Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2: PBNC 2022, 1 - 4 November, Beijing & Chengdu, China (Springer Proceedings in Physics, 284) 9789811987793, 9811987793

Table of contents :
Acknowledgment
Contents
Contributors
Optimal Analysis of Periodic Tests of Steam-Driven Auxiliary Feeding Pumps in QINSHAN Phase II NPP of CNNC
1 Introduction
2 Basic Situation of Steam-Driven Auxiliary Feeding Pumps
2.1 The Function of Auxiliary Water Supply System
2.2 Test of Steam-Driven Auxiliary Feeding Water Pumps
3 Optimization Objective of Auxiliary Feed Water Steam Pumps Tests
4 Optimal Analysis Method of Overhaul Project
5 Optimization Analysis
5.1 Safety Margin
5.2 Quantitative Risk Assessment
5.3 Experience Feedback
6 Conclusion
References
Fire Human Reliability Analysis for C-2 Nuclear Power Plant
1 Introduction
2 Fire HRA Methodology for Post-initiator HFEs
2.1 Identification of Post-initiator HFEs for Fire Scenarios
2.2 Screening Analysis
2.3 PSFs and Fire Effects to Consider
3 HFE Identification and Preliminary Quantitative Analysis for C-2 NPP
4 Detailed Quantification of C-2 Fire HFEs
5 Dependency and Uncertainty Analysis
6 Conclusion
References
Design and Application of Temperature Compensation System For Composite Material Spindle Measurement
1 Introduction
2 Temperature Compensation System Design
2.1 Hardware Structure Design
2.2 Software Architecture Design
3 Calibration Method Study
3.1 Determination of Calibration Methods
3.2 Calibration Experiments
4 Temperature Compensation Experiment and Compensation Coefficient Fitting
4.1 Warm Complement Fitting Method
4.2 Temperature Supplement Experiment and Data Fitting
5 Verification of the Effect of Temperature Compensation on the Measurement Result
6 Conclusion
References
The Research and Practice of PHM Technology in the Electrical System of Nuclear Plant
1 Introduction
2 Application of PHM Technology in Industrial Field
2.1 Aerospace PHM Systems
2.2 Intelligent Ship PHM System
2.3 High-Speed Rail PHM System
3 PHM Technology in Nuclear Power Plants
4 Thinking and Inspiration
5 Practice of PHM Technology in Electrical System of Nuclear Power Plant
6 Conclusion and Prospect
References
Failure Analysis of Valve Seat Cracking of Pneumatic Control Valve in Nuclear Power Plant
1 Introduction
2 Experiment
2.1 Visual Observation
2.2 Component Analysis
2.3 Hardness Test
2.4 Metallographic Observation
2.5 Microscopic Observation
3 Analysis and Discussion
4 Conclusions
References
Study on the Effect of Dispersant on the Properties of Ion Exchange Resins in Secondary Loop of Nuclear Power Plant
1 Introduction
2 Introduction
2.1 Chemicals and Equipments
2.2 Physicochemical Property Experiment
2.3 Adsorption Property Experiment
2.4 Dynamic Test of Anion Resin Bed
3 Results and Discussion
3.1 Determination of PAA Concentration at Room Temperature
3.2 Physicochemical Property of PAA Adsorbed and Regenerated Resin
3.3 Adsorption Property of PAA on Resin
3.4 Dynamic Test of Anion Resin Bed
4 Conclusion
References
The Analysis and Improvement of Fuel Damage Fraction Calculate
1 Introduction
2 Typical Thermal-Hydraulic Subchannel Model
3 Fuel Damage Fraction Calculation
4 Optimization of the Calculate Method
5 Conclusion
References
The Research on Phased Array Ultrasonic Inspection Technology in Nuclear Power Plant Thin Butt Weld Plate
1 Introduction
2 Development of Inspection Technology
2.1 Structural Characteristics of Thin Butt Weld Plate
2.2 Craft Design
2.3 Craft Modeling
3 Phased Array Craft Verification
3.1 Testing Equipment and Blocks
3.2 Validation Test Results
3.3 Application Results
4 Conclusion
References
Primary Water Stress Corrosion Cracking of Nickel-Based Alloys
1 Introduction
2 Application of Nickel-Based Alloys in NPPs
3 Study on PWSCC Behavior of Nickel-Based Alloys
3.1 Effect of Materials on PWSCC Behavior
3.2 Effect of Stress on PWSCC Behavior
3.3 Effect of Primary Water on PWSCC Behavior
4 Study on PWSCC Mechanism
5 Management of PWSCC of Nickel-Based Alloys Components in NPPs
5.1 Application and Management Activities of Nickel-Based Alloy Components
5.2 Management Requirements for PWSCC of Nickel-Based Alloy Components
6 Application and Management Suggestions About PWSCC for China NPPs
References
Study of Application Optimization of SPAR-H Human Reliability Analysis Method
1 Introduction
2 Introduction of SPAR-H Method
3 Optimization of the Definition and Selection for Each PSF
3.1 Available Time
3.2 Supplementary Instructions for Stress, Complexity PSFs
4 Case Study
4.1 Case of Different Evaluation Methods for Available Time PSF Analysis
5 Conclusion
References
Comparison of Nuclear Fuel Manufacturing Management Requirements Between HAF003 and IAEA Nuclear Safety Regulations
1 General
2 The Basic Requirements of HAF003
2.1 The Basic Structure of HAF003
2.2 The Safety Guidelines Related to the Manufacture of Nuclear Fuel Components
3 IAEA Nuclear Safety Laws and Their Development
3.1 Code on the Safety of Nuclear Power Plants: Quality Assurance (50-C-QA)
3.2 Quality Assurance for Safety in Nuclear Power Plants and Other Nuclear Installations (50-C/SG-1996)
3.3 The Management System for Facilities and Activities (GS-R-3 2006)
3.4 Leadership and Management for Safety (GSR Part2 2016)
3.5 Analysis of Compatibility of IAEA Calendar Standards
4 Changes of Safety and Quality Management in Domestic Nuclear Energy Industry
5 Conclusion
Environmentally-Assisted Fatigue Study of Charging Nozzle Under Multiaxial Stress History Based on Fluid-Solid Interaction Method
1 Introduction
2 Methodology
2.1 Fluid-Structure Coupling Governing Equation
2.2 Simplified Elastic-Plastic Fatigue Analysis Method
2.3 Environmental Assisted Fatigue Analysis Method
3 Numerical Simulation Analysis
3.1 Transient Load
3.2 Fluid-Structure Interaction Analysis
4 Results and Discussion
4.1 Stress Calculation Result
4.2 Fatigue Analysis
4.3 Environmentally-Assisted Fatigue Analysis
5 Conclusions
References
Condition Monitoring Based Equipment Health Management
1 Management Strategies of Equipment Health
2 FMEA Analysis and Condition Monitoring Task List
3 Condition Evaluation
3.1 Deviation Analysis
3.2 Methods of Condition Evaluation
4 Prediction Technology
4.1 AIC (An Information Criterion) Criterion
4.2 Prediction Example
5 Conclusion
References
Double-Ended Shear Fracture Accident of Short Casing Inner Tube in Small Modular Reactor
1 Introduction
2 Model Structure
3 Calculation Method
4 Calculation Results and Analysis
4.1 Double-End Shear Fracture of Short Casing Inner Tube at OTSG
4.2 Double-End Shear Fracture of Short Casing Inner Tube at Main Pump
5 Conclusions
References
Dissolution Behavior of Nickel(II) Oxide/Nickel(II) Hydroxide Colloids in the Oxidation Operation Process During Shutdown of a PWR
1 Introduction
2 Experimental Section
2.1 Chemical Reagents
2.2 Preparation of NiO/Ni(OH)2 Colloids
2.3 Study of the Dissolution Behavior of NiO/Ni(OH)2 Colloids
2.4 Instruments and Characterizations
3 Results and Discussion
3.1 Characterization of NiO/Ni(OH)2 Colloids
3.2 Dissolution Behavior of NiO/Ni(OH)2 Colloids
4 Conclusions
References
Application of Fiber Bragg Grating Technology in Perimeter Security of Nuclear Power Plants
1 Introduction
2 Design of FBG Perimeter Intrusion Detection System
2.1 Detection Layer
2.2 Monitoring and Analyzing Layer
2.3 Application Layer
3 Working Modes of Alarm
3.1 Single-Point Mode
3.2 Comprehensive Mode
3.3 Self-adaptive Mode
4 Application Test
4.1 Testing Conditions
4.2 Results
5 Conclusions
References
Experimental Study on Manufacturing Technology of Intermediate Heat Exchanger Tube-Tubesheet Joint in Sodium-Cooled Fast Reactor
1 Introduction
2 Structure of Intermediate Heat Exchanger and the Tube-Tubesheet Joint
2.1 Main Design Parameters of Intermediate Heat Exchanger
2.2 Connecting Form of Tube-Tubesheet Joint
3 Experiment Contents
3.1 Holes Processing in Tubesheet
3.2 Strength-Welding and Hydraulic Expansion
4 Analysis of Test Results
4.1 Analysis of Hydraulic Expansion Test Results
4.2 Analysis of Strength-Welding Test Results
5 Conclusions
References
PIRT Research on RCCA Ejection Accident
1 Introduction
2 Description and Acceptance Criteria for RCCA Ejection Accident
3 Typical Phenomena and Key Parameters of RCCA Ejection Accident
4 Single Variable Sensitivity Analysis
4.1 Analysis Methods and Cases
4.2 Results and Discussion
5 Application of RCCA Ejection Accident PIRT in DNB ALARP Analysis
6 Conclusion
References
Research on Passive Single Failure Conditions of LB-lOCA
1 Introduction
2 Overview of Passive Single Failure Criterion
2.1 Passive Single Failure Criterion in China
2.2 Passive Single Failure Criterion in United State
2.3 Passive Single Failure Criterion in Europe
2.4 Passive Single Failure Criterion in IAEA
3 Analysis of Mode Passive Single Failure
3.1 Leakage, Blockage and Bypass
3.2 Check Valve Failure
4 Passive Single Failure Criterion Applied to LBLOCA
4.1 Analysis Method
4.2 Limiting Passive Single Failure Assumption
4.3 Analysis of LBLOCA
5 Conclusion
References
Analysis of Liquid Metal Cooled Reactor Safety Analysis Software FRTAC Applied To Pipeline Breach Ejection Experiment
1 Introduction
2 Edwards’ Pipe Break Experiment
3 Marviken Jit11 Experiment
4 Conclusions
References
Study on the Rationality of Superposition Principle of Accident and Fire Based on Probabilistic Assessment
1 Introduction
2 Requirements from Codes and Standards
3 Rationality Analysis
4 Conclusions
References
Research on the Correlation of Influencing Factors Impinging Operating Events Fluctuation for NPPs
1 Introduction
2 Selection of Research Methods
2.1 Empirical Methods
2.2 Data Source
3 Empirical Analysis
3.1 Stationarity Test
3.2 Granger Test of Causality
3.3 Multivariate Ljung-Box Q Statistics
3.4 Establishment of the VAR Model
3.5 Testing of the VAR Model
3.6 Pulse Response Analysis
3.7 Pulse Response Analysis
4 Conclusions
References
Influence of a Film-Forming Amine on Repeated Adsorption and Regeneration of Ion Exchange Resins
1 Introduction
2 Test Contents
2.1 Test Equipment and Chemicals
2.2 Test Contents
3 Test Results and Discussion
3.1 Static Immersion Tests
3.2 Regeneration Test
4 Conclusions
References
Maintenance Scheme Analysis of Accumulator on Emergency Direct Current Distribution Panel for Nuclear Power Plant
1 Introduction
2 Introduction to the Accumulator on the DC Power Distribution Panel
3 Analysis of the Accumulator Discharge Test
4 Design Optimization Analysis of Accumulator on DC Distribution Panel
4.1 Addition of Temporary Load
4.2 Addition of Standby Accumulator Group
5 Conclusions
References
Development and Application of Rod Based Guaranteed Shutdown State (RBGSS) Technology for Heavy Water Reactor
1 Introduction
2 Application and Implementation of the Program
2.1 Modification
2.2 Modification of Affected Production and Operation Files
2.3 Program Implementation
3 RBGSS Earnings
3.1 Save Reactor Detoxification Time During Critical Period
3.2 Increase Unit Operation Flexibility
3.3 Mitigate the Consequences of Accident Conditions
3.4 Reduce Material Degradation
3.5 RBGSS Revenue Analysis
4 Summary
References
The Strategy of Developing Safety Benchmark for VVER Multi-reactor Periodic Safety Review (PSR)
1 Introduction
2 Development of PSR Safety Benchmark
2.1 Necessity of PSR
2.2 Scope and Process of PSR
2.3 Safety Benchmark Development Strategy
3 Principles of Developing the Safety Benchmark [4, 5]
4 Prepare Vver Unit Group Reactor Safety Benchmark Report
5 Conclusion
References
Research on Aging Management and Life Assessment of Low-Voltage Cables in Nuclear Power Plants
1 Introduction
2 Aging Management of the Low-Voltage Cables
2.1 Low-Voltage Cable Management Object Screening
2.2 Analysis of Aging Mechanism of Low Voltage Cable
2.3 Low-Voltage Cable Aging Effect Detection
2.4 Low-Voltage Cable Aging Management Implementation
3 Low Voltage Cable Life Assessment
3.1 Thermal Aging Life Assessment
3.2 Radiation Aging Life Assessment
4 Conclusions
References
Discuss on Severe Accident Management Guidelines for an Advanced NPP
1 Introduction
2 Discuss on Different SAMG
2.1 Development History of SAMG
2.2 Description of Different SAMGs
2.3 Comparison
2.4 Conclusion
3 Research on Samg for an Advanced NPP
3.1 Definition
3.2 Principles
3.3 Steps
4 Conclusions
References
Theoretical Study on Negative Pressure Transport of Radioactive Waste Resin
1 Experiment Objective
2 Testing Program
2.1 Process Program
2.2 Test Bench Scheme
2.3 Experimental Simulation Scheme
3 Test Steps
3.1 Test Acceptance Criteria [2]
3.2 Transportation of Pure Resin
3.3 80% Resin Conveying
3.4 60% Resin Conveying
4 Summary of Results
References
Design and Verification of BNCT Beam Shaping Assembly Based on Genetic Algorithm
1 Introduction
2 Materials and Methods
2.1 Neutron Source
2.2 Beam Shape Assemblies
2.3 Calculation Process
3 Results and Discussions
4 Conclusion
References
Performance Monitoring and Back Pressure Curve Calculation of Condenser
1 Introduction
2 Performance Monitoring
3 Performance Evaluation
3.1 Tube Plugging Rate
3.2 Performance Improvement
4 Conclusions
References
Openfoam Simulation of Damping Controlled Fluidelastic Instability
1 Introduction
2 Numerical Model
2.1 Model Description
2.2 Simulation of Moving Tube
2.3 Discretization and Solution
3 Results and Discussion
4 Conclusions
References
Research on Aging Behavior and Life Prediction of Fluorine Rubber Seals in Nuclear Power Plants Under Silicone Oil Condition
1 Introduction
2 Experiment
2.1 Experimental Materials
2.2 Experimental Equipments
2.3 Trial and Test
3 Results and Discussion
3.1 Compression Set
3.2 Shore Hardness
3.3 Infrared Spectroscopy
3.4 Thermogravimetric Analysis
3.5 Life Evaluation Under Composite Conditions
4 Conclusions
References
Super High Jacking Installation Technology of Ring Crane Arch Frame During Nuclear Power Plant Maintenance Period
1 Introduction
2 Project Overview
3 Special Equipment Design
3.1 Overall Structure Design
3.2 Boom System Design
3.3 Fixture System Design
4 Introduction of Super-High Jacking Installation Technology
4.1 Introduction of a Wireless Monitoring System
4.2 Super High Jacking Installation and Construction Process
4.3 Super-High Segmented Rotary Jacking Installation
5 Engineering Application
6 Conclusions
References
Separation of Strontium from Other Fission Products in High Level Liquid Waste by TODGA
1 Introduction
2 Experimental
3 Results and Discussion
4 Conclusions
References
Effects of Normal Load, Thickness of Oxide Layer and Number of Cycles on Fretting Wear of PWR Fuel ROD Cladding
1 Introduction
2 Experiments Details
2.1 Experiment Materials
2.2 Fretting Wear and Corrosion Testing Facility
2.3 Experiment Procedure
3 Results and Analysis
3.1 Effects of Normal Load on Fretting Wear and Corrosion
3.2 Effects of Number of Wear Cycles on Fretting Wear and Corrosion
3.3 Effects of Oxide Layer Thickness on Fretting Wear and Corrosion
4 Conclusions
Bibliography
Fault Detection for Redundant Measurement Channels
1 Introduction
2 Method Principle
2.1 Independent Component Analysis (ICA)
2.2 SA Based Signal Estimation
3 Model Validation
3.1 Introduction of Research Object
3.2 Health Data Analysis
3.3 Pressure Drift Data Analysis
4 Conclusions
References
Disorder Induced by Gamma Irradiation in Borosilicate Glasses
1 Introduction
2 Material and Experiments
2.1 Material
2.2 Experiments
3 Results and Discussions
3.1 Ultraviolet and Visible Spectra
3.2 Raman Spectra
4 Conclusions
References
Preliminary Evaluation of Impact Forces in Rotor Drop of the AMB-rotor System in Space Reactor
1 Introduction
2 Contact Impulse of the Dropping Rotor
3 Contact Force Between Rotor and Auxiliary Bearing
4 Results and Discussion
5 Conclusions
References
Error Analysis of Set Pressure Test of Main Steam Safety Valve in CPR1000 Nuclear Power Plant
1 Introduction
2 Characteristics of MSSV Set Pressure Testing
3 Failure Modes and Causes
3.1 Equipment and Environmental Factor
3.2 Test Method
3.3 Sample Analysis and Data Evaluation
4 Results and Discussion
5 Conclusions
References
Development and Application of the Control Rod Guide Tube Inspection Equipment
1 Introduction
2 Inspected Object
2.1 Inspection Environment
2.2 Common Defects
3 Schematic Design
3.1 Inspection Principle
3.2 Schematic Design and Selection
3.3 Safety Measures
4 Application
5 Conclusion
References
A Potential Threat Risk Evaluation Method for Nuclear Facilities
1 Introduction
2 Modeling
3 Logic Analysis
4 Case Description
5 Conclusion
References
An Improved Hazop Method Was Used to Analyze the Safety of Hydrogen Production System in Nuclear Power Plant
1 Introduction
2 Development Process of Improved Hazop Analysis Method
3 Application Case of Improved Hazop Method
4 Conclusions
References
Research on the Optimal Management of Very Low Level Radioactive Waste
1 Introduction
1.1 A Subsection Sample
2 Management Status of VLLW Abroad
2.1 USA
2.2 UK
2.3 France
2.4 Germany
3 Management Status of VLLW in China
4 Optimal Management of VLLW
4.1 Rapid Classification Measurement
4.2 Restriced Clearance
4.3 Landfill Disposal
5 Suggestion
References
Research on Contact Response of Active Compliant Assembly of Nuclear Power Maintenance Robot
1 Introduction
2 Contact Response of Peg-in-Hole Assembly
2.1 Peg-in-Hole Assembly Models
2.2 Convergence Analysis
3 Results
4 Discussion
5 Conclusions
References
Digital Design and Improvement of PWR Rod Position Measurement System
1 Introduction
2 Measurement Method and Defects of Analog Rod Position
3 All-Digital Rod Position Measurement System
4 Improvement Ideas and Implementation
4.1 Compensation and Resistance Adjustment by the Algorithm
4.2 Interference Reduction by the Algorithm
4.3 Intelligent Integration
5 Conclusions
References
Numerical Study on Radiative Heat Transfer Performance of ACP100 Passvie Containment Air Cooling System
1 Introduction
2 Pysical Model
2.1 Structure and Composition
2.2 Working Principle
3 Cfd Model
3.1 Governing Equations
3.2 Simulation Boundary Conditions
3.3 Mesh Schemes Irrelevance Analysis
4 Results and Analysis
4.1 The Effect of Wall Emissivity of the Steel Shell on Radiative Heat Transfer Performance of the PAS
4.2 The Effect of Wall Emissivity of the Concrete Shell on Radiative Heat Transfer Performance of the PAS
5 Conclusions
References
Influence of Zirconia Addition on the Leaching Stability of Borosilicate Glass
1 Introduction
2 Methods
3 Results and Discussion
3.1 FTIR Spectra
3.2 GIXRD
3.3 Leaching Rate
4 Conclusions
References
Best Estimate Plus Uncertainty Analysis of a Pressurizer Surge Line Break LOCA on China’s Advanced PWR
1 Introduction
2 Accident Simulation
2.1 Description of Relap5 Model
2.2 Results and Analysis of Basecase
3 Uncertainty Quantification and Sensitivity Analysis
3.1 Selection of Uncertain Parameters
3.2 Wilks Nonparametric Statistics
3.3 Uncertainty Analysis
3.4 Sensitivity Analysis
4 Conclusions
References
Fault Diagnosis of Nuclear Power Plants Based on 1D-CNN with Dual Attention Mechanism
1 Introduction
2 The Proposed Method
3 Case Study
3.1 Dataset Information
3.2 Experiment Setup
4 Results and Discussion
4.1 Analysis of the Model Training Process
4.2 Comparison with Baseline Models
4.3 Anti-noise Ability
5 Conclusions
References
Experiments of Polydisperse Aerosol Transport Progress in Horizontal and Vertical Pipes
1 Introduction
2 Method
2.1 Experimental Setup
2.2 Aerosol Condition
2.3 Instrument Calibration
3 Results Analysis and Discussion
3.1 Influence of Flow Direction
3.2 Influence of Re
3.3 Model Validation
4 Conclusion
References
Calculation of the Decommissioning Radiation Field of Nuclear Power Plants Based on the Coupling of MC and Point Kernel Integration
1 Introduction
2 The MC-KP Coupling Method
3 Single Source Calculation Model
3.1 Calculation Model Description
3.2 Analysis of Calculation Results
3.3 Influencing Factors of Calculation Accuracy and Time
4 Calculation of the Radiation Field of Qinshan I
4.1 Calculation Model of Qinshan I
4.2 Analysis of Calculation Results
5 Conclusion
References
Influence Analysis of Dirt on PAS Heat Transfer Performance of ACP100
1 Introduction
2 Computational Model
2.1 Model of Dirt with Different Areas
2.2 Model of Dirt with Different Locations
2.3 Model of Dirt with Different Thickness
3 Results and Analysis
3.1 Influence Analysis of Dirt Area on PAS Heat Transfer
3.2 Influence Analysis of Dirt Locations on PAS Heat Transfer
3.3 Influence Analysis of Dirt Thickness on PAS Heat Transfer
4 Conclusion
References
An Unsupervised Learning-Based Framework for Effective Representation Extraction of Reactor Accidents
1 Introduction
2 Proposed Method
3 Experiment and Analysis
3.1 Dataset Acquisition
3.2 Optimization Objectives and Evaluation Metrics
3.3 Experiment Conditions
3.4 Auto-Encoder Performance
3.5 Diagnosis Performance
4 Conclusion
References
TAC-1 Project Quality Management Research Based on Configuration Management
1 Introduction
2 TAC-1 Project Configuration Identification and Baseline
3 Configuration Status Statistics
3.1 Physical Configuration Status
3.2 Information Management System
4 Configuration Control
4.1 EW Mechanism and Process
4.2 FCR Mechanism and Process
4.3 NCR Mechanism and Process
5 Configuration Review
6 Conclusions and Recommendations
References
Research on Diffusion of Oceanic Radionuclides Deposited from Atmosphere Under Nuclear Leakage Accidents
1 Introduction
2 Research Object
3 Calculation Model
4 Boundary Conditions
4.1 Boundary Condition of the Ocean
4.2 Boundary Condition of Area Source
5 Conclusion
5.1 Surface Diffusion in Summer
5.2 Vertical Diffusion in Summer
5.3 Surface Diffusion in Winter
5.4 Vertical Diffusion in Winter
6 Conclusion
References
An Overview of R&D Activities on High Level Liquid Waste Partitioning at Tsinghua University, China
1 Introduction
2 Technology and Key Progresses
2.1 Chemical Fundamentals
2.2 Key Progresses
3 Conclusion
References
Analysis of the Economic Prospect of Nuclear Heat Supply by Megawatt-Class Nuclear Power Units from the Perspective of Carbon Peaking and Carbon Neutrality
1 Introduction
2 Great Opportunities for Nuclear Heating Under Carbon Peaking and Carbon Neutrality Targets
2.1 Analysis of China Heating Status
2.2 The Government Issued Relevant Policies, the Development of Nuclear Heating Sustains Positive
3 The Main Factors Affecting the Economy of Nuclear Heating Retrofit Project
4 Calculate the Financial Benefit of Different Heating Demands in the Case of Low-Pressure Steam Supply
5 Summary
References
The Study on Monitoring of Different Chemical Forms of Tritium and Tritium-Induced Radiation Dose
1 Introduction
2 Experiment
2.1 Tritium Collection
2.2 Tritium Sample Preparation
2.3 Tritium Measurement
3 Dosimetry Model of Tritium
3.1 Dosimetry Model for Breath Inhalation
3.2 Dosimetry Model for Skin Absorption
4 Results and Discussion
5 Conclusion
References
Improvement and Realization of AGC Strategy in Nuclear Power Plants
1 Introduction
2 AGC Strategy
2.1 AGC of Conventional Thermal Power Plants
2.2 AGC of EPR™ Nuclear Power Plant
3 Major Constraints for AGC Function in EPR™
3.1 Operating Range for AGC
3.2 Load Change Limit
3.3 Frequency Regulation Dead Band
3.4 Reactor Response to Power Fluctuation Under Normal Operating Conditions
3.5 Inherent Low Frequency Oscillation of Gross Load
4 Evolution of AGC Strategy for EPR™
4.1 Newly Added the Justification of Available Load Range for AGC
4.2 Newly Added ΔP Limitation
4.3 Newly Added Step Limitation for AGC Command
4.4 Prerequisites for AGC
5 Conclusion
References
Study on Simulation Method of CRUD Deposition Behavior on Nuclear Fuel
1 Introduction
2 Model
2.1 CAMPSIS Software
2.2 Brief Conclusion of CAMPSIS Theory
2.3 Crud Deposition Model
3 Calculations Result
3.1 Validation Case
3.2 Result of CGN Unit A
4 Conclusions
5 Funding category
References
Evaluation on the Release of Key Nuclides in Cemented Waste Form in a Rock Cavern Disposal Site
1 Introduction
2 Computing Software and Model Building
2.1 Conceptual Model
2.2 Mathematical Model
2.3 Compartment Model
2.4 Parameter Value
3 Calculation Result Analysis
3.1 Release Rate
3.2 Total Release Activity and Release Ratio
4 Conclusions
References
Research and Validation of RCP Temperature Automatic Control During Cold Shutdown Stage in a GEN-III Nuclear Power Plant
1 Introduction
2 Primary Temperature Control Principle
2.1 General Introduction of Primary Temperature Control
2.2 Flow Control
2.3 Temperature Control
2.4 Mode Detection
3 Test Validation and Optimization
3.1 Stability of RHR Primary Temperature Control System
3.2 Temperature Response in Stability Test
3.3 Flow Response in Stability Test
3.4 Automatic Cool Down
3.5 Automatic Heat up
4 Problem Analysis and Treatment
5 Summary
References
Application Status and Prospect of Two-Dimensional Graphene for Hydrogen Isotope Separation
1 Introduction
2 Hydrogen Isotope Separation Technologies
2.1 Cryogenic Distillation
2.2 Combined Electrolysis Catalytic Exchange
2.3 Crystalline Materials
2.4 2D Materials
3 Hydrogen Isotope Separation by Graphene
3.1 Theoretical Calculation
3.2 Experimental Research
4 Conclusions
References
Research on Two Power Reduction Strategies of Cpr1000 Unit and Its Influence on Axial Power Deviation Delta I
1 Introduction
2 Power Reduction Strategy and Influence Factors of the Axial Power Deviation
2.1 Power Reduction Strategy
2.2 Impact Factors of Axial Power Deviation During Power Reduction
3 Analysis of Parameter and Axial Power Deviation Trend During Power Reduction
3.1 Parameter Analysis During Power Reduction by G Rod Following
3.2 Trend Analysis of Axial Power Deviation of Power Reduction by G Rod Following
3.3 Parameter Analysis of Boronation Power Reduction
3.4 Trend Analysis of Axial Power Deviation of Boronation Power Reduction
4 Conclusion and Suggestion
References
Analytical Displacement Analysis for an Inverted U-Shaped Tunnel Excavated in Anisotropic Rock Mass
1 Introduction
2 Theories
2.1 Conformal Mapping
2.2 Basic Equations
2.3 The Solution of Complex Functions
3 Results and Discussion
3.1 Basic Formulas for Displacement
3.2 The Process of Displacement Solution
3.3 Example Analysis
4 Conclusion
References
Calculation of Cs Migration Behaviorin Argillaceous Material
1 Introduction
2 Argillaceous Material
2.1 Basic Conception
2.2 Argillaceous Texture
2.3 Tamusin Argillaceous Properties
3 Migration of Nuclides
3.1 Seepage
3.2 Adsorption of Cs by Argillaceous
3.3 Diffusion
4 Migration Simulation
4.1 Migration Model
5 Conclusions
References
Radiological Consequences and Risk Analysis for On-Site Workers During PSA Fault Sequences
1 Introduction
2 Acceptance Criteria of Radiological Risk Control for On-Site Workers
2.1 Acceptance Criteria of Overall Individual Risk of Death for On-Site Workers Under Fault Conditions
2.2 Acceptance Criteria of Frequency-Dose Target for Any Single Accident
3 Radiological Risk Assessment Methodology for On-Site Workers
3.1 Selection and Grouping Principles for Fault Sequences
3.2 Categorization of On-Site Workers During Accident Conditions
3.3 Calculation Methods of Worker Dose During Accident Conditions
4 On-Site Worker Radiological Risk Assessment for a Typical Third-Generation NPP
4.1 Assessment Scope and Results
4.2 Results Discussion
5 Conclusions
References
Study of the Capacity Building for Interim Storage of PWR Spent Nuclear Fuel in China
1 Introduction
2 Interim Storage Capacity Building
2.1 Requirement for Interim Storage of SNF
2.2 Capacity Vacancy for Interim Storage of SNF
2.3 Comparison Between Dry and Wet Storage
3 Management of SNF
3.1 Polices
3.2 Legislative Framework
3.3 Relevant Laws Specific for SNF Management
3.4 Challenges
4 Suggestions
4.1 Capacity Building
4.2 Management
5 Conclusions
References
The Study and Suggestions on the Civil Nuclear Safety Equipment Standards in China
1 Introduction
2 The Role of Civil Nuclear Safety Equipment Standards
3 The Management and Development of Civil Nuclear Safety Equipment Standards by NNSA
4 The Endorsement of Civil Nuclear Safety Equipment Standards by NNSA
5 Conclusions
References
Effect of Corrosion Damage on Structural Failure Models Under Different Boundry Conditions
1 Introduction
2 Theory of Acoutisc Damping
3 Theoretical Overthrow
3.1 Parameter Analysis of Fixed Support
3.2 Parameter Analysis of Hinged Support
4 Comparsion of Corrosion Damage Forms
5 Conclusion
References
Research on the Applicability of Experience Feedback of Multi-plants to Nuclear Safety Management of New Nuclear Power Plants
1 Introduction
2 Research and Applicability of Multi-plant Experience Feedback
2.1 Organization System
2.2 Document System
2.3 Information System
2.4 Daily Operation of Experience Feedback
2.5 Event Analysis Method
2.6 Experience Feedback Effectiveness Evaluation
2.7 Application of Experience Feedback
3 Conclusions
References
Drying-into-salt Technology of High Salinity Radioactive Liquid Waste
1 Introduction
2 Process Planning
2.1 Source Item Overview
2.2 Preliminary Process Design
2.3 Process Parameter Calculation
2.4 Key Equipment Design
3 Prototype Development and Test
3.1 Prototype Development
3.2 Test Process
4 Results and Discussion
References
Study on Key Technical Issues of Marine Environmental Safety Assessment of the Floating Nuclear Power Plant
1 Introduction
2 Environmental Safety Regulatory Requirements and Applicability
2.1 General Regulatory Requirements of the FNPP
2.2 Regulatory Requirements for the Relocation of the FNPP
2.3 Requirements for Marine Environmental Safety Impact Assessment
3 Marine Environment Characteristics and Site Suitability
3.1 Marine Environment Characteristics and Parameter Data Acquisition
3.2 Environmental Requirements and Site Suitability
4 Feasibility of Radioactive Waste Treatment and Discharge
4.1 Characteristics of Treatment and Discharge of Radioactive Waste
4.2 Discharge Conditions and Feasibility of Radioactive Waste
5 Rationality of Environmental Monitoring Plan
5.1 Source Term Characteristics and Monitoring Nuclides
5.2 Monitoring Points and Monitoring Equipment
6 Environmental Risk Controllability of Accident
6.1 Radioactive Accident Release and Radiation Effects
6.2 Marine Platform Accidents and Risk Controllability
6.3 Emergency Plan and Feasibility of Implementing Emergency
7 Conclusions and Prospects
References
Research on the Application of Tension Monitoring System of the Interception Net in Nuclear Power Plant
1 Introduction
2 Design and Construction of Tension Online Monitoring System
2.1 Overall Design of Tension Online Monitoring System
2.2 On-site Installation
3 Influence Factor Analysis of Tension
3.1 Brief Description of Research Conditions
3.2 Determination of Main Influencing Factor of Tension
4 Characterization Methods of Catches
5 Conclusions
References
Study on Preparation Technology of UCL4 From CCl4
1 Introduction
2 Experimental Section
2.1 Materials
2.2 Experimental Device
2.3 Preparation of Uranium Trioxide
2.4 Preparation of Uranium Tetrachloride
2.5 Analytical Methods
3 Results and Disscussion
3.1 Thermodynamic Calculation
3.2 Phase Structure Identification
4 Conclusions
References
Development and Application of High-Accuracy Metal Fuel Performance Analysis Code Based on Fem Method
1 Introduction
2 Analysis Model
2.1 Finite Element Method Model
2.2 Metal Fuel Model
3 Solution Procedure
4 Validation of Metal Fuel for Sodium Cooled Fast Reactor
5 Result and Analysis
5.1 The Influence of Metal Fuel Wire-wrap
5.2 The Asymmetry of X425 Fuel Rod
6 Conclusions and Perspective
References
Site Condition Sensitivity Analysis Based on the Sub-structuring Method for a Base-Isolated Nuclear Building
1 Introduction
2 Base Isolation Analysis Method Considering Soil-structure Interaction
2.1 Numerical Analysis Methods
2.2 Simulation of Isolation Bearing Characteristics
3 Base Isolation Design Scheme
3.1 General Information
3.2 Isolation Bearing
3.3 Finite Element Model
4 Analysis Parameters
4.1 Site Condition Parameters
4.2 Earthquake Wave
5 Sensitivity Analysis of Site Parameters
5.1 Linear Constitutive Model
5.2 Bilinear Constitutive Model
6 Conclusions
References
Modelling Analysis of Non-uniform Flow and Heat Transfer in Parallel Rectangular Channels During Flow Blockage Condition
1 Introduction
2 Numerical Models
2.1 One-Dimension Two-Fluid Model
2.2 Two-Dimension Heat Conduction Model
3 Selection and Validation of Flow and Heat Transfer Models
3.1 Test Cases
3.2 Friction Model
3.3 Single-Phase Heat Transfer Model
3.4 Two-Phase Heat Transfer Model
4 Blockage Modeling of IAEA’S 10mw Reactor
4.1 Boundary Conditions
4.2 Single Channel Blockage
4.3 Two-Dimensional Heat Conduction
5 Conclusions
References
Research Progress of SCR Denitration Catalyst in NOx Exhaust Gas Treatment
1 Introduction
2 Scr Technology
3 Catalyst System
3.1 Noble Metal Catalysts
3.2 Metal Oxide Catalysts
3.3 Molecular Sieve Catalyst
4 Catalyst Recycling
5 Conclusions
References
Laser Decontamination Experiments for Radioactive Contaminated Metals
1 Introduction
2 Laser Decontamination Experiment Equipment
3 Experiment Scheme for Laser Decontamination
4 Laser Decontamination Test Results
4.1 No-radioactive Process Experiment Results
4.2 Radioactive Process Experiment Results
5 Conclusion
References
The Optimization Design of the Reactor Coolant System Based on Optimus
1 Introduction
2 Dynamic Analysis of Reactor Coolant System
2.1 Non-linear Finite Element Model
2.2 Seismic Loads
3 Optimization Analysis
3.1 Sensitivity Analysis
3.2 Optimization Model and Result
4 Conclusion
References
Study on Near Surface Radionuclide Activity Under Vehicle Heat Pipe Small Modular Reactor Accident Condition
1 Introduction
2 Research Object
2.1 Regional Model
2.2 Accident Model
2.3 Representative Nuclide
2.4 Representative Nuclide
3 Computational Modle
3.1 Regional Model
3.2 Dry Settlement Model
3.3 Wet Settlement Model
3.4 Release Height Model
3.5 Decay Model
3.6 Calculation Process
4 Calculation Results and Analysis
4.1 Release Height Parameter
4.2 Sedimentation Flux
4.3 Atmospheric Diffusion of Nuclides at a Wind Speed of 5.5 m/s
4.4 Atmospheric Diffusion of Nuclides at a Wind Speed of 2.45 m/s
4.5 Comparison of Nuclide Diffusion Under Different Atmospheric Conditions
5 Conclusion
References
Multi-physics Numerical Investigation on the Mechanical Stirring in a Cold Crucible Melter
1 Introduction
2 Descriptions of Laboratory Facilities and Mathematical Models
2.1 Laboratory Facilities
2.2 Mathematical Models
3 Comparisons of Power Density Distribution Between 650 and 800 mm CCIM
3.1 Power Density Distribution of the Φ650 mm CCIM
3.2 Power Density Distribution of Φ800 mm CCIM
4 Comparisons of Temperature Distribution Among Different Powers in the 650 mm CCIM
5 Stirring Effects on Flow Field in the 650 mm CCIM
6 Conclusions
References
Development and Validation of Source Term Model of Corrosion Products in the Primary Circuit
1 Introduction
2 Theoretical Model
2.1 Burnup Equations
2.2 Chebyshev's Rational Approximation Method (CRAM)
3 Model Validation
3.1 Ling’ Ao Unit 1 Data
3.2 Ling’ Ao Unit 2 Data
3.3 Ling’ Ao Unit 3 Data
3.4 Ringhals Unit C Data
3.5 Validation Results Discussion
4 Conclusions
5 Funding Category
References
A Review on Adsorption Mechanisms and Distribution Coefficient (Kd) of Cesium in Clay/Host Rock
1 Introduction
2 Mechanism of Cesium Adsorption on Minerals
3 Parameters Which Influence Adsorption of Cesium on Minerals
3.1 Concentration of Cesium
3.2 pH
3.3 HA
3.4 Effect of Cations
3.5 Properties of the Minerals
4 Recommended Kd Value of Cs Adsorbed on Specified Minerals
4.1 Data Collection and Treatment
4.2 Construction of Cumulative Distribution Functions (CDF) to Describe Kd Variability
4.3 Obtaining the Recommended Kd Value for the Sorption of Cs on Bentonite
4.4 Obtaining the Recommended Kd Value for the Sorption of Cs on Granite
4.5 Obtaining the Recommended Kd Value for the Sorption of Cs on Clay
5 Conclusions
References
Is Perchlorate Innocent in the Study of Weak Complexations of Nitrate with Metal Ions by Spectrophotometric Titration?
1 Introduction
2 Experimental
2.1 Chemicals
2.2 Spectrophotometric Titrations
3 Results and Discussion
3.1 Effect of the Concentration Ratio of NO3- to Metal Ions
3.2 Effect of the Concentration Ratio of ClO4− to Metal Ions
3.3 Effect of the Activity Coefficient
3.4 Discussion on the Complexation Ability of Perchlorate
4 Conclusions
References
Design and Experimental Study of Ultra High Pressure Water Jet Decontamination Device
1 Introduction
2 Improved Design of High Pressure Water Cleaning Device
2.1 Principles of High-Pressure Water Jets
2.2 Improvement and Design of the High-Pressure Water Cleaning Device
2.3 The PLC Control System
3 Experimental Verification
3.1 The Experimental Sample
3.2 Experimental Result
3.3 Flow Influence Law Test
4 Conclusions
References
Analysis of Shock Wave and Containment Behavior Under Severe Accident Internal Hydrogen Explosion Based on CEL Method
1 Introduction
2 Numerical Simulation Method and Parameters
2.1 CEL Method
2.2 Parameters
3 Numerical Simulation Method Validation
4 Calculation
4.1 Model
4.2 Result
5 Conclusion
References
Study on the System Simulation for the Heat Pipe Failure Transient of Heat Pipe Cooled Reactor
1 Introduction
2 Reactor Structure
3 Numerical Model
3.1 Point Reactor Kinetics Model
3.2 Core Heat Transfer Model
3.3 Heat Pipe Model
3.4 Thermoelectric Generator Model
4 Heat Pipe Failure Accident Analysis
5 Conclusion
References
Prediction of LOCA Break Size Based on 1D Convolutional Neural Network
1 Introduction
2 1D Convolutional Neural Network
3 LOCA Simulation
4 1D-CNN Model Training
4.1 Data Preprocessing
4.2 Model Training
4.3 Results
5 Conclusions
References
Quality Control of Small Batch product in Manufacturing Process
1 Research Background and Current Situation
2 Research Objectives
2.1 Test Characteristics
2.2 Analysis of Difficulties
2.3 Research Objectives
3 Research Content
3.1 Determination of Welding Process Parameters
3.2 Control Chart Drawing
4 Conclusion
References
Study on the Influence of Reactor System LOCA Modeling Mode on Dynamic Analysis
1 Introduction
2 Model Establishmen
3 Analytical Theory
4 Computing Method
5 Result Analysis
6 Conclusion
References
Development and Verification of Solver for Natural Circulation Flow and Heat Transfer Simulation Base on Openfoam
1 Introduction
2 Models and Algorithms
2.1 Basic Solver icoFoam
2.2 IcoFoam Secondary Development
3 Code Verification
4 Conclusion
References
Economic Indicator Analysis of HPR1000 Process Pipe Engineering in the Integrated Technology Corridor
1 Introduction
2 Study on the Quantity Indicator of Gb Corridor
2.1 Methods
2.2 The Quantity Indicators in Every System
2.3 The Quantity Indicators in Every Representation
2.4 The Overall Quantity Indicator
3 Study on the Technical and Economic Indicators of GB Corridor
3.1 The Distribution of Technical and Economic Indicators in Different Systems
3.2 The Distribution of Technical and Economic Indicators in Different Specifications
3.3 Overall Technical and Economic Indicators
4 The Application and Accuracy Verification of Indicators
5 Conclusions
References
A Kinetic Model for Researching the Rolling Bearing Load Inversion Method
1 Introduction
2 Dynamic Model of Rolling Bearing
2.1 System State Model Construction
2.2 Nonlinear Contact Force of Rolling Bearing
3 Load Inversion Method
3.1 Construction of System State Space Model
3.2 Dynamic Load Inversion Calculation Process
4 Bearing Dynamic Load Test Verification
5 Conclusion
References
Solar Energetic Particles Hazard Assessment for UK HPR1000
1 Introduction
2 Solar Energetic Particles Safety Assessment
2.1 Hazard Effects
2.2 General Approach
2.3 Design Basis Values
2.4 Safety Assessment Process
2.5 Protection Measures
2.6 Potential Gaps and Design Improvement
3 Conclusion
References
Safety Analysis for Uncontrolled Withdrawal of Rod Cluster Control Assembly Bank After Extend Low Power Operation
1 Introduction
2 Analysis Methodology
2.1 Calculation Method
2.2 Basic Assumption
3 The Influence of ELPO on Core Power Distribution
3.1 The Influence of ELPO on Radial Power Distribution
3.2 The Influence of ELPO on Axial Power Distribution
3.3 The Characteristics of Radial Core Power Distribution After ELPO at Zero Power
3.4 The Characteristics of Axial Core Power Distribution After ELPO at Zero Power
4 The Influences of ELPO on the Result of URWZ
4.1 The Influence on Specific Nuclear Parameters in URWZ
4.2 The Influence on Nuclear Power in URWZ
4.3 The Influence on DNBR in URWZ
5 Conclusions
References
Discussion on the Application Prospect of Airborne Geophysical Prospecting in Site Selection and Construction of Nuclear Waste Disposal Sites
1 Introduction
2 Analysis of the Ability of Aero Geophysical Prospecting to Solve Geological Problems
3 The Application Prospect of Aero Geophysical Prospecting in the Construction of Nuclear Waste Disposal Site
4 Conclusion
References
Study on China’s Nuclear Power Development and Nuclear Safety Regulation Under the Background of Carbon Peaking and Carbon Neutrality
1 Introduction
2 Current Situation of International Carbon Peaking and Carbon Neutrality
3 Carbon Emission Status of Energy and Power Industry
4 Characteristics and Development Trend of Different Power Sources
4.1 Characteristics of Each Generating Power Supply
4.2 Structural Changes of Power System
4.3 Development Speed of Each Power Supply
5 Prediction of the Development Trend of Nuclear Power in China
5.1 Constraints of Various Power Generation Modes
5.2 Prediction of Nuclear Power Development Trend
6 Achievements and Challenges of Nuclear Safety
6.1 Achievements
6.2 Challenges
7 Conclusions and Suggestions
References
Effect of Different Improved Designs for ARE Sensors on Core Safety
1 Introduction
2 ARE001MP Function and Failure Consequence
2.1 ARE001MP Function
2.2 Failure Consequence
3 Improvement Project
4 Qualitative Analysis
4.1 Improvement of Safety
4.2 Risks
4.3 Comparative Analysis
5 PSA Analysis
5.1 Improvement of Safety
5.2 Risks of Case 1
5.3 Risks of Case 2
6 Conclusions
Bibliography
Determination Method and Application of Risk Threshold of Nuclear Power Plant Configuration
1 Introduction
2 Configuration Risk Management Threshold Determination Method
2.1 Quantitative Risk Index
2.2 Implementation of Risk Thresholds Abroad
2.3 Risk Threshold Determination Method
3 Application Practice
3.1 PSA Model Information of Nuclear Power Plant
3.2 Risk Threshold Determination
3.3 Risk Threshold Verification
4 Conclusions
Bibliography
High-Fidelity Drum Controller Design of Thermionic Space Nuclear Reactor
1 Introduction
2 Methodologies
2.1 Neutron Kinetics Model
2.2 Thermal Model of TFE
2.3 Model Predictive Control Design
2.4 Coupling Algorithm for System Simulation Control Platform
3 Results
3.1 Steady-State Results Analysis
3.2 Control Analysis of Transient Conditions
4 Conclusion
References
A Revision on CEFR Main Vessel Leakage Rate Measurement Based on Argon Chamber Numerical Simulation
1 Introduction
2 Main Vessel Leakage Rate Measurement Test
2.1 Methods of Leakage Rate Measurement
2.2 Analysis of Influencing Factors of Main Vessel Leakage Rate Measurement
3 Simulation Analysis of the Main Vessel Argon Chamber Temperature
3.1 Argon Chamber Temperature Simulation
3.2 Rapid Temperature-Rise Test
3.3 Leakage Rate Measurement Test
4 Conclusions
References
Emergency Power Supply System Scheme and Safety Analysis of a Project in a Newly-Built Large Nuclear Chemical Plant
1 Introduction
2 Design Principle and Process
2.1 Project Overview
2.2 Radiochemical Safety Level Loads Analysis
2.3 Emergency Power Supply Schemes
2.4 Emergency Power Supply Scheme in This Project
2.5 Supporting Facilities of the System
3 Operating Mode
3.1 Under Normal Condition
3.2 One Transformer Fault
3.3 Under Accident Condition
4 Safety Analysis
5 Conclusions
Bibliography
Total Quality Management in the Power Supply Design of a Project in a Newly-Built Large Nuclear Chemical Plant
1 Introduction
2 Characteristics and Functions of Total Quality Management
2.1 Development of Total Quality Management
2.2 Combination of Electrical Design and Total Quality Management
3 Application of TQM in the Project
3.1 Demand Determination
3.2 Construction Planning
3.3 Construction Process
3.4 Construction Effect
4 Conclusions
Bibliography
Surrogate Models Based on Back-Propagation Neural Network for Parameters Prediction of the PWR Core
1 Introduction
2 Methodology
2.1 Specification of the Reactor Core
2.2 Dataset Generation
2.3 ANNs Framework Design
2.4 Hyper-parameters Optimization
3 Results and Discussion
4 Conclusion
References
Comparison of SCC Results by Different Test Methods for Alloy 600 in High Temperature Water
1 Introduction
2 Effect of Temperature on PWSCC
3 Effect of Cold Work on PWSCC
4 Effect of Thermal Treatment
5 Conclusions
References
Development of New Spiral Throttling Device
1 Introduction
2 Analysis of Throttling and Pressure Reducing Principle of Throttling Device
3 Research on Design Method of Spiral Throttling Device
4 Turbulence Calculation Model [6]
4.1 Standard K- Model
4.2 RNG K- Model
4.3 Realizable K- Model
5 Comparative Analysis of Calculation and Test Results
5.1 Comparison of Calculation Results of Different Turbulence Models
5.2 Comparative Analysis of CFD Calculation Results and Test Results
6 Conclusions
References
Study on Corrosion Behavior of T-22 Alloy in Ultrahigh Temperature Impure Helium and Air
1 Introduction
2 Experimental Materials and Process
2.1 Experimental Materials
2.2 Experimental Process
3 Results and Discussion
3.1 Alloy Mass Change
3.2 Analysis of Alloy Morphology and Element Distribution
3.3 Alloy Carbon Content Analysis
4 Conclusion
References
Research and Application of Viscoelastic Artificial Boundary for Soil and Nuclear Power Plant Structure Dynamic Interaction Analysis
1 Introduction
2 Boundary Condition and Ground Motion Input Method
3 Soil and NPP Structure Dynamic Interaction Analysis
3.1 Finite Element Model
3.2 Comparative SSI Analyses
3.3 Influence of Oblique Incident Ground Motion on Seismic Response of NPP
4 Conclusion
References
Research on Mitigation Measures for Severe Accident Source Terms of Small Modular Reactor
1 Introduction
2 Calculation Models
2.1 ISAA Code
2.2 Calculation Model Settings
2.3 Typical Severe Accident Sequences Selection
3 Calculation Results
3.1 Analysis of Calculation Results of Containment Spray
3.2 Analysis of Double Containment Calculation Results
4 Summary
References
Study on the Over-Reading of Venturi Flow Measurement of ARE System in M310 Nuclear Power Unit
1 Problem Description
2 Background Information
3 Cause Investigation
3.1 Measuring Principle of Venturi Flow Element
3.2 Possible Causes of Measurement Over-Reading
3.3 Auxiliary Feed Water Valve Closedown Test
4 Influence of Vortex on Measurement Result
4.1 Properties of Vortex
4.2 Conservation Law of Angular Momentum
4.3 Influence of Vortex on Flow Measurement Results
4.4 Source of Vortex
4.5 Simulation Calculation
4.6 T-joint Measurement Results
5 Conclusions
References
Exploration and Practice of Industrial Aesthetics in Design of the Guohe One Nuclear Power Plant
1 Introduction
2 Principles and Applications of Industrial Aesthetics
2.1 The Intertwined Development of Industrial Architecture and Industrial Aesthetics
2.2 The Meaning of Industrial Aesthetics in Design of the Nuclear Power Building
3 Industrial Aesthetic Design of the Guohe One Nuclear Power Plant
3.1 Aesthetic Design for General Layout Space
3.2 Plan Layout and Facade Composition
3.3 Selection and Scrutiny of Facade Colors
3.4 Rational Interior Decoration and Design
4 Conclusions
Bibliography
Author Index

Citation preview

Springer Proceedings in Physics 284

Chengmin Liu Editor

Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2 PBNC 2022, 1–4 November, Beijing & Chengdu, China

Springer Proceedings in Physics

284

Indexed by Scopus The series Springer Proceedings in Physics, founded in 1984, is devoted to timely reports of state-of-the-art developments in physics and related sciences. Typically based on material presented at conferences, workshops and similar scientific meetings, volumes published in this series will constitute a comprehensive up to date source of reference on a field or subfield of relevance in contemporary physics. Proposals must include the following: – Name, place and date of the scientific meeting – A link to the committees (local organization, international advisors etc.) – Scientific description of the meeting – List of invited/plenary speakers – An estimate of the planned proceedings book parameters (number of pages/articles, requested number of bulk copies, submission deadline). Please contact: For Americas and Europe: Dr. Zachary Evenson; [email protected] For Asia, Australia and New Zealand: Dr. Loyola DSilva; [email protected]

Chengmin Liu Editor

Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2 PBNC 2022, 1–4 November, Beijing & Chengdu, China

Editor Chengmin Liu Nuclear Power Institute of China Chengdu, China

ISSN 0930-8989 ISSN 1867-4941 (electronic) Springer Proceedings in Physics ISBN 978-981-19-8779-3 ISBN 978-981-19-8780-9 (eBook) https://doi.org/10.1007/978-981-19-8780-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Acknowledgment

Thanks to all the members of the PBNC 2022 Conference Committees.

Contents

Optimal Analysis of Periodic Tests of Steam-Driven Auxiliary Feeding Pumps in QINSHAN Phase II NPP of CNNC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wang Zilong Fire Human Reliability Analysis for C-2 Nuclear Power Plant . . . . . . . . . . . . . . . Yongping Qiu, Yucheng Zhuo, Guixue Zhu, Jiandong He, and Xiao Tan

1

8

Design and Application of Temperature Compensation System For Composite Material Spindle Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wang Bin

16

The Research and Practice of PHM Technology in the Electrical System of Nuclear Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wang Zhijian and Sun Yao

27

Failure Analysis of Valve Seat Cracking of Pneumatic Control Valve in Nuclear Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ming-lei Hu, Wei Zhang, Ke Xu, Jie Wen, and Zhu Xu

37

Study on the Effect of Dispersant on the Properties of Ion Exchange Resins in Secondary Loop of Nuclear Power Plant . . . . . . . . . . . . . . . . . . . . . . . . Shunlong Yang, Shenao Wu, Rong Cao, Jun Wang, Hongyu Lai, Yu Wang, Chunsong Ye, and Tichun Dan The Analysis and Improvement of Fuel Damage Fraction Calculate . . . . . . . . . . Lei Lei, Ma Guoqiang, and Zou Xiang The Research on Phased Array Ultrasonic Inspection Technology in Nuclear Power Plant Thin Butt Weld Plate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hu Chenxu, Liu Hui, Jin Xiaoming, and Li Bingqian Primary Water Stress Corrosion Cracking of Nickel-Based Alloys . . . . . . . . . . . Han Yaolei, Peng Qunjia, Mei Jinna, and Xie Bin Study of Application Optimization of SPAR-H Human Reliability Analysis Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tan Xiao, Qiu Yongping, Zhuo Yucheng, Lei Wenjing, and Hu Juntao

46

58

67

78

91

viii

Contents

Comparison of Nuclear Fuel Manufacturing Management Requirements Between HAF003 and IAEA Nuclear Safety Regulations . . . . . . . . . . . . . . . . . . . Mengyao Tong Environmentally-Assisted Fatigue Study of Charging Nozzle Under Multiaxial Stress History Based on Fluid-Solid Interaction Method . . . . . . . . . . Hongbo Gao, Min Yu, Runfa Zhou, Mingya Chen, Lei Lin, Decheng Xu, and Shuai Zhou Condition Monitoring Based Equipment Health Management . . . . . . . . . . . . . . . Shuang-Han Ling, L. I.-Shi Ke, Jiang-Fei Sheng, and Li-Jun Huang

103

111

129

Double-Ended Shear Fracture Accident of Short Casing Inner Tube in Small Modular Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Han Feng, Huafa Chen, Jiang Yang, Ren Liang, and Zihao Guo

144

Dissolution Behavior of Nickel(II) Oxide/Nickel(II) Hydroxide Colloids in the Oxidation Operation Process During Shutdown of a PWR . . . . . Fuhai Li, Genxian Lin, Yun Sun, Hang Qiao, and Jun Fang

155

Application of Fiber Bragg Grating Technology in Perimeter Security of Nuclear Power Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hai-Rong Lu, Xu-Tao Bai, Xiao-Chen Zhang, Bo Yao, and Jin-Fei Zhang Experimental Study on Manufacturing Technology of Intermediate Heat Exchanger Tube-Tubesheet Joint in Sodium-Cooled Fast Reactor . . . . . . . Guangdong Song, Binbin Qiu, Xin li, Huajin Yu, and Lijun Zhou

166

173

PIRT Research on RCCA Ejection Accident . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zang Liye, Zhang Guanzhong, Li Qiang, Zhao Xiaohan, Wang Xiong, and Ouyang Yong

181

Research on Passive Single Failure Conditions of LB-lOCA . . . . . . . . . . . . . . . . Bao Guogang, Wang Weiwei, Liang Ren, Lin Zhikang, Wang Xiong, and Ouyang Yong

191

Analysis of Liquid Metal Cooled Reactor Safety Analysis Software FRTAC Applied To Pipeline Breach Ejection Experiment . . . . . . . . . . . . . . . . . . Yonggang Cao, Wenjun Hu, Pengrui Qiao, and Lei Zhao

200

Study on the Rationality of Superposition Principle of Accident and Fire Based on Probabilistic Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Li Xiaobo, Ji Jiangwei, Chen Yuxiu, Huang Huan, and Ding Xiaojiao

211

Contents

ix

Research on the Correlation of Influencing Factors Impinging Operating Events Fluctuation for NPPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Zhu, Qinmai Hou, Xiaoming Qian, Longquan Yang, and Yaqi Wang

218

Influence of a Film-Forming Amine on Repeated Adsorption and Regeneration of Ion Exchange Resins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lu Jundong, Jiang Xiaobin, Deng Jiajie, Li Xinmin, and Fang Jun

230

Maintenance Scheme Analysis of Accumulator on Emergency Direct Current Distribution Panel for Nuclear Power Plant . . . . . . . . . . . . . . . . . . . . . . . . Zhe Xu, Hongzhen Liu, Zongqiang Yang, Zhiguo Xie, and Tingwei Ma

240

Development and Application of Rod Based Guaranteed Shutdown State (RBGSS) Technology for Heavy Water Reactor . . . . . . . . . . . . . . . . . . . . . . Xingjin Shi, Chonmei Wang, Zhongguo Liu, Yongjie Zhan, and Zhixin Deng

246

The Strategy of Developing Safety Benchmark for VVER Multi-reactor Periodic Safety Review (PSR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenge Yu, Youyi Li, Yang Sun, Pengfei Zhao, and Xiye Lang

252

Research on Aging Management and Life Assessment of Low-Voltage Cables in Nuclear Power Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gu-Jian Ma and Ping Chen

259

Discuss on Severe Accident Management Guidelines for an Advanced NPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pingting Jiang

266

Theoretical Study on Negative Pressure Transport of Radioactive Waste Resin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jilong Zheng, Guipeng Li, Zhaoming Wang, and Kai Luo

277

Design and Verification of BNCT Beam Shaping Assembly Based on Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yaoqi Luo, Marcus Seidl, and Xiang Wang

306

Performance Monitoring and Back Pressure Curve Calculation of Condenser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaili Xu, Chang Gao, Tianqi He, and Shuhang Yan

316

Openfoam Simulation of Damping Controlled Fluidelastic Instability . . . . . . . . Zhipeng Feng, Shuai Liu, Huanhuan Qi, Xuan Huang, and Xi Lv

323

x

Contents

Research on Aging Behavior and Life Prediction of Fluorine Rubber Seals in Nuclear Power Plants Under Silicone Oil Condition . . . . . . . . . . . . . . . . Zhu Xu, Tiaobing Xiao, Zhe Xie, Yinqiang Chen, and Chun Gui

329

Super High Jacking Installation Technology of Ring Crane Arch Frame During Nuclear Power Plant Maintenance Period . . . . . . . . . . . . . . . . . . . . . . . . . . Zhishuang Liu, Jianjian Wu, Kejun Li, Furong Zhang, and Jie Su

337

Separation of Strontium from Other Fission Products in High Level Liquid Waste by TODGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nong Shuying, Chai Youqi, Yang Anbo, Zhao Qiaozi, Wang Jian, Li Tianfu, and Li Lianshun Effects of Normal Load, Thickness of Oxide Layer and Number of Cycles on Fretting Wear of PWR Fuel ROD Cladding . . . . . . . . . . . . . . . . . . . Li Dongxing, Hu Yong, Wang Hui, and Xin Long

347

354

Fault Detection for Redundant Measurement Channels . . . . . . . . . . . . . . . . . . . . . Peigen Cao, Wenfei Li, Haiyan Zhan, Feng An, and Xiaojun Xia

366

Disorder Induced by Gamma Irradiation in Borosilicate Glasses . . . . . . . . . . . . . Shikun Zhu, Xu Chen, Fan Yang, Kemian Qin, Jiangjiang Mao, Xiaoyang Zhang, Tieshan Wang, and Haibo Peng

375

Preliminary Evaluation of Impact Forces in Rotor Drop of the AMB-rotor System in Space Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yulan Zhao, Hongchun Ding, Guangchun Zhang, Kunlin Cheng, and Haochun Zhang Error Analysis of Set Pressure Test of Main Steam Safety Valve in CPR1000 Nuclear Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guo Yibo, Jiang Taikeng, and Yu Yuan Development and Application of the Control Rod Guide Tube Inspection Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pengfei Zhang, Qianfei Yang, Chenming Zeng, Shuangyin Wang, Jialong Shu, Chao Ma, and Jianrong Wu A Potential Threat Risk Evaluation Method for Nuclear Facilities . . . . . . . . . . . Chenliang Yuan, Liang Ma, Pin Zhang, Yutong Liu, Xiaocong Zhang, Ziyi Li, Duoyi Zhang, and Xiaoyuan Wan An Improved Hazop Method Was Used to Analyze the Safety of Hydrogen Production System in Nuclear Power Plant . . . . . . . . . . . . . . . . . . . Jianfeng Yang, Bingcheng Feng, Hangding Wang, and lihuang huan

384

400

412

419

428

Contents

xi

Research on the Optimal Management of Very Low Level Radioactive Waste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Li, Liu Jianqin, Qin Xiang, Gao Kai, and Guo Xiliang

436

Research on Contact Response of Active Compliant Assembly of Nuclear Power Maintenance Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lele Duan, Xi Wang, and Jianwen Chen

444

Digital Design and Improvement of PWR Rod Position Measurement System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huang Yuan, Chang Zhengke, Liu Yiqian, Lu Jian, and Huang Jing

459

Numerical Study on Radiative Heat Transfer Performance of ACP100 Passvie Containment Air Cooling System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Feng, Mingrui Yu, Hongliang Wang, Zhuo Liu, Xu Han, and Yidan Yuan Influence of Zirconia Addition on the Leaching Stability of Borosilicate Glass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoyang Zhang, Fan Yang, Jiangjiang Mao, Shikun Zhu, Xu Chen, Kemian Qin, Haibo Peng, and Tieshan Wang

470

481

Best Estimate Plus Uncertainty Analysis of a Pressurizer Surge Line Break LOCA on China’s Advanced PWR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cuiting Peng, Yao Yao, Ye Yang, Chengcheng Deng, and Jun Yang

490

Fault Diagnosis of Nuclear Power Plants Based on 1D-CNN with Dual Attention Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gensheng Qian and Jingquan Liu

506

Experiments of Polydisperse Aerosol Transport Progress in Horizontal and Vertical Pipes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhichao Gao, Lili Tong, and Xuewu Cao

515

Calculation of the Decommissioning Radiation Field of Nuclear Power Plants Based on the Coupling of MC and Point Kernel Integration . . . . . . . . . . . Yufei Guo, Yanfang Liu, Li Wang, Shuiqing Liu, and Hangzhou Zhang

530

Influence Analysis of Dirt on PAS Heat Transfer Performance of ACP100 . . . . Hongliang Wang, Yu Feng, Mingrui Yu, Zhuo Liu, Xu Han, and Yidan Yuan An Unsupervised Learning-Based Framework for Effective Representation Extraction of Reactor Accidents . . . . . . . . . . . . . . . . . . . . . . . . . . . Chengyuan Li, Meifu Li, and Zhifang Qiu

540

549

xii

Contents

TAC-1 Project Quality Management Research Based on Configuration Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xu Hanjie, Dong Cuicai, and Han Wei

565

Research on Diffusion of Oceanic Radionuclides Deposited from Atmosphere Under Nuclear Leakage Accidents . . . . . . . . . . . . . . . . . . . . . . Zichao Li, Rongchang Chen, Zheng Wang, Chen Liu, and Tao Zhou

574

An Overview of R&D Activities on High Level Liquid Waste Partitioning at Tsinghua University, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chao Xu and Jing Chen

584

Analysis of the Economic Prospect of Nuclear Heat Supply by Megawatt-Class Nuclear Power Units from the Perspective of Carbon Peaking and Carbon Neutrality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiao-lei Song, Xin Li, and Song-kun Jiao The Study on Monitoring of Different Chemical Forms of Tritium and Tritium-Induced Radiation Dose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mengjie Zhou, Yao Wu, Li Wang, Tao Jin, Ting Shen, Jianqiang Wang, and Min Pei Improvement and Realization of AGC Strategy in Nuclear Power Plants . . . . . . Jing Chuanxiang and Zhao Yuntao Study on Simulation Method of CRUD Deposition Behavior on Nuclear Fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiao-han Liu, Kai-yuan Wang, Yong Lu, Ya-Ni Liu, and Xin Jin Evaluation on the Release of Key Nuclides in Cemented Waste Form in a Rock Cavern Disposal Site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ren Yaqing, Xie Wenzhang, Li Kunfeng, Lin Peng, Du Yingzhe, Liu Xiajie, and Li Li Research and Validation of RCP Temperature Automatic Control During Cold Shutdown Stage in a GEN-III Nuclear Power Plant . . . . . . . . . . . . Fei Song, Hang Liu, Zhenhua Luan, Jianfeng Qiao, and Peng Liu Application Status and Prospect of Two-Dimensional Graphene for Hydrogen Isotope Separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ruixi Gao, Li Lin, Zhenchen Li, Yi Liang, Wenlu Gu, Jingjie Yang, Jiabing Yan, and Jiheng Fan

589

596

607

616

629

643

654

Contents

xiii

Research on Two Power Reduction Strategies of Cpr1000 Unit and Its Influence on Axial Power Deviation Delta I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhenhua Zhang, Bo Zhang, and Chaoying Zheng

668

Analytical Displacement Analysis for an Inverted U-Shaped Tunnel Excavated in Anisotropic Rock Mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shaojie Wang, Xinli Zhao, Dongmei Wang, and Di Jiang

679

Calculation of Cs Migration Behaviorin Argillaceous Material . . . . . . . . . . . . . . Heng Zhang and Li Hong Hui Radiological Consequences and Risk Analysis for On-Site Workers During PSA Fault Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenwang Ran, Jing Zhou, Weifeng Lv, Quan Gong, and Jun Xiong Study of the Capacity Building for Interim Storage of PWR Spent Nuclear Fuel in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lei Shi, Chen Chen, Na Ma, Honglin Zhang, Yang Su, Jian Hu, and Chao Chen The Study and Suggestions on the Civil Nuclear Safety Equipment Standards in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ting Liu, Zhiyuan Liu, Meng Chang, Dahu Song, Yiman Dong, Bingchen Huang, Shujie Jiang, Yanxin Lv, and Li Huang

689

700

712

724

Effect of Corrosion Damage on Structural Failure Models Under Different Boundry Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sheng Qian, Yu Sun, Yuzhao Huangfu, and Ke Zhang

729

Research on the Applicability of Experience Feedback of Multi-plants to Nuclear Safety Management of New Nuclear Power Plants . . . . . . . . . . . . . . . Shuang Zhang, Xiaoyan Sun, Wenwen Zhao, Chao Gao, and Yang Li

742

Drying-into-salt Technology of High Salinity Radioactive Liquid Waste . . . . . . Fan Jiheng, Luo Feng, Wu Guanghui, Li Zhenchen, Jia Zhanju, Zhang Hangzhou, Muo Shuangrong, Fan Chunxin, and Gao Ruixi

753

Study on Key Technical Issues of Marine Environmental Safety Assessment of the Floating Nuclear Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . Yueping Xu, Lianghui Liu, Xiaofeng Zhang, and Naigui Tao

762

Research on the Application of Tension Monitoring System of the Interception Net in Nuclear Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaolin Liu, Wei Meng, Qian Huang, Jiecong Liu, and Shuai Wang

773

xiv

Contents

Study on Preparation Technology of UCL4 From CCl4 . . . . . . . . . . . . . . . . . . . . . Qiaojuan Wei, Fuchen Ma, Gangqiang Yang, Dan Xu, and Jianxin Feng

783

Development and Application of High-Accuracy Metal Fuel Performance Analysis Code Based on Fem Method . . . . . . . . . . . . . . . . . . . . . . . Qihao Tao, Bo Zhang, Yongbo Hui, and Jianqiang Shan

793

Site Condition Sensitivity Analysis Based on the Sub-structuring Method for a Base-Isolated Nuclear Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoying Sun, Yingying Gan, Jian Chen, and Dongyang Wang

809

Modelling Analysis of Non-uniform Flow and Heat Transfer in Parallel Rectangular Channels During Flow Blockage Condition . . . . . . . . . . . . . . . . . . . Jiayue Chen, Huandong Chen, Xiaoyu Wang, and Zefeng Wang

827

Research Progress of SCR Denitration Catalyst in NOx Exhaust Gas Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tang Jinlong, Xu Yan, and Wu Yuyong

839

Laser Decontamination Experiments for Radioactive Contaminated Metals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wu Xiaojiang, Li Zhihua, Jiang He, Yu Dawan, Wang Shuai, Wen Xiaojun, Wen Jin, Liu Maoquan, Zhao Wan, and Cao Junjie

845

The Optimization Design of the Reactor Coolant System Based on Optimus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuan Yanli and Ye Xianhui

853

Study on Near Surface Radionuclide Activity Under Vehicle Heat Pipe Small Modular Reactor Accident Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Haolei, Zhou Tao, Xu Peng, and Tang Jianyu

860

Multi-physics Numerical Investigation on the Mechanical Stirring in a Cold Crucible Melter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ye Hong, Yusong Li, Dongdong Zhu, Dongsheng Qie, Runci Wang, and Shengdong Zhang Development and Validation of Source Term Model of Corrosion Products in the Primary Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liu Ya-ni, Jin Xin, Liu Xiao-han, Wang Tao, and Chen Wei-lin A Review on Adsorption Mechanisms and Distribution Coefficient (K d ) of Cesium in Clay/Host Rock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuling Wu, Sheng Fang, Jingchi Zhang, Xinyuan Mo, and Longcheng Liu

874

885

898

Contents

Is Perchlorate Innocent in the Study of Weak Complexations of Nitrate with Metal Ions by Spectrophotometric Titration? . . . . . . . . . . . . . . . . . . . . . . . . . Yuning Yang, Zhijin Zhao, Jing Chen, Jianchen Wang, and Taoxiang Sun Design and Experimental Study of Ultra High Pressure Water Jet Decontamination Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chengtian Zhang, Lei Teng, Xisan Chen, Yongling Zhang, Zhijun Sun, Zhiyong Su, Wei Xue, and Tao Long Analysis of Shock Wave and Containment Behavior Under Severe Accident Internal Hydrogen Explosion Based on CEL Method . . . . . . . . . . . . . . Li Rongpeng, Liu Mengsha, Gao Ge, Yao Di, and Jiang Di Study on the System Simulation for the Heat Pipe Failure Transient of Heat Pipe Cooled Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chao Tan, Suizheng Qiu, Chenglong Wang, Zeqin Zhang, Zhengquan Xie, and Fei Li Prediction of LOCA Break Size Based on 1D Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hao Wang and Zheng Liu Quality Control of Small Batch product in Manufacturing Process . . . . . . . . . . . Limei Peng, Pengbo Ji, Yueqing Qian, Yong Yang, Hongyu Tian, and Junling Han Study on the Influence of Reactor System LOCA Modeling Mode on Dynamic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liu Shuai, Huang Xuan, Feng Zhipeng, Zeng Zhongxiu, Qi Huanhuan, and Jiang Xiaozhou Development and Verification of Solver for Natural Circulation Flow and Heat Transfer Simulation Base on Openfoam . . . . . . . . . . . . . . . . . . . . . . . . . Zhengyu Gong, Miao Liang, Wenlan Ou, Qiwen Pan, Ling Zhang, and Zhixing Gu

xv

913

928

937

947

964

973

984

993

Economic Indicator Analysis of HPR1000 Process Pipe Engineering in the Integrated Technology Corridor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1002 Yang Shi, Wen-An Li, Qian Yu, Li-Juan Chen, and Dan Fa A Kinetic Model for Researching the Rolling Bearing Load Inversion Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1014 Donglin Liu, Renqiong Wu, Xianhe Shang, Chun Zeng, Zhong Xu, Haojun Ma, Kun Luo, and Lili Zhu

xvi

Contents

Solar Energetic Particles Hazard Assessment for UK HPR1000 . . . . . . . . . . . . . 1023 Xujia Chen, Guanghua Yang, and Xunjia Zhuo Safety Analysis for Uncontrolled Withdrawal of Rod Cluster Control Assembly Bank After Extend Low Power Operation . . . . . . . . . . . . . . . . . . . . . . . 1031 Kun Xiong and Xin Wang Discussion on the Application Prospect of Airborne Geophysical Prospecting in Site Selection and Construction of Nuclear Waste Disposal Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1040 Pei-jian Wang, Xiang Zhang, Bin Wei, Yuanzhi Wang, and Xianhong Wu Study on China’s Nuclear Power Development and Nuclear Safety Regulation Under the Background of Carbon Peaking and Carbon Neutrality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1045 Fangqiang Chen, Zhaotong Li, Qingsong Wang, Chaoying Zheng, and Shuai Chen Effect of Different Improved Designs for ARE Sensors on Core Safety . . . . . . . 1054 Chen Shijun, Chen Lihui, Luo Wenbo, and Zhang Kuan Determination Method and Application of Risk Threshold of Nuclear Power Plant Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1061 Shijun Chen, Zichun Wang, Wenbo Luo, and Lihui Chen High-Fidelity Drum Controller Design of Thermionic Space Nuclear Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1070 Jianghan Fu, Zhao Jin, Chenglong Wang, Zhiwen Dai, Wenxi Tian, Guanghui Su, and Suizheng Qiu A Revision on CEFR Main Vessel Leakage Rate Measurement Based on Argon Chamber Numerical Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1084 Xin-Yuan Bian, Guo-Tu Ke, Jian Zhang, and De-Kang Luo Emergency Power Supply System Scheme and Safety Analysis of a Project in a Newly-Built Large Nuclear Chemical Plant . . . . . . . . . . . . . . . . 1096 Shuo Gao, Meng Wang, and Shizhong Tian Total Quality Management in the Power Supply Design of a Project in a Newly-Built Large Nuclear Chemical Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . 1102 Shuo Gao, Zhi Huang, and Shizhong Tian Surrogate Models Based on Back-Propagation Neural Network for Parameters Prediction of the PWR Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1109 Xinyan Bei, Maosong Cheng, Xiandi Zuo, Kaicheng Yu, and Yuqing Dai

Contents

xvii

Comparison of SCC Results by Different Test Methods for Alloy 600 in High Temperature Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1123 Xiaohui Li, Panpan Wu, Xinhe Xu, Zhanpeng Lu, and Tongming Cui Development of New Spiral Throttling Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1131 Gao Chang, Tianqi He, Xu Kaili, and Shuhang Yan Study on Corrosion Behavior of T-22 Alloy in Ultrahigh Temperature Impure Helium and Air . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1140 Haoxiang Li, Wei Zheng, Bin Du, Huaqiang Yin, Xuedong He, Tao Ma, and Xingtuan Yang Research and Application of Viscoelastic Artificial Boundary for Soil and Nuclear Power Plant Structure Dynamic Interaction Analysis . . . . . . . . . . . 1149 Dongyang Wang, Xiaoying Sun, Ziqiao Liu, and Yingying Gan Research on Mitigation Measures for Severe Accident Source Terms of Small Modular Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1159 Tao Xu, Dujuan Han, Bin Zhang, Junlong Wang, Jiajia Liu, Yirui Wu, Chao Tian, Mingming Xia, and Haifu Ma Study on the Over-Reading of Venturi Flow Measurement of ARE System in M310 Nuclear Power Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1169 Gongzhan Wang, Xianhe Shang, Pengfei Fan, Yongxiang Zheng, Jiangbo Gao, Qian Min, Xiasheng Lei, Qiang Liu, and Fang Li Exploration and Practice of Industrial Aesthetics in Design of the Guohe One Nuclear Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1183 Yingrong Wang, Xiaojie Hu, Ming Wang, Chunguang Liu, and Hui Song Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1193

Contributors

Feng An Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Yang Anbo Science and Technology Research Institute of CNNC 404 Co., Ltd., Jiayuguan, Gansu, China; CNNC 404 Chengdu nuclear technology engineering design and Research Institute Co., Ltd., Chengdu, Sichuan, China Xu-Tao Bai Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Xinyan Bei Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China Xin-Yuan Bian China Institute of Atomic Energy, Beijing, China Wang Bin Research Institute of Physical and Chemical Engineering of Nuclear Industry, Tianjin, China Xie Bin Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Li Bingqian CGNPC Inspection Technology Corporation, Suzhou, Jiangsu, China Peigen Cao Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Rong Cao China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China Xuewu Cao Shanghai Jiao Tong University, Minhang, Shanghai, China Yonggang Cao China Institute of Atomic Energy, Fangshan, Beijing, China Gao Chang China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China; Research Institute of Nuclear Power Operation, Wuhan, Hubei, China Meng Chang Nuclear and Radiation Safety Centre of MEE Beijing, Beijing, China Chao Chen China Institute of Nuclear Industry Strategy, Beijing, China Chen Chen CNNC Xinneng Nuclear Engineering Corporation, Taiyuan, China Fangqiang Chen Nuclear and Radiation Safety Center, MEE, Beijing, China Huafa Chen China Nuclear Power Technology Research Institute, Shenzhen, China Huandong Chen Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-Sen University, Zhuhai, China Jian Chen China Nuclear Power Engineering Co., Ltd., Beijing, China

xx

Contributors

Jianwen Chen Research Institute of Nuclear Power Operation, Wuhan, Hubei, China Jiayue Chen Sino-French Institute of Nuclear Engineering and Technology, Sun YatSen University, Zhuhai, China Jing Chen Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Li-Juan Chen Economic Evaluation Division, China Nuclear Power Engineering Co., Ltd., Beijing Institute of Industrial Engineering, Beijing, China Lihui Chen Suzhou Nuclear Power Research Institute Co., Ltd., Shenzhen, China Mingya Chen Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Ping Chen Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Rongchang Chen China Waterborne Transport Research Institute, Beijing, China Shijun Chen Suzhou Nuclear Power Research Institute Co., Ltd., Shenzhen, China Shuai Chen Nuclear and Radiation Safety Center, MEE, Beijing, China Xisan Chen Nuclear Power Institute of China, Chengdu, Sichuan, China Xu Chen School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, China Xujia Chen China Nuclear Power Engineering Co., Shenzhen, Guangdong, China Yinqiang Chen China Nuclear Power Operation Technology Co., Ltd., Wuhan, Hubei, China Kunlin Cheng School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China Maosong Cheng Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China Hu Chenxu CGNPC Inspection Technology Corporation, Suzhou, Jiangsu, China Jing Chuanxiang China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Fan Chunxin Sichuan Provincial Engineering Laboratory of Nuclear Facility, Nuclear Power Institute of China, Chengdu, China Tongming Cui Shanghai University, Shanghai, China Dong Cuicai China Nuclear Power Engineering Co., Ltd., Beijing, China Yuqing Dai Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China

Contributors

xxi

Zhiwen Dai Xi’an Jiaotong University, Xi’an, Shaanxi, China Tichun Dan China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China Yu Dawan CNNP Nuclear Power Operation Management Co., Ltd., Haiyan, China Chengcheng Deng Department of Nuclear Engineering and Technology, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, China Zhixin Deng Qinshan Nuclear Power, Haiyan, Zhejiang, China Jiang Di China Nuclear Power Engineering Company, Beijing, China Yao Di China Nuclear Power Engineering Company, Beijing, China Hongchun Ding School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China Yiman Dong Nuclear and Radiation Safety Centre of MEE Beijing, Beijing, China Li Dongxing China Institute of Atomic Energy, Beijing, China Bin Du Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Lele Duan Research Institute of Nuclear Power Operation, Wuhan, Hubei, China Dan Fa Economic Evaluation Division, China Nuclear Power Engineering Co., Ltd., Beijing Institute of Industrial Engineering, Beijing, China Jiheng Fan Nuclear Power Institute of China, Chengdu, Sichuan Province, China Pengfei Fan CNNO Operations Management Co., Ltd., Haiyan, Zhejiang, China Jun Fang Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Sheng Fang China Institute of Atomic Energy, Beijing, China Bingcheng Feng Suzhou Nuclear Power Research Institute, Shenzhen, China Han Feng China Nuclear Power Technology Research Institute, Shenzhen, China Jianxin Feng The 404 Company Limited, CNNC, Lanzhou, China Luo Feng Sichuan Provincial Engineering Laboratory of Nuclear Facility, Nuclear Power Institute of China, Chengdu, China Yu Feng China Nuclear Power Engineering Co., Ltd., Beijing, China Zhipeng Feng Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Jianghan Fu Xi’an Jiaotong University, Xi’an, Shaanxi, China Yingying Gan China Nuclear Power Engineering Co., Ltd., Beijing, China

xxii

Contributors

Chang Gao China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China Chao Gao Suzhou Nuclear Power Research Institute Co., Ltd., Equipment Management Center, Shenzhen, Guangdong, China Hongbo Gao Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Jiangbo Gao CNNO Operations Management Co., Ltd., Haiyan, Zhejiang, China Ruixi Gao Nuclear Power Institute of China, Chengdu, Sichuan Province, China Shuo Gao China Nuclear Power Engineering Co., Ltd., Beijing, China Zhichao Gao Shanghai Jiao Tong University, Minhang, Shanghai, China Gao Ge China Nuclear Power Engineering Company, Beijing, China Quan Gong China Nuclear Power Design Co., Ltd., Shenzhen, Guangdong, China Zhengyu Gong College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, Sichuan, China Wenlu Gu Nuclear Power Institute of China, Chengdu, Sichuan Province, China Zhixing Gu College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, Sichuan, China Wu Guanghui Sichuan Provincial Engineering Laboratory of Nuclear Facility, Nuclear Power Institute of China, Chengdu, China Zhang Guanzhong CNPRI, Shenzhen, Guangdong, China Chun Gui China Nuclear Power Operation Technology Co., Ltd., Wuhan, Hubei, China Yufei Guo Nuclear Power Institute of China, Chengdu, China Zihao Guo China Nuclear Power Technology Research Institute, Shenzhen, China Bao Guogang Nuclear Safety Analysis Engineering, China Nuclear Power Technology Research Institute Co., Ltd., Shanghai, China Ma Guoqiang Nuclear and Radiation Safety Center, Beijing, China Dujuan Han Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Junling Han CNNC Key Laboratory on New Materials Research and Application Development, Baotou, China; China North Nuclear Fuel Co., Ltd., Baotou, Inner Mongolia, China Xu Han China Nuclear Power Engineering Co., Ltd., Beijing, China Zhang Hangzhou Sichuan Provincial Engineering Laboratory of Nuclear Facility, Nuclear Power Institute of China, Chengdu, China Xu Hanjie China Nuclear Power Engineering Co., Ltd., Beijing, China

Contributors

xxiii

Zhang Haolei Department of Nuclear Science and Technology, School of Energy and Environment, Southeast University, Nanjing, China; Institute of Nuclear Thermal-Hydraulic Safety and Standardization, Beijing, China; National Engineering Research Center of Power Generation Control and Safety, Nanjing, China Jiandong He Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China Jiang He CNNP Nuclear Power Operation Management Co., Ltd., Haiyan, China Tianqi He China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China Xuedong He Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Ye Hong Department of Radiochemistry, China Institute of Atomic Energy, Beijing, China Qinmai Hou Nuclear and Radiation Safety Center, MEE,, Beijing, China Jian Hu China Institute of Nuclear Industry Strategy, Beijing, China Ming-lei Hu CNNC Nuclear Power Operations Management Co., Ltd., Haiyan, Zhejiang, China Wenjun Hu China Institute of Atomic Energy, Fangshan, Beijing, China Xiaojie Hu State Nuclear Power Technology Corporation Ltd., Shanghai, China Huang Huan China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China lihuang huan Suzhou Nuclear Power Research Institute, Shenzhen, China Bingchen Huang Nuclear and Radiation Safety Centre of MEE Beijing, Beijing, China Li-Jun Huang Suzhou Nuclear Power Research Institute, Shenzhen, Futian Distric, China Li Huang Nuclear and Radiation Safety Centre of MEE Beijing, Beijing, China Qian Huang Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Xuan Huang Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Zhi Huang China Nuclear Power Engineering Co., Ltd., Beijing, China Yuzhao Huangfu Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Qi Huanhuan Science and Technology on Reactor System Design Technology Laboratory, Chengdu, Sichuan, China

xxiv

Contributors

Li Hong Hui China Institute for Radiation Protection, Taiyuan, Shanxi, China Liu Hui CGNPC Inspection Technology Corporation, Suzhou, Jiangsu, China Wang Hui China Institute of Atomic Energy, Beijing, China Yongbo Hui Xi’an Jiaotong University, Xi’an, Shaanxi, China Pengbo Ji China North Nuclear Fuel Co., Ltd., Baotou, Inner Mongolia, China; CNNC Key Laboratory on Fabrication Technology of Reactor Irradiation Special Fuel Assembly, Baotou, China Deng Jiajie Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Lu Jian CNNC Nuclear Power Operation Management Co., Ltd., Haiyan, Zhejiang, China Wang Jian CNNC 404 Co., Ltd., Jiayuguan, Gansu, China Di Jiang Structural Technology Research Center of Nuclear Engineering, China Nuclear Power Engineering Co., Ltd., Beijing, China Pingting Jiang Shenzhen, Guangdong, China Shujie Jiang Nuclear and Radiation Safety Centre of MEE Beijing, Beijing, China Ji Jiangwei China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Liu Jianqin Institute for Radiation Protection, Taiyuan, Shanxi, China Tang Jianyu Department of Nuclear Science and Technology, School of Energy and Environment, Southeast University, Nanjing, China; Institute of Nuclear Thermal-Hydraulic Safety and Standardization, Beijing, China; National Engineering Research Center of Power Generation Control and Safety, Nanjing, China Song-kun Jiao China Nuclear Power Engineering Co., Ltd., Shijiazhuang, Hebei, China Fan Jiheng Sichuan Provincial Engineering Laboratory of Nuclear Facility, Nuclear Power Institute of China, Chengdu, China Tao Jin The Reactor Operation and Application Research Sub-institute, Nuclear Power Institute of China, Chengdu, China; Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste Management, Chengdu, China Wen Jin Nuclear Power Institute of China, Chengdu, China Xin Jin China Nuclear Power Technology Research Institute Shenzhen, Guangdong, China Zhao Jin Xi’an Jiaotong University, Xi’an, Shaanxi, China

Contributors

xxv

Huang Jing CNNC Nuclear Power Operation Management Co., Ltd., Haiyan, Zhejiang, China Tang Jinlong China Nuclear Power Engineering Co., Ltd., BeiJing, China Mei Jinna Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Fang Jun Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Lu Jundong Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Cao Junjie Sichuan Provincial Engineering Laboratory of Nuclear Facilities Decommissioning and Radwaste Management, Nuclear Power Institute of China, Chengdu, China Hu Juntao Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China Gao Kai Institute for Radiation Protection, Taiyuan, Shanxi, China Xu Kaili China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China Guo-Tu Ke China Institute of Atomic Energy, Beijing, China L. I.-Shi Ke Suzhou Nuclear Power Research Institute, Shenzhen, Futian Distric, China Zhang Kuan Suzhou Nuclear Power Research Institute Co., Ltd., Shenzhen, China Li Kunfeng China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong Province, China Hongyu Lai Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Xiye Lang JNPC, CNNP, Lianyungang, Jiangsu, China Lei Lei Nuclear and Radiation Safety Center, Beijing, China Xiasheng Lei CNNO Operations Management Co., Ltd., Haiyan, Zhejiang, China Chengyuan Li Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Fang Li CNNO Operations Management Co., Ltd., Haiyan, Zhejiang, China Fei Li CNNC Key Laboratory on Nuclear Industry Simulation, Wuhan, Hubei, China Fuhai Li Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Guipeng Li CNNP Xiapu Nuclear Power Co., Ltd., Ningde, Fujian, China Haoxiang Li Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Kejun Li Central South University of Forestry and Technology, Changsha, China

xxvi

Contributors

Li Li China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong Province, China Meifu Li Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Wen-An Li Economic Evaluation Division, China Nuclear Power Engineering Co., Ltd., Beijing Institute of Industrial Engineering, Beijing, China Wenfei Li Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Xiaohui Li Shanghai University, Shanghai, China Xin li China Institute of Atomic Energy, Beijing, China Xin Li China Nuclear Power Engineering Co., Ltd., Shijiazhuang, Hebei, China Yang Li Suzhou Nuclear Power Research Institute Co., Ltd., Equipment Management Center, Shenzhen, Guangdong, China Youyi Li JNPC, CNNP, Lianyungang, Jiangsu, China Yusong Li China Institute of Atomic Energy, Beijing, China Zhang Li Institute for Radiation Protection, Taiyuan, Shanxi, China Zhaotong Li Nuclear and Radiation Safety Center, MEE, Beijing, China Zhenchen Li Nuclear Power Institute of China, Chengdu, Sichuan Province, China Zichao Li China Waterborne Transport Research Institute, Beijing, China Ziyi Li Nuclear Security Technology Innovation Center of Hebei Province, The Fourth Research and Design Engineering Corporation of CNNC, Shijiazhuang, China Miao Liang College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, Sichuan, China Ren Liang China Nuclear Power Technology Research Institute, Shenzhen, China Yi Liang Nuclear Power Institute of China, Chengdu, Sichuan Province, China Li Lianshun Science and Technology Research Institute of CNNC 404 Co., Ltd., Jiayuguan, Gansu, China Chen Lihui Suzhou Nuclear Power Research Institute Co., Ltd., Shenzhen, China Genxian Lin Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Lei Lin Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Li Lin Nuclear Power Institute of China, Chengdu, Sichuan Province, China Shuang-Han Ling Suzhou Nuclear Power Research Institute, Shenzhen, Futian Distric, China

Contributors

xxvii

Chen Liu China Waterborne Transport Research Institute, Beijing, China Chunguang Liu State Nuclear Power Demonstration Plant Co., Ltd., Weihai, Shandong, China Donglin Liu CNNC Nuclear Power Operation Management Co., Ltd., Haiyan, China Hang Liu China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, People’s Republic of China Hongzhen Liu China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Jiajia Liu Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Jiecong Liu Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Jingquan Liu Department of Engineering Physics, Tsinghua University, Beijing, China Lianghui Liu Suzhou Nuclear Power Research Institute, Suzhou, China Longcheng Liu China Institute of Atomic Energy, Beijing, China; KTH Royal Institute of Technology, Stockholm, Sweden Peng Liu China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, People’s Republic of China Qiang Liu CNNO Operations Management Co., Ltd., Haiyan, Zhejiang, China Shuai Liu Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Shuiqing Liu Nuclear Power Institute of China, Chengdu, China Ting Liu Nuclear and Radiation Safety Centre of MEE Beijing, Beijing, China Xiao-han Liu China Nuclear Power Technology Research Institute Shenzhen, Guangdong, China Xiaolin Liu Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Ya-Ni Liu China Nuclear Power Technology Research Institute Shenzhen, Guangdong, China Yanfang Liu Nuclear Power Institute of China, Chengdu, China; Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste Management, Chengdu, China Yutong Liu Nuclear Security Technology Innovation Center of Hebei Province, The Fourth Research and Design Engineering Corporation of CNNC, Shijiazhuang, China Zheng Liu CNNC Key Laboratory on Severe Accident in Nuclear Power Safety, CNPE, Beijing, China

xxviii

Contributors

Zhishuang Liu China Nuclear Industry 23 Construction Co., Ltd., Beijing, China Zhiyuan Liu Nuclear and Radiation Safety Centre of MEE Beijing, Beijing, China Zhongguo Liu Qinshan Nuclear Power, Haiyan, Zhejiang, China Zhuo Liu China Nuclear Power Engineering Co., Ltd., Beijing, China Ziqiao Liu China Nuclear Power Engineering Co., Ltd., Beijing, China Zang Liye Nuclear Safety Analysis Engineering, China Nuclear Power Technology Research Institute Co., Ltd., Shanghai, China; CNPRI, Shanghai, China Tao Long Nuclear Power Institute of China, Chengdu, Sichuan, China Xin Long University of Science and Technology Beijing, Beijing, China Hai-Rong Lu Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Yong Lu China Nuclear Power Technology Research Institute Shenzhen, Guangdong, China Zhanpeng Lu Shanghai University, Shanghai, China Zhenhua Luan State Key Lab of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University Hangzhou, Zhejiang, People’s Republic of China De-Kang Luo China Institute of Atomic Energy, Beijing, China Kai Luo CNNP Xiapu Nuclear Power Co., Ltd., Ningde, Fujian, China Kun Luo School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China Wenbo Luo Suzhou Nuclear Power Research Institute Co., Ltd., Shenzhen, China Yaoqi Luo College of Nuclear Science and Technology, Harbin Engineering University, Harbin, China Weifeng Lv China Nuclear Power Design Co., Ltd., Shenzhen, Guangdong, China Xi Lv Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Yanxin Lv Nuclear and Radiation Safety Centre of MEE Beijing, Beijing, China Chao Ma CGNPC Inspection Technology Co., Ltd., Suzhou, Jiangsu, China Fuchen Ma The 404 Company Limited, CNNC, Lanzhou, China Gu-Jian Ma Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Haifu Ma Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China

Contributors

xxix

Haojun Ma CNNC Nuclear Power Operation Management Co., Ltd., Haiyan, China Liang Ma Nuclear Security Technology Innovation Center of Hebei Province, The Fourth Research and Design Engineering Corporation of CNNC, Shijiazhuang, China Na Ma China National Uranium Corporation, Beijing, China Tao Ma Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Tingwei Ma China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Jiangjiang Mao School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, China Liu Maoquan Nuclear Power Institute of China, Chengdu, China Wei Meng Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Liu Mengsha China Nuclear Power Engineering Company, Beijing, China Qian Min CNNO Operations Management Co., Ltd., Haiyan, Zhejiang, China Xinyuan Mo China Institute of Atomic Energy, Beijing, China Wenlan Ou College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, Sichuan, China Qiwen Pan College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, Sichuan, China Min Pei The Reactor Operation and Application Research Sub-institute, Nuclear Power Institute of China, Chengdu, China; Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste Management, Chengdu, China Cuiting Peng Department of Nuclear Engineering and Technology, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, China Haibo Peng School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, China Limei Peng CNNC Key Laboratory on New Materials Research and Application Development, Baotou, China; China North Nuclear Fuel Co., Ltd., Baotou, Inner Mongolia, China Lin Peng China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong Province, China Xu Peng Institute of Nuclear Thermal-Hydraulic Safety and Standardization, Beijing, China; National Engineering Research Center of Power Generation Control and Safety, Nanjing, China;

xxx

Contributors

School of Nuclear Science and Engineering, North China Electric Power University, Beijing, China Huanhuan Qi Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Gensheng Qian Department of Engineering Physics, Tsinghua University, Beijing, China Sheng Qian Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Xiaoming Qian Nuclear and Radiation Safety Center, MEE,, Beijing, China Yueqing Qian CNNC Key Laboratory on New Materials Research and Application Development, Baotou, China; China North Nuclear Fuel Co., Ltd., Baotou, Inner Mongolia, China Li Qiang CNPRI, Shenzhen, Guangdong, China Hang Qiao Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Jianfeng Qiao China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, People’s Republic of China Pengrui Qiao China Institute of Atomic Energy, Fangshan, Beijing, China Zhao Qiaozi The Third Branch of China Nuclear 404 Co., Ltd., Jiayuguan, Gansu, China Dongsheng Qie China Institute of Atomic Energy, Beijing, China Kemian Qin School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, China Binbin Qiu Xi’an Jiaotong University, Xi’an, China Suizheng Qiu School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an, Shanxi, China Yongping Qiu Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China Zhifang Qiu Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Peng Qunjia Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Wenwang Ran China Nuclear Power Design Co., Ltd., Shenzhen, Guangdong, China Liang Ren CNPRI, Shenzhen, Guangdong, China Li Rongpeng China Nuclear Power Engineering Company, Beijing, China Gao Ruixi Sichuan Provincial Engineering Laboratory of Nuclear Facility, Nuclear Power Institute of China, Chengdu, China

Contributors

xxxi

Marcus Seidl PreussenElektra GmbH, Hannover, Germany Jianqiang Shan Xi’an Jiaotong University, Xi’an, Shaanxi, China Xianhe Shang CNNC Nuclear Power Operation Management Co., Ltd., Haiyan, China; CNNO Operations Management Co., Ltd., Haiyan, Zhejiang, China Ting Shen The Reactor Operation and Application Research Sub-institute, Nuclear Power Institute of China, Chengdu, China; Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste Management, Chengdu, China Jiang-Fei Sheng Suzhou Nuclear Power Research Institute, Shenzhen, Futian Distric, China Lei Shi China Institute of Nuclear Industry Strategy, Beijing, China Xingjin Shi Qinshan Nuclear Power, Haiyan, Zhejiang, China Yang Shi Economic Evaluation Division, China Nuclear Power Engineering Co., Ltd., Beijing Institute of Industrial Engineering, Beijing, China Chen Shijun Suzhou Nuclear Power Research Institute Co., Ltd., Shenzhen, China Jialong Shu CGNPC Inspection Technology Co., Ltd., Suzhou, Jiangsu, China Liu Shuai Harbin Engineering University, Harbin, China; Science and Technology on Reactor System Design Technology Laboratory, Chengdu, Sichuan, China Wang Shuai Nuclear Power Institute of China, Chengdu, China Muo Shuangrong Sichuan Provincial Engineering Laboratory of Nuclear Facility, Nuclear Power Institute of China, Chengdu, China Nong Shuying Science and Technology Research Institute of CNNC 404 Co., Ltd., Jiayuguan, Gansu, China; CNNC 404 Chengdu Nuclear Technology Engineering Design and Research Institute Co., Ltd., Chengdu, Sichuan, China Dahu Song Nuclear and Radiation Safety Centre of MEE Beijing, Beijing, China Fei Song China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, People’s Republic of China Guangdong Song China Institute of Atomic Energy, Beijing, China Hui Song State Nuclear Power Demonstration Plant Co., Ltd., Weihai, Shandong, China Xiao-lei Song China Nuclear Power Engineering Co., Ltd., Shijiazhuang, Hebei, China Guanghui Su Xi’an Jiaotong University, Xi’an, Shaanxi, China

xxxii

Contributors

Jie Su China Nuclear Industry 23 Construction Co., Ltd., Beijing, China Yang Su China Institute of Nuclear Industry Strategy, Beijing, China Zhiyong Su Nuclear Power Institute of China, Chengdu, Sichuan, China Taoxiang Sun Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Xiaoyan Sun Suzhou Nuclear Power Research Institute Co., Ltd., Equipment Management Center, Shenzhen, Guangdong, China Xiaoying Sun Key Laboratory of Earthquake Engineering and Engineering Vibration, China Earthquake Administration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, China; China Nuclear Power Engineering Co., Ltd., Beijing, China Yu Sun Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Yun Sun Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Yang Sun JNPC, CNNP, Lianyungang, Jiangsu, China Zhijun Sun Nuclear Power Institute of China, Chengdu, Sichuan, China Jiang Taikeng Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Chao Tan CNNC Key Laboratory on Nuclear Industry Simulation, Wuhan, Hubei, China Xiao Tan Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China Naigui Tao Suzhou Nuclear Power Research Institute, Suzhou, China Qihao Tao Xi’an Jiaotong University, Xi’an, Shaanxi, China Wang Tao China Nuclear Power Technology Research Institute, Shenzhen, Guangdong, China Zhou Tao Department of Nuclear Science and Technology, School of Energy and Environment, Southeast University, Nanjing, China; Institute of Nuclear Thermal-Hydraulic Safety and Standardization, Beijing, China; National Engineering Research Center of Power Generation Control and Safety, Nanjing, China Lei Teng Nuclear Power Institute of China, Chengdu, Sichuan, China Chao Tian Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Hongyu Tian CNNC Key Laboratory on New Materials Research and Application Development, Baotou, China; China North Nuclear Fuel Co., Ltd., Baotou, Inner Mongolia, China

Contributors

xxxiii

Shizhong Tian China Nuclear Power Engineering Co., Ltd., Beijing, China Wenxi Tian Xi’an Jiaotong University, Xi’an, Shaanxi, China Li Tianfu CNNC 404 Chengdu nuclear technology engineering design and Research Institute Co., Ltd., Chengdu, Sichuan, China Lili Tong Shanghai Jiao Tong University, Minhang, Shanghai, China Mengyao Tong Yuanmen Road CJNF, YiBin, Sichuan, China Xiaoyuan Wan Nuclear Security Technology Innovation Center of Hebei Province, The Fourth Research and Design Engineering Corporation of CNNC, Shijiazhuang, China Zhao Wan Sichuan Provincial Engineering Laboratory of Nuclear Facilities Decommissioning and Radwaste Management, Nuclear Power Institute of China, Chengdu, China Chenglong Wang School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an, Shanxi, China Chonmei Wang Qinshan Nuclear Power, Haiyan, Zhejiang, China Dongmei Wang Structural Technology Research Center of Nuclear Engineering, China Nuclear Power Engineering Co., Ltd., Beijing, China Dongyang Wang China Nuclear Power Engineering Co., Ltd., Beijing, China Gongzhan Wang CNNO Operations Management Co., Ltd., Haiyan, Zhejiang, China Hangding Wang Suzhou Nuclear Power Research Institute, Shenzhen, China Hao Wang Department of Engineering Physics, Tsinghua University, Beijing, China Hongliang Wang China Nuclear Power Engineering Co., Ltd., Beijing, China Jianchen Wang Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Jianqiang Wang The Reactor Operation and Application Research Sub-institute, Nuclear Power Institute of China, Chengdu, China; Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste Management, Chengdu, China Jun Wang Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Junlong Wang Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Kai-yuan Wang China Nuclear Power Technology Research Institute Shenzhen, Guangdong, China Li Wang Nuclear Power Institute of China, Chengdu, China; Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste

xxxiv

Contributors

Management, Chengdu, China; The Reactor Operation and Application Research Sub-institute, Nuclear Power Institute of China, Chengdu, China Meng Wang China Nuclear Power Engineering Co., Ltd., Beijing, China Ming Wang State Nuclear Power Technology Corporation Ltd., Shanghai, China Pei-jian Wang Airborne Survey and Remote Sensing Center of Nuclear Industry, Shijiazhuang, China Qingsong Wang Nuclear and Radiation Safety Center, MEE, Beijing, China Runci Wang China Institute of Atomic Energy, Beijing, China Shaojie Wang Structural Technology Research Center of Nuclear Engineering, China Nuclear Power Engineering Co., Ltd., Beijing, China Shuai Wang Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Shuangyin Wang CGNPC Inspection Technology Co., Ltd., Suzhou, Jiangsu, China Tieshan Wang School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, China Xi Wang Research Institute of Nuclear Power Operation, Wuhan, Hubei, China Xiang Wang College of Nuclear Science and Technology, Harbin Engineering University, Harbin, China Xiaoyu Wang Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, China Xin Wang China Nuclear Power Technology Research Institute, Shenzhen, Guangdong, China Yaqi Wang China Institute of Nuclear Industry Strategy, Beijing, China Yingrong Wang State Nuclear Power Demonstration Plant Co., Ltd., Weihai, Shandong, China Yu Wang Department of Energy Chemistry Engineering, School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei, China Yuanzhi Wang Airborne Survey and Remote Sensing Center of Nuclear Industry, Shijiazhuang, China Zefeng Wang Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, China Zhaoming Wang CNNP Xiapu Nuclear Power Co., Ltd., Ningde, Fujian, China Zheng Wang China Waterborne Transport Research Institute, Beijing, China Zichun Wang Suzhou Nuclear Power Research Institute Co., Ltd., Shenzhen, China

Contributors

xxxv

Bin Wei Airborne Survey and Remote Sensing Center of Nuclear Industry, Shijiazhuang, China Han Wei China Nuclear Power Engineering Co., Ltd., Beijing, China Qiaojuan Wei The 404 Company Limited, CNNC, Lanzhou, China Chen Wei-lin China Nuclear Power Technology Research Institute, Shenzhen, Guangdong, China Wang Weiwei CNPRI, Shanghai, China Jie Wen CNNC Nuclear Power Operations Management Co., Ltd., Haiyan, Zhejiang, China Luo Wenbo Suzhou Nuclear Power Research Institute Co., Ltd., Shenzhen, China Lei Wenjing Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China Xie Wenzhang China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong Province, China Jianjian Wu China Nuclear Industry 23 Construction Co., Ltd., Beijing, China Jianrong Wu CGNPC Inspection Technology Co., Ltd., Suzhou, Jiangsu, China Panpan Wu Shanghai University, Shanghai, China Renqiong Wu School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China Shenao Wu China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China Xianhong Wu Airborne Survey and Remote Sensing Center of Nuclear Industry, Shijiazhuang, China Yao Wu The Reactor Operation and Application Research Sub-institute, Nuclear Power Institute of China, Chengdu, China; Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste Management, Chengdu, China Yirui Wu Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Yuling Wu China Institute of Atomic Energy, Beijing, China Mingming Xia Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Xiaojun Xia Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Liu Xiajie China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong Province, China

xxxvi

Contributors

Qin Xiang Institute for Radiation Protection, Taiyuan, Shanxi, China Zou Xiang Nuclear and Radiation Safety Center, Beijing, China Ye Xianhui Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Liu Xiao-han China Nuclear Power Technology Research Institute, Shenzhen, Guangdong, China Tan Xiao Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China Tiaobing Xiao China Nuclear Power Operation Technology Co., Ltd., Wuhan, Hubei, China Jiang Xiaobin Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Li Xiaobo China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Zhao Xiaohan CNPRI, Shanghai, China Wu Xiaojiang Sichuan Provincial Engineering Laboratory of Nuclear Facilities Decommissioning and Radwaste Management, Nuclear Power Institute of China, Chengdu, China Ding Xiaojiao China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Wen Xiaojun Nuclear Power Institute of China, Chengdu, China Jin Xiaoming CGNPC Inspection Technology Corporation, Suzhou, Jiangsu, China Jiang Xiaozhou Science and Technology on Reactor System Design Technology Laboratory, Chengdu, Sichuan, China Zhe Xie China Nuclear Power Operation Technology Co., Ltd., Wuhan, Hubei, China Zhengquan Xie CNNC Key Laboratory on Nuclear Industry Simulation, Wuhan, Hubei, China Zhiguo Xie China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Guo Xiliang Institute for Radiation Protection, Taiyuan, Shanxi, China Jin Xin China Nuclear Power Technology Research Institute, Shenzhen, Guangdong, China Li Xinmin Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Jun Xiong China Nuclear Power Design Co., Ltd., Shenzhen, Guangdong, China Kun Xiong China Nuclear Power Technology Research Institute, Shenzhen, Guangdong, China

Contributors

xxxvii

Wang Xiong CNPRI, Shenzhen, Guangdong, China Chao Xu Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Dan Xu The 404 Company Limited, CNNC, Lanzhou, China Decheng Xu Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Kaili Xu China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China Ke Xu CNNC Nuclear Power Operations Management Co., Ltd., Haiyan, Zhejiang, China Tao Xu Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Xinhe Xu Shanghai University, Shanghai, China Yueping Xu Suzhou Nuclear Power Research Institute, Suzhou, China Zhe Xu China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Zhong Xu CNNC Nuclear Power Operation Management Co., Ltd., Haiyan, China Zhu Xu China Nuclear Power Operation Technology Co., Ltd., Wuhan, Hubei, China Huang Xuan Science and Technology on Reactor System Design Technology Laboratory, Chengdu, Sichuan, China Wei Xue Nuclear Power Institute of China, Chengdu, Sichuan, China Liu Ya-ni China Nuclear Power Technology Research Institute, Shenzhen, Guangdong, China Jiabing Yan Nuclear Power Institute of China, Chengdu, Sichuan Province, China Shuhang Yan China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China Xu Yan China Nuclear Power Engineering Co., Ltd., Beijing, China Fan Yang School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, China Gangqiang Yang The 404 Company Limited, CNNC, Lanzhou, China Guanghua Yang China Nuclear Power Engineering Co., Shenzhen, Guangdong, China Jianfeng Yang Suzhou Nuclear Power Research Institute, Shenzhen, China Jiang Yang China Nuclear Power Technology Research Institute, Shenzhen, China Jingjie Yang Nuclear Power Institute of China, Chengdu, Sichuan Province, China

xxxviii

Contributors

Jun Yang Department of Nuclear Engineering and Technology, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, China Longquan Yang Beijing Research Institution of Uranium Geology, Beijing, China Qianfei Yang CGNPC Inspection Technology Co., Ltd., Suzhou, Jiangsu, China Shunlong Yang China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China Xingtuan Yang Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Ye Yang Department of Nuclear Engineering and Technology, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, China Yong Yang CNNC Key Laboratory on New Materials Research and Application Development, Baotou, China; CNNC Key Laboratory on Fabrication Technology of Reactor Irradiation Special Fuel Assembly, Baotou, China Yuning Yang Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Zongqiang Yang China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Yuan Yanli Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Bo Yao Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Sun Yao China Nuclear Power Engineering Co., Ltd., Beijing, China Yao Yao Department of Nuclear Science and Technology, School of Energy and Environment, Southeast University, Nanjing, China; Institute of Nuclear Thermal-Hydraulic Safety and Standardization, Jinan, China; National Engineering Research Center of Power Generation Control and Safety, Nanjing, China Han Yaolei Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Ren Yaqing China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong Province, China Chunsong Ye Department of Energy Chemistry Engineering, School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei, China Guo Yibo Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Huaqiang Yin Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China

Contributors

xxxix

Du Yingzhe China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong Province, China Liu Yiqian CNNC Nuclear Power Operation Management Co., Ltd., Haiyan, Zhejiang, China Hu Yong China Institute of Atomic Energy, Beijing, China Ouyang Yong CNPRI, Shenzhen, Guangdong, China Qiu Yongping Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China Chai Youqi The Third Branch of China Nuclear 404 Co., Ltd., Jiayuguan, Gansu, China Huajin Yu China Institute of Atomic Energy, Beijing, China Kaicheng Yu Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China Min Yu Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Mingrui Yu China Nuclear Power Engineering Co., Ltd., Beijing, China Qian Yu Economic Evaluation Division, China Nuclear Power Engineering Co., Ltd., Beijing Institute of Industrial Engineering, Beijing, China Wenge Yu JNPC, CNNP, Lianyungang, Jiangsu, China Chenliang Yuan Nuclear Security Technology Innovation Center of Hebei Province, The Fourth Research and Design Engineering Corporation of CNNC, Shijiazhuang, China Huang Yuan CNNC Nuclear Power Operation Management Co., Ltd., Haiyan, Zhejiang, China Yidan Yuan China Nuclear Power Engineering Co., Ltd., Beijing, China Yu Yuan Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China Zhuo Yucheng Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China Zhao Yuntao China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Chen Yuxiu China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China Wu Yuyong China Nuclear Power Engineering Co., Ltd., BeiJing, China Chenming Zeng CGNPC Inspection Technology Co., Ltd., Suzhou, Jiangsu, China Chun Zeng CNNC Nuclear Power Operation Management Co., Ltd., Haiyan, China

xl

Contributors

Haiyan Zhan State Grid Ruijin City Electric Power Supply Branch, Ruijin, Jiangxi, China Yongjie Zhan Qinshan Nuclear Power, Haiyan, Zhejiang, China Xiaoyang Zhang School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, China Bin Zhang School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China Bo Zhang Nuclear and Radiation Safety Center, MEE, Beijing, China; Xi’an Jiaotong University, Xi’an, Shaanxi, China Chengtian Zhang Nuclear Power Institute of China, Chengdu, Sichuan, China Duoyi Zhang Nuclear Security Technology Innovation Center of Hebei Province, The Fourth Research and Design Engineering Corporation of CNNC, Shijiazhuang, China Furong Zhang China Nuclear Industry 23 Construction Co., Ltd., Beijing, China Guangchun Zhang School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China Hangzhou Zhang Nuclear Power Institute of China, Chengdu, China; Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste Management, Chengdu, China Haochun Zhang School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China Heng Zhang China Institute for Radiation Protection, Taiyuan, Shanxi, China Honglin Zhang China Institute of Nuclear Industry Strategy, Beijing, China Jian Zhang China Institute of Atomic Energy, Beijing, China Jin-Fei Zhang Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Jingchi Zhang China Institute of Atomic Energy, Beijing, China Ke Zhang Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China Ling Zhang College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, Sichuan, China Pengfei Zhang CGNPC Inspection Technology Co., Ltd., Suzhou, Jiangsu, China Pin Zhang Nuclear Security Technology Innovation Center of Hebei Province, The Fourth Research and Design Engineering Corporation of CNNC, Shijiazhuang, China Shengdong Zhang China Institute of Atomic Energy, Beijing, China

Contributors

xli

Shuang Zhang Suzhou Nuclear Power Research Institute Co., Ltd., Equipment Management Center, Shenzhen, Guangdong, China Wei Zhang CNNC Nuclear Power Operations Management Co., Ltd., Haiyan, Zhejiang, China Xiang Zhang Airborne Survey and Remote Sensing Center of Nuclear Industry, Shijiazhuang, China Xiao-Chen Zhang Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China Xiaocong Zhang Nuclear Security Technology Innovation Center of Hebei Province, The Fourth Research and Design Engineering Corporation of CNNC, Shijiazhuang, China Xiaofeng Zhang Suzhou Nuclear Power Research Institute, Suzhou, China; School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, China Yongling Zhang Nuclear Power Institute of China, Chengdu, Sichuan, China Zeqin Zhang School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an, Shanxi, China Zhenhua Zhang Nuclear and Radiation Safety Center, MEE, Beijing, China Jia Zhanju Sichuan Provincial Engineering Laboratory of Nuclear Facility, Nuclear Power Institute of China, Chengdu, China Lei Zhao China Institute of Atomic Energy, Fangshan, Beijing, China Pengfei Zhao JNPC, CNNP, Lianyungang, Jiangsu, China Wenwen Zhao Suzhou Nuclear Power Research Institute Co., Ltd., Equipment Management Center, Shenzhen, Guangdong, China Xinli Zhao Structural Technology Research Center of Nuclear Engineering, China Nuclear Power Engineering Co., Ltd., Beijing, China Yulan Zhao School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China Zhijin Zhao Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China Li Zhenchen Sichuan Provincial Engineering Laboratory of Nuclear Facility, Nuclear Power Institute of China, Chengdu, China Chaoying Zheng Nuclear and Radiation Safety Center, MEE, Beijing, China Jilong Zheng CNNP Xiapu Nuclear Power Co., Ltd., Ningde, Fujian, China Wei Zheng Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China

xlii

Contributors

Yongxiang Zheng CNNO Operations Management Co., Ltd., Haiyan, Zhejiang, China Chang Zhengke Nuclear Power Operation Research (Shanghai) Co., Ltd., Pudong, Shanghai, China Li Zhihua CNNP Nuclear Power Operation Management Co., Ltd., Haiyan, China Wang Zhijian China Nuclear Power Engineering Co., Ltd., Beijing, China Lin Zhikang CNPRI, Shenzhen, Guangdong, China Feng Zhipeng Science and Technology on Reactor System Design Technology Laboratory, Chengdu, Sichuan, China Zeng Zhongxiu Science and Technology on Reactor System Design Technology Laboratory, Chengdu, Sichuan, China Jing Zhou China Nuclear Power Design Co., Ltd., Shenzhen, Guangdong, China Lijun Zhou China Institute of Atomic Energy, Beijing, China Mengjie Zhou The Reactor Operation and Application Research Sub-institute, Nuclear Power Institute of China, Chengdu, China; Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste Management, Chengdu, China Runfa Zhou Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Shuai Zhou Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China Tao Zhou School of Energy and Environment, Southeast University, Dhaka, Bangladesh Dongdong Zhu China Institute of Atomic Energy, Beijing, China Guixue Zhu Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China Lili Zhu Suzhou Veizu Equipment Diagnosis Technology Co., Ltd., Suzhou, China Shikun Zhu School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, China Wei Zhu Nuclear and Radiation Safety Center, MEE,, Beijing, China Xunjia Zhuo China Nuclear Power Engineering Co., Shenzhen, Guangdong, China Yucheng Zhuo Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China Wang Zilong China National Nuclear Power Co., Ltd., Haiyan, Zhejiang, China Xiandi Zuo Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China

Optimal Analysis of Periodic Tests of Steam-Driven Auxiliary Feeding Pumps in QINSHAN Phase II NPP of CNNC Wang Zilong(B) China National Nuclear Power Co., Ltd., Haiyan, Zhejiang, China [email protected]

Abstract. By applying the risk-guided decision-making method based on PSA technology to carry out an overhaul optimization analysis for QINSHAN phase II NPP, the frequency of regular tests, preventive maintenance cycle and execution time can be determined more effectively and reasonably. On the premise of ensuring safety, reasonably optimize the overhaul workload, reasonably shorten the overhaul period, reduce the radiation dose of staff, reasonably reduce the unnecessary burden of the nuclear power plant, make more efficient use of resources, and further improve the safety, reliability and economy of the nuclear power plant in QINSHAN area. The analysis method used in this paper is the recommended method given by RG1.174 and RG1.177, and the judgment is given comprehensively. Combined with the characteristics of the PSA optimization project, the pilot project of the steam-driven auxiliary feed pump is used for the optimization analysis pilot for detailed analysis. According to the requirements of the overhaul optimization work, combined with the principle of project optimization and screening, the optimization objectives are formulated: under the condition that the test method is not adjusted temporarily, the main consideration is to extend the regular test period, thereby reducing the average annual test time. Through the optimization analysis, the safety of the optimized unit is determined, and the feasibility of the optimization project is demonstrated. Keywords: PSA · Steam-driven auxiliary feeding pumps · Optimal analysis

1 Introduction Through the preliminary optimization, screening and analysis of the overhaul of the QINSHAN phase II NPP of CNNC, and fully analyzed and discussed the optimization of the overhaul activities with the operation and maintenance personnel of the QINSHAN phase II NPP of CNNC, combined with the characteristics of the PSA optimization project, the model with the PSA model was selected. Appropriate projects to optimize. By applying the risk-informed decision-making method based on PSA technology to carry out an overhaul optimization analysis for QINSHAN phase II NPP, the frequency of regular tests, preventive maintenance cycle and execution time can be determined more effectively and reasonably. On the premise of ensuring safety, reasonably optimize © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1–7, 2023. https://doi.org/10.1007/978-981-19-8780-9_1

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the overhaul workload, reasonably shorten the overhaul period, reduce the radiation dose of staff, reasonably reduce the unnecessary burden of the nuclear power plant, make more efficient use of resources, and further improve the safety, reliability and economy of the nuclear power plant in QINSHAN area.

2 Basic Situation of Steam-Driven Auxiliary Feeding Pumps 2.1 The Function of Auxiliary Water Supply System Auxiliary feeding water system (ASG) is used as a water supply backup system to provide feeding water to the secondary side of the steam generator when the main feeding water is lost. Substitute the main water supply system (ARE) and start the water supply system (APD) operation under the following conditions: (1) Reactor startup and reactor coolant system heating; (2) Thermal shutdown; (3) Cool the reactor to a state where the residual heat removal system (RRA) can be put into operation. The secondary side of the steam generator is filled with water and maintained at its water level (initial water filling and refilling after cold shutdown) using an electric auxiliary feed pump. When the APD system fails, the auxiliary feed water pump (electric or steam-driven) can also be used to maintain the water level on the secondary side of the steam generator. Deaerator units are used to provide demineralized and deoxygenated water to the ASG system and the reactor boron water replenishment system (REA) tank. The ASG system is a dedicated security facility. In the event of an accident in any of the normal water supply systems (CVI, CEX, ABP, ARE, APD), the ASG system operates to export the residual heat of the core until the reactor coolant system reaches a state where the RRA system can be put into operation. The heat of the reactor coolant is transferred to the secondary circuit system through the steam generator supplied by the ASG system to generate steam; the secondary circuit system steam is discharged into the condenser or to the atmosphere through the GCT system. 2.2 Test of Steam-Driven Auxiliary Feeding Water Pumps The function of the steam-driven auxiliary feeding pumps is to ensure the flow required for safety throughout the operation. The purpose of periodic testing is to check: • The steam-driven auxiliary feed pump starts correctly and reaches the rated speed; • the pump is able to maintain its characteristics at rated speed; • All operating parameters are stable.

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The test under normal operating conditions of water supply to the steam generator corresponds to the test of the electric pump, but starts with the flow regulating valve fully open and the maximum steam pressure condition. The tests will be carried out under thermal shutdown conditions. Under the thermal shutdown condition of each refueling shutdown (the rated pressure of the steam generator design is 8.6 MPa), a start-up and operation test under the feeding water condition of the steam generator is carried out.

3 Optimization Objective of Auxiliary Feed Water Steam Pumps Tests The tests under normal operating conditions for water supply to the steam generator, on the one hand, has steam demand, and on the other hand, the test directly injects cold water from the auxiliary feeding water tank into the steam generator, so the test must be carried out under the thermal shutdown condition. The important critical path in the overhaul work is also the work goal of this optimization plan. According to the requirements of the overhaul optimization work, combined with the principle of project optimization and screening, the optimization objectives are formulated: under the condition that the test method is not adjusted temporarily, the main consideration is to extend the regular test period, thereby reducing the average annual test time. The main optimization plan for the steam-driven auxiliary feeding water system is to adjust the regular test interval of the steam-driven auxiliary feeding water pump to two refueling cycles (2 years), and only one of them is periodically tested for each refueling cycle. Therefore, after an overhaul does not implement PT ASG 006/007, the average overhaul time can be effectively shortened.

4 Optimal Analysis Method of Overhaul Project The analysis method used in this analysis is to adopt the recommended method given by RG1.174 [1] and RG1.177 [2], and give a comprehensive judgment. In implementing risk-informed decision making, LB changes are expected to meet a set of key principles. Some of these principles are written in terms typically used in traditional engineering decisions (e.g., defense-in-depth). While written in these terms, it should be understood that risk analysis techniques can be, and are encouraged to be, used to help ensure and show that these principles are met. These principles include the following: (1) The proposed change meets the current regulations unless it is explicitly related to a requested exemption (i.e., a specific exemption under 10 CFR 50.12, “Specific Exemptions”). (2) The proposed change is consistent with a defense-in-depth philosophy. (3) The proposed change maintains sufficient safety margins. (4) When proposed changes result in an increase in CDF or risk, the increases should be small and consistent with the intent of the Commission’s Safety Goal Policy Statement.

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(5) The impact of the proposed change should be monitored using performance measurement strategies. Each of these principles should be considered in the risk-informed, integrated decision making process, as illustrated in Fig. 1.

Fig. 1. Principles of risk-informed integrated decision-making

The risk-acceptance guidelines presented in this regulatory guide are based on the principles and expectations for risk-informed regulation discussed in this regulatory guide and are structured as follows. Regions are established in the two planes generated by a measure of the baseline risk metric (CDF or LERF) along the x-axis, and the change in those metrics (CDF or LERF) along the y-axis (Figs. 2 and 3). Acceptance guidelines are established for each region as discussed below. These guidelines are intended for comparison with a full-scope (including internal and external hazards, at power, low power, and shutdown) assessment of the change in risk metric and, when necessary, as discussed below, the baseline value of the risk metric (CDF or LERF). However, it is recognized that many PRAs are not full scope and PRA information of less than full scope may be acceptable. Specifically, it is to determine the modified part of the PSA model of the power plant for the change plan, and carry out quantitative analysis on this, so as to give the quantitative analysis result. If the model coverage is not enough to support the change, the evaluation will be given by means of estimation and so on result. If the evaluation result of the power plant is located in Region I, the change is not allowed. If the risk brought by the change is within the scope of Region II or Region III, a comprehensive judgment shall be given in combination with other aspects of analysis.

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5

Fig. 2. Acceptance guidelines for core damage frequency

Fig. 3. Acceptance guidelines for large early release frequency

5 Optimization Analysis 5.1 Safety Margin This optimization plan adjusts the test under normal operating conditions for water supply to the steam generator. For the auxiliary feeding water steam pump, there is still a small flow test once every two months to ensure its availability. Therefore, this scheme can maintain the safety margin inherent in the new design and system design, and overall the safety margin is not seriously degraded. The changes to the periodic test of auxiliary water supply did not destroy or affect the defense purpose of the 5 layers of defense in depth, which can maintain the defense in depth theory. 5.2 Quantitative Risk Assessment The extension of the regular test period will reduce the reliability of the equipment, which will reduce the reliability of the auxiliary feeding water steam pump to quickly start to perform water injection to the steam generator after an accident. This results in an increase in the frequency of damage to the core of the power plant. For power operating conditions and low-power shutdown conditions, the first-level PSA model of internal events is adjusted accordingly. Specifically, the demand failure

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probability after adjustment of the test period can be calculated according to the following formula: P = λ ∗ Ttest/2 where the test period is T test and the backup failure rate is λ, assuming that unplanned demand occurs during the regular test period. Due to the change of the periodic test period, the start-up failure probability can be linearly treated as a function of the periodic test period. Therefore, the start-up failure probability of the steam-driven auxiliary feed pump is adjusted in the model, and the new power plant core damage frequencies are calculated. As shown in Tables 1 and 2 [3]: Table 1. Basic event unreliable parameters Basic event

Initial failure probability

New failure probability

3ASG003POP_FD

4.05E−05

8.10E−05

3ASG001TCN_FD

3.86E−03

7.72E−03

3ASG004POP_FD

4.05E−05

8.10E−05

3ASG002TCN_FD

3.86E−03

7.72E−03

Table 2. Power plant core damage frequency. Operating conditions

Power operation

Low power and shutdown conditions

Base CDF (1/year)

7.67E−06

3.25E−06

New CDF (1/year)

7.70E−06

3.27E−06

According to the above analysis results, it can be seen that the change of the core damage frequency of the power plant caused by the adjustment of the regular test cycle of the steam-powered auxiliary feed pump is: CDF = 5.0E − 08/reactor year < 1.0E − 05/reactor year. According to the quantitative evaluation guidelines given in RG1.174, it can be seen that the risk impact of this change is in Region III, and this change can be implemented without considering the impact of LERF and other disaster risks. After the change is implemented, the NPP should revise the emergency diesel generator set inspection program and other management procedures, update all relevant documents required for the operation of the NPP, and provide corresponding training to personnel to track and timely feedback the impact of the change.

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5.3 Experience Feedback Judging from the historical maintenance and fault conditions of the equipment, from the time of commercial transportation to the end of 2009, no equipment failure or major defects were found. Most of these defects in corrective maintenance were found in daily inspections, and these defects It has nothing to do with the function of the device verified by this test. The defects found were corrected, and the problems with high frequency were technically transformed to avoid recurrence. Operational experience shows that the auxiliary feed water steam-driven auxiliary feed pump has good reliability. From the level of supervision, for the auxiliary feeding water pumps, there are recirculation flow test with a cycle of 2 months and a vibration inspection of the pump set with a cycle of 4 months to verify the performance of the equipment, and there are daily inspections to find out the possible performance degradation of the equipment in time. This supervision is helpful in detecting pump defects.

6 Conclusion According to the above analysis, the periodic test optimization plan of the steam-driven auxiliary feed pump meets the requirements of the principle of “guaranteeing a small increase in risk” in the risk-oriented decision-making, and the optimization project can be implemented. Through the optimization analysis, the safety of the optimized unit is determined, and the feasibility of the optimization project is demonstrated. In the followup, the optimization analysis and evaluation of other projects can be continued according to this article, so as to shorten the overhaul time and reduce the overhaul workload as a whole.

References 1. U.S. Nuclear Regulatory Commission.: RG1.174: An approach for using probabilistic risk assessment in risk-informed decisions on plant specific changes to the licensing basis. Revision 2, 7–16 (2011) 2. U.S. Nuclear Regulatory Commission.: RG1.177: An approach for plant-specific, riskinformed decision-making: technical specifications. Revision 1, 6–12 (2011) 3. China Nuclear Power Engineering Co., Ltd.: Probabilistic safety analysis report on power operation conditions of the No. 1 and No. 2 PWR units of Qinshan Nuclear Power Plant. Revision A, 691–719 (2020)

Fire Human Reliability Analysis for C-2 Nuclear Power Plant Yongping Qiu(B) , Yucheng Zhuo, Guixue Zhu, Jiandong He, and Xiao Tan Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China [email protected]

Abstract. As one of the essential tasks of fire Probabilistic Safety Assessment (PSA), fire Human Reliability Analysis (HRA) has important impact on analysis results and quality of a fire PSA. The scope of a fire HRA mainly includes preinitiator HRA and post-initiator HRA. In this paper, the screening methodology for the post-initiator HRA of CHASHMA Nuclear Power Plant Unit 2 (C-2) is first introduced briefly with the screening criteria, and the performance shaping factors (PSFs) that should be considered in the fire HRA are studied. Next the postinitiator human failure events (HFEs) for C-2 nuclear power plant are identified and the preliminary quantitative analysis is presented with the screening method. Finally, the detailed quantification of HFEs with the Standardized Plant Analysis Risk Human Reliability Analysis (SPAR-H) method is described briefly and the analysis results are all obtained. The dependency analysis and uncertainty analysis in C-2 fire HRA are also briefly discussed in this paper. Keywords: Fire human reliability analysis · Post-initiator · Human failure events · Performance shaping factors · Dependency analysis

1 Introduction Human failure events (HFEs) for fire scenarios in nuclear power plants are important factors affecting the safety of nuclear power plants. As one of the essential tasks of fire Probabilistic Safety Assessment (PSA), fire Human Reliability Analysis (HRA) has important impact on results and quality of a fire PSA. The scope of a fire HRA mainly includes pre-initiator HRA and post-initiator HRA. The HRA method used for fire HRA is usually consistent with the internal events HRA method. In CHASHMA Nuclear Power Plant Unit 2 (C-2) PSA, the Technique for Human Error Rate Prediction (THERP) method is applied for pre-initiator human failure events (HFEs) analysis, and the Standardized Plant Analysis Risk Human Reliability Analysis (SPAR-H) method is applied for the detailed analysis of post-initiator HFEs. The analysis process of pre-initiator HRA in fire HRA is consistent with the internal event HRA. No new pre-initiator HFE is identified after a thorough review of the fire PSA model and internal events PSA model. The pre-initiator HFEs analysis is thus not further described in this paper. For the post-initiator HRA, after identifying the HFEs list, the initial model quantification is performed with screening human error probabilities where appropriate. For the risk-important HFEs, a detailed analysis is carried out with SPAR-H method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 8–15, 2023. https://doi.org/10.1007/978-981-19-8780-9_2

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In this paper, the screening methodology for the post-initiator HRA is first introduced briefly with the screening criteria, and the performance shaping factors (PSFs) that should be considered in the fire HRA are studied. Next the post-initiator HFEs for C-2 nuclear power plant are identified and the preliminary quantitative analysis is presented with the screening method. Finally, the detailed quantification of HFEs is described briefly and the analysis results are all obtained. The dependency analysis and uncertainty analysis in the C-2 fire HRA are also briefly discussed.

2 Fire HRA Methodology for Post-initiator HFEs The fire HRA process is shown in Fig. 1. 2.1 Identification of Post-initiator HFEs for Fire Scenarios Identification of essential HFEs includes review of the applicability of existing HFEs from internal events PSA. The scope of review includes fire induced initiating events, related training materials (in particular fire related), etc. The new HFEs are mostly from review of fire specific procedures. These new actions include fire related MCR actions and local actions due to fire. In considering the decision to leave the MCR and performing the necessary subsequent actions, the following PSFs should be considered [1]: • The procedural/training approach and explicitness/clarity of the criteria for abandoning the MCR; • The potential confusion about the need to evacuate the MCR, e.g., because of spurious signals and confusing indications; • The potential impact of crew reluctance to abandon the MCR; • The general effects of crews no longer having access to all the information in the MCR; • The number and complexity of the actions; • The number of different locations to be visited; • The adequacy of the human-machine interface at the remote shutdown and/or local panels; and so on. Based on above principles, a complete review of exiting HFEs from internal events, fire response procedure and other related system documents, the HFEs under fire scenarios are identified. 2.2 Screening Analysis The screening method provides initial human error probabilities (HEPs) for preliminary fire PSA quantification, and helps to rank important scenarios. This ranking can be used to determine scenarios that need further detailed analysis. Before an analyst can quantify the reliability of an operator action, the HRA analyst needs to initially determine whether the operator action is feasible. If an operator action is not feasible, the HEP could be set to 1.0 and no screening value should be used.

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Iden fy and Define

Qualita ve Assessment Quan fica on (Screening and Detailed Analysis)

Recovery

Dependency

Uncertainty

Documenta on Fig. 1. Fire HRA process

There are four sets of screening criteria in fire HRA [2]. Criteria for set 1 apply only to existing HFEs in the internal events PSA. A factor of 10 could be multiplied to the internal events PSA probability values, or the same HEPs from the internal events PSA can be used. Set 2 addresses a special case for HFEs modeled in related scenarios in the internal events PSA that the Set 1 criteria are not met. Screening values of 0.1 or 10 times the internal events PSA HEPs can be used. Set 3 addresses (1) new HFEs added to the fire PSA to account for fire-specific effects or (2) prior internal events PSA HFEs that need to be significantly altered or modified during the identification and definition step (see Sect. 2.1) to reflect fire effects in the fire PSA. Set 4 addresses actions involved with MCR abandonment and all subsequent actions.

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The fire screening criteria are described in detail in [2] and summarized in Table 1 with relative HEPs [3]. Table 1. Screening criteria of fire HRA Screening criteria Short-term human actions Set 1: similar to internal events HFE but with some fire effects Set 2: similar to Set 1 but with spurious equipment or instrumentation effects in one safety-related train/division Set 3: new fire HFEs or prior internal events HFEs needing to be significantly modified as a result of fire conditions Set 4: alternate shutdown (including MCR abandonment)

Long-term human actions

Definition

HEP Value

Definition

HEP value

Required within first hour of fire/trip

10 times internal Performed ~1h events HEP after fire/trip (fire effects no longer dynamic, 0.1, or 10 times equipment damage internal events HEP, whichever understood, and fire does not is greater significantly affect ability of operators to perform action)

Same as internal events HEP

1.0

0.1, or 10 times internal events HEP, whichever is smaller

0.1, or 10 times internal events HEP, whichever is smaller

1.0 for initial screening, or 0.1 following qualitative analysis

2.3 PSFs and Fire Effects to Consider The screening method provides initial human error probabilities (HEPs) for preliminary fire PSA quantification, and helps to rank important scenarios. This ranking can be used to determine scenarios that need further detailed analysis. The PSFs that should be considered in the detailed fire HRA quantitative analysis are provided as follows [1, 2]: • Available staffing resources—Fire can introduce additional demands for staffing resources beyond what is typically assumed for handling internal events; • Applicability and suitability of training/experience; • Suitability of relevant procedures and administrative controls;

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• Availability and clarity of instrumentation (cues to take actions as well as confirm expected plant response); • Time available and time needed to complete the action, including the impact of concurrent and competing activities; • Environment in which the action needs to be performed; • Accessibility and operability of equipment to be manipulated; • The need for special tools; • Communications; etc. The detailed PSF analysis due to fire scenarios are described in [1, 2]. When the detailed analyses for the more important HFEs are performed, there are several cases in which credit for human actions should not be given or for which credit should be very limited. These include: • Tasks needing significant activity and/or communication among individuals while wearing Self-Contained Breathing Apparatus (SCBA); • The fire could cause significant numbers of spurious equipment activations and affect the reliability of multiple instruments; • Actions to be performed in fire areas or actions needing operators or other personnel to travel through fire areas; • Actions need the usage of equipment that could have been damaged such that even manual manipulation may be very difficult or unlikely to succeed; • Actions to be performed without the basic needs of procedure direction, training, special tools, or sufficient time; etc.

3 HFE Identification and Preliminary Quantitative Analysis for C-2 NPP In C-2 fire HRA, two new HFEs are identified in addition to those that are included in internal event PSA. One is the action related to MCR abandonment and the other is the HFE caused by the operation of fire protection system. For preliminary quantitative analysis, the screening method presented in Sect. 2.2 is followed. The method supports assignment of screening values by addressing the conditions that can influence crew performance during fires, ensuring that the time available to perform the necessary action is appropriately considered (given the other activities ongoing in the accident sequence), and ensuring that potential dependencies among HFEs modelled in a given accident sequence are addressed. Based on the screening criteria (sets 1, 2, 3 and 4) and time window type of each HFE, the conservative HEP value is assigned to provide HEPs for preliminary fire PSA quantification and rank the fire sequences.

4 Detailed Quantification of C-2 Fire HFEs After preliminary quantification of fire PSA model, 19 risk-significant HFEs are selected for further analysis, including 17 HFEs from internal events PSA and 2 new fire specific HFEs.

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The detailed fire HRA quantification for C-2 NPP is carried out with SPAR-H method. According to the specific design of C-2 and combined with the operator interview results for fire HRA, additional assumptions and considerations are determined in addition to those used in internal events HRA, and some of them are as follows: • There are local alarm panels (several, in main buildings) and central fire alarm control panel (CFCP, in MCR) of C-2. When fire occurs, the CFCP will send fire alarm. Then the shift supervisor (SS) in MCR will be responsible to actuate fire response, and notify the local operator to confirm and mitigate the fire and inform the plant fire brigade to extinguish the fire. In reality, fire information can be obtained and confirmed from various fire information sources, such as fire alarms, plant patrol, fire information gathered by local operator and auto actuation of fire pumps, etc. In this assessment, only the fire alarm in MCR is considered conservatively. • If a reactor trip occurs resulting from fire events, the prolonging of overall action time should be taken into account due to the simultaneous responses of reactor trip and fire events. Therefore, for existing events in internal event HRA, during fire scenarios, additional diagnosis time should be considered. • Fire alarm has high clarity and is easy to be positioned by operators in MCR. • For fire HFEs from existing HFEs in the internal events PSA, it is normally assumed that the total available time window is the same, unless that the fire event impacts the whole accident sequence obviously. For fire-specific HFEs, the available time windows are obtained from the fire scenario analyses. • Following an event, all of the interactions implemented should be directed by the procedures. The actions without directions of procedures are generally not considered except that the actions can be supported by operator interview results or other specific basis, and if these actions are considered, the PSFs should be considered for the specific conditions to modify the HEPs. • The levels of each PSF should be modified appropriately compared to internal events if the analyzed fire HFEs are considered to be affected by the fire scenarios. The HEPs of detailed analysis for fire HFEs are obtained based on the above methodology. Compared to the internal event HFEs, the HEPs are increased due to the modifications of PSFs under fire scenarios. For example, the HEP of operators fail to recognize the need or perform the SRC depressurization and cooldown during a small break loss of coolant accident under fire scenario increases about 4 times compared to the relevant internal event HEP due to the modification of levels of PSFs such as available time, complexity, etc.

5 Dependency and Uncertainty Analysis Dependency analysis is considered in detail in C-2 fire HRA and the methodology in [3] is applied. The factors considered to impact the dependency of operator actions include: (1) same or different crew, (2) time in close or not, (3) same location or different location, (4) the availability of additional cues. The decision tree of dependency analysis is shown in Fig. 2. The dependency level definition and the equations used to calculate conditional HEPs in NUREG/CR-1278 are used.

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The dependency levels of C-2 fire HFEs are basically the same as those in internal event HRA, but the conditional HEPs of fire HFEs are different compared to those similar internal event HFEs since the unconditional HEPs are different. These HFEs are finally inputted into the PSA fault tree and event tree model as basic events. The conditional HEPs of dependent events are analyzed to substitute the unconditional HEPs in the quantification of core damage sequences by setting the rules of the MCSs (minimal cut sets) post processing. The uncertainty analysis in [3] is also applied for C-2 NPP and a beta distribution is used for the post-initiator fire HFEs.

Crew

Time

Location

Additional Cues

Complete Dependency

Same Different

High Dependency

Close Yes Not Close Same

Same

Dependency Level

N/A

Different Yes N/A Different Close Not Close

Moderate Dependency High Dependency Low Dependency Moderate Dependency Moderate Dependency

Low Dependency

Fig. 2. Decision tree of post-accident HFE dependency

6 Conclusion In this paper, the fire HRA process is firstly described. The screening methodology for the post-initiator HRA is presented with the screening criteria, and the performance shaping factors that should be considered in the detailed fire HRA are studied. Then the post-initiator HFEs for C-2 NPP are identified and the preliminary quantitative analysis is performed. Finally, based on the risk-important HFEs identified from the results of C-2 PSA/HRA, 19 risk-significant HFEs are selected for detailed quantification. The dependency analysis and uncertainty analysis are also included in the C-2 fire HRA and all the results are input into the fire PSA model to complete the development of C-2 PSA model.

References 1. Kassawara, R.P., et al.: EPRI/NRC-RES Fire PRA Methodology for Nuclear Power Facilities, NUREG/CR-6850, EPRI 1011989 (2005) 2. Cooper, S., et al.: Fire Human Reliability Analysis Guidelines, EPRI 1023001, NUREG-1921, EPRI/U.S. NRC (2012)

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3. Gertman, D., et al.: The SPAR-H Human Reliability Analysis Method, NUREG/CR-6883, Idaho National Laboratory (2005) 4. Swain, A.D., Guttmann, H.E.: Handbook of Human Reliability Analysis with Emphasis on Nuclear Power Plant Applications, NUREG/CR-1278, Sandia National Laboratories/U.S. NRC (1983)

Design and Application of Temperature Compensation System For Composite Material Spindle Measurement Wang Bin(B) Research Institute of Physical and Chemical Engineering of Nuclear Industry, No. 168, Jintang Road, Hedong District, Tianjin, China

Abstract. In order to solve the measuring precision undulation of the composite material spindle length, which result in the environment temperature. An independent temperature measuring system was designed. Further more, the calibration method of temperature measuring system was also built to solve the sensor calibration problem. According to the temperature compensation experiment and fitting method research, a temperature compensation model was set up. The verification of experimental results show that the measuring capability and precision was improved faced to the composite material spindle. Keywords: Composite material spindle · Temperature compensation · Temperature calibration · Compensation curve · Experimental verification

1 Introduction Composite material spindle is the core component of important special equipment in the field of nuclear fuel cycle, which is assembled by a variety of different materials and components of different structures, and its axial length is greatly affected by temperature, so the measurement data at different temperatures will produce different judgment results. Therefore, an independent temperature compensation system needs to be established to improve the consistency of the measurement results of the device at different temperatures. The calibration of the temperature sensor in the temperature compensation system will directly affect the accuracy of the compensation result, and it is necessary to study and establish a reasonable and reliable temperature sensor accuracy calibration method to ensure the overall accuracy of the temperature compensation system. In this paper, a temperature compensation system with temperature monitoring and real-time compensation function based on the geometric measurement device will be designed, and the corresponding calibration method will be established to realize the real-time compensation of the axial length measurement of the composite material spindle and improve the measurement accuracy of the axial size of the composite material spindle under variable temperature conditions.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 16–26, 2023. https://doi.org/10.1007/978-981-19-8780-9_3

Design and Application of Temperature …

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2 Temperature Compensation System Design 2.1 Hardware Structure Design The temperature measurement system is composed of three parts: a temperature sensor, a signal processing unit and a display module. According to the temperature acquisition requirements of the temperature compensation system, the overall structure design of the device fully considers the layout requirements of the temperature measurement point, and the temperature sensor 1 and 2 are set on the device matrix, respectively, of which the temperature sensor 1 is used to monitor the ambient temperature, and the temperature sensor 2 is used to monitor the temperature of the device matrix; Temperature sensors 3 and 4 are set on the upper and lower end faces of the standard (or workpiece), and the two sensors monitor the temperature at both ends of the standard (or workpiece) to determine whether the standard (or workpiece) has sufficiently constant temperature and reaches the temperature balance. The location of the temperature measurement point is shown in Fig. 1.

Fig. 1. Hardware structure diagram of the temperature compensation system

2.2 Software Architecture Design The factors that affect the accuracy of a measurement device are manifold, and one of the most important factors to consider is temperature. The most suitable conditions for calibrating equipment and measuring parts are 20 °C. In practice, it has been shown that the inspection equipment is often used under workshop conditions or conditions that do not fully meet the requirements of the standard measurement environment, while

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the measured part is measured at different stages of the manufacturing cycle [1]. The software module calculates the compensation amount based on the temperature of the part, the temperature of the standard part, the temperature of the device body, and the preset expansion coefficient of each component. This compensation amount is added to the measured value of the part to characterize the true value of the part. The empirical calculation formula for temperature compensation is: (initial measurement reading) + (equipment body compensation) + (compensation of parts) + (compensation of standard parts) = measurement after compensation. From the above calculation formula, it can be seen that the equipment body, workpiece, and standard part will produce precision drift caused by temperature changes, therefore, each factor that produces precision drift should be compensated accordingly, but the actual situation is often that the equipment, workpiece, and standard parts have large differences in materials, structure, size, etc. Therefore, it is necessary to carry out temperature impact experiments for each factor that produces temperature drift, establish a step-by-step compensation mechanism, and finally optimize the measurement accuracy. Through the above analysis, the setting of the temperature sensor is added during the design of the device, which is used to monitor the ambient temperature, equipment temperature, standard temperature and workpiece temperature, and after the measurement is completed, the measurement program calculates the difference between each temperature drift link and the standard temperature, and calculates the correction compensation amount to compensate for the change in the measured value due to the temperature[2]. The amount of compensation for the specimen = [the expansion coefficient of the specimen × (the temperature of the specimen − 20 °C)] + [the expansion coefficient of the device × (the temperature of the device − 20 °C)]; The amount of compensation for the workpiece = [expansion coefficient of the workpiece × (temperature of the workpiece − 20 °C)] + [expansion coefficient of the device × (temperature of the device − 20 °C)]. The final corrected measurement is a theoretical truth equivalent to a standard measurement temperature of 20 °C [3]. According to the above analysis, in order to obtain accurate measurement results, it is necessary to establish a temperature compensation system that includes three levels of compensation function for the equipment body, the standard and the workpiece, and determine the compensation coefficient by carrying out experimental research on the temperature expansion of the equipment, the standard and the workpiece, and implement the compensation step by step, and finally realize the optimization of the measurement accuracy. The structure of the temperature compensation system is shown in Fig. 2.

3 Calibration Method Study 3.1 Determination of Calibration Methods Regarding the calibration of the temperature measurement system, the discrete component method is mostly used, that is, the components and instruments that constitute the temperature measurement system are calibrated separately.

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Fig. 2. Structural diagram of the temperature compensation system

This calibration method is offline, and in-situ calibration of the temperature measurement system cannot be performed. The temperature compensation system of the composite rotary body detection device should belong to the typical embedded temperature measurement system, and its temperature measurement system is a subsystem of the device, and in order to embed the overall device by pre-installation, it is necessary to take the corresponding calibration method for precision calibration. For the measurement value traceability method of the embedded temperature measurement system, according to whether the temperature sensor has the function of reset after disassembly, it is divided into two ways: laboratory discrete component calibration method and field calibration method. Portable in-line calibration point thermometers are more suitable for the accuracy calibration of embedded temperature measurement

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systems, with easy operation and traceable accuracy [4]. Composition of portable in-line calibrators: (1) (2) (3) (4)

Select high-precision I. class, type N or K armored thermocouples; New high-precision compensation wire NXST, as shown in Table 1; High-precision portable digital display instrument; Temperature gun.

Table 1. Point thermometer compensates for wire calibration deviations Type SCST/°C

NXST/°C

Temp

1

2

3

4

20

− 0.36

− 0.48

− 0.04

0.18

50

− 1.33

− 2.10

− 0.21

0.15

Structure and performance of portable online temperature calibrator Portable online temperature calibrator structure for the field is a thermocouple, display instrument, temperature gun trinity. The display gauge is a three-digit display with peak hold. If special needs are required, it can also be equipped with a memory storage function. The online temperature calibrator adopts a combination structure, the gun body is removable, and the thermocouple and protective tube can be replaced, which is accurate and reliable, and is easy to use. Figure 3 shows the structure diagram of a portable in-line temperature calibrator. In order to reduce costs and simplify the structure, armored thermocouples are used instead of temperature guns. Portable in-line temperature calibrators are monolithic calibration, and each calibrator is accompanied by a calibration certificate from a Nationally Recognized Calibration Laboratory (CNAL). 3.2 Calibration Experiments The use of on-site calibration method of the device temperature monitoring system for precision calibration, the use of the incubator to the composite rotary body detection device standard calibration device temperature set in the range of 18–30 °C, every 2 °C for a calibration point, respectively, with the equipment temperature monitoring system and portable point thermometer to detect the real-time temperature of the standard proofreading parts, record the detection values of the two, and find the difference, experimental data and calculation results as shown in Table 2. Since the monitoring error of the temperature control system is less than 1 °C, it can be seen from the above experimental results that the accuracy of the standard device system and the indication error of the calibrated system are less than 1/3 of the monitoring error, which meets the requirements of the national traceability standard, so the measurement accuracy of the temperature monitoring system of the device meets the design requirements.

Design and Application of Temperature …

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Fig. 3. Portable point thermometer

Table 2. Calibration data for temperature monitoring system Temp/°C

18

20

22

24

26

28

30

System/°C

18.3

19.8

22.6

23.8

26.5

27.7

29.6

Thermometer/°C

18.6

19.5

22.4

23.5

26.7

27.9

29.4

Difference/°C

0.3

0.3

0.2

0.3

0.2

0.2

0.2

4 Temperature Compensation Experiment and Compensation Coefficient Fitting 4.1 Warm Complement Fitting Method For the temperature sensor measurement error is relatively large by the temperature, the temperature measurement data value must be corrected by using the method of temperature compensation. Using the principle of least squares linear fitting [5], the best value of the truth value to be measured is the sum of squares that allows the deviation of each measurement to be the minimum. Its mathematical expressions are: k 

(xi − x0 )2 = S(x0 ) = min

i=1

A straight line fitted by applying the least squares rule is called a straight line fitting. Let the two physical quantities y and x have a functional relationship y = a + bx, a and b are the two pending parameters, and now the a and b parameters are estimated by the n group measurements {xi , yi } (i = 1, 2, …, n) of x and y.

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The error of x in the two quantities of x and y is relatively small and negligible, while y has a measurement error and is measured with equal precision. According to the principle of least squares, in order for S to reach a minimum value, the first-order partial derivative of S to a and b should be zero, and the second-order partial derivative should be greater than zero. In fact, since S is always greater than zero, there must be a minima. The first-order partial derivative is zero and collated to determine an optimal linear equation (regression equation) y = a + bx based on the measurements of the n-group {xi , yi } (i = 1, 2, …, n), and the two pending parameters a and b in the equation take their best estimates. r indicates how well the functional relationship between two variables is linear, and the value of r is always between 0 and 1. The closer the r value is to 1, the more dense the experimental data distribution is near the resulting straight line, and the more it conforms to the obtained straight line, that is, it makes sense to fit the line. 4.2 Temperature Supplement Experiment and Data Fitting (1) Equipment and standard parts temperature compensation experiment and temperature compensation curve fitting In order to carry out temperature compensation for the composite material spindle, it is necessary to first conduct a temperature compensation experimental study on the standard part and the equipment body, and after excluding the influence of the temperature drift of the standard part and the equipment, carry out the temperature compensation study for the rotary body itself, establish the compensation coefficient, and implement the compensation [5]. In order to calculate the expansion coefficient of a component, the dimensions of the component (gauge or part) at an ambient temperature of 20 °C must be recorded. When performing a temperature compensation study, this size will be used as an environmental reference. It is then heated and cooled, and the coefficient of expansion is calculated using dimensional deviations from the relative environment. The calibration standard and workpiece are placed in the measurement room at a constant temperature of 20 °C, the equipment is fully constant to 18–30 °C, the constant temperature time is greater than 6 h, and only at 18 °C, the calibration standard is used to calibrate the equipment, and it is no longer calibrated at other temperatures, and each temperature is continuously measured 5 times, and the average measurement of the workpiece is recorded. The compensation coefficients are generated using minitab software, and the compensation curve is shown in Fig. 4. According to the data fitting curve, the compensation equations of the device are: C1 = L − 0.002628C C2 = L + 0.003629C Among them, C 1 and C 2 measure the real-time temperature, C measure the standard temperature of 20 °C, and L is the length of the workpiece. The correlation coefficients are r 1 = − 0.9864, r 2 = − 0.9841, 0.8 ≤ |r| ≤ 1 are strong correlations, and the fitting calculation is reasonable.

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Fig. 4. Temperature coefficient fitting curve. a Equipment temperature compensation curve. b Standard part temperature compensation curve

As can be seen from Equation, when the temperature of the standard and the workpiece is relatively constant, with the continuous increase of temperature, the measurement result is constantly decreasing, indicating that when the temperature rises, the device body undergoes deformation expansion, resulting in a longer distance from the measuring point, in order to maintain contact with the workpiece during measurement, the sensor is closer to the original position of the workpiece, so the measured value shows a decreasing trend; When the temperature of the equipment and the standard is relatively constant, with the continuous increase of the temperature, the measurement results continue to increase, indicating that when the temperature rises, the workpiece (regarded as the standard) deforms and expands, and the axial length increases. (2) Composite material rotation body temperature supplement experiment and temperature compensation curve fitting In order to implement accurate temperature compensation for the composite material spindle, on the basis of realizing accurate temperature compensation for the equipment

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body and calibration parts, the experimental study of the temperature expansion coefficient of the composite material spindle was carried out, and the compensation coefficient was established and the compensation was implemented (Fig. 5).

Fig. 5. Axial length L1 and L2 temperature coefficient fitting curves of composite shiroden bodies. a L1 temperature compensation curve. b L2 temperature complement curve

According to the above temperature experimental fitting curve, the resulting L1 and L2 length compensation equations are as follows: L1 = L + 0.01114C, L2 = L + 0.01016C wherein, the correlation coefficient r 1 = − 0.9858, r 2 = 0.9902, 0.8 ≤ |r| ≤ 1 is a strong correlation, and the fitting formula is reasonable.

5 Verification of the Effect of Temperature Compensation on the Measurement Result In order to verify the effect of temperature compensation on the equipment body and calibration parts, a large temperature difference compensation verification experiment was

Design and Application of Temperature …

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carried out. The temperature change of the equipment and the standard part is controlled within a large temperature difference range (more than 5 °C), and the temperature of the workpiece is relatively constant (within 2 °C of the temperature difference), which fully verifies that in the case of the relatively stable temperature of the workpiece, the compensation effect of the system on the equipment and the calibration parts is carried out, and a total of 20 sets of temperature crossover experiments are carried out, and the experimental results are shown in Table 3. Table 3. Compensation effect verification data sheet Parameter Maximum value

L1/mm

L2/mm

Before

After

Before

After

0.096

0.017

0.109

0.021

As can be seen from Table 3, through temperature compensation, the axial length parameter of the composite material spindle has been significantly reduced in the measurement range at different temperatures, the measurement consistency is better, and the measurement repeatability is greatly improved.

6 Conclusion (1) In order to solve the problem of fluctuation of the axial length measurement accuracy caused by the ambient temperature change at the production site of the composite material spindle, an independent temperature monitoring system is designed and developed to achieve temperature compensation for the axial dimensional measurement value of the composite material spindle in the variable temperature environment. (2) The on-site calibration method of temperature detection accuracy of the temperature compensation system was established, which solved the problem of traceability of temperature monitoring accuracy, and the calibration results showed that the temperature detection error of the temperature compensation system was less than 1/3 of the design accuracy, which met the requirements of relevant national standards. (3) The temperature compensation experiment based on the temperature compensation system and the study of the fitting method of the temperature compensation curve were carried out, and the minimum squares method was used to fit the temperature compensation curve, and the measurement repeatability and accuracy of the compensation device were improved to varying degrees, which could meet the real-time temperature compensation requirements of the axial length and size of the composite material spindle in the production site environment, and effectively improve the measurement accuracy of the composite material spindle.

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References 1. McKeown, P.A., Weck, M., Bonse, R.: Ann. CIRP 44(2), 589 (1995) 2. Bryan, J.: Ann. CIRP 39(2), 645 (1990) 3. Hongzhang, D.: Calculation of coefficient of thermal expansion of engineering metal materials. J. Zhejiang Univ. Technol. 28(4), 358–366 (2000) 4. Chao, J., Jie, C., et al.: A temperature sensor and dynamic calibration method. China Sci. Technol. Inf. 23, 56–57 (2017) 5. Jimin, S., Haiyan, W., et al.: Linear fitting and application of least squares method to temperature sensor temperature measurement data. Univ. Phys. Exp. 32(2), 81–84 (2019)

The Research and Practice of PHM Technology in the Electrical System of Nuclear Plant Wang Zhijian(B) and Sun Yao China Nuclear Power Engineering Co., Ltd., Beijing, China [email protected]

Abstract. Recently, Prognostic and Health Management (PHM) technology has brought about a rapid change in maintenance strategy. This paper first introduces the development history of PHM technology, and briefly describes the aerospace, achievements and experience of PHM technology applied in aerospace, shipbuilding, high-speed rail and other industries. Therefore, the above industrial experience and explosion for the establishment of electrical PHM system are summarized. Furthermore, practical and exploration of PHM technology applied in electrical system of a nuclear power project is introduced in detail. Taking the intelligent MV cabinet as the example application object, through analyzing the actual operation and maintenance data, the main fault types and proportion of MV cabinet are determined. And the sensors are set up reasonably to form the intelligent MV cabinet design scheme. Thus, on this starting point, MV cabinet PHM system as well as nuclear power plant electrical operation and maintenance system are able to establish. Finally, the main problems and challenges existing in PHM technology are listed for better development. Keywords: Prognostic and health management · Nuclear power plant · Electrical system · Practical exploration · PHM

1 Introduction At the beginning of the last century, engineers fixed equipment only after broken down. Around 1940, the maintenance way gradually developed into periodic maintenance due to the reliability requirements during the war. Since 1970, the concept of equipment maintenance began to change from passive maintenance to active maintenance. Engineers began to explore the use of machine learning, artificial intelligence and other methods to predict the health status of equipment. The Prognostic and Health Management (PHM) technology has emerged as the times require [1–3]. As shown in Fig. 1, although equipment continues to be systematized and complicated, with the synchronous development of maintenance strategies, the complexity of maintenance skills also increases, and the advantages and benefits (such as reliability and economical efficiency, etc.) also gradually increase. At present, the common architecture of PHM technology is OSA-CBM (open system architecture for condition-based maintenance) System proposed by the United States © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 27–36, 2023. https://doi.org/10.1007/978-981-19-8780-9_4

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W. Zhijian and S. Yao Complexity 1970 Systematic ensemble coordinate maintenance 1940 Development of maintenance technology owe to wars State Maintenance Regular Maintenance

Reliablility Maintenance

Enhance Diagnosis

Prediction Health Management

1980 Diagnosis and warn system working or break down by machine lea rning and artificial intelligence methods.

Fault Maintenance

2 00 0 Pr e d ict th e future health of system a n d de t e r mi ne th e maintenance strategies Advantage

Fig. 1. Development of maintenance concept

defense department. CBM system consists of 7 functional modules, including data acquisition, feature extraction, condition monitoring, health assessment, fault prediction, maintenance decision and human-machine interface. According to different demand scenarios, CBM system can be divided into three types, namely centralized architecture, distributed architecture and hierarchical fusion architecture [4, 5], as shown in Fig. 2. (a)

(b) Center Fault Management Controller

Part N

Sensor N

health assessment

format conversion

fault prediction maintenance decision

merge health assessment fault prediction

. . .

Sub N Fault Management Controller

. . .

model library

. . .

Sub 2 Fault Management Controller

merge

message receiving module

Sensor 2

Sub 1 Fault Management Controller

Sensor 1

Part 2

model library

Data Transmission

Part 1

message receiving module

Equipment System

Equipment System Integrated Display Controller

format conversion

maintenance decision

Equipment Maintenance Support Organization System Level

Sensor 2

fault prediction

Sensor N

Sensor 1

Sub 1

Sensor 2

Sub 2

Sensor N

. . .

Sensor 1

Fault Diagnosis

. . .

Sensor Module Level

Anomaly Detection

. . .

Sub Model System Library Level

Equipment System Integrated Display Controller Feedback Control

Knowledge correction

(c)

Sub N

Equipment System Sub 2

. . .

Sub 1

Sub N

Equipment System

Fig. 2. PHM architecture. a Centralized architecture b distributed architecture c hierarchical fusion architecture

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As shown in Fig. 2a, centralized architecture is simple, the information transmission path is clear, and all data needs to be uploaded to the central fault management controller for unified processing. Therefore, the central fault management controller is required to be powerful. This architecture is suitable for situations where equipment space is limited and the data center is separated from the field equipment. As shown in Fig. 2b, the subsystems in a distributed architecture operate as independent centralized systems. This architecture is suitable for the situation where there are many equipment systems but the system functions can be clearly distinguished. However, it lacks of comprehensive final decision-making at the system level. As shown in Fig. 2c, hierarchical fusion architecture is the integration of centralized architecture and distributed architecture, which is mainly suitable for large-scale complex electromechanical systems of modern equipment. PHM technology was originally born in the demand for maintenance capability of carrier-borne aircraft of the United States Army. In recent years, PHM technology has been successfully applied in military equipment, and has received great attention from academia and industry. It is gradually developing into aerospace, shipbuilding, high-speed rail, electric power and other fields.

2 Application of PHM Technology in Industrial Field 2.1 Aerospace PHM Systems With the continuous development of aerospace industry, the safety, reliability and maintenance support of aviation equipment are facing higher and higher requirements. For aircraft, maintenance costs account for 95% of the total cost of its 95% life. It is necessary to promote the application of PHM technology in aerospace aircraft and reduce maintenance costs. Throughout all kinds of advanced models at home and abroad, their advancement is inseparable from the guarantee of PHM system. The F135 engine health management system for the American F35 represents the highest level of PHM technology in the aerospace field today. As shown in Fig. 3, this system can make the life of F35 fighter reach 8000h. Compared with the original maintenance means, the average operation and maintenance time, aircraft deployment time, maintenance personnel demand, support equipment demand, equipment support and other aspects are greatly improved, which significantly ensures flight safety and reduces the whole life cost [6–8]. In view of the limited volume of the aircraft, the central processing unit of its PHM system is generally set on the ground, so that the centralized system structure is usually adopted. This requires an efficient and real-time air-to-ground data transmission system between the airborne system and the ground system for bidirectional transmission. At present, air-to-ground data communication system of PHM system of civil passenger aircraft gradually uses air-to-ground broadband data link. The broadband can reach ten or even nearly one hundred megabytes, which can realize the complete “real-time down transmission” capability of aircraft data.

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Optimize Degree

120% 100%

100%

100%

80% 60%

60% 50%

40%

100%

100%

67%

100%

57% 40%

20% 0%

Fig. 3. Effect comparison between PHM technology and common operation and maintenance technology

2.2 Intelligent Ship PHM System Because ships work in the “three high” offshore environment of high temperature, high humidity and high salt fog for a long time, and are affected by complex working conditions such as surge, fatigue, corrosion and variable load, various parts often have different degrees of failure, resulting in the suspension, failure and even death of ship travel mission. The application of PHM technology in intelligent ships will effectively guarantee the safe and reliable navigation of ships. Hyundai Heavy Industries is a leader in the research and development of PHM technology in the shipbuilding field. Its smart ships based on PHM technology can reliably monitor and warn important equipment, significantly improving the reliability of ships. Nippon Line has transmitted the operation data of four container ships to the intelligent ship operation and maintenance platform, creating a “digital twin” corresponding to the physical ship which can achieve ship performance testing, state-based maintenance and inspection and other functions. At present, our country has accelerated the research and development in the field of intelligent ship, the Chinese group’s wisdom “of PHM system adopts the shipboard and shore-based two centralized architecture, based on Marine equipment for monitoring information and a small amount of additional sensors, using multi-source monitoring, early diagnosis, trend prediction, the performance optimization of many fields such as technology, The transformation from accurate equipment to accurate information has been realized, meeting the needs of the owners for use, maintenance, management and decision-making” [9, 10]. The key technologies of PHM in ship systems are: (1) Ship information fusion technology to achieve comprehensive standardized data collection and support mutual learning and sharing; (2) Quantitative analysis and evaluation can be carried out on a comprehensive and unified analysis platform through self-learning by using ship comprehensive state analysis and evaluation technology;

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(3) Using ship-shore integration technology and big data lightweight model transmission technology to realize information interaction and collaborative decisionmaking; (4) Maintenance decision support technology is adopted to develop modular and component-based software architecture, and equipment with algorithmic function modules is customized according to users. 2.3 High-Speed Rail PHM System High-speed railway is a typical complex electromechanical system, which integrates the components of mechanical, electrical, gas and thermal physical domains in a distributed and networked manner. Therefore, the performance mode of high-speed railway equipment fault is highly complicated. PHM technology can predict the life of high-speed railway components based on the running state, change the original regular maintenance mode regardless of cost security, and realize efficient, accurate and low-cost operation and maintenance. Foreign leading countries in the field of rail transit technology have conducted a lot of research and application in PHM. Japan Shinkansen continuously monitored the highspeed trains on Tokaido Shinkansen, collected and analyzed the operation status data of each important component and equipment, which reduced the number of maintenance personnel by 1/3 and significantly reduced the faults. GE’s RM&D system can remotely monitor and diagnose high-speed rail equipment, combine reliability and cost to provide intelligent maintenance advice, which can save about $1 billion a year. China’s rail transit demonstration project takes green intelligent rail transit vehicles as the “mobile terminal”, integrates intelligent on-board state monitoring and fault disaster monitoring system, which is networking and intelligent, and establish “full life cycle service system of rail transit equipment based on Internet of Things” [11, 12]. Typical high-speed rail PHM system adopts hierarchical fusion architecture, including three subsystems: vehicle-mounted PHM system, vehicle-ground data transmission system and ground-based PHM system. The vehicle-ground data transmission system adopts two data transmission modes, namely real-time information transmission through 3G/4G/LTE/ satellite and broadband information transmission through Wi-Fi, which can fully guarantee security, confidentiality and integrity, and has functions of identity identification, access control and transmission backup. In addition, in the traction motor insulation aging fault prediction function, the fault library is mainly established by simulating different fault environments and assigning a certain number of samples to conduct different fault tests in order to obtain fault data. In the absence of fault data samples, such test data strictly in accordance with the test specifications and fully considering the failure environment are reliable (Fig. 4). Electrical equipment in nuclear power plant has similar complexity with aerospace, shipbuilding and high-speed rail. That’s why PHM technology applicated in nuclear power plant can also be appropriate reference to this technical route, such as the large capacity high bandwidth of data transmission, digital twin power plants, data acquisition method based on the failure data from offline test, etc.

W. Zhijian and S. Yao different load

Fault Test

different environment different samples

Data acquisition and storage

offline test

Equipment signal acquisition

Signal processing

Online monitoring

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Feature extraction

Fault signal analysis Fault signal characteristic

Healthy management platform

Equipment operation state

Life prediction

Fig. 4. Schematic diagram of traction motor PHM system

3 PHM Technology in Nuclear Power Plants Recently, PHM technology has popularized and applied by the owners, design institutes and suppliers of nuclear power plants, and some preliminary achievements have been obtained. Sanmen nuclear power plant has customized the equipment health management system of AP1000, which covers key equipments on the nuclear islands. The comprehensive state awareness system based on the actual operation data can learn from space and time dimensions respectively, and predict the future state of the device by combining the current state of the device, operation and maintenance information, as well as environmental information. Key functions include as follows, advanced anomaly identification based on multi-dimensional sensor information fusion, dynamic threshold alarm system based on state estimation algorithm, and fault predication algorithm based on growing fault cases and rule bank, so as to achieve efficient and accurate fault diagnosis. Qinshan nuclear Power Plant has moved forward with the pilot to built the equipment reliability management system, then carried out the pilot promotion in other nuclear power plants. The system has updated several versions. The system establishes normal model according to normal operation database, abnormal fault model for warning and repairing according to fault database. After obtaining the equipment operation data, the system automatically compares it with the model and enters the “deterioration identification loop” to analyze whether the operation parameters of the nuclear power plant system are normal or not. The data confirmed as abnormal operating conditions is entered into the fault prediction module for further identification, early warning or model correction. The system has warned many fault events of equipment since it was put into operation, and it has been confirmed by manual disassembling equipment review, which effectively avoids sudden accidents and has good economic benefits.

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4 Thinking and Inspiration The understanding of PHM technology in nuclear power plants and other industrial fields has important implications for the establishment of PHM system for the electrical system of nuclear power plants. The following describes the PHM system from six aspects: architecture, data acquisition, data screening, health monitoring, fault prognostic and modular integration. (1) Architecture. The PHM system of electric equipment in nuclear power plant should adopt hierarchical fusion architecture. As the electrical equipment of the nuclear power plant is scattered, local analysis function is required so that the operation inspectors can find and handle faults in time. At the same time, the overall information needs to be managed as a whole. Analyze data from time and space dimensions, and then establish a big data fault model. The hierarchical fusion architecture first preprocesses data through local processors, and then establishes a centralized data processing center to comprehensively analyze global information. (2) Data acquisition. On the basis of fully considering the common fault types and characterizations of the equipment, a sensor selection library should be established, in which similar sensor functions such as temperature rise monitoring, insulation monitoring, and mechanical characteristic monitoring should be listed one by one to form a sensor selection specification, so as to select suitable monitoring equipment for electrical equipment with different performance requirements. In order to ensure the reliability of the sensor, it is necessary to set up a backup. For important equipment, sensors of different brands but the same function can be placed in the same position to serve as backup for each other and monitor each other, which is beneficial to avoid missed or false reports of faults caused by sensor failures. (3) Data screening. In order to improve the quality of big data, it is necessary to establish data standards and standardize the data to make it consistent and easy to analyze. Establish data interconnection, which is conducive to comparing and analyzing the reliability of data, and then establish dirty database, so as to quickly identify and clear dirty data. Use statistics, clustering and classification methods to remove noise points and abnormal points. At the same time, in view of the lack of equipment fault data in nuclear power plants, offline tests can be carried out to obtain data with reference to the high-speed rail model. (4) Health monitoring. Due to the variable state of electrical equipment, adaptive threshold is used for health monitoring. Adaptive threshold needs to judge the equipment status through horizontal or vertical comparison according to the overall situation of similar equipment or the state change trend of the same equipment. Once a state change is detected, the equipment fault can be warned in time. Set the hierarchical early warning mode, which means to optimize maintenance tasks based on equipment early warning information, diagnosis conclusion, remaining working life and equipment importance level. This mode is helpful to determine the best maintenance content and schedule. On the premise of ensuring the safety of operation,

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get the minimum maintenance task requirements and reasonably allocate maintenance resources. (5) Fault prediction. Data driven method is the first choice for the complex and interactive electrical equipment group of nuclear power plants. However, because there are few fault cases, it is necessary to establish a test platform to create test data of different faults. Although the initial fault cases are not rich enough and rely on manual intervention, with the continuous improvement of case data and enhancement of the self-learning ability of the expert base, data-driven automatic fault diagnosis technology will be more mature, which can be gradually separated from manual intervention. (6) Modular integration. In order to solve the problem of difficult acceptance of humancomputer interaction module caused by numerous types of electrical equipment and equipment manufacturers in nuclear power plants, a modular and component-based software architecture should be established. The scheme standardizes and modularizes the fault diagnosis and health management of different equipment, which is convenient for expansion, integration and unified management. After fully understanding the design requirements of PHM system, package different diagnostic rules for different equipment manufacturers. According to the user’s diagnostic tasks, start different diagnostic rule packages and perform corresponding services.

5 Practice of PHM Technology in Electrical System of Nuclear Power Plant In order to promote the application of PHM technology in the electrical system of nuclear power plant, based on the method of system engineering, considering the actual needs of nuclear power plant, the maturity of existing industrial technology and the economic evaluation results, a nuclear power project takes the intelligent medium voltage switchgear as an example to demonstrate the application of PHM technology, which is gradually promoted in subsequent projects. According to the actual operation and maintenance data of the nuclear power plant, the main fault types and proportions of the medium voltage switchgear can be obtained, as shown in Fig. 5. Through further analysis of the causes of the above faults, the following conclusions can be drawn: (1) Mechanical faults are mainly caused by abnormal conditions of the mechanical structure of circuit breaker handcarts, which are manifested as component damage, refusal to open or close, operation error, etc. The health level of the operating mechanism can be predicted by installing an angular displacement sensor and a pressure sensor on the circuit breaker mechanism to calculate the opening and closing time, trip and speed of it. (2) Insulation faults are mainly caused by aging and damage of insulation materials, which are manifested as partial discharge or even insulation puncture, such as phase to phase or phase to ground short circuit. According to different types of partial

The Research and Practice of PHM Technology … secondary faults, 11.90%

current carrying faults, 14.50%

35

other faults, 2.40%

mechanical faults, 44.50%

insula on faults, 26.70%

Fig. 5. Distribution diagram of fault types of medium voltage switchgear

discharge, such faults can be predicted by setting ultrasonic sensors and ground wave sensors at appropriate positions, which can effectively give early warning in the early stage of insulation deterioration. (3) Current carrying faults are mainly caused by local heating when load current flows through busbar termination, circuit breaker plum blossom contact and other positions prone to poor contact, which are manifested as local abnormal temperature rise. Such faults can be monitored by setting various temperature sensors. (4) Secondary faults are mainly caused by the abnormal operation of the mechanical part or motor of the circuit breaker handcart, which are manifested as operation error, refusal to open or close, etc. Such faults can be predicted by measuring the current waveform of motor and opening and closing coil. Furthermore, according to the above information, set sensors reasonably to achieve the intelligent design scheme of the medium voltage cabinet. Collect data and transmit it to the electrical intelligent operation and maintenance platform through the bus. Through data analysis and processing, an operation and maintenance system with the functions of condition monitoring, fault warning and health assessment is established.

6 Conclusion and Prospect At present, various industries strive to develop PHM technology, and some successful cases have been achieved in military industry. With the rapid development of artificial intelligence and machine learning technology, PHM technology is bound to usher in a new development peak. At the same time, we should also notice that there are some urgent challenges in PHM application: (1) Failure prediction models of various equipment become increasingly specialized, distributed and fragmented, resulting in certain limitations of PHM data and technology when used across industries and scenarios, and have limited universality; (2) For application scenarios with less fault cases and fault data, how to obtain a prediction model with high reliability based on limited operation data restricts the promotion and application of PHM technology to a certain extent. The off-line test of high-speed rail industry is one of the feasible methods at present.

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(3) At present, the construction of fault prediction model needed to run large data owned by the plant owner. However, industry design institute or suppliers are the main organization of researching PHM technology, who have few operation data assets. Thus, large operation data owner and PHM technology possessor belong to different organizations. The way to quantify the value of data assets affects the depth of cooperation of both sides, and then influence the development of PHM technology. It is urgent to establish data specific confirmation and data asset value evaluation system.

References 1. Chen, L., Jian, M., Zili, W.: A state of the art review on PHM technology. Comp. Meas. Control 24(09), 1–4 (2016) 2. Chen, X.: Intelligent Maintenance and Health Management, vol. 10, pp. 15–18. China Machine Press, Beijing (2018) 3. Zhou, J., Li, P., Zhou, Y., et al.: Toward new-generation intelligent manufacturing. Engineering 4(4), 11–20 (2018) 4. Hui, L., Zhenyu, L., Weiqiang, J., et al.: Current research and challenges of deep learning for remaining useful life prediction. Comput. Integr. Manuf. Syst. 27(01), 34–52 (2021) 5. Qiuyue, D., Yao, H., Chao, D.: Overview of prognostics and health management architecture. Aviat. Mainten. Eng. 01, 70–74 (2021) 6. Li, X., Wang, H., Shen, Y., et al. : Application and development of integrated vehicle health management system in aviation field. Comp. Meas. Control 23(4), 1069–1072 + 1079 (2015) 7. Lei, Y.: Intelligent fault diagnosis and remaining useful life prediction of rotating machinery || Preface 2017, ix–x 8. Volponi, A.J.: Gas turbine engine health management: past, present, and future trends. J. Eng. Gas Turbines Power: Trans. ASME (2014) 9. Yunlong, L., Le, Q., Hongwei, W.: Overall architecture design of distribution product fault prediction and health management. Sci. Technol. Inno. 17, 93–95 (2021) 10. Jigyasu, R., Shrivastava, V., Singh, S. : Smart classifier based prognostics and health management of induction motor. Mater. Today: Proc. 2021(2) 11. Yi, Y., Doretto, G.: Boosting for transfer learning with multiple sources. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13–18 June 2010. IEEE (2010) 12. Karkouch, A., et al.: Data quality in Internet of Things: a state-of-the-art survey. J. Netw. Comp. Appl. 73, 57–81 (2016)

Failure Analysis of Valve Seat Cracking of Pneumatic Control Valve in Nuclear Power Plant Ming-lei Hu1(B) , Wei Zhang1 , Ke Xu1 , Jie Wen1 , and Zhu Xu2 1 CNNC Nuclear Power Operations Management Co., Ltd., Haiyan, Zhejiang, China

[email protected] 2 China Nuclear Power Operation Technology Co., Ltd., Wuhan, Hubei, China

Abstract. Cracks were found on the alloy layer of the valve seat when a pneumatic control valve was disassembled during the overhaul of the nuclear power plant, and the erosion corrosion traces were found on the surface of valve leaf spring, valve core and valve seat. It was found that the alloy layer of the valve seat was the brittle Ni–Cr alloy, and the alloy layer had defects such as holes and impurity particles through macroscopic observation, composition test, hardness test, metallographic observation and microscopic observation. It was found that the valve seat step was deformed due to the sealing pressure of the valve core. The plasticity of the Ni–Cr alloy layer was much lower than that of the 316 valve seat, and the microcracks occurred after deformation exceeded a certain amount, which eventually lead to the overall cracking of the alloy layer. There were steam erosion traces and steam erosion products on the surface of leaf springs. It indicated that the sealing structure of the valve had been damaged, which accelerated the cracking of the valve seat. Keywords: Valve seat · Alloy layer · Crackle · Leaf spring · Erosion corrosion

1 Introduction The maintenance personnel of the nuclear power plant found the cracks on the valve seat when performing the physical inspection and repair task of a pneumatic control valve in the feedwater deaerator system. The operating temperature of the valve was 280 °C, and the pressure was 8.3 MPa. The operating medium of the valve was steam. The valve would start-up quickly in case of sudden power reduction of the steam turbine, such as tripping or load shedding. The valve was only enabled when the unit was at low power and it was normally closed during daily full power operation. The valve type belonged to pneumatic diaphragm automatic regulating valve. The valve seat was made of 316 stainless steel and coated with tungsten carbide on the surface. During the previous maintenance, the components had been steam eroded, and the newly replaced valve seat had operated for 19 months since this failure. During the disassembly and maintenance, it was found that there were obvious cracks in the coating of the inner ring of the valve seat, and the sealing surface had been deformed. At the same time, there were erosion marks on the surface of the valve core and the leaf spring as a buffer and anti-seismic © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 37–45, 2023. https://doi.org/10.1007/978-981-19-8780-9_5

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between the valve body gasket and the valve cover. Cracking of the valve seat would lead to internal leakage of the valve and affected the stable operation of the unit.

2 Experiment 2.1 Visual Observation The macro morphology of the valve seat was shown in Fig. 1. The valve seat was circular, and there were two rectangular bosses at the top and bottom when viewed from the section. The thickness of the upper step was about 3.4 mm and the height was about 7 mm. When the valve was closed, the upper surface of this step formed a sealing fit with the sealing surface of the valve core. The height of the lower step was about 2.5 mm and the width was about 11.5 mm. The surface of the step was bright black, and the lower step contacted the valve seat gasket. There were four longitudinal cracks and one circumferential crack on the upper step, and the longitudinal crack extended to the inner diameter of the step. The lower step was intact and no crack was found. The inner diameter surface of the valve seat was the alloy coating area, which was dark brown when operating under high temperature for a long time. The cracks distributed in the circumferential direction correspond to the chamfer position on the outside of the step. The step had obvious deformation, and the inner diameter surface had become two planes. After the disassembly of the valve, the components were inspected, and it was found that there was steam erosion on the leaf spring, valve core and the external surface of the cracked valve seat. The area with serious erosion showed gully morphology, and the surface of the area with slight erosion had obvious roughness. The above phenomena indicated that the valve had steam internal leakage during operation. 2.2 Component Analysis As show in Table 1, the chemical composition of the valve seat base was analyzed. The results showed that the chemical composition of the valve seat matrix meet the composition requirements of 316 stainless steel. As show in Table 2, the chemical composition of alloy coating was analyzed by portable alloy analyzer. The analysis results of alloy coating showed that the metal was composed of Ni, Cr, Fe, Cu and Mo elements, and the main component of tungsten carbide was not detected. The results showed that the coating on the inner diameter surface of the valve seat was not the designed tungsten carbide coating. In fact, it was a Ni–Cr alloy layer, and the composition of the coating didn’t meet the design requirements. 2.3 Hardness Test As show in Table 3, the hardness of the substrate and inner diameter surface coating of valve seat was tested. The average hardness of 316 base of valve seat was 145HV, which meet the requirement that the hardness of 316 stainless steel was no more than 200HV. The average hardness of the alloy coating on the inner diameter was 761HV. However, the original designed coating was tungsten carbide coating, and the coating hardness of

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Fig. 1. Macro morphology of valve seat Table 1. Analysis results of valve seat matrix composition (wt%) Element C

Si

Mn

S

P

Cr

Mo

Ni

Seat base

0.40

1.00

0.003

0.024

16.31

2.13

11.07

0.021

316 ≤ 0.08 ≤ 1.00 ≤ 2.00 ≤ 0.03 ≤ 0.045 16.00–18.00 2.00–3.00 10.00–14.00 standard value

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Element

Cr

Mo

Ni

Cu

Fe

Coating

18.97

3.47

63.85

2.18

11.53

this material was generally above 1200HV. It could be concluded that the hardness of the nickel chromium alloy coating used in practice could not meet the hardness performance requirements of the design material. Table 3. Results of hardness test (HV) Position

Measured value

Average value

Seat base

145

150

144

143

143

145

Coating

773

762

740

765

764

761

2.4 Metallographic Observation As show in Fig. 2, it was the microstructure of valve seat substrate and inner ring coating. From Fig. 2a, it could be seen that the microstructure of valve seat matrix was austenitic structure, which meets the microstructure requirements of 316 stainless steel. From Fig. 2b, it could be seen that the internal structure of the alloy layer on the inner surface of the valve seat was uniform, and no obvious defects were found. From Fig. 2c, it could be seen that there were obvious hole defects at the joint surface between the coating and the valve seat substrate, with a diameter of 30 µm or more. It indicated that the coating quality of the inner ring of the valve seat was unqualified, and the hole defect would lead to the decrease of the adhesion of the alloy coating and easy to crack. From Fig. 2d, it could be seen that there were also hole defects on the coating surface. 2.5 Microscopic Observation There were two kinds of cracks in the coating on the inner surface of the valve seat, one was longitudinal crack and the other was circumferential crack. As show in Fig. 3, it was the microscopic observation of longitudinal cracks. Figure 3a showed that there were many holes and fine particles on the sealing surface of the valve seat step. It could be seen that the longitudinal crack on the valve seat only existed in the coating and didn’t extend into the stainless steel matrix. Figure 3b showed the bifurcation direction of the crack, and it could be judged that the crack extends from right to left. In other words, the crack initiated in the interface area between stainless steel matrix and Ni–Cr alloy and extended to the outer surface of the coating until it penetrates the coating. Based on the above situation, it could be concluded that there were many hole

Failure Analysis of Valve Seat Cracking of Pneumatic …

a

b

c

d

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Fig. 2. Metallographic structure of valve seat substrate and coating. a Valve body base, b coating, c coating joint surface, d: coating surface)

a

b

Fig. 3. Longitudinal crack micro morphology. a Crack initiation zone, b final fracture zone

defects in the interface area between the substrate and coating, and the crack originates from the hole on the sealing surface. As show in Fig. 4, it was the microscopic analysis results of circumferential crack in inner diameter of valve seat. There were obvious plastic deformation marks on the upper step, which was formed by the contact and rolling of the sealing surface of the valve core and the sealing surface of the valve seat when the valve was closed. The sealing step was

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obviously bent and deformed outward, and the deformation point corresponded to the chamfer position on the outside of the step. There was stress concentration in the initial deformation area of the coating, and the crack was also located here. The crack opening was large and there were many internal particle oxides. The typical morphological characteristics had been covered and could not be observed.

Fig. 4. Microstructure of circumferential crack

As show in Fig. 5, it was the energy spectrum test results of the hole area at the interface between the coating and the substrate. From the energy spectrum results, it could be seen that there were obvious differences between the chemical elements near the hole and the components of the coating and matrix, mainly o, Al, C and other elements. These elements mainly come from some oxides, which were caused by the unclean substrate before preparing the surface alloy layer.

3 Analysis and Discussion The base material of the valve seat was 316 austenitic stainless steel, and there was no obvious abnormality in the results of microstructure and hardness analysis. There were different degrees of erosion marks on the leaf spring, valve core and valve seat surface of the valve. The surface layer of the inner diameter of the valve seat was Ni–Cr alloy, which was not tungsten carbide of the original design. It’s hardness was about 760HV. However the hardness of general tungsten carbide spraying layer could reach more than 1200HV, which was far lower than that of tungsten carbide coating. The hardness of the coating did not meet the design requirements. In addition, the surface layer of Ni–Cr alloy had obvious quality defects. There were a large number of holes on both sides of the alloy layer, with a diameter of up to 30µm above, and there were many holes and fine particles between the alloy layer and the stainless steel substrate, which led to the weak bonding surface. The two kinds of cracks in the inner ring of the valve seat: longitudinal cracks on the sealing surface of the step and circumferential cracks on the inner diameter surface. Both types of cracks were on the Ni–Cr alloy layer and didn’t extend into the stainless steel matrix. The cracks originated from the holes and impurity particles in the interface between the alloy layer and the matrix, and extended to the surface of the coating until they penetrate the coating. Both types of cracks on the valve seat were related to the deformation of the step on the valve seat. When the valve was closed, both the valve core and the valve seat

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Fig. 5. Energy spectrum test results of interface between coating and substrate

were pressurized to form a sealing effect, and the sealing surface of the valve core was matched with the sealing surface of the upper step of the valve seat. The valve seat was subjected to the sealing pressure of the valve core, and its upper step was extruded from inside to outside to expand and deform. The stress structure diagram of both was shown in Fig. 6. The thickness of the upper step of the seat was only 3.4 mm, and the long-time stress on the upper step made this deformation very easy to occur. The original design of valve coating was tungsten carbide coating, but the actual coating was Ni–Cr alloy coating. Compared with tungsten carbide, Ni–Cr alloy coating had low strength and high brittleness. Its plasticity was much lower than that of 316 stainless steel matrix, and cracks would occur easily when the deformation exceeded a certain value. The circumferential deformation at the sealing surface of the step was the largest, where longitudinal cracks were formed. The outer chamfer position of the upper step corresponded to the starting position of deformation, where a whole circle was a stress

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concentration area, forming a circumferential crack. The holes and impurity particles in the Ni–Cr alloy layer were the weak areas of the coating, so the crack originates formed the joint surface of the two and extended to the surface of the coating until it passed through the coating. There were erosion marks on the valve leaf spring, valve core and valve seat, indicating that the valve has internal leakage. To a certain extent, it reduced the stability of the valve structure and promoted the crack propagation of the valve seat. Through the above analysis, the upper step structure was relatively weak, resulting in its deformation. The Ni–Cr alloy layer on the inner diameter surface didn’t meet the original design material, and there were holes and impurity particles. These were the root causes of valve seat cracking. The valve had internal leakage, which promoted the cracking of the valve seat.

Fig. 6. Stress diagram of valve seat and valve core

4 Conclusions The valve seat was squeezed by the valve core when the valve was closed, which resulted in the deformation of its upper step. The Ni–Cr alloy layer on the surface was brittle, and there were defects in it. When the deformation exceeded a certain amount, the Ni–Cr alloy layer cracked. The steps on the valve seat were relatively weak, and the deformation caused by the extrusion of the valve core, the inconsistent coating material and defects were the root causes of the valve seat cracking. Valve internal leakage was the contributing factor.

References 1. Xu, X.: Research and design of shape memory alloy composite variable section leaf spring. Shandong University of Science and Technology (2011) 2. Shengting, L.I.: Erosion wear and treatment measures of valve pipeline in Shenhua coal direct liquefaction project. Inner Mongolia Petrochem Ind 7, 5 (2016) 3. He, R., Li, Y., Wang, L., et al.: Study on erosion of PWR containment wastewater discharge pipeline and valve. Valve (003), 21–22, 25 (2015) 4. Bian, C., Zhang, W., Liu, H., et al.: Development and application of new erosion mitigation technology for the inner wall of small-size valves in nuclear power plants. Electroplat. Finish. 40(22), 7 (2021)

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5. Xinwei, W., Xiaoyan, Z., Beidi, Z., et al.: Study on cracking of laser cladding layer of nickel based tungsten carbide cermet. China Laser 24(6), 7 (1997) 6. Liang, Z., Xing, J., Bao, C., et al.: Erosion wear resistance of tungsten carbide/iron matrix cast composites. Foundry 49(5), 3 (2000) 7. Zhenting, W., Huahui, C.: Microstructure and friction and wear properties of tungsten carbide particle reinforced metal matrix composite coating. J. Tribol. 25(3), 4 (2005) 8. Wang, H., Xia, W.: Study on wear resistance of hard phase nickel base coating containing tungsten carbide. J. Tribol. (1995) 9. Pei, X., Xiaohua, W.: Application of J-integral in plane indentation boundary cracking of coating substrate. J. Petrochem. Colleges Univ. 26(4), 4 (2013) 10. Guo, C.: Analysis of properties and influencing factors of carbon nanotube reinforced nickel matrix composite coating. Beijing University of Chemical Technology (2007)

Study on the Effect of Dispersant on the Properties of Ion Exchange Resins in Secondary Loop of Nuclear Power Plant Shunlong Yang1(B) , Shenao Wu1 , Rong Cao1 , Jun Wang2 , Hongyu Lai2 , Yu Wang3 , Chunsong Ye3 , and Tichun Dan1 1 China Nuclear Power Operation Technology Corporation, Ltd., Wuhan, Hubei, China

[email protected] 2 Fujian Fuqing Nuclear Power Co. Ltd., Fuqing, Fujian, China 3 Department of Energy Chemistry Engineering, School of Power and Mechanical Engineering,

Wuhan University, Wuhan, Hubei, China

Abstract. The dispersant, polyacrylic acid (PAA), is increasingly used in the secondary loop of nuclear power plants to inhibit corrosion product deposition in steam generators. Therefore, it is necessary to study the effect of dispersant on the properties of ion exchange resins in the secondary loop of nuclear power plant. In this paper, an extensive resin testing program was initiated to generate additional data on the potential effects of PAA on pressurized water reactor (PWR) condensate polishing resins, with a particular high molecular weight nuclear grade PAA (Mw, 7–15w). The physicochemical properties, which including ion exchange capacities and the other three parameters, and adsorption dynamic boundary model was used to research the adsorption property, finally, the bench scale was setup to determine the effect of PAA on possible sodium leakage after regeneration by dynamic test of anion resin bed. It was useful for power plant to evaluate the risk of PAA in the secondary loop of nuclear power plants before application. Keywords: Dispersant · Polyacrylic acid · Physicochemical property · Ion exchange resins · Dynamic test

1 Introduction The role of pressurized water reactor (PWR) steam generator (SG) deposit corrosion products causing heat-transfer losses, thermal-hydraulic instabilities through blockage of tube support plate, and reductions in plant output has been well established during the last few decades. Accordingly, over the past few decades, utilities have spent considerable resources to limit or reduce the accumulation of corrosion product deposits within PWR SG and thereby lower the risk of such deposit fouling. Reducing the corrosion product in feedwater system and Removing fouling accumulated in the SG have been used as the mainly approaches. All of the deposit removal efforts are often effective, but can be costly and can carry risks of extended outages or incomplete effective, particularly in crevice locations [1, 2]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 46–57, 2023. https://doi.org/10.1007/978-981-19-8780-9_6

Study on the Effect of Dispersant on the Properties …

47

In recent years, Polyacrylic acid (PAA), a macromolecule polymer dispersant, has been developed as a mean to limit the deposition of corrosion iron inside SG, which was added to the PWR secondary system during full power operation, SG wet layup, or long-path recirculation (LPR) [3, 4]. Although many studies have been done on the dispersion effect of PAA used in the secondary circuit water system, there is very little information on the influence of PAA on resin. However, one concern with the use of dispersants in the secondary cycle is the potential effect it may have on the ion exchange resins that are used to purify the water in the condensate and blowdown systems, due to the organic fouling. Gönder et al. reported that organic fouling such as humic acid could cause the exchange capacity losses in anion resin, because of the blockage of humic acid [5]. Additionally, if PAA irreversibly attaches to any of the anion exchange sites, the carboxylic acid of ion exchange site existing in resin by a large amount of groups would be converted to the sodium salt during the regeneration process with NaOH. These groups would then effectively act as weak cation exchange sites. Na+ would be exchanged for hydronium ion during normal operation causing sodium leakage from the resin. All of these conditions will affect the water quality of the secondary system of nuclear power plant and lead to the decline of chemistry performance index, which is not expected to be seen in the process of PAA application. Therefore, it is necessary to study the influence of PAA on the process performance for resin bed by monitoring the compatibility between PAA and resin. In this paper, bench tests were carried out to analyze the physicochemical property, adsorption performance and process performance of resin by five cycles PAA desorption and resin rinse, then, a conclusion can be get that no apparent effect for resins, even though PAA concentration is magnitude larger than expected during typical PWR applications.

2 Introduction 2.1 Chemicals and Equipments Polyacrylic acid (PAA, M.W, 7000–150000) was independently provided by nuclear power plant, mainly composed of polyacrylic acid and isopropyl alcohol, dried at 80 °C for 2 h in oven before used, to evaporate the isopropyl alcohol. Other chemicals are analytical pure. Deionized water was used throughout the experiments, the conductivity and the total organic carbon (TOC) of deionized water was less than 2.0 µS/cm and 0.1 mg/L respectively. The equipments were shown in Table 1. Nuclear grade gel type resins were provided by nuclear power plant, which were used in the condensate polishing system, the new anion and cation resins were pretreated with NaOH and HCl respectively according to the National Standard GB/T 5476-2013 [6], and then flushed with deionized water until the conductivity is less than 2µs/cm, and then centrifuge dewatered for fresh resins, which were used for subsequent tests. 2.2 Physicochemical Property Experiment The physicochemical properties of resin were detected according to Table 2. The average particle size of resin was determined by laser particle size analyzer, and the refractive index was set as 1.6.

48

S. Yang et al. Table 1. The mainly equipments used during the experiment

Name

Type

Detection level

Manufacturer

Shaking table

TS-110X30

T ± 0.1 °C

China

High temperature circulating water bath

GYY-50L

T ± 0.1 °C

China

Conductivity meter

DDB-303A

± 0.01 µS/cm

China

pH meter

PHBJ-260

± 0.1 pH

China

Total carbon analyzer

TOC-L

0.1 mg/L

Japan

Laser diffraction particle size meter

WingSALD II

± 0.01 µm

Japan

ContrAA700

Na+ ± 0.1 µg/L

Germany

Atomic absorption spectrometer

Table 2. The determination methods for physicochemical property experiment [7–9] No.

Content

Method

1

Average particle size

Laser particle size analyzer

2

Specific gravity in wet state

GB/T 8330-2008

3

Bulk density in wet state

GB/T 8331-2008

4

Total exchange capacity

GB/T 5760-2000

2.3 Adsorption Property Experiment Fresh resins (2.5 g) were added into a conical flask containing 100 mg/L PAA solution, which was adjusted pH (pH 9.6–9.8) by ammonia for checking test, then, the flask was placed in a water bath shaker at 55 °C, oscillated at a constant speed 100 r/min. The solution was sampled at 5, 10, 20, 30, 45, 60, 90, 120, 180, 240, 300, 360, 480 min, TOC analyzer was used to measure the TOC of the solution at each time, and the concentration of PAA was calculated according to the TOC-PAA standard curve of the solution. Resin adsorption capacity Qt (mg/g) can be calculated according to Eq. (1). When the change value of C t is less than 1 mg/L, the adsorption reaction reaches equilibrium, and Qt was the equilibrium adsorption capacity of resin at this time. Qt = (C0 − Ct )V /m

(1)

where C 0 and C t are the concentration of PAA at initial and equilibrium time (mg/L), V is the volume of PAA solution (L), and m is the weight of resin (g). According to the adsorption equilibrium time, the resins with weights of 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4 and 1.6 g were weighed and separately added into conical flask contained 100mL PAA solution, when the adsorption reaction reaches equilibrium, the saturated adsorption quantity Qe (mg/g) of the resin can be calculated according to Eq. (2). Qe = (C0 − Ce )V /m

(2)

Study on the Effect of Dispersant on the Properties …

49

where C 0 and C e are the concentration of PAA at initial and saturated adsorption equilibrium time (mg/L), V is the volume of PAA solution (L), and m is the weight of resin (g). 2.4 Dynamic Test of Anion Resin Bed During the regeneration process of anion resin by NaOH solution, a small part of PAA irreversibly attaches to any of the anion exchange sites, the carboxylic acid groups would be converted to the sodium. These groups would then effectively act as weak cation exchange sites. Sodium would be exchanged for OH– during normal operation causing sodium leakage from the resin, especially at the initial stage of anion resin bed operation. So, the bench scale test was performed to determine the effect of PAA on possible sodium leakage after regeneration. According to GB/T 5476-2013, fresh resin, 100 mg/L PAA adsorbed and regenerated resins for 1 (ARs 1cycle) and 5 cycles (ARs 5cycles) were respectively put into anion resin bed, and dynamic test was carried out at the initial stage of anion resin bed operation.

Fig. 1. Schematic diagram of the dynamic test of anion resin bed (1, thermostatic water tank. 2, pump. 3, flow meter. 4, anion resin bed. 5, sampling point. S1, high performance mixed bed).

Figure 1.shows the schematic diagram and Table 3 shows the test conditions. The effluent from anion resin bed outlet was sampled and monitored, and its Na+ concentration was determined by atomic absorption method, and then the influence of PAA on anion resin bed was analyzed and evaluated. The checking experiment was carried out with H2 SO4 instead of PAA.

50

S. Yang et al. Table 3. The dynamic test condition of anion resin bed

Parameter

Tank volume (L)

Diameter of anion resin bed (mm)

Temperature (°C)

Flow rate (L/min)

Running time (min)

Resin bed height (mm)

Data

30

25

55

1

30

550

3 Results and Discussion 3.1 Determination of PAA Concentration at Room Temperature The concentration of PAA can be determined by the content of TOC in solution, which was measured using TOC-L total organic carbon analyzer (Shimadzu Company, JPN). The standard curve for the content of TOC and the concentration of PAA in solution showed well correlated (R2 > 0.999) (Fig. 2). The linear relationship between PAA and TOC in the solution was as Eq. (3). PAA = 1.8386 ∗ TOC + 1.1457

(3)

600 500

PAA(mg/L)

400 300 200 y = 1.8386x + 1.1457 R² = 0.9998

100 0 0

100

200

300

TOC (mg/L)

Fig. 2. The linear relationship between PAA and TOC

3.2 Physicochemical Property of PAA Adsorbed and Regenerated Resin Physicochemical property of 100 mg/L PAA adsorbed and regenerated resins were determined according to the method shown in Table 2. The fresh cation resins (FCRs), adsorbed cation resins (ACRs) and regenerated cation resins for 1 cycle (RCRs) were shown in Table 4. From this table, it can be known that four kinds of parameters were detected and characterized for PAA adsorbed and regenerated cation resins, there were no obvious change for all of these parameters, especially for total exchange capacity,

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51

Table 4. Physicochemical property of PAA adsorbed and regenerated cation resins Type

FCRs

Average particle size

Specific gravity in wet state

Bulk density in wet state

Total exchange capacity

(µm)

(g/mL)

(g/mL)

(mmol/g)

687.44

1.19 ± 0.04

0.77 ± 0.02

5.13 ± 0.02

ACRs

690.43

1.19 ± 0.02

0.78 ± 0.04

5.11 ± 0.05

Change rate (%)

0.44

− 0.08

0.52

− 0.38

RCRs

684.77

1.21 ± 0.06

0.78 ± 0.04

5.16 ± 0.04

Change rate (%)

− 0.39

1.85

1.03

0.51

change rate is less than 2%. So, it can be concluded that 100 mg/L PAA had little influence on physicochemical property of cation resin. The fresh anion resins (FARs), adsorbed (AARs) and regenerated (RARs) anion resins in 100mg/L PAA solution for 1cycle and 5cycles were shown in Table 5. Similar to cation resin, after adsorbed and regenerated 5cycles in 100 mg/L PAA, the change rates of most parameters for anion resins are very tiny, and the total exchange capacity changes slightly, range from − 2.47 to − 3.93%. It is probably because residual PAA occupies the exchangeable site of anion. If the decrease rate of exchange capacity for anion resin was more than 50%, the resin can be considered as spent resin. So, the checking experiments were carried out with 100 mg/L H2 SO4 instead of PAA, as shown in Table 5. The similar change rates were observed in the experiment for H2 SO4 range from − 1.12 to − 4.09% for total exchange capacity. This phenomenon may be attributed to the decreased desorption efficiency, which results in the residue of adsorbent, and the degraded partial ion exchange site after many cycles. Therefore, there is no obvious effect for 100 mg/L PAA on anion resin. 3.3 Adsorption Property of PAA on Resin Figure 3a shows the curve of the PAA adsorption capacity of cation resin with time. It can be seen that cation resins have almost no adsorption capacity for PAA, the adsorption rate of cation resin for PAA is less than 2% at any pH condition, neither ion exchange nor physical adsorption, so the effect of PAA on anion resins were paid more attention at the subsequent tests. Figure 3b shows the curve of PAA adsorption capacity of anion resin with time. It can be seen that the adsorption process of resin on PAA is divided into three stages. The adsorption capacity of the resin for PAA increased rapidly in the first 45 min, slowly from 60 to 360 min, and basically remained unchanged after 360 min. Therefore, the change of PAA adsorption rate of anion resins was analyzed in the adsorption process of 0–45 min and 60–360 min respectively. In order to further study the PAA adsorption rate of anion resin, the dynamic boundary model was used to fit the PAA adsorption capacity of anion resin within 0–45 min and 60– 360 min. The dynamic boundary model is widely used to describe the resin adsorption process. In this model, PAA is firstly adsorbed on the outer layer of the resin, and then

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S. Yang et al. Table 5. Physicochemical property of PAA adsorbed and regenerated anion resins

Type

Average particle size

Specific gravity Bulk density in wet state in wet state

Total exchange capacity

(µm)

(g/mL)

(g/mL)

(mmol/g)

637.60

1.10 ± 0.03

0.67 ± 0.02

4.51 ± 0.01

AARs

635.80

1.09 ± 0.02

0.66 ± 0.01

4.33 ± 0.02

Change rate (%)

− 0.28

− 0.57

− 1.05

− 3.93

RARs

636.85

1.09 ± 0.03

0.67 ± 0.03

4.40 ± 0.04

Change rate (%)

− 0.12

− 0.72

− 0.55

− 2.47

AARs

634.28

1.10 ± 0.02

0.66 ± 0.01

4.33 ± 0.05

Change rate (%)

− 0.52

− 0.18

− 1.46

− 4.01

RARs

634.37

1.11 ± 0.03

0.66 ± 0.05

4.34 ± 0.04

Change rate (%)

− 0.51

0.91

− 1.04

− 3.69

AARs

/

1.11 ± 0.02

0.67 ± 0.01

4.32 ± 0.01

Change rate (%)

/

1.18

0.33

− 4.09

RARs

/

1.10 ± 0.01

0.67 ± 0.06

4.46 ± 0.03

Change rate (%)

/

0.31

0.43

− 1.12

AARs

/

1.11 ± 0.03

0.67 ± 0.03

4.30 ± 0.03

Change rate (%)

/

0.46

− 0.08

− 4.52

RARs

/

1.10 ± 0.04

0.66 ± 0.05

4.44 ± 0.06

Change rate (%)

/

− 0.45

− 0.97

− 1.61

FARs 1 cycle in 100 mg/L PAA

5cycle in 100 mg/L PAA

1 cycle in 100 mg/L H2 SO4

5 cycle in 100 mg/L H2 SO4

gradually occurs on the inner surface of the resin with the progress of the adsorption reaction, and also, a certain moving boundary exists between these two layers. So, the adsorption process can be generally divided into three steps, including membrane diffusion, particle diffusion and chemical reaction. The rate controlling step of the whole process depends on the slowest step of the three steps. The equations of these models are shown as follows. Membrane diffusion : F = kt

(4)

Particle diffusion : 3 − 3(1 − F)2/3 − 2F = kt

(5)

Study on the Effect of Dispersant on the Properties …

53

0.10

a

Q(mg/g)

0.08

0.06

0.04 pH=4.10

0.02

pH=9.64 0.00

0

20

40

60

80

100

120

t(min)

3.50

b 3.00

Q (mg/g)

2.50 2.00 1.50 1.00 pH=4.10 pH=9.64

0.50 0.00

0

200

400

600

t (min)

Fig. 3. The curve of PAA adsorption capacity of resin with time. a The cation resins. b The anion resins

Chemical reaction : 1 − (1 − F)1/3 = kt

(6)

where k is adsorption rate constant, t is adsorption time, min. F is ion exchange coefficient, min. Table 6 shows linear fitting at different pH in the PAA adsorption dynamic boundary model. It can be seen that the linear fitting relationship at the first 45 min is the best for the particle diffusion model step, the linear correlation coefficients range from 0.979 to 0.995, this is because when the macromolecule PAA diffuses from the surface of the resin to the interior of the resin, the diffusion movement of PAA molecules is slow due to the narrow pores of the anion resins, so the rate controlling step is particle diffusion control. With the extension of PAA adsorption time, at 60–360 min, the linear fitting relationship is the best for the chemical reaction step, so the resin adsorption process

54

S. Yang et al.

of PAA was controlled by chemical reaction between PAA and the exchange group of anion resin. In addition, the pH of the solution has a great influence on the PAA adsorption capacity of the anion resin. When the solution is in lower pH, the adsorption rate of anion resin to PAA is faster and higher, then, when the solution is converted to alkaline condition, the adsorption rate decreases obviously. All the resins used in this experiment are strong alkali type, so that, alkaline condition can inhibit ion exchange reaction and slow down the adsorption rate of PAA on anion resin, which is consistent with the actual situation of ammonia saturated cation resin bed in nuclear power plant. Table 6. The linear fitting at different pH of each step in PAA adsorption dynamic boundary model Time

0–45 min

60–360 min

Controlling Membrane Particle step diffusion diffusion

Chemical Membrane Particle Chemical reaction diffusion diffusion reaction

Parameter

R2

R2

k(× R2 10–3 )

pH = 4.10

0.773

0.979 7.22

pH = 9.64

0.878

0.995 3.88

R2

R2

R2

0.876

0.722

0.900

0.927 1.10

0.933

0.963

0.994

0.995 1.55

k(× 10–3 )

In order to further study the relationship between the saturated adsorption capacity (Qe ) of anion resin for PAA and the concentration of PAA at equilibrium (C e ) under different experimental conditions, Langmuir and Freundlich isothermal adsorption models were carried to express the mathematical relationship according to Sect. 2.2, the equations of these models were shown in Eqs. (7) and (8) respectively. Langmuir isothermal adsorption model is suitable for homogeneous adsorption or monolayer adsorption process. Freundlich isothermal adsorption model is usually used to describe non-ideal adsorption or multilayer molecular adsorption processes on the heterogeneous surfaces. Qe = Qm ∗ KL ∗ Ce /(1 + KL ∗ Ce )

(7)

Qe = KF ∗ Ce1/N

(8)

where Qm is the maximum adsorption capacity of resin, mg/g. K L is the Langmuir isothermal adsorption equilibrium constant, L/mg. K F is the Freundlich isothermal adsorption equilibrium constant, mg/g*(L/mg)1/N , N is the Freundlich correlation coefficient. In Langmuir models, Qm showed the maximum adsorption capacity of PAA under the experimental conditions, N is used to indicate the adsorption effect, good performance when N is more than 1 in the Freundlich model. The equilibrium constant K L and K F of Langmuir and Freundlich model respectively can judge the degree of reaction, the higher the value, the more complete the adsorption of PAA by resin. Langmuir and Freundlich isothermal adsorption models were used to fit the adsorption saturation test data of PAA on anion resin under different experimental conditions,

Study on the Effect of Dispersant on the Properties …

55

shown as Table 7. The isothermal adsorption model equation has a good fitting correlation coefficient (R2 > 0.90) for the adsorption of PAA. It is also can be shown that the adsorption capacity of anion resin to PAA is related to pH of solution. When the solution was acidic, the Qm value of PAA adsorbed by anion resin was 57.85 mg/g, and N was greater than 1. When the solution was change to alkaline condition, the Qm was 16.63 mg/g, and N was less than 1. And the Qm , K L and K F of resin adsorption process were higher at acidic condition than alkaline condition. So, it can be conclude that PAA was easier to be adsorbed by resin at acidic condition, but more difficult to be adsorbed at alkaline condition, which could be attributed to that excessive OH− can inhibit the ion exchange reaction at alkaline conditions, and then reduce the adsorption capacity of anion resin to PAA. Table 7. Equation parameters of Langmuir and Freundlich isothermal adsorption model of anion resin for PAA. Model

pH 4.10

pH 9.64

Langmuir model

K L (L/mg)

9.32 × 10–5

2.33 × 10–14

Qm (mg/g)

57.85

16.63

R2

0.948

0.985

KF [(mg/g) * (L/mg)1/N ]

3.52 × 10–1

6.02 × 10–4

N

1.23

0.57

R2

0.910

0.919

Freundlich model

3.4 Dynamic Test of Anion Resin Bed In order to verify the influence of the PAA adsorbed and regenerated resin on the water quality of the mixed resin bed in nuclear power plant, the dynamic test of the resin bed according to Sect. 3.3 was carried out. During the test, the anion resin bed was used to instead of the mixed resin bed, which was used to avoid the absorption of sodium by the cation resin, and the high concentration of PAA was injected to carry out adsorption balance, which is much higher than the concentration during the operation of nuclear power plant. The concentration of Na+ at the outlet of anion bed was detected by AAS, as shown at Table 8. From this table, it can be known that the concentration of Na+ at the outlet of anion bed is less than 1 ppb at any time, even though 5 cycles in 100 mg/L PAA for RARs, which is lower than the water quality limit value of condensate polishing system and steam generator blowdown system in nuclear power plant. It can be concluded that although high concentration of PAA may occupy a small amount of anion exchange groups, no obvious influence can be found at the regenerated anion resin outlet. The checking test using H2 SO4 instead of PAA was further proved the effectiveness of the test.

56

S. Yang et al. Table 8. The concentration of Na+ at the outlet of anion bed

Type

Time/(min) 0

1

5

10

15

20

25

30

FARs

NDa

ND

ND

ND

ND

ND

ND

ND

1 cycle in 100 mg/L PAA

ND

ND

ND

ND

ND

ND

ND

ND

5 cycles in 100 mg/L PAA

ND

ND

ND

ND

ND

ND

ND

ND

1 cycle in 100 mg/L H2 SO4

ND

ND

ND

ND

ND

ND

ND

ND

5 cycles in 100 mg/L H2 SO4

ND

ND

ND

ND

ND

ND

ND

ND

a Not Detectable, the detection limit is less than 1ppb

4 Conclusion In this paper, the effects of PAA on the physicochemical property of adsorbed and regenerated resins were studied, especially for the PAA adsorption dynamic boundary model on the anion resin, and dynamic test of anion resin bed was used to further evaluate the influence of PAA on the regenerated anion resin. The results showed that high concentration of PAA had no obvious effect on the physicochemical properties of resin. The adsorption boundary model showed that PAA was controlled by particle diffusion and chemical reaction during 0–45 min and 60–360 min of resin adsorption step, respectively. The results of dynamic test showed that the sodium content at the outlet of the regenerated anion resin bed was lower than the limit value of the resin bed in nuclear power plant system. All of these results provide a theoretical basis for the application of PAA in nuclear power plants.

References 1. Yin, J., Liu, X., Chen, J., Lee, S., Huang, Z.: Polyacrylic acid, a highly efficient dispersant for aqueous processing of tantalum carbide. Ceram. Int. 43, 3654–3659 (2017) 2. Joshi, A.C., Rufus, A.L., Velmurugan, S.: Poly(acrylic acid-co-maleic acid), a polymer dispersant for the control of oxide deposition over nuclear steam generator surfaces. J. Nucl. M 498, 421–429 (2018) 3. Fruzzetti, K.: Dispersants for pressurized water reactor secondary side fouling control: sourcebook for online and offline applications volumes 1 and 2. EPRI, Palo Alto, CA: 1025317 (2012) 4. Fruzzetti, K.: Assessment of possible polyacrylic acid (PAA) dispersant effects on South Texas project unit 1 condensate demineralizer resin performance: addendum 1. EPRI, Palo Alto, CA: 3002005417 (2015) 5. Gönder, Z.B., Kaya, Y., Barlas, I.H.: Capacity loss in an organically fouled anion exchanger. Desalination 189, 303–307 (2006) 6. GB/T 5476-2013 Methods of pretreatment for ion exchange resins, Beijing, China (2013) 7. GB/T 5757-2008 Determination of moisture holding capacity of ion exchange resins, Beijing, China (2008) 8. GB/T 5760-2000 Determination for exchange capacity of anion exchange resins in hydroxylic form, Beijing, China (2000)

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9. DB/T 1854-2018 Evaluation of kinetic behavior of strongly basic anion exchange resin for water treatment in power plant, Beijing, China (2018)

The Analysis and Improvement of Fuel Damage Fraction Calculate Lei Lei, Ma Guoqiang, and Zou Xiang(B) Nuclear and Radiation Safety Center, Beijing, China [email protected]

Abstract. The fuel damage fraction is a key parameter of performance for pressurized water reactor under Condition III and IV transients. Usually, the amount of fuel rods experiencing departure from nuclear boiling (DNB) is calculated to represent the Fuel Damage Fraction. The conventional method only calculates the amount of fuel rods experiencing DNB at the minimum departure from nuclear boiling ratio (DNBR) reached moment using subchannel analysis model. This article reviews the simplifying assumptions of this method, points out that only calculating the minimum DNBR reached moment is not appropriate. Since the calculation of fuel damage fraction requires the calculation of critical fuel rod power rate of the moment which contains iterations, the calculation of a series of moments would be complicated. An improved method is presented which can not only ensure the result is conservative but also do not increase the computational complexity. The increased method adds a checking step after the calculation of fuel rod critical power at the minimum DNBR reached moment. It verifies whether the critical power is conservative in any other moments. If a fuel rod of critical power experiences DNB in any other moment, the critical power at that moment should be recalculated. The newly calculated critical power should be check again by the checking step. At last, the fuel damage fraction should be calculated with this checked critical power. Compared to the calculation of fuel damage fraction in every moment of the accident, the number of times of the critical power calculation is effectively reduced. In this way, the result would be conservative and the complexity of calculation is acceptable. Keywords: Fuel damage · DNB · Subchannel

1 Introduction At present, in order to calculate the fuel damage fraction, the thermal-hydraulic subchannel model is used to simulate the core, which is often simplified when modeling. The flow paths between the fuel rods in the core are simulated as channels. Each channel is divided into several nodes along the flow direction, and the turbulence between the channels is considered [1]. The minimum departure of nucleate boiling ratio (DNBR) of the channel is calculated according to local parameters such as pressure, temperature, flow rate and heat flux. The subchannel model does not simulate each flow path between

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 58–66, 2023. https://doi.org/10.1007/978-981-19-8780-9_7

The Analysis and Improvement of Fuel Damage Fraction Calculate

59

the fuel rods one by one [2]. Some flow paths are combined into one channel for simulation. In a typical subchannel calculation model, one channel may simulate multiple inter-rod channels [3]. Therefore, it is not possible to calculate the minimum DNBR for each fuel rod or to directly calculate the amount of fuel rods experiencing DNB. According to the above model, theoretically, after calculating the fuel damage fraction of each moment, the maximum value represents the fuel damage fraction of the accident. However, calculating the fuel damage fraction at a single time requires iterative calculations to reduce the FH, which is complex. The computational burden of calculation at each moment is too large. In practice, usually engineers tend to calculate the fuel damage fraction at the minimum DNBR reached moment. But in fact, the fuel damage fraction at the minimum DNBR reached moment is not surely the highest through the accident, only calculating the minimum DNBR reached moment is not appropriate. This article presents an improved method that can not only ensure the conservativeness of the result but also does not increase the computational complexity.

2 Typical Thermal-Hydraulic Subchannel Model In this paper, a typical core sub-channel model is established. The model simulates a pressurized light water reactor. The absolute pressure of the reactor system under normal operating conditions is 15.5 MPa. The core consists of 177 fuel assemblies, which are identical in structural design, except for the enrichment of uranium dioxide fuels. The fuel assembly consists of a fuel assembly frame and 264 fuel rods arranged in a 17 × 17 configuration. The fuel assembly frame is composed of 24 guide tubes, 1 instrument tube, 11 grid frames, the upper tube socket, lower tube socket and connecting piece. The fuel assembly contains eight structural grids. The nuclear fuel consists of UO2 and UO2 – Gd2 O3 fuel pellets, which are encapsulated in fuel cladding. The key parameters of the fuel assembly are shown in Table 1, and the core fuel loading arrangement and assembly schematic are shown in Figs. 1 and 2. Table 1. Key parameters of the reactor core Parameter

Value

Pressure

15.5 MPa

Number of assembles

177

Number of fuel rods in one assembly

264

Considering the symmetry of the core arrangement, a quarter of the core is modeled to simulate the whole core [2]. For the homogeneous core, the center fuel component of the whole core is the hottest component, so the simulations focus on the central core 1/4 thermal component, divided into 29 sub-channels, as shown in Fig. 3. Each component is modeled as one channel for the remaining part of the core. The heating circumference (the perimeter of the contact surface with the channel and the fuel rod,

60

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Fig. 1. Fuel loading arrangement (first cycle)

used to characterize the heat transfer area) and the hydraulic diameter are modeled. These parameters are calculated from the core geometry parameters. For the axial node division, the channels are divided into nodes by the bottom of each grid, and generally the upper nodes are longer than lower nodes. A total of 48 nodes are divided. Each node needs to input the geometric information, and the pressure drop factor of each grid. The axial power distribution in the active region of the core is distributed as the end of the cycle life, the axial power outside the active region is 0. The heat flux density is a key parameter in the subchannel calculation, which is affected by total core power and power distribution. The power distribution is inputted as a normalized power factor distribution whose peak value is the peak power factor FDH. Usually, the full power operating condition is used as the benchmark condition, the power factor of each sub-channel can be calculated using reactor physics. This gives the reference power factor distribution, as well as the reference power peak factor. In the subchannel calculation, according to the input parameter FDH and the referenced power factor distribution, the power factor distribution can be obtained through the normalizing calculation. In the normalizing calculation, the subchannels are divided into three regions. In the first region, the power factor of a channel is positively correlated with FH. In the second region, the power factor of the subchannel remains unchanged. In the third region, the power factor of the subchannel is inversely related to FH. The regional division in this typical core subchannel model is shown in Fig. 4.

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Fig. 2. Schematic of assembly

3 Fuel Damage Fraction Calculation The fuel damage is irreversible; therefore, the fuel damage of an accident should be the maximum value of the fuel damage at each moment. In engineering applications, because the fuel damage fraction calculation requires iterative calculations, the fuel damage fraction is calculated only at a specific moment in order to reduce the complexity of the calculation. It calculates the critical power factor of the fuel rod which happens to occur DNB. Then the critical power factor is used to compare with the fuel statistic

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Fig. 3. Schematic of 1/4 central component subchannel

Fig. 4. Schematic of power regions

curve, and the fuel rod fraction whose power is higher than the critical power factor is the fuel damage fraction. The calculation process can be described in three steps.

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Step 1: The minimum DNBR of the whole accident process is calculated by the subchannel code. The minimum DNBR reached moment is recorded, and the subsequence steps are implemented at this point. Step 2: Continuously reduce the power peak factor FH while maintaining the other parameters unchanged, so that the distribution of the power is more uniform and the maximum power of single fuel rods decreases, until the minimum DNBR of the whole core equals the DNBR limit value. At this point, the fuel rod with maximum power happens to experience DNB. This FH is recorded as a critical fuel rod power factor, which represents the fuel rod power that happens to experience DNB. Step 3: Use the fuel rod statistic curve to find the ratio of fuel rods whose power is higher than the critical fuel rod power factor, this ratio is the result of fuel damage fraction calculation. This method basis on the assumption that the fuel damage fraction reaches the maximum value at the minimum DNBR reached moment. This means when the hottest fuel rod reaches the most dangerous moment, the fuel damage fraction will be the highest at the same time. In fact, this assumption could not be proved theoretically. It is possible that the fuel damage fraction at another moment is bigger than the minimum DNBR reached moment. The results of the typical PWR subchannel calculation at two moments can be an example of this condition. Table 2 lists the parameters and the results of these two moments. Compare the results of these two moments, the minimum DNBR reached moment is moment A, but the fuel damage fraction of moment B is higher. Table 2. Comparison of the parameters of moment A and B Moment Minimum DNBR Fuel damage fraction (%) Critical power factor

A

B 0.967

11.9

0.969 12.1

1.5426

1.5404

Pressure (bar)

158.8898

157.1483

Inlet temperature (°C)

291.0841

290.9471

Normalized core power

1.007085

0.999388

Normalized flow rate

0.65910

0.65282

4 Optimization of the Calculate Method As mentioned above, it is inappropriate to use the fuel damage fraction at a minimum DNBR reached moment to represent the largest fuel damage fraction. It is necessary to compare the fuel damage fraction at each moment and select the maximum value. In fact, the calculation of the fuel damage fraction is complex, therefore, this paper presents an optimized method, with this method, only one or a few moments need to be calculated to ensure the fuel damage fraction is the maximum.

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Fig. 5. Normalized fuel statistic curve

Since the fuel statistic curve is monotonically decreasing, as shown in Fig. 5, the fuel damage fraction increases while the critical power factor decreases. Therefore, if we could find the minimum critical power factor through the whole accident process, the fuel damage fraction at this moment is certainly the maximum. Furthermore, if the fuel rod of critical power does not experience DNB at any other moments, that is, the critical power factor is sufficiently conservative; on the contrary, if the fuel rod of critical power experiences DNB at any other moments, the critical power factor should be recalculate at this moment. Therefore, the fuel damage fraction calculation could be more conservative and simpler after adding a few checking steps. The Optimized steps of calculation are shown in Fig. 6. The first 2 steps are the same as the original method. After step 2, the critical fuel rod power factor at a minimum DNBR reached moment is obtained. Step 3: Set the value of FH to the critical power factor calculated in step 2. Calculate the minimum DNBR through the whole accident process while keeping the FH constant. In this calculation, the power of the hottest fuel rod corresponds to the critical power factor. If the minimum DNBR does not exceed the limit means the fuel rod of this critical power does not experience DNB at any moment through the accident. Therefore, the critical power factor is conservative. On the contrary, if the minimum DNBR exceeds the limit, recalculate the critical power factor at the exceeding moment until the critical power factor is conservative. Step 4. Use the fuel rod statistic curve to find the ratio of fuel rods whose power is higher than the critical fuel rod power factor, this ratio is the result of fuel damage fraction calculation. In this method, the calculation of the critical power factor is calculated only in a few moments, and the result is the minimum critical power factor through the whole accident process. It can effectively reduce the number of iterations.

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Fig. 6. Optimized fuel damage fraction calculate method process

5 Conclusion The calculation of fuel damage fraction needs to get the minimum critical power factor of every moment through an accident. In order to simplify the calculation, usually use the critical power factor of minimum DNBR reached moment to represent the minimum critical power. Analysis and examples could prove that this simplification is not appropriate. An optimized fuel damage fraction calculation method is presented, this method adds a checking step to the original process. In the optimized method, the minimum DNBR with constant FH is additionally calculated, but the calculation of the critical power factor which needs iteration is reduced. Generally, this method reduces the complexity of fuel damage calculation and ensures the conservative of the result.

References 1. Jingwu, Z.: Thermal hydraulic analysis of sub-channel. Nucl. Power Eng. 3(1), 49–54 (1982) 2. Wolf, L., Faya, A.: A subchannel code for steady-state and transient thermal-hydraulic analysis of BWR fuel pin bundles. Energy Laboratory Report No MIT-EL-78-023, September, 1978 3. Kelly, J.E., Kao, S.P.: A two-fluid model for light water reactor subchannel transient analysis. MIT Energy Laboratory Electric Utility Program Report No. MIT-EL-81-014 4. Thurgood, M.J., Kelly, J.M.: Model description. NRC Advanced Code Review Group Meeting, Silver Spring, Maryland (1980) 5. Kelly, J.M., Stewart, C.W.: A two fluid thermal-hydraulics code for reactor core and vessel analysis: Mathematical modeling and solution methods. Nucl. Technol. 100 (1992) 6. Rosehart, R.G., Rogers, J.T.: Turbulent interchange mixing between subchannels in closepacked nuclear fuel bundles, part 1: single-phase coolant generalized 7. Carlucd, L.N.: Single and two-phase mixing in rod bundles: critical review, new relationships and implementation methods. AECL Report, FFC-FCT- 221, RC-2289 (1999) 8. Stewart, C.W.: The model and the method. BNWL-2214, Battelle Pacific Northwest Laboratories (1973)

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9. Touml, I., Bergeron, A.: A three dimensional two-phase flow computer code with advanced numerical methods for nuclear applications. Nucl. Eng. Des. 200, 139–155 (2000)

The Research on Phased Array Ultrasonic Inspection Technology in Nuclear Power Plant Thin Butt Weld Plate Hu Chenxu(B) , Liu Hui, Jin Xiaoming, and Li Bingqian CGNPC Inspection Technology Corporation, Suzhou, Jiangsu, China [email protected]

Abstract. For developing the ultrasonic phased array inspection craft in thin plate butt weld, in order to improve the efficiency and reliability of the weld quality assessment, to ensure the stability and impermeability of the thin plate butt weld, this paper uses the phased array sound field theory, through the simulation method to establish the propagation model of sound field in the weld, designs the phased array craft and verify the feasibility and effectiveness of the phased array craft by the simulation test and on-site application. The results show that the developed craft can accurately evaluate and effectively determine the defects in the area inspected, and shorten the inspection period. Therefore, this technology can be applied to the ultrasonic inspection of the weld in the thin plate. Keywords: Thin plate butt weld · Ultrasonic · Phased array

1 Introduction There are a lot of thin plate structures in nuclear power plants, such as the steel liner of spent fuel pools, the steel structure of containment domes, and the partition structure in some equipment. This kind of thin plate structure is mostly assembled by welding, and plays the role of support, protection and shielding. The necessity of the thin plate structures inspection in pre-service and in-service inspection has been clearly defined in the international code, such as《Design and Construction Rules for Mechanical Components of RWR Nuclear Islands》(RCC-M) and 《Rules for Construction of Nuclear Facility Components Division》(ASME), the thin butt weld plate is the focus of pre-service and in-service inspection. At present, RT(radiographic testing) technology is widely used for the thin butt weld plate in nuclear power plant. The X-ray is selected as the ray source, the ray source and the weld center or the groove surface of weld is in the same plane, using single-wall viewing. However, due to the particularity of RT, its implementation process takes a long time, and cross-operation is not allowed. The subsequent film washing and radiographic interpretation process also takes a amount of time, which greatly occupies the pre-service and in-service inspection period. Meanwhile, the different thin plates also have different structures and laying positions, which bring certain difficulty to the film arrangement, and a considerable area of unreachable area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 67–77, 2023. https://doi.org/10.1007/978-981-19-8780-9_8

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The ultrasonic phased array inspection technology has obvious advantages in the inspection of thin butt weld plate. The phased array probe realizes the deflection and focus of sound by delayed-excitation chips, focusing at multiple depths can optimize the shape and effective range of the sound beam at different depths, to ensure sound beam coverage, reliable detection capability, sensitivity and signal-to-noise ratio. The phased array inspection technology has B scan, D scan, S scan and C scan at the same time, and the defect display is also intuitive [1]. This paper will take the 6 mm thickness butt weld with high strength carbon steel plate as an example to analyze its structural characteristics and develop a targeted ultrasonic phased array inspection technology to ensure detection sensitivity, resolution and efficiency. The inspection technology developed in this paper can provide reference for the testing of other thin plates of materials, thickness and groove.

2 Development of Inspection Technology By analyzing the structure, material and welding process of the object inspected, to design and develop the ultrasonic phased array inspection technology. 2.1 Structural Characteristics of Thin Butt Weld Plate The object inspected is the thin butt weld plate with a thickness of 6 mm. The material is high strength carbon steel. The butt weld is formed by manual arc welding. The groove form is V-shaped and the groove angle is 30°. There are residual heights on both sides of the butt weld and typical defects such as porosity, slag inclusion and non-fusion mostly appear at the root of the weld. Due to the limited position of the weld in the nuclear power plant, the root side of the weld was selected as the inspection surface (Fig. 1).

Fig. 1. Structure of weld

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2.2 Craft Design According to the structural characteristics and inspection difficulties of the object, to choose the appropriate phased array probe and the corresponding phased array rules, and to analyse the sound field characteristics of the selected probe and phased array rules by simulation [3]. First of all, the echo of the defect is between the upper and lower surface of the plate. In the thin butt weld plate, the propagation time of ultrasonic wave is short, the surface wave, defect wave and bottom wave is too close to separate. So, choosing the probe with high frequency and small chips size can improve the craft resolution and distinguish defect signals from structural signals. Secondly, from the weld root side to inspect, due to the existence of residual height, the probe across the weld can not effectively couple, the defects located in the root and middle of the weld need to be inspected by using the primary reflection wave. So, the appropriate probe wedge angle and phased array rule should be selected to ensure the final deflection and focusing depth of the beam can cover the whole area by primary wave and primary reflected wave, and the defects in the range of full thickness can be detected. Thus, at the beginning of the craft design, choose the liner phase array probe, 32 elements, 5 MHz frequency, the single element’s width e is 0.21 mm, the center distance between adjacent elements p is 0.31 mm, the element’s length W is 9 mm, the probe is equipped with β = 60° s-wave wedge to realize sector scanning (Fig. 2).

Fig. 2. Chart of PA probe

Analyzing the sound field and the rules by theoretical calculation and simulation.

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For the phased array probe, the rule needs to be completed in the near field area. In the thin butt weld plate ultrasonic inspection, the focus depth should cover both primary and primary reflection depth, twice the plate thickness, total 12 mm, according to the law of cosines, the focus acoustic path should reach 12/cosβ = 12/cos60 = 24 mm. Through theoretical calculation, to analyze whether the near field area of phased array probe covers the range of inspection depth. The sound field of phased array probe actually spans the wedge and object, the nearfield length N  in the object is the near-field length of the sound field minus the distance from the incident point to the imaginary sound source. Under the condition that all the chips are excited, the near-field length N  in the object is: N  = N − L2 =

tan α Fs cos β − L1 π λs2 cos α tan β

(1)

N is the length of the near-field area of the sound field, mm; F s is the area of sound source, mm2 ; λs2 is the wavelength of shear wave; α is the incidence angle of sound beam, °; β is the refraction angle of sound beam, °; L 1 is the distance from sound source to the incident point, mm; L 2 is the distance from the incident point to the indication, mm. According to the selected probe and the relevant parameters of the object inspected (F s = 88.4 mm2 ; λs2 = 0.65 mm; α = 45.9°; β = 60°; L 1 = 11.5 mm), the focusing length of the phased array probe selected in the object is 24.2 mm. So, the near field length of the phased array probe selected in the object can contain the minimum length required by the phased array rules, which can meet the requirements of the craft design. By means of simulation, to analyze the distribution of sound field of phased array probe in natural state and the characteristics of sound field in rules state (Fig. 3). According to the simulation data, when the phased array probe is in no rules state, the maximum sound pressure depth is 12 mm, and the maximum sound pressure – 6 dB depth with better sensitivity and resolution can cover the inspected weld. The phased array rules should have a proper range of sector scan angle and focusing depth, to meet the primary wave detection on the upper surface and middle part of weld, and a reflection wave detection on the root of the weld (scanning surface), to form the corresponding B scan, D scan, S scan and C scan, distinguish and detect structure and defect signals clearly. Select angle—depth focus rule, the angle range is 40°–75°, the corresponding depth range is 15–5 mm, the step is 1°. According to the simulation data, under the angle-depth focusing rule, the maximum sound pressure depth of the selected phased array probe is 3–12 mm, and the depth range of the maximum sound pressure – 6 dB covers the inspected weld. 2.3 Craft Modeling The verification test block model is established by simulation software, including calibration test block and simulation test blocks. The calibration test block is used for craft calibration as a reference for evaluating defects, according to EN ISO 13588:2012 standard to design, choose 2 mm holes with different depths as calibration reflector to draw

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The Distribution of the Sound Field in Natural State

The Cumulative distribution of Sound Field in rules State Fig. 3. The distribution of the sound field

time-gain curve (TCG), the TCG is a basis for indication evaluation. The simulation test blocks are used for validation of the craft designed. Its structure, size, material and welding process are exactly the same as the object inspected, due to the common defects of the thin butt weld plate are non-fusion and porosity, so design and insert corresponding planner and volume defects in upper, middle and root of the welds, to verify and analyze the inspection ability of the primary and primary reflection wave by angle-depth focus rule. The design defects are shown in Table 1. To simulate calibrating on the calibration block and simulate scanning on the simulation block by using the design craft. To measure amplitude according to the calibration reflector amplitude, using – 6 dB method to measure the length of the indication, and on this basis to evaluate the indication. To record and evaluate the indication according to the acceptance standard of nuclear power plant ultrasonic inspection for some 6 mm thickness butt welds. The simulation data and analysis results are shown in Table 2. Through the summary and analysis of the simulation data, the designed phased array inspection craft can effectively detect the planar and volume defects required by the standards and the nuclear power plant acceptance standard, and measure the amplitude and length of the defects. The maximum length error is + 1.4 mm. Through the evaluation of amplitude and length, the grade of defects and whether qualified or not can be determined.

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No.

Simulation defect type

Diameter/mm

height × length/mm

The distance to scanning surface/mm

1

Porosity in center of weld

1.5

/

2.5 (Middle)

2

Porosity on fusion line close to probe side (scanning direction)

1.5

/

2.5 (Middle)

3

Porosity on fusion line far to probe side (scanning direction)

1.5

/

2.5 (Middle)

4

Non-fusion on fusion line close to probe side (scanning direction)

/

1×6

5 (The upper of weld)

5

Crack in center of weld /

1×6

5 (The upper of weld)

6

Non-fusion on fusion line far to probe (scanning direction)

/

1×6

5 (The upper of weld)

7

Non-fusion on fusion line close to probe (scanning direction)

/

1×6

0(The root of weld)

8

Crack in center of weld /

1×6

0(The root of weld)

9

Non-fusion on fusion line far to probe (scanning direction)

1×6

0(The root of weld)

/

3 Phased Array Craft Verification Perform the verification test by using the craft developed, to compare the test results with the RT results, and demonstrate its reliability and substitutability. After the background full verification, to perform the nuclear power plant on-site application, and explain the application effect. 3.1 Testing Equipment and Blocks In the verification test, the phased array ultrasonic instrument is TOPAZ portable phased array ultrasonic instrument with the matching software to calculate the phased-array rule. The probe is 5 MHz, 32 array elements, the center distance of adjacent array elements is 0.31 mm, the gap is 0.1 mm, and the length of array elements is 9 mm. The angle and depth focusing settings are realized through 60° s-wave wedge. According to the requirements of the phased array inspection craft developed, to prepare the calibration test blocks for calibrating the time base and sensitivity of the craft. The calibration test block is made of material with the same acoustic properties

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Table 2. Result of simulation data and analyze No.

Amplitude/dB

Length/mm

The difference from the actual length/mm

Indication evaluate result

1

F2 – 14.2

3.8

0.8

No pass

2

F2 – 13.3

4.0

1.0

No pass

3

F2 – 14.9

4.4

1.4

No pass

4

F2 – 1.2

7.2

1.2

No pass

5

F2 – 1.3

7.2

1.2

No pass

6

F2 – 3.2

7.4

1.4

No pass

7

F2 + 3.0

7.0

1.0

No pass

8

F2 + 0.7

7.2

1.2

No pass

9

F2 – 4.7

7.4

1.4

No pass

as the object. The raw material of the block has no defects greater than or equal to the equivalent of 2 mm flat bottom hole, and the calibration reflector is 2 mm hole. Preparing the simulation blocks to verify the phased array inspection craft developed. According to the standard requirements, the material, size and welding process of the simulating blocks are exactly the same as the object inspected, and representative artificial defects including planar and volume defects are distributed on the upper, middle and root of the weld. There are 5 simulation blocks in this test, the blocks contain 15 defects of different types and positions, which meet the standard requirements (Fig. 4). 3.2 Validation Test Results By using the phased array craft designed to calibrate the time base and sensitivity on the calibration block, acquire the data of five simulation blocks, measure the amplitude and length of the indications, analyse the characteristics of the indications, and determine whether they were qualified or not. In addition, by using RT to inspect five simulation blocks, summarize the inspection results, compare with ultrasonic phased array inspection results, and illustrate the detection and quantitative ability of ultrasonic phased array inspection craft designed (Table 3). Verification test results show that: (1) The ultrasonic phased array inspection craft can detect 15 artificial defects contained in 5 test blocks and measure them, and the difference between them and the design value is within the inspection requirements. The craft can effectively and accurately evaluate the artificial defects of different types and positions. (2) Compared with the RT, the ultrasonic phased array inspection craft is closer to the real results in quantitative aspect; at the same time, it overcomes the disadvantage of duration caused by RT, which can not cross operation and occupy long period.

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Fig. 4. The simulation blocks

3.3 Application Results There are many uncertain factors in the actual inspection environment and weld quality, it is necessary to complete application testing by using the craft designed, further to verify the adaptability in the nuclear power plant. The craft designed has been applied to the thin butt weld plate inspection in a nuclear power plant as a method to evaluate the weld quality. By using the phased array craft designed to calibrate the time base and sensitivity on the calibration block, acquire the data of the thin butt welds plates in nuclear power plant, measure the amplitude and length of the indications, analyse the characteristics of the indications, and determine whether they were qualified or not. Mark the defect range on the unqualified welds, cut the welds by destructive method, measure the defects, summarize the measurement data, which was compared with the ultrasonic phased array inspection results, to illustrate the detection and quantitative

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Table 3. Result of verification test Defect No.

Defect type RT

1-3/1

Ultrasonic phased array inspection

Indication size/mm

Evaluation result

Amplitude/dB

Indication length/mm

Evaluation result

Porosity

1.5

No pass

F2 − 13.2

2

No pass

1-3/2

Porosity

1.5

No pass

F2 − 13.6

3

No pass

1-3/3

Porosity

1.5

No pass

F2 − 12.6

2

No pass

3-2/1

Porosity

1.5

No pass

F2 − 13.2

3

No pass

3-2/2

Porosity

1.5

No pass

F2 − 12.0

4

No pass

3-2/3

Porosity

1.5

No pass

F2 − 10.0

2

No pass

4-1/1

Slag

1.5

No pass

F2 − 12.5

3

No pass

4-1/2

Slag

1.2

Pass

F2 − 10.5

3

No pass

4-1/3

Slag

1.2

Pass

F2 − 11.8

3

No pass

194172/1

Incomplete fusion

10

No pass

F2 + 2.0

8

No pass

194172/2

Incomplete fusion

10

No pass

F2 − 1.5

12.1

No pass

194172/3

Incomplete fusion

5

No pass

F2 − 6.0

9.1

No pass

194173/1

Incomplete fusion

9

No pass

F2 − 0.9

7.1

No pass

194173/2

Incomplete fusion

10

No pass

F2 + 4.2

9.6

No pass

194173/3

Incomplete fusion

5

No pass

F2 + 1.3

8.0

No pass

ability of the ultrasonic phased array inspection craft in the on-site environment (Fig. 5; Table 4). On-site application results: (1) The ultrasonic phased array inspection technology can detect the unqualified defects in the thin butt weld plate in nuclear power plant, and evaluate the defects according to the analysis of the acquire data, the results are consistent with the destructive testing results, and the technology has the ability to detect and evolution. (2) The on-site environment and weld surface state do not have unacceptable influence on the evaluation results of the technology. The technology can complete the on-site inspection project.

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Fig. 5. The on-site data and destructive testing Table 4. Result of on-site application Defe ct No.

Ultrasonic phased array inspection

Destructive data

Amplitude /dB

Indication length/mm

Evaluation results

The actual length/mm

Evaluation results

1

F2 − 6.5

3

No pass

2

No pass

2

F2 − 8.2

5

No pass

2

No pass

3

F2 − 0.1

7

No pass

3

No pass

4 Conclusion Through the theoretical analysis, simulation modeling, experimental verification and onsite application of the ultrasonic phased array inspection technology developed in this paper for the thin butt weld plate of the nuclear power plant, the following conclusions are obtained:

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(1) Through the analysis of ultrasonic phased array craft’s sound field and simulation modeling data, it can be seen that, the sound field of the craft developed can effectively cover the inspected area in both the natural state and the sector-scan-focusing state, the craft has good sensitivity and resolution, which can detect different types and positions of unacceptable defects, measure the amplitude and length of defects, evaluate their harm degree, and determine whether the weld is qualified or not. (2) Through the analysis of the verification test results of the ultrasonic phased array craft developed, it can be seen that, the craft can detect artificial defects in simulation blocks designed according to the standard, and effectively measure and evaluate the indications. (3) Through comparative analysis of the on-site application results by using the technology developed and damage inspection results, it can be seen that, the technology can complete the inspection task in the complex environment, detect and evaluate the excessive defects, the results are consistent with the damage inspection results; the phased array ultrasonic inspection has significant advantage in time over radiographic inspection. The ultrasonic phased array inspection technology developed in this paper can effectively detect the excessive defects within the range of primary wave and primary reflected wave in the welds, compared with the conventional technology, it has better sensitivity and resolution and lower time cost. At the same time, the development and verification method of the technology can also be applied to the thin plats in nuclear power plant to improve the efficiency of craft development.

References 1. Zhao, L., Zhiyuan, K., Xianglin, Z.: Application of full focus imaging technique in weld inspection of thin plate. NDT 42(4), 14–16 (2018) 2. Yang, X., Liu, W., Hu, Y.: Research on performance of weld joints in P265GH steel plate for nuclear power pressure equipment. WISCO Technol. 55(6), 30–33 (2017) 3. Yang, Z.: Study on deconvolution denoising of ultrasonic testing signal for butt weld between thin plate, pp. 8–10. Harbin University of Science and Technology, Harbin (2018)

Primary Water Stress Corrosion Cracking of Nickel-Based Alloys Han Yaolei(B) , Peng Qunjia, Mei Jinna, and Xie Bin Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China [email protected], {pengqunjia,xiebin}@cgnpc.com.cn

Abstract. The primary water stress corrosion cracking (PWSCC) occurs in the nickel-based alloys of pressurized water reactor nuclear power plants (NPPs) in contact with the primary water environment. In order to improve the operation and management level of nuclear power plants, it is necessary to study the PWSCC problem and its management. This paper introduces the types of nickel-based alloys, the influencing factors and mechanisms for the PWSCC. Based on the international treatment methods and management requirements for PWSCC of nickel-based alloys components in NPPs, management suggestions are proposed for NPPs in China to deal with PWSCC of nickel-based alloys in the design and manufacturing stage and operation and maintenance stage. Keywords: Nuclear power plant · Nickel-based alloy · Primary water stress corrosion cracking

1 Introduction Nickel-based alloy is used as the material of key components in the primary coolant system of nuclear power plants (NPPs). The material reliability of nickel-based alloys in the high temperature water has become one of the important factors that affects the long-term safe operation of NPP [1, 2]. Alloy 600, used in the early stage of NPPs, suffers from primary water stress corrosion cracking (PWSCC) after decades of service in NPPs. PWSCC is widely concerned in the industry because it directly affects the integrity of the pressure boundary of the primary coolant system. Because of the better PWSCC resistance of alloy 690 than that of alloy 600, now most alloy 600 components in NPPs have been replaced by alloy 690 components. However, some alloy 600 materials are still used in some places where they cannot be replaced or the replacement cost is too high. Therefore, it is necessary to analyze the behavior and mechanism of PWSCC of nickel-based alloys used in NPP and manage it.

2 Application of Nickel-Based Alloys in NPPs Nickel-based alloy is defined as the alloy that the mass fraction of nickel content is more than 50% (mass fraction, same as below). Nickel-based alloys used in NPPs are © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 78–90, 2023. https://doi.org/10.1007/978-981-19-8780-9_9

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mostly nickel-chromium-iron system. Because of its excellent corrosion resistance, it is widely used in reactor vessel internals, control rod drive mechanism (CRDM), steam generator (SG) tube and other components, as well as the same and different metal weld joints. The compositions of nickel-based alloys commonly used in NPPs are listed in Table 1. The construction of nuclear power began in the 1950s. At that time, the structural materials of key components were mainly alloy 600. Alloy 82 and alloy 182 were mainly used for its welding. In the 1970s, it was found that there was intergranular PWSCC in such materials and their weld metals. Since then, a series of studies have proved that the content of chromium in nickel-based alloy is directly related to its resistance to PWSCC. Nickel-based alloy containing 30% chromium (wt%) has the best resistance to stress corrosion cracking. As a result, alloy 690 containing 30% chromium (wt%) was developed in the industry, and soon became the main material for manufacturing various radiation and corrosion-resistant components in NPPs. In the 1980s, weld metals alloy 52 and alloy 152 matched with alloy 690 were also developed and widely used.

3 Study on PWSCC Behavior of Nickel-Based Alloys PWSCC is a process from local defect initiation to slow steady-state propagation caused by corrosion behavior accelerated by sensitive material structure, stress and primary corrosive medium [3–6]. The main influencing factors of PWSCC in NPP include materials (chemical composition, weld organization and structure, grain boundary, surface state, etc.), high temperature and high pressure water environment (corrosion potential, dissolved hydrogen and dissolved oxygen in water, impurity anion, irradiation neutron flux, etc.), and stress (residual strain, stress caused by welding and cold work), etc. At present, the research on PWSCC behavior of nickel-based alloys are mainly carried out for alloy 690, alloy 600 and their welding alloys. By characterizing the microstructure of the material, the PWSCC behavior under different stress and hydrochemical environment are studied to obtain the effect of material, mechanical and hydrochemical factors on PWSCC behavior. Moreover, combined with the research on the corrosion behavior of nickel-based alloy, the PWSCC mechanism of nickel-based alloy is revealed. 3.1 Effect of Materials on PWSCC Behavior The most common PWSCC mode of nuclear power structural materials is intergranular cracking. Due to segregation, the chemical composition at the grain boundary of the material may be significantly different from that in the matrix. In high temperature water, chemical composition, grain boundary segregation, the distribution of precipitation and carbide and grain boundary structure have a great influence on PWSCC behavior. In addition, surface treatment, weld and other factors also have a certain impact on the PWSCC behavior of nickel-based alloys. 3.1.1 Effect of Chemical Composition on PWSCC Behavior of Nickel-Based Alloys The study of Ni–Cr–Fe alloys containing 18% chromium shows that the alloys produce transgranular corrosion when the nickel content is less than 25%; When nickel content

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H. Yaolei et al. Table 1. Composition of the nickel-based alloys used in NPPs (wt%) Ni

Cr

Fe

Alloy ≥ 72.0 600

14.0–17.0

Alloy ≥ 67.0 82

Si

Mn

P

6.00–10.00 ≤ 0.15

≤ 0.5

≤1

–

18.0–22.0

≤ 3.0

≤ 0.10

≤ 0.50

2.5–3.5

≤ 0.030

Alloy ≥ 59.0 182

13.0–17.0

≤ 10.0

≤ 0.10

≤ 1.0

5.0–9.5

≤ 0.030

Alloy ≥ 58.0 690

27.0–31.0

7.0–11.0

≤ 0.05

≤ 0.5

≤ 0.5

–

Alloy ≥ 52 52

28.0–31.5

7.0–11.0

≤ 0.04

≤ 0.50

≤ 1.0

≤ 0.020

Alloy ≥ 47 152

28.0–31.5

7.0–12.0

≤ 0.05

≤ 0.75

≤ 5.0

≤ 0.03

Alloy ≥ 70.00 X750

14.0–17.0

5.0–9.0

≤ 0.08

≤ 0.5

≤ 1.00

–

≤ 0.08

≤ 0.35

≤ 0.35

≤ 0.015

Alloy 50.00–55.00 17.00–21.00 Bal 718

C

Cu

Nb + Ta

Ti

Al

Mo

Bal

Alloy ≤ 0.015 600

≤ 0.5

–

–

–

–

–

Alloy ≤ 0.015 82

≤ 0.50

2.0–3.0

≤ 0.75

–

–

≤ 0.50

Alloy ≤ 0.015 182

≤ 0.50

1.0–2.5

≤ 1.0

–

–

≤ 0.50

Alloy ≤ 0.015 690

≤ 0.5

–

–

–

–

–

Alloy ≤ 0.015 52

≤ 0. 30

≤ 0.1

≤ 1.0

≤ 1.10

≤ 0.50

≤ 0.50

Alloy ≤ 0.015 152

≤ 0.50

1.0–2.5

≤ 0.50

≤ 0.50

≤ 0.50

≤ 0.50

Alloy ≤ 0.01 X750

≤ 0.50

0.70–1.20

2.25–2.75 0.40–1.00 –

Alloy ≤ 0.015 718

≤ 0.30

4.75–5.50

0.65–1.15 0.20–0.80 2.80–3.30 ≤ 0.006

S

–

is more than 65%, the alloys suffer from intergranular corrosion; When the nickel content is between 25 and 65%, the alloys neither suffer from transgranular corrosion nor intergranular corrosion. The International Atomic Energy Agency (IAEA) combined with the research experience of the industry, made statistics on the impact of nickel

Primary Water Stress Corrosion Cracking of Nickel-Based Alloys

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content of nickel-based alloy on PWSCC behavior [3]. It was found that the nickel content of alloy 600 was in the high sensitivity area of PWSCC while the nickel content of alloy 690 was in the low sensitivity area. PWSCC has been found on nickel-based alloy components with 18–22% chromium content in NPPs (such as alloy 82 weld metal). However, PWSCC has not been found in materials with chromium content higher than 22%, including alloy 690 (27–31% chromium content). In addition, trace elements can significantly change the PWSCC resistance of nickel-based alloys. Alloy 600 with high content of carbon leads to the depletion of chromium at grain boundary after heat treatment. The trace element phosphorus in alloy 600 hinder the dislocation movement in the solid solution, which slightly increases the resistance to PWSCC of alloy 600. The experimental results show that the presence of carbon in solid solution greatly increases the resistance to PWSCC in reducing water. However, slight Cr depletion significantly promotes the occurrence of intergranular PWSCC in oxidizing water. 3.1.2 Effect of Microstructure and Distribution of Grain Boundary Carbides on PWSCC Behavior Was et al. [4] found that the grain boundary carbide in mill-annealled (MA) alloy 600 is mainly Cr7 C3 , while the grain boundary carbide of alloy 600 after special heat treatment (600TT) is mainly Cr23 C6 . Cr23 C6 is coherent with the matrix on one side of the grain boundary and has high bonding strength with the grain boundary, so it can significantly improve the resistance to PWSCC in the primary water. Leonard et al. [5] studied the effect of carbide distribution in alloy 600 with and without special heat treatment on PWSCC. It was found that the grid continuous grain boundary carbides enhanced the resistance to PWSCC of alloy 600 in reductive high temperature water. However, the increase of intragranular carbides reduced the resistance to PWSCC of alloy 600. 3.1.3 Effect of Grain Boundary Structure on PWSCC Behavior Grain boundaries include can be divided into random large angle grain boundary, coincident position lattice (CSL) grain boundary and small angle grain boundary. Small angle grain boundaries and CSL grain boundaries have better resistance to PWSCC, while random large angle grain boundaries are prone to PWSCC. Yun et al. [6] studied the intergranular corrosion behavior of alloy 600 in simulated primary water. Intergranular corrosion is related to the characteristics of grain boundaries. Random large angle grain boundaries are seriously oxidized, while special grain boundaries are hardly oxidized. It is considered that grain boundary embrittlement caused by random large angle grain boundary oxidation is the main reason for intergranular PWSCC of alloy 600. 3.1.4 Effect of Element Distribution in Weld Metals on PWSCC Behavior Peng et al. [7] studied the relationship between the behavior of interdendritic PWSCC in alloy 182 and the micro-distribution of chemical components on the dendrite grain boundary. It was found that the heterogeneous distribution of chromium on the interdendritic facet may be in response to interdendritic PWSCC in alloy 182. The relevant research on alloy 182 [8] found that higher carbon/niobium ratio led to the formation of

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more continuous grain boundary carbides, which can reduce the resistance to PWSCC of alloy 182, and at the same time increase the probability of hot cracks. In addition, grain boundary carbides may also lead to local stress concentration, which reduces the resistance to PWSCC of alloy 182. 3.1.5 Effect of Overlay on PWSCC Behavior Alexandreanu et al. [9] studied the PWSCC behavior of alloy 52M overlay. By overlaying 52M weld metal on alloy 182 weld metal of J-groove, it is found that when the crack extends from alloy 182 weld metal to 52M weld metal, the crack propagation rate is faster at the junction of double-layer weld metal. However, the crack propagation rate is slower in 52M weld metal. 3.1.6 Effect of Surface Treatment on PWSCC Behavior Han et al. [10, 11] studied the effect of electropolishing treatment on the corrosion and PWSCC behavior of alloy 600 and alloy 182 weld metal in high temperature water. It was found that electropolishing increased the content of chromium on the surface, which attributed to formation of the Cr-enriched, protective oxide on electropolished surface. It prevented the diffusion of oxygen along the grain boundary. In particular, formation of the protective, blunted oxide at grain boundaries on electropolished surface mitigated the intergranular corrosion and caused a lower SCC susceptibility of Alloy 182. 3.1.7 Effect of Weld Defects on PWSCC Behavior Although alloy 690 and alloy 52/152 weld metals have the resistance to PWSCC, defects such as ductility-dip cracking, partial penetration and solidification crack are often found in weld joints. These defects may increase the PWSCC susceptibility. In order to improve the welding performance, a series of alloy 52/152 weld metals have been developed. Alloy 52i/152i reduces chromium content and increasing niobium. Although the above welding defects can be avoided, the reduction of chromium content results in the decrease of the resistance to PWSCC. Alloy 52m/152m and alloy 52mss/152mss increase niobium/molybdenum, resulting in the formation of precipitates, which enhance grain boundary pinning effect and reduce DDC. However, the precipitates increased the possibility of solidification cracks [12]. Therefore, it is necessary to further develop new nickel-based alloy weld metals in the future to avoid welding defects and increase the resistance to PWSCC at the same time. 3.2 Effect of Stress on PWSCC Behavior The existence of residual stress or strain reduced the resistance to PWSCC of nickelbased alloys. In the experiment of PWSCC initiation, micro cracks are more likely to initiate in places with large residual strain (i.e. local orientation difference). The main causes of residual stress/strain are cold work and weld.

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83

3.2.1 Effect of Cold Work The residual strain caused by cold work significantly increased the crack growth rate of PWSCC. Andresen et al. [13] systematically studied the PWSCC expansion behavior of nickel-based alloys and stainless steels in high temperature water. The research shows that the residual strain caused by cold work or weld significantly increased the crack growth rate of PWSCC, especially in the strain range of 25–30%. Even alloy 690 with good corrosion resistance has a high crack growth rate in this range. Bruemer et al. [14] studied the effect of cold work on PWSCC crack growth rate of alloy 690 pipe and plate in simulated primary water. After cold work, the specimens show a higher tendency of intergranular PWSCC. The PWSCC crack growth rate increases with the increase degree of cold work deformation. When the degree of cold work deformation is large, the crack growth rate increases significantly, which means the resistance to PWSCC significantly decreases. 3.2.2 Effect of Weld Residual Stress Mychailo et al.[15] studied the PWSCC behavior of alloy 152/52 dissimilar weld joints and found that the resistance to PWSCC is the worst near the heat affected zone (HAZ) and fusion line on the carbon steel side with large residual stress of dissimilar welded joints. On the 304 stainless steel side, the crack growth rate is the fastest near the fusion line. Therefore, the welding residual stress has a significant effect on the initiation and propagation process of PWSCC. 3.3 Effect of Primary Water on PWSCC Behavior The hydrochemical parameters of primary water have an important impact on the PWSCC behavior of nickel-based alloys, such as corrosion potential, dissolved hydrogen, dissolved oxygen and so on. With the increase of corrosion potential, the resistance of nickel-based alloys to PWSCC decreases. The increase of dissolved oxygen increase the corrosion potential, thus increasing the resistance to PWSCC. Andresen et al. [16] summarized the experimental data related to the crack growth rate and corrosion potential of nickel-based alloys, stainless steels and other materials. The crack growth rate decreases with the decrease of corrosion potential in high temperature water. The dissolved hydrogen concentration affects the thermodynamic and kinetic process of corrosion, thus affecting the occurrence of PWSCC [17–19]. It is generally considered that the effect of dissolved hydrogen on the crack growth rate of PWSCC in nickel-based alloys is related to the Ni/NiO phase transformation. The crack growth rate reaches the peak near the amount of dissolved hydrogen corresponding to the phase transformation. Xu et al. [20] studied the influence mechanism of dissolved hydrogen content in the primary water environment on the corrosion behavior of alloy 182 weld metal. It was found that when the hydrogen content was high, nickel was stable and the oxide film formed was very thin, mainly chromium oxide. When the concentration of dissolved hydrogen is low, the oxide film is thick, and NiO or spinel oxide particles containing more nickel are formed in the outer layer. Therefore, it is considered that the decrease of dissolved hydrogen content promote nickel dissolution and chromium oxidation at the crack tip of PWSCC and reduce the resistance to PWSCC.

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4 Study on PWSCC Mechanism In order to explain the PWSCC of nickel-based alloy in high temperature water, slip oxidation film rupture model, environmental coupling fracture model and internal oxidation model are proposed by researchers. (1) Slip oxidation film rupture model The model considers that under the condition of external load, the crack tip produces local plastic strain. When the strain is greater than a certain limit, the matrix begins to produce sliding steps, which leads to the rupture of the protective oxide film (passive film) at the crack tip, and the exposed fresh surface dissolves rapidly, resulting in crack propagation. The experimental results show that the main factor controlling the crack growth rate is the crack tip strain rate. Ford and Andresen et al. [21, 22] considered that the average crack growth rate was exponentially related to the strain rate at the crack tip, and put forward the formula of average crack growth rate: n vt = Aεct

(1)

where: vt is the average crack growth rate; εct is the crack tip strain rate; A and n are constants related to material and environment, respectively. The model is widely recognized because it puts forward quantitative calculation formulas and is consistent with some cracking characteristics. (2) Environmental coupling fracture model The model considers that the charge is conserved in the process of corrosion cracking, that is, the sum of the current density in the crack and the current density produced by hydrogen oxidation, oxygen reduction and metal dissolution is zero. The model can well explain the phenomena observed in many experiments, such as the effects of dissolved oxygen content, conductivity and stress intensity factor on the crack growth rate, and predict the accumulation of chloride ions at the crack tip and the acidification of the crack tip environment. (3) Internal oxidation model The model is mainly proposed for PWSCC of nickel-based alloy in primary water environment. This model considers that during the diffusion of oxygen through the oxide film and metal interface to the metal lattice, chromium is preferentially oxidized near the interface to form chromium oxide due to the high activity of chromium, and lead to the depletion of chromium around it. Panter et al. [23] studied the effect of oxide film on the initiation process of PWSCC of alloy 600, and found the depletion of chromium in the bottom metal of chromium rich oxide film. Besides, oxide at the grain boundary in the matrix was also found. Therefore, it is considered that the internal oxidation mechanism is one of the possible PWSCC mechanisms of alloy 600.

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5 Management of PWSCC of Nickel-Based Alloys Components in NPPs 5.1 Application and Management Activities of Nickel-Based Alloy Components Because a large number of NPPs are built earlier, the PWSCC problem of alloy 600 material has not been found or has not attracted enough attention at that time of construction. Therefore, alloy 600 material is widely used in these power plants. During the long-term service, it is found that the PWSCC problem of alloy 600 material is serious, and many failure cases occurred. For example, when a NPP was refuelling after 15.78 effective power years, it was found that there were axial cracks at the connection between the nozzle of five CRDMs and the J-groove weld. Alloy 600 is used for the CRDM tube, and alloy 182 welding alloy is used as the weld material. 308L stainless steel is used as the cladding on the inner side of the pressure vessel top cover. The crack caused in the service process is a single intergranular crack. The rock sugar pattern is caused by PWSCC. In addition, the secondary crack branch is also caused by PWSCC, showing typical intergranular characteristics. The axial crack starts at the nozzle and extends into the weld, so the PWSCC of penetration nozzle weld is the root cause of boric acid leakage. The use and management methods of alloy 600 in NPPs are shown in Table 2. It can be seen from Table 2 that the management activities for alloy 600 components in NPPs mainly include: replacing alloy 600 components and/or welds with 316L stainless steel and/or welds and alloy 690 and/or welds, implementing corresponding management and inspection activities. 5.2 Management Requirements for PWSCC of Nickel-Based Alloy Components By classifying different combinations of components, materials and environment, the PWSCC management requirements of nickel-based alloy (including alloy 600, alloy 690, etc.) components of NPPs are shown in Table 3 [24]. The management requirements of nickel-based alloy components are summarized as follows: (1) Through in-service inspection, visual, surface, volume inspection and other methods can be used to check whether there is cracking in the components or welds. (2) Through the leakage monitoring from the primary side to the secondary side of SG, the integrity of SG divider plate to tubesheet weld, tube and tube plug to tubesheet weld can be monitored. (3) The resistance to PWSCC can be improved by controlling the concentration of various elements in water within a certain range through hydrochemical management. (4) Through boric acid leakage management, the base material of cladding nickel-based alloy components can be prevented from corrosion and the stress of cladding metal can be greatly improved.

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H. Yaolei et al. Table 2. Application and management activities of alloy 600 components in NPPs

NPP Alloy 600 components

Management activities

A

Pressurizer instrument probe nozzle weld; primary coolant system temperature and pressure probe welds

Repair activities: repair by cutting out the original weld metal and welding it again with new weld metal; Preventive management: Based on conservative decisions, repaired all other alloy 600 instrument nozzle welds of the reactor coolant system

B

Surge line piping nozzle welds; Nozzle weld of pressurizer spray pipe; Safety valve safe end; Reactor pressure vessel (RPV) head penetration welds; SG tube

Repair activities: alloy 690 weld overlay repair no. 2 unit pressurizer nozzle weld; Preventive management: OVERALL replacement of RPV head, penetration welds change to alloy 690 material; SG whole replacement, the new SG tube using alloy 690

C

All kinds of nozzle welds of primary coolant system; pressurizer safety valve nozzle weld; pressurizer instrument nozzle weld; RPV instrument nozzle weld; SG tube; SG head blowdown nozzle

Repair activities: replacement by 316L stainless steel and alloy 690 to repair pressurizer safety valve weld; Preventive management: establish the aging management programme of other welds

D

All kinds of nozzle welds in the hot leg of primary coolant system

Repair activities: take repair activities to the nozzle

E

The RPV head; CRDM shell weld

Preventative management: replacement of a new RPV head without alloy 600

F

All kinds of nozzle weld of primary coolant system; pressurizer gas chamber instrument nozzle weld

Repair activities: use piping repair technology to repair instrument pipe nozzle

G

RPV nozzle safe ends and welds

Preventive management: establish the aging management programme

H

SG tube; vessel internals

Preventive management:establish the aging management programme

I

RPV head penetration weld; CRDM weld; Preventive management: establish the SG tube; SG tube supports aging management programme

J

RPV supports; CRDM shell weld; RPV instrument nozzle safe ends and welds; RPV penetration welds

Preventive management: establish the aging management programme

K

RPV head penetration weld; SG tube

Preventive management: establish the aging management programme

Primary Water Stress Corrosion Cracking of Nickel-Based Alloys

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Table 3. PWSCC aging management of nickel-based alloy components Components

Aging management program

Control rod drive head penetration: nozzle welds

A, B, C

Control rod drive head penetration: pressure housing

A, B

Core support pads; core guide lugs

A, B

Nozzle safe ends and welds: inlet; outlet; safety injection

A, B, C

Penetrations: head vent pipe (top head); instrument tubes (top head)

A, B, C

Penetrations: instrument tubes (bottom head)

A, B, C

Piping, piping components and piping elements

A, B, C

Pressurizer components

A, B

Pressurizer instrumentation penetrations; heater sheaths and sleeves; heater bundle diaphragm plate; manways and flanges

A, B, C

Pressurizer surge and steam space nozzles; welds

A, B, C

Pressurizer: spray head

B, D

Instrument penetrations and primary side nozzles, safe ends, welds

A, B, C

Primary side components: divider plate

B

Tube plugs

E, B

Tubes and sleeves

E, B

Tube-to-tubesheet welds

B

A: ASME Section XI Inservice Inspection, Subsections IWB, IWC, and IWD; B: Water Chemistry; C: Cracking of Nickel-Alloy Components and Loss of Material Due to Boric Acid-Induced Corrosion in RCPB Components (PWRs Only); D: One-time inspection; E: Steam Generators

6 Application and Management Suggestions About PWSCC for China NPPs Nickel-based alloy components of China NPPs are mainly used in the key components of the primary coolant system. Some examples of the use of nickel-based alloys in a NPP in China are following: RPV bottom penetration, SG divider plate, SG tube sheet cladding, etc. Through the above research and analysis, in order to avoid the impact of PWSCC on the safe operation of NPPs, the following suggestions are put forward for the use of nickel-based alloy components in NPPs in China. Design and manufacturing stage (1) Expand the use of alloy 690 and reduce the use of alloy 600. (2) Adopt heat treatment and welding processes that can effectively reduce the residual stress of materials.

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(3) The internal stress level of materials under various working conditions should be considered in component design, and a certain margin should be maintained between this level and yield strength. As far as possible, the principal stress direction of the components should be avoided to be perpendicular to the solidification direction of the nickel-based alloy weld. (4) The surface cold machining should ensure that the surface roughness meets the requirements of the technical specifications and prevent machining tool marks or other PWSCC sensitive sources on the surface of the components. Operation and maintenance stage: (1) Based on the detailed evaluation of alloy 600 components and/or welds, it is determined whether to replace the materials of corresponding components with other materials with high resistance to PWSCC. (2) Referring to the relevant management requirements of NUREG-1801 “Generic Aging Lessons Learned (GALL) Report”, strict management and inspection activities for nickel-based alloy components are implemented through in-service inspection programme, water chemistry programme, SG management programme, etc. (3) The PWSCC failure cases of components and materials during the NPP operation should be timely feedback to promote the improvement and optimization of design, manufacturing and installation to prevent the recurrence of similar stress corrosion failure cases.

Acknowledgement. This study was financially supported by the National Natural Science Foundation of China (No. 51901019 and 52071018) and the Scientific Research and Innovation Project of China General Nuclear Power Group (Research on Overall Scheme and Key Technology of Steam Generator Replacement in NPPs).

References 1. Xu, X.L., et al.: Corrosion protection design and aging management of structural materials for pressurized water reactor nuclear power plant. Corr. Protect. 37(7), 534–543 (2016) 2. Sam, K.: Application of nickel-base alloy weld metals in nuclear power industry. In: International Forum on Energy Engineering Welding. China Mechanical Engineering Society and its Welding Branch, Harbin, pp. 127–135 (2005) 3. IAEA. : Assessment and management of ageing of major nuclear power plant components important to safety: steam generators: IAEA-TECDOC-981. IAEA, Vienna Austria, pp. 37–40 (1997) 4. Was, G.S., Lian, K.: Role of carbides in stress corrosion cracking resistance of alloy 600 and controlled-purity Ni–16%Cr–9%Fe in primary water at 360 °C. Corrosion 54, 675–688 (1998) 5. Leonard, F., Cottis, R.A., Vaillant, F., et al.: Mechanistic studies of stress corrosion cracking of nickel-based alloys in high temperature high pressure PWR environment. In: Proceedings of the 14th International Conference on Environmental Degradation of Materials in Nuclear

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24. NUREG-1801, Rev. 2. Generic Aging Lessons Learned (GALL) Report. NRC, Rockville, MD, USA (2010)

Study of Application Optimization of SPAR-H Human Reliability Analysis Method Tan Xiao, Qiu Yongping(B) , Zhuo Yucheng, Lei Wenjing, and Hu Juntao Shanghai Nuclear Engineering Research and Design Institute Co., Ltd., Shanghai, China [email protected]

Abstract. The Standardized Plant Analysis Risk Human Reliability Analysis method (SPAR-H method) is widely used in the analysis of post-accident human failure events (HFEs) at present. Although SPAR-H methodology has presented the definition of each level of the eight performance shaping factors (PSFs), the detailed judgment criteria and analysis process of the PSF levels are not described definitely and in detail, which may result in the uncertainty of the calculated human error probability (HEP). In order to solve above problems, supplementary guidance in the process of identifying PSF and rating PSF is given when using SPARH method to quantify HFE based on the SPAR-H Step-by-Step Guidance. The PSFs, such as available time, stress and complexity are further studied. Combined with an example, the improvement and optimization of “available time” PSF are explained. Theoretical research and case analysis show that, relevant optimization suggestions can improve the PSF analysis process in SPAR-H method, and more reasonable HEPs could be obtained, therefore enhance the accuracy of quantitative results of human reliability analysis (HRA). Keywords: Standardized Plant Analysis of Risk Human Reliability (SPAR-H) · Performance Shaping Factors (PSFs) · Human Reliability Analysis (HRA) · Available time

1 Introduction Human Reliability Analysis (HRA) is an important element of Probabilistic Safety Assessment (PSA) for nuclear power plants [1]. Usually three types of human failure events (HFEs) are defined in HRA, i.e., pre-accident HFEs, initiating event-related HFEs, and post-accident HFEs [1–3]. Post-accident HFEs which are performed in response to an initiating event, take place following initiating events when the operator is following the procedures and training to bring the plant to a safe state. These actions are usually the most important human interactions to be considered in the PSA [3]. In recent years, the HRA method of nuclear power plants is in continuous development, and the development and application of IDHEAS method [4] and fire HRA method [5] have made breakthroughs. However, the Standardized Plant Analysis Risk Human Reliability Analysis method (SPAR-H method) of nuclear power plants is still one of the most widely used HRA methods in China. SPAR-H is the HRA method used in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 91–102, 2023. https://doi.org/10.1007/978-981-19-8780-9_10

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the Accident Sequence precursor Standardized Plant Risk Analysis Model (ASP/SPAR) developed by the Nuclear Regulatory Commission (NRC) and Idaho National Laboratory (INL) [6]. In the analysis of post-accident HFEs, the possible HFE is identified and defined through the review procedures and accident process, and then the HFE is analyzed by SPAR-H method. SPAR-H divides the task into diagnosis and action; the basic error probability (BHEP) of diagnosis is 1.0E−2, the BHEP of action is 1.0E−3, then corrected with 8 performance shaping factors (PSFs) (available time, stress, complexity, experience/training, procedures, human-machine interface (HMI), fitness for duty, work processes), and the final human error probability (HEP) is obtained by combining the correlation. The SPAR-H method is characterized by simplicity, easy to use, and easy acceptance by HRA analysts, however the SPAR-H method is also an easily misused method [7]. Although this method describes the eight PSFs and their rating basis, the definition of PSF and rating criteria are not clear and detailed enough, which is easy to confuse analysts, thus affecting the calculation accuracy of the final HEP. In order to solve the problems of SPAR-H method in engineering practice, this paper first briefly describes the analysis process of SPAR-H method, and then combines the SPAR-H guidelines [11] (referred to as guidelines), optimizing the definition and rating criteria of PSFs by HRA analysts in quantifying HFE using SPAR-H method, focusing on the available time, stress and complexity PSFs. Then the improved optimization of the “available time” PSF is illustrated. Relevant optimization suggestions improve the PSF rating in the SPAR-H method to obtain a HEP more in line with the engineering reality and enhance the credibility of the HRA quantification results.

2 Introduction of SPAR-H Method Post-accident HFEs are analyzed using the SPAR-H method using the following analysis steps: (1) Identify and define the post-accident HFEs. (a) Systematic analysis of relevant procedures to determine the individual operator response required for each event sequence; (b) For each human action to be considered, the corresponding HFE is defined to reflect the impact of not performing the correct human action. (2) The SPAR-H method is used to analyze the HFE determined in step (1) to obtain the corresponding HEP. Figure 1 shows the flow chart of HFE by the SPAR-H method. The calculation step of HEP is: step 1—categorizing the HFE as diagnosis and action; step 2—rate the PSFs; step 3—calculate PSF-modified HEP; and step 4—accounting for dependency. Since PSF rating is a key step in the application of SPAR-H method, this paper focuses on step 2 “rating PSF”; because the three PSFs—available time, stress and complexity have a great impact on the actual quantitative analysis of the engineering, the following focuses on the classification of the three PSFs. In this paper, further detailed guidance as required in engineering practice is given compared to that in [6] so as to enhance the credibility of the HRA quantification results.

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Before rating PSFs, HFE needs to be identified and defined, and each PSF needs to be checked for the accident scenario of HFE to solve two basic problems. First, whether there is adequate information to judge the influence of the PSF; second, whether the selected PSF is a “main performance shaping factors”. Only those PSFs with sufficient information to allow an informed judgment and are identified as “main performance shaping factors” can be further evaluated and quantified.

Fig. 1. Calculation flow-chart of post-accident HFE by SPAR-H method

3 Optimization of the Definition and Selection for Each PSF The eight PSFs in SPAR-H method are available time, stress, complexity, experience/training, procedures, ergonomics/HMI, fitness for duty and work processes. During engineering application, the definition of PSF in [6] is not clear and detailed. Based on

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the implementation guidelines, this paper focuses on the definition and rating criteria of three PSFs—available time, stress and complexity, because the above PSFs are more closely combined with the analyzed HFE characteristics and the actual situation of nuclear power plants, and more likely to change under different events and different power plant conditions. In addition, the rating content is quite different in the [6] and the implementation guidance, and the PSFs not discussed were basically classified in the two documents. 3.1 Available Time “Available time” refers to the amount of time that an operator or a crew has to diagnose and act upon an abnormal event. When discussing the “available time” PSF, it is assumed that the time window, the required time of diagnosis and the required time for action are all consistent with the actual engineering situation obtained through procedures and operators’ interview, and the credibility (uncertainty) of the data is not considered. For the “available time” PSF, the followings are noted. First, in the assessment of “available time” PSF, it looks at the available time relative to required time to complete the task. The required time is not the time required for immediate action execution. It also includes the whole process from observing indicators, monitoring power plant parameters, collecting and processing information, and interacting with crew members. In diagnosis and action, the classification criteria for PSF levels of “available time” PSF are different, and the corresponding judgment criteria for specific PSF levels is shown in Table 1. The time margin is the difference between the required time and the available time [12]. Using the concept of “time margin” can somewhat simplify the evaluation of the “available time” PSF. If the “time margin” is used to reevaluate the five levels of the “diagnosis part” of “available time” PSF, “available time” PSF level classification method as shown in Table 1 can be obtained. For diagnosis, NUREG-6883 varies from the implementation guidance for “inadequate time”, “barely adequate time” and “nominal time”; the same criteria for “extra time” and “expansive time”. If the total time window is known, when allocating the available time of “diagnosis” and “action” in previous engineering practice, take the total time window minus the required time for diagnosis as the available time for action, and the total time window minus the action time as the available time for diagnosis, available time allocation is too optimistic, implementation guidance gives a more reasonable method. The steps are as follows: (1) First, estimate the nominal time of action (average or minimum time plus a small period of time); (2) If there is enough time to perform the action, the available time PSF of the action will be rated as “nominal time” level (3) Assign the remaining time (total time window minus the nominal time of action) to the diagnosis as the available time of diagnosis. Among them, the implementation guidance suggests assigning a “nominal time” level for the action part, leaving as much available time as possible for the diagnosis part.

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Table 1. Rating criteria of “available time” PSF in NUREG-6883 and the guideline PSF rate

Rating criteria for diagnosis and action in NUREG-6883

Rating criteria for diagnosis in implementation guidance

Inadequate time

If the operator cannot diagnose the Time margin is negative problem/execute the appropriate action in the amount of time available, no matter what she/he does, then failure is certain

Barely adequate time

For diagnosis: 2/3 the average time Time margin is zero required to diagnose the problem is available: For action: there is just enough time to execute the appropriate action

Nominal time

On average, there is sufficient time There is small time margin to diagnose the problem

Extra time

For diagnosis: time available is between one to two times greater than the nominal time required, and is also greater than 30 min: For action: Time available ≥ 5× time required

The time margin is greater than zero but less than the time required; the time available is greater than the time required

Expansive time

For diagnosis: time available is greater than two times the nominal time required and is also greater than a minimum time of 30 min; For action: Time available ≥ 50× time required

The time margin exceeds the time required; the time available is much greater than the time required

When the total time window is sufficient, the PSF level of action part can be evaluated as “nominal time” or “extra time” according to the ratio to the “diagnosis part”. In the case analysis of Sect. 4, the corresponding engineering examples are discussed on the allocation of available time. 3.2 Supplementary Instructions for Stress, Complexity PSFs Combined with the implementation guidance, the optimization of the reference basis for HFE by SPAR-H method is given in this section, because the above PSFs have a great impact on the specific quantitative analysis of engineering practice. In NUREG-6883, “stress” PSF refers to the level of undesirable conditions and circumstances that impede the operator from easily completing a task; “complexity” PSF refers to how difficult the task is to perform in the given context. Comparison of the ratings of stress and complexity PSFs between the guideline and NUREG-6883 is shown in Table 2. The criteria for the definition of “stress” PSF and the criteria for “high” and “extreme” stress levels are clarified. For the “complexity”

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PSF, the examples of “high” and “moderately” complexity levels are added, and three judgment criteria for the “obvious diagnosis” level are given. Note that for the “obvious diagnosis” level in the complexity PSF, [6] presents a steam generator heat transfer tube rupture (SGTR) case. In current engineering practice, analysts tend to assess the “complexity” PSF level of the HFE as “nominal” or above, but in fact [6] recommends classifying such HFE as a “obvious diagnosis” PSF level. When analyzing the complexity level of human actions after SGTR accidents, attention should be paid to avoid too conservative rating.

4 Case Study Based on the analysis example of an HFE, the PSF of available time are rated by current engineering practice and by method introduced in implementation guidance, the final HEP results of this example are compared and illustrated. 4.1 Case of Different Evaluation Methods for Available Time PSF Analysis (1) Initiating event: Medium break Loss of Coolant Accident (MLOCA) (2) HFE description: After the MLOCA initiating event, the operator failed to recognize the need to open a steam generator atmospheric release valve to cool the reactor coolant system (RCS). (3) Post-accident personnel response: The operator enters reactor accidental shutdown or safety injection procedure according to the shutdown signal, go to the corresponding steps, turn to the primary or secondary circuit coolant loss procedures, go to the corresponding steps, turn to EOP for LOCA procedure, go to the corresponding steps, open the steam generator atmospheric release valve for the primary circuit cooling operation. The total time window of the mentioned HFE is approximately 80 min, and the signal delay time is 1 min, that is, the total available time is 79 min. Combined with the results of the operator interviews, the operator takes 30 min to complete diagnosis and 5 min for action. The PSF ratings obtained by using current engineering practices for the above examples are shown in Table 3. For Available Time PSF, the PSF rating method used in current engineering practices is to take the total time window minus the required time for diagnosis as the available time for action, and the total time window minus the action time as the available time for diagnosis, and the allocation of available time for the action and diagnosis section is shown in Fig. 1a. With the available time allocation method described in the implementation guidance, the allocation of total available time is: the average time is the available time for action and the available time for diagnosis is the total time window minus available time for action, as shown in Fig. 1b. Comparison of Fig. 2 shows an overlap in the allocation of available time. At this point, the PSF level determined by comparing the required time and available time will be more optimistic, and will also have an impact on the total HEP results. The

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Table 2. Comparison of rating criteria of “stress, complexity” PSFs between NUREG-6883 and guideline PSFs

Rating criteria in NUREG-6883

Implementation guidance to NUREG-6883

Stress

“Extreme”—a level of disruptive stress in which the performance of most people will deteriorate drastically. This is likely to occur when the onset of the stressor is sudden and the stressing situation persists for long periods. This level is also associated with the feeling of threat to one’s physical well-being or to one’s self-esteem or professional status, and is considered to be qualitatively different from lesser degrees of high stress (e.g., catastrophic failures can result in extreme stress for operating personnel because of the potential for radioactive release)

No difference in rating, but note that: ➀ Often stress results from limited time, high complexity, poor procedures, poor training, poor work processes, or poor crew dynamics. However, the analyst should make an effort to avoid any “double counting” of specific influencing factors ➁ The key to assigning a level to this PSF is the distinction between high and extreme stress. Extreme stress is qualitatively different from high stress, and is likely to occur if a problem is prolonged, such as multiple equipment failures, if the crew has had prolonged difficulties controlling plant parameters, or in situations where there is a severe threat to personnel or plant safety

“High”—A level of stress higher than the nominal level (e.g., multiple instruments and annunciators alarm unexpectedly and at the same time; loud, continuous noise impacts ability to focus attention on the task; the consequences of the task represent a threat to plant safety) “Nominal”—The level of stress that is conducive to good performance Complexity “High complexity”—Very difficult to perform. There is much ambiguity in what needs to be diagnosed or executed. Many variables are involved (i.e., unfamiliar maintenance task requiring high skill)

“High complexity”—Very difficult to perform. There is much ambiguity in what needs to be diagnosed or executed. Many variables are involved, with concurrent diagnoses (or actions). For example, an unfamiliar equipment line-up is required that involves defeating interlocks on valves (continued)

implementation guidance method first determines the available time of the action part, and the remaining time in the total time window is allocated to the diagnosis part, making the allocation clearer and avoiding the generation of overlapping parts. When the total time window is sufficient, the PSF grade of the action part can be rated as “nominal time” or “extra time” based on the ratio of the total time window to

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PSFs

Rating criteria in NUREG-6883

Implementation guidance to NUREG-6883

“Moderately complexity”—Somewhat difficult to perform. There is some ambiguity in what needs to be diagnosed or executed. Several variables are involved, perhaps with some concurrent diagnoses or actions (i.e., evolution performed periodically with many steps)

“Moderately complexity”—Somewhat difficult to perform. There is some ambiguity in what needs to be diagnosed or executed. Several variables are involved, perhaps with some concurrent diagnoses (or actions). For example, an atypical system startup is executed requiring the manual connection of backup power supplies

“Nominal”—Not difficult to perform. There is little ambiguity. Single or few variables are involved

“Nominal”—Not difficult to perform. There is little ambiguity. An easily managed number of variables or inputs are involved. The organization of information or execution of steps is relatively straightforward with little potential for confusion. Also, nominal should be chosen when this PSF is judged as not being a performance driver (continued)

the diagnosis part and the action part. For the above cases, it is calculated as “nominal time” and “extra time” respectively: (a) When the action available time PSF takes the “nominal time” level, the diagnosis part takes the “expansive time” level. (b) When the action available time PSF takes “available time ≥ 5×” level, the diagnosis part takes the “extra time” level. Through the case analysis of the specific HFE of the available time PSF, the results of the same HFE calculated by comparing the current engineering practices and the implementation guidelines are given in Table 4.

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Table 2. (continued) PSFs

Rating criteria in NUREG-6883

Implementation guidance to NUREG-6883

“Obvious diagnosis”—Diagnosis becomes greatly simplified. There are times when a problem becomes so obvious that it would be difficult for an operator to misdiagnose it. The most common and usual reason for this is that validating and/or convergent information becomes available to the operator. Such information can include automatic actuation indicators or additional sensory information, such as smells, sounds, or vibrations. They indicate a SGTR. Diagnosis is not complex at this point; it is obvious to trained operators

“Obvious diagnosis”—Diagnosis becomes greatly simplified. There are times when a problem becomes so obvious that it would be difficult for an operator to misdiagnose. The most common and usual reason for this is that validating and/or convergent information becomes available to the operator. Such information can include automatic actuation indicators or additional sensory information, such as smells, sounds, or vibrations. There are three characteristics needed to qualify a diagnosis as obvious. First, the situation needs to be relatively simple, a single event only. Second, the indications need to be clear and unambiguous. Third, it needs to be something the operators have experienced before (at least in training)

Table 3. PSF levels using the current engineering practice PSF

Diagnosis

Action

PSF level

Multiplier PSF level

Multiplier

Expansive time

0.01

Available time

Expansive time

Stress

High

2

Stress

High

Complexity

Moderately complexity

2

Complexity

Moderately complexity

Experience/training

Nominal

1

Experience/training

Nominal

Procedure

Diagnostic/symptom oriented

0.5

Procedure

Diagnostic/symptom oriented

Ergonomics/HMI

Nominal

1

Ergonomics/HMI

Nominal

Fitness for duty

Nominal

1

Fitness for duty

Nominal

Working process

Nominal

1

Working process

Nominal

Available time

For the allocation of the available time in the diagnosis and action parts, the implementation guidelines take the “total time window minus the action available time” as the

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T. Xiao et al. Total time window Diagnosis required time

Action available time

Diagnosis available time

Action required time

(a) Current engineering practice method

Total time window Diagnosis required time

Action required time

Diagnosis available time

Action available time

(b) Implementation guideline method (selected) Fig. 2. Schematic diagram of the apportion of available time between diagnosis and action using the current engineering practice and guideline

Table 4. Comparison of HEP results with different time proportion HEP results comparison

Current engineering practice method

Implementation guideline method (a)

Implementation guideline method (b)

Diagnosis HEP 2.00E−04

2.00E−04

2.00E−03

Action HEP

4.00E−04

4.00E−03

4.00E−04

Total HEP

6.00E−04

4.20E−03

2.40E−03

available time of the diagnosis part, effectively avoiding the generation of the overlap between diagnosis and action part available time. By comparing the HEP analysis results between the current engineering practice and the implementation guidelines, the calculation results of the current engineering practice are too optimistic. For the total HEP, the total HEP values under the two considerations of the implementation guidelines are more conservative than the current engineering practices.

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The analysis results of the two consideration methods of the implementation guidelines are basically equivalent, and any one data or small value can be selected as the final result. The division of the available time for the diagnosis and action part is more clear and reasonable, and the final calculation results should be more in line with the engineering practices. Implementation guidelines provide several flexible ways to allocate the total available time between the diagnosis and action parts. In the subsequent engineering application, the priority method selection can be studied to reach an agreement in the industry and facilitate a better application.

5 Conclusion The SPAR-H method is easy to use and is prone to misuse. In the engineering application of SPAR-H, the selection of PSF level has a significant impact on the calculation results of HEP. This paper mainly combines the relevant guidance in [6] and [11] and the current engineering application situation in China, studies the HFE calculation step 2 “PSF rating” in SPAR-H method, and further studies the definition of available time, pressure and complexity PSFs and the reference basis of the rating process. Finally, the different evaluation methods of “available time” PSF are analyzed and compared. Compared with the current engineering practice, the rating of PSFs which described in the guideline is clearer and more accuracy and the final analysis and calculation results are more reasonable, which provides reference for the optimization of SPAR-H method in engineering practices.

References 1. Kolaczkowksi, A., et al.: Good Practice for Implementing Human Reliability Analysis (Final Report), NUREG/CR-1792, U.S. Nuclear Regulatory Commission (2005) 2. National Nuclear Safety Administration. HAD 02/19, First Class Probabilistic Safety Analysis of Nuclear Power Plant. Beijing: NNSA (2021) 3. Development and Application of Level 1 Probabilistic Safety Assessment for Nuclear Power Plants, Specific Safety Guide No. SSG-3, Vienna: IAEA (2010) 4. Zhuo, Y., Qiu, Y., Hujuntao, et al.: Study of application of IDEAHS method. Atomic Energy Sci. Technol. 55(4), 678–684 (2021) 5. Zhuo, Y., Qiu, Y., Hejiandong.: Study of human reliability method in scoping method of fire PSA. Nucl. Sci. Technol. 34(4), 537–540 (2014) 6. Gertman, D., et al.: The SPAR-H Human Reliability Analysis Method, NUREG/CR-6883, U.S. Nuclear Regulatory Commission (2005) 7. Forester, J.A., et al.: The International HRA Empirical Study: Lessons Learned from Comparing HRA Methods Predictions to HAMMLAB Simulator Data. NUREG-2127, U.S. Nuclear Regulatory Commission (2014) 8. Qiu, Y., et al.: The applicability analysis of SPAR-H method in MCR. In: The 6th Nuclear Energy Industry PSA Seminar, Hangzhou. China Nuclear Energy Industry Association (2018) 9. Tao, Q., et al.: Application of SPAR-H method in human reliability analysis of digital nuclear power plants. Nuclear Power Engineering 42(3), 127–131 (2021) 10. Jianqiao, L., et al.: Identification of correlation among performance shaping factors of SPAR-H method. Nucl. Power Eng. 42(4), 144–150 (2021)

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11. Whaley, A.M., et.al.: SPAR-H Step-by-Step Guidance, INL/EXT-10-18533, Idaho National Laboratory (2011) 12. Cooper, S.: EPRI/NRC-RES Fire Human Reliability Analysis Guidelines (Final Report), NUREG-1921. U.S. Nuclear Regulatory Commission (2012)

Comparison of Nuclear Fuel Manufacturing Management Requirements Between HAF003 and IAEA Nuclear Safety Regulations Mengyao Tong(B) Yuanmen Road CJNF, YiBin, Sichuan, China [email protected]

Abstract. Under the national strategy of ‘going out’ with the nuclear industry, we urgently need to understand the similarities and differences between domestic nuclear safety regulations and international nuclear safety laws in the manufacture of nuclear fuel components. This paper briefly introduces the relationship and differences between Quality Assurance Safety Regulations for Nuclear Power Plants A Code of Practice (HAF003(91)) regulations, guidelines and International Atomic Energy Agency (IAEA) nuclear safety regulations. A proposal has been made, which introduce the latest requirements of international general nuclear safety and quality management at an appropriate time. This suggestion will help company employees better understand and implement nuclear safety regulations in design, production and technical services. Keywords: Nuclear fuel components · Manufacture · Quality assurance · HAF003 · IAEA nuclear safety regulations

1 General Quality Assurance Safety Regulations for Nuclear Power Plants issued by the National Nuclear Safety Administration in 1986 are based on Code on the Safety of Nuclear Power Plants: Quality Assurance (IAEA 50-C-QA), which was revised in 1991 and is still in use today. It is applicable to on-land stationary thermal neutron reactor nuclear power plants. However, the latest version of IAEA regulation is (GSR Part 2-2016) revised 3 times. Although HAF003 has not changed over the years, the nuclear industry still closely follows the trend of IAEA’s nuclear safety management. The quality and safety management of nuclear component manufacturing basically meet the requirements of IAEA nuclear safety regulations. The integration of health, environment, safety, quality and other systems will become a development direction, considering the trend of foreign exchanges of nuclear power industry in the future.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 103–110, 2023. https://doi.org/10.1007/978-981-19-8780-9_11

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2 The Basic Requirements of HAF003 2.1 The Basic Structure of HAF003 Quality Assurance Safety Regulations for Nuclear Power Plants (HAF0400 (1986) Rev.1), the earliest quality assurance regulation of nuclear facilities in China, puts forward principles and objectives for the quality assurance of production and operation in the nuclear energy industry. It was revised to HAF0400 (1991 Rev.2) in 1991, and its application scope was extended to’ other nuclear facilities’. After verifying the inaccuracies in the translation, in 1998, it was republished and changed to HAF003 (1991), which is still in use today. HAF003 (1991) puts forward the basic requirements that must be met for quality assurance of nuclear power plants and other nuclear facilities. It also proposes a set of quality-assurance measures that must be taken to ensure the items and activities of nuclear power plants. Nuclear facility operators, all contractors and subcontractors must comply with it. For nuclear fuel component manufacturers, the regulation include quality assurance activities that need to be implemented to ensure the safe operation of nuclear power plants, when providing nuclear fuel components and services. The basic structure of HAF003 (1991) is divided into 13 chapters, including General and 12 aspects of organizational management and technical management measures, which are collectively referred to as the 12 basic elements of nuclear facilities quality assurance system. It is in line with the general order in which work is carried out by operators of nuclear installations. 1. General stipulate the basic requirements that must be met by the quality assurance of nuclear power plants and nuclear facility operators. 2. The nuclear facility operators must formulate a quality assurance program (Chap. 2 Quality Assurance Program), stipulate the formulation and implementation of such quality assurance documents as the quality assurance program, working procedures and detailed rules, and plan its own quality assurance work in advance. They must establish quality assurance organizations (Chap. 3 Organization), clarify the responsibilities of departments and personnel, and the requirements of management department to regularly review the status and practicability of the outline. 3. Any work must be carried out in accordance with the established documents, so the requirements of document control should be specified first (Chap. 4 Document control). Then requirements for the design (Chap. 5 Design control) and procurement (Chap. 6 Procurement control) process need to be formulate. When working, it is necessary to strictly take control measures on items (Chap. 7 Item control) and carry out process control (Chap. 8 Process control). 4. In order to ensure quality, the organization must carry out quality supervision, inspection and testing (Chap. 9 Inspection and test control), at same time, control the non-conformities in the work (Chap. 10 Non-conformance control) and take corrective actions on them (Chap. 11 Corrective actions). All work shall be recorded in accordance with the requirements (Chap. 12 Record). 5. The organization shall inspect and evaluate the completion of the work (Chap. 13 Audit).

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2.2 The Safety Guidelines Related to the Manufacture of Nuclear Fuel Components The HAF003 (1991) consists of 10 guidelines, 7 of which relate to nuclear fuel components manufacturing: 1. HAD003/01 Formulation of quality assurance program for nuclear power plant 2. HAD003/02 Quality assurance organization of nuclear power plant 3. HAD003/03 Quality assurance in procurement of items and services for nuclear power plants 4. HAD003/04 Quality assurance record system of nuclear power plant 5. HAD003/05 Quality assurance audit of nuclear power plant 6. HAD003/08 Quality assurance in item manufacturing of nuclear power plant 7. HAD003/10 Quality assurance in procurement, design and manufacture of nuclear fuel components

3 IAEA Nuclear Safety Laws and Their Development 3.1 Code on the Safety of Nuclear Power Plants: Quality Assurance (50-C-QA) Based on US federal regulations and combined with the experience of quality assurance activities of other member states, IAEA published 50-C-QA in 1978, and recommended each member state to use it. It is the prototype of HAF003 (1991), and it can be said that HAF003 (1991) is basically equivalent to it. 3.2 Quality Assurance for Safety in Nuclear Power Plants and Other Nuclear Installations (50-C/SG-1996) In 1996, the IAEA revised the original 50-C-QA and promulgated the 50-C/SG-1996. The basic requirements defined by this Code constitute the foundation of a comprehensive quality assurance programme. They are divided into three functional categories: Management, Performance and Assessment. Management at all levels shall regularly assess the processes for which it is responsible. Extend the original inspection to independent assessment. It emphasizes the responsibilities and requirements of managers, staff and job evaluators, which is not different from 50-C-QA and HAF003. 10 basic requirements are put forward in the 3 sections of management, performance and assessment: 1. Management: Quality assurance programme; Training and qualification; Nonconformance control and corrective actions; Document control and records. 2. Performance: Work; Design; Procurement; Inspection and testing acceptance. 3. Assessment: Management self-assessment; Independent assessment. In the appendix of the code, 10 basic requirements have been supplemented. There are also 14 guidelines that provide guidance for tasks, of which the following 8 are related to the development and manufacturing of nuclear fuel components:

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50-SG-Q1 Establishing and implementing a quality assurance programme 50-SG-Q2 Non-conformance control and corrective actions 50-SG-Q3 Document control and records 50-SG-Q4 Inspection and testing for acceptance 50-SG-Q5 Assessment of the implementation of the quality assurance programme 50-SG-Q6 Quality assurance in procurement of items and services 50-SG-Q7 Quality assurance in manufacturing 50-SG-Q8 Quality assurance in research and development

3.3 The Management System for Facilities and Activities (GS-R-3 2006) In 2006, the IAEA made a comprehensive revision of 50-C-Q and published the general safety requirements The Management System for Facilities and Activities (GS-R-3). This Safety Requirements publication defines the requirements for establishing, implementing, assessing and continually improving a management system. A management system designed to fulfil these requirements integrates safety, health, environmental, security, quality and economic elements. It ensures safety by considering the impact of all activities not only within the individual management system, but also in terms of overall security. From GS-R-3, it uses the term ‘management system’ rather than ‘quality assurance’. The term management system reflects and includes the initial concept of ‘quality control’ (controlling the quality of products) and its evolution through quality assurance (the system to ensure the quality of products) and ‘quality management’ (the system to manage quality). Meantime, the scope of application of the code has been expanded. The code incorporates the requirements of ISO 9001:2000 and ISO 14001:1996. However, most domestic companies don’t implemented its requirements for management system integration. Therefore, neither GS-R-3 nor the subsequent revision of GS-R Part2 can be applied directly. The code puts forward 21 basic requirements in 5 sections of management system, management responsibility, etc., including: 1. Management System A total of 4 requirements: General requirements, Safety culture, Grading the application of management system requirements, Documentation of the management system. 2. Management Responsibility A total of 5 requirements: Management commitment, Satisfaction of interested parties, Organizational policies, Planning, Responsibility and authority for the management system. 3. Resource Management A total of 3 requirements: Provision of resources, Human resources, Infrastructure and the working environment. 4. Process Implementation A total of 3 requirements: Developing processes, Process management, Generic management system processes.

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5. Measurement, Assessment and Improvement A total of 6 requirements: Monitoring and measurement, Self-assessment, Independent assessment, Management system review, Non-conformances and corrective and preventive actions, Improvement. The code includes a supporting guideline: Application of the Management System for Facilities and Activities (GS-G-3.1), which contains 4 appendices: Transition to Integrated Management System, Activities in the Document Control Process, Activities in the Procurement Process, Performance of Independent Assessment, and 3 references: Electronic Document Management System, Media for Record Storage, Record Retention and Storage. 3.4 Leadership and Management for Safety (GSR Part2 2016) In 2016, the IAEA revised the GS-R-3 and published the Leadership and Management for Safety (GSR Part2). The main framework of the code includes 5 aspects and 14 requirements. See Table 1 for details. Compared with GS-R-3, GSR Part2 requires that the prime responsibility for safety must rest with the person or organization responsible for facilities and activities that give rise to radiation risks, and the management system should consider the interaction between technology, organization and human factors. The code connects with other relevant safety standards of IAEA, and is oriented to ensure the realization of basic safety objectives, highlighting safety responsibility, safety leadership, safety management and safety culture. The integrated management system is a means to meet the above requirements and achieve safety goals, and quality assurance is one of several elements that need to be considered. Its main modifications include: (a) It based on achieving the basic safety goals set by the IAEA’s Fundamental Safety Principles (SF-1): establish, maintain and continuously improve safety leadership and management; all practical efforts must be made to prevent and mitigate nuclear or radiation accidents; protect people and the environment from ionizing radiation; ensure facilities and activities meet the requirements of safety, quality and management standards. (b) The scope of application is clearer: facilities and activities causing radiation risk; life cycle of facilities; the whole process of the activity. In addition to nuclear facilities, the scope is extended to the use of nuclear technology and industrial activities of natural radioactive materials with protection and safety requirements; the life cycle extends to the stage of decommissioning or closure, and site closure; activities extended to radioactive waste management and radioactive material transportation. (c) Management system needs to consider more complete requirements: human, organizational and social factors are added, in addition to safety, health, environment, security, quality and economic factors. Properly resolve conflicts in various decisions and ensure that security is not damaged or compromised by other decisions. Require safety and security measures to be designed and applied collaboratively to identify and resolve the potential impacts of each other.

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Aspects

Requirements and Contents

Responsibility for safety

Requirement 1

Achieving the fundamental safety objective

Leadership for safety

Requirement 2

Demonstration of leadership for safety by managers

Management for safety Responsibility for Requirement 3 integration of safety into the management system

Responsibility of senior management for the management system

Requirement 4

Goals, strategies, plans and objectives

Requirement 5

Interaction with interested parties

The management system Requirement 6

Integration of the management system

Requirement 7

Application of the graded approach to the management system

Requirement 8

Documentation of the management system

Management of resources

Requirement 9

Provision of resources

Management of processes and activities

Requirement 10 Management of processes and activities Requirement 11 Management of the supply chain

Culture for safety

Requirement 12 Fostering a culture for safety

Measurement, assessment and Improvement

Requirement 13 Measurement, assessment and improvement of the management system Requirement 14 Measurement, assessment and improvement of leadership for safety and of safety culture

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(d) Add or enhance concepts and requirements: management leadership; nuclear security culture; Integrated management system; radiation risk analysis and management; organizational policy planning; communicate and report information with relevant parties; knowledge and information management; change management; independent review prior to major safety decisions; supply chain management etc.. (e) Refined the considerations of the classification method: Security importance and complexity of organization, facility operation or activity execution; hazard and degree of potential risks related to safety, health, environment, security, quality and economic factors; possible safety consequences arising from the occurrence of failure or unexpected events, or inappropriate or incorrectly implemented activity plans. (f) Use independent evaluation and self-evaluation to regularly evaluate safety and quality performance and the suitability and effectiveness of the management system. Apply internal and external experience feedback, technological progress and research and development results, and good practices to manage. It also requires regular reviews of leadership and safety culture. 3.5 Analysis of Compatibility of IAEA Calendar Standards According to the development process of IAEA quality assurance standards, each version of the standard has been revised or added requirements according to the development direction of nuclear safety and international quality management system. Therefore, those that meet the requirements of the latest version of the standard can also cover the requirements of the previous versions.

4 Changes of Safety and Quality Management in Domestic Nuclear Energy Industry Based on the above comparison, it can be seen that the structure and content of HAF have not changed in recent years. However, since most companies in the nuclear energy industry can usually obtain system certifications such as quality management based on the adoption of HAF regulations, which effectively supplements the management requirements in HAF regulations. Nuclear Safety Law of the People’s Republic of China which implemented on January 1, 2018 provides a legal basis for ensuring nuclear safety, preventing and responding to nuclear accidents, safe use of nuclear energy, protecting the safety and health of the public and employees, and protecting the ecological environment. In 2017, in terms of nuclear safety culture construction, based on fully studying the development status of nuclear safety culture at home and abroad, the National Nuclear Safety Administration, in accordance with the Nuclear Safety Culture Policy Statement, compiled and published the Nuclear Safety Culture Characteristics, which provides an authoritative reference for the nuclear safety culture construction of the nuclear energy industry. In terms of nuclear safety management, in January 2021, the Ministry of Ecology and Environment promulgated Decree No. 18 Safety Regulations for Nuclear Power Plant

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Management System, which is applicable to the establishment and implementation of the management system for nuclear power plants within the territory of the People’s Republic of China and in other sea areas under its jurisdiction. It adopts the five main frameworks of safety responsibility, safety leadership, and safety management in GSR Part2, stipulates some of the same or similar management requirements, and is more suitable for the management status of the domestic nuclear industry. It is a nuclear safety management regulation with unique Chinese characteristics.

5 Conclusion IAEA’s nuclear safety standards have been revised for three times, and are developing towards the direction of “great quality” and “great safety”. Although HAF003 has not changed, which is basically equivalent to 50C, the domestic nuclear energy industry still closely follows the IAEA nuclear safety management trend. Based on the establishment of management systems such as ISO9001 and ISO14001, the company has comprehensively carried out nuclear safety culture construction, and the quality and nuclear safety management of nuclear component manufacturing basically meets the current IAEA standards. Considering the needs of the nuclear power industry in the next few years, the integration of health, environment, security, quality and other management systems will become a development direction.

Environmentally-Assisted Fatigue Study of Charging Nozzle Under Multiaxial Stress History Based on Fluid-Solid Interaction Method Hongbo Gao(B) , Min Yu, Runfa Zhou, Mingya Chen, Lei Lin, Decheng Xu, and Shuai Zhou Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China [email protected]

Abstract. Based on the real transient load data of a second generation pressurized water reactor (PWR), 3D full-scale transient computational fluid-structure interaction (FSI) numerical analysis method was employed for effective characterizing the real thermal field and the stress field of the charging nozzle in PWR. Based on the stress field data obtained by fluid-structure coupling calculation, fatigue analysis and environmentally-assisted fatigue analysis were carried out under multiaxial stress history. Compared with the traditional design fatigue analysis results based on ideal design transient load, it is concluded that although the thermal shock response by traditional design analysis based on ideal design transient load was more severe and the fatigue results was also more conservative, this method could not effectively characterize the fatigue severity of different zones of the nozzle. The FSI numerical analysis based on the real transient load data can not only effectively reflect the fatigue state in different zones of the nozzle, but also effectively reduce the excessive conservative margin in the traditional design fatigue analysis. Considering the influence of reactor water environment on fatigue, the calculation assumptions in traditional design analysis are too conservative. However, the environmentally-assisted fatigue analysis method using fluid-structure coupling stress field data and based on multi-axial stress history can effectively reduce the unnecessary conservative margin and make the fatigue calculation results meet the requirements of the code. The environmentally-assisted fatigue analysis method adopted in this paper can provide reliable reference for equipment design, inservice inspection and renewal of operation license for fatigue sensitive area of nuclear power plant. Keywords: Charging nozzle · Fluid-Solid interaction · Environmentally-assisted fatigue · Multiaxial stress

1 Introduction The reactor pressure boundary contains radioactive water and maintains structural integrity during the life of the plant. According to ASME Code Section III NB [1] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 111–128, 2023. https://doi.org/10.1007/978-981-19-8780-9_12

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and RCC-M Code Section B [2], fatigue is a major factor to be considered in the design of pressure vessels and pipelines in nuclear power plants, and the cumulative usage factor (CUF) should be proved to be less than 1 during the whole design life. In the design stage of nuclear power plant, the fatigue curves used in fatigue calculation according to ASME or RCC-M code are all fatigue curves obtained by using smooth solid small sample test in room temperature air environment, without considering the influence of reactor water environment on fatigue behavior [3]. However, it has been found in recent years [4–6] that the reactor water environment has a significant impact on the fatigue life of carbon steel, low-alloy stainless steel, austenitic steel and nickel-ferrochrome alloy. The U.S. Nuclear Regulatory Commission (NRC) has required new nuclear plants and those applying for license renewal to consider environmentallyassisted fatigue (EAF) issues, and detailed guidelines for considering EAF issues are provided in NUREG/CR 6909 [7]. In the chemical and volume control system (RCV) of the primary nuclear auxiliary system of PWR, the charging line is responsible for regulating the water quality and maintaining the concentration of boric acid in the primary circuit, among which the cold water is generally 50 °C [8]. During the operation of the nuclear power plant, severe thermal fatigue usually exists at charging nozzle due to the severe cyclic thermal shock load generated by the interaction of hot and cold water [9, 10]. However, the current recording of transient events in nuclear power plants is mainly concerned with events that have a serious impact on the reactor pressure vessel (RPV). Although some thermal transients in auxiliary systems can lead to severe thermal shock in charging nozzle, they are not continuously and effectively tracked because they have no significant impact on the RPV. The operating experience of some nuclear power plants indicates that the expected number of such transient events may exceed the design cycles at 60 years of operation. In the traditional design fatigue analysis, the nozzle area is usually simplified into several heat zones based on the design transient load, and the fatigue analysis is carried out by calculating the convective heat transfer coefficient according to the empirical formula. This oversimplification may not reflect the wall temperature distribution of the charging nozzle, which affects the effective fatigue life evaluation. In the in-service inspection, the selection of thermal fatigue sensitive positions of pipelines is based on the fatigue analysis results of the original design [11, 12], whose accuracy directly affects the accuracy of the scope of in-service inspection. NUREG/CR-6260 [13], a technical report of NRC, identified charging nozzle as one of the most typical fatigue sensitive locations in PWR, and pointed out that monitoring the real fatigue status of this location is the most effective management means to solve such problems. In addition, due to the influence of EAF is not considered in the fatigue analysis of the original design of nuclear power plants, and the implementation rules for considering EAF recommended by NUREG/CR 6909, ASME Code Case 792 [14] and RCC-M code (2016) are very conservative. For typical fatigue sensitive locations (such as charging nozzle), the CUF obtained after considering the environmental impact factor (Fen) usually exceeds 1, which greatly increases the cost of equipment design and license renewal and makes it more difficult for nuclear power plants to obtain operating licenses. In order to solve the problem, more accurate methods are needed to calculate the fatigue

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characteristics of components to reduce unnecessary conservative margins. However, the current fatigue analysis of nuclear power plant equipment is based on conservative assumptions to obtain conservative analysis results, and there are few studies on the actual fatigue characteristics of charging nozzle under the actual operating state. As a part of the pressure boundary of the primary circuit, the charging nozzle is not allowed to leak during operation. Therefore, it is of great technical significance and engineering application value to study the environmentally-assisted fatigue characteristics of the charging nozzle under the real transient conditions during operation to prevent the sudden occurrence of failure accidents. This paper aims to explore a fatigue analysis method for fatigue sensitive components in nuclear power plants based on the current design criteria, so as to avoid the overconservative problem when considering EAF. Therefore, as a typical fatigue sensitive position in PWR, the charging nozzle is selected as the research object. Based on the real transient data, 3-D full-size transient fluid-structure coupling numerical analysis was used to simulate the structural response of the charging nozzle under the thermal impact. The calculation area not only includes the fluid flow and heat transfer in the pipe, but also includes the solid area where the pipe is located, so that the simulation results are more consistent with the actual temperature field and stress field. EAF analysis is carried out using fluid-structure coupling stress field data and based on multi-axial stress history, and compared with the results of traditional design fatigue analysis to determine the effectiveness and feasibility of this analysis method, which will provide a reliable basis for the fatigue sensitive area of nuclear power plant in equipment design, service inspection and operation license renewal.

2 Methodology 2.1 Fluid-Structure Coupling Governing Equation Hooper [15], Gong Jinke [16] et al. obtained the calculation results highly consistent with the experimental results through coupling calculation of all physical processes in fluid and solid. The result of research shows that the stress field data obtained by this method can greatly reduce the over-conservative margin caused by conservative assumptions. In this paper, in order to reduce the excessive conservative margin, the phenomenon of hot and cold mixed flow at charging nozzle was calculated by fluid-structure coupling to obtain more accurate stress field data. For three-dimensional incompressible flow, the conservation equation is as follows [17]. Mass conservation equation:   (1) ∇ · ρf ν = 0 Momentum conservation equation:   ∂ρf ν (2) + ∇ · ρf νν − τf = ff ∂t In which, t is time and ff is volume vector. ρf is fluid density, ν is fluid velocity vector, τf is shear force tensor, which can be expressed as: τf = (−p + μ∇ · ν)I + 2μe

(3)

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In which, p is fluid pressure, μ is dynamic viscosity, and e is velocity stress tensor. Energy conservation equation:   ∂(ρhhot ) ∂p − + ∇ · ρf νhhot = ∇ · (λ∇T ) ∂t ∂t + ∇ · (τ · ν) + ν · ρff + SE

(4)

The conservation equation of stress field calculation is derived from Newton’s second law: ρs d¨s = ∇ · σs + fs

(5)

In which, ρs is density, σs is Cauchy stress tensor, fs is volume force vector, and d¨s is local acceleration vector in solid. Thermal deformation caused by temperature difference: fT = αT · ∇T

(6)

At the interface of fluid-structure interaction, for the conservation of variables such as stress τ , displacement d , heat flow q and temperature T in the fluid domain and solid domain, the following four equations should be satisfied: ⎧ τf · nf = τs · ns ⎪ ⎪ ⎨ df = ds (7) ⎪ qf = qs ⎪ ⎩ Tf = Ts

2.2 Simplified Elastic-Plastic Fatigue Analysis Method The stress intensity is used for fatigue life analysis of nuclear class 1 pipelines and equipment, and the stress component in element body at a certain time is shown in Fig. 1. The principal stresses (σ1, σ2, σ3, and σ1 ≥ σ2 ≥ σ3) are obtained by solving the cubic Eq. (8). The stress intensity SI at this time is (σ1 − σ3).   σ03 − σx + σy + σz σ02  2 2 2 σ0 + σx σy + σy σz + σz σx − σxy − σxz − σyz  2 2 =0 (8) − σx σy σz + 2σxy σyz − σz σxy − σy σyz According to the design requirements of nuclear class 1 pipeline in ASME or RCC-M code, for any two instants i and j, the range of primary stress and secondary stress Sn (i, j) (used for plastic correction in simplified fatigue elastic-plastic analysis) is calculated according to Eq. (9). Sn (i, j) = C1

|P0 (i, j)|D0 D0 + C2 Mi (i, j) 2t 2I

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Fig. 1. Stress component in element body

1 Eα| T1 (i, j)| + C3 Ea,b 2(1 − υ) × |αa Ta (i, j) − αb Tb (i, j)| +

(9)

In which, C1 , C2 , C3 are primary stress and linearized secondary stress index; P0 (i, j) is the pressure fluctuation caused by the i and j instants; D0 is outer diameter of pipe; t is Nominal pipe thickness; I is inertia moment of the pipe; υ is Poisson ratio; Mi (i, j) is square root of bending moment caused by the i and j instants; E, α are Young’s modulus and thermal expansion coefficient at room temperature;

T1 (i, j) is nonlinear part of temperature distribution along wall thickness caused by the i and j instants; Ea,b is Young’s modulus at ambient temperature in two regions; αa , αb are coefficient of thermal expansion at a and b; Ta (i, j), Tb (i, j) are the range of temperature. For two instants i and j, the range of total stress Sp (i, j) should be calculated in the fatigue analysis of the pipeline: |P0 (i, j)|D0 D0 + K2 C2 Mi (i, j) 2t 2I 1 1 K3 Eα| T1 (i, j)| + Eα × | T2 (i, j)| + 2(1 − ν) 1−ν

Sp (i, j) = K1 C1

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+ K3 C3 Ea,b × |αa Ta (i, j) − αb Tb (i, j)|

(10)

In which, K1 , K2 , K3 are peak of Stress index;

T2 (i, j) is nonlinear part of temperature distribution along wall thickness caused by the i and j instants. If the stress caused by loads such as internal pressure and bending moment obtained through finite element method, stress indices C1 , C2 and C3 have been included in the analysis model, the values of pipeline stress indices K1 , K2 and K3 are selected according to ASME code. The loads pair may belong to the same or different transients. The alternating stress intensity corresponding to the load pair is as follows: Salt (i, j) = Ke (p, q)

Sp (i, j) Ee 2 E

(11)

In which, Ke (p, q) is the elastic-plastic strain correction coefficient, which is determined by Sn (i, j); Ee is Elastic modulus corresponding to the fatigue curve. Taking the amplitude of alternating stress intensity under each load cycle, the allowable cycle number Nf under the corresponding amplitude was searched on the fatigue curves in ASME, RCC-M or other codes, so the usage factor of the corresponding load cycle was 1/Nf . After obtaining the usage factor of all cycles, the CUF can be obtained by adding these usage factors, which should be less than 1 according to ASME code. 2.3 Environmental Assisted Fatigue Analysis Method In the design stage of nuclear power plant, the design curves used in fatigue calculation according to ASME or RCC-M code are all fatigue curves obtained by using smooth solid small sample test in room temperature air environment, which do not fully consider the influence of reactor water environment on fatigue behavior. More than 20 years of experimental studies in Japan and the United States have shown that the reactor water environment significantly reduces the fatigue life of carbon steel, low alloy steel, austenitic stainless steel and nickel base alloy. The U.S. Nuclear Regulatory Commission (NRC) explicitly requires in regulatory Guidance RG 1.207 [18] that EAF issues must be considered in the design of new light water reactor nuclear power plants and in the application for license renewal of existing plants. In the research report NUREG/CR 6909, detailed implementation rules for considering EAF are given, in which the influence of environment is considered through fatigue life environmental correction factor Fen . It is defined as the ratio of fatigue life in room temperature air environment to fatigue life in operating temperature water environment, which is affected by the sulfur content, temperature, dissolved oxygen, strain rate and other factors of ferrite material. According to NUREG/CR 6909, for stainless steel, Fen is calculated as follows:   (12) Fen = exp 0.734 − T  ε˙  O

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In which, T  , O and ε˙  are the parameters affecting service temperature, oxygen content and strain rate respectively. And the environmental cumulative usage factor after considering the environmental impact is as follows:

CUFen = UFk × Fenk (13) In which, Fenk is fatigue life environmental correction factor during the k-th transient pair; UFk is usage factor of the k-th transient pair without considering environmental effects. CUFen is environmental cumulative usage factor after considering the environmental impact. In engineering applications, it is found that the current EAF analysis methods are excessively conservative (for example, the method recommended by Japanese Society of Mechanical Engineers and the method recommended by NRC), which can easily lead to CUFen greater than 1.0 after considering the environmental impact. In order to reduce unnecessary conservative margin, a method based on multi-axial stress history is recommended. The Fenk for a stress cycle can be calculated as: n Fenk,i εi n Fenk = i=2 (14) i=2 εi In which, Fenk,i is fatigue life environmental correction factor at time point I, which can be calculated by methods recommended in NUREG/CR 6909;

εi is Change in strain at Point i; n is number of time points in the stress cycle;

εi is calculated based on six stress components as follows:  ) from point i-1 The principal stress range (σ1 , σ2 , σ3 ) and stress intensity range (σSI to point i are calculated, taking into account the possibility of principal stress direction change. σyi σzi σxyi σyzi σxzi σxi − σxi−1 σyi−1 σzi−1 σxyi−1 σyzi−1 σxzi−1  σ σ = σx σy σz σxy yz xz

(15)

If the stress cycle under consideration is composed of two extreme stress states that are not chronologically continuous (e.g., the peak of one transient pairs with the valley of a different one), then the strain increment and strain rate between the discontinuity should not be considered in the Fen formulation. • Calculate the sign of the stress intensity range. The sign of the numerically largest principal total stress range i-1 to i determines whether the strain increment is primarily increasingly tensile or increasingly compressive in nature.

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Sgn = the sign (1 or −1) of the maximum of σ1  , σ2  , σ3  . • The increasingly tensile change in strain εi , can be calculated as follows:

 σSI K if Sgn = 1

εi = E(Tmin ) e 0 Otherwise

(16)

In which, E(Tmin ) is elastic modulus at the minimum temperature.

3 Numerical Simulation Analysis 3.1 Transient Load The charging line is used to maintain the program water level of the voltage regulator and the primary water volume under different powers, whose flow rate changes frequently during the operation of PWR. Based on the fatigue analysis results of the original design of the charging nozzle and the transient parameters of the unit studied during nearly 30 years of operation, this paper analyzes the transient load with the maximum thermal impact of the nozzle (charging and discharging simultaneously closed and opened), and the design cycles of this transient is 200 times. During the transient, other parameters are relatively stable except for the obvious change of charging flow rate and temperature. Therefore, the simplified assumption in calculation is that the water temperature in RCP is 297 °C, the flow rate is 17.25 m/s, and the pressure is 15.5 MPa. Figure 2 shows the ideal design transient curve of charging flow rate and temperature and the real transient data of PWR, which are quite different from each other. Compared with the ideal design transient, the injection flow is smaller and the temperature change is relatively gentle. 3.2 Fluid-Structure Interaction Analysis Thermal shock load will lead to huge thermal stress at charging nozzle, but the effect of pipeline deformation caused by thermal stress on the flow field inside the pipeline can be ignored, so unidirectional coupling analysis method is adopted in the fluid-structure coupling calculation. Data transfer between flow field and structure field adopts active query difference method. The fluid-structure coupling model is shown in Fig. 3. The flow and heat transfer calculations are carried out in Fluent. In order to ensure the solving accuracy of the flow field at the nozzle, the implicit solver with three-dimensional, double precision and pressure base is selected. In order to better describe the boundary layer flow on the inner surface of the nozzle, the Realizable k−ε 2 equation turbulence model was selected in the calculation [19, 20]. Simple algorithm was used to solve the equation for pressure and velocity coupling, and second-order upwind scheme was used for convective phase difference scheme [21, 22]. In order to ensure the efficiency of heat utilization, the outer wall of primary pipeline in nuclear power plant is usually equipped with thermal insulation layer, so the outer wall of pipeline is assumed to be adiabatic in calculation. Fluid-structure interface of thermal boundary is restricted by the interaction between water and wall of pipeline caused by, therefore regardless of interface

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200 150 100 50 0

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Volumetric flow/m3 h-1

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(b) Volumetric flow Fig. 2. Temperature and volumetric flow curve during analyzed transient

temperature and heat flux are all part of the calculation results, is not a known condition, setting of coupling boundary, in the process of solving solver according to the flow field of grid near interface variables directly and dynamically calculate the heat transfer on the pipe wall. The corresponding boundary conditions are as follows: the upstream side of main pipe and the charging line are set as the velocity inlet, and the velocity of the upstream side of main pipe is set as 17.25 m/s. The velocity of charging line is written into the measured curve parameters in Fig. 2 through UDF files. The downstream side of main pipe is set as a static pressure outlet with a pressure of 15.5 MPa.

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In stress field calculation, temperature field parameters obtained in fluid dynamics calculation were directly applied. In order to avoid the interference of main pipe length in the model on calculation results, the degrees of freedom of upstream end face of the main pipe were coupled to the center of the main pipe just below the nozzle, and the axial degrees of freedom of downstream end face of the main pipe and the end face of the charging line were coupled.

Fig. 3. The fluid-structure coupling model

4 Results and Discussion 4.1 Stress Calculation Result The fluid-structure coupling calculation method based on measured transient (Method 1) and the traditional design analysis method based on ideal design transient (Method 2) were used to analyze the temperature field and stress-strain field of charging nozzle. In traditional design analysis, such nozzle is usually simplified into two heat zones in order to simplify calculation settings, that is, the nozzle area and the main pipe area are respectively set as independent heat zones [8]. In order to reflect the process of gradual mixing of hot and cold water in the nozzle, as shown in Fig. 4, the charging nozzle was optimized into 7 thermal zones in the calculation of method 2. The temperature field at the maximum response time point obtained by the two calculation methods is shown in Fig. 5. Although the overall temperature distribution at the nozzle obtained by the two methods is close by optimizing the setting of thermal zones, there are still some differences in details. In method 2, different temperature and heat transfer convection coefficients were applied to different thermal zones, and a large temperature gradient appeared at the boundary of adjacent thermal zones. In method 1, the temperature gradient of the temperature field calculated based on the real flow field environment is small and the change is relatively gentle. The stress intensity distribution at the nozzle at the time of maximum thermal shock under analyzed transient is shown in Fig. 6.

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Fig. 4. Heat zones settings

Compared with method 1, the thermal shock stress response obtained by method 2 is more severe, but the maximum thermal shock location obtained by the two methods is quite different. The maximum zone of thermal stress calculated by method 2 is located at the inner wall of the upper end of the thermal sleeve of the nozzle. Figure 7 shows Variation history of stress components under thermal shock at the maximum stress position. The amplitude of the alternating stress calculated by the two methods is quite different, and the variation of the active stress component is also different. In the setting of thermal zones, it is generally considered that the cold water is almost not heated in this area. Under ideal design transient, both the cold-water injection stage when the valve is opened and the injected water rapidly heats up need to bear large thermal shock. In the actual operation, the mixing of hot and cold water and the heating rate control of injected water play a significant role in alleviating the thermal shock. The thermal sleeve is filled with high temperature water whose temperature is almost the same as that of the primary circuit. After the cold water is injected, the temperature difference of over 100 °C is formed in the thickness transition zone at the top of the thermal sleeve. In addition, according to ASME Code Section III, the thickness transition zone belongs to the 1/3 thickness transition zone, and a larger stress index should be considered in fatigue analysis. Therefore, in actual operation, the transition zone of the thickness of the top of the thermal sleeve should be the zone where the thermal shock has the greatest influence. The thermal stress calculated by method 1 is at this zone, where the thermal shock characteristics are effectively represented.

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(a) Method 1

(b) Method 2 Fig. 5. Temperature field distribution calculated by two methods

4.2 Fatigue Analysis Combined with the stress field distribution in the structural analysis, the zones with the maximum stress in the results calculated by the above two methods is selected as the evaluation zones, and the Stress Classification Line (SCL) zones were shown in Fig. 8. According to the fatigue analysis method of ASME Code Section III, simplified elastic-plastic fatigue analysis of the linearized stress of the two methods was carried out by using 3D rain flow method. The fatigue analysis results of the 3 SCLs are shown in Table 1. The fatigue analysis results of method 2 are not proportional to the real fatigue effect of each SCL. Among the 3 SCLs, the CUF of SCL3 with the most serious actual fatigue effect is the smallest. The CUF of SCL2 with the smallest actual fatigue effect was the largest.

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(a) Method 1

(b) Method 2 Fig. 6. Stress intensity distribution at maximum thermal shock

It can be seen from the results that method 1 can effectively represent the severity of fatigue state in different areas of the nozzle, while greatly reducing the conservative margin caused by ideal assumption of method 2. For SCL3 with the most severe fatigue in method 2, CUF obtained by method 1 decreased by 99.16%. For SCL1, the actual maximum fatigue location, CUF obtained by method 1 also decreased by 17.38%. 4.3 Environmentally-Assisted Fatigue Analysis Based on the multi-axial stress history, the environmentally-assisted fatigue is further calculated. In this paper, the design cycles of the analyzed transient in the 40-year design life are 200 cycles. As shown in Table 2, even under a single transient, after comprehensively considering the cumulative fatigue effect and the influence of reactor

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400

x

xy

y

yz

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300 200 100 0 -100

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100

150

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Time/s

(b) Method 2 200

x

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0

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(b) Method 2 Fig. 7. Variation history of stress components under thermal shock

water environment on fatigue, the CUFen of SCL3 at the end of 40 years is calculated by method 2 as 1.346, which has exceeded the design limit in ASME Code. The CUFen calculated by method 1 at the end of 40 years is still far less than 1, reducing the unnecessary conservative margin by 99.03%. Therefore, after considering EAF effect in nuclear power plant design and license renewal, the calculation assumption in method 2 is too conservative, which will increase the cost and difficulty of equipment design and license renewal application.

5 Conclusions Based on the real transient load data of a second generation PWR nuclear power unit, 3D full-size transient fluid-structure coupling numerical analysis was carried out for the

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Fig. 8. Stress classification line zones

Table 1. Fatigue analysis results Zones

Methods

Sp /MPa

Ke

Salt /MPa

Nallow

U1cycle

Decline rate/%

SCL1

Design

305.21

1.48

496.91

4516

0.00022

17.38

FSI

398.62

1.00

438.50

5466

0.00018

Design

417.46

1.33

615.19

2000

0.00050

FSI

302.78

1.00

335.48

13688

0.00007

Design

528.90

1.53

894.99

581

0.00172

FSI

194.90

1.00

215.56

69541

0.00001

SCL2 SCL3

85.39 99.16

charging nozzle. The complex thermal boundary of the fluid-solid interface was obtained by solving the coupling solution between the fluid domain and the solid domain, so as to effectively characterize the real temperature field and structure field of the charging

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Zones

Methods

CUF 40Y

F en

CUF en 40Y

Decline rate/%

SCL1

Design

0.044

4.84

0.214

21.03

FSI

0.037

4.61

0.169

SCL2

Design

0.100

4.82

0.482

FSI

0.015

4.51

0.066

SCL3

Design

0.344

3.91

1.346

FSI

0.003

4.61

0.013

86.31 99.03

nozzle under analyzed transient. Based on the stress field data obtained by fluid-structure coupling calculation, fatigue analysis and environmentally-assisted fatigue analysis was carried out under multi-axial stress history. By comparing the results of traditional design analysis method based on ideal design transient, the following conclusions are obtained: (i)

Compared with the calculation results based on the fluid-structure coupling method, the thermal shock stress response obtained by the traditional design analysis method is more stringent, but due to the limitation of the thermal zone hypothesis, it can’t effectively reflect the real thermal shock response and severity in different areas of the nozzle. (ii) The fatigue result of traditional design analysis based on ideal design transient is not proportional to the real fatigue effect of each zone, and the fatigue state of each zone of the nozzle can’t be effectively represented, which may lead to misleading selection of thermal fatigue sensitive position in service inspection. (iii) Based on the results of this case study, the fluid-structure coupling calculation method based on measured transient parameters can effectively reduce the unnecessary conservative margin caused by ideal assumptions in the traditional design analysis method based on ideal design transient. CUF decreased by 17.38% at locations with the highest actual fatigue. Based on the results of this case study, the calculation assumptions in traditional design analyses have been too conservative after considering the effects of reactor water environment on fatigue, which will increase the cost and difficulty of equipment design and license renewal application. By using fluid-structure coupling stress field data and based on multi-axial stress history to carry out environmentally-assisted fatigue analysis, it is calculated that the CUFen of each evaluated zone at the end of the 40 years is still far less than 1, reducing the unnecessary conservative margin by 99.03%. Therefore, the environmentally-assisted fatigue analysis method adopted in this paper can effectively avoid the over-conservative problem after considering environmentally-assisted fatigue, and can provide reference for nuclear power plant equipment design, in-service inspection and operation license renewal.

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References 1. ASME.: ASME boiler and pressure vessel Code, Section III, NB 3200, Design, New York (2013) 2. RCC-M.: Design and construction rules for mechanical components of PWR nuclear islands (2016) 3. Fang, Y., Wang, Q., Chu, Q., et al.: Analysis and evaluation methodology of effect of light water reactor coolant environment on fatigue life of class 1 components. Atomic Energy Sci. Technol. 47(11), 2114–2119 (2013) 4. Chopra, O.K., Shack, W.J.: The effect of lwr coolant environments on the fatigue life of reactor materials. In: ASME Pressure Vessels and Piping Conference, vol 47527, pp 191–204 (2006) 5. Mehta, H.S., Gosselin, S.R.: An environmental factor approach to account for reactor water effects in light water reactor pressure vessel and piping fatigue evaluations. Am. Soc. Mech. Eng., New York, NY (United States) (1996) 6. Chopra, O.K., Stevens, G.L., Tregoning, R., et al.: Effect of light water reactor water environments on the fatigue life of reactor materials. J. Press. Vessel Technol. 139(6) (2017) 7. Chopra, O.K., Shack, W.J.: Effect of LWR coolant environments on the fatigue life of reactor materials. Argonne National Laboratory, NUREG/CR-6909 (2007) 8. Gilman, T., Chinthapalli, A., Hoehn, M.: Stress-based environmental fatigue monitoring of PWR charging nozzle. In: ASME 2013 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, V01AT01A039-V01AT01A039 (2013) 9. Hong-Lei, A.I., Bin, Z., Xi-Feng, L.U., et al.: Elastoplastic stress analysis for charging nozzle of PWR primary piping. Press. Vessel Technol. 67–73 (2016) 10. Gray, M.A., Cranford III, E.L., Donavin, P.R.: Application of environmental fatigue penalty factors and implications for design analyses. In: ASME Pressure Vessels and Piping Conference, vol 47527, pp 205–212 (2006) 11. Carey, J.: Thermal cycling screening and evaluation model for normally stagnant non-isolable reactor coolant branch line piping with a generic application assessment. EPRI, CA: 1009552, December 2004 12. Carey, J.: Management of thermal fatigue in normally stagnant non-isolable reactor coolant system branch lines. EPRI, CA, 1011955 (2004) 13. Ware, A.G., Morton, D.K., Nitzel, M.E.: Application of NUREG/CR-5999 interim fatigue curves to selected nuclear power plant components. Nuclear Regulatory Commission, NUREG/CR-6260 (1995) 14. Code Case N-792, Cases of ASME boiler and pressure vessel code[S]. ASME (2010) 15. Hellenkamp, M., Pfeifer, H.: Thermally induced stresses on radiant heating tubes including the effect of fluid—structure interaction. Appl. Therm. Eng. 94, 364–374 (2016) 16. Yang, L.I., Lifeng, M.A., Jiang, Z., et al.: Temperature field analysis of roll heated by fluidsolid coupled heat transfer. J. Mech. Eng. (24), 10 (2018) 17. Tan, W.: Numerical heat transfer. Xi’an Jiaotong University Press (2001) 18. RG1. 207, Guidelines for Evaluating Fatigue Analyses Incorporating the Life Reduction of Metal Components due to the Effects of the Light-Water Reactor Environment for New Reactors[S]. U. S. Nuclear Regulatory Commission, Washington, DC (2007) 19. Zhang, X., Jianping, F.U. et al.: Applicability analysis of k-ε turbulence models on numerical simulation of internal flow field of recoil brake. Explosion and Shock Waves 31(5), 516–520 (2011) 20. Xiuhong, Z., Dandan, B., et al.: Numerical simulation and characteristic analysis of novel whirlwind supercharger. China Mech. Eng. 46(24), 118–122 (2010)

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21. Ju, Y.S.: Thermal conduction and viscous heating in microscale Couette flows. J. Heat Transfer 122(4), 817–818 (2000) 22. Deng, J., Shao, X.M., Fu, X., et al.: Evaluation of the viscous heating induced jam fault of valve spool by fluid–structure coupled simulations. Energy Convers. Manage. 50(4), 947–954 (2009)

Condition Monitoring Based Equipment Health Management Shuang-Han Ling(B) , L. I.-Shi Ke, Jiang-Fei Sheng, and Li-Jun Huang Suzhou Nuclear Power Research Institute, CGN Building, No. 2002 Shennan Avenue, Shenzhen, Futian Distric, China [email protected]

Abstract. Good equipment operating condition is important to power enterprise safety and economic operation. Through monitoring and capturing condition parameters of equipment units, using deviation analysis technology to evaluate the parameters, condition monitoring based equipment health management can provide equipment current health condition and future development trend, which provides more complete information and scientific theory support for maintenance strategies application. Keywords: Health management · Monitoring · Deviation analysis · SVM · AIC

1 Management Strategies of Equipment Health Condition monitoring based equipment health management strategies include four parts: formulating condition monitoring task list, implementing condition monitoring task, condition evaluation and trending. The effectiveness of condition monitoring task list is the premise of strategies implementation. Firstly, the failure mode and effect analysis at component level is performed by FMEA (Failure Modes and Effects Analysis), and each failure mode is evaluated to determine the appropriate condition monitoring task list. Later, analyzing data generated during the implementation of monitoring activity to evaluate equipment condition. Finally, the equipment of poor performance indicated by condition evaluation is carried out trend analysis to guide the maintenance plan. Equipment health management strategy implementation process is shown in Fig. 1.

Fig. 1. Implement process of equipment health management strategies

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 129–143, 2023. https://doi.org/10.1007/978-981-19-8780-9_13

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Fig. 2. FMEA analysis and condition monitoring task formulation

2 FMEA Analysis and Condition Monitoring Task List FMEA analyzes all possible failure modes of system/equipment and their possible effects on system/equipment, and predicts possible problems and potential failures of components or parts, so as to take preventive measures to avoid defects [1]. After getting

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failure mode list, mechanism and symptom of failure mode are analyzed, and the optimal condition monitoring task list is developed for each failure mode according to the analysis results. It should be clear that not all equipment need to use condition monitoring technology to manage, and the current condition monitoring technology cannot control all failure modes. Maintenance, failure finding, improvements and other measures are irreplaceable. This paper only discusses to the equipment management appropriate to condition monitoring technology (Fig. 2).

3 Condition Evaluation 3.1 Deviation Analysis Condition monitoring technology is the basis of condition evaluation. Only correctly determine whether the equipment operation is normal can effectively evaluate its status. Condition monitoring technology is divided into two forms: online monitoring and off-line monitoring. For off-line monitoring, comparing with standard value is usually used to determine whether the equipment is abnormal. On-line monitoring can evaluate equipment condition in real time, the traditional method is to evaluate equipment condition by monitoring whether the parameter has reached the standard value. In this paper, a new evaluation method—deviation analysis is adopted. Since there are various forms of equipment failure, it is difficult to accurately describe all the failure modes by mathematical methods. However, the normal operating conditions of equipment are relatively stable. Therefore, if we can use mathematical methods to calculate the expected value of parameter which is under normal condition of equipment, when the operating data deviates from the expected value, it can be considered that the equipment condition is abnormal. As Fig. 3 shows, even under normal condition, the diversification of parameters is complex. But the parameters are correlative. As Fig. 4a shown, the gear case temperature and thrust bearing temperature of the circulating water pump have an approximate linear relation. When a failure happens, the relation changes into logarithmic-related, as seen in Fig. 4b. Selecting the parameters with relevance as the training data of model, then the more relevant between the data, the more accurate the model could describe equipment parameters. Data modeling is conducted by using operating parameter under normal condition of equipment as basic data. The model after learning can be used to calculate the expected value of the equipment operating parameters in real time. If the deviation between the actual value and the expected value is greater than the setting value, then the equipment is considered to be abnormal. This is the core idea of deviation analysis. In this paper, we choose SVM (support vector machine) as a mathematical modeling tool. SVM achieves a global optimal solution, which has better generalization ability and no requirement for types of data distribution. The process of modeling is essentially a convex optimization problem [2–4]:   1 2 ω +C ξi∗ + ξi 2 l

min Q =

i=1

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46

45

44

0

20

40

60

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100

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serial number

Fig.3. Thrust bearing temperature under normal condition

⎧ ⎨ yi − ω · ∅xi − b ≤ ξi∗ + ε s.t. ω · ∅xi + b − yi ≤ ξi + ε ⎩ ∗ ξi , ξi ≥ 0, i = 1, 2, · · · , l

(1)

where, xi is training data vector, yi is expected value, C is penalty factor; ξi∗ and ξi are incoming relaxation factors; ε is the loss function; ω, b are the parameters to be solved. The resulting function model is: f (x) =

l      αi − αi∗ K xi · xj + b

(2)

i=1

where, αi , αi∗ are Lagrangian coefficient, K is a kernel function, the specific derivation process is not repeated in this paper. Taking the temperature monitoring model of thrust bearing of circulating pump in a nuclear power plant as an example, thrust bearing temperature, gearbox temperature, motor drive end bearing temperature and motor radial bearing temperature have a strong correlation, so they can be put into a model to describe the thrust bearing temperature, that is: the expected value set Y = (y1 , y2 , . . . , yn ), where y1 , y2 , . . . , yn is the time series of thrust bearing temperature under normal conditions; training data vector set X = (x1 ,x2 , . . . ,xn ), xi = (x1i ,x2i ,xxi ), where 1 ≤ i ≤ n, x1i , x2i , x3i respectively represent the gearbox temperature at the moment of i, the bearing temperature of motor drive end and the motor radial bearing temperature at the moment of i. In this paper, 120 h of operating parameters are selected as the training data from the normal operation data of the circulating water pump for one month. The maximum error of the training result and the expected value is 1.1%. It is necessary to pay attention that the training process doesn’t need to deliberately pursue accuracy, because excessive training may lead to weaken generalization ability of model. The training results are shown in Fig. 5. Another 20 h of data is taken to test the model and finding that the maximum error between the regression prediction value of model and test data is 1.7%. This indicates that the model can accurately describe changes in parameters under normal equipment operating conditions, as seen in Fig. 6.

gear case temperature (℃)

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59

58

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38

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thrust bearing temperature (℃)

a Normal Condition

gear case temperature (℃)

59.0

58.5

58.0

57.5

57.0

44

46

48

50

52

54

thrust bearing temperature (℃)

b

Abnormal Condition

Fig. 4. Relation of thrust bearing temperature and gear case temperature

After the model is tested by normal data, the simulation experiment is carried out on the model by using the condition data of equipment abnormal operating condition. Simulation experiment results are shown in Figs. 7, and 8 is the deviation between expected value and failure data. In the early stage, equipment is in relatively good condition, the expected value of model calculation is close to the actual parameter, and the deviation is less than 3%, and the trend of change is consistent. With the deterioration of the parameter, the deviation becomes more and more obvious. Criteria to determine

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46.5

training data training result 46.0

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45.0

44.5

44.0 0

20

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60

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100

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serial number Fig. 5. Training results

Fig. 6. Testing results of normal data

whether the equipment is in an abnormal state: when the deviation of four consecutive moments in seven moments is 15%, an alarm is issued, and the equipment is in an abnormal state, as seen the red area in Fig. 8. The temperature of thrust bearing of circulating pump takes limit management, 65 °C below is the normal operation temperature of equipment, 80 °C is the temperature for high temperature alarm. In the earlier failure, because the temperature doesn’t reach 65 °C, the traditional method cannot identify that the equipment has deviated from the normal state, but the simulation results could give

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Fig. 7. Simulation experiment results

Fig. 8. Deviation

alarm when the temperature rises to 52 °C equipment abnormality could be this found 60 h in advance. 3.2 Methods of Condition Evaluation 3.2.1 Equipment Condition Evaluation Different monitoring activities focus on different equipment characteristics, and the importance of equipment characteristics is different, so before scientifically evaluating

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equipment condition, it is needed to classify the monitoring parameters according to equipment characteristics. The condition monitoring parameters are divided into evaluation subgroups based on the equipment characteristics they react. Taking transformers as an example, the parameters can be divided into electrical characteristics group, mechanical characteristic group and gas in oil group. The parameters are evaluated separately according to different groups, and different weights are given. Finally, the evaluation of the different dimensions is integrated by using mathematical method, and the condition evaluation of the whole equipment Q is obtained. n 

Q=

ω · Qi , Qi =

i=1

n 

ωi · qi

(3)

i=1

ω is the weight of the evaluation subgroup, Qi is the score of the ith evaluation subgroup, ωi is the weight of the ith parameter of the evaluation subgroup and qi is the score of the ith parameter of the evaluation subgroup. It should be noted that the weight here is not fixed, the weight will change under special situation: when the evaluation of one or several important parameters is lower than “Good”, the weight of these parameters will increase, and the weight of sum will reach 80%. When the evaluation of important parameters is lower than “General”, the equipment condition evaluation is equivalent to the evaluation of the parameter. For example, gas in oil reaches the upper limit of standard value, then regardless of the evaluation result of the other equipment subgroups, the overall evaluation result of equipment is “Very Poor” and corrective action is required immediately (Table 1). Table 1. Equipment condition rating Condition grade

Excellent

Good

General

Score

[90, 100]

[80,90]

[70,80)

Condition grade

Poor

Very poor

Score

[60,70)

[0,60)

3.2.2 Off-Line Monitoring Parameters Evaluation Off-line monitoring mainly refers to monitoring equipment through relevant test equipment, such as partial discharge detection. The evaluations of these monitoring results mainly refer to relevant standards and expert experience to set different parameter scope, and according to the interval of parameters to obtain corresponding state score q (Table 2). q=A+B·

y − ymin ymax − ymin

(4)

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Table 2. Evaluation of test parameters A = 90, B = 100

A = 80, B = 90

A = 70, B = 80

Parameter scope

(ymin , ymax )

(ymin , ymax )

(ymin , ymax )

Evaluation

Excellent

Good

General

A = 60, B = 70

A = 0, B = 60

/

Parameter scope

(ymin , ymax )

(ymin , ymax )

/

Evaluation

Poor

Very poor

/

Among them, A and B represent the upper and lower limits of the condition score interval, and y is parameter value, ymax and ymin are the upper and lower limits of the parameter interval of the parameter y. 3.2.3 Online Monitoring Parameters Evaluation Since online monitoring uses the deviation analysis technology, in the case that the monitoring results do not meet the traditional alarm value, the parameter interval is running deviation, the application condition is to trigger deviation alarm. When the traditional alarm value is reached, the rating is “Poor” or “Very Poor” according to the alarm level (Table 3). Table 3. Deviation evaluation method A = 90, B = 100

A = 80, B = 90

A = 70, B = 80

Deviation

(0,0.05)

(0.05, 0.1)

(0.1,0.15)

Evaluation

Excellent

Good

General

A = 60, B = 70

A = 0, B = 60

/

Deviation

(0.15, 0.25)

(0.25,1)

/

Evaluation

Poor

Very poor

/

q=A−B·

y − ymin ymax − ymin

(5)

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4 Prediction Technology From the perspective of equipment health management, predictive maintenance and condition-based maintenance are obviously better than periodic maintenance. Maintenance system changes from periodic maintenance to predictive maintenance and condition-based maintenance is an inevitable trend of development, and the realization of maintenance system changes is based on the development and mature of prediction technology, so the study of trend analysis techniques is necessary. In this paper, the prediction technology is SVM, and the principle will not be repeated [5]. 4.1 AIC (An Information Criterion) Criterion For  the time series {x1 , x2 , x 3 , · · · , xn }, {xn } is the predicted target value, the previous xn−1 , xn−2 , xn−3 , · · · ,xn−p is used as the correlation quantity to establish the mapping relationship between autocorrelation input x = {xn−1 , xn−2 ,xn−3 , · · · , xn−m } and yn = {xn−m+1 }, the process is called modeling or training, where, p is the dimension of x (also called step p). Different p will affect the training results of the model, this study uses AIC criterion to determine the applicable p value. Proceeding from extracting the maximum amount of information from the observed sequence, AIC criterion defines the criterion function [6, 7]: AIC(p) = lnσˆ 2 +

2p N

(6)

where, N is the number of observed samples; P is the selected model order; σ 2 is the model residual variance. The model order when the AIC value is the smallest is the applicable model step.

4.2 Prediction Example 4.2.1 Data Form Taken in Modeling The establishment of prediction model requires historical operation data as a support, there are two specific forms: one is to use historical data of the equipment itself to model, the method is known as ontology data modeling; in the case of lacking data of the equipment itself, the historical anomaly data of the same type of equipment is used to model and the individual information is evaluated with the overall information, this method is called overall data modeling. 4.2.2 Modeling Prediction of Ontology Data Using ripple voltage test data of a nuclear island charger in second generation nuclear power plant as an example to carry out research. The data is sorted by time (Table 4). AIC criterion is used to evaluate model of different order, and the relationship between AIC and p is obtained (Fig. 9).

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Table 4. Ripple voltage peak-to-peak (v) Serial number

1

2

3

4

5

6

7

Data

0.11

0.13

0.14

0.14

1.2

1.55

2.2

0 -2

AIC

-4 -6 -8 -10 -12 -14 1

2

3

4

step P

Fig. 9. AIC & p-ontology data

Table 5. Sample and test data of second order model Unit: v

Y

X1

X2

Sample data

0.14

0.11

0.13

0.14

0.13

0.14

1.20

0.14

0.14

1.55

0.14

1.20

2.2

1.2

1.56

Test data

When the order is 2, the AIC value is the smallest, so the second order is chose to model, the sample data and the test data are as follows (Table 5): The Lagrangian coefficient α and the parameter b are solved according to the method described in Sect. 4 to establish the prediction function [8]. yt =

4    αi − αi∗ K(xi · x) + B

(7)

i=1

Using test data to test the prediction effect of model and get prediction results (Fig. 10):

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3.0

ripple voltage p-p (V)

2.5 2.20

2.0 1.55

1.5

1.19 1.20

1.0 0.5 0.0

1.55

0.139

0.140

0,140

0

1

predictive value test data

0.142

2

3

4

5

serial number Fig. 10. Prediction results

The model predicts that the value of the fifth timing point is 2.9, the actual value is 2.2, the error is 31.8%, the trend is correct. 4.2.3 Overall Data Modeling Prediction The overall data is shown in Table 6, and the seventh data of ontology data is test data and is not trained. The remaining data are used to establish the autocorrelation mapping relationship according to the following rules: x = {xn−1 , xn−2 , xn−3 , · · · , xn−m }, yn = {xn−m+1 }. Table 6. Training data (unit: v) Serial number

Ontology data

Experience data 1

Experience data 2

1

0.11

0.08

0.11

2

0.13

0.13

0.13

3

0.14

0.11

0.14

4

0.14

0.14

0.21

5

1.20

1.23

1.13

6

1.55

1.33

1.73

7

2.2

2.03

2.57

The AIC criterion is used to evaluate the model of different step, and the relationship between AIC and p is obtained (Fig. 11):

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-6.8

AIC

-7.0

-7.2

-7.4

-7.6

-7.8

-8.0 1

2

3

4

5

step P Fig. 11. AIC & p—overall data

When the order is 4, the AIC value is the smallest, so choose the fourth step modeling, the sample data and the test data are as follows (Table 7): Table 7. Samples and test data of fourth order model Unit: v

Y

X1

X2

X3

X4

Sample data

1.2

0.11

0.13

0.14

0.14

1.55

0.13

0.14

0.14

1.20

1.23

0.08

0.13

0.11

0.14

1.33

0.13

0.11

0.14

1.23

2.03

0.11

0.14

1.23

1.33

1.13

0.11

0.13

0.14

0.21

1.73

0.13

0.14

0.21

1.13

Test data

2.57

0.14

0.21

1.13

1.73

2.2

0.14

0.14

1.20

1.55

After the training is completed, the prediction results of the model are tested with the test data, and the prediction result is obtained (Fig. 12): The model predicts that the value of the third timing point is 2.39, the actual value is 2.2, the error is 8.6%, the trend is correct. It can be seen from the above results that the prediction effect that uses the overall data modeling is more accurate than the ontology data modeling. The reason is that overall data modeling uses the empirical data of the same kind of equipment. The empirical

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ripple voltage p-p (V)

2.39

predictive value test data

2.20

2.0

1.55

1.5

1.55 1.20

1.0

1.20

1

2

3

serial number Fig. 12. Forecast results

data contains complete data of such equipment during parameter degradation process. Therefore, the model can use the empirical data of other equipment to evaluate the current individual equipment, and thus obtain the changed trend and predicted value that are appropriate for such equipment. In particular, because of limited data of this study, we cannot conclude that overall data modeling is more effective than ontology data modeling. Theoretically, when historical empirical data of individual is complete, better predictions can also be obtained. 4.2.4 Overall Data Modeling Prediction If the predicted value has enough margin relative to standard value, this indicates that the equipment can continue to run, so maintenance time could delay to next test to assess; if the predicted value has been relatively close to standard value or falls within the standard value area, condition monitoring period should be shortened or maintenance task should be arranged (Fig. 13).

5 Conclusion In this paper, condition monitoring based equipment health management strategy is established by carrying out FMEA analysis to identify the condition monitoring task list, introducing the deviation analysis technology to evaluate the characteristic parameters and obtaining equipment overall condition through condition evaluation technology, and adopting trend prediction technology to evaluate the equipment operation reliability to arrange maintenance tasks as well as prevent occurrence of accidents. The following questions need to be further studied:

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predictive ripple voltage p-p (V)

3.4 3.2 3.0

standard value

2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 2

serial number Fig. 13. Predictive application

(1) When facing massive data, the real-time of variance analysis model need to be improved; (2) If the correlation between the parameters of the equipment is weak, the error of deviation analysis model is larger after training.

References 1. Failure mode and effects analysis for system of nuclear power plants.NB/T 20096-2012. p. 12 (2012) 2. Chakraborty, K.: Forecasting the behavior of multivariate time series using neural networks. Neur. Netw. 05, 961–970 (1992) 3. Chen, B., Zhao, H., Ru, Z.: Probabilistic back analysis for geotechnical engineering based on Bayesian and support vector machine. J. Central South Univ. 12, 276–284 (2015) 4. Yang, X.G., Xue, X., Chen, Xin.: Application of a support vector machine for prediction of slope stability. Sci. China 2, 89–96 (2014) 5. Zhang, W.B.: Research on State Trend Prediction and Fault Diagnosis Methods for Turbogenerator Unit, pp.44–57. Zhe Jiang Univercity (2009) 6. Qin, X.Y., Bu. Y.Y., Xia, Y.-M.: Surface reconstruction for micro-landform based on RBF neural network optimized by AIC criterion. J. Central South Univ. Technol. 05, 816–819 (2004) 7. Li, W., Jiang, H., Wang, X.: Multiple criteria method for autoregressive model selection. Statist. Decis. 18, 24–25 (2010) 8. Zhou, J.: Study on the Methods for Detecting the Conditions of Key Deviees and Predieting Their Remaining Lives. Xidian University, vol. 10, pp. 47–74 (2006)

Double-Ended Shear Fracture Accident of Short Casing Inner Tube in Small Modular Reactor Han Feng(B) , Huafa Chen, Jiang Yang, Ren Liang, and Zihao Guo China Nuclear Power Technology Research Institute, Shenzhen, China [email protected]

Abstract. The double-ended shear fracture accident is one of the key accidents that determines the operating power and affects the safety margin of nuclear power plant. The research on it is of great significance to the safe operation of nuclear power plant. The main section of Small Modular Reactor (SMR) adopts short casing design, which is different from the traditional pressurized water reactor. In this paper, SMR is taken as the research object. And the accident results of the doubleended shear fracture of short casing inner tube at the once through steam generator (OTSG) and the main pump are investigated. The LOCUST program is used to establish the overall model of SMR. The influence of different break connection methods, different break volumes, and different break resistance coefficients on the accident results is considered. The research shows that, after the double-ended shear fracture of short casing inner tube at OTSG and the main pump, the natural circulation can be established in the intact loop of the reactor primary circuit. And the residual heat of the reactor core can be successfully exported through the secondary side of OTSG. Keywords: Double-Ended shear fracture · SMR · Natural circulation

1 Introduction The International Atomic Energy Agency (IAEA) [1] defines a reactor with an electric power below 300 MW as SMR. The compact integrated design is adopted in the structure of SMR, where the main components of the primary circuit of the reactor (including the reactor core, steam generator, etc.) are arranged in the pressure vessel. This structure avoids the occurrence of large break loss of coolant accident (LB-LOCA) and greatly improves the inherent safety of the reactor. SMR adopts modular design and manufacture, which effectively simplifies the process system and shortens the construction period. Compared with the traditional pressurized water reactor, SMR has flexibility in layout, less initial investment, and can effectively solve the problem of power transmission in small and medium power grids. SMR has attracted widespread attention domestically and abroad. At present, many countries such as China, the United States, South Korea and Russia have carried out the research and development of SMR [2]. Kim et al. [3] conducted safety analysis on steam pipeline breakage, water supply pipeline breakage and double-end fracture of voltage regulator pipeline of SMART small © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 144–154, 2023. https://doi.org/10.1007/978-981-19-8780-9_14

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reactor. The research results showed that SMART small reactor has accident mitigation ability and can export heat in natural circulation way after accident. Yang et al. [4] established a small reactor model based on RELAP program in order to verify the scheme design of passive safe injection system for small reactor. This study simulates LOCA conditions such as small breaks in the cold pipe section, DVI double-ended fractures and ADS false start-up. The simulation results show that the primary circuit of the small reactor can achieve effective cooling and depressurization, and the reactor core will not be overheated, which verifies the rationality and safety of the small reactor design. Huang et al. [5] carried out an experimental study on the double-ended rupture of the wave tube of the modular small reactor voltage stabilizer, obtained the accident response characteristics of the passive safety system, and verified the safety performance of the system. Although domestic and foreign scholars have conducted safety analysis on various rupture accidents of SMR, the current research does not involve the doubleended shear fracture of short casing inner tube at OTSG and the main pump. Although this accident will not cause the loss of the coolant capacity in the primary circuit of the reactor, it will affect the heat transfer capacity of the secondary circuit. It is vital to study the double-end shear fracture of the short casing at OTSG and the main pump. In order to verify the system safety of SMR, the thermal hydraulic analysis software LOCUST is used to analyze the system operating characteristics of SMR after the doubleend shear fracture of the short casing inner tube at OTSG and the main pump respectively.

2 Model Structure SMR coolant system uses a two-loops design with concentrically arranged double casing connections between the pressure vessel and OTSG, the pressure vessel and the main pump. Coolant inlet and outlet nozzles are arranged on the pressure vessel assembly for each loop, which are connected with the inlet and outlet nozzles of a steam generator and a main pump respectively. A diversion ring cavity is arranged between the pressure vessel and the in-reactor component hanging basket assembly to form the flow passage of the reactor coolant inside the pressure vessel from the steam generator outlet to the main pump inlet. OTSG is mainly composed of pressure vessel, tube sheet, spiral tube bundle assembly, water supply tube bundle assembly, tube bundle support assembly, sleeve assembly, etc. The primary circuit coolant flows down through the rising section of the OTSG and transfers heat to the spiral coil, and then rises from the bottom of the OTSG to the cold leg of the short casing. The main pump is located in the reactor cabin, and a check valve is set at the outlet of each main pump. The function of the check valve is to ensure that there will be no bypass flow before the coolant is injected into the reactor core during the safe injection stage after the main pump is shut down. The main pipe adopts double casing form, the inner pipe is heat pipe, and the outer pipe is cold pipe. In this way, the number of open holes corresponding to the primary circuit of pressure vessel, OTSG and main pump can be reduced by half. The difficulty of processing and manufacturing of main equipment can be reduced, and the manufacturing cycle can be shortened (Fig. 1).

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Fig. 1. SMR structure diagram

3 Calculation Method The thermal-hydraulic analysis software LOCUST was used for accident analysis of SMR. LOCUST is safety analysis software independently developed by China Nuclear Power Technology Research Institute. It is mainly used for safety analysis of accident conditions. The LOCUST calculation model of SMR is established, and the control volume is divided reasonably. The biggest difference between SMR and pressurized water reactors is that SMR use OTSG and casing main pipeline. OTSG conducts heat exchange through the fluid swept spiral tube bundle. During the simulation, the PIPE module is used to simulate the fluid channels on the primary and secondary sides of OTSG, where the PIPEs are connected by thermal components to achieve heat transfer. The inner pipe of the casing pipe section is a hot pipe and the outer pipe is a cold pipe. The PIPE module is used to simulate the two fluid channels of the cold and hot pipe section, assuming that there is no direct energy exchange between the two fluid channels. Assuming that SMR is in normal operation at the beginning, the transient simulation calculation of the double-end shear fracture of the short casing inner pipe at OTSG and the main pump is carried out. The important thermal-hydraulic phenomena and safety characteristics in the transient process of accident are analyzed. Moreover, the effects of different breach simulation methods, breach volumes and breach resistance settings on the safety characteristics of SMR were compared.

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4 Calculation Results and Analysis 4.1 Double-End Shear Fracture of Short Casing Inner Tube at OTSG After the double-end shear fracture of short casing inner tube at OTSG, the coolant flow from the core outlet to OTSG will decrease, which will affect the heat transfer capacity of OTSG at this side. Based on the steady operation of SMR, a break was added to the inner tube of the short casing of one of the loops of the OTSG. The VALVE module was used to connect the inner pipe with the outer pipe to realize the simulation of the double-end shear fracture condition of the inner pipe, as shown in the Fig. 2. In the figure, 102 is the inner pipe and 114 is the outer pipe. During simulation, 102 are broken into two sub-components, which are connected to 114-01 respectively.

Fig. 2. Double-end shear fracture of short casing inner tube at OTSG

Figures 3, 4, 5 and 6 show the change of the flow rate of each key part over time after the accident. In the figure, the red line indicates the intact loop, and the black line indicates the broken loop. After the accident, it can be seen from Fig. 3 that the flow rate of the broken loop is lower than that of the intact loop. Figure 4 shows that there has been a steady flow of injection from the inner tube 102-01 to the outer tube 114-01. Figure 5 shows the flow rate of the junction 871, where the flow rate is negative, that is, the coolant will flow from the outer tube 114-01 into the inner tube 102-02. Figure 6 shows the flow rate of the short casing inner tube 872, with less difference between the intact loop and the broken loop. The simulation results show that the natural circulation can be successfully established in the intact loop, and the broken loop can continue to cool the coolant on the primary side of OTSG due to the normal operation of ASHR. The coolant on the primary side of OTSG gradually establishes natural circulation due to the density difference. The coolant, which has completed heat transfer during the natural circulation process, is mixed with the hot coolant at the core outlet in the inner tube 102-01. Part of the coolant flows to the OTSG ascending section for heat transfer

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Fig. 3. Short casing inner tube 102-01 flow rate

Fig. 4. Junction 872 flow rate

again, the other part coolant flows to the guide ring cavity, and finally enters the pressure vessel again to be heated by the reactor core. In this way, part of the residual heat of the reactor core can be taken out. In addition, a supplementary study was carried out on the OTSG double-end shearing condition, where the sensitivity analysis was mainly carried out on the breaking connection method, the outer tube volume and the resistance coefficient. Among them, the break connection methods connected to the same outer pipe control body and different outer pipe control body are compared. The new break connection method is shown in the Fig. 7.

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Fig. 5. Junction 871 flow rate

Fig. 6. Short casing inner tube 102-02 flow rate

Figures 8 and 9 show the change of the flow rate of each key part with time for different break connection methods. The black line is connected to the same outer tube control body (break mode 1), and the red line is connected to a different outer tube control body (break mode 2) (Fig. 10). The simulation results show that with the new break connection method, the flow difference at the junction 872 is small. The flow at the junction 871 is significantly reduced, that is, the flow rate from the inner tube 102-01 to the inner tube 102-02 is reduced. In addition, there is flow rate from the outer tube 114-01 to the outer tube 114-02, because the broken loop was connected to the voltage stabilizer. And the flow

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Fig. 7. Double-end shear fracture of short casing inner tube at OTSG (break mode 2)

Fig. 8. Junction 872 flow rate

direction returned to normal after connecting the voltage stabilizer to the intact loop. The research found that the reactor core temperature showed a decreasing trend, so the break connection method can also successfully export the residual heat of the reactor core. The sensitivity analysis of the outer tube volume was carried out. The original outer tube volume was enlarged by 10 times, which found that the volume change had little effect on the results. The sensitivity analysis of the resistance coefficient was carried out. The simulation results of the resistance coefficients of 0.5, 10 and 1000 were explored. Although the flow rate from the outer tube to the inner tube becomes less, it does not affect the establishment of the overall natural circulation, so the residual heat of the reactor core can be exported smoothly.

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Fig. 9. Junction 871 flow rate

Fig. 10. Valve flow rate of outer tube

4.2 Double-End Shear Fracture of Short Casing Inner Tube at Main Pump Compared with the double-end shear fracture of short casing inner tube at OTSG, the double-end shear fracture of short casing inner tube at the main pump will not affect the coolant flow rate to the OTSG. The main pump breach simulation method is the same as that at OTSG. The changes of the parameters of each key part over time in the intact loop and the broken loop are compared, as shown in the Fig. 11. In the figure, the red line indicates the intact loop, and the black line indicates the broken loop (Figs. 12, 13 and 14). The simulation results show that the double-end shear fracture of short casing inner tube at main pump does not directly affect the natural circulation of the heat source (reactor core) and the cold source (ASHR). After the accident occurs, the two OTSGs can gradually establish natural circulation, so as to take away the residual heat of the reactor core smoothly. Compared with the double-end shear fracture of short casing at

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Fig. 11. Reactor secondary circuit pressure

Fig. 12. Middle OTSG temperature

OTSG, the temperature of the primary circuit drops faster, that is the heat transfer on the secondary side is better.

5 Conclusions The double-ended shear fracture accident is one of the key accidents that determines the operating power and affects the safety margin of nuclear power plant. In order to verify the system safety of SMR, a simulation study of the double-ended shear fracture accident is carried out. This paper uses the software LOCUST to simulate the double-end shear fracture of the short casing inner tube at OTSG and the main pump, and studies the important thermal-hydraulic phenomena and safety characteristics in the transient process of the accident. In the case of double-end shear fracture of short casing inner tube at OTSG, the effects of different break simulation methods, break volumes and break resistance on the safety characteristics of SMR were compared.

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Fig. 13. Main pump short casing outer tube flow rate

Fig. 14. Main pump short casing inner tube flow rate

The research results show that whether it is the double-end shear fracture of short casing inner tube at OTSG or the double-ended shear fracture of the short casing inner tube at the main pump, the primary circuit of SMR can successfully establish natural circulation. The residual heat of the reactor core can be stably taken away.

References 1. IAEA.: Advance in SMR Technology Development. IAEA, Vienna (2014) 2. Vladimir, K.: Small and medium sized reactors: development status and deployment potential/IAEA activities in support of SMR development and deployment. INPRO/SMR Briefing, IAEA, 30 September, USA (2008) 3. Kim, H.C., Chung, Y., Bae, K.H., et al.: Safety Analysis of SMART, GENES4/ANP2003, Kyoto, Japan (2003)) 4. Jiang, Y., Zhikang, L., et al.: Preliminary study on coolant loss accident of small reactor. Atomic Energy Sci. Technol. 50(07), 1232–1237 (2016). (In Chinese)

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5. Zhigang, H., Yan, Z., et al.: Experimental study on double-end rupture of wave tube of small reactor voltage regulator. Nucl. Power Eng. 42(6), 5 (2021) (In Chinese)

Dissolution Behavior of Nickel(II) Oxide/Nickel(II) Hydroxide Colloids in the Oxidation Operation Process During Shutdown of a PWR Fuhai Li(B) , Genxian Lin, Yun Sun, Hang Qiao, and Jun Fang Suzhou Nuclear Power Research Institute Co. Ltd., Suzhou, Jiangsu, China [email protected]

Abstract. NiO/Ni(OH)2 is one of the main components of corrosion products in the primary loop of PWRs. Co-58, one of the primary radionuclides and contributors to collective radiation dose (CRE), is produced by the activation of Ni-58. Therefore the behavior of NiO/Ni(OH)2 during plant shutdown transients attracts the interest of researchers. The pore diameter of the filters used in most PWRs is 0.1 and 0.4 μm. The removal efficiency of NiO/Ni(OH)2 colloids (< 0.1 μm) by the filters is very low. NiO/Ni(OH)2 colloids is unstable and tends to deposite on the pipeline surface of the primary loop during shutdown leading to deposition of Co-58 due to co-deposition effect, contributing to CRE. The chemical conditions of the coolants during the oxidation operation process greatly affect the dissolution and removal efficiency of NiO/Ni(OH)2 colloids. In this work, NiO/Ni(OH)2 colloids with particle size of 0–80 nm were first prepared and characterized. Then the effects of temperature (60–80 °C), concentration of Li (pH), concentration of H2 O2 (0–20 ppm) and reaction time (0–24 h) on the dissolution behavior of NiO/Ni(OH)2 colloids were investigated. The results showed that the dissolution of NiO/Ni(OH)2 colloids was quite slow. From 8 h to more than 24 h was needed to reach reaction equilibrium. The addition of H2 O2 accelerates the dissolution of NiO/Ni(OH)2 colloids, which may be explained by the weak acidity, oxidizability and activation effect of H2 O2 . The best efficiency was achieved with 5 ppm H2 O2 . Maintaining high temperature at 80 °C and acidic pH was also favorable for the dissolution of NiO/Ni(OH)2 colloids. The results obtained in this work may provide important references and guidances for the improvement of dissolution and removal of NiO/Ni(OH)2 colloids during shutdown of PWRs, thus reducing CRE. Keywords: Dissolution · Nickel(II) oxide · Oxidation operation process · Shutdown · PWR

1 Introduction Great efforts have been devoted to reducing the collective radiation dose (CRE) of PWRs by all the staff members and relevant researchers [1, 2]. The deposition of activated corrosion products on the pipeline surface of primary loop during shutdown of PWRs © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 155–165, 2023. https://doi.org/10.1007/978-981-19-8780-9_15

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contributes to 80–95% of the CRE [3, 4]. NiO/Ni(OH)2 , together with Ni, Ni–Fe ferrites, are the main components of corrosion products in the primary loop of PWRs. Co-58, one of the primary radionuclides and contributors to CRE, is produced by the activation of Ni-58. In addition, Ni-rich corrosion product deposits on fuel such as NiO/Ni(OH)2 contribute to the axial offset anomaly (AOA) [5]. Therefore the behavior of NiO/Ni(OH)2 during plant shutdown transients has attracted the interest of researchers. NiO/Ni(OH)2 may exist in particles (≥ 0.1 μm) and colloids (< 0.1 μm). The pore diameter of the filters used in most PWRs is 0.1 and 0.4 μm at present. The removal efficiency of NiO/Ni(OH)2 colloids by the filters is very low. NiO/Ni(OH)2 colloids are unstable and may deposite on the pipeline surface of the primary loop leading to deposition of Co-58 due to co-deposition effect, contributing to the CRE. The oxidation operation process with addition of H2 O2 has been widely employed by PWRs worldwide, in which the coolant becomes acidic and oxidative. The corrosion products are oxidized and dissolved. The radionuclides are removed by ion exchange resins. The oxidation operation process is a very important process, during which the chemical conditions of primary loop, such as pH, temperature and concentration of H2 O2 , greatly affect the dissolution and removal efficiency of NiO/Ni(OH)2 colloids [6]. However, specialized studies on the dissolution behavior of NiO/Ni(OH)2 colloids in the oxidation operation process are still needed as far as we know. In this work, NiO/Ni(OH)2 colloids with particle size of 0–80 nm were first prepared and characterized. Then the effects of temperature (60–80 °C), concentration of Li (pH), concentration of H2 O2 (0–20 ppm) and reaction time (0–24 h) on the dissolution behavior of NiO/Ni(OH)2 colloids were investigated. The mechanism is also discussed. The best chemical condition for the dissolution and removal of NiO/Ni(OH)2 colloids was suggested, which may provide important references and guidances for the improvement of the oxidation operation process of PWRs.

2 Experimental Section 2.1 Chemical Reagents Nickel chloride hexahydrate (NiCl2 •6H2 O) (AR), boric acid (H3 BO3 ) (AR), lithium hydroxide (LiOH) (AR), hydrazine (N2 H4 ) solution (85 wt%, AR) and hydrogen peroxide (H2 O2 ) solution (30 wt%, AR) were purchased from Sinopharm Chemical Reagent Co., Ltd. Ultra-pure water (18.3 M • cm) was used in all experiments. 2.2 Preparation of NiO/Ni(OH)2 Colloids Taking Daya Bay PWR as an example, the temperature in the primary loop is about 300 °C, while the pressure is 155 atm during the power operation. The pH is adjusted by H3 BO3 and LiOH (B–Li buffer solution) to be weakly alkaline with pH300 °C = 7.2. Dissolved hydrogen (H2 ) is injected into the coolant to maintain a reductive environment, inhibiting the radiolysis of water. To simulate the weak alkalinity of the coolant at 25 °C, pH25 °C should be 9.8, generating the same concentration of OH– based on the calculation of neutral pH at different temperature by Chemwork, as shown in Table 1. The pH25°C

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of the B-Li buffer solution with 100 ppm Li and 9.2 ppm B is about 9.8 as determined by experimental measurements. N2 H4 is added in the solution to simulate the reducibility of the coolant. Table 1. Neutral pH of pure water at different temperatures T/°C

25

100

150

200

250

300

Neutral pH

7.0

6.13

5.81

5.64

5.59

5.69

To prepare NiO/Ni(OH)2 colloids, N2 H4 solution was added into the B-Li solution at 25 °C under magnetic stirring (400 rpm). Then NiCl2 solution was added into the B– Li–N2 H4 solution. The composition of the final B–Li–N2 H4 –NiCl2 solution was shown in Table 2. After 2 h reaction NiO/Ni(OH)2 colloids were filtered by vacuum filtration and then redispersed in water by sonicating. The above steps were repeated until Cl– was not detected in the filtrate. NiO/Ni(OH)2 colloids were collected and dispersed in water. Table 2. The composition of reaction solution to prepare NiO/Ni(OH)2 colloids Composition

HBO3 (ppm)

LiOH (ppm)

N2 H4 (ppm)

NiCl2 (mM)

Concentration

572

220

50

1.5

2.3 Study of the Dissolution Behavior of NiO/Ni(OH)2 Colloids Taking Daya Bay PWR as an example, during the oxidation operation process, the concentration of B is kept 2200–2400 ppm, while the concentration of Li is kept 0– 1.3 ppm. The temperature is usually between 60 and 80 °C. While the concentration of H2 O2 is not higher than 20 ppm. The experimental conditions to study the dissolution behavior of NiO/Ni(OH)2 colloids are shown in Table 3. In a typical experiment, the as-prepared NiO/Ni(OH)2 colloids solution in a flask was placed in the electro thermostatic water bath under magnetic stirring. The B–Li– H2 O2 solution was prepared in another flask, which was pre-heated to experimental temperature. The B–Li–H2 O2 solution was then added into the NiO/Ni(OH)2 colloids solution. The total mass of the final solution was 400 g in all experiments. After the reaction started, about 20 mL solution was taken at different time, filtered immediately by a needle filter (0.22 μm). The content of dissolved Ni in the filtrate was measured by ICP-OES, and then normalized by total Ni. 2.4 Instruments and Characterizations Ultra-pure water was generated by Milli-Q® (Merck Co. Ltd.). The morphology and composition of NiO/Ni(OH)2 colloids was examined with high resolution transmission

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Table 3. Experimental conditions to study the dissolution behavior of simulated Co colloids T (°C)

B (ppm)

Li (ppm)

H2 O2 (ppm)

Total Ni (ppm))

Time (h)

80

2350

0

0, 5, 10, 20

≈5

0–24

0.5

0, 5, 10, 20

1.3

0, 5, 10, 20

0

0, 5, 10, 20

≈5

0–24

0.5

0, 5, 10, 20

1.3

0, 5, 10, 20

0

0, 5, 10, 20

≈5

0–24

0.5

0, 5, 10, 20

1.3

0, 5, 10, 20

70

60

2350

2350

electron microscopy (HR-TEM) (JEOL 2010) at 200 kV. The content of Ni in the filtrate was measured by ICP-OES (SPECTRO ARCOS EOP).

3 Results and Discussion 3.1 Characterization of NiO/Ni(OH)2 Colloids The color of NiCl2 solution was green, which turned into grey-green after addition of B–Li–N2 H4 solution. A significant Tindal effect was observed after reaction indicating the formation of NiO/Ni(OH)2 colloids. The morphology of NiO/Ni(OH)2 colloids was characterized by TEM. As shown in Fig. 1, irregular spherical NiO/Ni(OH)2 colloids were successfully prepared. The diameter of the colloids was measured statistically by Nano Measurer as shown in Fig. 2. The diameters of all NiO/Ni(OH)2 colloids were less than 80 nm with an average diameter about 30 nm. HR-TEM was conducted to characterize the composition of NiO/Ni(OH)2 colloids. As shown in Fig. 3, lattice fringes were observed on the surface. The lattice fringes of each nanoparticle were along the same direction, illustrating the as-prepared NiO/Ni(OH)2 colloids were single crystal. The interplanar crystal spacing was 0.22 nm, which is very close to the (1 1 1) lattice spacing of NiO (PDF 47–1049) and the (1 0 1) lattice spacing of Ni(OH)2 (PDF 04-0850) (Table 4) [7]. The components of the prepared colloids were NiO/Ni(OH)2 . 3.2 Dissolution Behavior of NiO/Ni(OH)2 Colloids The dissolution behavior of NiO/Ni(OH)2 colloids by H2 O2 (0 ppm, 5 ppm, 10 ppm, 20 ppm) and Li (0 ppm, 0.5 ppm, 1.3 ppm) at 60, 70, 80 °C, was investigated, while the concentration of B was fixed at 2350 ppm. The pH of B–Li buffer solution in this study at 25, 60, 70 and 80 °C was calculated by PHREEQC, as shown in Table 5. All the solutions were weak acid.

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Fig. 1. TEM images of NiO/Ni(OH)2 colloids.

Fig. 2. Size distribution of NiO/Ni(OH)2 colloids.

3.2.1 Effect of H2 O2 The effect of H2 O2 on the dissolution behavior of NiO/Ni(OH)2 colloids was studied with 0 ppm, 0.5 ppm, 1.3 ppm Li at 70 °C, as shown in Fig. 4. The dissolution of NiO/Ni(OH)2 colloids was a slow rection, which didn’t reach equilibrium after 24 h reaction. Only about 70–80% colloids were dissolved, suggesting NiO is rather unreactive towards mineral acids [8]. With addition of 5 ppm H2 O2 , the dissolution rate of NiO/Ni(OH)2 colloids and the ratio of dissolved Ni2+ in the solution was increased. As H2 O2 is a weak acid, it slightly enhances the acidity of the solution, thus promoting the dissolution of NiO/Ni(OH)2 colloids. H2 O2 is also an oxidizing agent. The dissolution of NiO is accelerated in the presence of oxidizing agents, as reported by several literatures [8–10]. It is because the mechanism of the dissolution of NiO in water or acids is a rather complex surface

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Fig. 3. HR-TEM images of NiO/Ni(OH)2 colloids. Table 4. Standard XRD patterns of NiO, Ni and Ni(OH)2 [7].

NiO (PDF 47-1049)

Ni (PDF 04-0850)

Ni(OH)2 (PDF 14-0117)

2θ/°

(h k l)

d/nm

37.25

(2 0 0)

0.24

43.28

(1 1 1)

0.21

62.88

(2 2 0)

0.15

75.41

(3 1 1)

0.13

44.51

(1 1 1)

0.20

51.85

(2 0 0)

0.18

76.37

(2 2 0)

0.12

92.94

(3 1 1)

0.11

19.26

(0 0 1)

0.46

33.06

(1 0 0)

0.27

38.54

(1 0 1)

0.23

52.1

(1 0 2)

0.18

59.05

(1 1 0)

0.16

62.73

(1 1 1)

0.15

chemical reaction involving Ni(III) reactive intermediate on the dissolving surface [8– 11], which will not be discussed here. Besides, many transition metal oxides including NiO are able to catalyze the decomposition of H2 O2 [12, 13]. During the catalyzing process NiO/Ni(OH)2 colloids may be activated, of which the dissolution was promoted. As the physical properties of catalyzer such as phase, shape, density, particle size, etc. usually change before and after reaction

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Table 5. Calculated pH of B–Li buffer solution at 25, 60, 70 and 80 °C by PHREEQC Li (ppm)

0

0.5

1.3

Pure water

pH60 °C

4.83

5.53

5.93

6.51

pH70 °C

4.80

5.47

5.86

6.40

pH80 °C

4.77

5.42

5.81

6.30

pH25 °C

4.96

5.80

6.20

7.00

[14, 15]. NiO + 2H+ = Ni2+ + H2 O

(1)

Ni(OH)2 + 2H+ = Ni2+ + 2H2 O

(2)

+ H2 O2  HO− 2 +H

(3)

However, too much H2 O2 is unfavorable for the dissolution of NiO/Ni(OH)2 colloids. With addition of 10 and 20 ppm H2 O2 , the dissolution rate of NiO/Ni(OH)2 colloids and the ratio of dissolved Ni2+ in the solution decreased instead. This may be explained by the formation of passive film on the surface of the colloids, as H2 O2 can act as passivator in weak acidic, neutral and weak alkaline conditions [16–18]. The more H2 O2 is present, the more quickly the passive film forms, and the stronger the corrosion resistance of the passive film is [19, 20]. Therefore, a proper concentration of H2 O2 , about 5 ppm, is appropriate for the dissolution of NiO/Ni(OH)2 colloids. 3.2.2 Effect of Temperature The effect of temperature on the dissolution behavior of NiO/Ni(OH)2 colloids with 0 and 0.5 ppm Li was shown in Figs. 5 and 6 respectively. A notable effect of temperature on the dissolution of NiO/Ni(OH)2 colloids was observed. The dissolution rate of NiO/Ni(OH)2 colloids was accelerated at 80 °C compared with that at 60 °C. The dissolved Ni2+ reached its maximum in 8 h. It indicates that 80 °C is the most favorable temperature for the dissolution of NiO/Ni(OH)2 colloids. This is perhaps due to the faster molecular movement at higher temperature and the higher concentration of H+ at 80 °C as indicated by the calculated pH in Table 5. 3.2.3 Effect of Li The effect of Li on the dissolution behavior of NiO/Ni(OH)2 colloids at 60 and 80 °C was shown in Fig. 7. It was expected that the dissolution of NiO/Ni(OH)2 colloids was faster at lower pH with low concentration of Li, as shown in Fig. 7a. However, this effect was not observed at 80 °C (Fig. 7b), which may be due to the fast reaction rate.

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Fig. 4. The effect of H2 O2 concentration on the dissolution behavior of simulated Co colloids at 70 °C with a 0 ppm, b 0.5 ppm, c 1.3 ppm Li.

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Fig. 5. The effect of temperature on the dissolution behavior of NiO/Ni(OH)2 colloids with 0 mg/kg Li and a 0 mg/kg, b 5 mg/kg, c 10 mg/kg, d 20 mg/kg H2 O2 .

4 Conclusions In this work, simulated Ni colloids with particle size of 0–80 nm were first prepared. The composition of simulated Ni colloids was characterized as NiO/Ni(OH)2 . Then the effects of temperature, concentration of Li (pH), H2 O2 and reaction time on the dissolution behavior of NiO/Ni(OH)2 colloids during the simulated oxidation operation process were investigated. The results showed that the dissolution of NiO/Ni(OH)2 colloids was quite slow. From 8 h to more than 24 h was needed to reach equilibrium. The addition of proper amount of H2 O2 promotes the dissolution of NiO/Ni(OH)2 colloids, which may be explained by the weak acidity and activation effect of H2 O2 . The best efficiency was achieved with 5 ppm H2 O2 . Temperature showed a notable effect on the dissolution of NiO/Ni(OH)2 colloids. Maintaining high temperature at 80 °C and acidic pH was favorable for the dissolution of NiO/Ni(OH)2 colloids. The results obtained in this work may provide important references and guidances for the improvement of dissolution and removal of NiO/Ni(OH)2 colloids during the oxidation operation process of PWRs.

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Fig. 6. The effect of temperature on the dissolution behavior of NiO/Ni(OH)2 colloids with 0.5 mg/kg Li and a 0 mg/kg, b 5 mg/kg, c 10 mg/kg, d 20 mg/kg H2 O2 .

Fig. 7. The effect of temperature on the dissolution behavior of NiO/Ni(OH)2 colloids with 0 ppm H2 O2 at a 60 °C and b 80 °C

References 1. Information System on Occupational Exposure.: Occupational exposures at nuclear power plants. Twentyfirst Annual Report of the ISOE Programme (2011)

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2. Cao, Q.J., Zheng, J.G., Liu, L.Y., et al.: Radiation source term and its measurement of primary system in PWR NPPs. Radiat. Protect. Commun. 35(04), 38–41 (2015) 3. Rocher, A., Berger, M.: Impact of main radiological pollutants on contamination risks (ALARA) optimization of physico chemical environment and retention technics during operation and shutdown. EDF, Portoroz Workshop, Session 2 (2004) 4. Yang, M.C., Chen, D.G.: Practive and experience of occupational exposure control in the outages of daya bay nuclear power plant. Radiat. Protect. Z1, 144–154 (2004) 5. Palino, G.F., Miller, M.R., Sawochka, S.G.: Behavior of nickel/nickel oxide in PWR enviromments. EPRI, Palo Alto, CA, 1001397 (2001) 6. Lin, G.X., Li, F.H., Fang, J., et al.: Reduced deposition of 110m ag colloids by improved oxidation operation process during shutdown of a PWR. J. Radioanal. Nucl. Chem. 331, 111–118 (2022) 7. Zhang, Y., Xu, R.D., Qin, Z.Y., et al.: Facile preparation of porous sheet–sheet hierarchical nanostructure NiO/Ni–Co–Mn–Ox with enhanced specific capacity and cycling stability for high performance supercapacitors. RSC Adv. 10, 22422–22431 (2020) 8. Grygar, T., Jandov, J., Kl˘ımov, Z.: Dissolution reactivity of NiO obtained by calcination of pure and contaminated Ni-hydroxides. Hydrometallurgy 52: 137–149 (1999) 9. Nii, K.: On the dissolution behavior of NiO[J]. Corros. Sci. 10, 571–583 (1970) 10. Blesa, M.A., Morando, P.J., Regazzoni, A.E.: Chemical Dissolution of Metal Oxides. CRC, Boca Raton (1994) 11. Figueroa, C.A., Sileo, E.E., Morando, P.J., et al.: Dissolution of nickel oxide in oxalic acid aqueous solutions. J. Colloid Interface Sci. 244, 353–358 (2001) 12. Liu, Z.Y., Li, H.B., Wang, X.R.: Study of the catalytic decomposition of H2 O2 . Ann. Rep. China Instit. Atomic Energy 371 (2009) 13. Xin, Y.X., Zhang, F., Jia, X.W., et al.: Research progress of catalysts used for hydrogen peroxide decomposition. Petrochem. Ind. Technol. 24(06), 50–51 (2017) 14. Wang, Z.H., Heng, N.N., Wang, X.B., et al.: Surface and morphology structure evolution of metal phosphide for designing overall water splitting electrocatalyst. J. Catal. 374, 51–59 (2019) 15. Shang, M.F., Zhao, T.T., Bao, H.L., et al.: In situ XAFS characterization of bimetallic nanoparticle catalysts PtCo/C structure changes in the working conditions. Nucl. Tech. 39(06), 060101 (2016) 16. Jiang, B., Zhang, J.B., Zhang, J.P.: Low concentration hydrogen peroxide passivation technic. Clean. World 26(04), 15–17 (2010) 17. Ma, Q., Yang, D.W., Ren, Z., et al.: Effect of hydrogen peroxide passivation content on corrosion resistance of carbon steel. Clean. World 28(05), 11–14 (2012) 18. Li, G.J., Ji, Q.R., Cai, Y.C., et al.: Research of environmentally friendly passivation using citric acid formulations. J. Tianjin Univ. Sci. Technol. 27(01), 48–51 (2012) 19. Feng, Z.M., Deng, W.Y., Li, X.R.: Passivation of boiler by hydrogen peroxide after acid cleaning. Technol. Water Treat. 29(03), 180–181 (2003) 20. Zhou, H.Y., Li, J.H., Liu, B.: Study on corrosion inhibition process of electroless nickel coating. Surf. Technol. 43(05), 81–86 (2014)

Application of Fiber Bragg Grating Technology in Perimeter Security of Nuclear Power Plants Hai-Rong Lu(B) , Xu-Tao Bai, Xiao-Chen Zhang, Bo Yao, and Jin-Fei Zhang Suzhou Nuclear Power Research Institute Co., Ltd., Suzhou, Jiangsu, China [email protected]

Abstract. Aiming at the questions in applying the traditional fiber-optic vibration system in the perimeter security of nuclear power plants. Hence, the fiber bragg grating (FBG) technology is introduced in this field. With the implementation of data collection, comparative analysis, optimization of characteristic signals extraction, and developing intelligent intrusion prevention strategies, the intrusion prevention efficiency can be further improved. Actual tests and the field application have demonstrated that the FBG system has such advantages of flexible layout, high accuracy, low false alarm rate and easy maintenance. Therefore it is worthy of being popularized. Keywords: Fiber bragg grating (FBG) · Perimeter security · Intrusion detection · Intelligence · Nuclear power plants

1 Introduction As large nuclear power plants have been put into operation, the public pays special attention to the safe operation of nuclear power plants and the control of nuclear material. At present, the security system (Physical Protection System) of commercial nuclear power plants basically uses foreign design and products. For example, domestic nuclear power plants mostly adopt a foreign fiber-optic vibration sensing system as one of the anti-intrusion technologies. This system was introduced in the last century and was then integrated into the field of national nuclear power. It uses the vibration sensing fiber-optic cable to detect possible intrusion in the specific defense area (generally in a range of 50–100 m). The processor converts the optical signal into electrical signal, which will be compared and analyzed subsequently. In this way such intrusion actions as climbing, cutting or pulling the fence facility can be distinguished and the alarm will be triggered [1]. In its practical application, the traditional fiber-optic vibration sensing system has following problems: The procurement and delivery cycle is long. It is unable to be customized based on the on-site condition. Repairing is inconvenient and can only be realized by replacing the detection cable. Accurate positioning is impossible for detection is only targeted to the defense area. The system is closed and higher using demand cannot be achieved. The system lacks self-adaption and can only make analysis based on the onsite condition and recent alarm data. The system sensitivity should be further upgraded © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 166–172, 2023. https://doi.org/10.1007/978-981-19-8780-9_16

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[1–3]. This paper discusses the application of fiber Bragg grating (FBG) technology and introduces a self-adaptive intelligent perimeter intrusion detection system.

2 Design of FBG Perimeter Intrusion Detection System Fiber gratings refer to the spatial phase gratings formed in the fiber core due to the photosensitivity of fiber materials, thus generating a narrowband (projection or reflection) filter or reflector to change and control the propagation of light in the fiber core [2, 3]. Using the FBG sensor as the sensing element, the perimeter intrusion detection system is sensitive to vibration. It demodulates the wavelength of the reflected light and then analyses the data. With data comparison, the system confirms the specific intrusion events and their locations. The main optical fiber is both an information carrier, and a sensitive signal medium. It is characterized by the large capacity and high speed information transmission. With the demodulation and processing of various parameters, including amplitude, phase, frequency and the state of polarization, it can be highly sensitive to minute variation of various physical quantities and chemical quantities. Therefore, the system can achieve long-distance passive perception and has such advantages as covering wide detection range, and realizing front-end passive detection and high positioning accuracy [2]. The basic architecture of the system can be divided into three layers according to different functions, namely the detection layer, the monitoring and analyzing layer and the application (interaction) layer. 2.1 Detection Layer The detection layer is located at the front end of the entire system. It mainly uses a multiple of sensors distributed in series on the optical fiber line to monitor and collect the vibration information from the detection object line. It then converts the information into distinguishable data such as light intensity, wavelength, etc. By analyzing the data variation and its correspondence to vibration, the on-site vibration information can be restored and long-term real-time monitoring is thus to be achieved. Sensors are using the FBG technology and are fully sealed and packaged. The exterior of the detection layer is customized and optimized based on actual application scenarios to meet the requirements of operating environment. 2.2 Monitoring and Analyzing Layer The monitoring and analyzing layer transforms and sorts out the information obtained in the detection layer, extracts and analyzes signal characteristics, and then provides various forms of alarm through pattern recognition and intelligent algorithm. 2.3 Application Layer The application layer analyzes and organizes the monitored data and then sends it to the remote monitoring center through LAN or Internet to realize remote intelligent monitoring. It is combined with specific fields and displays the monitored data in a user-friendly interface so as to realize human-computer interaction [4] (Fig. 1).

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Fig. 1. Operating interface of FBG perimeter intrusion detection system

3 Working Modes of Alarm The anti-intrusion alarm follows the basic requirements of “little omission, less false alarm, accurate positioning and prompt alert”. Based on theoretical analysis and verified by test data samples, the alarm system is continually optimized through engineering application so as to ensure accurate and timely output, so as to improve accuracy and reduce false alarm rate. 3.1 Single-Point Mode Single-point mode is the most basic function in the anti-intrusion alarm system, which mainly depends on analyzing and organizing the front-end vibration signals, extracting corresponding characteristic signals and comparing them with those of various actions in the characteristic action library, and then confirming the actions such as climbing, cutting, pulling, knocking (one-time knock, continuous knock, touching), animals interfering (birds, etc.), environmental interference (resonance, wind and rain, overloaded transit, etc.). Based upon the judgements, intrusion actions will be recognized and the alarm will be triggered (Fig. 2). 3.2 Comprehensive Mode In the single point mode, different actions may generate similar characteristic threshold values, which is easy to cause misjudgments. Meanwhile, in order to improve the recognition rate, the system adopts time-domain signals to analyze and extract characteristic values, which requires a large quantity of characteristic values and takes a lot of calculation. Under special weather circumstances, such as on windy and rainy days, the recognition rate is reduced, leading to higher rate of false alarm. Besides, the one-way arrangement on the front end makes it much affected by the on-site installation method.

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real-time signals data processing

characteristic value extraction （vibration peak value、 vibration time、signal-to-noise ratio、envelope calculation...）

characteristic action library

sensitivity setting

characteristic comparison matched

unmatched

action matching

action confirmation

non-intrusion action

intrusion action

alarm output

alarm inhibition

Fig. 2. Single-point mode

There is some delay in part of the sensors in collecting vibration signals, which is even longer when such actions happen as tapping the mesh bottom or treading on the bracket. Therefore, in order to further reduce false alarm, maintain high recognition rate and shorten the confirmation time, a comprehensive recognition mode is designed. This mode is similar to the single-point recognition mode. It sets the average value of the sensor’s variation in certain time periods as the fiducial value. The concept of the correction wavelength of one single sensor, namely the difference between the actual wavelength and the fiducial value is considered as one of the characteristic values for

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making judgements. At the same time, such values as relative signal-to-noise ratio and SNR of the sensor itself are also included in the recognition process (Fig. 3).

Fig. 3. Comprehensive mode. Notes (1) relative signal-to-noise ratio of sensors = sensors peak intensity/median of the maximum peak intensity of all sensors in the channel within n seconds. (n refers to the time periods), (2) signal-to-noise ratio of the sensor itself = the peak intensity of the sensor/median of the (m) maximum peak intensity of a single sensor in the last n seconds. (m refers to the number of the maximum peak intensity, n refers to the time periods)

3.3 Self-adaptive Mode The system sets high, medium and low sensitivity for the selection of characteristic threshold value under different circumstances. However, as the actual application environment is changing, it is likely to lead to false alarm in a very small range. Therefore, for the specific range of the mesh installation, a self-adaptive algorithm of single sensor characteristic threshold value is designed. The main process works as follows: after the single-point sensor detects the vibration, the information is to be compared with the alarm information of the adjacent sensors in a specific time period; if it is judged as discrete alarm, the threshold value will be dynamically adjusted to improve the alarm threshold, and the new threshold will be used in the confirming process (new threshold = threshold value + adjustment value); If the discrete alarm of does not appear in the following 24 consecutive hours, then the threshold value will be dynamically adjusted to the original set value under this sensitivity (new threshold = threshold value -adjusted value).

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4 Application Test 4.1 Testing Conditions After the system operates for six consecutive months, artificial methods are adopted to conduct performance tests. To ensure validity, tests are generally organized based on the following criteria: Testing area: random, no region or network is specified. • Testing personnel: randomly selected. • Testing methods: climbing, tapping, pulling, simulated cutting, crossing, touching, etc. (including unconventional testing methods). • Testing location: any position of the mesh, fence support column, etc. 4.2 Results The testing personnel randomly organized 36 intrusion tests within the range where the 720 m optical cable is distributed at the front end. The results are as follows (Table 1). Table 1. Data comparison between FBG perimeter intrusion detection system (new system) and the fiber-optic vibration system (original system) Comparison items

Alarm of the new system (%)

Alarm of the original system (%)

Calculation method

Alarm triggering times

97

64

Alarm times / total testing times

Priority number of triggering alarms

35

26

Priority times/total alarm times, only counting the average alarm-triggering times of two systems

Abnormal single point-triggered alarms

9

52

Single point-triggered alarms/total alarms, counting the average alarm-triggering times of two systems

Notes (1) Alarm triggering times: the number of alarms triggered by the on-site simulated intrusion actions; (2) Priority number of triggering alarms: the number of alarms that either system was triggered prior to the other; (3) Abnormal single point-triggered alarms: the number of alarms on adjacent locations or multiple alarms on the same location when the single-channel testing is triggered

According to the test data and the test analysis when the alarm is triggered, it can be seen that: • Compared with the original system’s intrusion alarm mechanism, the new system’s detection optical cable at the front end can be customized according to the site, so as to confirm the positioning of the intrusion on single mesh and facilitate quick response.

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• The new system has superior intrusion alarm controlling performance. No false triggering occurred during the test, except only once when the alarm was not triggered (the original system did not trigger either). The main reason is that the testing personnel touched the mesh with the back of his hand, then neither of the two systems collected effective vibration signals. • The new system has better vibration sensitivity than the original system. The new system can respond to such cases as tapping, kicking the lower part of the mesh, climbing the bracket and other intrusion actions, while these are hard to be detected by the original system. • In the new system, there were twice when adjacent channels simultaneously triggered alarms. According to the field survey, in those two alarms, the testing mesh was relatively loose. It kept vibrating after being tapped, which affected the adjacent mesh, causing the adjacent sensors to collect vibrating signals and trigger the alarm. By comparison, there were twelve abnormal single point-triggered alarms in the original system, which was mainly due to the top-down arrangement of the system. When a single point was triggered, a multiple of points would collect the intrusion information and then triggered the alarm.

5 Conclusions Considering defects of the traditional fiber-optic vibration system, such as poor adaptability, inconvenient operation and maintenance, high false alarm, etc., the FBG perimeter intrusion detection system is an applicable alternative. The new system can achieve high accuracy and has strong self-adaptive competence. By means of development and application, the new system enlarges the variety of anti-intrusion products and provides more alternatives for the perimeter security system of nuclear power plants.

References 1. Sun, H.-B., Lu, H.-R., Xiao, W.-W., Ai, D.-W.: The application of distributed fiber-grating technology on the vibration detection in the security and protection system of nuclear power plant. Electr. Safety Technol. 20(1), 54–56 (2018) 2. Lei, Y.-T.: Security and Optoelectronic Information-Security Monitoring Technology Foundation. Publishing House of Electronic Industry, (2016) 3. Liu, L.: Application of FBG vibration sensor for intrusion detection in oil tank farm. Petrochem. Safety Environ. Protect. Technol. 28(6), 32–35, 39 (2012) 4. Lu, H.-R., Cai, B.-G., Zhu, L.: Optimization of security and protection system of switch station in nuclear power station. Electr. Safety Technol. 20(1), 54–56 (2018)

Experimental Study on Manufacturing Technology of Intermediate Heat Exchanger Tube-Tubesheet Joint in Sodium-Cooled Fast Reactor Guangdong Song1(B) , Binbin Qiu2 , Xin li1 , Huajin Yu1 , and Lijun Zhou1 1 China Institute of Atomic Energy, Beijing, China

[email protected] 2 Xi’an Jiaotong University, Xi’an, China

Abstract. Intermediate Heat Exchanger (IHX) is one of the most important equipments in a sodium-cooled pool type fast reactor, which transports the heat from the primary sodium to the secondary sodium. The manufacturing quality of tube-tubesheet joints is the most important mark of the quality of intermediate heat exchanger. In this paper, the key process parameters of IHX tube-tubesheet joint manufacturing are studied and the manufacturing process suitable for mass production of tube-tubesheet joints is determined. Keywords: Sodium-Cooled Fast Reactor Intermediate Heat Exchanger Tube-Tubesheet Joint Manufacturing Technology Experimental Study

1 Introduction Intermediate Heat Exchanger (IHX) is one of the most important equipment in a sodiumcooled pool type fast reactor, which transports the heat from the primary sodium to the secondary sodium. The safety objectives of the IHX are to isolate the radioactive primary sodium from the secondary sodium and to provide a physical barrier to the transport of radioactive sodium out of the containment boundary. The tube bundle assembly of the intermediate heat exchanger is the core functional assembly of the whole equipment, which is mainly composed of upper tube sheet, lower tube sheet and heat exchange tubes. The manufacturing quality of tube-tubesheet joints is the most important mark of the quality of intermediate heat exchanger. The selection of the connection type and manufacturing process parameters will directly affect the strength and design life of the tube-tubesheet joints and even the intermediate heat exchanger. In order to accurately evaluate the various factors affecting the performance of tube to tubesheet joints, the test method is usually used [1, 2]. In this work, through the process reliability test of the intermediate heat exchanger tube-tubesheet joints, a large number of samples of tube-tubesheet joints trial production has been completed, the sensitivity of key process parameters has been mastered, the weak part of process scheme has been optimized, and the solidification of manufacturing © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 173–180, 2023. https://doi.org/10.1007/978-981-19-8780-9_17

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process parameters has been completed, which can provide technical reference for the intermediate heat exchanger product manufacturing.

1.protective shell 2. Fastening flange 3. shielding block 4. drain pipe 5. outlet cavity 6. upper tubesheet 7. inlet window 8. center tube 9. heat exchange tube 10. support plate 11.shell 12.outlet window 13.lower tubesheet 14. distributing plate 15. shell cover

Fig. 1. Structure schematic of IHX

2 Structure of Intermediate Heat Exchanger and the Tube-Tubesheet Joint IHX is a vertical shell and tube type heat exchanger with primary radioactive sodium on shell side and secondary sodium on tube side. In order to facilitate the connection of piping, layout and equipment itself, the sodium coolant in the secondary loop main cooling system is imported and exported coaxially in the IHX (the sodium is imported through central down pipe). The overall structure of the intermediate heat exchanger is shown in Fig. 1.

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2.1 Main Design Parameters of Intermediate Heat Exchanger The intermediate heat exchanger is a nuclear safety level 2 equipment. The research object of this paper is a domestic fast reactor intermediate heat exchanger, and its main technical parameters are shown in Table 1. Table 1. Technical characteristics of intermediate heat exchanger Number

Item

Unit

Value

1

Coolant

–

Liquid sodium

2

Design temperature

°C

500–600

3

Design pressure

MPa

1–2

4

Material of heat exchange tube/tubesheet

–

TP 316H/316H

5

Specification of heat exchange tube

mm

16 × 1.2

6

Layout of tube bundle

–

Concentric arrangement

2.2 Connecting Form of Tube-Tubesheet Joint Considering the requirement of sodium removal and ensuring the tightness and strength of the welded joint, the end of heat exchange tube of intermediate heat exchanger is flush with the tube sheet, and the combination of “self-fusible welding without filler wire + hydraulic expansion” is used for the tube-tubesheet joint manufacture. The structure of tube-tubesheet joint is shown in Fig. 2 (upper tube sheet). The specification of the heat exchange tube is 16 × 1.2 mm. The thickness of the tubesheet is 300 mm and within 15 mm from the end of the secondary side is the location of the expansion area. The non-expanded area of heat exchange tube and the tube-hole is controlled at 3–6 mm, and the other areas of the heat exchange tube in the tubesheet holes are hydraulic expansion area.

3 Experiment Contents In order to accurately evaluate the influence of various process parameters on the reliability of tube-tubesheet joint, the joint expansion test and the joint welding test of intermediate heat exchanger were carried out respectively. The joint expansion test of intermediate heat exchanger mainly evaluates the influence of expansion pressure and holding time on expansion performance; for joint welding test, according to experience, the factors affecting the welding quality and welding performance include welding current, welding voltage, welding speed, pulse frequency, etc., in which the influence of welding current on welding formation, joint quality, throat size and root micro crack is significant. The upper limit, middle limit and lower limit of welding current are used for joint welding test.

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Fig. 2. Structure of tube-tubesheet joint

3.1 Holes Processing in Tubesheet Deep-hole drilling technology was used for holes processing in IHX tubesheet. Holes in tubesheet is shown in Fig. 3. The sizes of holes in tubesheet were measured after deep-hole drilling.

(a Tubesheet for welding test (b)Tubesheet for expansion test Fig. 3. Holes in tubesheet

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3.2 Strength-Welding and Hydraulic Expansion The welding test and hydraulic expansion test of tube-tubesheet joint are carried out after positioning expansion. Nondestructive testing for welded tube joints includes visual inspection, liquid penetrant inspection, radiographic inspection and helium leak detection. Other tests include metallographic inspection and pull-out force test. In addition to visual inspection and pull-out force test, the size of the heat exchange tube before and after expansion should be measured. Welding and expanding process of tube and tubesheet and joints after welding are shown in Fig. 4.

Fig. 4. Welding and expanding process of tube and tubesheet and joints after welding

4 Analysis of Test Results 4.1 Analysis of Hydraulic Expansion Test Results 4.1.1 Expansion Data Statistics According to GB/T 151[3], the calculation formula of expansion degree is k = (d2di-b)/(2δ) × 100%, where d2 is the inner diameter of the heat exchange tube after expansion, di is the inner diameter of the heat exchange tube before expansion, b is the radial clearance between the heat exchange tube and the tube sheet hole, and δ is the wall thickness of the heat exchange tube. 247 holes were used in the expansion test, and the average expansion was 2.45%. According to GB/T 4882-2001[4]: the statistical processing and interpretation of data, the lower limit of 95% confidence interval was 2.23%, and the lower limit of 99% confidence interval was 2.20%.In order to investigate the effect of expansion parameters on the process stability, the expansion pressure of 2700 bar, 2800 bar and 2900 bar was used. The positioning expansion torque was 4.5 N·m, and the holding time was 5 s and 7 s respectively. The statistical results of expansion are shown in Table 2. The results show that the average expansion degree is more than 2% when the expansion pressure is 2800 ± 100 bar and the holding time is 5–7 s.

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Expansion pressure (bar)

Holding time (s)

No. of holes

Average degree of expansion (%)

2700

5

40

2.10

2800

5

42

2.24

2900

5

41

2.03

2700

7

41

2.67

2800

7

42

3.13

2900

7

41

2.50

4.1.2 Pull-Out Force Data Statistics The pull-out force test was carried out on all expansion tube holes. The average pull-out force of 247 tube holes was 13620.6 N, and the minimum value was 9409 N. According to the calculation method of statistical processing and interpretation of data in GB/T 4882-2001, the lower limit of 95% confidence interval was 13055 N, and the lower limit of 99% confidence interval was 13014 N. The test results are shown in Table 3. Table 3. Statistical results of expansion pull-out force Expansion pressure (bar)

Holding time (s)

No. of holes

Average of expansion pull-out force (%)

2700

5

40

2.10

2800

5

42

2.24

2900

5

41

2.03

2700

7

41

2.67

2800

7

42

3.13

2900

7

41

2.50

4.2 Analysis of Strength-Welding Test Results 4.2.1 Welding Throat Size Data Statistics There are three plates for welding test and at least 30 tube holes shall be used for metallographic inspection of each test plate according to ASME section IX QW193.1.3[5]. The selected inspection area shall be cut into 4 sections to obtain the welding throat size and check whether there is root crack. Figure 5 shows a typical metallographic photograph of a single tube joint with a magnification of 10. No root crack was found in all tube joint sections. Table 4 shows the statistical data of weld throat size. The overall average value of welding throat of all tube joints is 1.299 mm, and the welding throat size of single

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tube joint is greater than 1.20 mm. The test results show that when the welding current is 85–105 A, the welding voltage is 10 V and the welding parameter is 75 mm/min, the throat size of the tube joint meets the requirements.

(a)3 o'clock direction

(b) 6 o'clock direction

Fig. 5. The throat size of the tube joint

Table 4. Statistical data of weld throat size Test plate No.

Number of holes

Average size

Lower limit of 99% confidence interval

IHX-H1

33

1.296

1.287

IHX-H2

36

1.293

1.283

39

1.307

1.296

108

1.299

1.293

IHX-H3 Total

4.2.2 Pull-Out Force Data Statistics 105 holes of three test plates were selected for pull-out force test, and all the fracture positions appeared on the heat exchange tube near the weld joint. The lower limit of 99% confidence interval was 38270 N, and the upper limit was 38824 N. The test results are shown in Table 5. The test results show that when the welding current is 85–105 A, the welding voltage is 10 V and the welding parameter is 75 mm/min, the pull-out force of the tube joint meets the requirements. 4.2.3 Non-destructive Test Result Helium leak detection, liquid penetrant inspection and radiographic inspection were carried out for all tube-tubesheet welding joints according to ASME BPVC-III division 5. All test results meet the ASME requirements.

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Test plate No.

Number of holes

Welding current

Welding voltage

Average of welding pull-out force

IHX-H1

33

70–78 A

12 V

37455

IHX-H2

36

78–86 A

12 V

38644

36

86–95 A

12 V

39452

105

70–78 A

12 V

38547

IHX-H3 Total

5 Conclusions Based on the reliability test of expansion and welding process of a large number of samples(355 pipe holes in total), the expansion pressure 2800 ± 100 bar and the holding time 5–7 s are selected as the expansion processing parameters of IHX. In the meantime, the welding current 85–105 A, the welding voltage 10 V and the welding speed 75 mm/min are selected as the welding processing parameters of IHX. The results show that the expansion and welding process parameters drawn up in the process reliability test of intermediate heat exchanger tube joints are reasonable, the indexes of tube joints meet the predetermined requirements, and the quality of tube joints is stable, which can provide guidance for mass production of products.

References 1. Qian, C.D., Yu, H., et al.: Reliability study of the hydraulically expanded tube-to-tubesheet joint. J. Pressure Vessel Technol. 128, 408–413 (2006) 2. Tian, X., Huang, X., Fu, Z.: Determination of expanding pressure of hydraulically expanded tube-to-tubesheet joint. Adv. Mater. Res. Korea (2010) 3. GB/T151-2014 Heat Exchangers. Standards Press of China, Beijing 4. GB/T4882-2001 Statistical Interpretation of Data-Normality Tests. Standards Press of China, Beijing 5. ASME BPVC III-2015

PIRT Research on RCCA Ejection Accident Zang Liye1,2(B) , Zhang Guanzhong3 , Li Qiang3 , Zhao Xiaohan2 , Wang Xiong3 , and Ouyang Yong3 1 Nuclear Safety Analysis Engineering, China Nuclear Power Technology Research Institute

Co., Ltd., Shanghai, China [email protected] 2 CNPRI, Shanghai, China 3 CNPRI, Shenzhen, Guangdong, China

Abstract. Phenomenon Identification and Ranking Table (PIRT) mainly refers to: for the specified accident type of the specified power plant, analyze and identify the phenomena and processes existing in this accident, and sort them according to their impact on the main safety criteria to form PIRT. Many PIRT studies have been performed for LOCA, while there is few PIRT studies for Reactivity Initiated Accident (RIA). Thus, PIRT for RCCA ejection accident (a quintessential RIA) of a three-loop PWR is developed in this paper. Firstly, the safety criterion of RCCA ejection accident is obtained by the technical analysis of regulations and standards. Then, the main phenomena and key parameters of the RCCA ejection accident are obtained by analyzing the process of transient. Finally, the impacts of each key parameter on the calculation results are quantitatively evaluated by single variable sensitivity analysis, and the importance of each parameter is judged. Based on the results, the PIRT of RCCA ejection accident is obtained. In addition, the PIRT is applied to DNB ALARP analysis in RCCA ejection accident, of which the evaluation result is successfully used to confirm the validity of ranking in the PIRT. This study provides a basis for the methodological optimization and conservative demonstration of subsequent RCCA ejection accident. Keywords: PIRT · Single variable sensitivity analysis · DNB

1 Introduction In 1988, U.S.NRC revised 10CFR50.46 to allow the use of realistic evaluation models, which required quantification of the uncertainty of the calculation results, known as the BEPU (Best estimate plus uncertainty) method. In order to adapt to and support the revised regulatory requirements, NRC has led the development of the quantitative analysis method of uncertainty, known as the CSAU (Code Scaling, Applicability, and Uncertainty) method. In CSAU, the concept of Phenomena Identification and Ranking Table (PIRT) is firstly put forward. PIRT is the basis of CSAU method, and the formulation of PIRT Table is a key step of CSAU [1]. PIRT is mainly aimed at the designated accident type of the designated power plant, analyzing and identifying the phenomena and processes existing in the accident, sorting © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 181–190, 2023. https://doi.org/10.1007/978-981-19-8780-9_18

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according to their impacts on the main safety criteria. Sensitivity analysis is often used to quantitatively evaluate the impacts of input parameters. PIRT provides the basis for the subsequent analysis process, which can be used to determine the applicability of the code, establish the evaluation and verification matrix, and quantify the uncertainty range of parameters related to important phenomena [2–5]. Many PIRT studies have been performed for loss of coolant accident (LOCA), while there is few PIRT studies on the reactivity-initiated accident (RIA). Thus, this study takes the RCCA ejection accident of a Pressurized Water Reactor (PWR) nuclear power plant as the research object, quantitatively evaluates the influence degree of each input parameter on the calculated results through single variable sensitivity analysis, then determines the importance degree of each parameter, providing a basis for the subsequent methodology optimization and conservative demonstration of RCCA ejection accident. The analysis process is as follows: 1. Determine the target parameters of RCCA ejection accident, namely find the key parameters which affect acceptance criteria threshold; 2. Identify the main phenomena and key parameters of RCCA ejection accident; 3. Perform sensitivity analysis to confirm the selected key parameters; 4. Make a conclusion.

2 Description and Acceptance Criteria for RCCA Ejection Accident The rod cluster control assembly (RCCA) ejection accident is defined as the scenario where an RCCA is vertically ejected from the reactor core due to the mechanical failure of the RCCA drive mechanism housing at the top of the pressure vessel. It leads to a rapid reactivity transient by inducing an uncontrolled positive reactivity, followed by a nuclear power increase and a distortion of power distribution. The magnitude of the inserted reactivity is related to the inserted depth of the ejected RCCA. The nuclear power increase is limited by reactivity feedback effects, such as the Doppler feedback effect caused by the fuel temperature increase. Nuclear power increase and distortion of power distribution may result in Departure from Nucleate Boiling (DNB) among the adjacent fuel channels, and the potential failure for the fuel cladding. Simultaneously, the increase of core power in transient will lead to the increase of reactor coolant system (RCP) pressure, which has the risk of over pressure. RCCA ejection accident is classified as a DBC-4 event. The fuel integrity, integrity of RCP and radiological consequence might be challenged in this fault. In terms of fuel integrity and integrity of RCP, the specific criteria are considered in this study as listed: 1. The enthalpy of fuel pellet must be less than design limit (942 J/g for non-irradiated fuel and 837 J/g for irradiated fuel); 2. The fuel cladding temperature must not be greater than design limit (1482 °C); 3. The amount of fuel rods experiencing DNB must not exceed design limit (10%); 4. The melting fuel pellet amount at the hot spot must remain below the design limit (10%);

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5. The coolant pressure peak must be less than design limit; 6. For the high burnup assemblies (Average burnup ≥ 47000 MWd/tU), all the above acceptance criteria shall be still valid. In addition, it also needs to meet the following criteria: (a) The fuel enthalpy rise must be less than design limit(239J/g): (b) The fuel cladding temperature must not be greater than design limit (700°C).

3 Typical Phenomena and Key Parameters of RCCA Ejection Accident After the RCCA is ejected vertically from the reactor core, an uncontrolled positive reactivity insertion will be induced in the core, leading to a nuclear power excursion and a power distribution distortion. During the initial stage, the nuclear power increase will be limited by reactivity feedback effects, i.e. the feedback effect. Then, the reactor trip is triggered by the “High neutron flux (power range)” signal or “High positive neutron flux rate” signal, causing a rapid decrease in nuclear power. The reactor core controlled state is reached by the actuation of the reactor protection system. Compared with LOCA, the fluid in the loop during RCCA ejection accident is singlephase fluid, and the accident phenomenon mainly involves various phenomena in the reactor core, including reactivity feedback, neutron dynamics in the reactor core, and shutdown. 1. Reactivity feedback Including the reactivity changes caused by fuel temperature, moderator temperature, etc., using different reactivity feedback coefficients will affect the core power, and then have a great influence on the results. 2. Neutron dynamics parameters of the reactor core Including effective delayed neutron fraction and prompt neutron lifetime. Different core neutron dynamics parameters will affect the reactor cycle and the increase rate of reactor power, and then affect the calculation results. 3. Emergency shutdown Shutdown is an important means to alleviate the consequences of RCCA ejection accident. The time of shutdown determines the process of core power change. Based on the core state parameters that affect the analysis of RCCA ejection accident, 6 key parameters are identified, including: (a) Ejected RCCA rod worth (b) Moderator temperature coefficient (c) Prompt neutron lifetime

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(d) Doppler temperature coefficient (e) Effective delayed neutron fraction (f) Emergency shutdown time

4 Single Variable Sensitivity Analysis 4.1 Analysis Methods and Cases Based on the analysis methodology of RCCA ejection accident, nuclear data calculation, core power transient simulation, DNB analysis, fuel thermal transient analysis and reactor coolant system pressure transient analysis were carried out. Single variable sensitivity analysis is used to quantitatively evaluate the influence of each input parameter on the calculation results. A total of 8 cases were calculated, classified as follows: Case 1: the base conservative case. Considering the most penalizing transient critical case (EOL-20%FP), and the uncertainty and margin of key input parameters are considered on the basis of the calculated envelope values. Case 2: On the basis of case1, the uncertainty and margin of key parameter 1 (ejected RCCA rod worth) are released, that is, only the calculated envelope value is considered, and other parameters are consistent with case 1. By analogy, key parameter values of each case are shown in Table 1. 4.2 Results and Discussion Table 2 shows the analysis results of each case, including DNB results, fuel thermal transient results(hot spot results and high burnup results) and overpressure transient results. It indicates that the results of target parameters in each case are all within the limit value. In case 1, due to the conservative consideration of the uncertainty and margin of each key parameter, the comprehensive results are the worst and can envelop other cases. Sensitivity analysis was used in this analysis to quantitatively judge the relative importance of the phenomenon, and the sensitivity factors were defined as follows: Si =

∂y ∂xi y xi / / ≈ y xi y xi

(1)

xi : Key parameters corresponding to important models; xi : The offset change of key parameters; yi : The target parameter (PCT or Fraction of fuel rod experiencing DNB, etc.); yi : The change of target parameter. Figures 1 and 2 show the influence of key parameters on fraction of fuel rod experiencing DNB, peaking cladding temperature, peaking fuel pellet temperature, maximum fuel pellet enthalpy, peaking cladding temperature (high burnup analysis), maximum fuel pellet enthalpy rise (high burnup analysis) and pressure peak value. The change rate of target parameters (y/y) in each case is shown in Fig. 1, and the sensitivity factor Si in each case is shown in Fig. 2. The conclusions are as follows:

Best-estimated value plus uncertainty and margin

Best-estimated value

---

---

---

---

---

Case 1 (base case)

Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

---

---

---

Best-estimated value

---

---

Best-estimated value plus uncertainty and margin

Prompt neutron lifetime

3

Note “---” indicates that the value of this key parameter is consistent with case 1

---

---

---

---

Best-estimated value

---

Best-estimated value plus uncertainty and margin

Ejected RCCA rod Moderator worth temperature coefficient

Key parameter

2

1

Key parameter No.

---

---

Best-estimated value

---

---

---

Best-estimated value plus uncertainty and margin

Doppler temperature coefficient

4

Table 1. Key parameters and analysis cases

---

Best-estimated value

---

---

---

---

Best-estimated value plus uncertainty and margin

Effective delayed neutron fraction

5

Best-estimated value

---

---

---

---

---

Best-estimated value plus uncertainty and margin

Emergency shutdown time

6

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Case Fraction of Peaking Peaking Maximum Peaking fuel rod cladding fuel pellet fuel pellet cladding experiencing temperature temperature enthalpy temperature DNB (high burnup analysis)

Maximum Pressure fuel pellet peak enthalpy value rise (high burnup analysis)

%

°C

°C

J/g

°C

J/g

MPa

1

5.17

997

2426

522.61

643

136.81

16.02

2

4.95

947

2339

492.73

612

118.28

15.97

3

4.93

976

2396

511.03

630

129.49

16.01

4

5.19

996

2424

522.22

642

136.72

16.02

5

5.07

959

2364

500.53

619

122.70

15.99

6

5.12

971

2382

507.54

627

127.76

16.00

7

5.17

996

2425

522.19

642

120.74

16.01

1. Sensitivity of target parameters: The change of key parameters has the most obvious effect on the fuel enthalpy rise (high burnup analysis), because: This study is based on the EOL 20%FP of the RCCA ejection accident, and the low temperature failure mode of pellet cladding mechanical interaction (PCMI) is more likely to occur in the low power level and high burnup fuel rods. When the key parameters change, the effect on pressure is least obvious. The reasons are as follows: At the beginning of the RCCA ejection accident, the Doppler feedback effect can restrain the surge of the core power. Then the emergency shutdown of the reactor is triggered by the nuclear protection signal, the nuclear power and thermal power decrease rapidly. Since the high power maintains in extremely short duration, the risk of primary over pressure is low. 2. Identification and ranking of key parameters: The ejected RCCA rod worth is ranked first, because the ejected RCCA rod worth directly affects the introduction of the positive reactivity, and plays a decisive role in the height and width of the nuclear power pulse. The secondary important parameters are the Doppler temperature coefficient and effective delayed neutron fraction. The reasons are as follows: The Doppler temperature coefficient is a transient temperature coefficient, which responds quickly to power changes. The Doppler feedback will make the power flat, thus limiting the deposition of energy. The effective delayed neutron fraction will affect the neutron cycle rate and thus the rate of power growth. Reactor control actually takes advantage of delayed neutrons.

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Fig. 1. The influence of key parameters on the change rate of target parameters

Fig. 2. The influence of key parameters on sensitivity factors

Although its fraction is small, it is not negligible in the study of reactor transient process and reactor control. The third important parameter is the moderator temperature coefficient. After the RCCA ejection accident, the core power surge will also be inhibited by the moderator temperature feedback. Although the moderator temperature change lags behind the fuel

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temperature change for a period of time, the influence of moderator temperature feedback cannot be ignored. Compared with the above key parameters, the impact of emergency shutdown time and prompt neutron life on RCCA ejection accident is relatively slight. The rod drop time of emergency shutdown determines the introduction speed of negative reactivity. However, due to the rapid inhibition effect of reactive feedback at the early stage of the accident, the peak value of core power (i.e. the turning point of power change) usually appears before the rod drop of emergency shutdown, so the rod drop time of emergency shutdown has no obvious influence on the target parameters.

5 Application of RCCA Ejection Accident PIRT in DNB ALARP Analysis According to the results in Sect. 4, as far as DNB analysis is concerned, core related parameters--Ejected RCCA rod worth, Doppler temperature coefficient, delayed neutron fraction and moderator temperature coefficient have obvious effects on DNB fraction results, while prompt neutron life has no significant effects on DNB fraction results. Based on this conclusion, DNB ALARP analysis is carried out for RCCA ejection accidents of PWR nuclear power plants with different items of the same reactor type. In the analysis of RCCA ejection accident, the ejected RCCA rod worth is a key parameter that affects the results of DNB fraction regardless of the worst condition or not. In the analysis, the ejected RCCA rod worth covering all fuel cycles and RCCA configuration is used. If the ejected RCCA rod worth is reduced, the occurrence of transient criticality can be avoided, thus reducing the DNB fraction. As shown in Table 3, when the ejected RCCA rod worth decreases to 64% of the base case, the fraction of fuel rod experiencing DNB decreases from 8.0 to 5.0%. When the ejected RCCA rod worth decreases to 57% of the base case, the fraction of fuel rod experiencing DNB decreases from 8.0 to 0.9%. At the initial stage of RCCA ejection accident, the power surge of core can be restrained by Doppler feedback effect. The minimum absolute value of Doppler temperature coefficient was considered conservatively in the analysis. Increasing the minimum absolute value of Doppler temperature coefficient can reduce the DNB fraction. As shown in Table 3, when the minimum absolute value of the Doppler temperature coefficient increases to 2.56 times that of the base case, the DNB fraction decreases from 8.0 to 7.3%. The minimum value of effective delayed neutron share is considered conservatively in the analysis of rod accident. As shown in Table 3, when the effective delayed neutron fraction increases to 1.59 times that of the basic case, the DNB fraction decreases from 8.0 to 2.0%. The minimum absolute value of moderator temperature coefficient is considered conservatively in RCCA ejection accident analysis. As shown in Table 3, when the minimum absolute value of moderator temperature coefficient increases to 1.15 times that of the basic case, the DNB fraction remains at the level of 8.0%. Due to the small increase, the DNB fraction does not decrease effectively.

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The minimum absolute value of prompt neutron lifetime is taken in the analysis of RCCA ejection accident. As shown in Table 3, when the minimum absolute value of prompt lifetime increases to 2.19 times that of the base case, the DNB fraction remains at the level of 8.0%, indicating that the prompt neutron life has no significant influence on the DNB fraction result. In conclusion, the key influencing factors and relevant improvement direction of DNB ALARP analysis of RCCA ejection accident is determined, and the analysis results are consistent with the PRIT ranking law of in Sect. 4. All of these improvements can reduce the fraction of fuel rod experiencing DNB. However, these improvements also have a wide range of adverse effects, such as operational flexibility, refueling design and economy. Therefore, the advantages and disadvantages should be weighed and relevant improvement schemes should be adopted selectively. Table 3. Results of DNB ALARP analysis Analysis case

Fraction of fuel rod experiencing DNB

Base case: The most penalizing case for DNB analysis

8.0%

Sensitivity case 1: Same as base case, but ejected RCCA rod worth is 64% of the base case

5.0%

Sensitivity case 2: Same as base case, but ejected RCCA rod worth is 57% of the base case

0.9%

Sensitivity case 3: Same as base case, but Doppler 7.3% temperature coefficient is 2.56 times of the base case Sensitivity case 4: Same as base case, but delayed neutron fraction is 1.59 times of the base case

2.0%

Sensitivity case 5: Same as base case, but moderator 8.0% temperature coefficient is 1.15 times of the base case Sensitivity case 6: Same as base case, but prompt neutron lifetime is 2.59 times of the base case

8.0%

6 Conclusion The influence degree of each key parameter is quantitatively evaluated by single variable sensitivity analysis, and the importance of each parameter is judged. Based on the results, a PIRT of RCCA ejection accident is obtained. In addition, the PIRT is applied to DNB ALARP analysis in RCCA ejection accident, of which the evaluation result is successfully used to confirm the validity of ranking in the PIRT. This study provides a basis for the methodological optimization and conservative demonstration of subsequent RCCA ejection accident.

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References 1. NUREGCR-5249, Rev. No. 4, Quantifying Reactor Safety Margins Application of CSAU (1989) 2. Greene, K.R., Fletcher, C.D., et al.: Utilizing elements of the CSAU PIRT to qualify a PWR Non-LOCA transients system code 3. Wilson, G.E., Boyack, B.E.: The role of the PIRT process in experiments, code development and code applications associated with reactor safety analysis. Nucl. Eng. Des. 186, 23–37 (1998) 4. Miglierini, B., Kozlowski, T.: Investigation of VVER-1000 rod ejection accident according to the phenomenon identification and ranking tables for PWR. Prog. Nucl. Energy 108, 438–444 (2018) 5. Boyack, B.E., et al.: Phenomena Identification and Ranking Tables (PIRTs) for Rod Ejection Accidents in Pressurized Water Reactors Containing High Burnup Fuel. US NRC document. NUREG/CR-6742 (2001)

Research on Passive Single Failure Conditions of LB-lOCA Bao Guogang1(B) , Wang Weiwei2 , Liang Ren3 , Lin Zhikang3 , Wang Xiong3 , and Ouyang Yong3 1 Nuclear Safety Analysis Engineering, China Nuclear Power Technology Research Institute

Co., Ltd, Shanghai, China [email protected] 2 CNPRI, Shanghai, China 3 CNPRI, Shenzhen, Guangdong, China

Abstract. In the accident analysis of PWR nuclear power plants, it is usually assumed that a single passive failure occurs 24 h after the initial event. However, according to the feedback from the operation experience of nuclear power plants, the single failure of some passive equipment may occur at any time of operation, which challenges the current hypothesis of accident analysis. In the LBLOCA analysis, the situation that passive single failure occurs within 24 h of the initial event was conservatively considered for the first time. Firstly, the active and passive single failure conditions that may appear in the analysis of LBLOCA are comprehensively combed, and conservative analysis assumptions are obtained. Then, the LBLOCA analysis is performed using LOCUST-K code which is selfdeveloped by CGN. Considering that a passive single failure occurs within 24 h of the initial event, the most conservative single failure assumption in LBLOCA analysis was that only one train of safety injection system is available. The results of LBLOCA analysis show that even if a passive single failure occurs within 24 h of the initial event, safety protection system still has enough capacity to ensure the safety of plant and has enough safety margins. Keywords: Single failure criteria · Passive single failure · LBLOCA

1 Introduction The single failure criterion should be considered in the system availability analysis of the deterministic safety analysis. According to single failure criterion, one single failure should be assumed for the safety system required to mitigate the initiating event, in addition to the initiating failure and any secondary failure. According to the selected acceptance criteria, assume a single system or component failure causing the most challenge to the safety system [1]. A single failure is a failure that results in the loss of capability of a system or component to perform its intended safety function(s) and any consequential failure(s) that result from it. The single failure criterion is a criterion (or requirement) applied to a system such that it must be capable of performing its task in the presence of any single failure [2]. A single failure is usually divided into an active single © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 191–199, 2023. https://doi.org/10.1007/978-981-19-8780-9_19

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failure and a passive single failure. In fact, the internationally common single failure criterion requires that for Design Basis Accidents (DBAs), a passive failure is only a single failure considered in the long-term stage (24 h after the initiating event) [3]. Thus, there is the risk of a single failure considered in the nuclear power plant deterministic safety analysis that does not meet the “most challenge” assumption. Therefore, in the present study, firstly the international general passive single fault criterion is sorted out as comprehensively as possible, and the necessity to consider the passive single fault within 24 h of the initiating event was proposed (Sect. 2). Subsequently, the typical Large Break Loss of Coolant Accident (LBLOCA) was selected as the research object to conduct a single fault analysis of the safety system required to alleviate the LBLOCA, and to obtain the most challenging single fault hypothesis (Sect. 3).Then, the typical LBLOCA accident calculation is conducted to demonstrate the impact of the most challenging single failure hypothesis on the accident consequences (Sect. 4). Finally, the conclusions are given.

2 Overview of Passive Single Failure Criterion 2.1 Passive Single Failure Criterion in China 2.1.1 HAF102-2016 In China, Nuclear Power Plant Design Safety Regulation law HAF102-2016 [4] is applied. The criteria requirements for a single failure are consistent with the internationally accepted single failure criteria, mainly including: (1) A single failure criterion must be applied to each safety combination included in the nuclear power plant design. (2) When applying a single failure criterion to a combination or system, spurious action must be regarded as a mode of failure. (3) Cases not complying with a single failure criterion must be extremely individual and must be clearly justified in the safety analysis. (4) The design must be due to the failure of the inactive component unless it can be confirmed in a single failure analysis with high confidence that the failure of the component is highly unlikely and keeps its function free from the assumed initiating event. 2.1.2 GB/t13626-2021 Application of the single-failure criterion for nuclear power plant safety systems (GB/T13626-2021) [5] specified single failure criterion. The safety systems shall perform all required safety functions for a design basis event in the presence of the following: (1) Any single detectable failure within the safety systems concurrent with all identifiable but non detectable failures. (2) All failures caused by the single failure.

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(3) All failures and spurious system actions that cause or are caused by the design basis event requiring the safety function. The single failure could occur prior to, or at any time during, the design basis event for which the safety system is required to function. This regulations are same as IEEE Standard for Application of the Single-Failure Criterion to Nuclear Power Generating Station Safety Systems [6]. 2.1.3 NB/T 20402-2017RK Single Failure Criteria for Pressured Water Reactor Fluid Systems Important to Safety (NB/T 20402-2017RK) [7] specified single failure criteria are: In the short stage of the accident, only active single failure is considered; In the long stage, the single failure can be active or passive failure; if the mode of passive failure is leakage, the analysis of the actual passive failure mechanism in the system is needed, and the leakage flow rate also needs to be determined. 2.2 Passive Single Failure Criterion in United State Utility Requirement Document (URD) [8] Volume II requires consideration of passive single failures, including valve leaks, seals, flange leaks, etc. The appendix to Volume II, Chap. 5 also states that the single failure assumption of passive devices in fluid systems is long-term and consistent with current regulatory practice. 2.3 Passive Single Failure Criterion in Europe 2.3.1 United Kingdom Office for Nuclear Regulation (ONR) in United Kingdom issues Safety Assessment Principles (SAPs) for nuclear facilities [9]. SAPs EDR.4 Required that during any normally permissible state of plant availability, no single random failure, assumed to occur anywhere within the systems provided to secure a safety function, should prevent the performance of that safety function. (1) Consequential failures resulting from the assumed single failure should be considered as an integral part of the single failure. (2) A system that is the principal means of fulfilling a Category A safety function should, other than in exceptional circumstances, always be designed to meet the single failure criterion. However, other systems which make a contribution to fulfilling the same safety function, but are independent of the principal system, do not necessarily need to meet the single failure criterion. SAPs FA.6 specified the single failure applications in design basis analysis, that is, each design basis fault sequence should include single failures in the safety measures in accordance with the single failure criterion (Principle EDR.4). In addition to SAPs and their technical evaluation guidelines, there are relevant good practice requirements in the United Kingdom. The Size-well B plant design considers

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a short-stage passive single failure and represents good practice in PWR technology in the United Kingdom. Therefore, the ONR proposed in the Generic Design Assessment (GDA) review that short-term passive single failures must be considered. 2.3.2 Western European Western European issued Report about WENRA Safety Reference Levels for Existing Reactors (2020) [10] requires single failure as follows: The worst single failure shall be assumed in the analyses of design basis events. However, it is not necessary to assume the failure of a passive component, provided it is justified that a failure of that component is very unlikely and its function remains unaffected by the postulated initiating events. 2.3.3 European Utility Requirements European Utility Requirements (EUR) [11] Volume II Chap. 1 specified passive single failure as following: “In a single failure analysis, the failure of passive parts is not assumed if the design, manufacture, installation, inspection and maintenance of this part reaches a high level of quality. However, assuming that the passive component does not fail, consider the length of time required for the component after the initiating event.” Further stated in the note, “passive leakage is not considered for the first 24 h after the initiating event”. 2.4 Passive Single Failure Criterion in IAEA International Atomic Energy Agency issued Safety of Nuclear Power Plants: Design [2], Specific Safety Requirements about single failure criterion. (1) Spurious action shall be considered to be one mode of failure when applying the single failure criterion17 to a safety group or safety system. (2) The design shall take due account of the failure of a passive component, unless it has been justified in the single failure analysis with a high level of confidence that a failure of that component is very unlikely and that its function would remain unaffected by the postulated initiating event.

3 Analysis of Mode Passive Single Failure 3.1 Leakage, Blockage and Bypass According to engineering experience, the common passive failure mode in the safety system is mainly leakage, blockage and bypass. Bypass and blockage will reduce the flow of safety systems, and their limiting scenario is complete loss of flow in one train of safety systems. Leakage not only causes lower flow, but also the available fluid capacity. And the limiting scenario is complete loss of one train of safety systems and their capacity. If the leaked liquid is under the high temperature and high pressure state, the leakage will also cause other safety systems to work under harsh environment, or even appear secondary failure. Therefore, as mentioned in Sect. 2, if the mode of passive

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failure is leakage, the analysis of the actual passive failure mechanism in the system is needed, and the leakage flow rate also needs to be determined. However, in Sect. 2, the consideration of leakage is generally conditional: (1) 24 h after the initiating event. (2) Leakage flow rate does not exceed 200 L/min. In 2021, two reactor safety systems at the Civaux nuclear power plant cracked. Then similar problems are found and half of the reactors are suspended in France. Although the pipe cracking problem was found in the inspection, which meets the requirements of “In a single failure analysis, the failure of passive parts is not assumed if the design, manufacture, installation, inspection and maintenance of this part reaches a high level of quality” in EUR, which makes the requirement of “high level of quality” not easy to be met in each aspect. Therefore, it is necessary to consider the most challenging situation of the passive failure of the safety system from the perspective of the deterministic safety analysis. For example, the limit scenario of leakage due to pipe cracking (complete leakage), one train of safety system and its capacity are completely lost, should be assumed as one kind of single failure in design basis accident analysis. 3.2 Check Valve Failure In the safety analysis of nuclear power plants, a check valve is usually treated as a passive and highly reliable equipment whose failure is not considered. Thus its design and safety assessment do not involve the application of a single failure criterion [12]. However, with the accumulation of the actual operation experience of nuclear power plants, more and more on-site feedback information shows that the check valve may fail and the consequences may be very serious. Several practical cases are given in Reference 6, and subsequent studies have also proved that a failure of the check valve really exists [13]. The GDA review highly concerned about the check valve opening failure, and explicitly requires that the check valve failure should be considered in the short-term analysis of the accident. The U.S. Nuclear Regulatory Commission (NRC) also concerns the check valve failure in 10 CFR Part 50. Therefore, it is necessary to consider the most challenging situation of the check valve failure in the safety system in deterministic safety analysis. For example, failure to open the check valve may cause one train of safety system to lost completely which should be assumed as one kind of single failure in DBA analysis.

4 Passive Single Failure Criterion Applied to LBLOCA LBLOCA is the most representative accident condition in PWR nuclear power plants. 4.1 Analysis Method LOCUST-K software was independently developed by CGN and was used to analyze LBLOCA. The analysis steps are as follows:

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(1) Select the analysis object: LBLOCA. This step aim to obtain the short-term LBLOCA limit condition without considering the passive single failure. (2) Determine the safety systems used in LBLOCA. Nuclear power plants usually have special safety systems to alleviate the consequences of LBLOCA, such as safety injection system, emergency feed water system. (3) Analysis the passive single failure of safety systems determined in step 2, to obtain the limiting passive single failure assumption. (4) Analysis of LBLOCA. Based on the limiting passive single failure assumption, update analysis LBLOCA. (5) The effect of the passive single failure on the accident consequences is demonstrated by comparing with the short-term LBLOCA limiting results without considering the passive single failure (step 1). Other assumptions remained unchanged except for the single failure assumption, including initial conditions, safety functions, core power distribution and other conservative selections which may lead to higher cladding peak temperature setting. 4.2 Limiting Passive Single Failure Assumption The use of the single failure principle in a typical LBLOCA accident analysis is consistent with international practice which only considers a passive single fault in the long-term stage (24 h after the initiating event).Therefore, the loss of one train of emergency diesel generators is usually selected as the worst single failure condition. In this case, the safety injection pump in the safety injection system and the feed water pump in the emergency system will fail simultaneously.

Fig. 1. Analysis of passive single failure in safety injection system

The most challenging passive single failure scenario, namely, complete loss of one train of injection system due to leakage or check valve open failure (Fig. 1) or complete loss of one train of emergency feed water system (Fig. 2), which are discussed in Sect. 3.

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Fig. 2. Analysis of passive single failure in emergency feed water system

Safety injection system consists of the following three parts: (1) Medium Head Safety Injection(MHSI), (2) Low Head Safety Injection(LHSI), (3) Accumulator (ACC). The failure of one train safety injection system after considering the passive single failure refers to the complete loss of the safety injection flow due to the limiting condition of pipe leakage or check valve open failure, including the active flow from the injection pump and the passive flow from accumulator (ACC). In contrast, the failure of the safety injection system without considering the passive single failure refers to the safety injection flow decrease due to the failure of the safety injection pump, however the safety injection flow from ACC is still effective (Table 1). Table 1. Comparison of the single failure conditions MHSI

LHSI

ACC

ASG

Base case

Invalid

Invalid

Valid

Invalid

PSF case

Invalid

Invalid

Invalid

Valid

4.3 Analysis of LBLOCA The results of the peak cladding temperature are shown in Fig. 3. The results show that the Peak Cladding Temperature (PCT) of LBLOCA is 1101.7 °C after considering active single failure (Base Case), the same as considering passive single failure (PSF Case). However, during 25–250 s, the PCT in PSF case keep higher than that of Base Case, and the PCT was higher than 700 °C. That means the equivalent cladding oxidation (ECR) in PSF case higher than that in Base case. Further calculations indicate that the ECR in PSF case was lower than 10%. This shows that, the internationally common single failure criteria requirements does not guarantee that the results of the LBLOCA accident analysis are more conservative.

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Fig. 3. Peak cladding temperature in LBLOCA

That is, the internationally common single failure criterion is not a single failure of the system or component that poses the most challenge to the safety system. Therefore, from the perspective of the deterministic safety analysis, it is necessary to consider the passive single failure within 24 h of the initial event. The results also show that, even considering the passive single failure within 24 h of the LBLOCA accident, the cladding peak temperature is still lower than 1204 °C, and equivalent cladding oxidation still lower than 17%, required by the acceptance criteria, with a relatively high margin. Thus, it is proved that even considering the passive single fault within 24 h of the LBLOCA accident, the safety system has enough capacity to ensure the safety design goal.

5 Conclusion This study begins with a review of the internationally universal passive single failure criterion. Then the passive failure mode and the actual operation experience of the nuclear power plant are proposed. For typical LBLOCA accidents, the impact of passive single failure is considered. The results show that even if the passive single failure occurs within 24 h of the initial event, the safety protection system of nuclear power plant still has enough capacity to ensure the safety of the power plant, and enough safety margin exists. Meanwhile, the passive single failure within 24 h of the initial event is recommended to consider in deterministic safety analysis.

References 1. National Nuclear Safety Administration.: Deterministic Safety Analysis of Nuclear Power Plant. National Nuclear Safety Administration, Beijing (2021) 2. International Atomic Energy Agency.: Safety of Nuclear Power Plants: Design. IAEA (2016) 3. Hua, Z., Shuhong, W.: Consideration and safety review of passive single failure in UK EPR analysis. Nuclear Power. 6, 1–4 (2019) 4. National Nuclear Safety Administration.: Nuclear Power Plant Design Safety Regulations. National Nuclear Safety Administration, Beijing (2016)

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5. National Standardization Administration.: Application of the Single-Failure Criterion for Nuclear Power Plant Safety Systems. National Standardization Administration, Beijing (2021) 6. IEEE Standard Association.: IEEE Standard for Application of the Single-Failure Criterion to Nuclear Power Generating Station Safety Systems. IEEE Std 379™-2014 (Revision of IEEE Std 379-2000) 7. National Energy Administration.: Single Failure Criteria for Pressured Water Reactor Fluid Systems Important to Safety. National Nuclear Safety Administration, Beijing (2017) 8. EPRI.: Advance Light Water Reactor Utility Requirements Documents. EPRI, California (2008) 9. Office for Nuclear Regulation.: Safety Assessment Principles for Nuclear Facilities, 2014 Edition, Revision1 (2020) 10. WENRA Safety Reference Levels for Existing Reactors. WENRA (2020) 11. EUR.: European Utility Requirements for LWR Nuclear Power Plants. EUR, Villeurbanne (2012) 12. Chang, M., Peng, X., et al.: Application of single failure criteria in safety analysis of check valves in NPPs. Nuclear Safety 13(2), 35–39 (2014) 13. Wang, H., Yin, K., Zhang, W.: Failure analysis of check valve in safety injection system of a nuclear power plant. Nuclear Power Eng. 38(3), 141–144 (2017)

Analysis of Liquid Metal Cooled Reactor Safety Analysis Software FRTAC Applied To Pipeline Breach Ejection Experiment Yonggang Cao, Wenjun Hu, Pengrui Qiao(B) , and Lei Zhao China Institute of Atomic Energy, Fangshan, Beijing, China

Abstract. In order to verify the ability of the liquid metal cooled reactor safety analysis code FRTAC to simulate the leakage of pipeline bursts, so as to analyze the three-circuit burst leakage accidents of sodium-cooled fast reactors. In this study, FRTAC code was used to carry out modeling calculations for the EDWARDS’ pipeline burst injection experiment (high temperature and high-pressure water injection) and the Marviken JIT11 experiment (high temperature and high-pressure water vapor injection), and compared with the experimental results and the calculation results of the RELAP5 program. The analysis results show that the changes of flow rate and pressure simulated by FRTAC code are very close to the experimental results, and the change trend is in good agreement with the experimental results. Therefore, FRTAC code can be used to simulate the release of high temperature and high-pressure water or water vapor when the pipeline is broken. Keywords: Loss of water accident · Safety analysis · Programe verification

1 Introduction Loss of heat sink accident (LOHS) [1, 2] refers to an accident in which the temperature of the primary circuit coolant entering the core is too high due to the failure of the secondary or tertiary circuits, resulting in insufficient cooling capacity of the core. In liquid metal cooled fast reactor, the typical loss of heat sink accident includes the rupture of the main water supply pipeline and the rupture of the main steam pipeline. The water supply pipeline rupture accident is defined as the rupture of the three-circuit water supply pipeline, which results in insufficient water entering steam generator to ensure the heat transfer capacity of steam generator. The steam pipeline rupture accident is defined as the rupture of the steam pipeline, which causes a large amount of steam to be ejected from rupture, the steam outlet pressure and temperature drop rapidly, and a critical saturated injection occurs at rupture. According to the size of rupture, it can be divided into small break accident and large break accident (instantaneous double-ended rupture of pipeline). The large break accident and small break accident of water reactor all occur in the primary and secondary loop system of reactor. At present, the development of water reactor is very mature, and analysis of LOCA is also very comprehensive. A great deal of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 200–210, 2023. https://doi.org/10.1007/978-981-19-8780-9_20

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research work has been done on mechanical model and related procedure. Liquid metalcooled fast reactor such as sodium-cooled fast reactor [3, 4] use the design of three circuits of sodium-sodium-water. When three-circuit rupture accident occurs, change of the three-circuit cooling capacity can only be affected by the transmission of the secondary circuit. The dynamic response process of main cooling system is different from the water reactor. Therefore, the transient analysis of three-circuit rupture accident in sodium-cooled fast reactor is very important, and an effective system safety analysis code is needed to analyze the accident. This code can not only simulate the flow, temperature and pressure changes of high-temperature and high-pressure steam and water in threecircuit pipeline, but also simulate the operation of sodium-cooled fast reactor system loop. FRTAC is safety analysis code for liquid metal cooled reactor system independently developed by China Institute of Atomic Energy. The development of FRTAC code [5] involves multiple disciplines such as reactor physics, reactor thermal hydraulics, reactor safety, reactor system and equipment, and reactor control. The code has developed numbers of modules such as thermal hydraulics module, neutron dynamics module and burst discharge. The code includes various control bodies such as common hydraulic component of reactor (pipe, liquid pool, pump, valve, buffer tank), thermal component (fuel rod, heat exchange tube), neutron component (reactivity feedback, reactivity introduction). FRTAC code adopts a variety of numerical algorithms such as symmetric matrix solution and asymmetric sparse matrix solution, which can simulate multiple flowing media such as water/water vapor, sodium, lead/lead-bismuth, air, etc. When loss of heat sink accident occurs in third circuit of sodium-cooled fast reactor, the high-temperature and high-pressure water or water vapor is ejected from the rupture. At this time, the spraying process of water or water vapor is very violent, accompanied by sharp phase transition process. Whether the rupture ejection phenomenon is simulated correctly is the basis for FRTAC code to analyze loss of heat sink accident of sodiumcooled fast reactor. This paper investigates the high-temperature and high-pressure water jetting experiment “EDWARDS’ pipe break experiment” [6, 7] and the high-temperature and high-pressure water vapor jetting experiment “Marviken JIT11 experiment” [8, 9]. FRTAC code was used to model the two experiments, and the results were compared with experimental results to verify the ability of FRTAC code to simulate the pipeline rupture.

2 Edwards’ Pipe Break Experiment The break experiment which was simulated was one performed originally by Edwards. The apparatus consisted of a straight pipe four meters long and initially filled with water. The pipe was pressurized and heated, then allowed to depressurize by rupturing a glass disk at one end. The flow process in the pipe and at the discharge provides a good test for a transient two-phase flow model. Early in development of RELAP5, this experiment was used to check out the hydrodynamic model since the geometry is simple and Edwards data is well documented. The Edwards pipe break problem is used as a base case. Table 1 gives the basic parameter of experiment. In 1980, Ransom et al. [10, 11] of Idaho National Laboratory in USA used RELAP5 code to simulate Edwards’ pipe break experiment to verify two-phase flow model. In

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Parameter name

Valve

Pipe length/m

4.09

Pipe cross-sectional area/m2

1.0956 * 10–4

Break size/m2

0.95317 * 10–4

Initial water temperature/°C

228.85

Initial pressure/MPa

7

the simulation process, the pipe is divided into 20 control bodies, as shown in Fig. 1, the control bodies 1, 5, 7, 10, 15, 19, 20 are respectively connected with the experimental instrument measurement positions 7, 6, 5, 4, 3, 2, 1 correspond.

Fig. 1. Modeling diagram of RELAP5 for Edwards’ test

According to the basic parameter of Edwards’ pipe break experiment and the modeling method of RELAP5, FRTAC code was used to model the experiment. Figure 2 shows the modeling diagram of FRTAC, the experimental pipe is divided into 20 nodes, corresponding to the 20 control bodies of RELAP5. The break is simulated by a pipe with a length of 0.01 m and an area of 0.95317 * 10−4 m2 , and the pressure boundary condition TDV is used to simulate the environment (ambient atmospheric pressure 100 kPa, temperature 20 °C).

Fig. 2. Modeling diagram of FRTAC code for Edwards’ test

Figures 3 and 4 show the comparison of pressure change at the position of pipe break head and pipe tail at the moment of break (0.02 s). The pressure at the break end decreases rapidly to about 2.4 MPa in a very short transient time. The pressure drop rate simulated by FRTAC and RELAP5 is faster than the experiment valve, the main reason is that program simulation is an ideal experimental condition, while friction and measurement errors exist in the actual experimental process, so the simulation result of code are more ideal.

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Fig. 3. Pressure of pipe break position at the moment of breaking

Fig. 4. Pressure of pipe end position at the moment of breaking

In the figure, the pressure simulated by RELAP5 has a large pressure undershoot process, and the final stability is consistent with experimental result. The pressure change result measured by experiment also show this process. The pressure undershoot process simulated by RELAP5 is steeper than experimental results. The pressure propagation speed calculated by RELAP5 is the fastest (0.00165 s), followed by the calculation result of FRTAC (0.00185 s), both of which are greater than the propagation speed of Edwards’ experiment (0.00325 s). As in Fig. 3, the pressure undershoot in the calculation result of RELAP5 is more obvious, which is basically consistent with experimental results, while the pressure calculation result of FRTAC do not show this process. The reason is that the delayed effect of nucleation and budding properties are considered in steam generation model of RELAP5, which is not considered in FRTAC. The pressure undershoot process only occurs in very short transient time and has little impact on long-term calculation. Figures 5, 6, and 7 show the comparison of pressure changes at the head position, the middle position (control body 7, the instrument position 5) and the tail position of the

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pipe within 0.5 s. The variation trend of the pipeline pressure simulated by FRTAC in the long-term period is basically consistent with the experimental result and the simulation result of RELAP5. The two-phase flow model of FRTAC code can accurately simulate the discharge phenomenon of the high temperature and high-pressure pipeline, so FRTAC code can be used to simulate loss of heat sink accident of the sodium-cooled fast reactor.

Fig. 5. Pressure of pipe break position within 0.5 s

Fig. 6. Pressure of pipe middle position within 0.5 s

Figure 8 shows the comparison of the cavitation fraction calculated by FRTAC code at the instrument position 5 in the middle of pipeline with the experimental result and the calculation result of RELAP5 code. The cavitation fraction calculated by FRTAC code and RELAP5 code is very close, the curve is very smooth, and there is no fluctuation. The experimental result show obvious fluctuations, but the general trend was the same.

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Fig. 7. Pressure of pipe end position within 0.5 s

Fig. 8. Void fraction of pipe middle position within 0.5 s

3 Marviken Jit11 Experiment The Marviken Power Station is a heavy-water cycle boiling water reactor that was never commissioned. The reactor retains a complete steam supply system, using oil-fired boilers to supply steam to the turbine. The Marviken separation effect experiment is a series of experiments conducted by various countries on the steam supply system of the Marviken Power Station in the late 1970s. A total of 27 Critical Flow Test (CFT) were carried out, and 35 reports were published on related test procedure, equipment parameter and technical evaluation. After CFT project ended, the Jet Impingement Test (JIT) project was carried out. The project focused on measuring the load of a fluid jet impinging on a flat plate, while producing a series of critical flow data. One of the tests, named JIT11, only sprays high-temperature, high-pressure saturated steam. JIT11 experimental device is shown in Fig. 9. The left picture is a pressure vessel, which contains high-temperature and high-pressure water (mass 145 * 103 kg) and water vapor (mass 5 * 103 kg), in which the maximum subcooling degree of water is less than 3 °C. An 18 m-high standpipe is installed in the pressure vessel, which is connected

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to the lower port of the pressure vessel and the discharge pipe, so as to ensure that saturated steam is sprayed from the nozzle and prevent the liquid high temperature and high-pressure water in the pressure vessel from being sprayed together from the nozzle. On the right is a schematic diagram of the discharge tube and nozzle. The ball valve switch is used to control the experiment, in which nozzles with different inner diameter and length can be replaced to simulate the effect of the nozzle aspect ratio on the spray. Table 2 presents the device dimension and input parameter for Marviken JIT11 experiment. In this experiment, the nozzle is controlled to spray a steady stream of saturated steam by means of depressurization and boiling of slightly supercooled water. Table 2. Basic parameter of Marviken JIT11 test Parameter name

Valve

Vessel volume/m2

420

Vessel inside diameter/m

5.22

Height of vessel/m

21

Height of standpipe/m

18

Outside diameter of standpipe/m

1.04

Wall thickness of standpipe/mm

8.8

Height of discharge pipe/m

7.929

Area of discharge pipe/m2

0.4441

Height of nozzle/m

1.18

Area of nozzle/m2

0.0702

Initial pressure/MPa

4.982

Final pressure/MPa

1.88

Initial water level/m

10.2

Final water level/m

8.0

Initial water inventory/kg

145 * 103

Initial steam inventory/kg

5 * 103

Maximum subcooling/°C

(Trial)”. There are 9 elements including preventive measures to mitigate and control aging and deterioration, detection of aging effects, monitoring of aging effects and prediction of deterioration trends, mitigation of aging effects, acceptance criteria, corrective actions, feedback of operating experience and R&D results, and quality management. After the establishment of the outline, the power plant low-voltage cable aging management implementation should be carried out according to the outline management requirements, the implementation data should be collected, and the aging state assessment should be carried out regularly. It should be noted

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that since the EAB test of cables is a destructive test, if all cables are evaluated according to the EAB test, the aging state will have an adverse effect on the normal operation of the cables. The determination of the cable aging benchmark curve provides test samples and test benchmarks for the assessment and qualification test of the cable aging state during the operation of the nuclear power plant. At the same time, the power plant should conduct an aging management review on the aging management activities of low-voltage cables in a timely manner. The review content should include the recognition of aging, the monitoring of aging, and the mitigation of aging effects. The review frequency is generally 5–10 years/time [6].

3 Low Voltage Cable Life Assessment 3.1 Thermal Aging Life Assessment The low-voltage cable thermal aging life assessment model is the Arrhenius rate constant empirical model, referred to as the Arrhenius model. The Arrhenius model is commonly used and accepted in the long-term thermal aging assessment of rubber materials. The principle of the Arrhenius model is that in the aging process of rubber materials, due to the long-term physical and chemical changes between rubber molecules or atoms, adverse reactions inside the rubber occur, resulting in the degradation and failure of rubber properties. With the accumulation of time, such adverse changes will become more and more, and eventually lead to the failure of the insulation performance of the rubber material. The aging rate of the rubber material depends on the reaction rate of this adverse reaction, and the faster aging rate of the rubber material means that the life of the rubber material is relatively short. Arrhenius in 1889 obtained the relationship between the performance degradation rate of a rubber product and its activation energy and temperature as:   −Ea k = A exp RT k——material aging degradation rate; A——frequency coefficient (a constant related to the evaluated material); Ea ——activation energy (eV); R——Boltzmann constant (8.617 × 10–5 eV/K); T ——thermodynamic temperature (K). A large number of experiments have proved that when the chemical reaction temperature of the rubber material does not exceed 500K, the activation energy will remain constant. When the test temperature does not exceed 500K, another common form of the Arrhenius equation is to express the relationship between the aging times t1 and t2 when the material reaches the same aging state at different temperatures T1 and T2, as follows: α = t1 /t2 = exp[Ea /R(1/T1 − 1/T2 )] α

proportional coefficient, or extrapolation coefficient;

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t1 The aging degradation time for the material to reach a specific aging state under temperature T1; t2 The aging degradation time for the material to reach the same aging state as the temperature T1 when the material operates at the temperature T2. The insulation and sheath of low-voltage cables are made of rubber, and the above formula is also applicable. According to the above formula, it can be seen that the aging effect of the aging and degrading t1 time of the cable material at the temperature T1 is equivalent to the aging effect of the aging and degrading t2 time at the temperature T2. Therefore, using the Arrhenius equation, a certain material is exposed to a higher temperature for a shorter time. By extrapolation, the time required for the material to achieve the same aging effect at a lower temperature can be calculated. The Arrhenius equation can be used to obtain the accelerated aging temperature to reach a certain aging state at a given test time, and can also be used to obtain the test time to reach a certain aging state at a given accelerated aging temperature. Specific method: (1) Carry out the EAB test of the elongation at break of the actual service cable (or the reserved cable) to obtain the EAB value X; (2) Bring X into the cable thermal aging benchmark curve to obtain the aging time t2 in the X state; (3) Using the Arrhenius equation, calculate t1 through t2; (4) If the actual service time t is less than t1, the aging state is worse; otherwise, it is better. Using the thermal aging life assessment model, the aging state of the cable running for a long time t1 at the actual ambient temperature T1 is simulated under laboratory conditions with a high accelerated aging temperature T2 and a short test time t2. The model can also be used to extrapolate and evaluate the thermal aging state of cables under different temperature conditions, and provide guidance for obtaining the state index values of cables under different thermal aging states using various cable aging state monitoring methods. 3.2 Radiation Aging Life Assessment According to the cable test standard [7] and related studies, radiation aging is related to the cumulative dose of radiation. The radiation accelerated aging test is performed on the cable sample, so that the cumulative radiation dose received is equivalent to the cumulative radiation dose of the cable during its service life, that is, the radiation aging effect of the cable sample is considered to be the same. Therefore, the radiation aging life model is presented in the form of a combination of the extrapolation function β and the reference curve. For the test object, based on the cumulative damage of radiation, the extrapolation function β of the actual service radiation of each cable relative to the test radiation is as follows. β = t1 = D/d1

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t1 The aging degradation time for the cable to reach a specific aging state under the actual service dose rate d1; D The radiation dose experienced by the cable when the radiation dose rate d2 reaches the same aging state as the actual service dose rate d1. Specific method: (1) Carry out the EAB test of the elongation at break of the actual service cable (or the reserved cable) to obtain the EAB value X; (2) Bring X into the cable radiation aging benchmark curve to obtain the radiation dose D in the X state; (3) Using the radiation aging life assessment model, calculate t1 through d1; (4) If the actual service time t is less than t1, the aging state is worse; otherwise, it is better.

4 Conclusions The research work on aging management and life assessment of low-voltage cables is an important activity to ensure that the aging effects of low-voltage cables in nuclear power plants can be effectively monitored, detected, evaluated, controlled and mitigated, and it is also an important work to ensure their expected functions. Comprehensive and effective aging management and life assessment can not only effectively improve the reliability of low-voltage cables, reduce unit operation safety risks and economic losses caused by temporary maintenance and emergency maintenance, but also determine whether the power plant can have long-term safe operation. At present, relevant foreign institutions have carried out a lot of basic scientific research work in the field of cable aging, and achieved certain results, and my country’s aging management technology system is also in a critical period of establishment, and various power plants and scientific research units should also keep up with the pace. Master the core technology of cable aging and life assessment and comprehensively promote and apply it as soon as possible to ensure the safe and reliable operation of my country’s nuclear power plants.

References 1. Tao, L.: Aging state monitoring technology of low-voltage cables in nuclear power plants. Wire Cable 4, 34–36 (2009) 2. Aging Management of Nuclear Power Plants, HAD103-12, 2012[EB]. National Nuclear Safety Administration 3. Technical Policy for Validity Extension [EB]. 2015, National Nuclear Safety Administration 4. Shengbo, Z.: Research on aging monitoring of class 1E cables in nuclear power plants. Machinery 44(5), 23–25 (2017) 5. IEEE 1205-2000. IEEE guide for assessing monitoring and mitigating aging effects on class 1E equipment used in nuclear power generating stations 6. Aging management. Internal data of power plant 7. IEEE Std 383TM-2015. IEEE standard for qualification class 1E electric cables and field splices for nuclear power generating stations

Discuss on Severe Accident Management Guidelines for an Advanced NPP Pingting Jiang(B) Shenzhen, Guangdong, China [email protected]

Abstract. If the primary coolant releases continuously or secondary heat sink turns unavailable for a period, the core will become uncovered and consequently leads to core melt, and this resultant event is usually called a Severe Accident (SA). Severe accident is the most serious accident in a nuclear power plant. In order to relieve the consequences of a severe accident, the severe accident management guideline(SAMG) is established to protect the third barrier in the NPP as long as possible and reduce the fission products release as lower as possible. After Fukushima Daiichi nuclear accident, severe accident prevention and mitigation are more concerned in all over the world, and lessons and challenges are investigated and discussed [1]. One of particular note is that mitigation capabilities should be enhanced to adequately complement the already accident prevention features of a nuclear power plants and strengthen the SAMG to improve the response capability. This paper summarizes the common form of SAMGs by investigation, gain insights from the surveys [2–4] on lessons learnt from Fukushima Daiichi nuclear accident, and gives a suggestion of the recommended framework of SAMG for the advance NPP. Keywords: Severe accident management · Framework · Diagnosis

1 Introduction According to the report [5] of International Atomic Energy Agency (IAEA), severe accident (SA) is defined as “an accident involving degradation of the reactor core leading to significant core damage”. Though there is a very low probability of core damage accident, the SAMG should be taken into account to mitigate the accident consequences in case the severe accident occurs. It is an indispensable procedure after accidents and encompasses the actions taken during the course of an accident by the plant operating and technical staff to [6]: (a) Prevent core damage. (b) Terminate the progress of core damage if it begins and retain the core within the reactor vessel (RV). (c) Maintain containment integrity as long as possible. (d) Minimize offsite releases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 266–276, 2023. https://doi.org/10.1007/978-981-19-8780-9_28

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Severe accident management, in effect, extends the defense-in-depth principle to plant operating staff by extending the operating procedures well beyond the plant design basis into severe fuel damage regimes, with the operator skills and creativity to find ways to terminate accidents beyond the design basis or to limit offsite releases after such an accident.

2 Discuss on Different SAMG As SAMG is indispensable, it is important to develop a proper SAMG to enhance the capability to cope with severe accidents. A short introduction of SAMG is given in this chapter. 2.1 Development History of SAMG The primary study on SAMG started in the 1980s. Initially, only full power operation mode is considered in the SAMG. In recent years, more and more NPPs are developing SAMG covering other operation mode, and also spent fuel pool (SFP). Especially after the Fukushima accident, many countries and regions proposed relevant requirements which pointed that it is not enough to consider only full power operation mode in SAMG, but also shutdown operation mode and accident occurred in spent fuel pool. The development of SAMG in united states (US) began in 1980s. Nowadays, US has developed a serious of accident procedures including EOP and SAMG to handle different nuclear accidents. The widely used type of SAMG in the world is developed by Westinghouse, which is called WOG SAMG. After Fukushima Daiichi nuclear accident, US prompted three groups, Westinghouse Owners Group, Combustion Engineering Owners Group and Babcock & Wilcox Owners Group, to formulate a new framework of SAMG, which is called PWROG SAMG [7]. In the late 1980s, France began to study on severe accident management to improve the EOP, which is called GIAG. In recent years, AREVA have developed new severe accident management guidelines called OSSA for EPR NPP. In China, the Daya Bay NPP began severe accident management strategy study in 1999 and had the first-implemented SAMG in 2005. And other NPPs in China implemented SAMGs successively from then on, with the form similar with WOG SAMG. After Fukushima Daiichi nuclear accident, severe accidents in shutdown mode and in spent fuel pool are concerned. 2.2 Description of Different SAMGs Due to different types of NPPs in China, the SAMG also varies. Generally, there are three main forms of SAMG, including WOG SAMG, OSSA, and integrated SAMG. WOG SAMG [8] is widely used in generation-II NPPs with two diagnosis flowcharts. One is called severe accident diagnosis (DFC) with severe accident guidelines (SAGs) at the initial stage. The other is called diagnosis and severe challenge status tree (SCST) with severe accident challenge guidelines (SCGs) when the safety shields are under serious threat. The two diagnosis flowcharts work simultaneously.

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OSSA [9] proposes a single diagnostic tool, valid from entry conditions up to the final state. Firstly, three SA Safety Functions are identified according to the progression of a severe accident and key parameters are fingered with determinate value. And a looping flowchart allows for the continuous monitoring of three prioritized SA Safety Functions. The diagnosis and the actions in OSSA are more like EOP. The integrated SAMG develops an approach based on a new diagnostic tool within an easily used table. As high-level actions are summarized [10], the priorities can be determined by sensitive calculations. The more comprehensive the sensitivity cases are done, the more applicable the priorities and the setpoints are. Considering the complex situation in a real accident, a simple flowchart is important and more work should be done in advance. 2.3 Comparison In ordered to figure out a suitable SAMG for advanced NPPs, the comparison of three SAMGs are as follows: Table 1. Comparison of the SAMGs Types

Scope

WOG SAMG

1. Initial WOG only considers full power 2. Revised WOG considers different operation modes and SFP after Fukushima

OSSA

1. Initial OSSA considers different operation modes (SFP is not considered) 2. Revised OSSA considers different operation modes and SFP after Fukushima

Integrated SAMG Integrated SAMG that developed after Fukushima had considered different operation mode and SFP Types

Diagnosis

WOG SAMG

WOG provides two diagnosis flowcharts, DFC and SCST

OSSA

OSSA provides a flowchart like EOP based on three safety function

Integrated SAMG Integrated SAMG provides a diagnosis table based on the strategies Types

Guidelines about shutdown mode

WOG SAMG

Guidelines about shutdown mode are added directly in the previous guidelines in WOG

OSSA

OSSA considers different operation mode in the beginning, including closed and not closed states

Integrated SAMG Integrated SAMG regards guidelines about shutdown mode as independent guidelines and gives a clear entrance (continued)

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Table 1. (continued) Types

Guidelines about spent fuel pool(SFP)

WOG SAMG

Guidelines about SFP are added as a new guideline in WOG, and the other guidelines are modified if relevant

OSSA

Diagnosis about SFP is added at the end of the previous OSSA after Fukushima, but the strategies are linked to the original guideline in EOP

Integrated SAMG Integrated SAMG develops separate guidelines for SFP, and considers both DEC-A and severe accidents Types

Form of guidelines

WOG SAMG

WOG(revised) provides one series of guidelines including different operation modes and SFP

OSSA

OSSA(revised) provides one series of guidelines including closed and not closed states and SFP

Integrated SAMG Integrated SAMG provides a complete series of guidelines including four separated parts(at power SAMG, shutdown SAMG, refueling SAMG and SFP SAMG) Types

Operational staff in SAMG

WOG SAMG

Two teams consist of personnel in main control room (MCR) and technical support centre (TSC)

OSSA

The same as WOG SAMG

Integrated SAMG The same as WOG SAMG

It can be seen from Table 1 that: (a) The scope of the three SAMGs is almost the same, only the detailed guidelines differ from each other. (b) The great difference among the three SAMGs including three aspect: i.

The diagnosis way: WOG considers two flowcharts simultaneously, while OSSA and integrated SAMG consider one flowchart. According to the study on lessons from Fukushima, people are always in a mass and hardly to think over calmly under such severe scenarios. Thus, a simple diagnosis is more suitable, and WOG SAMG seems not a good choice. OSSA provides one diagnosis in the form of a flowchart like EOP. It caters to the operational staff and gives a direct instruction on the basis of parameters that helps to reduce human error and lighten the burden of staffs. The shortage of diagnosis in OSSA is that the flowchart is fixed but the situation under severe accidents is uncertain. Though there is a NOTE at the beginning, operational staff will probably obey the procedure step by step. Besides, flexible strategies are not considered in it too.

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The integrated SAMG provides one diagnosis flowchart in the form of a table. It combined the two diagnosis flowcharts in WOG, and gives one diagnosis table on the basis of strategies and relevant parameters. The operational staff firstly fills in the current value of key parameters in the table, and then turns into the relevant guidelines according to the priority. Guidelines are written in original form that considers benefit and cost, and flexible strategies are considered as substitute actions [11]. Though it is more simply than WOG SAMG, it is complex compared to OSSA. ii. Considerations on SFP: WOG and OSSA consider accidents in SFP partially. WOG adds a new parameter and a new special guideline for SFP in the previous WOG SAMG. OSSA only adds a monitor in the diagnosis and links to guidelines in EOP. The integrated SAMG specialized complete guidelines for SFP. Though the probability of severe accidents in SFP is low, SAMG should better consider bad situations. Maybe complete guidelines for SFP need to be further discussed, guidelines for severe accidents in SFP should be considered. Once severe accidents happen, a usable procedure is of importance, according to the lessons from Fukushima. Guidelines for shutdown mode are mostly the same as for SFP, the discussion is omitted here. iii. Form of guidelines: WOG and OSSA consider guidelines for shutdown mode and SFP, and add one or two guidelines in the previous SAMG. The integrated SAMG provides complete guidelines for each one. Obviously WOG and OSSA are simple in the form and have fewer guidelines. However the integrated SAMG are more targeted. Here after is the comparison between them (Fig. 1).

EOP

SAMG

Emergency Entry Operating Procedure

WOG SAMG:

OSSA:

1. criterion entrance about shutdown mode and SFP added: 2. two guidelines about shutdown mode and SFP added.

1. criterion entrance about shutdown mode added: 2. no guidelines added. The guidelines about SFP links to EOP.

The integrated SAMG:

PSAMG (for at power and NS/SG mode)

SSAMG (for shutdown mode while RHR is aligned)

RSAMG (for refueling mode while the reactor pool has been charged)

SFSAMG (for SFP)

Fig. 1. Comparison between the SAMGs

(c) The operational staffs of the three SAMGs are the same which including MCR and TSC.

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2.4 Conclusion According to the comparison in 2.3, the three SAMGs have their advantages and disadvantages. (a) WOG SAMG is the earliest one among the three, and it is widely used in generationII NPPs. Operational staffs are familiar with the form and know the rules. If NPPs want to revise their SAMG, then WOG SAMG is a good choice. (b) OSSA supplies a simple diagnosis, but it considers less about uncertain scenarios. If NPPs are simplified ones, and only a few strategies can be used, the OSSA is a better choice. (c) The integrated SAMG gives a diagnosis table that is simple and along with complete guidelines for each strategies. It is a good choice for generation-III NPPs with more severe accident strategies and flexible strategies.

3 Research on Samg for an Advanced NPP As three main types of SAMGs are described and compared in Chap. 2, the integrated SAMG seems better suits the newly designed NPPs with many strategies. This chapter will take an advanced plant of generation-III NPPs for example to explain how to develop it. Firstly, definition and principles should be determined. Then the framework, the strategies and the setpoints should be defined. 3.1 Definition The SAMG is an integrated guidance to deal with severe accidents with the following goals: (a) Minimize radioactive release to the environment; (b) Maintain the containment integrity as long as possible and restore to a stable controlled state; and (c) Restore residual heat removal to bring the plant to a controlled, stable state.

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3.2 Principles Main principles should be taken into account are listed as follows: (a) (b) (c) (d)

Operational staff should monitor the entrance of the SAMG. Once SAMG used, the EOP is no longer applicable which means it is irreversible. Limiting fission products release is important than core reflooding. Responsibility/authority of MCR and TSC shall be defined clearly in the design. And if TSC is available, the responsibility is transferred to the TSC.

3.3 Steps A good SAMG should consider the characteristics of the plant. Main steps are listed below [12]: (a) Comb the whole procedures and make clear the boundary and the entrance of SAMG. EOP and SAMG are procedures to deal with accidents. While EOP focuses on protecting core integrity, SAMG pays attention to the accidents with significant core damage. Thus, the transition is the key parameters which indicate the core damage status. According to the analysis, the core outlet temperature (COT) and dose rate in the containment are chosen as the entry (while for SFP, water level and dose rate are chosen). (b) Figure out the plant operational states and the characteristics of each one. There are many standard operation modes in an NPP, and they should be combined with the consideration of similar thermodynamics and reactor physical characteristics to form the final SAMG. A simple framework of the full-scope SAMG is shown in Fig. 2. As the left three (PSAMG, SSAMG and RSAMG) are deal with severe accident occurred in the core, the staff can only choose one SAMG in the red box. Considering accidents may occur in the SFP meanwhile, the staff can use SFSAMG simultaneously. (c) Calculate the dominant and typical accident sequences to gain insights, and then define proper strategies.

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Severe Accident Management Guideline (SAMG)

Power operation severe accident management guideline (PSAMG)

Shutdown operation severe accident management guideline (SSAMG)

Refuelling operation severe accident management guideline (RSAMG)

Spent fuel pool severe accident management guideline (SFSAMG)

Fig. 2. Framework of the final SAMG

Find out the dominant and typical accident sequences and do analysis on a set of strategies to mitigate accidents, including dedicated equipment, other equipment and non-permanent equipment. Based on PRAs and severe accident analyses, the risk associated with severe core damage accidents can be reduced through effective mitigations in SAMG. Here after are the strategies on the basis of analysis results. i.

ii. iii.

iv.

v.

vi.

Primary system depressurization: the strategy helps to prevent containment overheating by high-pressure melt ejection and reduce the stress on the steam generator tubes. It helps to get a lower pressure for primary injection too. In the strategy, dedicated valves or other valves can be used; Corium retention: the strategy helps to remove the heat from the corium pool and preserve the integrity of RPV. A specific system is designed in the NPP; Steam generators re-flooding: the strategy helps to remove heat in the secondary side. Furthermore, it protects the tubes from failure and scrub fission products leaked from the primary side; Core re-flooding: the strategy helps to remove the heat in the core. It is a direct way and is suggested to be used as earlier as possible. If the corium pool is formed at the bottom of RPV, it may be abandoned due to the risk of RPV failure since there is a sharp rise of pressure once injection; Containment pressure control: the strategy helps to keep the containment pressure under its design limit and maintain the integrity of the containment. According to the calculation, both negative pressure limit and high pressure limit should be noted in SAMG; Hydrogen control: the strategy helps to prevent containment failure because of hydrogen combustion. In the strategy, both hydrogen concentration and containment pressure are monitored and judged. Thus, it is closely linked to containment pressure control strategy;

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vii. Fission products control: the strategy helps to protect the emergency personnel and the public from high radiation. In the strategy, fission products release path is identified and proper actions may be implemented; viii. Containment flooding: the strategy is used only when the above strategies are ineffective and there are no more strategies to use. Thus, containment flooding is the last way to mitigate the severe accidents. ix. As for SFP, the strategies contain SFP re-flooding, restoring cooling and fission products control. Because of lower decay heat and large water inventory in SFP, the process of accidents in SFP is much slower compare to the reactor core. There is more time available to mitigate accidents. Besides, the probability of re-criticality is low according to the analysis. The tap water and sea water may be injected into SFP if necessary. (d) Choose a suitable SAMG and the diagnosis flowchart. Considering it is a large-sized advanced pressurized water reactor that has many mitigation strategies and each strategy has different equipment, the integrated SAMG is chosen(detailed reasons can be seen in Chap. 2.3 & 2.4). Diagnosis table of SFP is presented as an example (Table 2). Firstly, current value of three key parameters are remarked (water level in SFP, temperature in SFP and release level on site due to SFP), and TSC fill the correct blanks. After that, TSC enter the corresponding guideline one by one according to the diagnosis rule (risk level-1 in red is number one priority, risk level-2 in orange is number two priority, risk level-3 in yellow is number three priority, and then risk level-4 in green). Table 2 Diagnosis table

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(e) Define priorities and setpoints. Since the mitigation measures are teased, the priorities of the mitigation measures should be further calculated, and so as the setpoints which indicate the status of the core or the spent fuel in each strategy. An effective accident management ensures the optimal and maximum safety benefits through pre-planned strategies. It needs a large amount of calculations to get reliable results, but it is the most important part in SAMG. The more sensitivity analysis are done, the more proper the priorities and setpoints are chosen. (f) Develop guidelines and assistant aids. In order to make each strategy in SAMG work well, system and available devices need to be sorted out. Finally, the guidelines for strategies are written and necessary calculation aids are given. Then development of a complete set of procedures and guidelines are accomplished.

4 Conclusions The SAMG can expand the accident management scope and capability of the plant. It is an expansion of the existing emergency operation procedures and a significant improvement that enhance the severe accident mitigation capacity. According to the study of the design and implementation in different NPPs in different countries, three main SAMGs are presented, and detailed comparisons are done with advantages/disadvantages put forward. Then a short conclusion on applicability of the three SAMGs is proposed. In addition, SAMG in advanced NPPs is discussed with the definition, principles and steps of developing the SAMG clarified.

References 1. IAEA. The Fukushima Daiichi Accident: Report By The Director General (2015) 2. NRC. TI 2515/183, Follow up to the Fukushima Daiichi Nuclear Station Fuel Damage Event (2011) 3. NRC. TI 2515/184, Availability and Readiness Inspection of Severe Accident Management Guidelines (SAMGs) (2011) 4. IAEA. Report on severe accident management in the light of the accident at the Fukushima Daiichi NPP (2015) 5. IAEA. Accident Analysis for Nuclear Power Plants, International Atomic Energy Agency— Safety Reports Series No.23 (2002) 6. IAEA. DS483, NS-G-2.15 Severe Accident Management Programmes for Nuclear Power Plants (2015) 7. Vayssier, G.: Benefits and Limitations of the New Consolidated PWROG Severe Accident Management Guidance (SAMG)—A Review of Some Critical Issues (2014) 8. Huh, C.: Evaluation of SAMG Effectiveness in View of Group Decision (2011) 9. Marco, G.: CEPR Severe Accident Management Guidelines (OSSA): Operating Guidance for TST – Immediate Actions and Diagnosis (2018) 10. EPRI. Severe Accident Management Guidance Technical Basis Report, Volume 1 (2012)

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11. NEI 12-06. Diverse and Flexible Coping Strategies (FLEX) Implementation Guide, Revision 4 (2016) 12. Solovjanov, O.: Implementation of Severe Accident Management Guidelines to Shutdown and Low-Power Modes for VVER and PWR Plants. Westinghouse Electric Belgium S.A. (2010)

Theoretical Study on Negative Pressure Transport of Radioactive Waste Resin Jilong Zheng(B) , Guipeng Li, Zhaoming Wang, and Kai Luo CNNP Xiapu Nuclear Power Co., Ltd, Ningde, Fujian, China [email protected]

Abstract. Usually, the transportation of radioactive waste resin adopts the method of positive pressure transportation. But the usage of positive pressure transportation will occur in the process of pipe blocking, blockage and other things, so it is necessary to control the proportion of resin and water, and the use of artificial control of the flow of desalination. This leads to higher doses of radiation being received by workers. Therefore, in view of the above reasons, this paper adopts the way of negative pressure to carry out the transportation of radioactive waste resin, in order to improve the automation of the transport process, reduce blockage and other things, so as to improve the work efficiency and reduce the radioactive dose accepted by staff. At the same time, the device to deal the radioactivity waste water and resin is needed to satisfy the whole and new process. We use the software to draw the 3D model and to check the details, use the software to calculate the heating efficiency and power. Not only above, but also we need a new method to deal with the waste resin to minimize the volume of the waste resin in order to save the space of storage which cost a large number of manpower, materials and finances to built. With the slogan, ALARA, as low as reasonably achievable, we need find a solution to solve above questions that trouble us long. Therefore, we check much paper, calculate the heat efficiency, and do a number of tests to verify the theory and device. Keywords: Radioactive waste resin 1 · Negative pressure conveying 2 · Resin to water ratio 3 · Waste resin drying device 4 · Waste resin transport tank 5

At present, nuclear power plants and nuclear facilities require high service life for mechanical equipment in the main circuit, in which pipelines, mechanical sealing surface of the main pump, bearings, metal connectors and other requirements for fluid media containing impurities are very low. At home and abroad, the way of resin adsorption for fluid filtration, after a period of time with highly radioactive impurities will be adsorbed on the resin, through the replacement of resin to ensure fluid performance. After the use of waste resin can not be reused or regeneration, can only be treated, belong to consumables. At present, in nuclear power plants and nuclear waste treatment facilities, waste resins with radioactivity need to be transferred to the corresponding plant for treatment. The waste resin transport equipment usually used in domestic power stations relies on the waste resin pump to load and unload the waste resin. Qinshan nuclear power, Tianwan nuclear power, Sanmen nuclear power and so on all take the method to adjust the potion demineralized water and resin to transfer the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 277–305, 2023. https://doi.org/10.1007/978-981-19-8780-9_29

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mixture, otherwise it is easy to happen blockage in the pipe at the interface box joint, quick joints joint, and further more radioactive substances, water, containing tritium deuterium easy to leak, resulting in plant room of radioactive dose increase. Through the results of the experience feedback, domestic and foreign research comparison, using resin negative pressure adsorption process. This test is used to verify the feasibility of negative pressure adsorption and simulate the negative pressure transportation of resin in the engineering site. The test bench will be built according to the 1:1 size of the project.

1 Experiment Objective (1) Verify the feasibility of negative pressure transportation of waste resin treatment system; (2) Understanding the resin conveying situation under different vacuum degrees; (3) Further study, under certain negative pressure, resin and water can be transported in different proportions; (4) The operation method of negative pressure conveying.

2 Testing Program 2.1 Process Program Waste resin negative pressure transport system is divided into waste resin receiving part and waste resin unloading part, in order to fully develop the feasibility of related technology and equipment. Among them, the process route of receiving part is as follows: Waste resin → waste resin receiving tank → waste resin transfer tank (1.5 m2 ) → waste resin transport equipment → waste resin transport tank. The process route of unloading system is as follows: Waste resin transport equipment → waste resin transport tank → waste resin transfer tank(1.5 m2 ) → waste resin receiving tank → conical dryer → back-end treatment. 2.1.1 Resin Receiving System Process Plan In order to prevent pipeline blockage during waste resin transportation, waste resin transportation equipment will adopt negative pressure transportation, conveying pump, vacuum pump and other auxiliary transportation equipment can provide enough pressure head and flow rate to prevent waste resin deposition in the pipeline, and waste resin and water in an appropriate proportion into the waste resin transportation tank. Waste resin transport tank can enter the corresponding plant and room, and connect hoses and quick joints in the room. The interface device is designed to ensure that the hose can be easily emptied before the quick connector is disconnected.

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(1)

First, the waste resin transfer tank vacuum pump is used to pump the pressure in the waste resin transfer tank from 1 bar to 0.2 bar, forming a micro negative pressure, and to prepare for the subsequent process.

(2)

The resin in the waste resin storage tank is transported to the waste resin transfer tank through the system pipeline, and the pressure in the waste resin transfer tank can be observed to rise to 1 bar.

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Because the resin is insoluble in water, after standing for a period of time, the resin and water will be stratified. The waste resin transfer tank mixing pump is used to remove excess water from the mixture.

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(4) (5)

(6)

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The liquid level is measured by the guided wave radar level gauge in the waste resin transfer tank, and the height H is recorded by PLC control system. The water in the desalting tank is discharged into the waste resin transfer tank, and the amount of desalting water is not more than H to prevent water spill over.

Demineralized water and resin are mixed through mixing pump of waste resin transfer tank to prevent resin hardening, precipitation and block formation in subsequent preparation work.

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Drive the waste resin transport tank [1] to the specified position, and connect the waste resin transport tank with the system through the exhaust hose and the resin transport hose of the waste resin transport tank.

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(8)

Vacuum pump is used to vacuum the waste resin transport tank and form negative pressure. The pumped gas is discharged into the radioactive gas treatment system to prevent accidental diffusion of radioactive gas.

(9)

Use the negative pressure of the waste resin transport tank to suck the resin in the waste resin transfer tank. When the waste resin is received by the waste resin transport tank, the waste resin transport equipment will prevent leakage and ensure that the waste resin has a sufficient flow rate to prevent the waste resin from settling in the pipeline.

(10) Muddy the resin in the waste resin transfer tank, and vacuum it again with the waste resin transport tank vacuum pump.

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(11) Use desalinated water to flush the transportation lines and hoses.

(12) Remove the exhaust hose and resin conveying hose of the waste resin transport tank.

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2.1.2 Resin Unloading System Process Plan Waste resin transport equipment unloading waste resin: waste resin transport equipment will waste resin and water mixture into the waste resin receiving tank, waste resin transport tank is provided with a set of car interface box, interface box is provided with waste resin transport and drainage fast joint. Quick joint adopts double ball valve type dry quick joint, manual connection and disconnect. In order to prevent pipe blockage during waste resin transportation, waste resin unloading from waste resin transportation tank also adopts negative pressure transportation; and have rinse water to clean. Since the unloading process is similar to the receiving process, it will not be described here. 2.2 Test Bench Scheme 2.2.1 Process Scheme The main function of waste resin transfer tank is to temporarily store between radioactive waste resin workshop and radioactive treatment workshop before transfer. The resin is transported to the transfer tank by screw pump and transported to the waste resin transport tank by negative pressure adsorption. During each transfer, external hoses with quick joints are loaded into tankers, which are then transported to the radioactive waste treatment plant. In order to prevent pipeline blockage during the transportation of waste resin, waste resin and water should be mixed in an appropriate proportion, and negative pressure transportation should be adopted for loading and unloading. The corresponding conveying pump, vacuum pipeline or vacuum pump and other auxiliary transportation equipment should provide sufficient pressure head and flow rate. The setting of waste resin transfer tank facilitates the measurement of waste resin transferred, ensuring that the mixture ratio of waste resin and water transported each time is the best. Moreover, because the volume of waste resin transfer tank is smaller than the volume of the tank, it is impossible to lead overload overflow accident during negative pressure transportation, which greatly improves the safety of waste resin transportation. Waste resin transfer tank is a vertical cylindrical container, mechanically sealed, the normal operating temperature is 5°–40°, the pressure is slightly negative pressure. A spray pipe is arranged above the transfer tank to clean the waste resin particles attached to the inner wall of the equipment. The nozzle arrangement covers the whole inner surface and has enough flushing power to clean away the waste resin particles. As the waste resin will precipitate at the bottom of the equipment after standing for a period, the agitator should be installed in the center of the top. Set vacuum tube mouth DN32, infusion tube mouth DN50; The liquid level meter is convenient for external observation of the liquid level in the tank, the pipe uses a hose, and the prototype tank is equipped with a glass mirror to visually observe the changes in the tank. Pipes and valves are made of 304 stainless steel. The waste resin transfer tank is equipped with a 60 RPM, 1.5 kW (frequency conversion) stirring paddle, and a circulating pump to fully stir the mixture of resin and water in the tank to prevent resin hardening, precipitation, block, etc. The main process routes verified by the negative pressure test scheme of radioactive waste resin are as follows:

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Test process route of receiving part: simulated waste resin receiving tank (IBC bucket) → simulated waste resin transfer tank (1.5 m3 ) (tank 1) → simulated waste resin transport equipment—waste resin transport tank (tank 2). Unloading part of the test process route: simulated waste resin transport equipment— waste resin transport tank (tank 2) → simulated waste resin transfer tank (1.5 m3 ) (tank 3) → simulated waste resin receiving tank (IBC bucket). The test bench will be designed in accordance with the ratio of 1:1 (prototype: engineering machine) to restore the actual working conditions to the maximum extent. The test bench consists of the following equipment:

1 General drawing of resin negative pressure conveying equipment

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2 Description of resin negative pressure conveying equipment

3 Waste resin transfer tank

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2.2.2 Test Bench Equipment Details No

Name

Data

No

function

1

Waste resin transfer tank

Effective volume: 1.5 m3

1

Simulated waste resin transfer tank engineering machine

2

Waste resin transfer tank

Effective volume: 1.5 m3

1

Simulated waste resin transfer tank engineering machine

3

Prototype of shielding container for waste resin transport tank

Effective volume: 1 m3

1

Simulation of waste resin waste resin transport tank shielding container

4

vacuum pump

VS-65

3

Used for vacuumizing

5

air compressor

OTS-1500X2

3

Used for adjusting negative pressure and positive pressure

6

flexible tube

DN50, DN80

N

Used for conveying resin

7

The IBC barrel

1 m3

4

Resin storage and rinse water storage

8

Out of the pump

Maximum flow 16.5 m3 /h

1

Used for tank 3 resin discharge

9

Cycling pump

–

3

Used for circulating stirring of resin in tank

10

Agitator

Equipped with three-phase motor

3

Used for mixing resin in tank

Note The installation of the test bench is independent of each other. The waste resin transfer tank prototype (2 sets) and the waste resin transport tank shielded transport vessel prototype (1 set) are installed on the bottom plate of 1500 × 2200 × 100 to connect the pipes and control system respectively

2.3 Experimental Simulation Scheme Negative pressure conveying test materials. Material

No

Resin GR-5723

1 ton

Water

2 ton

2.3.1 Simulation of Waste Resin Receiving/Unloading System The configuration of the two systems is the same as follows:

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The test is equipped with a vacuum pump VSV65, which can pump 65 m3 of air per hour, absorb 0.2 bar of waste resin transfer tank for about 2–3 min. (1 Bar = 0.1 mpa = 1 atmosphere, relative pressure 0.2 bar = −0.08 mpa), vacuum pump motor 3P, 1.5 kW. 2 IBC Ton Barrels Volume is 1 m3 . 1 oil-free air compress, OTS-1500X2, power 3 KW, as pneumatic valve air source. A set of PLC automatic control system, automatic operation of the system, control system brand for XINJE, and the use of touch screen, control valve, liquid level, pump automatic operation. On the controller, the vacuum degree can be adjusted, and can automatically stop the vacuum pump when the set vacuum degree is reached, and the real-time liquid level display, control and record through the replica level gauge. The whole system bottom plate assembly, convenient transportation, handling. 2.3.2 Simulation of Waste Resin Transport Tank Inside tank of waste resin transport tank 1200X1000, working temperature of equipment: normal temperature, working vacuum degree −0.1 mpa, vacuum tube mouth DN32, infusion tube mouth DN50; Pipes and valves are made of 304 stainless steel. The waste resin transport tank is equipped with resin pump circulating stirring and stirring paddle stirring. Test equipped with vacuum pump, air compressor, PLC automatic control system is consistent with the loading and unloading system.

3 Test Steps IBC barrels (1 m3 ) are used to simulate the waste resin receiving tank, which was connected to the waste resin transfer tank through a hose. IBC barrel is equipped with a fixed proportion of resin mixture for test according to the test requirement, the proportion separated is 100% resin, 80% resin, 60% resin.

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3.1 Test Acceptance Criteria [2] Meeting the performance requirements of negative pressure transportation, no blockage of pipelines, no running and dripping, ensure that there is no residual resin in the transfer tank, residual resin is easy to remove, less suction times. 3.2 Transportation of Pure Resin Resin mass: m = 0.7 t, density 1.117 g/cm3 , volume 0.627 m3 .

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IBC barrel pure resin picture

(1) IBC bucket to waste resin transfer tank (tank 1): Start the vacuum pump and open the relevant valve through the control panel to pump the pressure in the waste resin transfer tank to −0.08 mpa. Open the liquid inlet valve and transfer the resin in IBC tank to the waste resin transfer tank under negative pressure. The first resin was extracted at a height of approximately 20 cm IBC barrels (approximately 0.2 m3 ) and the pressure is relieved to −30 kPa.

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Continue to extract to −80 kPa, due to the serious air intake, after several times of extraction, the IBC bucket to tank 1 resin transport. Open the stirring paddle and start the circulating pump to realize the mixing of waste resin and water in the waste resin transfer tank; (2) Waste resin transfer tank (tank 1) Waste resin transport tank (tank 2). Close all valves in the receiving system. Through the hose, the bottom outlet of the waste resin transfer tank is connected with the fast interface of the inlet of the waste resin transport tank (tank 2); Open the vacuum pump of the simulated waste resin transport tank and pump the pressure in the tank to −0.08 mpa. Open the outlet valve and inlet valve of tank 1 and tank 2 respectively, and transfer the resin in tank 1 to tank 2. At this time, the pressure of tank 2 is observed to increase, and repeat steps 5 and 6 when it rises to atmospheric pressure. And record the transmission stop pressure and transmission times. When the resin conveying in tank 1 is finished, close the circulating stirring and liquid outlet valve of tank 1; Close the inlet valve of tank 2 and open the stirring and circulating stirring of tank 2. Open the vacuum valve of tank 1, the extraction pressure is −0.08mpa, open the water inlet valve, and enter the cleaning water through the cleaning water pipe connected to IBC bucket to flush the resin stored in the inner wall of tank 1, and indirectly verify the amount of resin temporarily stored in the inner wall, and record the results. Resin transport from tank 1 to tank 2 was completed by two extraction (−80 kPa vacuum). Among them, the vacuum pressure relief from −80 to −34 kPa, stop conveying; Vacuum pressure relief from −80 to −33 kPa, complete the delivery.

Processing after coveying, the resin in the tube

Continue to extract to −80 kPa, due to the serious air intake, after several times of extraction, the IBC bucket to tank 1 resin transport. Open the stirring paddle and start the circulating pump to realize the mixing of waste resin and water in the waste resin transfer tank. (3) Waste resin transport tank (tank 2) transport resin to waste resin transfer tank (tank 3)

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The bottom outlet of tank 2 is connected with the upper inlet of tank 3, and fixed firmly; Start the vacuum pump of tank 3 and pump the pressure of tank 3 to about −0.08 mpa. Start the liquid inlet valve of tank 3 and the liquid outlet valve of tank 2, transfer the resin in tank 2 to tank 3 through negative pressure, observe the resin transportation in the pipeline until the resin is finished, and record the times of transportation and pressure relief. After the resin is completely transported to tank 3, open the circulating stirring of tank 3. When the resin is pure (resin content is 100%), a certain amount of clean water is extracted by opening the cleaning water valve (the ratio of resin to water is 6:4 and the amount of water is inhaled). Resin transport from tank 2 to tank 3 is completed after one extraction (−80 kPa vacuum).

Vacuum display

Resin conveying process from tank 2 to tank 3

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Tank 3 into the cleaning water: open the liquid outlet valve, the resin in tank 3 to IBC bucket, until the resin transport is completed, and record the pipeline resin transport situation. By default, the resin goes completely into tank 3, i.e., 0.7 t of resin, 0.627 m3 of resin, and 0.42 m3 of water.

Volume of water inhaled

(4) Resin from tank 3 is exported to IBC barrels. After stirring, the pump draws out the resin and water mixture in tank 3.

Resin and water mixture in pump tank 3

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3.3 80% Resin Conveying

Initial state: resin and water ratio 8:2; End status: resin output in IBC barrel

(1) IBC barrel to waste resin receiving tank (tank 1) The first time can quickly extract the mixture of resin and water, and pressure relief to –16 kPa; Subsequently, a vacuum of −80 kPa was extracted. Due to the serious air intake, resin transportation from IBC bucket to tank 1 was completed after multiple extraction. (2) Waste resin receiving tank (tank 1) conveying resin to waste resin transfer tank (tank 2). After one extraction (−80 kPa vacuum) to complete resin transport from tank 1 to tank 2; Vacuum pressure relief from −80 to −4 kPa, complete delivery;

Output process Pressure to –4 kpa

(3) Waste resin transfer tank (tank 2) transport resin to waste resin transport equipment—Waste resin transport tank (tank 3). Resin transport from tank 2 to tank 3 is completed by two extraction (−80 kPa vacuum);

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Vacuum pressure relief from −80 to −27 kPa, stop conveying; Vacuum pressure relief from −80 to −30 kPa, complete the delivery.

Resin conveying process from tank 2 to tank 3

(4) Resin from tank 3 is exported directly to IBC barrels. Start stirring in tank 3 and pump out the mixture of resin and water in tank 3.

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Resin and water mixture in pump tank 3

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Tank 3 to IBC bucket to complete delivery

(5) Tank 1 is filled with cleaning water. After absorbing and opening the water inlet valve of tank 1, a certain amount of water was extracted and stirred before being transported from tank 1 to tank 2. During the conveying process, no obvious resin was observed in the pipeline.

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After the cleaning water from tank 1 enters tank 2, it is transferred from tank 2 to tank 3 after opening and stirring, and a small amount of resin is found in the conveying process. 3.4 60% Resin Conveying

Initial state: resin to water ratio 6:4. End status: resin output in IBC barrel

(1) IBC barrel to waste resin receiving tank (tank 1) Vacuum pressure −80 kpa

6 tank 1 after resin input liquid level display changes

The first time can quickly extract the mixture of resin and water, and pressure relief to about −10 kpa; Subsequently, a vacuum of −80 kpa was extracted. Due to the serious air intake, after three times of extraction, the resin transportation from IBC bucket to tank 1 was completed.

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(2) Waste resin receiving tank (tank 1) Conveying resin to waste resin transfer tank (tank 2) Vacuum degree −80 kPa; Resin transportation from tank 1 to tank 2 is basically completed after one extraction (−80 kPa vacuum); Vacuum pressure relief from −80 to −3kPa, complete the delivery.

Tank 2 completes resin transport

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Transporting

(3) Waste resin transfer tank (tank 2) transport resin to waste resin transport equipment—Waste resin transport tank (tank 3) Vacuum degree −83 kPa.

Tank 3 is ready for resin input

Resin input from tank 2 to tank 3 was completed after 3 extraction (−80 kPa vacuum).

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Vacuum pressure relief from −80 to −19 kPa, stop conveying. Vacuum pressure relief from −80 to −4 kPa, stop conveying. Vacuum pressure relief from −80 to −2 kPa, complete the delivery.

Tank 3 completes the resin input

Resin conveying process from tank 2 to tank 3

(4) Resin from tank 3 is exported directly to IBC barrels. Start stirring in tank 3 and pump out the mixture of resin and water in tank 3.

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Resin and water mixture in pump tank 3

Tank 3 is delivered to IBC barrel

(5) Tank 1 is filled with cleaning water. After vacuumizing and opening the water inlet valve of tank 1, a certain amount of water was extracted and stirred before being transported from tank 1 to tank 2. During the conveying process, no obvious resin was observed in the pipeline. After the cleaning water from tank 1 enters tank 2, it is transferred from tank 2 to tank 3 after opening and stirring, and no obvious resin is found in the conveying process.

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4 Summary of Results Through a series of tests, the fluidity of radioactive waste resin in the process of negative pressure transportation is verified, the pipeline will not be blocked, and the feasibility of negative pressure transportation is verified. Through the above theoretical analysis and experiment, the feasibility of negative pressure transportation of radioactive waste resin is verified, which provides a practical basis for subsequent engineering machines. (1) When the resin of IBC bucket enters the waste resin transfer tank, a large amount of air is mixed into the tank, so it will need to be transported by vacuum for many times. When the resin of the waste resin transfer tank is transferred to the simulated waste resin transport tank, the resin transport can be successfully completed under the vacuum pressure of −0.08 mpa. Due to the hose inserted the IBC barrels, barrels brash open state, hose nozzle under the liquid level, the liquid seal, to inhale the mixture of resin and water well, the above the dew in the liquid level, will be mixed with air, leading to a sharp rise in the vacuum degree within the tank, pumping ability to drop, and at the same pressure drop, suction resin reduced capacity, suction requires repeated many times, In order to achieve the purpose of transferring resin. Subsequent to test can be used to switch to the IBC barrels for waste resin transfer cans and other containers in sealing pressure way, so as to ensure the sealing performance of container hose connection, improve the vacuum degree, in order to reduce the number of suction, reduce staff workload, reduce the risk of workers receive radiation dose, reduce the number of equipment operation, improve the overall system performance. Thus, it can be seen that negative pressure transportation and positive pressure transportation need to ensure the integrity of the sealing of the whole system, the difference is that the former is to complete the transportation smoothly and quickly, and the latter is to ensure that the liquid and resin do not leak. The overall process meets the performance requirements of negative pressure transportation, no blockage of pipelines, no running and dripping, no residual resin in the transfer tank, residual resin is easy to remove, less suction times. (2) The test used pure resin, 80% resin and 60% resin test. Under certain negative pressure, resin and water can be transported in different proportions. Fully understand the resin conveying situation under different vacuum degrees, and provide theoretical and practical basis for the compilation of subsequent operation manual, specification and debugging outline. When pure resin (100% resin) is transported, there will be residue of resin in the inner wall of the tank (tank adhesion), which can be completely transported after cleaning with spray water, stirrer and circulating pump; When the resin is in a complete state, the theory and system verification of transportation are feasible, and there will be no system failure caused by over-design accidents, which also provides a guarantee for the subsequent practical operation process. Even if the resin storage time is long and then water absorption, expansion, it does not affect the operation of the system.

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When 80 and 60% of the resin conveying, can be done to wash the tank with water, no obvious found resin retention, only adhere to the wall of the container of resin. It also provides experimental data for reasonable mixed values. (3) Waste minimization is achieved by adjusting the appropriate resin and water ratio. The back-end of waste resin treatment is drying and dewatering. Usually, the conical dryer [3] is used to heat and remove water, and then cement solidification and other processes are processed. In dehydration, the less steam is produced, the less pressure is placed on the radioactive ventilation system and the longer its operating life is, resulting in less other derived nuclear waste. Subsequently, a cone dryer is needed to verify the total amount of steam generated by the system under different ratios of resin and water, including flushing water, circulating water, condensed water, resin water and other moisture.

References 1. Huang, G., Sun, S.: Development of vehicle-mounted waste resin receiving device for nuclear power plant. Atom. Energy Sci. Technol. 48(5) (2014) 2. Mozes, G., Kristof, M.: Bituminous solidification, disposal, transport and burial of spent ionexchange resins. Part of a coordinated.programme on treatment of spent ion exchange resins. Final Report for the Period 1 June 1980–30 June 1983 (1983) 3. Chen, B.: Treatment technology of low-medium waste resin in nuclear power plant. Radiat. Protect. Commun. 30(1) (Total No. 175) (2020)

Design and Verification of BNCT Beam Shaping Assembly Based on Genetic Algorithm Yaoqi Luo1 , Marcus Seidl2 , and Xiang Wang1(B) 1 College of Nuclear Science and Technology, Harbin Engineering University, Harbin 150001,

China [email protected] 2 PreussenElektra GmbH, 30457 Hannover, Germany

Abstract. Most of the recent research in boron neutron capture therapy (BNCT) mainly focuses on the accelerator neutron source for its stability and safety. The therapeutic effect of BNCT can be outstandingly improved if the neutron beam is suitable. However, the neutron generated by the accelerator cannot be directly applied to BNCT due to its high energy, and the neutron beam should be moderated in the beam shaping assembly (BSA). The traditional BSA design is carried out by artificial combination and empirical judgment, which is non-optimized. Considering the effectiveness of the genetic algorithm in multi-objective optimization, the BSA was optimized by combining the genetic algorithm with the Monte Carlo program in this study. The epithermal neutron flux was significantly higher than the threshold and the volume of the entire facility was dramatically reduced when all in-air parameters meet the standards established by the International Atomic Energy Agency (IAEA). The dosimetric validation was carried out in a simulated human brain phantom, the results confirmed that the neutron beam can embody its value in clinical parameters, which can be applied to the treatment of deep-seated tumors. Keywords: Boron neutron capture therapy · Beam shaping assembly · Genetic algorithm

1 Introduction Boron neutron capture therapy (BNCT) has gained widespread attention as a new precision radiotherapy modality. Because of the high neutron quality requirement for BNCT, accelerator-based neutron sources have become a hot research topic in recent years by reason that their ability to provide neutrons of sufficient yield and suitable energy. The current mainstream accelerate-based neutron sources utilize the 7 Li(p, n)7 Be and 9 Be(p, n)9 B reactions, which easily produce low-energy neutrons and reduce the difficulty of subsequent processing of the neutron beam [1]. The International Atomic Energy Agency (IAEA) has made some recommendations on the quality of the BNCT neutron beam, where the dose produced by fast neutrons and gamma should be low enough to reduce damage to normal tissues, as well as given the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 306–315, 2023. https://doi.org/10.1007/978-981-19-8780-9_30

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reference values for the epithermal neutron energy boundary (0.5 eV–10 keV), requiring that most of the neutron energy be concentrated in the epithermal neutron range to achieve deep treatment [2]. The neutron energy produced by the accelerator-based neutron source is concentrated in the fast neutron (> 10 keV) energy range, which does not meet the requirements for BNCT treatment and the neutron beam needs to be moderated and shaped by the Beam Shaping Assembly (BSA) to accomplish all of the recommendations. In recent years, various BSA schemes have been designed for different neutron source types, and the design process of these schemes is to calculate the parameters by manually selecting the material and varying the material thickness in fixed steps, which is influenced by the selected material and the step size and is still non-optimized [3–5]. Considering the effectiveness of genetic algorithms in the calculation of multiobjective optimization problems, in this work, the Non-dominated Sorting Genetic Algorithm (NSGA-III) is introduced into the BSA design. The epithermal neutron flux was significantly higher than the threshold when all in-air parameters meet the recommendations. To verify the clinical efficacy of the neutron beam for BNCT, the dose distributions in tumor and normal tissues were calculated based on a simulated human brain phantom. The MCNP code is used in the entire modeling and dose calculation.

2 Materials and Methods 2.1 Neutron Source The reaction threshold of 7 Li(p, n)7 Be was lower than that of 9 Be(p, n)9 Be, which were 1.88 meV and 2.057 meV, respectively, and the neutron yield is higher at lower proton energy [6]. The neutron source is produced by bombarding a lithium target with a proton beam with an energy of 2.5 meV and the beam current is 30 mA [7]. The energy and angular distribution of the neutron source outgoing neutrons were first calculated, and then made into a secondary neutron source term file for subsequent calculations to save time. The neutron energy spectrum is shown in Fig. 1.

Fig. 1. Neutron spectrum for the proton source with an energy of 2.5 meV and a beam current of 30 mA

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2.2 Beam Shape Assemblies The BSA structure adopts the traditional cylindrical design, and the distribution is composed of a target, moderator, reflector, filter and collimator, which is shown in Fig. 2. The moderator is designed with three layers of different material superposition designs to achieve a favorable moderating effect. Moderator materials need to have a small neutron absorption cross-section and a large fast neutron scattering cross-section to effectively slow down fast neutrons to the epithermal neutron energy region; reflector materials need to have a high elastic scattering cross-section and a low absorption cross-section to reduce neutron leakage and improve neutron flux; thermal neutron filters need to absorb excess thermal neutrons in the beam to reduce damage to superficial tissues; gamma filters use materials with the higher atomic number to effectively reduce the gamma component. The material candidate of each part is selected from Table 1.

Fig. 2. The layout of the BSA model

Table 1. List of material candidates for each section Elements

Material

Moderator

Polyethylene, TiF3 , MgF2 , BeO, Al, Fe, Teflon, Al2 O3 , H2 O

Reflector

Al2 O3 , BeO, Pb, Ni, Bi

Thermal neutron filter

Cd, B, LiF, B4 C

Gamma filter

Pb

Collimator

Boron-containing polyethylene (10 wt%)

2.3 Calculation Process IAEA [2] has specific reports on neutron beam quality for BNCT, as shown in Table 2. Obviously, this is a multi-objective optimization problem, the NSGA-III algorithm is chosen to automatically find the optimum for each parameter as the effectiveness for it.

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The NSGA-III algorithm, proposed by DEB. in 2014 [8], maintains the diversity of the population by introducing widely distributed reference points, adapts the crowdedness ranking, and is more capable of searching for solutions in high-dimensional spaces. The algorithm randomly selects the material of each part from Table 1 and randomly generates the component thickness to automatically generate the MCNP input file. MCNP calculates the four parameters in Table 2 and takes them as the optimization target. With the recommended value as the calculation limit, and iterates repeatedly until the final result is optimal. The calculation process is shown in Fig. 3. Table 2. IAEA recommendations for BNCT Paraments

Units

Limits

epith

n·cm−2 ·s−1

≥ 1 × 109

th /epith

–

≤ 0.05

Dfast /epith

Gy·cm2

≤ 2 × 10–13

Dγ /epith

Gy·cm2

≤ 2 × 10–13

Fig. 3. Flow chart of genetic algorithm calculation

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The tally segment for the in-air figures of merit is a cylinder, with 16 cm diameter and 0.5 cm thick, located at the BSA outlet. The thermal neutron flux and the epithermal neutron flux are counted by the F4 card in MCNP, based on the ENDF/B-VII database and the number of neutron cycles is 100,000. The dose contaminated by fast neutron and gamma is calculated from the corresponding kerma factors. The calculation of the in-air figures of merit is only a preliminary evaluation method, but the calculation of clinical parameters of neutron beam is necessary to measure the treatment effect on patients. The classical brain model, Snyder head phantom [9], is usually used as the computational object. Figure 4 shows a cross-sectional view of the phantom, where the grey shell is skin, the green layer is a skull and the red core represents the brain. The tally region is along the direction of the neutron beam center. The Snyder head phantom is determined by three ellipsoidal equations. Material compositions of the phantom are referenced by the ICRU report 46 [10]. The equations for skin, skull and brain are given as follows, respectively:  x 2  y 2  z 2 + 10.3 + 7.3 = 1, 8.8 

 x 2 8.3

+

 x+1 2 6.5



+

y 2 9.8



+

y 2 9.0



+

 z 2 6.8  z 2 6

= 1, = 1,

Fig. 4. The profile map of Snyder head phantom

The biological effects of BNCT consist of four dose components: (1) Boron dose (DB ), which results from Boron neutron capture reaction; (2) thermal neutron dose (DN ), which is due to the 14 N(n, p)14 C reaction; (3) fast neutron dose (Dfast ), that arises from the 1 H(n, n)1 H reaction; (4) gamma dose (Dγ ), which is produced by the contaminate of the primary neutron beam and photons generated by nuclear reactions in the tissue. The total biological dose (DT ) is obtained by a sum of the above components multiplied by corresponding weighting factors: DT = ωN DN + ωfast Dfast + ωγ Dγ + CBE · DB , where ωN , ωfast , and ωγ are weighting factors for thermal neutrons, fast neutrons, and gamma, while CBE is short for Compound Biological Effectiveness [11]. Based on that

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the relative biological effectiveness is defined as the ratio of the absorbed dose of X-rays to the absorbed dose required for other radiation doses to cause the same biological effect, ωN and ωfast are taken to be 3.2, ωγ is determined to be 1, the CBE in normal tissue and tumor are 1.35 and 3.8, respectively [12]. The 10 B concentration in tumor and healthy tissue is 35 ppm and 10 ppm respectively to ensure a 10 B concentration ratio (tumor: normal) greater than 3.5:1 [13]. For the figures of merit, several paraments have been used to evaluate the neutron beam: Advantage Depth (AD) is the depth when the dose at the tumor is equal to the maximum dose to normal tissue; Advantage Depth Dose Rate (ADDR) is the maximum dose rate to normal tissue; Treatable Depth (TD) is the depth where the tumor dose falls below twice of the maximum dose to normal tissue; Average Ratio (AR) is the ratio between the total tumor and normal tissue dose, usually integrated from the surface to AD; Treatment Time (TT) is defined as the radiation time when the total dose of normal tissue reached the maximum allowable dose (12.5 Gy).

3 Results and Discussions For the calculation process in-air, the parameters were changed by the genetic algorithm, and the program was called to automatically generate an MCNP input card to calculate the target value. The size of each population was set to 20, and the maximum evolution algebra was set to 50 generations. At the end of the iteration, the individual with optimal parameters is selected from the population as the design scheme. From the results, the optimal scheme is shown in Table 3. The in-air figures of merit are represented in Table 4. Compared with other schemes with the same structure, our proposed BSA has significantly improved the epithermal neutron flux and effectively reduced the volume of the device. The optimized neutron energy spectrum is also shown in Fig. 5, from which it can be seen that most of the neutrons are in the epithermal neutron range (Green section). Table 3. Material and thickness of the optimized scheme Component

Our-proposed

Moderator1

Teflon (7.3 cm)

Case 1 [14]

Case 2 [7]

Case 3 [15]

Moderator2

D2 O (20.0 cm) MgF2 (35.0 cm) MgF2 (21.8 cm) MgF2 (17.1 cm) – – Al (1.0 cm)

Moderator3

BeO (2.2 cm)

–

–

Reflector

Pb (16.0 cm)

Gr (30.0 cm)

Pb (28.0 cm)

Pb (35.0 cm)

Thermal neutron filter B (2.0 mm)

Cd (2.0mm)

LiF (0.4 mm)

LiF (0.2 cm)

Gamma filter

–

Pb (1.0 cm)

Bi (1.0 cm)

Table 5 presents the in-phantom figures of merit, the designed BSA can be applied to the therapy of deep tumor location as AD reaches 9.89 cm and TD reaches 7.50 cm, which is larger than any other program. ADDR is also greater than the other two protocols, and patients can receive more doses per unit of time, which can effectively reduce

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Y. Luo et al. Table 4. In-air paraments for the designed BSA

BSA models

epith (n·cm−2 ·s−1 )

th /epith

Dfast /epith (Gy·cm2 )

Dγ /epith (Gy·cm2 )

Reflector thickness (cm)

Our-proposed

2.32E+09

3.31E-02

1.98E-13

8.72E-14

16.00

Case 1 [14]

1.65E+09

5.00E-02

0.66E-13

2.63E-13

30.00

Case 2 [7]

1.26E+09

3.30E-02

1.77E-14

1.48E-14

28.00

Case 3 [15]

1.02E+09

3.72E-02

1.97E-13

0.99E-13

35.00

Fig. 5. Pre-optimized and post-optimized neutron spectrum

the treatment time (28.94 min). AR is a little larger than case3, indicating that better therapeutic benefits can be obtained. Because the tumor boron concentration set in case1 is 65 ppm, no comparison is made. Figure 6 shows the depth-dose curve in the tumor and healthy tissue, all the paraments are derived from it. To visualize the effect of each component on the tumor, Fig. 7 shows the dose produced by the different reactions in the tumor. As can be seen from it, the physical dose of BNCT is mainly contributed by boron capture thermal neutron. The primary dose accounts for a small percentage of the gamma dose, which is mainly generated by the capture reactions in the tumor. Fast neutron dose declines dramatically owing to the fast neutron being a small proportion in the primary neutron beam and they are rapidly moderated in the tissue. The kerma coefficients for the 10 B are referenced from Zamenhof [16]. The kerma coefficients for neutrons and gamma refer to the ICRU report 46 [10], where a double logarithmic interpolation is used for neutrons with energies below 0.0253 eV.

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Table 5. In-phantom figures of merit for the designed BSA BSA models

ADDR (Gy/min)

AD (cm)

TD (cm)

TT (min)

AR

Our proposed

0.432

9.89

7.50

28.94

4.61

Case 2 [7]

0.208

7.93

–

60.10

–

Case 3 [15]

0.301

8.95

6.80

41.53

4.43

Fig. 6. The depth-dose curve for the tumor and healthy tissue

Fig. 7. Depth distributions of the dose profiles for the tumor

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4 Conclusion A preliminary optimization calculation of the BSA scheme for neutron generation from 2.5 meV/30 mA proton beam bombardment of lithium target, which is based on a genetic algorithm combined with the MCNP software is carried out in this paper. Taken an effective attempt for the application of artificial intelligence in the optimization design of BSA. The epithermal neutron flux and facility volume are optimized compared with existing similar equipment. Furthermore, a simulated human brain phantom dosimetric analysis of the optimized neutron beam was performed to verify the clinical applicability of the BSA scheme. From the results, the design scheme takes an excellent performance in the treatment of deep penetration. Nevertheless, the influence of the component materials and thickness on the calculated parameters is mainly considered during the optimization process, and the classical cylindrical design is used, without considering the influence of the geometric structure. The structure of a particular geometry may have a great influence on the results.

References 1. Liskien, H., Paulsen, A.: Neutron production cross sections and energies for the reactions 7Li (p, n) 7Be and 7Li (p, n) 7Be∗. At. Data Nucl. Data Tables 15(1), 57–84 (1975) 2. IAEA-TECDOC-1223.: Current Status of Neutron Capture Therapy. International Atomic Energy Agency, Vienna (2001) 3. Cumberlin, R.L.: Clinical research in neutron capture therapy. Int. J. Radiat. Oncol. Biol. Phys. 54(4), 992–998 (2002) 4. Coderre, J.A., et al.: Neutron capture therapy of the 9L rat gliosarcoma using the pboronophenylalanine-fructose complex. Int. J. Radiat. Oncol.-Biol.-Phys. 30(3), 643–652 (1994) 5. Menéndez, P.R., et al.: BNCT for skin melanoma in extremities: updated Argentine clinical results. Appl. Radiat. Isot. 67(7–8), S50–S53 (2009) 6. Lee, C.L., et al.: A Monte Carlo dosimetry-based evaluation of the reaction near threshold for accelerator boron neutron capture therapy. Med. Phys. 27(1), 192–202 (2000) 7. Li, G., et al.: Design of beam shaping assembly for an accelerator-based multi-terminal BNCT facility. Nuclear Phys. Rev. 38(1), 80–88 (2021) 8. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using referencepoint-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2013) 9. Goorley, J.T., Kiger Iii, W.S., Zamenhof, R.G.: Reference dosimetry calculations for neutron capture therapy with comparison of analytical and voxel models. Med. Phys. 29(2), 145–156 (2002) 10. Scott, J.A.: Photon, electron, proton and neutron interaction data for body tissues: ICRU report 46. International Commission on Radiation Units and Measurements, Bethesda, pp. 7–200 (1992) 11. Coderre, J.A., Morris, G.M.: The radiation biology of boron neutron capture therapy. Radiat. Res. 151(1), 1–18 (1999) 12. Kiger Iii, W.S., Sakamoto, S., Harling, O.K.: Neutronic design of a fission converter-based epithermal neutron beam for neutron capture therapy. Nucl. Sci. Eng. 131(1), 1–22 (1999) 13. Coderre, J.A., et al.: Biodistribution of boronophenylalanine in patients with glioblastoma multiforme: boron concentration correlates with tumor cellularity. Radiat. Res. 149(2), 163– 170 (1998)

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14. Ganjeh, Z.A., Eslami-Kalantari, M.: Design and optimization of two-sided beam based on 7Li (p, n) 7Be source using in BNCT for brain and liver tumors. Nucl. Instrum. Methods Phys. Res., Sect. A 916, 290–295 (2019) 15. Torres-Sánchez, P., et al.: Optimized beam shaping assembly for a 2.1-MeV protonaccelerator-based neutron source for boron neutron capture therapy. Sci. Rep. 11(1), 1–12 (2021) 16. Zamenhof, R. G., et al.: Monte Carlo based dosimetry and treatment planning for neutron capture therapy of brain tumors. In: Neutron Beam Design, Development, and Performance for Neutron Capture Therapy, pp. 283–305. Springer, Boston, MA (1990)

Performance Monitoring and Back Pressure Curve Calculation of Condenser Kaili Xu(B)

, Chang Gao, Tianqi He, and Shuhang Yan

China Nuclear Power Operation Technology Corporation, Ltd., No. 1021 Minzu Dadao, Wuhan, Hubei, China [email protected]

Abstract. The Condenser is the key equipment of Conventional Island and the source in the heat cycle of the unit. It plays an important role in the safe operation of the power plant. The problems such as blocking, non-condensing gas and high Na+ content can lead to the performance degradation of condensers. Therefore, it is necessary to evaluate the state of the equipment according to the existing measure points and chemical parameters. This article used the on-line measure points trend analysis and heat transfer performance calculation methods for condenser performance monitoring, evaluation and equipment condition monitoring module development. The healthy state of the equipment can be predicted based on the trend analysis of the on-line measurement of the equipment and the calculation value of the thermal performance parameters such as end difference and heat transfer coefficient. After the equipment history operation data evaluation and parameter sensitivity analysis equipment is in good condition. The equipment performance monitoring module can meet the performance monitoring requirements of the heat exchanger. Keywords: Condenser · Performance monitoring · Back-pressure curve

1 Introduction The design purpose of the condenser is to improve the thermal efficiency of the unit by reducing the back pressure of the turbine [1]. In the condenser, the exhaust steam of the turbine is cooled by circulating cooling water (sea water), and the condensate is recovered as the feed water of the steam generator. Pipe plugging and non-condensable gas will affect the heat exchange effect of the condenser. The leakage of tube will lead to the increase of sodium ion content in condensate and affect the safe operation of the unit. Therefore, it is necessary to carry out condenser condition monitoring and online performance evaluation in order to identify the problems of the condenser in time. In this project, the parameter range of performance monitoring is formulated for the condenser of a domestic nuclear power plant, and aiming at the problem that the blockage rate of the condenser has exceeded the limit, the verification calculation of the blockage rate is carried out to avoid false alarm in the monitoring. Back pressure is an important performance index of condenser [2]. The back pressure of condenser will be affected by seawater temperature. By calculating the theoretical © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 316–322, 2023. https://doi.org/10.1007/978-981-19-8780-9_31

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pressure under different seawater temperatures and power levels and drawing the backpressure curve, the power reduction procedure of the unit can be optimized under the water chamber isolation condition. The economy of unit operation will be improved.

2 Performance Monitoring The condenser is divided into two independent condensers A and B. Each low-pressure cylinder corresponds to one condenser, and the two condensers are connected at the throat by a steam balance channel. The cold side of each condenser is divided into two columns, and each column has separate inlet and outlet water Chambers and titanium tubes [3] (Fig. 1, 2).

Fig. 1. Schematic diagram of condenser

CCW is short for circulating cooling water. The technical specification and design parameters of condenser are shown in Table 1. The selection principle of equipment performance monitoring parameters is the parameters that can effectively reflect the equipment status, independent of the acquisition mode, frequency and source [4]. According to different collection methods, the monitoring parameters can be divided into three categories. • On-line parameters, such as temperature, pressure, flow rate, liquid level, etc. • Indirect parameters, such as heat transfer end difference, heat transfer coefficient, dirt heat resistance, etc. • Non-online parameters, such as pipe plugging rate, maintenance, in-service inspection, chemical analysis and parameters obtained from regular tests. Different types of parameters come from different sources. The online parameters come from the PI system and can be used for performance monitoring, early warning or

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Fig. 2. Flow chart of condenser

Table 1. Design parameter Items

Value

Type

Double shell single flow

Back pressure

4.9 kPa

Volume

1.142 × 109 kcal/h

CCW inlet temperature

18.8 °C

CCW temperature rise

9.0 °C

Cleanness factor

85%

Tube external diameter

25.4 mm

Tube thickness

0.50 mm

Cooling surface

55,000 m2

Margin

1.1%

alarm. The indirect parameters are obtained through online calculation. Offline parameters are divided into two categories. The plugging rate is updated in the monitoring data after being obtained by the equipment engineer through maintenance. Chemical parameters, such as sodium ion content and oxygen content, are uploaded to the chemical system after regular detection. In Equipment Reliability Database (ERDB) system, PI system data, online calculation results, pipe plugging rate and chemical parameters are integrated to form a unified condenser performance monitoring (Fig. 3).

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Fig. 3. Performance monitoring

3 Performance Evaluation 3.1 Tube Plugging Rate In actual operation, the plugging rate of one condenser (1.26%) exceeds the designed tube plugging margin (1.1%), and an alarm signal is generated. At the same time, the condenser operates well and other monitoring parameters are in a healthy state [5]. In this case, it is determined that the design plug rate margin set in the pipe plugging rate monitoring does not meet the monitoring needs, and it is necessary to re-calculate according to the operating conditions. The calculation results of pipe plugging rate are shown in Table 2. The calculation condition of unit 1 is winter condition, and the calculation condition of unit 2 is summer condition [6]. Table 2. Plugging rate calculation table Parameter

Unit

Design value

1#

2#

Turbine power

MW

2062

2063

2061

Generator power

MW

728

729

715

Thermal load

MW

1314

1332

1326

CCW inlet temp

°C

18.8

8.38

24.74

CCW outlet temp

°C

27.8

20.1

33.12

Temperature Raise of CCW

°C

9

11.73

8.39

Condensate temperature

°C

32.6

26.76

36.6

back pressure

kPa

4.9

3.7

6

Saturated water temperature

°C

32.54

27.68

36.28

End difference

°C

4.74

7.57

3.16 (continued)

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Parameter

Unit

Design value

1#

2#

Supercooling degree

°C

−0.06

0.92

−0.32

CCW flow rate

kg/s

36882

28344

39441

heat transfer area

m2

54419.2

54885

53911

LMTD

°C

8.46

6.58

12.19

Plugging rate

%

1.1

2.08

1.97

3.2 Performance Improvement In the nuclear power plant, the condenser has 4 rows of water chambers. When the CCW pump needs to stop for maintenance or the condenser water chamber needs isolated maintenance, the reactor power should be reduced first to ensure the safe operation of the condenser after the pump is stopped or the condenser water chamber is isolated [7]. The back-pressure curve calculation process is shown in Fig. 4.

Fig. 4. The back-pressure curve calculation process

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There are no circulating water flow measurement points in the NPP, so it is necessary to calculate the head of CCW pump and obtain the circulating water flow according to the pump characteristic curve [8, 9]. Local system model is shown in Fig. 5. The meanings of each component represented in Fig. 5 are as follows.

Fig. 5. Local system model

1, 2-CCW pump, 3, 4, 6, 7-condenser, 5-sea water, 8, 9, 10, 11, 12, 13-valve, 14seawater. In order to reduce the power loss and improve the operation economy, the way of reducing power is re-evaluated by calculating the theoretical back pressure of condenser at different sea water temperature and power levels. 10 kPa is assumed to be the back-pressure warning value. When the cooling water temperature is below 22 °C, power reduction is not required. When the cooling water temperature is from 22 to 28 °C, the power level reduces to 95% FP. When the water temperature is from 28 to 30 °C, the power level reduces to 90% FP (Fig. 6).

Fig. 6. Trend chart of back pressure variation with seawater temperature in a single pump

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Performance evaluation needs to monitor the performance parameters of the condenser in real time to find out and feedback the operation of the condenser. At the same time, it is necessary to seek the improvement of the performance of the condenser or the power of the unit. By solving the theoretical back pressure of the condenser, it can reduce the power loss of the unit and improve the economic benefit by guiding the power level during non-stop maintenance.

4 Conclusions 1. Real-time monitoring of condenser temperature, pressure, liquid level, and other online parameters, thermal performance parameters, such as heat transfer coefficient, heat transfer end difference and other periodic calculation and monitoring, chemical parameters such as Na+ concentration, dissolved oxygen and other periodic analysis and monitoring, can comprehensively monitor the operation of the condenser. 2. The plugging rate of one condenser exceeds the designed tube plugging margin and alarms. According to the operation condition, the designed value 1.1% is judged not suitable for the current operation condition. The plugging margin of condenser in summer is re-evaluated as 1.97%, and that in winter is 2.08%. 3. The calculation of theoretical back pressure of condenser at different sea water temperature and power levels will guide the power drop level when CCW pump stops or the condenser water chamber is isolated in operation.

References 1. Li, Y., Lu, D.: Design and analysis of throttle orifice applying to small space with large pressure drop. Nuclear Power Engineering 34(04), 126–129 (2013) 2. Yu, L., Li, H.: Research for numerical simulation of large pressure drop pipe throttling orifice with short distance. Journal of Yangtze University 8(3), 68–70 (2011) 3. Zou, Z.: Candu-6 Nuclear Power Plant System and Operational Conventional Island System, 1st edn. Atomic Energy Press, Beijing (2010) 4. Mollerus Engineering Corporation. EPRI-NP-7552 Heat Exchanger Performance Monitoring Guidelines (1991) 5. Yang, S.: Heat Transfer, 4th edn. Higher Education Press, Beijing (2006) 6. Crytzer, K.: EPRI-3002008015 Comprehensive Tube Plugging Guidelines (2016) 7. Rausch, M.H., Leipertz, A.: Dropwise condensation of steam on iron implanted titanium surfaces. International Journal of Heat and Mass Transfer 51(1), 423–430 (2010) 8. Shao H.: Energy Conservation Technology 34(3), 250–253 (2016) 9. Chen, Z., Yang, Z.: A certain type of nuclear power steam turbine condenser side operation analysis. Journal of thermal power generation 47(10), 142–146 (2018)

Openfoam Simulation of Damping Controlled Fluidelastic Instability Zhipeng Feng(B) , Shuai Liu, Huanhuan Qi, Xuan Huang, and Xi Lv Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China [email protected]

Abstract. A parallel triangular tube bundle with a single flexible tube is taken as the object, and the most dangerous mechanism of flow induced vibration in the tube bundle structure, fluidelastic instability (FEI) is studied based on the opensource CFD tool OpenFOAM. Firstly, a numerical model describing the fluidstructure interaction of the tube bundle is established by combining computational fluid dynamics with the vibration equation of the tube; Then, the variation of lift coefficient, drag coefficient, lateral displacement and lift spectrum with velocity is discussed. The results show that the open-source tool OpenFOAM can be easily used to predict the fluidelastic instability of tube bundle. Keywords: Fluidelastic instability · OpenFOAM · Tube bundle · Flow-induced vibration

1 Introduction Flow-induced vibration is still the main limiting factor affecting the performance of steam generator in the nuclear energy field. Fluidelastic instability is considered to be the most destructive flow-induced vibration mechanism. In case of fluidelastic instability, the amplitude of the tube will increase sharply in a short time. Severe vibration will cause damage to the steam generator and lead to nuclear leakage. Especially after the steam generator failure of San Onofre nuclear power plant (SONGS) due to excessive flowinduced vibration in 2012, researchers began to focus on the fluidelastic instability in tube bundle. Since the 1960s, the fluidelastic instability of tube bundle has been an important research topic. A series of theoretical models have been proposed [1], and some important reviews have been published [2, 3]. Price [4] and Paidousis [5] made a detailed comment on the theoretical model of fluidelastic instability in tube bundle. Chen [6, 7] pointed out that the fluidelastic instability is caused by two fluid-structure interaction mechanisms. The first mechanism is related to the fluid coupling of adjacent tube vibration. This mechanism related to relative tube displacement is called fluid stiffnesscontrolled mechanism, or stiffness mechanism. The second fluidelastic instability mechanism is related to the fluid force component related to the tube velocity, which is called fluid damping controlled mechanism, or damping mechanism. For the fluid damping © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 323–328, 2023. https://doi.org/10.1007/978-981-19-8780-9_32

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controlled fluidelastic instability, the fluid force acting on the tube is proportional to the vibration velocity of the tube, and this mechanism could occur with only one degree of freedom. For a long time, the study of fluidelastic instability in tube bundle structure mainly depends on experiments. Many semi-empirical models [1] proposed need to determine numerous parameters with the help of a large number of experiments, which greatly limits the practical application of these semi-empirical theoretical models. Moreover, these models ignore the influence of turbulence intensity, Reynolds number and other factors. The flow through the tube bundle is usually viscous and turbulent, and computational fluid dynamics (CFD) is a very useful tool, which can be used to solve many fluid dynamics and fluid-structure interaction problems. In fact, the best possibility to describe the flow in detail without empirical data is to use CFD model. Among many CFD software, OpenFOAM has been widely used in the fields of computational fluid dynamics, computational heat transfer, fluid-structure interaction and multiphase flow due to its open source and user free customization [8], but it has less application in the prediction of fluidelastic instability of tube bundle structure. In this paper, a parallel triangular tube bundle with a single flexible tube was selected as the research object, the unsteady Reynolds averaged Navier Stokes (uRANS) equation is solved based on the open source CFD tool OpenFOAM, and the Newmark integral method is used to predict the motion of the single tube, so as to reveal the key parameters and flow response affecting the damping controlled fluidelastic instability mechanism, At the same time, the feasibility of open source tool OpenFOAM in the instability prediction of tube bundle fluidelastic is discussed.

2 Numerical Model 2.1 Model Description Figure 1 shows the simulated tube bundle and computational domain. The pitch to diameter ratio of parallel triangular tube bundle is Pr = P/D = 1.5. The computational domain includes 21 rows and 7 columns of tubes with a diameter of 30 mm, in which the dark tubes can vibrate freely in the transverse direction (the moving tube), and the upper and lower sides are half tubes. The inlet is positioned at a distance equivalent to 15D upstream of the first row. This inlet length was found to be suitable after a sensitivity study conducted by Hassan et al. [9]. In order to accurately capture the boundary layer region of the current turbulence model, the number and size of boundary layer elements are determined according to the dimensionless distance (y+ ), Reynolds number Re and length scale D from the wall. For SST Turbulence model, y+ ≤ 1 can ensure that the viscous sublayer can be solved reasonably. Unstructured mesh is used in other areas, and the smooth transition from boundary layer element to unstructured mesh element is ensured. Stretch the generated two-dimensional mesh by 10mm along the depth direction, and finally get the mesh for OpenFOAM calculation. The inlet is set as uniform flow, and the turbulence intensity is taken as 1%. The outlet is set as the inletOutlet condition. The inletOutlet allows the fluid to flow out or into the domain, so the fluid will not be forced to flow to the outlet direction. Except that

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the tube in the center of the 11th row is a moving wall, the upper and lower sides of the flow field and other tube surfaces are fixed nonslip walls. According to the average gap velocity, the Reynolds number range in this simulation is Re = 2 × 103 ~ 4 × 104 .

15D

U0

Fig. 1. Computational zone

2.2 Simulation of Moving Tube The moving tube is regarded as a single degree of freedom system in transverse direction, which means that the movement of the moving tube will not move along the streamwise due to drag force. Therefore, the arrangement of tube bundles will not change significantly: ms y¨ + cs y˙ + ks y = F(¨y, y˙ , y, U0 )

(1)

where, y, y˙ , y¨ are the displacement, velocity and acceleration of the tube respectively, ms , cs , ks are the mass, damping and stiffness of the tube respectively, F(¨y, y˙ , y, U0 ) is the lift force acting on the tube. The Newmark integral method is used to solve the equation, and the position and grid of the tube are updated at each time step. The flow field simulation is based on the open source CFD tool OpenFOAM to solve the unsteady Reynolds averaged Navier Stokes equations (uRANS). The SST model is used for turbulence simulation. The SST model can better simulate the non-equilibrium boundary layer problems (such as flow separation), and is in good agreement with the experimental results in the flow prediction of the tube bundle [10]. 2.3 Discretization and Solution The time derivative is an implicit first-order Euler scheme, the convection term is a linear upwind scheme, and the diffusion term is a non-orthogonal correction central difference scheme. The momentum equation is solved by Geometric-Algebraic Multigrid and Gauss-Seidel smoother. Other quantities are solved by smootherSolver and symGaussSeidel smoother. The motion boundary solution is based on the sixDoFRigidBodyMotion solver. The grid partition adopts Scotch method, and the parallel solution is based on MPI.

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3 Results and Discussion When simulating the free vibration of a moving tube, the mass per unit length of the tube is ml = 1 kg/m, the natural frequency is f = 10Hz, the damping ratio ζ = 1%, and the fluid is water and air. The mass damping parameters are MDP = 0.07 and MDP = 56.99 respectively, and the mass damping parameter is defined as MDP = ml δ/ρD2 , δ = 2πζ, ρ is the fluid density and D is the tube diameter. The inlet velocity is dimensionless with the tube diameter and vibration frequency, U r = U g /fD, U g is the gap velocity, defined as U g = U 0 Pr /(Pr − 1), Pr is the pitch to diameter ratio of the tube bundle, and U 0 is the inlet velocity. Based on the inlet velocity, the lift coefficient and drag coefficient are defined as: Cl =

Fl Fd , Cd = 2 0.5ρU0 Ay 0.5ρU02 Ax

(2)

where, Fl and Fd are the lift and drag force acting on the tube respectively. ρ is the fluid density and U0 is the inlet velocity. Ax and are the projected areas of the tube in the streamwise direction and the transverse direction, respectively. All quantities are taken from the moving tube. C lRMS , C dRMS , yRMS represent the root mean square of lift coefficient, root mean square of drag coefficient and root mean square of transverse displacement respectively. Figure 2 shows the changes of lift coefficient, drag coefficient and transverse displacement with velocity. It can be seen that, in general, the drag coefficient and lift coefficient decrease with the increase of velocity, and the transverse Ay displacement increases with the increase of velocity. The transverse displacement does not have a monotonic relationship with the change of velocity, which is similar to the response form measured by Weaver [11] in the water tunnel in the early stage. From Fig. 2, there is no clear correlation between the fluid force coefficients and the transverse displacement. At the velocity where the transverse displacement changes sharply, no obvious critical point of the fluid force coefficients is found. Therefore, it is difficult to determine the critical velocity through the abrupt point of the slope of the amplitude velocity curve.

(a)MDP=56.99

(b)MDP=0.07

Fig. 2. Fluid forces and transverse displacement versus velocity

The response characteristics of the system have a peak value of transverse displacement in the velocity range of Ur = 25 ~ 40. In order to further verify this conclusion,

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Figs. 7, 8 compare and analyze the results of flow past stationary tube bundle and fluidstructure interaction of tube bundle, where FSI represents fluid-structure interaction and static represents flow past stationary tube bundle. The response of the tube for the static case is obtained by applying the fluid force calculated by flow past stationary tube bundle to the dynamic equations of the tube, and then solving it by the fourth-order Runge-Kutta method. As can be seen from Fig. 3, for MDP = 56.99, the fluid forces of FSI and static are almost the same, indicating that motion has little effect on fluid forces; For MDP = 0.07, the lift of FSI is much greater than that of static, indicating that the fluid-structure interaction has an obvious amplification effect on fluid forces, which is similar to that of amplification effect of vibration on fluid forces when vortex-induced vibration occurs in flexible tube [12]. Figure 4 shows the comparison of transverse displacements. For MDP = 56.99, when Ur is small, the main mechanism is turbulence excitation. Therefore, there is little difference between FSI and static. With the increase of Ur, the displacement of FSI increases significantly, indicating that the fluidelastic instability gradually dominates the motion; For MDP = 0.07, although the root mean square of the maximum transverse displacement has reached 12%D, there is little difference between FSI and static. From the comparison of displacement, the system is still dominated by turbulent excitation. By comparing the forced vibration under flow past stationary tubes and vibration under fluid-structure interaction, combined with the spectral characteristics of lift, it can more comprehensively reflect whether the dominant excitation mechanism in the system is turbulence excitation, vortex shedding or fluidelastic instability.

(a)MDP=56.99

(b)MDP=0.07

Fig. 3. Fluid forces

(a)MDP=56.99

(b)MDP=0.07

Fig. 4. Transverse displacement

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4 Conclusions Based on the open source CFD tool OpenFOAM, the damping controlled fluidelastic instability prediction model of parallel triangular tube bundle is established successfully. The dominant excitation mechanism in the system can be reflected more comprehensively by comparing the forced vibration under flow past stationary tubes and vibration under fluid-structure interaction. The open-source tool OpenFOAM can be easily used to predict the fluidelastic instability behavior of tube bundle. Furthermore, investigations such as CFD analysis, fluidstructure interaction calculation and model construction based on OpenFOAM also provide new ideas, new methods and new approaches for CFD numerical method research and software development. Acknowledgements. The authors are grateful for the supports provided by the National Natural Science Foundation of China (No. 51606180, 11872060, 11902315) and China Scholarship Council.

References 1. Price, S.J.: A review of theoretical-models for fluidelastic instability of cylinder arrays in cross-flow. Journal of Fluids and Structure 9(5), 463–518 (1995) 2. Paidoussis, M.P.: A review of flow induced vibrations in reactors and reactor components. Nucl. Eng. Des. 74, 31–60 (1983) 3. Weaver, D.S., Fitzpatrick, J.A.: A review of cross-flow induced vibrations in heat exchanger tube arrays. Journal of Fluids and Structure 2(1), 73–93 (1988) 4. Price, S.J.: An investigation on the use of Connors’ equation to predict fluidelastic instability in cylinder arrays. J. Pressure Vessel Technol. 123, 448–453 (2001) 5. Paidoussis, M.P., Price, S.J.: The mechanisms underlying flow-induced instabilities of cylinder arrays in crossflow. J. Fluid Mech. 187, 45–59 (1988) 6. Chen, S.S.: Instability mechanisms and stability criteria of a group of circular cylinders subjected to cross flow. I. Theory. Journal of Vibration, Acoustics, Stress, and Reliability in Design 105, 51–58 (1983) 7. Chen, S.S.: Instability mechanisms and stability criteria of a group of circular cylinders subjected to cross flow. II. Numerical results and discussion. Journal of Vibration, Acoustics, Stress, and Reliability in Design 105, 253–260 (1983) 8. Robertson, E., Choudhury, V., Bhushan, S., et al.: Validation of OpenFOAM numerical methods and turbulence models for incompressible bluff body flows. Comput. Fluids 123, 122–145 (2015) 9. Hassan, M., Gerber, A., Omar, H.: Numerical estimation of fluidelastic instability in tube arrays. ASME Journal of Pressure Vessel Technology 132(4), 041307 (2010) 10. Khalifa, A., Weaver, D., Ziada, S.: Modeling of the phase lag causing fluidelastic instability in a parallel triangular tube array. Journal of Fluids and Structures 43, 371–384 (2013) 11. Weaver, D.S., Yeung, H.C.: Approach flow direction effects on the cross-flow induced vibrations of a square array of tubes. J. Sound Vib. 3, 469–482 (1983) 12. Feng, Z., Zang, F., Zhang, Y.: Flow field characteristics analysis of flow induced heat transfer tube vibration. Nuclear Power Engineering 35(2), 71–75 (2014)

Research on Aging Behavior and Life Prediction of Fluorine Rubber Seals in Nuclear Power Plants Under Silicone Oil Condition Zhu Xu(B) , Tiaobing Xiao, Zhe Xie, Yinqiang Chen, and Chun Gui China Nuclear Power Operation Technology Co., Ltd, Wuhan, Hubei, China [email protected]

Abstract. The comprehensive aging experiments of fluororubber sealing materials in the nuclear power plant were carried out in the condition of pressure load, silicone oil and temperature from 135 °C to 165 °C. Then, compression set, Shore hardness, infrared spectroscopy and thermogravimetric analysis were tested for original samples and aging samples. The results showed that the performance of compression set gradually declines, but the Shore hardness did not see a significantly and continuously increasing or decreasing trend. The fourier infrared spectroscopy and thermogravimetric analysis results had no great change in different aging samples. The samples were mutually affected by degradation and crosslinking during aging process. In the life model which set compression as the key parameter, the predicted life of fluororubber seals was 148.28 years at 50 °C when the end point of compression set was 40%. Keywords: Fluororubber · Compression set · Shore hardness · Aging behavior · Life prediction

1 Introduction A large number of fluororubber seals are used on the crucial mechanical equipments and facilities in nuclear power plants. However, compared with metal parts, fluororubber seals will gradually degrade with time under the collective effect of environmental factors such as temperature, medium, pressure, and oxygen [1–3], and even lose their performance and lead to failure of nuclear power equipments. With the increasing attention to nuclear power safety, the nuclear plant needs to take certain preventive measures for fluororubber seals to establish a systematic and reliable management mode, which can not only ensure the safe and efficient operation of equipment, but also reasonably reduce the cost of nuclear power plants. Xian Yi studied the thermal decomposition kinetics of fluororubber [4], Zhang Xiaojun studied the aging mechanism of fluororubber seals under humid and heat conditions [5], Zhang Luping analyzed the change of glass transition temperature during the aging process of special fluororubber [6]. However, there are few domestic studies on the aging behavior and lifetime of fluororubber seals for nuclear power plants. In this paper, the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 329–336, 2023. https://doi.org/10.1007/978-981-19-8780-9_33

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aging experiments of the fluororubber seal were carried out in the condition of temperature, silicone oil and pressure, and then testing the performance of compression set, Shore hardness, infrared spectroscopy and thermogravimetric analysis of the aging samples. The performance change behavior with time of fluororubber seals under the combined conditions of silicone oil and pressure was studied, and the life prediction model was established at 50 °C.

2 Experiment 2.1 Experimental Materials The fluororubber seals were made into B-type samples according to GB/T 7759.12015[7]. The samples were cylinders with about 13 mm diameter and 6.3mm height. The test medium was AK 350 dimethyl silicone oil produced by WACKER Company, which was consistent with the oil type used in nuclear power plants. The flash point and burning point of silicone oil were 260 °C and 410 °C, respectively. The pre-test confirmed that that silicone oil could be used on accelerated aging tests. 2.2 Experimental Equipments The experimental equipments and instruments mainly used in these experiments included heat aging box, compression set fixture, oil drum, micrometer, Shore hardness tester, Fourier infrared spectrometer, and thermogravimetric analyzer. The corresponding equipment models and manufacturers were shown in Table 1. 2.3 Trial and Test With reference to the two standards of GB/T 3512-2014[8] and GB/T 7759.1-2015 [7], we first set the rubber sample under the compression set fixtures to compress and deform, the compression rate were 25%. Then put the fixture into stainless steel oil drums filled with AK 350 silicone oil and sealed with tin foil. Finally the drums was placed in a heat aging box, and the temperature of the heat aging box was set to 135 °C, 145 °C, 155 °C, and 165 °C, respectively. The compression set fixtures were taken out at different aging times.Then the fixtures were released when they cooled down to the standard laboratory temperature within 30–120min. And then, we measured and recorded the recovery height and compression set of the rubber samples by using micrometer after the rubber samples released naturally for one hour. After that, the fluororubber samples were put back into the heat aging box for the composite accelerated aging test again. According to the compression set (C value) result of the rubber samples, we draw the curve of the relationship between the compression set and time with temperature and established the life model of the fluororubber according to HG/T 3087-2001[9], which predicts the service lifetime at 50 °C. For the some retained samples, we measure its Shore hardness value according to GB/T 531.1–2008 [10], observe its macroscopic appearance according to the equipment usage method, and measure the Fourier transform infrared spectroscopy and thermogravimetric analysis curve everytime.

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Table 1. Main experimental equipment and instruments Name

Model

Heat aging box

401B

Compression set fixture

Homemade

Oil drum

Homemade

Micrometer

25mm

Shore hardness tester

Shore A

Fourier infrared spectrometer

Spectrum two

Thermogravimetric analyzer

TGA2

3 Results and Discussion 3.1 Compression Set The relationship between the value of 1-compression set (1-C) and time (t) of the fluororubber samples at the aging temperature from 135 °C to 165 °C was shown in Fig. 1. With the increase of aging time, value of 1-C of the fluororubber samples gradually decreased. At the same aging time, the higher the aging temperature is, the faster the value if 1-C declines. With the increase of aging time, the decreasing trend of value of 1C gradually flattened. The results shown that under the influence of pressure, silicone oil and temperature, the fluororubber samples suffered a certain degradation, and the molecular chain lost flexibility, resulting in a gradual decline in the compression set properties of the fluororubber, especially in the early stage of aging. Obviously, with the increase of aging time, the number of degradable molecules decreases, and the decline trend of the value of 1-compression set (1-C) gradually flattens. Therefore, the compression set index can better evaluate the actual aging state of fluororubber. 3.2 Shore Hardness The relationship between Shore hardness (Shore A) and time (t) of the fluororubber samples at the aging temperature from 135 °C to 165 °C was shown in Fig. 2. The initial hardness of the fluororubber was 70 Shore A, and the hardness fluctuated around 70 Shore A with the increase of aging time. There is no obvious trend of continuous increase or decrease of the hardness value [11, 12]. The results showed that under the influence of pressure, silicone oil and temperature, the molecular chains of the fluororubber samples were degraded and cross-linked at the same time. As a result, the hardness value of fluororubber samples did not show a clear trend of increase or decrease with the increase of aging time. Therefore, the hardness index cannot well evaluate the actual aging state of fluororubber. 3.3 Infrared Spectroscopy Figures 3 and 4 show the Fourier infrared spectroscopy of fluororubber samples with different aging times at 135 °C and 155 °C. The results had two broad band intensities

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Fig. 1. The relationship between value of 1-C and time (t)

Fig. 2. The relationship between hardness(Shore A) and time (t)

between wavenumbers 1400 to 1000 cm−1 , which is a typical C–F vibrational absorption peak. Another absorption peak could also be observed around wavenumber 880– 720 cm−1 , which is consistent with the infrared spectroscopy of fluororubber. At different aging times, the characteristic peaks of the spectra of the fluororubber samples did not shift significantly, indicating that the typical molecular structure of the rubber samples did not change significantly. But the absorption peak area between 1100–1000 cm−1 gradually decreased with the increase of time, indicating that the C–F bond was broken and recombined, and degradation occurred. 3.4 Thermogravimetric Analysis The thermogravimetric curves of the rubber samples corresponding to different aging temperatures and different aging times were shown in Fig. 5. The thermogravimetric curves of the fluororubber samples after aging under different conditions almost overlap. Compared with the original sample, the change of the reaction initiation temperature point of the sample after aging was small, indicating that the fluororubber samples had better thermal stability below 165 °C. The longer the accelerated aging of the fluororubber samples is, the smaller the reaction initiation temperature is. According to

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Fig. 3. Infrared spectroscopy of samples with different aging time at 135 °C

Fig. 4. Infrared spectroscopy of samples with different aging time at 155 °C

Fig. 5. Different aging time of thermogravimetric analysis test results

the different stages of the thermogravimetric curves, the composition of fluororubber includes moisture and volatile non-rubber components before 300 °C, rubber polymer

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components at 300 ~ 550 °C, carbon black components and residual ash residues above 550 °C. 3.5 Life Evaluation Under Composite Conditions Referring to HG/T 3087-2001[9], value of 1-compression set (1-C) and time (t) at 135 °C, 145 °C, 155 °C, and 165 °C were fitted according to the exponential relationship: P = B × e−kt

α

The values of B, k, a from 165 °C to 135 °C fitting were shown in Table 2. The R2 values of the fitting curves of compression set at each temperature were greater than 90%, indicating a good fit of the life curve. Table 2. Key parameters of life prediction model T(°C)

B

k

a

1/(T)

lnk

R2

165

1.0031

0.1192

0.56

0.002283

-2.12695

0.9223

155

1.0006

0.0723

0.56

0.002336

-2.62693

0.9728

145

0.9771

0.0583

0.56

0.002392

-2.84215

0.9769

135

0.9853

0.0446

0.56

0.002451

-3.11002

0.9691

According to Table 2, the linear equation for the relationship between lnk and 1/T is ln k = − 5634.1 × 1/T + 10.651 When the service condition of the fluororubber seal was 50 °C, k50 = 0.0011227 was calculated according to the fitting curve of lnk and 1/T, and B50 = 0.9915 was calculated by taking the average value of fitting B values of four aging temperatures. At 50 °C, the life curve model of the performance P and time t represented by the compression set C value of the fluororubber sample was C = 1 − 0.9915 × e−0.0011227×t

0.56

When C = 0.3, the predicted service life t0.3 of the fluororubber sealing sample under the condition of silicone oil at 50 °C was 77.05 years; when C = 0.4, the prediction of the service life of the fluororubber sealing sample under the condition of silicone oil at 50 °C was 77.05 years. Life t0.4 was 148.28 years. According to the manufacturer, the predicted life of the fluororubber sealing ring at 50 °C was 126.4 years [13], and the result was basically consistent with the prediction results of this model (Figs. 6 and 7).

4 Conclusions For the fluororubber seal of nuclear power plant, the composite accelerated aging test method of temperature-medium-load was used to study the aging behavior of parameters such as compression set and Shore hardness, and the conclusions are as follows:

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Fig. 6. Fitting curve of 1/(T) and lnk

Fig. 7. Life prediction model of fluororubber samples at 50 °C

During the composite aging process of temperature-medium-load, the value of 1-C of the fluororubber sample gradually decreased with the increase of aging time, and the hardness did not show a significantly and continuously increasing or decreasing trend. It may be that under the action of load, grease and temperature, the degradation of the fluororubber sample made the molecular chain lose flexibility, and some small molecules also had cross-linking effect, which was the result of mutual influence. When the fluororubber sample established the performance-temperature-time relationship curve with the compression set, the exponential relationship had a good fit. When the compression set was 40% as the end of life, the predicted life t0.4 was 148.28 years.

References 1. Chen, X., Liu, L., Xu, J.: High performance sealing rubber used for ships (III) Study on thermal aging properties of fluorinated rubber vulcanizate. China Elastomerics. 06, 16–18 (2004) 2. Kader, M.A., Bhowmick, A.K.: Thermal ageing, degradation and swelling of arcylate rubber, fluororubber and their blends containing polyfunctional acrylates. Polym. Degrad. Stab. 79, 283–295 (2003) 3. Yilan, S., Hui, L., Junling, L.: Study on the thermal-oxidizing aging of fluororubber with dynamic thermal mechanical analyzer. Organic Chemical Industry. 01, 30–32 (2017)

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4. Xian, Y., Zhang, Y., Wu, Y.: Study on thermal decomposition dynamics and ageing of fluororubber. Sichuan Chemical Industry. 01, 13–16 (2016) 5. Zhang, X., Chang, X., Zhang, S.: The hygrothermal aging mechanism of fluororubber sealing materials. Lubrication Engineering 05, 38–40+45 (2013) 6. Luping, Z., Yan, S., Hui, L.: Study on the change of the glass transition temperature of fluoroelastomer in the aging process. China Elastomerics. 04, 34–36 (2011) 7. GB/T 7759-2015: Rubber, vulcanized or thermoplastic—determination of compression setPart 1: At ambient or elevated temperatures 8. GB/T 3512-2014: Rubber, vulcanized or thermoplastic-accelerated ageing and heat resistance tests-air-oven method 9. HG/T 3087-2001: Method of accelerated determination for Shelf-life of rubber static sealing parts 10. GB/T 531.1-2008: Rubber,vulcanized or thermoplastic-Determination of indentation hardnes-Part 1: Duromerer method (shore hardness) 11. Duan, Y., Wang, Y., Yu, Y.: Study on thermal oxidative aging resistance of fluoro rubber. China Rubber Industry. 07, 768–771 (2018) 12. Deng, J., Zheng, Y., Pan Z. : Study on thermal oxidative aging properties of different rubber materials. Special Purpose Rubber Products 06, 17–20+36 (2019) 13. Winwood, J: Qualification test report of Lisega hydraylic snubbers. Lisega Report. 16–21 (1996)

Super High Jacking Installation Technology of Ring Crane Arch Frame During Nuclear Power Plant Maintenance Period Zhishuang Liu1(B) , Jianjian Wu1 , Kejun Li2 , Furong Zhang1 , and Jie Su1 1 China Nuclear Industry 23 Construction Co., Ltd, Beijing 101300, China

[email protected] 2 Central South University of Forestry and Technology, Changsha 410004, China

Abstract. The self-provided maintenance arch frame of the ring crane is the necessary equipment for the ring crane maintenance. The maintenance and replacement parts of the ring crane can be lifted by using its jacking mechanism, and the overhaul task of the ring crane trolley can be safely and efficiently completed. In order to meet the requirements of rapid installation of arch frame in the construction period and overhaul time window period of the in-service nuclear power plant, a set of super high jacking installation equipment and installation process of in-service ring crane arch frame is designed. The design of the boom, fixture, hydraulic, and strength check of critical parts of the jacking installation equipment was introduced. The super high segmented jacking rotation installation of the maintenance arch frame of the nuclear power plant in service, the wireless monitoring system in the construction process and the specific construction process were described in detail. The construction equipment has been successfully applied to the maintenance of a nuclear power plant. The construction period was reduced by more than 20 days, and about 240 million yuan increased the revenue. Keywords: Ring crane maintenance arch · Jacking equipment · Construction technology · Jacking installation · In-service nuclear power plant

1 Introduction In order to optimize the hoisting process during the maintenance of the ring crane, a nuclear power plant was added a self-provided maintenance arch frame and related hoisting facilities in the design of the ring crane [1]. However, during the construction of the project, it was found that there was interference between the design height of the arch frame and the height of the dome’s sprinkler system. In order to seize the critical milestone in the installation of the dome, the installation of the maintenance arch frame was cancelled [2]. After the formal operation of the nuclear power plant, when the trolley of the ring crane fails, it is necessary to carry out maintenance and replace accessories in time. The maintenance arch is a necessary facility for the maintenance of the ring crane [3], various repair components can be lifted with the help of its lifting mechanism. If the

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 337–346, 2023. https://doi.org/10.1007/978-981-19-8780-9_34

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maintenance arch is not installed, the overhaul task of the ring crane trolley cannot be completed. The conventional installation method is to hoist and install the maintenance arch frame by installing a hoisting system on the central arch of the ring crane. The operation cycle is long and complex, and there is a risk of falling objects from high altitudes. It is not suitable for nuclear power plants that have been commercialized for power generation [4, 5]. In order to complete the installation of the maintenance arch frame safely, efficiently and reliably within a limited time, it is necessary to develop a set of special super-high jacking construction equipment and corresponding construction techniques [6].

2 Project Overview The layout of the ring crane of the nuclear power plant is shown in Fig. 1. The selfprovided maintenance arch is installed on the ring crane bridge at one side of the central arch. The top of the arch beam is kept at a sufficient distance from the dome of the nuclear island to ensure the installation space of the dome internal components and the safe distance between the maintenance arch and the spray pipe in the dome during the ring crane operation. Before hoisting the dome, a domestic nuclear power plant found that the designed self-provided maintenance arch was too high, which interfered with the spray pipe inside the dome. Therefore, the self-provided maintenance arch was removed, and the hoisting of the dome was completed first, and then the installation of the maintenance arch was completed in the later nuclear power maintenance window. The maintenance arch to be installed is 6.85 m high and 12.7 m wide. After installation, a safe distance of 1.55 m is reserved between the upper part of the arch and the dome, and the installation position is 6.4m from the central arch axis. The internal diameter of the seated ring crane is 20.9 m, the clear spacing of the bridge is 8m, and the height of the guardrail on the bridge is 1.2 m. Nuclear island

Central arch frame

self-provided maintenance arch frame

containment

Ring crane 1550 6400

Fig. 1. Schematic diagram of the ring crane structure of a nuclear power plant (unit: mm)

The lifting installation ring crane maintenance arch is mainly composed of arch crossbeam, auxiliary crossbeam, upper and lower legs and electric hoist. The dimensions, weight and installation height of each component are shown in Table 1.

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Table 1. Information on the equipment of the jacking installation ring crane overhauling the arch frame Part name

Size (mm)

Weight (kg)

Installation height (m)

Arch frame beam

12600 × 500 × 550

3736

33.40

Upper outrigger

2450 × 500 × 550

1228

33.40

Electric hoist

1780 × 1100 × 1530

1700

33.40

Lower outrigger

3500 × 500 × 550

1504

30.40

Auxiliary beam

12520 × 500 × 534

1916

30.40

3 Special Equipment Design 3.1 Overall Structure Design The lifting installation equipment of ring crane arch frame is shown in Fig. 2, and the main performance parameters are shown in Table 2. Hydraulic and control system

Boom system

Bodywork

Fixture system

Outriggers Rotary mechanism

Fig. 2. Schematic diagram of construction equipment for jacking installation

The jacking installation equipment is mainly composed of outriggers, fuselage, boom system, fixture system, hydraulic and control system, etc. After the jacking installation equipment is transported to the designated place in the nuclear island, the outrigger shall be spread out, and a box-shaped base plate should be placed at the bottom of the outrigger to reduce the contact pressure between the equipment and the ground. The luffing mechanism of the boom system will luffing the telescopic boom from the horizontal position to the vertical position, place the ring crane arch into the equipment fixture for automatic clamping through the on-board crane, then the telescopic boom will carry out the telescopic action to lift the ring crane arch to the target height, adjust the ring crane arch to the installation position through the body rotation action, and conduct flange connection, and finally complete the ring crane arch installation. The lifting equipment is powered by electric motor, and it adopts hydraulic transmission system and remote wireless remote control operation.

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Design parameters

Numerical value

Maximum jacking weight (t)

12

Maximum jacking height (m)

34

Maximum rotation speed (r/min)

0.7

Rotation angle range (°)

± 180°

Boom luffing range (°)

-2°-92°

Boom length (m)

9.59–29.36

Equipment size (l × b × h, m)

12.565 × 3.0 × 2.845

The total mass of equipment (t)

29.6

3.2 Boom System Design The boom system is the core component of the equipment to achieve high installation and maintenance of the arch. It is mainly composed of four section telescopic boom with regular octagon structure, three built-in telescopic cylinders and luffing cylinders, as shown in Fig. 3. The four telescopic booms are named as the first boom, the second boom, the third boom and the fourth boom from the outside to the inside. The end of the first boom is hinged with the fuselage, and the first telescopic cylinder is hinged with the first boom and the second boom respectively, and so on; The regular octagonal structure of the telescopic boom can ensure the stiffness of the boom in all directions and prevent large deformation in the jacking process, which will affect the installation accuracy of the arch. The three telescopic oil cylinders realize the extension of each telescopic arm of the boom from the inside to the outside. When the extension stroke of the third telescopic oil cylinder is in place, the boom lifting action is completed. The boom luffing cylinder adopts double acting hydraulic cylinders, which are respectively connected with the side of the first boom and the rotating platform of the fuselage, so that the boom can reach the vertical working state from the horizontal state, and can ensure that the boom exceeds the vertical state (92°) in a small angle, so as to eliminate the impact of station position error and equipment position error on the installation. 3.3 Fixture System Design The fixture system is the key component of the equipment to realize the independent clamping and jacking of the maintenance arch. It adopts the structural design that can be opened on one side to facilitate the clamping and disengagement of the maintenance arch. As shown in Fig. 4, the fixture system is mainly composed of mounting seat, bearing seat, first and second clamping arms, clamping cylinder and swing cylinder. The clamp bearing base is hinged with the boom mounting base on one side, and the opposite side is connected by the swing oil cylinder. The first clamping arm is a fixed arm connected with the bearing base, and the second clamping arm is a movable arm hinged with the middle of the bearing base. The end is hinged with another position of the bearing base

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Fixture

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Section 2 ~ 4 arm (retracted state)

Boom luffing cylinder

Section 1 arm

Bodywork

Fig. 3. Schematic diagram of the boom structure

through the clamping oil cylinder. The upper part of the fixture bearing seat provides a sitting space for the maintenance arch; The second clamping arm retracts the clamping maintenance arch through the clamping oil cylinder, and a pressure sensor is installed in the rod free cavity of the clamping oil cylinder to detect the clamping pressure, so as to avoid the sliding of the maintenance arch and reduce the high-altitude operation of personnel during the jacking process; Through the swing oil cylinder, the telescopic boom can swing back and forth under the working condition of clamping the maintenance arch, and it can be adjusted within a certain angle range to eliminate the influence of the deformation of the telescopic boom on the installation accuracy of the maintenance arch. Two electromagnet clamping blocks are installed on each clamping arm, which do not generate magnetism before the clamping action, but generate magnetism during clamping and jacking installation to increase the clamping force. After the maintenance arch is installed in alignment, the power is cut off to eliminate magnetism.

4 Introduction of Super-High Jacking Installation Technology 4.1 Introduction of a Wireless Monitoring System For the case that the dome has been covered, the space for installation of the jacking maintenance arch is very limited. The upper space cannot touch the dome and related facilities. The lower space needs to cross the guardrail and cable sliding frame of the ring crane main beam. The overall jacking of the maintenance arch is very likely to cause interference and accidents. Therefore, the scheme of sectional jacking and wireless monitoring is determined. As shown in Fig. 5, the jacking maintenance arch is divided into section 1 and section 2, in which section 1 is composed of the lower outrigger and the auxiliary beam.

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First clamping arm

Clamping block

Clamping cylinder

Second clamping arm

Fixture carrier Boom mount

Swing cylinder Boom

Fig. 4. Schematic diagram of the fixture system structure

After the auxiliary beam is installed and fixed on the lower outrigger and the ring girder, the connection of the auxiliary beam is removed, and the lifting equipment lifts it back to the ground. Wireless inclination sensors are installed on the crossbeams of section 1 and section 2 to monitor the overall inclination state of section 1 and section 2 caused by the flexible deformation of the boom during the lifting rotation installation process in real time, and display and adjust it on the display instrument to ensure the installation accuracy of the lifting arch frame; Ultrasonic sensors are uniformly distributed outside the upper and lower outriggers to detect the distance between segment 1 and 2 and the surrounding existing equipment in real time during the lifting and rotating installation process, and wirelessly transmit the data to the display instrument for processing and display, so as to ensure the safe distance between the fixed arch extension equipment and the surrounding structures and avoid collision; The flange surface at the bottom of the outrigger is equipped with a laser rangefinder to measure the horizontal displacement during the seating and installation of sections 1 and 2, and conduct real-time accurate measurement during the hole positioning of the connecting flange, so as to provide data support for the final accurate positioning. 12600mm 550mm

2450mm

beam

wireless tilt sensor

Ultrasonic sensor

upper outrigger segment 2

3500mm

Auxiliary beam (After the lower outrigger is installed and fixed, the connection is released and it falls back)

lower outrigger segment 1

Laser rangefinder

Fig. 5. Arch frame segment and wireless sensor distribution

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4.2 Super High Jacking Installation and Construction Process The construction process of super-high jacking installation mainly includes: (a) (b) (c) (d) (e) (f) (g)

Nuclear reactor core pool anti-foreign object arrangement; Jacking rotation action space measurement; Hoisting and introduction of construction equipment and maintenance arch; Construction equipment jacking position determination; Ground assembly and segmented jacking and rotating installation; Inspection and acceptance test of arch frame; Construction equipment exit and site cleaning.

The nuclear reactor core pool is fully enclosed with three layers of protection to prevent foreign objects. Move the aisle walkway of the reactor core refueling tank to the designated position, as shown in Fig. 6. After strictly checking and ensuring the stability of the railings around the pool, three layers of nylon rope, safety net, and plastic sheet are laid in sequence, and each layer is reliably fixed on the aisle walkway and the railing around the pool. Due to the extended operation time of the unit, whether the structure of the main beam of the ring crane is deformed, and whether the actual span between the left and right outriggers of the maintenance arch is changed relative to the theoretical design size, it also needs to be strictly confirmed. The installation-related data such as the position of the arch installation base, the clearance of jacking and rotation, and the position of possible interference points are collected.

Fig. 6. Nuclear Reactor Core Pool Anti-foreign Matter Arrangement

After the maintenance arch frame and construction equipment are protected against radiation pollution, they are hoisted into the site according to “small first, then large, first inside and outside.” Equipment larger than the hoisting channel, such as arch frame beams, auxiliary beams, aerial work vehicles, and jacking tooling, are simulated by software, carefully confirm the lifting height and lifting posture, and repeat the adjustment before they can be successfully introduced through the hoisting channel. The position of the construction equipment jacking operation shall be determined by pulling the wire at the center of the base of the outrigger of the arch frame. The four hydraulic outriggers can adjust the level of the construction equipment getting off and the angle of the boom

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system, adjust the center of the fixture and the center of the base of the arch outriggers and determine the working position of the construction equipment. 4.3 Super-High Segmented Rotary Jacking Installation The arch frame adopts the construction scheme of super high segmented jacking and rotating installation. The specific process is as follows: (a) The auxiliary beam and the lower outrigger are hoisted to the designated position by the 10t hook of the ring crane, and then the group is bolted to form the segment 1. At this time, the main arm of the jacking equipment retracts to the shortest, and the luffing is 90 º; (b) The segment 1 is hoisted to the fixture of the jacking equipment by the 10t hook of the ring crane, and fixed by the independent clamping. During the clamping process, the aerial work vehicle is used to assist in monitoring and adjusting the position to ensure the center alignment; (c) The main arm of the jacking equipment extends out of the jacking segment 1 in sequence, and through the bodywork slewing mechanism, segment 1 has a sufficient safety distance when passing through the gap between the ring crane main beam, as shown in Fig. 7(a). When the flange surface at the bottom of the lower outrigger of the jack-up segment 1 is higher than the guardrail of the main beam of the ring crane, turn it counterclockwise to make the flange surface of the normal outrigger face the installation base, as shown in Fig. 7(b). (d) The jacking equipment slowly adjusts the jacking height of the main boom and, at the same time, cooperates with the wireless monitoring system to realize the positioning of the lower outriggers of the arch frame segment 1 to the installation base. Moreover, use the aerial work vehicle to complete the bolting and fastening of the lower outriggers of the arch frame segment 1 and the mounting seat of the main beam of the ring crane, as shown in Fig. 7(c). Then release the connection between the lower outriggers and the auxiliary beam, separate the auxiliary beam of the jacking equipment from the lower outriggers, rotate it back down and jack the auxiliary beam from the jacking equipment fixture to the ground with a 10T hook. (e) Use the same method as above to rotate and jack up the arch frame segment 2 on the lower outrigger connection seat that has been fixed. The jacking equipment clamps are automatically released to open and close the vehicle to complete the installation of the arch frame, as shown in Fig. 7d–f. During the entire super-high jacking and rotating installation process, pay attention to various monitoring values on the wireless acquisition and display instrument in real-time. Stop the operation immediately when an alarm occurs to troubleshoot the problem.

5 Engineering Application The construction equipment and process technology for the super-high jacking and rotating installation of the ring crane arch frame of the in-service nuclear power plant

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(a) Jacking segment 1

(b) Rotating through the ring crane main beam

(c) Segment 1 lower outrigger drop installation

(d) Jacking segment 2

(e) Rotating through the ring crane main beam

(f) Section 2 drop installation

345

Fig. 7. Schematic diagram of the installation process of the super-high segmented rotary jacking of the arch frame

described in this paper, which has been successfully applied in the reassembly project of the self-provided maintenance arch frame of the ring crane of Ling’ao Phase II Units 3 and 4 of the Daya Bay Nuclear Power Plant. This technology shortens the original 30day construction period to 7 days, satisfactorily guarantees the project progress, quality and construction safety, and greatly reduces the time window, manpower, materials and other inputs for shutdown and maintenance of commercial nuclear power plant (Fig. 8).

6 Conclusions In this paper, the research subject is the Lingao phase II l308/l407 overhaul “ring crane self-provided maintenance arch reconstruction” project, which carries out the research on the ultra-high jacking installation technology of the ring crane maintenance arch in the in-service nuclear power plant, develops the special jacking installation equipment, and develops the ultra-high sectional jacking rotation installation process in combination with the on-site construction conditions. The following conclusions can be drawn:

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Fig. 8. Engineering applications

(a) The particular jacking installation and construction equipment adopt a multihydraulic controlled lowering structure, a four-section telescopic boom with an octagonal stable cross-section, a special single-side opening and closing fine-tuning clamp, a dual-motor power system, and redundantly designed electronic control, hydraulic system. The equipment has cons jacking weight and high jacking height and can rotate and fine-tune to realize precise jacking seat installation under limited conditions. (b) The super-high segmented jacking and rotating installation process for the ring crane arch frame have been successfully applied to the re-installation of the ring crane arch frame of Ling’ao Phase II Units 3 and 4. Compared with the traditional construction process, this process dramatically reduces the time occupied by the ring crane, significantly improve installation efficiency. Reduce labor intensity and create considerable economic benefits.

References 1. Zhuang, Y.J., Wang, D., Liang, J.: Analysis and countermeasure of the impact of maintenance arch bridge of polar crane on the dome. Mechanical & Electrical Engineering Technology 41(06), 157–160 (2012) 2. Wang, Y.X., Huo, Y.B.: Erection and management for polar crane of Ling’ao NPP phase II. China Nuclear Power 4(04), 325–337 (2011) 3. Yan, P.F., Feng, L., Li, K., et al.: Experience feedback from commissioning of ap1000 project nuclear island polar crane. Nuclear Power Engineering 35(S1), 120–123 (2014) 4. IAEA: Safety requirements for nuclear power plants (2012) 5. IAEA: Basic safety principles for nuclear power plants (1999) 6. Liu, Z.S., Wu, J.J., Su, J., Dai, S.F.: Construction technology of ultra-high jacking installation in ring crane maintenance arch frame of nuclear power plant in service. Architecture Technology 52(10), 1173–1176 (2021)

Separation of Strontium from Other Fission Products in High Level Liquid Waste by TODGA Nong Shuying1,2(B) , Chai Youqi3 , Yang Anbo1,4 , Zhao Qiaozi3 , Wang Jian5 , Li Tianfu4,5 , and Li Lianshun1 1 Science and Technology Research Institute of CNNC 404 Co., Ltd., Jiayuguan, Gansu, China

[email protected] 2 CNNC 404 Chengdu Nuclear Technology Engineering Design and Research Institute Co.,

Ltd, Chengdu, Sichuan, China 3 The Third Branch of China Nuclear 404 Co., Ltd, Jiayuguan, Gansu, China 4 CNNC 404 Chengdu nuclear technology engineering design and Research Institute Co., Ltd.,

Chengdu, Sichuan, China 5 CNNC 404 Co., Ltd., Jiayuguan, Gansu, China

[email protected]

Abstract. Large amounts of high level liquid waste (HLLW) has been generated from the reprocessing of spent nuclear fuel employing the PUREX process. It contains complex composition including various fission products (FPs), corrosion products and minor actinides (MAs). Some of these nuclides, such as strontium-90 (90 Sr), can be used in military and medical fields and have commercial value. The separation of strontium is not only about to develop the value of HLLW but also reduce the radioactivity of the liquid waste for disposal. Separating of Sr from other elements including Ln, Y, Cs, Ru, Fe, Mo, Zr, etc. is essential to obtain available strontium products. In this work, N,N,N ,N -Tetraoctyldiglycolamide (TODGA) was used for the purpose focusing on removing fission products and corrosion products other than Sr. Firstly, 0.05 mol/L TODGA was used as extractant to separate Sr from Ln, Zr and Y, by which process Ln, Zr and Y were extracted to the organic phase and Sr entered the raffinate. Meanwhile, Ru, Fe and Cs were in the aqueous phase together with Sr. 0.2 mol/L TODGA was further used to contact with the raffinate, by which Sr was extracted and Ru, Fe and Cs were still in the aqueous phase being separated. After the Sr was stripped from the organic phase, Sr product was obtained. The separation method has been verified with non-radioactive simulated HLLW and radioactive genuine HLLW as feed liquid, proving its reliability. Keywords: Spent fuel reprocessing · Strontium-90 · Extraction · Fission products · TODGA

1 Introduction HLLW generated from reprocessing of spent nuclear fuel by PUREX process is highly acidic (HNO3 about 3 mol/L) and complex in composition. The elements in HLLW © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 347–353, 2023. https://doi.org/10.1007/978-981-19-8780-9_35

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include minor actinides (Americium (Am), Curium (Cm), etc.), fission products, including Lanthanides (Lanthanum (La), Neodymium (Nd), Europium (Eu), etc.), Strontium (Sr), Cesium (Cs), Ruthenium (Ru), Palladium (Pd), Niobium (Nb), Zirconium (Zr), etc., as well as many corrosion products (Iron (Fe), Nickel (Ni)) [1–4]. Since radio strontium 90 Sr is one of the major heat generators in nuclear waste, the severe conditions for waste repository design governed by decay heat would be reduced on its removal. Besides, 90 Sr (half-life: 28.79 year), as a pure beta emitter, whose decay daughter is 90 Y (half-life: 2.67 day), is used in medical radiotherapy [5–8]. Since the 1940s, people have been working to remove and recover 90 Sr from HLLW. The earliest reagents used include di(2-ethylhexyl)phosphoric acid, chlorinated cobalt dicarbollide and various derivatives of macrocyclic polyethers. The further modification of these works has led to the development of modern extraction methods, such as SREX, FPEX and UNEX, which are suitable for extracting 90 Sr from HLLW. SREX (strontium extraction) process uses di-t-butylcyclohexano-18-crown-6 (DtBu18C6) as extractant to efficiently and selectively extract Sr from acidic HLLW containing a variety of fission products and inert components [9–11]. FPEX (fission product extraction) process is based on the simultaneous extraction of Cs and Sr from HLLW, the extraction of Sr with DtBu18C6 and the extraction of Cs with BOBCalixC6 [12–14]. UNEX (universal extraction) process implemented by INL, America and KRI, Russia, uses 0.08 mol/L chlorinated cobalt dicarbollide, 0.5% polyethylene glycol 400 and 0.02 mol/L diphenylN,N-dibutylcarbamoyl methyl phosphine oxide as extractants [15, 16]. N,N,N ,N -tetra octyldiglycolamide (TODGA) is a widely concerned reagent in the treatment of HLLW because of its good extraction performance, excellent stability and easy to be obtained. Some studies have shown that TODGA displayed an affinity toward extraction of strontium from 2 to 3 mol/L nitric acid solutions [17–21]. In this work, for the HLLW after the removal of minor actinides, TODGA was used as the extractant to separate Sr from other fission products and corrosion products by batch extraction. Firstly, HLLW solution was extracted by a lower-concentrated TODGA to separate Sr from those with high distribution ratios such as Ln, Y and Zr. The raffinate was retained for a subsequent extraction with a higher-concentrated TODGA to separate Sr from elements with low distribution ratios such as Cs, Ru and Fe and Sr products was obtained after stripping. A preliminary hot test was conducted to verify the separation process.

2 Experimental TODGA was synthesized in our lab using a new method which hasn’t been published, and the purity analyzed by HPLC was > 96% (mobile phase: methanol, peak area: TODGA of 96.4%, ethyl acetate of 3%, di-n-octylamine of 0.6%). The organic TODGA solutions were prepared by dissolving precisely weighed amounts of TODGA and phase modifier into the kerosene diluent. The simulated aqueous solutions were prepared by dissolving appropriate amounts of nitrates into HNO3 solutions. All the metal nitrates are commercially available without further purification. The radioactive genuine HLLW was obtained from CNNC 404 Co., Ltd. In the experiments, 10 ml of organic and 10 ml of aqueous phases were contacted in a 20 mL glass-stoppered tube and mixed by an oscillator with 250 rmp for 15 min.

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Preliminary experiments showed that the equilibrium was reached after five minutes. After stewing for 5 min with adequate phase separation, an aliquot of each phase was taken for the measurement of the distribution ratio (DM ) that equals to the ratio of the concentration of M in the organic phase over that in the aqueous phase. All the extraction experiments were performed at 25 + 0.2 °C. Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-OES, Agilent 5110, USA) and Inductively Coupled Plasma Mass Spectrometer (ICP-MS, PerkinElmer NexlON 2000, USA) were used to measure the concentrations of metals in both phases for cold tests. Nuclear Magnetic Resonance Spectromete (NMR, Bruker 400MHz, Germany) was used to check the structure of TODGA. The activity of 90 Sr was determined by measuring the activity of its daughter 90 Y. The sample was put aside for 14 days and then 90 Y was extracted with an equal volume of 30% TRPO in kerosene. The activity was determined by use of a liquid scintillation analyzer (Packard 2200 CA). The 137 Cs and 152 Eu activity was determined with a Ge-Li detector (EG&G, USA).

Fig. 1. a Dependencies of DSr with concentrations of TODGA. Aqueous phase: 3mol/L HNO3 (initial concentration). b Extraction of Sr by 0.2 mol/L TODGA-0.5 mol/L TBP-OK in different initial HNO3 concentrations

3 Results and Discussion The extraction ability of strontium by TODGA increases with the rising of TODGA concentration, as shown in Fig. 1a. To provide insight into the composition of species of Sr2+ formed in the extraction, the dependences of DSr on the concentrations of TODGA and initial concentrations of HNO3 in aqueous phase were determined, respectively. Figure 1a shows the plot of logDSr and log[TODGA], and the slope is 2.28 ± 0.03. The experiments were implemented by 0–0.4 mol/L TODGA-0.5 mol/L TBP-kerosene with two phases of O:A = 1 and 3 mol/L HNO3 in aqueous phases. Figure 1b shows the plot between logDSr and log[HNO3 ]. The experiments were carried out by 0.2 mol/L TODGA-0.5 mol/L TBP-kerosene with two phases of O:A = 1. Based on the above results, the possible mechanism of strontium extraction by TODGA is as follows:  − (1) Sr2+ + 2 NO− 3 + 2 TODGA + HNO3  Sr NO3 2 (TODGA)2 ( HNO3 ) Figure 2 shows the distribution ratios of different fission and corrosion products extracted by TODGA under the condition of 3 mol/L HNO3 and O:A = 1. Kerosene

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Fig. 2. a Correlation between distribution ratios of Sr and concentrations of TODGA. Differences of distribution ratios between Sr and b Eu, c Zr, d Ru, e Cs, f Fe extracted by TODGA. (Initial concentration of HNO3 in aqueous phase is 3 mol/L, phase ratio O:A = 1)

was used as diluent and 0.5 mol/L TBP was used as phase modifier. As shown in Fig. 2a and b, the distribution ratio of Eu is significantly higher than that of Sr with the TODGA concentrations range of 0–0.4 mol/L. Specifically, when the TODGA concentrations range of 0.025–0.05 mol/L, DSr is very low (DSr < 0.1), and the distribution ratio of Eu is much higher (DEu > 10), which indicates that Sr will be in aqueous phase while Eu can be extracted into organic phase. Lanthanide elements usually have similar extraction properties and the performance of europium can be used as a reference for other lanthanides, which indicates that they have the same extraction trend under the same condition. Therefore, Sr can be separated from lanthanides by this simple TODGA contact. Figure 2c shows the distribution ratios of Sr and Zr, which indicates the much higher distribution ratio of Zr compared to that of Sr. Like Ln, Zr would be extracted and separated from Sr. In Fig. 2d, e, f comparisons of distribution ratios of Sr and Ru, Cs and Fe can be seen. As shown, the distribution ratios of Ru, Cs and Fe have been very low even when that of Sr has gone to an ascent. According to the distribution ratios, TODGA can be used to separate Sr from Ru, Cs and Fe when the using TODGA concentration is high enough to extract Sr. Based on the extraction properties, the separation of Sr from a large amount of other fission products and corrosion products can be achieved by adjusting the TODGA concentrations.

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According to the extraction performance, the process of separating Sr from other fission products and corrosion products in simulated HLLW by TODGA was designed and verified. Kerosene was used as diluent and 0.5 mol/L TBP was used as phase modifier in this process. The concentration of HNO3 in the feed liquid was 3 mol/L to simulate the HLLW. The initial metal concentrations can be seen in Table 1. Firstly, 0.05 mol/L TODGA was contacted with feed solution with O:A = 1. As seen in the result, DSr was 0.03, which indicated that Sr hadn’t been extracted remaining in aqueous phase. Meanwhile, the distributions ratios of Zr, Y and Eu, Gd, Nd were much higher (DZr > 700, DY > 500, DEu > 200, DGd > 400, DNd = 7.9), signifying that these elements were extracted being separated from Sr. However, the distributions of Cs, Ru, Fe were low (DCs < 0.01, DFe = 0.39, DRu = 0.6), implying Cs, Ru and Fe would keep in aqueous phase along with Sr. Thus, further separation was needed. The raffinate obtained from this process was further contacted with 0.2 mol/L TODGA, by which operation Sr could be extracted into organic phase (Dsr = 4.5) and Cs, Ru, Fe and other analogous fission and corrosion products were separated by keeping in aqueous phase according to the distribution ratios (DCs < 0.01, DFe = 0.39, DRu = 0.6). The final striping composition is shown in the Table 1. The schematic diagram of the whole process is shown in the Fig. 3. Table 1. Mass balance of metal ions under two extraction conditions Analyte

Activity/conc. of analyte in samples (mg/L) Striping (O:A = 1)

Feed

Raffinate after 0.05mol/L TODGA contacts

Raffinate after 0.2mol/L TODGA contacts

Sr

118

115

21

86

Cs

468

455

451

ND

Ru

17

16

10

ND

Mo

158

147

78

9.6

Pd

33

1.89

1

ND

Y

108

ND

ND

ND

Fe

56

57

41

0.82

Eu

140

ND

ND

ND

Gd

48

ND

ND

ND

Nd

790

89

ND

ND

La

249

69

ND

3.8

Organic phase in the first extraction: 0.05 mol/L TODGA-0.5 mol/L TBP-kerosene; Feed: simulated HLLW; Organic phase in the second extraction: 0.2 mol/L TODGA-0.5 mol/L TBP-kerosene; Aqueous phase: raffinate of the first extraction; Strip solution: 0.01 mol/L nitric acid; O/A = 1

To demonstrate the reliability of this process, further hot-tested verification using radioactive genuine HLLW as feed liquid has been implemented according to Fig. 3. Due to the limitation of experimental time and characterization means, only three elements

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Fig. 3. Flow-sheet for the separation of 90 Sr from other fission and corrosion products in HLLW

of Sr, Cs and Eu were traced. Separation of Sr from Eu and Cs were testified to be consistent with that of cold test, with Dsr of 0.01, DCs of < 0.01 and DEu of > 750 in the first contact and Dsr of 4.3, DCs of < 0.01 in the second contact, respectively.

4 Conclusions As a fission product in HLLW, strontium-90 finds extensive use in nuclear medicine and industry, and its research on separation is of great significance. Sr was successfully separated from other fission and corrosion products in HLLW using TODGA as extractant in this work. Two concentrations of TODGA were employed in a flow sheet with two contacts, separating Sr from Ln, Y, Zr, Cs, Ru and Fe etc. Specifically, with a low TODGA concentration (0.05 mol/L) extraction, Sr had a low distribution ratio remaining in the aqueous phase, while Ln, Zr and other elements with high distribution ratio were extracted into the organic phase, resulting in their separation. At a higher TODGA concentration (0.2 mol/L), Sr showed a high distribution ratio and could be extracted, Cs, Ru and Fe went into the raffinate separating from Sr. After two batch extractions by TODGA, a Sr product could be obtained getting rid of a large amount of fission and corrosion elements. This method has been tested using the genuine hot HLLW as feed liquid with verification of its consistency with that of cold test. The hot experiment will be further elaborated in the future work. Author Contributions. Nong Shuying and Chai Youqi worked equally to this work.

References 1. Veliscek-Carolan, J.: Separation of actinides from spent nuclear fuel: a review. J. Hazard. Mater. 318, 266–281 (2016) 2. Ewing, R.C.: Long-term storage of spent nuclear fuel. Nat. Mater. 14, 252–257 (2015) 3. Miguirditchian, M., Vanel, V., Marie, C., Pacary, V., Charbonnel, M.-C., Berthon, L., Hérès, X., Montuir, M., Sorel, C., Bollesteros, M.-J., Costenoble, S., Rostaing, C., Masson, M., Poinssot, C.: Americium recovery from highly active PUREX raffinate by solvent extraction: The EXAm process. a review of 10 years of R&D. Solvent Extr. Ion Exc. 38, 365–387 (2020)

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4. Chitnis, R.R., et al.: Separation and recovery of uranium, neptunium, and plutonium from high level waste using tributyl phosphate: countercurrent studies with simulated waste solution. Sep. Sci. Technol. 33, 1877–1887 (1998) 5. Nishiyama, Y., Hanafusa, T., Yamashita, J., Yamamoto, Y., Ono, T.: Adsorption and removal of strontium in aqueous solution by synthetic hydroxyapatite. J. Radioanal. Nucl. Chem. 307(2), 1279–1285 (2015). https://doi.org/10.1007/s10967-015-4228-9 6. Lumetta, G.J., Wagner, M.J., Jones, E.O.: Separation of strontium-90 from hanford high-level radioactive waste. Sep. Sci. Technol. 30, 1087–1101 (1995) 7. Happel, S., Streng, R., Vater, P., Ensinger, W.: Sr/Y separation by supported liquid membranes based on nuclear track micro filters. Radiat. Meas. 36, 761–766 (2003) 8. Pichestapong, P., Sriwiang, W., Injarean, U.: Separation of yttrium-90 from strontium-90 by extraction chromatography using combined Sr resin and RE resin. Energy Procedia 89, 366–372 (2016) 9. Sharma, J.N., Khan, P.N., Dhami, P.S., Jagasia, P., Tessy, V., Kaushik, C.P.: Separation of strontium-90 from a highly saline high level liquid waste solution using 4,4 (5 )-[di-tertbutyldicyclohexano]-18-crown-6 + isodecyl alcohol/n-dodecane solvent. Sep. Sci. Technol. 229 (2019) 10. Horwitz, E.P., Dietz, M.L., Fisher, D.E.: Srex: a newprocess for the extraction and recovery of strontium from acidic nuclear waste streams. Solvent Extr. Ion Exc. 9, 1–25 (1991) 11. Bai, F., He, C., Chen, G., Wei, J., Wang, J., Ye, G.: Synthesis of alkyl substituted dicyclohexano-18-crown-6 homologues for strontium extraction in HNO3 media. Energy Procedia 39, 396–402 (2013) 12. Xu, C., Wang, J., Chen, J.: Solvent extraction of strontium and cesium: a review of recent progress. Solvent Extr. Ion Exc. 30, 623–650 (2012) 13. Wang, J.: Co-extraction of strontium and cesium from simulated high-level liquid waste (HLLW) by calixcrown and crown ether. J. Nucl. Sci. Technol. 52, 171–177 (2014) 14. Riddle, C.L., et al.: Fission product extraction (FPEX): development of a novel solvent for the simultaneous separation of strontium and cesium from acidic solutions. Solvent Extr. Ion Exc. 23, 449–461 (2005) 15. Romanovskiy, V.N., Smirnov, I.V., Babain, V.A., Todd, T.A., Herbst, R.S., Law, J.D., Brewer, K.N.: The universal solvent extraction (Unex) process. I. development of the unex process solvent for the separation of cesium, strontium, and the actinides from acidic radioactive waste. Solvent Extr. Ion Exc. 19, 1–21 (2001) 16. Kumar, V., et al.: Separation of strontium from the raffinate solution of TEHDGA-actinide partitioning process: batch extraction and process development. Sep. Sci. Technol. 98, 118– 122 (2012) 17. Tachimori, S., Suzuki, S., Sasaki, Y., Apichaibukol, A.: Solvent extraction of alkaline earth metal ions by diglycolic amides from nitric acid solutions. Solvent Extr. Ion Exc. 21, 707–715 (2003) 18. Tian, G., Wang, J., Shen, Y., Rao, L.: Extraction of strontium from HLLW using N, N, N , N -tetraisobutyl 3-oxa-glutaramide. Solvent Extr. Ion Exc. 23, 519–528 (2005) 19. Zhu, Z.-X., Sasaki, Y., Suzuki, H., Suzuki, S., Kimura, T.: Cumulative study on solvent extraction of elements by N, N, N , N -tetraoctyl-3-oxapentanediamide (TODGA) from nitric acid into n-dodecane. Anal. Chim. Acta 527, 163–168 (2004) 20. Dhami, P.S., et al.: Separation and purification of 90Sr from PUREX HLLW using N,N,N ,N tetra(2-ethylhexyl) diglycolamide. J. Radioanal. Nucl. Ch. 296, 1341–1347 (2012) 21. Suzuki, Y.S.H., Sugo, Y., Apichaibukol, A., Kimura, T.: Extraction and separation of Am(III) and Sr(II) by N,N,N ,N -tetraoctyl-3-oxapentanediamide (TODGA). Radiochim. Acta 92, 463–466 (2004)

Effects of Normal Load, Thickness of Oxide Layer and Number of Cycles on Fretting Wear of PWR Fuel ROD Cladding Li Dongxing1(B) , Hu Yong1 , Wang Hui1 , and Xin Long2 1 China Institute of Atomic Energy, Beijing, China 2 University of Science and Technology Beijing, Beijing, China

Abstract. Fretting wear and corrosion experiments are carried out under a simulative primary circuit environment provided by autoclave, the loss of volume of zirconium alloy fuel rod cladding samples under different normal load (10, 20, 40 N), thickness of oxide layer (0.5, 2, 3.5 µm) and number of cycles(2 × 106 , 5 × 106 , 18 × 106 ) is measured and the micro structures of the wear scars are inspected and analyzed. The results show the minimum of volume loss is under the normal load of 10 N, the maximum is under 20 N and the volume loss under the normal load of 40 N is smaller than that of 20 N and bigger than that of 10 N. Different forms of friction and different forms of oxidation films are discovered under different normal loads. Samples with oxide layer of 2 µm and 3.5 µm have nearly the same loss of volume, and 0.5 µm samples have larger loss of volume. With the increasing of wear cycle number, the average wear rate rises at first then falls. Oxide layer on the surface plays an important role in the fretting wear of fuel rod cladding. Keywords: Pressurized water reactor · Zirconium alloy cladding · Fretting wear and corrosion · Grid-to rod fretting

1 Introduction In pressurized water reactors, the friction between fuel rod cladding and grid in fuel assemblies is a typical wear phenomenon. GTRF is mainly caused by flow induced vibration which means fuel rod would vibrate because of the flow of coolant in the first circuit. The corrosion of cladding is promoted by the sever environment in the first circuit including high temperature and pressure, strong irradiation, radiolysis product of coolant, the failure process of fuel rod is accelerated when fretting wear is combined with corrosion. To ensure the operation safety of nuclear power plant, avoid unexpected failure of fuel rod cladding, optimize the structure of fuel assembly, expand the life period, reduce the cost, it is important to learn about the rules of fretting wear and corrosion. Specimens used in this assay are made of Zr-4 alloy, fretting wear and corrosion behaviors of fuel rod cladding under simulated PWR first circuit environment are © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 354–365, 2023. https://doi.org/10.1007/978-981-19-8780-9_36

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observed. Impacts of normal load, number of wear cycles and thickness of prefabricated oxide layer on fretting wear and corrosion are studied. Through macro and micro analysis methods, how different parameters influence the form of wear scars, oxide layer and zirconium alloy basis and how they make difference to wear volume and average wear rate are explored,

2 Experiments Details 2.1 Experiment Materials Zr-4 fuel rod claddings are cut into 15 mm long short tubes, Ø6 holes are dug in the middle on one side of each tube. Some of the samples are put into an autoclave to generate oxide layer under 400 °C saturated steam. Under the guidance of oxide layer growth empirical formula, oxide layer with specific thickness is obtained by controlling the oxidation time. Metalloscope is used to check the thickness of oxide layer. 26 mm × 28 mm Zr-4 alloy plates are used to simulate the grid. Before the test, specimens are put into an ultrasonic cleaning machine, ultrapure water and ethyl alcohol are used as detergent, samples are dried after cleaning. 2.2 Fretting Wear and Corrosion Testing Facility The fretting wear testing machine is mainly made up of three parts: the autoclave and the holders inside, vibration generation and transmission mechanism and control system. The tube specimen and the plate specimen are separately hold by two holders in the autoclave, and are set to line contact mode. The plate specimen is kept still, the holder of the tube specimen is connected to the vibration generation mechanism, the mechanism is controlled by the software on the controlling computer, to generate fretting with specific frequency and amplitude. The actual fretting amplitude is monitored by a linear variable differential transformer (LVDT). Thermocouples and pressure sensor are used to monitor the temperature and pressure in the autoclave. The schematic diagram is shown in Fig. 1.

Fig. 1. The schematic diagram of the fretting wear testing machine

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2.3 Experiment Procedure Samples are fixed on the holders, holders are placed in the autoclave, springs are adjusted to suitable length to get specific normal load. After the autoclave is sealed, ultrapure water (specific resistance 18.25M·cm) with boric acid (CB3+ = 650ppm) and lithium hydrate (CLi+ = 3.5ppm) is added into the autoclave. After deoxygenation, autoclave is heated to 300 °C. When the water chemistry environment has become stable, vibration generation mechanism is motivated to generate relative motion between tube and plate sample. When the number of wear cycle has reached the set value, samples are taken out from the autoclave. After the fretting wear and corrosion test, tube samples are cleaned and dried, the 3D profile and wear volume are measured by 3D white light interfering profilometer. Laser scanning confocal microscope (LSCM, Olympus OLSED-2000) is used to observe the wear scar’s position, shape, distribution of wear scar. Scanning electron microscope (SEM, Thermo Fisher Scientific 10548-L) is used to get detailed information of wear scar’s surface. Some of the tube samples are cut using wire-electrode cutting, the notch is 1–2 mm away from the wear scar. Then the cut samples are made to resin samples. The resin samples are mechanical polished using SiC sandpaper up to 2500#, then are polished using diamond paste. The section samples are observed using SEM. After being etched for 5 s using etch agent (HF 10%, HNO3 45%, distilled water 45%, volume fraction), the section samples are washed using lots of water, then the section tissue is observed.

3 Results and Analysis 3.1 Effects of Normal Load on Fretting Wear and Corrosion The testing parameters, average wear volume and average friction coefficient of samples tested for exploring the impacts of normal load are shown in Table 1. The definition of friction coefficient can be referred to the Archard model. All the samples in Table 1 are tested under 100 µm fretting amplitude, 2 million wear cycles. As shown in Table 1, 20 N samples have the biggest average wear volume, 10 N samples have the smallest, but have little difference from 40 N samples. Typical samples which are numbered as TL34, TL31, TL38 (10, 20, 40 N) are chosen to perform further analysis. As shown in Fig. 2, wear scar on 10 N sample is irregular, wear scar on 20 N sample has some bright zones, wear scar on 40 N sample can be obviously divided into three parts. Surface images of 10 N sample under SEM backscattered electron imaging mode are shown in Fig. 3. Irregular dark zone can be seen in Fig. 3a, it can be inferred that there is oxide unordered accumulated on the surface. It can be seen that there is irregular dark zone near the bright zone, and scratch-like stripe is distributed in the bright zone. It can be inferred that during the fretting wear progress, the oxide layer in this place is damaged and slides along the surface for a short distance under the action of external force, then the basis is exposed and scratch is left on it. Associated with the irregularly distributed oxide on the surface, it can be inferred that the fretting process is not stable under 10 N, and is probably accompanied with shock.

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Table 1. Loss of volume, average loss of volume and average friction coefficient of sample under different normal loads NO

Normal Load/N

Loss of Volume/ × 107 µm3

Average loss of volume/ × 107 µm3

Average Friction Coefficient/ × 10−13 Pa−1

TL07

40

0.85

1.23

1.54

TL08

40

1.39

TL37

40

1.32

TL38

40

1.37

TL09

20

1.78

2.63

6.57

TL10

20

3.64

TL31

20

2.38

TL32

20

2.71

TL33

10

0.70

0.87

4.33

TL34

10

1.03

Fig. 2. Wear scars of samples under 10, 20, 40 N normal load

Fig. 3. Wear scar SEM photo of 10 N sample a wear debris, b special morphology

As shown in Fig. 4, bright zones are also discovered on the wear scar of 20 N sample under SEM backscattered electron imaging mode. EDS analysis is performed on the selected zone, higher zirconium content is discovered. Combined with the morphology, it can be inferred that the basis is exposed after the oxide layer drops off. Wear scar photo of 40 N sample under SEM secondary electron imaging mode is shown in Fig. 5. Compared with 10 N and 20 N sample. Wear scar of 40 N sample

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Fig. 4. Bright region on 20 N sample surface and EDS of Zr

is regular and well-distributed. It can be roughly divided into three parts, striation-like stripe which indicates fatigue can be seen in the middle of the wear scar. Dropped off oxide layer cannot be seen. The two sides of the wear scar are high and irregular. It can be inferred the fretting wear mainly takes place in the middle, the wear debris heap up on the two sides.

Fig. 5. Wear scar SEM photo of 40 N sample

Different morphologies of samples under different normal load indicate that different modes of fretting wear are promoted under different normal load, caused different wear debris distribution, oxide layer form, exposed basis. The higher the normal load is, the more regular wear debris accumulation and wear scar form become, and the better the oxide layer can cover the basis. Section samples are made after the samples are cut and polished. SEM is used to observe the section samples, as shown in Fig. 6a, the oxide layer and the basis are not tightly combined at the wear scar of 20 N sample. Void can be seen at the interface, the oxide layer drops off in some regions, and the basis is exposed. As shown in Fig. 6b, for 40 N sample, the oxide layer and the basis are tightly combined, the oxide layer is regular and intact. Even the oxide layer is damaged in some regions, the basis is still covered by it. The section image is in good consistent with the surface morphology. After the section samples are ion polished, electron backscattered diffraction (EBSD) is used to analyze the tissue under the wear scar. EBSD photos and number fraction of misorientation angle of blank sample and 40 N sample are shown in Fig. 7 and Fig. 8. 40 N sample’s number fraction of small misorientation angle is higher than that of blank sample’s, but does not have much difference. And no significant difference can be seen

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Fig. 6. Section SEM photo under the wear scar of 20 N sample 40 N sample a 20 N, b 40 N

in grain size, phase and grain orientation of blank sample and 40 N sample according to EBSD analysis results.

Fig. 7. EBSD photo of blank sample and the boundary angle

Fig. 8. EBSD photo of 40 N sample and the boundary angle

3.2 Effects of Number of Wear Cycles on Fretting Wear and Corrosion The average wear volume and average friction coefficient of samples tested for different wear cycles are shown in Table 2. It is indicated in the table that the average wear rate rises at first then decreases with the increase of wear cycles. The analysis is mainly performed on 2 million sample and 1800 million sample. The wear scar on 2 million sample and 18 million sample are shown in Fig. 9. In Fig. 9a, zones with dropped off oxide layer and layered oxide can be seen on the wear scar on 2 million sample. Compared with 2 million sample, the amount of layers of

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Table 2. Loss of volume, average loss of volume and average friction coefficient of sample tested for different wear cycles No.

Wear Cycles/ × 106

Loss of Vomlume/ × 107 µm3

Average Loss of Average Friction Cofficient/ Volume/ × × 10−13 Pa−1 107 µm3

TL47

2

2.36

2.11

2.64

TL48

2

1.85

TL41

5

8.25

8.53

4.27

TL42

5

8.80

TL45

18

13.62

TL46

18

8.24

10.93

1.52

Fig. 9. SEM photo of different wear cycle samples a 2 million, b 18 million

accumulated oxide on 18 million sample is much larger. It indicates that the oxide layer keeps growing and dropping off during the fretting process. The SEM photo of wear scar edge of 2 million sample and 1800 sample are shown in Fig. 10. In Fig. 10a, zones with dropped off oxide layer can be seen on the edge of wear scar on 2 million sample, part of such zones is covered by subsequent oxide layer. Under backscattered electron imaging mode, the wear scar and the region without friction can be easily distinguished from each other. In Fig. 10b, on the surface of 18 million sample, zones on which oxide layer once dropped off also can be seen, but such zones are all covered by subsequent oxide layer. The wear scar doesn’t have much difference with the region free from normal load in brightness under backscattered electron imaging mode, which indicates they have experienced the same degree of oxidation. Through comparing the 2 million sample and the 18 million sample, it can be interfered that the fretting wear corrosion process is campaigned with growing and dropping of oxide layer, and the average growing speed is higher than average dropping speed. It results in the decrease of fraction of basis directly take part in fretting wear. When the number of wear cycles is high enough, fretting wear mainly takes place on oxide layer. The decrease of average wear speed with the increase of wear cycles can be explained in this way,

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Fig. 10. SEM photo of wear scar edge of different wear cycle samples a 2 million, b 18 million

3.3 Effects of Oxide Layer Thickness on Fretting Wear and Corrosion The average wear volume and average friction coefficient of samples with different thickness of oxide layer are shown in Table 3. Samples with 0.5 µm prefabricated oxide layer have the largest loss of volume, 2 µm samples have the smallest, average loss of volume of 3.5 µm samples is a little bit larger than that of 2 µm samples. Table 3. Loss of volume, average loss of volume and average friction coefficient of samples with different oxide layer thickness No.

Oxide Layer Thickness/µm

Loss of Volume/ × 107 µm3

Average Loss of Volume/ × 107 µm3

Average Friction Coefficient/ × 10−13 Pa−1

A1

0.5

6.39

5.84

7.71

A2

0.5

5.29

B1

2

3.56

3.48

4.36

B2

2

3.40

C1

3.5

1.68

C2

3.5

5.98

3.83

4.80

It is shown in Fig. 11 that on the surface of 0.5 µm sample, the oxide layer drops off in some regions, bright fresh basis can be seen in such regions.

Fig. 11. SEM photo of 0.5 µm pre-oxidize layer sample

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The regions with dropped off oxide layer can be seen both on 2 µm sample and 3.5 µm sample, as shown in Fig. 12. However only little fresh basis be discovered compared with the 0.5 µm sample.

Fig. 12. SEM photo of samples of different pre-oxidize layer thickness a 2 µm, b 3.5 µm

From the section SEM photo of 0.5 µm sample, as shown in Fig. 13, fresh basis which is exposed because of the loss of oxide layer can be seen.

Fig. 13. Section SEM photo of 0.5 µm pre-oxidize layer sample

From Figs. 14 and 15, which show the SEM photos of 2 and 3.5µm sample, through comparing the region which have experienced fretting wear caused by normal load or not, it is noticed that the oxide layer on the wear scar only has become thinner and no dropped off zone can be seen.

Fig. 14. Section SEM photo of 2 µm sample oxidize layer

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Fig. 15. 3.5 Section SEM photo of 2 µm sample oxidize layer

4 Conclusions From the experiments above, conclusions can be drawn as following: 1. Different forms of fretting wear take place under different normal loads. Fretting wear is campaigned with shock under 10 and 20 N, leading to the damage of oxide layer and the exposer of zirconium alloy basis. 2. The grain in the tissue under the wear scar is slightly deformed by the normal load, the number fraction of small misorientation angle increases, but not significantly. Besides, no obvious influence of normal load can be seen on grain size, metallographic and grain orientation. 3. A tendency of rising at first then falling down in average wear rate can be seen with the increase of wear cycles. 4. Thin prefabricated oxide layer may be broken during fretting wear process and leads to larger loss of volume. If the prefabricated oxide layer is rather high, only reduction of oxide layer thickness may take place, different samples with different prefabricated oxide layer thickness would have similar loss of volume.

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Fault Detection for Redundant Measurement Channels Peigen Cao1(B) , Wenfei Li1 , Haiyan Zhan2 , Feng An1 , and Xiaojun Xia1 1 Fujian Fuqing Nuclear Power CO., LTD, Fuqing, Fujian, China

{caopg,liwf01,anfeng,xiaxj01}@cnnp.com.cn 2 State Grid Ruijin City Electric Power Supply Branch, Ruijin, Jiangxi, China

Abstract. Monitoring the status of nuclear power plant (NPP) units is the basis for their safe, stable, and efficient operation. The measurement channel acquires real-time parameters to realize the monitoring function of the unit. With the development of big data, information communication and AI technology, Redundant measurement channel verification technology, as a branch technology of online detection technology, can be used to verify the measurement channel of nuclear power plant safety level protection systems and important adjustment parameters. Analyzing the measurement data of the measurement channel online to determine whether its drift or failure, it solves many problems of the traditional verification method. This article research independent component analysis-sequential probability ratio test (ICA-SPRT), to explore the value and role of online monitoring technology in nuclear power plant redundant measurement. Keywords: Redundant channels · ICA · SPRT · NPP

1 Introduction The measurement channel acquires real-time parameters to realize the monitoring function of the unit. In order to ensure the effectiveness of the measurement channel, the staff will periodically perform cross-comparison and verification to evaluate their operating status [1]. This method has many disadvantages, such as failure to find problems online, and repeated inspection of health equipment also increases the probability of damage. The American Electric Power Research Institute (EPRI) first proposed the on-line monitoring (OLM) technology [2]. In 2006, the Nuclear Regulatory Commission (NRC) introduced the key issues and methods of OLM technology in detail in its report Nureg6895 [3]. OLM obtains the monitoring data of the measurement channel during the operation of the power plant, and analyzes the data by means of computer off-line processing to obtain the working performance of the measurement channel. OLM technology has adjustable sensitivity and strong effectiveness, which optimizes the maintenance cycle so as to reduce the maintenance cost and improve the economy and wisdom of power station operation. As a branch of OLM, redundant measurement channel verification technology can be used to verify the measurement channels of safety level protection system and important regulating parameters of nuclear power plants (NPP), so as to ensure the safe operation of NPP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 366–374, 2023. https://doi.org/10.1007/978-981-19-8780-9_37

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At present, there are many methods for verifying OLM redundant measurement channels, such as simple average (SA), instrumentation and calibration monitoring program (ICMP) and parity space. These methods require sufficient redundancy of measurement instruments. The more redundant instruments, the better the monitoring effect. When the redundancy of measurement channels is not high, it is difficult to distinguish fault measurement channels. Generally, there are three to four redundant measurement channels for important protection parameters of NPP. In the case that the redundancy does not meet the above methods, this paper uses independent component analysis and sequential probability ratio test to verify the measurement channels under low redundancy.

2 Method Principle In this paper, two steps of blind source signal separation and signal residual analysis are proposed to realize the on-line verification of redundant measurement channels. The first step is signal estimation. The main characteristics of the measurement channel signals are separated through independent component analysis (ICA). The channel noise, system noise, drift and other variables in the measured signals are eliminated through ICA, and the independent components of the monitoring variable characteristics of the measurement channel are restored. Finally, the signal residuals of each measurement channel are obtained; In the second step, sequential probability ratio test (SPRT) is used to test the residual error and predict the working condition of the measurement channel. For blind source signal separation, this paper compares and analyzes the traditional simple average (SA) algorithm and ICA algorithm to find the best evaluation method. 2.1 Independent Component Analysis (ICA) ICA developed in the late 1990s. It is a statistical method that can separate mixed blind source signals. Through the linear transformation of the measured data, the mixed signal is separated into several independent sub signals. ICA can be used to separate the real process signal from the independent channel noise, so it can be used as a data preprocessing or signal filtering method of other technologies. ICA can also be used to obtain the estimated value of real process variables from a group of redundant instruments. Its advantage is that its estimate is not affected by wrong data. ICA was proposed by Herault and Jutten [4] from the perspective of blind source signal separation. They believe that ICA can separate basic source signals from non Gaussian linear mixed signals. At the same time, they give the theoretical framework of the theory, such as application scope, constraints and estimation methods. Then ICA algorithm became popular and was applied in biomedicine, communication, image processing, speech recognition and so on. In 1997, Hyvarinen [5] proposed a fast fixed point algorithm based on negative entropy, namely FastICA algorithm. The emergence of FastICA algorithm has set off a new upsurge in ICA research and application, such as blind source signal separation, physiological data analysis, image processing, face recognition and so on. Fudan University, Shanghaijiaotong University, Southeast University and other universities in China have carried out the research and application of ICA algorithm. Wang Gang proposed a parameter adaptive FastICA algorithm based on

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generalized Gaussian model. Liu Ying discussed the solution method of FastICA based on MATLAB optimization toolbox function, and promoted the application of ICA in engineering practice. Ding used ICA for the first time for verification of redundant measurement channels in NPP. Jindian explored the specific application of ICA in abnormal monitoring of measurement channels. ICA is used to separate blind source signals. Suppose that the observation data are linearly mixed from the source signals. Namely: X = AS in the equation: is the order matrix composed of observation values of measurement channels; It is an order matrix composed of independent components. The blind source signal may include system noise, detected signal, channel noise; A is a mixed matrix of order composed of constants. Since the number of source signals is unknown, for simplicity, it is generally considered that a is a square matrix, that is, the number of observed signals is equal to the number of source signals. Since A and S are unknown, the unknown independent signal components, namely as, can only be estimated by solving the inverse matrix: Y = QX

(1)

In order to solve this problem, the fast ICA (FastICA) algorithm is used to separate independent components. Firstly, preprocessing can simplify the parameter estimation of ICA algorithm, eliminate the correlation between variables, and reduce the noise. The observed signals after whitening are uncorrelated, and their variances is one. B is defined as the whitening matrix, which is completed by the method of covariance matrix. FastICA algorithm separates the blind source signal through the non Gaussian property of the mixed signal. When the non Gaussian property of the blind source signal reaches the maximum, the separated independent components also reach the maximum. In information theory, negative entropy is a non Gaussian measure, which is defined as: NgR = HRGauss − H(R)

(2)

in the equation: is the same as the variance of Gaussian random variables, and H is the differential entropy of random variables. For Eq. (2), since the differential entropy of cannot be accurately calculated, the approximate Eq. (3) is adopted as follows: NgY = c{EgY − E[YGauss]}

(3)

in the equation: is an independent constant, the mean operation mean function in MATLAB, and G is a nonlinear function tanh(x). The maximum value in Eq. (3) is usually obtained at the extreme point of, and W is solved line by line, representing the k-th line in W, which is transformed into the extreme value at the time of calculation. The calculated iterative Eq. (4):  wk + 1 = EZgwkTZ − E gwkTZ)wk (4)

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in the equation:, is the first derivative of, is the second derivative of, and the initial value is randomly generated. After iteration, it is orthogonalized and normalized to obtain the unmixing matrix W, and then the estimated value y of each independent component is calculated through formula (1). Since the estimated value y of s calculated by FastICA is composed of the estimated values of the detected signal, noise, drift and other independent components, and the order of each independent component is uncertain, in order to accurately monitor the abnormal measurement channel, it is necessary to find out the corresponding independent component of the detected signal in each independent component, and restore and correct this component. By comparing the correlation coefficient between each independent component and the average value of the observed signal, this paper finds the independent component with the largest correlation coefficient as the estimated value of the detected signal: IC = argmax[C(E(X), Yi )]

(5)

in the equation: is the ith row of the observation data matrix of the redundant measurement channel; M is the number of redundant measurement channels, i.e. redundancy; Is the estimated value of this set of redundant measurement channels. in the equation, is the correlation coefficient function corrcoef in MATLAB, and is the ith line of the estimated value y. In this paper, the scale factor is introduced to optimize the estimated signal, and the optimized estimated value signal is calculated from Eq. (5), where the scale factor K is obtained from Eq. (6), then Y = kIc

(6)

k = E(M(X))/(M(Yi))

(7)

in the equation, m is the median function in MATLAB. 2.2 SA Based Signal Estimation The principle of SA algorithm is as follows:  Y = 1/m (i = 1, m)( xi )T

(8)

in the equation: (xi )T is the row of the observation data matrix of the redundant measurement channel; M is the number of redundant measurement channels; Y is the estimated value of this set of redundant measurement channels. SPRT algorithm is a statistical decision-making program based on sequence detection. When the measured signal removes the channel noise, it can be used for fault detection to determine the working state of the measurement channel. This method was proposed by Wald in 1947. Different from the traditional fixed sample test method, SPRT does not specify the total number of samples first. It starts from the first sample, takes

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a small number of samples first, determines the total number of samples according to the sampling results, and gives the judgment results. Sequential probability ratio test has been widely used in pipeline leakage, flight control, navigation, fault diagnosis, etc. in recent years, golz and others have used SPRT method for signal analysis [6]. The fault detection variable in this paper is the measurement channel residual, that is, the difference between the measured value and the estimated value of the measurement channel, and its probability density function is a Gaussian function. It is assumed to be the residual distribution in healthy state, which is the residual distribution in fault state. The mean and variance of the health state assumption are calculated from the data residuals of normal operation, and the mean of the fault is given by the engineering experience or the actual detection requirements.   t t 1  1  2 2 (xi − μ1 ) + (xi − μ0 ) R(t) = exp − 2 2σ0 i=1 2σ02 i=1 The SPRT test first needs to obtain the residual value distribution of the measurement channel under the normal working state. In this paper, firstly, the measurement data of the redundant measurement channel under the normal operating state are selected, and the estimated values of the two groups of measurement channels are obtained after being processed by ICA and SA methods respectively. The actual measurement data are subtracted to obtain the residual value of the measurement channel under the normal operating state, and the average value and variance are calculated, Get assumptions. Secondly, the hypotheses are compared by SPRT method. Finally, the working state of the measurement channel is determined according to the monitoring results.

3 Model Validation 3.1 Introduction of Research Object The main steam system (VVP) of the nuclear power plant is the hub of the primary and secondary circuits of the nuclear power plant, which transmits the saturated steam generated by the steam generator to the secondary circuit. There are three pressure measurement channels on each main steam pipe. Taking the three pressure measurement channels of a main steam pipe in Fujian Fuqing nuclear power plant as the object, this paper analyzes the effect of ICA-SPRT and SA-SPRT two redundant measurement channel verification methods. Three pressure measurement channels on the same steam pipeline that are manually verified to be qualified are selected as the research object. Then, through the nuclear power data acquisition and recording system, the historical operation data during full power operation are derived for analysis. The sampling period is 1 s and the analysis time is 100 min. In order to compare the test, the normal pressure measurement data is added to the artificial drift in the case analysis. 3.2 Health Data Analysis The steam pressure of the three pressure measurement channels during full power is shown in Fig. 1a, with 6000 sets of data for each channel. Through manual verification

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during the overhaul, it was found that the three measurement channels did not drift, and the zero point also met the standard, which was used as the health data for analysis. It can be seen from the actual measured values in Fig. 1a that during normal operation, the pressure of the main steam pipeline fluctuates within a certain range, and there are many factors causing the fluctuation, such as the main steam pressure fluctuation, the sensor site temperature change and noise. After ICA and SA processing of the actual measured data in Fig. 1a, the estimated value of the actual pressure of the main steam pipe in Fig. 1b can be obtained. It can be seen from the figure that since SA algorithm is a simple average of pressure values, its fluctuation range is significantly smaller than the estimated value of ICA algorithm. This method ignores the influence factors such as the noise of the measured signal, and the deviation between the estimated value and the measured value of each channel is small, which also leads to a small fluctuation range of SA in the subsequent calculation of residual error. Different from the estimated value of SA, ICA regards the measured data as a linear mixture of time-varying noise and actual pressure value. The actual pressure value is separated by algorithm as an independent component, and the final algorithm estimated value is obtained through correction. Compared with the simple average SA algorithm, ICA estimation value fluctuates more and has higher accuracy in principle. After the pressure measurement values are processed by ICA and SA methods, the mean and variance of residual distribution of redundant measurement channels are calculated, and the mean of residual distribution of each measurement channel is tested by SPRT. The SPRT false alarm rate is 0.01, the error range is 60000.01 = 60, and the false alarm rate is 0.001. See Table 1 for the corresponding sum value of the normal operation data of each channel. According to the principle, in a test data containing only random error, the standard deviation is obtained, and the interval follows the normal distribution. When it exceeds a certain interval range, it is defined as gross error instead of random error. At that time, the probability of mean test was 99.74%, and the calculation in this paper was completed by MATLAB software. When the alarm threshold is less than the likelihood ratio, that is, the measurement channel has found a fault at point i.

Fig. 1. Healthy sensor data a and ICA/SA estimation b

As shown in Table 2, after the residual value of the channel calculated by ICA and SA methods is verified by SPRT, the number of fault points of the measurement channel can be obtained. The fault points of channel 1 and channel 2 monitored by ICA algorithm are 25 and 47 respectively, but both are within the error range of 60, so the

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Channel

μ0 /10−4

δ0 /10−3

ICA 1

SA 0.411

8.32

ICA

SA

14.3

8.9

2

59.0

67.0

13.3

9.5

3

− 83.0

− 75.0

10.3

2.4

Table 2. Status discrimination Channel

Failures

Results

ICA

SA

ICA

SA

1

25

0

Healthy

Healthy

2

47

0

Healthy

Healthy

3

0

0

Healthy

Healthy

three measurement channels are judged to be healthy. In this example, the measurement data is given by the normal measurement channel, and the above judgment results are in line with expectations. 3.3 Pressure Drift Data Analysis In order to compare the fault analysis ability of ICA and SA to drift data, simulation drift is added to test the influence of the two data preprocessing methods on the determination of SPRT algorithm. The normal pressure channel measurement data still uses 6000 groups of data during the full power period of the previous example. During the test, the simulation drift is added to the monitoring data of channel 2. In the process of channel verification of actual nuclear power plant, the allowable accuracy of different channels is not consistent. Generally, the allowable error of a single measurement channel is 0.5%, and the maximum allowable inconsistency in the process of cross comparison is 1%. During the test, + 1% simulation drift is added to the 1000s of the monitoring data of channel 2. The observed values of the three pressure measurement channels after adding simulation signals are shown in Fig. 2a, and the estimated values after ICA and SA processing are shown in Fig. 2b. Compared with no simulation drift, the estimated value of SA algorithm gradually drifts upward after 1000s. After the simulation drift is added, the residual error of the pressure channel changes accordingly, After ICA algorithm processing, the residuals of channels 1 and 3 did not change significantly, and channel 2 drifted upward significantly after 1000s. This is because ICA algorithm separates the influence of drift and gives a more accurate estimate. The residuals of the three measurement channels of SA algorithm have obvious drift, with negative drift of channel 1 and 3 and positive drift of channel 2. When SA algorithm calculates the estimated value, the observed values of the three measurement channels

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are averaged, and the drift of channel 2 is averaged into the three channels. Thus, in the case of low redundancy, the traditional SA can not accurately identify and measure the faults in the same kind. ICA algorithm can more accurately identify the anomaly of the measurement channel by separating the independent components of the channel.

Fig. 2. Sensor data with drift a and ICA/SA estimation b

Under the condition that all parameters of SPRT algorithm remain unchanged, residual value test is conducted for the measurement channel after drift is added. SPRT algorithm is sensitive to the change of residual value, and can detect the fault of measurement channel after simulation drift. During ICA residual error check, the fault is judged by SPRT after adding simulation drift 1339s. By adjusting the sampling period, the decision time of SPRT can be affected. The ICA residual value test results show that the measurement channels 1 and 3 are normal, and the number of fault points of channel 2 is 3574. If the allowable range is exceeded, it is determined as a fault. The SA residual value detection results of the three channels are all faults, and the simulation drift data of channel 2 affects the fault points of channels 1 and 3. In the case of low redundancy, it is easy to cause the overall deviation of redundant measurement channels in NPP by simple average method. The logic of current distributed control system (DCS) for redundant measurement channels is that when one measurement channel exceeds 5% of the average value of other measurement channels, in this case, the small-scale drift of measurement signals will not be cut off by DCs, but will affect the overall average value of measurement channels and have a certain impact on the control of NPP. Through verification, under the common low measurement channel redundancy in NPP, ica-sprt algorithm can accurately determine the working state of the channel, and the detection accuracy and determination results are also due to the traditional SA method. Compared with the manual periodic calibration method commonly used in NPP, it shows higher timeliness and intelligence.

4 Conclusions This paper discusses the influence of OLM technology on the operation and maintenance of nuclear power plant, and introduces two algorithms of measurement channel verification under low redundancy of OLM technology. The example shows that ica-sprt algorithm is superior in redundant measurement channel inspection of nuclear power plant.

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ICA can separate the measurement channel noise and give a more accurate estimate of the real working conditions. This signal processing method can be used as a basis for fault diagnosis of measurement channel. SPRT algorithm is very sensitive to the change of measurement channel, and can quickly identify the abnormal measurement channel. The combination of the two is beneficial to improve the accuracy of redundant measurement channels and give early warning of abnormal operation in time. Therefore, the exploration and research of this method gives a recommended solution to the condition based equipment maintenance strategy, and also gives an optional direction for improving the intelligence of measurement and control in NPP.

References 1. Fantoni, P.F., Hoffmann, M.I., Shankar, R., et al.: On-line monitoring of instrument channel performance in nuclear power plant using PEANO. Prog. Nucl. Energy 43(1/4), 83–89 (2003) 2. Labbe, A., Abdul-Nour, G., Vaillancourt, R., et al.: Implementation of an on-line monitoring system for transmitters in a CANDU nuclear power plant. J Phys Conf Ser 364, 012121 (2012) 3. Davis, E., Funk, D., Hooten, D.: On-line monitoring of instrument channel performance: TR104965. California: EPRI (1998) 4. Hines, J.W., Seibert, R.: Technical Review of On-line Monitoring Techniques for Performance Assessment: NUREG-6895. Nuclear Regulatory Commission, Washington DC (2006) 5. Jutten, C., Herault, J.: Independent component analysis versus principal component analysis. Signal Processing IV, Theories and Applications 643–646 (1988) 6. Shanxie, D.J.: Detection of redundant pressure sensors in main steam system of NPP 58(04), 582–588 (2019)

Disorder Induced by Gamma Irradiation in Borosilicate Glasses Shikun Zhu, Xu Chen, Fan Yang, Kemian Qin, Jiangjiang Mao, Xiaoyang Zhang, Tieshan Wang, and Haibo Peng(B) School of Nuclear Science and Technology, Lanzhou University, Lanzhou, Gansu, China {tswang,penghb}@lzu.edu.cn

Abstract. Vitrification is the mainstream method for the immobilization of highlevel radioactive waste (HLW). As the common matrix of vitrification, the borosilicate glass and its irradiation tolerance have been widely studied. Two kinds of borosilicate glasses were irradiated by gamma rays with absorbed doses from 104 to 107 Gy at ambient temperature. Ultraviolet and visible absorption spectra and Raman spectra were employed to characterize the microstructural changes of borosilicate glass. The Urbach energy of borosilicate glass was obtained from the absorption spectra. The noise intensity of Raman spectra with different absorbed dose were obtained. In addition, the results show that the Urbach energy is positively correlated with the noise of Raman spectra. The disorder of glasses, which was characterized by the noise on Raman spectroscopy and Urbach energy from absorption spectroscopy, increased with the absorbed dose. Finally, compared with the result of Urbach energy, the disorder of the gamma-irradiated glass can be better reflected by the noise intensity of Raman spectra. The study of the gamma radiation effects on vitrification requires more attention than before. Keywords: Borosilicate glass · γ irradiation · Urbach energy · Microstructure

1 Introduction The borosilicate glass, the common matrix of vitrification to solidify the high-level radioactive waste (HLW) [1], will suffer from various irradiations during the long-term storage. As the main source of irradiation damage, the radiation effects of alpha and beta rays on borosilicate glass have been extensively studied, but the latest research proved that the radiation effects of gamma rays in borosilicate glass should be reckoned with. Griscom et al. studied the gamma radiation effect of silica glass and explained mechanism [2–7]. Ravneet Kau reported that the band gap of Ba-containing borosilicate glasses reduced and Urbach energy increased after gamma radiation, and discussed the change of corresponding boron structure [8, 9]. Du et al. presented the 10 kGy 60 Co gamma ray produced higher-concentration color centers in the borosilicate glass than 4 × 1012 e/cm2 1.85 meV electron [10]. Leon used gamma ray to irradiate different type of fused silica and concluded that radiation induced optical absorption depends on the material grade [11]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 375–383, 2023. https://doi.org/10.1007/978-981-19-8780-9_38

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The current way to characterize the disorder of materials is mainly through Urbach energy. Noise can be caused by a variety of factors, such as ambient, measurement and sample itself. If the variations of ambient and measurement are limited to the greatest extent possible, the main influence on noise variation comes from the sample itself. For the borosilicate glass irradiated by gamma ray, the change of noise can be regarded as the effects of irradiation. Two series borosilicate glasses have been irradiated by gamma ray and the radiation effects in the borosilicate glasses were studied in this work. The results were analyzed via absorption spectroscopy and Raman spectroscopy.

2 Material and Experiments 2.1 Material The borosilicate glasses with two compositions were prepared by melt-quenching method. The raw materials were melted and stirred for four hours at the temperature of 1300 °C and then cooled naturally to the room temperature. Afterward, in order to remove the residual stress, the cooled borosilicate glasses were reheated to the temperature of 500 °C for 24 h. Finally, the glasses were dimensioned into the 10 × 10 × 1 mm3 prisms and polished on both sides. The prepared borosilicate glasses were named NBS5 and NBS6, the compositions are listed in Table 1. Table 1. Compositions and properties of the borosilicate glasses NBS5

NBS6

Na2 O (mol%)

14.2

15.3

SiO2 (mol%)

67.3

64.5

B2 O3 (mol%)

18.6

20.2

R = Na2 O/B2 O3

0.76

0.76

K = SiO2 /B2 O3

3.62

3.20

Density (g/cm3 )

2.44

2.46

2.2 Experiments Two series of glasses were irradiated by a Cobalt-60 gamma source with the energies of 1.33 and 1.17 meV at a dose rate of 1 × 105 Gy/h under air conditions. The absorbed doses of borosilicate glass were from 1 × 104 Gy to 1 × 107 Gy. The Linear attenuation coefficient which is the photon energy decays to a distance of one part of the natural log in the borosilicate glass for the energy of 1.33 meV gamma ray is 1.4 cm−1 . Thus, the samples with a thickness of 1 mm can be considered uniformly irradiated. The Raman spectra of borosilicate glasses were acquired by a Laser Confocal Raman Micro spectroscopy (HORIBA, iHR550). The wavelength of the excitation laser was

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532 nm and the grating line was 1200 line/mm. The laser power was less than 10 mW. The Raman spectra, whose resolution is 1 cm−1 , were recorded from 200 to 1600 cm−1 . The Ultraviolet and Visible (UV) spectra of borosilicate glasses were measured with a UV-visible spectrophotometer (EU-2800D). The UV spectra ranged from 200 nm to 1100 nm with the resolution of 0.1 nm. The scanning depth was the entire thickness of the samples. The Raman spectra and UV spectra were measured at room temperature.

3 Results and Discussions 3.1 Ultraviolet and Visible Spectra

2.0 1.5

Abs (a.u.)

1.0

pristine 1E4Gy 6E4Gy 1E5Gy 3E5Gy

6E5Gy 1E6Gy 3E6Gy 6E6Gy 1E7Gy

NBS5

peroxy linkage

peroxy radical

0.5 0.0

pristine 1E4Gy 6E4Gy 1E5Gy 3E5Gy

1.5 1.0

6E5Gy 1E6Gy 3E6Gy 6E6Gy 1E7Gy

NBS6

0.5 0.0

2

3

4

5

Photon Energy (eV)

6

Fig. 1. UV spectra of NBS5 and NBS6 glasses at different absorbed doses

The UV spectra of NBS5 and NBS6 glasses are presented in Fig. 1. The solid line are pristine absorption spectra of NBS5 and NBS6 glasses, respectively. After irradiation, three absorption bands at 2.0, 3.0 and 3.8 eV emerged in both borosilicate glasses, and their optical absorption increased with the increase of absorbed dose. The absorption band at 2.0 eV correspond to peroxy radical [≡Si–O–O·(Oxy)] and non-bridging oxygen hole center [≡Si–O·(Oxy)] defect and the absorption band at 3.8 eV correspond to peroxy linkage (≡Si–O–O–Si≡) defect [12]. The structure of defect at 3.0 eV is unknown at present. Figure 1 presented that significant absorption bands appeared in these two glasses after gamma radiation. According to Beer-Lambert law, the induced absorbance is proportional to the concentrations induced by gamma radiation [10]. Wang et al. attributed the absorption to the valence electrons which were excited sufficiently by gamma radiation to leave the normal sites [13]. The band gap and Urbach energy correspond to the defect level and disorder of borosilicate glass respectively, which can be calculated by formula (1) and (2) [14–17]:   (hν − Eg )2 (1) α(ν) = B hν

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α(ν) = A exp(hν/Eu )

(2)

where α is the absorption coefficient, B is an energy-independent constant, Eg is the optical band gap (eV), hv is the photon energy (eV), A is a constant, Eu is the optical Urbach energy (eV). The diagrammatic sketch of the Urbach energy and band gap by take the 1 × 104 Gy absorbed dose of NBS5 glass as an example has been presented in Fig. 2.

Fig. 2. The diagrammatic sketch of how to calculate the Urbach energy and band gap

The band gap and the Urbach energy evolving with absorbed doses are exhibited in Fig. 3. It can be seen intuitively that the band gap decreases with the increase of the absorbed dose. The reason for such a phenomenon was that the bridging oxygen atoms transformed to non-bridging oxygen atoms by gamma radiation, which raises the top of the valence band [8]. For the Urbach energy, at the beginning, it hardly changed distinctly (the Urbach energy of pristine samples was directly assigned the value of absorbed doses at 1 × 104 Gy). When the absorbed doses were higher than 6 × 104 Gy, it began to rise with the increase of absorbed dose. The absorbed dose, at which the Urbach energy changed dramatically, was called the critical absorbed dose,

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which means after this absorbed dose, the defects caused by gamma radiation gradually account for the majority of the overall defects.

0.6

2

Band Gap (eV)

3

pristine

Urbach Energy (eV)

0.8

0.7

4

NBS5-Urbach Energy NBS6-Urbach Energy NBS5-Band Gap NBS6-Band Gap

0.5 103

104

105

106

107

1

Absorbed Dose (Gy)

Fig. 3. The Band gap and Urbach energy of borosilicate glasses evolved with absorbed doses

3.2 Raman Spectra The typical Raman spectra of gamma-irradiated NBS5 and NBS6 glasses were showed in Fig. 4. There are four regions in the Raman spectra of NBS5 and NBS6 glasses. The peak at 480 cm−1 corresponds to the bending vibration of the Si-O-Si bond [18–20]. The peak at 630 cm−1 could be assigned to the danburite-like structure, including two SiO4 and two BO4 tetrahedra [21, 22]. The region, from 830 to 1300 cm−1 was summarized as vibration of the Qn (n = 0,1,2,3,4) species, where Q represents the tetrahedral unit and n is the number of bridging oxygen atom (BO) per tetrahedron [21, 23–25]. The peak around 1400 cm−1 can be considered as a contribution of BO3 structure [26, 27]. In the Raman spectra of the two series of glasses, the closer to the end of the horizontal axis, the greater to the relative intensity of the peak at 1400 cm−1 . However, the peak at 1400 cm−1 did not change significantly due to the enhanced of the Photoluminescence spectra background, which can be attributed to non-bridging oxygen hole center (NBOHC) defects [28]. In fact, Raman spectra can provide little information for the analysis of structural characterization in this work. Nevertheless, if the curve of Raman spectra without smoothed, it can be found that the signal-to-noise ratio decreased rapidly with the increase of absorbed dose. So that the peak at 1400 cm−1 was almost drowned by noise at high absorbed doses. The noise of the Raman spectrum consists of ambient noise, measurement noise and sample noise. The ambient noise depends on the background optical conditions of the measurement environment. The sample noise is predominantly affected by sample structure and impurity. The measurement noise is related to the properties of the optical path and the detector. The ambient noise and measurement noise can be referred to as system noise. System noise is a constant for a specific measurement system.

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Relatived Intensity

2000

Danburite-like

1500

pristine 1E4Gy 6E4Gy 1E5Gy 3E5Gy

6E5Gy 1E6Gy 3E6Gy 6E6Gy 1E7Gy

pristine 1E4Gy 6E4Gy 1E5Gy 3E5Gy

6E5Gy 1E6Gy 3E6Gy 6E6Gy

Qn

[BO3]

1000 500

NBS5

0 2000 1500 1000 500

NBS6

0

400

800

1200

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Raman Shift (cm-1)

Fig. 4. Raman spectra of NBS5 and NBS6 glasses irradiated with different absorbed doses 9 in cr ea se do se Ab so rb ed

Noise

6

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3 Absorbed dose increase

0 0.5

0.6

0.7

0.8

Urbach Energy (eV)

Fig. 5. The noise of Raman spectra of gamma-irradiated borosilicate glasses evolved with Urbach energy

To exact the noise, the Raman spectra of borosilicate glasses irradiated by gammarays would be smoothed first. Then the smoothed spectra were subtracted the original spectra, the differences were the noise spectra. Finally, calculating the standard deviation of noise spectra by Eq. (3).  n 2 i=1 (xi − x) σx = (3) n−1 where σx is the noise of Raman spectra, xi (i = 1, 2, 3...n) is the i th value in the sample, x is the smoothed value in the sample, n is the total channel of Raman spectra. The error of noise spectra was calculated by Eq. (4). √ σ = σx / n (4)

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Figure 5 showed the relationship between the noise and the Urbach energy. The solid and half empty squares present NBS5 and NBS6 respectively. The arrow points in the direction of increasing absorbed dose. It can be seen that the trend of noise and Urbach energy is roughly the same, which proves that the noise of Raman spectrum can be used to characterize the disorder of borosilicate glass from the side. Nevertheless, there are also disadvantages in noise measurement, such as the spectra are required continuous measurement under the condition of constant systematic error. The test conditions are very demanding. Once the test conditions are disturbed, the noise will change greatly. To obtain the noise of Raman spectra, the measurement parameters of Raman spectroscopy should keep same for all the samples.

4 Conclusions In this work, the borosilicate glasses with two components were irradiated by gamma rays with the absorbed doses from 1 × 104 Gy to 1 × 107 Gy. The results of UV spectra indicated that the ≡Si–O–O·(Oxy) and ≡Si–O–O–Si≡ defects in the two glasses increased gradually with increasing absorbed dose. The changes in band gap and Urbach energy suggested that gamma ray irradiation would lead to the increase of disorder and the defect levels in borosilicate glasses. The noise extracted from Raman spectra was strongly correlated with Urbach energy, and it could also reflect the disorder level of borosilicate glasses. Acknowledgments. This work was supported by the National Natural Science Foundation of China (Grant Nos. 12175092, U1867207) and Fundamental Research Funds for the Central Universities of China (lzujbky-2021-kb11). The authors are appreciated the help from Hongyuan Co. Ltd. And technical support from the Public Center for Characterization and Test at Suzhou Institute of Nanotech and Nano-bionics. H. B. Peng was supported by INWARD coordinated research project [Ion Beam Irradiation for High Level Nuclear Waste Form Development, F11022] from IAEA.

References 1. Hench, L.L., Clark, D.E., Campbell, J.: High level waste immobilisation forms. Nucl. Chem. Waste Manage. 5, 149–173 (1984) 2. Griscom, D.L., Friebele, E.J.: Effects of ionizing radiation on amorphous insulators. Radiat. Eff. 65(1–4), 63–72 (1982) 3. Griscom, D.L.: Optical Properties and Structure of Defects in Silica Glass. Journal of the Ceramic Society of Japan 99, 923–942 (1991) 4. Griscom, D.L.: Self-trapped holes in pure-silica glass: a history of their discovery and characterization and an example of their critical significance to industry. J. Non-Cryst. Solids 352(23–25), 2601–2617 (2006) 5. Griscom, D.L.: A minireview of the natures of radiation-induced point defects in pure and doped silica glasses and their visible/near-IR absorption bands, with emphasis on self-trapped holes and how they can be controlled. Physics Research International 2013, 1–14 (2013)

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6. Griscom, D.L., et al.: Electron spin resonance studies of defect centers induced in a high-level nuclear waste glass simulant by gamma-irradiation and ion-implantation. J. Non-Cryst. Solids 258, 34–37 (1999) 7. Griscom, D.L., Sigel, G.H., Ginther, R.J.: Defect centers in a pure-silica-core borosilicate-clad optical fiber: ESR studies. J. Appl. Phys. 47(3), 960–967 (1976) 8. Kaur, R., Singh, S., Pandey, O.P.: Gamma ray irradiation effects on the optical properties of BaO–Na2 O–B2 O3 –SiO2 glasses. J. Mol. Struct. 1048, 78–82 (2013) 9. Kaur, R., Singh, S., Pandey, O.P.: FTIR structural investigation of gamma irradiated BaO– Na2 O–B2 O3 –SiO2 glasses. Physica B 407(24), 4765–4769 (2012) 10. Song, Z.L.: Color centers of a borosilicate glass induced by 10 MeV proton, 1.85 MeV electron and 60Co-γ ray. Radiation Physics and Chemistry (2013) 11. León, M., et al.: Gamma irradiation induced defects in different types of fused silica. J. Nucl. Mater. 386–388, 1034–1037 (2009) 12. Skuja, L.: Optically active oxygen-deficiency-related centers in amorphous silicon dioxide. J. Non-Cryst. Solids 239(1–3), 16–48 (1998) 13. Ttwa, B., et al.: γ-Irradiation effects in borosilicate glass studied by EPR and UV–Vis spectroscopies - ScienceDirect. Nucl. Instrum. Methods Phys. Res., Sect. B 464, 106–110 (2020) 14. Mojgan, et al.: Tl4CdI6 nanostructures: facile sonochemical synthesis and photocatalytic activity for removal of organic dyes. Inorganic chemistry (2018) 15. Panahi-Kalamuei, M., Salavati-Niasari, M., Hosseinpour-Mashkani, S.M.: Facile microwave synthesis, characterization, and solar cell application of selenium nanoparticles. J. Alloy. Compd. 617, 627–632 (2014) 16. Sabet, M., Salavati-Niasari, M., Amiri, O.: Using different chemical methods for deposition of CdS on TiO2 surface and investigation of their influences on the dye-sensitized solar cell performance. Electrochim. Acta 117(4), 504–520 (2014) 17. Davis, E.A., Mott, N.F.: Philosophical magazine conduction in non-crystalline systems v. conductivity, optical absorption and photoconductivity in amorphous semiconductors (1970) 18. Pasquarello, A., Car, R.: Identification of Raman defect lines as signatures of ring structures in vitreous silica. Phys. Rev. Lett. 80, 5145–5147 (1998) 19. Boizot, B., et al.: Raman study of b-irradiated glasses. J. Non-Cryst. Solids 243, 268–272 (1999) 20. Shimodaira, N., et al.: Microscopic structural changes of SiO2 glasses as a function of temperature investigated by in situ Raman spectroscopy. Physical Review B 73(21) (2006) 21. Manara, D., Grandjean, A., Neuville, D.R.: Advances in understanding the structure of borosilicate glasses: a Raman spectroscopy study. Am. Miner. 94(5–6), 777–784 (2009) 22. Manara, D., Grandjean, A., Neuville, D.R.: Structure of borosilicate glasses and melts: a revision of the Yun, Bray and Dell model. J. Non-Cryst. Solids 355(50–51), 2528–2531 (2009) 23. Parkinson, B.G., et al.: Quantitative measurement of Q3 species in silicate and borosilicate glasses using Raman spectroscopy. J. Non-Cryst. Solids 354(17), 1936–1942 (2008) 24. Windisch, C.F., et al.: Deep-UV Raman spectroscopic analysis of structure and dissolution rates of silica-rich sodium borosilicate glasses. J. Non-Cryst. Solids 357(10), 2170–2177 (2011) 25. Yadav, A.K., Singh, P.: A review of the structures of oxide glasses by Raman spectroscopy. RSC Adv. 5(83), 67583–67609 (2015) 26. Cormier, L., et al.: In situ evolution of the structure of alkali borate glasses and melts by infrared reflectance and Raman spectroscopies. Physics and Chemistry of Glasses—European Journal of Glass Science and Technology Part B 47(4), 430–434(5) (2006)

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27. Yano, T., et al.: Structural investigation of sodium borate glasses and melts by Raman spectroscopy. II. Conversion between BO4 and BO2O-units at high temperature. Journal of Non-Crystalline Solids 321(3), 147–156 (2003) 28. Mao, J.J., et al.: Simulation of beta decay effects in borosilicate glasses by changing composition. J. Non-Cryst. Solids 583, 121483 (2022)

Preliminary Evaluation of Impact Forces in Rotor Drop of the AMB-rotor System in Space Reactor Yulan Zhao(B) , Hongchun Ding, Guangchun Zhang, Kunlin Cheng, and Haochun Zhang School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China [email protected]

Abstract. The active magnetic bearing (AMB) is an ideal solution of the rotor support in the space reactor. The AMB are complete exemption of lubrication and well-adapted to the high temperature and intense radiation of the space reactor. Auxiliary bearings are utilized for temporary support and displacement limitation. When power outage happens, the rotor loses its support and drops down to the auxiliary bearing. The collision state at each moment is random and the interaction between the rotor and the auxiliary bearings is difficult to evaluate. Constrained by the structure, it’s hard to install force sensors. A preliminary method is proposed to evaluate the interaction between the rotor and the auxiliary bearings by analyzing the displacement signals of each DOF of the rotor based on specific contact conditions. The method can make up for the shortage that force sensors of the auxiliary bearing cannot be installed in the test rig and evaluate indirectly the force state of the rotor and the auxiliary bearings. It is beneficial to verify the reliability of the auxiliary bearings and can provide a certain theoretical basis for the application of the AMB-rotor system in the space reactor. Keywords: Active magnetic bearing · Auxiliary bearing · Rotor drop · Force analysis · Space reactor

1 Introduction In the rotating machinery of the dynamic power conversion system in the space reactor, the bearing needs to be used for more than ten years or even decades without maintenance, and the lubricant needs not to be affected by high temperature and intense radiation of the reactor [1]. The benefits of the active magnetic bearing (AMB) are well documented in terms of complete exemption of contact, wear contamination and lubrication, excellent endurance, and well-controlled performance [2]. Given the above advantages, the AMB is an ideal choice to support the high-speed rotor and provides a new solution of the rotor support in the space reactor. The application of the AMB to support the high-speed rotor in the space reactor can greatly reduce the difficulty of the overall layout design

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 384–399, 2023. https://doi.org/10.1007/978-981-19-8780-9_39

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and the manufacturing process of the total system and improve the dynamic behavior of the rotor. The AMB is limited by magnetic saturation and cannot bear overload. When the AMB fails due to internal failure, external disturbance or power outage, the rotor will lose support and fall. Therefore, auxiliary bearings (ABs) are required to provide temporary mechanical support for the rotor. When the AMB works normally, the rotor is in a stable suspension state and the ABs on standby. When the AMB fails to work, the rotor falls off and collides with the ABs, which are in working state. ABs mainly provide temporary support for the rotor and displacement limitation [3]. The reliability of the AMB system is determined by the capacity to endure collisions of the ABs, which is a key factor concerning whether the AMB can be applied to the reactor. The research and application of ABs in space reactor is very challenging. In the process of rotor drop, the interaction between the rotor and the ABs is mainly bounce and rub [4]. The rotor rotates around the axis of the rotor and the center of the AB inner race at a high speed and presents afterward and forward states [5]. With consideration of the interaction between the rotor and the ABs at both ends, the dropping rotor mainly presents cylinder and conical motion states. Horizontal rotor drop consists of free fall, impact, sliding and rolling [6]. Vertical rotor drop can be divided into free fall, axial impact and forward whirling [7]. The rubbing interaction mainly includes swing, collision and friction, among which collision and reverse friction have the most serious influence [8]. The whirling frequency reaches its maximum value at the first contact, and then decreases gradually with deceleration of the rotor [9]. The afterward whirling is propelled by the tangential friction of the falling rotor and reciprocating collisions continue due to large unbalance [10]. In addition, axial load is positively correlated with the whirling frequency [11]. The life span of ABs can be extended by reducing the clearance, the rotational speed, the support stiffness and increasing the support damping [12]. The dropping rotor motion is evaluated based on mechanical learning algorithm, whose parameters such as stiffness, damping and friction coefficient are taken as functions of the AB deformation and the rotor speed [13]. It is shown in [14] that the application of a novel rolling-sliding integrated AB with rolling and sliding can retard contact effectively and make the dropping orbit more stable. A research method to identify dropping rotor trajectory based on displacement signal processing is proposed in [15]. A re-suspension control strategy of dropping rotor based on discrete PID control algorithm is proposed according to trajectory identification [16]. A method is proposed to reveal the real contact force by decoupling the dynamic DOF signals of the dropping rotor acquired from displacement sensors according to the specific contact condition, which makes up for the deficiency that force sensors cannot be installed due to the constrained mechanical structure. This article aims at the further application of AMB in space reactor and provides corresponding support for the research of ABs in the AMB system.

2 Contact Impulse of the Dropping Rotor A vertical AMB rotor test rig is shown in Fig. 1. Auxiliary bearings and sensors are arranged respectively at the upper and lower ends of the rotor. The trajectory of the

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dropping rotor is obtained by data post processing. Force applied on the rotor can be calculated by rotor orbits. The span of the sensor is the same in x and y directions. The motion of the dropping rotor can be described as Fig. 2.

Fig. 1. The rotor of the helium circulator in the HTR-PM

Fig. 2. Dynamic motion of the rotor

The momentum and the moment of momentum of the rotor center can be illustrated as follows [17]: ⎧ Px = m˙x ⎪ ⎪ ⎪ ⎪ ⎪ y P ⎪ y = m˙ ⎪ ⎪ ⎪ ⎨ Pz = m˙z (1) ⎪ Hx = −IT φ˙ + IP θ ⎪ ⎪ ⎪ ⎪ ⎪ Hy = −IT θ˙ + IP φ ⎪ ⎪ ⎪ ⎩ Hz = IP  − (IP − IT )(θ φ˙ − φ θ˙ ) in which x, y and z denote the displacements of the x, y, and z axes, respectively. φ and θ denote the rotational angular around x and y axes, and  the rotating velocity. I T and I p denote the transverse and the polar moment of inertia. If the influence of

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(IP − IT )(θ φ˙ − φ θ˙ ) on H z is less than 5%, (IP − IT )(θ φ˙ − φ θ˙ ) can be appropriately ignored to simplify the calculation. The impulse and the impulse moment applied on the rotor during t are as follows17 : ⎧ Ix = Fx t = Pxt+t − Pxt ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ Iy = Fy t = Pyt+t − Pyt ⎪ ⎪ ⎪ ⎪ ⎨ Iz = Fz t = P t+t − P t + mgt z z (2) t+t ⎪ = M t = H − Hxt L ⎪ x x x ⎪ ⎪ ⎪ ⎪ ⎪ Ly = My t = Hyt+t − Hyt ⎪ ⎪ ⎪ ⎩ Lz = Mz t = Hzt+t − Hzt in which F x , F y and F z are the resultant force of the rotor center, M x , M y and M z are the resultant moment. Based on the above equations, the impact of rotor drop can be analyzed.

3 Contact Force Between Rotor and Auxiliary Bearing With consideration of the limited structure space in space reactor, components of the AMB-rotor system are closely combined. There is no enough space usually to install force sensors to measure the contact force state of ABs. Therefore, a compromise method, shown in Fig. 3, is put forward to indirectly evaluate contact force according to contact impulse applied on the rotor. Force evaluation of the auxiliary bearing is based on impulse and impulse moment applied on the contact region which are decomposed from the rotor center.

Fig. 3. Flow chart of contact force evaluation

Contact conditions is an essential prerequisite for analyzing the contact points between the rotor and the auxiliary bearing. During the process of the rotor drop, axial

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collision occurs between upper and lower surfaces of the rotor flange and the AB due to axial bounces. Also, radial collision occurs on the radial contact regions between the upper and lower ABs and the rotor due to radial contacts and bounces. The contact process is highly non-linear. Several contact conditions are assumed based on contact point number, such as single-point contact, two-point contact, three-point contact and surface contact, which are shown in Fig. 4.

Fig. 4. Contact condition between the rotor and the auxiliary bearing

Fig. 5. Contact forces applied on the radial contact region

Contact forces applied on the radial contact region are illustrated in Fig. 5. Accordingly, the upper radial contact force and the lower are derived in the following equations: ⎧ Fx1,2 = −Fn1,2 cos α1,2 − Ft1,2 sin α1,2 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ Fy1,2 = −Fn1,2 sin α1,2 + Ft1,2 cos α1,2 Mx1,2 = −Fy1,2 lb1,2 + Fz1,2 R cos α1,2 (3) ⎪ ⎪ ⎪ My1,2 = Fx1,2 lb1,2 + Fz1,2 R sin α1,2 ⎪ ⎪ ⎪ ⎩ Mz1,2 = Ft1,2 R in which F n , F t , F z denote the normal, tangential and vertical contact forces, α1,2 = y , 1 and 2 denote the upper and lower auxiliary bearings. In consideration of arctan xb1,2 b1,2

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the inclination of the dropping rotor, the inclination angle can be calculated as: γ = arctan

yb1 − yb2 xb1 − xb2

(4)

It’s assumed that the rotor drops down vertically. Accordingly, the axial contact force is seemed to be distributed equally. Interactions between the rotor and the ABs are decomposed according to contact conditions. (1) Single-point contact: Contact forces are derived from resultant force equations as follows:  Fx = Fx1,2 = −Fn1,2 cos α1,2 − Ft1,2 sin α1,2 Fy = Fy1,2 = −Fn1,2 sin α1,2 + Ft1,2 cos α1,2 Simultaneously, resultant moment is verified as follows: ⎧ ⎪ ⎨ Mx = Mx1,2 = −Fy1,2 lb1,2 + Fz1,2 R cos α1,2 My = My1,2 = Fx1,2 lb1,2 + Fz1,2 R sin α1,2 ⎪ ⎩ Mz = Mz1,2 = Ft1,2 R

(5)

(6)

(2) Two-point contact: The following equation set is well posed and closed. Thereby, contact forces can be derived as a unique solution. ⎧ Fx = Fx1 + Fx2 = −Fn1 cos α − Ft1 sin α − Fn2 cos β − Ft2 sin β ⎪ ⎪ ⎪ ⎪ ⎪ F = Fy1 + Fy2 = −Fn1 sin α + Ft1 cos α − Fn2 sin β + Ft2 cos β ⎪ ⎪ y ⎪ ⎨ F = F + F + mg z z1 z2 (7) ⎪ Mx = Mx1 + Mx2 = −Fy1 lb1 + Fz1 R cos α + Fy2 lb2 + Fz2 R cos β ⎪ ⎪ ⎪ ⎪ ⎪ My = My1 + My2 = Fx1 lb1 + Fz1 R sin α − Fx2 lb2 + Fz2 R sin β ⎪ ⎪ ⎩ Mz = Mz1 + Mz2 = Ft1 R + Ft2 R (3) Surface contact and three-point contact: On this occasion, force and moment equations need to be treated simultaneously with geometric simplification and thus be solved with well-posed assumptions. ⎧ Fx = Fx1 + Fx2 = −Fn1 cos α − Ft1 sin α − Fn2 cos β − Ft2 sin β ⎪ ⎪ ⎪ ⎪ ⎪ Fy = Fy1 + Fy2 = −Fn1 sin α + Ft1 cos α − Fn2 sin β + Ft2 cos β ⎪ ⎪ ⎪ ⎨ F − mg = F + F + F z z1 z2 a (8) ⎪ Mx = Mx1 + Mx2 = −Fy1 lb1 + Fz1 R cos α + Fy2 lb2 + Fz2 R cos β ⎪ ⎪ ⎪ ⎪ ⎪ My = My1 + My2 = Fx1 lb1 + Fz1 R sin α − Fx2 lb2 + Fz2 R sin β ⎪ ⎪ ⎩ Mz = Mz1 + Mz2 = Ft1 R + Ft2 R

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In allusion to surface contact combined with two-point contact, axial contact force is assumed to apply on the axis of the rotor. The normal components of the upper and lower radial contact forces are seemed to be equal. Also, vertical friction is neglected on radial contact regions. Based on the above assumptions, the resultant force and moment equations can be solved simultaneously. Three-point contact occurs hardly, which can be calculated according to upper and lower radial contacts. The sampling frequency is supposed with high enough accuracy. It is estimated whether the gap between the rotor and the constrained surface is smaller than the presupposed minimum value. Two sampling points at least need to be guaranteed between successive impacts.

4 Results and Discussion Simulation is carried out based on the AMB-rotor system of the HTR-PM helium blower, which is a representative application of the AMB in reactor. The parameters of the AMBrotor system are listed in Table 1. The axial rotation of the rotor is propelled by the motor. The other 5 DOFs are controlled by the AMB, in which axial displacement is constrained by axial AMB and radial displacements and rotation constrained by upper and lower radial ones. At each end of the rotor, a pair of ABs is installed. The upper ABs endure the whole axial impacts and mostly radial impacts and the lower endure partial radial impacts. Table 1. Parameters of the AMB rotor in the HTR-PM Parameters

Value

Installation

Vertical

Length of rotor, m

3.3

Mass of rotor, kg

3700

Moment of inertia, kg m2

192.2

Diameter of rotor, mm

260

Axial clearance of AB, mm

0.3

Radial clearance of AB, mm

0.3

Velocity of rotor, r/min

3000

In the test, the rotor velocity is adjusted to the rated speed, 50 Hz, by the frequency converter. After that, the power supply of the AMB is quickly cut off. Therefore, the rotor loses its support and falls to collide with the ABs. The varying displacements and speed are recorded until the speed decreases to lower than 5 Hz or reaches 0 Hz. The experimental data within 0.5 s after rotor drop is analyzed and processed. Figures 6 and 7 show the dropping orbits of the rotor at the upper and lower AB sections respectively. Figure 8 shows the axial displacement of the dropping rotor.

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Radial collision occurs when the displacement exceeds the nominal clearance. The lower whirling motion is developed completely. Synchronous full annular rub whirl is observed in the lower orbit. Conical motion is presented in the rotor drop process. The rotor drops with an inclination. As to the axial collision, the rotor drops to the axial surface of the upper AB with several bounces. At about 0.35 s, the bounce range reaches its peak value with an upward bounce height of more than 0.15 mm. The dropping rotor mainly goes through the stages of free fall, bounce, backward whirling and forward whirling.

Fig. 6. Rotor orbit in the cross-section of the upper AB

Fig. 7. Rotor orbit in the cross-section of the lower AB

The radial displacements and angles are derived from the above experimental data, which are presented in Figs. 9, 10, 11 and 12. The axial intersection angle is shown in Fig. 13. The velocities of the dropping rotor in x, y and z axes are shown in Figs. 14, 15 and 16, which are calculated based on differentiation of displacement signals. The velocities around x and y axes are shown in Figs. 17 and 18, correspondingly. According to the evaluation measure mentioned in the above, the resultant forces of the x, y and z axes are illustrated in Figs. 19, 20 and 21. Negative force value denotes the force direction opposite to the initial direction. The axial impact force is fully applied to the upper AB. The magnitude of the impact force in x, y and z directions is equal. The

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Fig. 8. Axial displacement of the rotor

Fig. 9. Displacement in the x axis

Fig. 10. Displacement in the y axis

axial impact force is slightly larger than the radial one. After the rotor drops down, the axial impact is more intense and axial bounces are more obvious, whose peak value is not significantly attenuated within the initial 0.5 s. Furthermore, based on the evaluation of contact conditions between the rotor and the ABs at each moment of the rotor drop process, the decoupling analysis of the resultant

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Fig. 11. Angle around the x axis

Fig. 12. Angle around the y axis

Fig. 13. Axial intersection angle

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Fig. 14. Velocity in the x axis

Fig. 15. Velocity in the y axis

Fig. 16. Axial velocity of the rotor

forces on the rotor is carried out to obtain radial and tangential forces of the upper and lower ABs as shown in Figs. 22, 23, 24 and 25.

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Fig. 17. Velocity around the x axis

Fig. 18. Velocity around the y axis

Fig. 19. Force in the x direction

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Fig. 20. Force in the y direction

Fig. 21. Force in the z direction

As to this experiment, the upper AB endures the whole axial impacts and part of radial impacts, the lower AB endures mostly radial impacts. At the upper contact region, bounces are more obvious. The radial and tangential forces have larger values in the initial period and then decrease slightly but increase in the later 0.5 s. The lower AB endures most of the rub in the initial dropping stage. The rotor drop process is highly non-linear. The initial state of each dropping process is difficult to keep consistent. Due to the randomness of the rotor drop, it is difficult to summarize the detailed impact characteristics between the rotor and the ABs according to a specific contact condition.

5 Conclusions Collisions between the dropping rotor and the auxiliary bearings are highly random and non-linear. It’s difficult to evaluate the contact force directly. A method is proposed

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Fig. 22. Normal contact force of the upper bearing

Fig. 23. Tangential contact force of the upper bearing

Fig. 24. Normal contact force of the lower bearing

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Fig. 25. Tangential contact force of the lower bearing

to reveal the contact force by decoupling the dynamic signals acquired from sensors according to the specific contact condition. This method can evaluate the real contact force in the process of rotor drop and make up for the deficiency that force sensors cannot be installed due to the constrained mechanical structure. It’s beneficial to the further application of AMB in the constrained interspace of the space nuclear reactor and provides a further theoretical reference for the evaluation of contact force in rotor drop of AMB system. Acknowledgements. This paper is supported by Heilongjiang Province Postdoctoral Research Foundation (LBH-Q21094) and the Characteristic discipline construction project of the Harbin Institute of Technology. Thanks are due to Professor Zhao, Lei and Associate Professor Yang, Guojun for assistance with the experiments and valuable discussion.

References 1. Xu, Y., Zhao, L., Yu, S., et al.: Study of safety and reliability design for active magnetic bearing-a survey. The first Chinese Academic Conference on Electromagnetic Bearings, Beijing (2005) 2. Schweitzer, G., Bleuler, H., Traxler, A.: Active magnetic bearings: basics, properties and application of active Magnetic Bearings. Switzerland: ETH (2010) 3. Yang, G., Yu, S.: Preliminary study on rotor dynamics of magnetic bearing for 10 MW high temperature gas-cooled reactor. Nuclear Power Engineering 24(6), 514–516 (2003) 4. Keogh, P.S., Yong, W.Y.: Thermal assessment of dynamic rotor/auxiliary bearing contact events. J. Tribol. 129(1), 143–152 (2007) 5. Nelson, F.C.: Rotor dynamics without equations. Int. J. COMADEM 10(3), 2–10 (2007) 6. Fumagalli, M., Schweitaer, G.: Measurements on a rotor contacting its housing, in 6th International Conference on Vibration in Rotating Machinery, IMechE Conference Transactions, Oxford, pp. 779–788 (1996) 7. Kang, X., Yang, G., Yu, S.: Dynamic behavior of the AMB’s vertical arranged rotor during its drop process. In: 22th International Conference on Nuclear Engineering, Prague, Czech (2014)

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8. Zhu, C.: Transient response of rotor drop on a rolling element backup bearing in a flexible rotor supported on active magnetic bearings. Acta Aeronautica et Astronautica Sinica 26(3), 349–355 (2005) 9. Masala, A., Vannini, G., Ransom, D., et al.: Numerical simulation and full scale landing test of a 12.5Mw vertical motorcompressor levitated by active magnetic bearings. In: Proceedings of ASME Turbo Expo, Vancouver, Canada (2011) 10. Su, Y., Gu, Y., Keogh, P.S., et al.: Nonlinear dynamic simulation and parametric analysis of a rotor-AMB-TDB system experiencing strong base shock excitations. Mech. Mach. Theory 155, 104071 (2021) 11. Vannini, G., Camatti, M., Masala, A., et al.: Full load testing of a 12.5 Mw vertical high speed subsea motor compressor. In: Proceedings of the Fortieth Turbomachinery Symposium, Houston, Texas (2011) 12. Lee, J.G., Palazzolo, A.: Catcher bearing life prediction using a rainflow counting approach. Journal of Tribology 134(3), 31101(15) (2012) 13. Sun, Z., Yan, X., Zhao, J., et al.: Dynamic behavior analysis of touchdown process in active magnetic bearing system based on a machine learning method. Science and Technology of Nuclear Installations (2017) 14. Liu, X., Zhou, Y., Yan, X., et al.: Experimental study on the novel rolling-sliding integrated auxiliary bearing in active magnetic bearing during rotor drop. Ann. Nucl. Energy 136, 107044 (2020) 15. Liu, T., Lyu, M., Wang, Z., et al.: An identification method of orbit responses rooting in vibration analysis of rotor during touchdowns of active magnetic bearings. J. Sound Vib. 414, 174–191 (2018) 16. Lyu, M., Liu, T., Wang, Z., et al.: A control method of the rotor re-levitation for different orbit responses during touchdowns in active magnetic bearings. Mech. Syst. Signal Process. 105, 241–260 (2018) 17. Zhao, Y., Yang, G., Liu, X., et al.: Research on dynamics and experiments about auxiliary bearings for the helium circulator of the 10 MW high temperature gas-cooled reactor. Ann. Nucl. Energy 95, 176–187 (2016)

Error Analysis of Set Pressure Test of Main Steam Safety Valve in CPR1000 Nuclear Power Plant Guo Yibo(B) , Jiang Taikeng, and Yu Yuan Fujian Fuqing Nuclear Power Co., Ltd., Fuqing, Fujian, China [email protected]

Abstract. Main Steam Safety Valve (MSSV) has been widely used to prevent overpressure from main steam systems in nuclear power plants. To confirm operability and valve opening with allowable pressure as established by the licensee’s Technical Specifications or by procedure acceptance criteria, periodical inspection should be achieved in every operation cycle. Auxiliary-lift devices used online for most MSSV partly weld with steam pipes. Other than off-site bench testing, it’s widely found set pressure of MSSV is not consistent with acceptance criteria in on-site testing. Such effects produce safety function unavailable status and bring unplanned maintenance work in the outage period. Although valve producers, testing diagnostic equipment vendors and engineering consulting firms had proactively developed solutions to address generic problems resulting in aging, material degrading, elastic coefficient of spring changing, and inappropriate maintenance, there is no general agreement about differential experimental data between online and offline testing. This study systematically reviews thousands of industry MSSV set pressure variation data and correlates observed variation with associated statistical and environmental factors, to develop an improved understanding of the setpoint drifting phenomenon. Based on current research, suggest the implications of on-site test conditions contributed to system uncertainty, and statistical methods contributed to random uncertainty. This study improved understanding of the setpoint drifting phenomenon while proposing an amendment method in the following project. Keywords: MSSV · Set point drift · System uncertainty · Random uncertainty

1 Introduction As important safety devices in the PWR nuclear power plants, the functions of main steam safety valves (MSSVs) are to provide overpressure protection for the steam generator and the main steam line. Spring-load structures are selected by most MSSVs used in CPR1000 PWRs. Pilot-Operated Pressure Relief Valves pilot-operated valve is also used in a few projects. Normally each CPR1000 unit is equipped with 21 MSSVs, which are arranged in 3 rows. Figure 1 displays the main steam system and the location of 3 MSSV rows. The © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 400–411, 2023. https://doi.org/10.1007/978-981-19-8780-9_40

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layout for each row of MSSVs is presented in Fig. 2. MSSVs need to be inspected regularly according to the design and operating specifications. The inspection items include dimensional inspection, hydrostatics, set pressure, leak tightness as well as performance tests, etc. Out of set pressure range from the acceptance, standards is generally observed within the industry, several report documents investigation to determine the probable cause for the observed initial high lift phenomenon. For instance, EPRI suggests that oxide accumulation between nozzles-disks surfaces contributes to some initial high-lift phenomena [1] during plant outages. Followon research assessed the high-lift phenomenon affected by various surface coatings and treatments of disk and nozzle materials metal [2]. EPRI and FISHER suggest the transient temperature distribution within the valve resulting from changes in inlet fluid and ambient temperature lead to set pressure reduction. The primary factor tending to reduce set pressure is the relaxation of spring rate with increased temperature, due to the strong effect of temperature on spring rate [3, 4]. Factors like equipment aging, the accuracy of inspection tools, and inspection procedure also contribute to the changes in MSSV set pressure values. China Nuclear Power Technology Research Institute suggests equipment aging and mismatch between testing tools and acceptance criteria also contributed to set pressure drifting [5]. A simple lumped parameter model has been developed by Jiangsu Nuclear Power Plant, which has shown capable of accurately predicting the temperature distribution within relief valves and resulting variations in set pressure for general combinations of inlet fluid and ambient temperature. In contrast to earlier findings, this article reviewed MSSV set pressure failure data based on massive plant test data. Systematic and random errors in the experiment were analyzed, and the relative effect and the overall uncertainty of the identifiable error sources are calculated. Additionally, regulatory concerns, recent industry topics, and helpful operational and maintenance reminders were presented to discuss opinions on the principal failure modes, mechanisms, causes, and efforts to improve MSSV performance. The result can be applied to add reliability management and testing tool improvements.

2 Characteristics of MSSV Set Pressure Testing To ensure the technical specification requirements are met. MSSV require various inspection in all life cycle. In the design and manufacture stage, the MSSV performance inspection generally follows the requirements of ASME PTC 25 and ASME OM. While the PM program, corrective maintenance program, and surveillance test program applied in an operating plant, included set pressure value verification, operational reliability, leakage rate, flow performance, etc. Valve design and operation specifications require MSSVs to operate within certain allowable tolerance limits depending upon valve service requirements and set pressure values. In which the set pressure is defined as where the valve disc has measurable movement in the opening direction due to inlet pressure. The CPR1000 technical specifications require each valve to be tested for set pressure in each fuel cycle. Normally MSSVs set pressure tested in NS/SG Mode during end-of-cycle coast-down. Following valve maintenance utilities typically verify valve operability by testing the repaired valve(s) during heat-up.

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Fig. 1. Main steam system (red) and main feed water system of CPR1000

Fig. 2. Seven MSSVs layouts on single array

The popular methods for set pressure testing include the bench test and online test. The bench test, which is widely used both in equipment manufacturing and on-site service, is normally carried out at room temperature with compressed gas. While the online test is applied in design operating conditions either under the combined adoption of a system pressure test with an auxiliary device test or under the full adoption of a system pressure test. For CPR1000 MSSVs are partly welded on the main steam line, and the online setting test by the auxiliary device is widely selected. The ASME BPVC PG 72.2 recommends the error range according to different pressure sections [6], shown in Table 1. The set pressure range of MSSV in most cases is been ± 1%, depending on each plant’s unique specification. Manufacturers and operators generally determine the error range by rounding numbers or proportions, For instance, the set pressure of CPR1000’s MSSVs ranges between 8.20 and 8.70 MPa [7], and the error range for the Sect. (1% of set pressure for great than 7.0 MPa) is ± 0.082–0.087 MPa according to BPVC, the actual set point tolerance value determined as ± 0.10 MPa. The ASME OM-2015 requires a minimum of 5min shall elapse between successive openings [8]. The number of openings at set pressure shall be sufficient to demonstrate

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Table 1. Set pressure range specified in ASME BPVC PG-72.2 Set pressure, Psi (MPa)

Tolerance, ± Psi (kPa)

≤ 70 (0.5)

2Psi (15 kPa)

> 70 (0.5), ≤ 300(2.1)

3% of set pressure

> 300 (2.1), ≤ 1,000(7.0)

10 Psi (70 kPa)

> 1000 (7.0)

1% of set pressure

satisfactory repeatability with a minimum of two consecutive openings within acceptance criteria. Under the ASME PTC-25-2001, the computed set pressure will be the average of at least the last three measured set pressure once established and stabilized [9]. Meanwhile, the TSG ZF001-2006 requires that the set pressure test should be conducted consecutively at least thrice [10]. So far design institutes, operators, and valve producers do not share a unified standard of MSSV testing numbers. Considering the cost and safety factor, the number of measurements is generally set to 2–3 times in the on-site test process. Usually on-site have only a limited set of observations, from which to infer the characteristics of the larger population.

3 Failure Modes and Causes As a Class-2 nuclear safety device, MSSV failure information was individually reviewed to categorize reported failure mode and cause. A failure mode refers to the way a MSSV fails, e.g., lift high, leakage. A failure cause refers to the physical cause of the failure. Failure causes can be related to human errors, mechanical defects, service stresses, aging, etc. The predominant failure mode for MSSV is lifting deviating from the desired pressure range. According to the EPRI records, the MSSV set pressure failure from the PWR nuclear power plants accounted for 52% of all failure records reported in the United States from 1974 to 1993. A nuclear power plant in Fujian accounted for 36% out of the acceptance criteria range in ten overhaul records. Meanwhile, another nuclear power plant in Guangdong accounted for 18% in 16 overhaul records, in most cases, the deviations acceptance criteria range also record as “setpoint drift”. Major causes of setpoint drift include the physical condition of the valve, maintenance practices, testing practices, as well as the physical environment of the installed condition of the valve, such as ambient and fluid temperature, vibration, and backpressure on conventional non-balanced valves. A review of the specific data indicates that most of these failures are outside the ± 1% allowable but are normally within the ± 3% range. These setpoint drifts are principally driven by the close tolerance between Technical Specification requirements and the actual ability of the valve to perform within the required pressure band. Meanwhile, the common failure traits include a higher defective rate of online test results than the bench test results and the higher defective rate of hot state tests than the cold state.

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3.1 Equipment and Environmental Factor Equipment aging was recorded as the primary cause of set value deviation in history fault classification, meanwhile aging is a rather vague classification, which is the effects seen by a component that remained unexercised for an extended period at extreme temperatures. Lubrication dries out due to high temperature. On the one hand, some unrecognized failure modes are classified as aging without further examination in some failure analyses. On the other hand out-of-range set point value is also observed in relatively brand new valves. This phenomenon appears to be normal for the type of valves being used by the industry. EPRI states that a deterministic relationship is present between the sealing surface adhesion and the high set pressure. The sealing surface adhesion occurs chiefly during the online hot state test with an auxiliary booster device. This is embodied in the high initial opening pressure (+ 7% maximum observed) of long-term non-operating MSSVs during a test, Often the first lift of the valve is outside the ± 1% range but is normally within the ± 3% range. Then, in many cases, the second, third and fourth lift tests result in the valve lifting within the ± 1% range without valve adjustment. Regarding the mechanism, oxide deposition occurs on the 422/316 stainless steel material, leading to changes in the actual area of the sealing surface. Based on this research, EPRI proposes to replace the component material with INCONEL 750X. The results of the equipment set pressure test are improved greatly after changing the material. Further, the institute proposes to optimize the sealing surfaces of valve clacks and seats through the surface coating and heat treatment. Environmental impacts refer to such test conditions as temperature, vibration, and medium state. Ambient temperatures can affect the valve’s normal temperature profile. Large ambient temperature transients may cause the valve to open outside the expected lift set pressure tolerance. Correction in the valve set pressure for these conditions must be made. Vibration may cause wear of the components that need relative motion inside the equipment, resulting in performance alterations. Figure 3 displays the vibration caused by high-speed steam flow in the Main Steam Isolation Valve, which causes the wear of adjacent MSSV components (shaft sleeves), further affecting the set value of MSSV. 3.2 Test Method The measuring process inevitably alters the characteristics of both the source of the measured quantity and the measuring system itself; thus, the measured value will always differ by some amount from the quantity whose measurement is sought. The main focus of any safety valve testing program is to ensure that the measurements obtained during testing will permit accurate set pressure verification. It is equally important to eliminate any variables present during the testing process that could affect the set pressure measurements [11]. Tight control of the testing parameters and equipment helps eliminate the introduction of errors and will ensure an accurate, repeatable test. The most accurate method for testing the set pressure of a valve is to test it in the exact condition that it is required to function. However, this requires the system to be taken to the conditions designed to protect against, but in producers’ factory test conditions are

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not completely consistent with the plant service conditions, especially in terms of equipment layout, system pressure, temperature, fluid characteristics, and operating interval. Meanwhile, the prototype tests are often fully emphasized and carefully organized, and the test. systems and samples are sufficiently commissioned and maintained. Under this setting, relatively ideal test data and error range can be obtained. At large-scale production in the factory, the products are generally inspected by bench test under ambient temperature.

Fig. 3. Wearout shaft sleeves of a MSSV caused by system vibration

Error range definition is greatly influenced by the test method. Compared to the online test, the set value from the bench test exhibits a low offset rate and a low standard deviation reflected by the test data[12]. Table 2 presents the bench and online test results of two groups of MSSVs. Both the standard deviation and maximum offset of the factory bench test are below 50% of those of the field online test. Table 2. Differences between bench testing and online test of set-pressure Test object

Sample size (N) Standard deviation Max error (MPa) Min error (MPa)

Group1 bench test 42

0.038

0.070

0.001

Group 2 bench test 42

0.033

0.110

0.000

Group1 online test 42

0.081

0.232

0.001

Group2 online test 42

0.070

0.219

0.004

Errors resulting from environmental factors and test methods are generally systematic errors, which are independent of the equipment structure type. Other structure types, such as pilot-operated safety valves, also present significant differences in the error range during the bench and online tests.

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3.3 Sample Analysis and Data Evaluation Error range provided in MSSVs technical specifications includes the systematic and random errors during experimental design, as well as the equipment manufacturing tolerance. The systematic errors have relatively fixed direction and amplitude, which vary with the seasons, operating conditions, and state of measuring instruments. The random errors vary chiefly with the number of samples, and the difference between a sample and overall characteristics decreases with the increasing number of samples, tending to follow a normal distribution. The MSSVs under the same operating condition in a certain nuclear power plant were evaluated as a whole population. Table 3 present the set pressure result of 75 MSSVs tested in ten fuel cycles. Table 3. Set pressure measurement in ten fuel-cycle Sample

Means (MPa)

Standard deviation (MPa)

Max deviation (MPa)

C1

8.489

0.155

0.387

C2

8.733

0.047

0.140

C3

8.627

0.075

0.210

C4

8.711

0.046

0.090

C5

8.627

0.050

0.183

C6

8.610

0.136

0.521

C7

8.583

0.093

0.239

C8

8.612

0.070

0.219

C9

8.763

0.078

0.245

C10

8.737

0.061

0.230

It is observed that the measurements from the aforementioned set pressure test are normally distributed, the sample mean is 8.649 MPa, which approaches the theoretical value of 8.700 MPa. The confidence interval (CI) is calculated by formulas (1) and (2) based on the overall distribution [13]. 

1 f(x) = √ e σ 2π

− (x−μ) 2

2



2σ

μ − Z0.45 σ < x < μ − z0.45 σ

(1) (2)

where x is the magnitude of a particular measurement, μ the true value of the population, σ the standard deviation of a population. The 95% CI of the ensemble average is obtained as x = 8.639 ± 0.163 MPa. The standard deviation of the corresponding design error range (8.700 ± 0.100 MPa) needs to reach 0.051 MPa.

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Test data were evaluated by the 2σ error probability (95% CI). Comparisons were made among the overall data of 10 overhauls, Table 4 presents test data from the same manufacturer’s equipment in different units and the test data of different manufacturers’ equipment under the same operating environment. As is clear, the CIs of the overall data and single overhaul test data are both below 95%. Table 4. Test result from difference power unit and producers Case

Population (N)

Mean (MPa)

Standard deviation

Z value

Confidence level (%)

Total 10 Fuel-Cycle

135

8.639

0.083

1.20

76

Sample A

15

8.610

0.136

0.69

50

Single Cycle B

15

8.763

0.078

0.81

58

Single Cycle C

27

8.737

0.061

0.61

46

* Test Sample A/B/C are from the different power unit

Samples A and B were provided by the same producer while C was from another producer

Due to consideration of equipment wear and production schedule, normally the plant online test measures the same valve 2–3 times and uses their average or consecutively qualified times as the evaluation basis. Given the increase in random deviation components caused by the small sample size, the t-distribution was employed to accomplish the small-sample CI verification by the formula (3): sx sx x − t α2 ,ν · √ < μ < x + t α2 ,ν · √ (95%) n n

(3)

where α is the μ the mean value of population x the mean value for sample.υ the degree of freedom, Sx the standard deviation of the sample. n the number of measurements. The 95% CI in the single-unit small-sample inspection (3 consecutive measurements) was obtained as x = 8.700 ± 0.207 MPa, which is greater than that in the large-sample inspection and approximately twice the design deviation. In each overhaul, 3 units (at least 1 unit per loop) are used for preventive disassembly inspection as The MSSV maintenance strategy. The results of the disassembly inspection and set pressure test are not necessarily related. For MSSVs whose results are judged as beyond range, adjustment is required until qualification according to the maintenance strategy. On the one hand, repeated adjustment tests within a small range increase the equipment wear and maintenance workforce. On the other hand, serious failure signs like aging and wear out may be covered by simple spring adjustment. MSSVs under the same specification and set value in two overhaul tests were regarded as the integral sample. Case 1 in Table 5 represents the set pressure measurement under identical conditions from begin and end of the fuel cycle, whereas case 2 represents the measurement after adjustment of 6 pcs in 15 MSSVs.

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CASE

Description

CASE 1

CASE 2

Previous cycle

Next cycle

Mean

x1 = 8.687 MPa

x2 = 8.763 MPa

Standard deviation

S1 = 0.062

S2 = 0.078

Population

n1 = 15

n2 = 15

Mean

x2 = 8.763 MPa

x2 = 8.702 MPa

Standard deviation

S2 = 0.078

S3 = 0.052

Population

n2 = 15

n3 = 15

The hypothesis to be tested is H0 : x1 = x2 x2 = x3 Ha : x1 = x2 x2 = x3 A sample comparison test was conducted using (4), (5): 2  2 S1 /n1 + S22 /n2 υ= 2 2 (S 21 /n1 ) /(n1 − 1) + (S22 /n2 ) /(n2 − 1) x1 − x2 t= 2 S1 /n1 + S22 /n2

(4) (5)

where x is the sample means, S the standard deviations, n the sizes of the two respective populations, and υ the degree of freedom, which is rounded down to the nearest integer. By calculating the means and standard deviations of each sample with Eq. 4 the degree of freedom (DOF) υ = 27(rounded down) and from Eq. 5 calculated the test statistic t = −2.954 For a confidence level (1 − α) equals 0.99, the value of t from the Student’s t table is 2.771, suggesting that there is not a significant difference between single fuel cycles at a 99% confidence level. After evaluation of the test results, a comparison should be made between the preadjustment and post-adjustment results. The sample DOF is υ = 25 (rounded), the t-statistic is t = − 2.520, and the corresponding critical value is 99% > ± ta/2υ > 95% (t0.005), suggesting that the ensemble averages of set pressure tests before and after adjustment differ insignificantly at a 95% CI. As revealed by the pre- and postadjustment correlation analyses, the test values before and after one integral cycle are generally stable, and the adjustment of some out-of-tolerance results does not contribute to the sample CI improvement.

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4 Results and Discussion Summarizing the above analyses, the CI of experimental design is lower than the ideal level, the overall uncertainty of the online test is greater than the ideal level, and the uncertainty of the bench test reaches the ideal level. The differences among random errors come chiefly from the sample size disparity, and the systematic errors contribute predominantly to the overall error. Regarding the elimination of measurement errors, manufacturers of equipment and inspection tools provide partial correction methods for identifiable common impact factors (e.g. instrument error, change of spring elasticity coefficient), which can be preset in the analysis software of highly integrated inspection tools. The correction methods are primarily for tools, and the conditions of plant equipment usage and test environment cannot be further distinguished. Temperature factors, as an example, vary greatly with changes in seasons, equipment layout, and thermal insulation. Deviations cannot be fully compensated for with fixed correction factors. Each valve may have a different temperature profile so just taking an area temperature will not provide the thermal information necessary for valve testing. An example of this condition is shown in Figs. 4 and 5, which is the actual temperature profile of three arrays of MSSVs. Depict the equipment temperature differences under different loops at identical times and under identical loops at different times. As is clear, there may be differences of over 20 °C between the yearon-year and vertical test temperatures, large ambient temperature transients may cause the valve to open outside the expected lift set pressure tolerance. Correction in the valve set pressure for these conditions must be made. Meanwhile, some owners recommended that safety valves going into service should be set to within the ± 1% tolerance and the ASME/ANSI OM allowance of tolerance of ± 3% for valves should be used as the acceptance criteria for in-service valves.

Fig. 4. MSSV temperature differentia between three loop

For products from mass manufacturing, the data points greater than 3σ are generally regarded as outliers in statistical methods, which may be discarded as unqualified products. In contrast, the outrange results of set pressure testing were adjusted and retested to ensure compliance with the technical specification tolerance before continuing with the test, without distinguishing between the magnitudes of the error range. If the set value exceeds a certain tolerance, it should imply that the equipment may have other problems, which are particularly hardly noticed when the operating pressure is higher than

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Fig. 5. MSSV Temperature differentia from unit operating stage

the protection set value, resulting in an increased potential risk of the MSSV operation failure.

5 Conclusions Based on the equipment failure analysis combined with the collected plant data, the MSSV testing method and conditions are explored, thereby deepening the understanding of the set value drift during the routine MSSV inspection. The following conclusions are reached: Testing method and conditions are the contributors to the systematic error of set pressure test results for MSSVs, while the selection of test sample number is the contributor to the random error of the test. Differences in the individual arrangement and usage conditions of equipment cannot be foresighted and corrected sufficiently in the test tool, and individual evaluation and correction of a single MSSV’s environment and usage characteristics are necessary during the experimental design. Difference between the factory test and plant test conditions leads to greatly varying results. During the model selection and error range setting, the “as build” test conditions should be distinguished from the “as found” test conditions. By developing pre- and post-test evaluation mechanisms, the confidence interval should determine based on the historical test records, and measuring instrument calibration records. Systematic and random errors can be reduced by comparing test instruments and properly increasing the number of samples. During the test result evaluation, the correction method can be proposed and correction conditions can be presented. Reliability analysis can be made based on accumulating and analyzing plant test data. Trend tracking and analysis can be carried out after establishing a reasonable confidence interval. By focusing on the out-of-boundary test values and optimizing the preventive maintenance items, excessive maintenance can be reduced and early detection of potentially defective equipment can be detected beforehand.

References 1. EPRI: Investigation of MSSV High first lift phenomenon in dresser 3700 Series Steam Safety Valves, EPRI, Palo Alto, CA, TR-113560 (2000)

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2. EPRI: Investigation of main steam safety valve seat/nozzle surface treatments, EPRI, Palo Alto, CA, 1003762 (2003) 3. EPRI: Method for Assessing temperature effects on relief valve set pressures, EPRI, Palo Alto, CA, 1003508 (2002) 4. FISHER: Installation and maintenance instructions for safety relief valves, FISHER (1997) 5. Li, T., Zhang, S.: Solution of main steam safety valve setting drift in daya bay nuclear power plant. Nuclear Science and Engineering 35(2), 264–270 (2015) 6. ASME Boiler & Pressure Vessel Code, Rules for Construction of Power Boilers. New Yoke (2004) 7. China Nuclear Power Engineering Co., Ltd. Technical Specification for Main Steam Safety Valves (2012) 8. ASME: Code for operation and maintenance of NPP, mandatory appendices, PWR Pressure Relief Device Testing, New York (2015) 9. ASME: Performance test codes, pressure relief devices performance test codes, New York (2005) 10. AQSIQ: Safety technical supervision regulation for safety valves, Beijing (2006) 11. BINE: Guidelines of main steam safety valve set pressure test NI STP VVP 51 (1998) 12. SAPAG: Main steam valve HE 6 R10 end of manufacturing report, France (2000) 13. ASME PTC 19.1-2005: ASME performance test codes, measurement uncertainty, New York (2005)

Development and Application of the Control Rod Guide Tube Inspection Equipment Pengfei Zhang(B) , Qianfei Yang, Chenming Zeng, Shuangyin Wang, Jialong Shu, Chao Ma, and Jianrong Wu CGNPC Inspection Technology Co Ltd, Suzhou, Jiangsu, China [email protected]

Abstract. The main function of the control rod guide tube (CRGT) is to guide the rod cluster control assembly (RCCA), its integrity can make sure that the RCCA’s drop-time meets the requirements of nuclear safety. This paper puts forward two kinds of visual inspection schemes by analyzing the inspection environment and common defects. A more conservative and stable scheme was selected because of the strict requirements of the inspection environment for foreign materials and radiation. The equipment is applied in the nuclear power plant and the results of visual inspection have guiding significance for the replacement of 6 relevant CRGTs. Keywords: Control rod guide tube · Inspection equipment · Visual inspection

1 Introduction In all nuclear power plants, the control rod drop-time test is mandatory to ensure that the chain reaction in the reactor core is stopped within a limited time and meet the requirements of safety shutdown of the reactor [1]. The main function of the control rod guide tube (CRGT) is to guide the stepping movement of the rod cluster control assembly (RCCA), avoid interference between the control rod and the CRGT guide plate, and ensure that the rod drop-time meets the nuclear safety requirements. Therefore, any problem with the CRGT is a major problem affecting the operation safety of the nuclear power plant. The vertical stepping movement of RCCA in CRGT and the vibration of control rod in CRGT will cause repeated impact of guide system, resulting in wear of RCCA and CRGT respectively. The operation experience shows that the wear of CRGT mainly occurs in the hole, ligament area and opening position of the CRGT guide card, which may cause the bonding effect between the control rod and the CRGT, lead to the jam of the control rod, and even cause the serious problem of increasing the rod drop-time of RCCA. In addition, the damage of CRGT caused by foreign materials during operation and overhaul will also bring practical hidden dangers to the operation safety of the unit.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 412–418, 2023. https://doi.org/10.1007/978-981-19-8780-9_41

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2 Inspected Object 2.1 Inspection Environment The CRGT, with a total length of about 4m, is composed of the upper assembly and the lower assembly, which are fixed on the upper support plate and the upper core plate of RPV by screws. The upper assembly is cylindrical in shape and contains 4 layers of guide plates inside. Two anti-rotation bars pass through each layer of guide plate and housing plate, and welded with the housing plate. The housing plate is located at the top of the upper assembly and has a hole with a diameter of 58.5mm in the center, which is the movement passageway of the control rod drive shaft; The lower assembly is square in shape and contains 6 layers of guide plates and one continuous guidance. The location and structure of CRGT is as illustrated in Fig. 1.

Fig. 1. The location and structure of CRGT

The interior of CRGT is almost a closed structure, with small entrance and large internal space. Moreover, the center distance of adjacent CRGT is very close, about 304mm. It is difficult to take the foreign materials out once they fall into the inside of the CRGT. Because the CRGT is located in the center of the RPV and its lower part is close to the fuel assembly, the internal radiation level of the CRGT is very high. In addition, the CRGT is located at a depth of about 8m underwater during in-service inspection, which has high requirements for equipment operation and positioning. 2.2 Common Defects There are two main types of common defects in the CRGT: wear and foreign material intrusion. There are two main causes of wear: displacement wear and vibration wear. Displacement wear is caused by friction when RCCA steps or falls freely in the guide hole of CRGT; The vibration wear is caused by the vibration and impact of the control rod in the CRGT because of the flow of coolant during reactor operation.

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Due to the short ligament length (about 3mm), the four E-holes (see Fig. 2 left picture) close to the center of the guide plate of CRGT are most likely to be worn in the direction pointing to the center, so the guide plate loses its guiding function and causes the control rod to fall out. In serious cases, it will also cause adverse consequences such as control rod sticking or damage. The right picture of Fig. 2 shows the case where the slot width of 10.05mm which is greater than the control rod diameter.

Fig. 2. E-holes (left) and severe wear of E-hole (right)

Foreign material intrusion is the most headache and inevitable problem in nuclear power plants (see Fig. 3 left picture). Foreign material is often difficult to find or remove, which will bring incalculable damage to the nuclear power plant. If the foreign material inside the CRGT is just near the slot or hole of the guide plate, it will cause damage to the control rod, RCCA wing or guide plate, and further affect the control rod drop-time. The right picture of Fig. 3 shows the deformation caused by foreign material intrusion.

Fig. 3. Foreign material (left) and deformation caused by foreign material (right)

3 Schematic Design 3.1 Inspection Principle At present, there are many kinds of inspection methods and equipment abroad [2–4], among which visual inspection is the simplest and minimum risky one. Before carry

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out the inspection, it is necessary to adjust the focal length, illumination and visual field of the camera on the calibration test block, use machine vision to identify the Ehole diameter D, slot width W, ligaments length L1 and L2 (see Fig. 4), and calibrate through the standard data of different wear conditions on the test block. During on-site inspection, the equipment will use the same parameters so as to ensure the reliability of the inspection system.

Fig. 4. Measurement parameter of E-hole

Because the E-hole is the most dangerous and easily to be worn, the strategy of accurate measurement of E-hole (see Fig. 5 left picture) and foreign material inspection of the overall guide card (see Fig. 5 right picture) is adopted in the inspection.

Fig. 5. Accurate measurement of E-hole (left) and foreign material inspection of the overall guide card (right)

3.2 Schematic Design and Selection Establish the coordinate system: according to the structure of CRGT, the equipment uses the upper surface of the housing plate at the top of CRGT upper assembly as the axial zero point, the cylindrical surface of the upper tube as the centering reference, and the part of the anti-rotation bar extending out of the housing plate as the circumferential reference to make the equipment coordinate system consistent with the CRGT coordinate system. The equipment mainly has two degrees of freedom, axial motion and circumferential rotation. The axial movement is completed by driving a hollow rack tube through a spur gear. The camera assembly is installed at the front end of the rack tube. The camera can be moved to the focal length position automatically at the upper surface of each guide plate. The circumferential rotation assembly is located at the lower part of the equipment and drives the axial motion assembly to rotate together through gear transmission, so as to realize the circumferential rotation of the camera assembly (see Fig. 6).

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Fig. 6. The structure of the equipment

Two visual inspection schemes are proposed on the premise that the design of the equipment body remains unchanged: Scheme 1 is the camera pop-up, that is, during the inspection condition, the camera pops out of the tube through the linkage mechanism (see Fig. 7), the camera is facing the position of E-hole, and the accurate measurement of the four E-holes can be completed by rotating the equipment. When the equipment moves from the current guide plate to the next guide plate to be inspected, the camera completely retracts to the inside of the tube to prevent collision with the guide plate. The camera of this scheme has small diameter, high resolution and high inspection accuracy, but its radiation protection performance is not good and its structure is complex.

Fig. 7. The structure of the scheme 1

Scheme 2 is the central arrangement of the camera, that is, the camera is fixed inside the hollow rank tube and located on the central axis of the CRGT guide plate. With the movement of the hollow rank tube, it reaches above the guide plate of each layer. Because the camera is not directly facing each E-hole, the inspection accuracy is slightly low. This scheme has large camera size and low resolution, but has good radiation protection performance, simple structure and no jamming risk. Although the emergency measures are designed in scheme 1 which can ensure that the camera can automatically retract to the inside of the tube under the action of the

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spring restoring force in the state of power failure or air failure, based on the strict requirements for the high radiation dose inside the CRGT, the possibility of introducing the foreign materials and the risk of equipment jamming, scheme 2 was finally selected at the slightly sacrifice of measuring accuracy. The measurement accuracy can be improved by measuring every 90° rotation. 3.3 Safety Measures There are many risks in operating in the reactor cavity, such as the inspection equipment damaging the relevant component of the nuclear power plant, introducing foreign materials, and the equipment itself is uncontrollable under extreme conditions. These safety issues must be paid attention to in the process of equipment development and use. In order to improve the operability of the equipment and prevent the collision of the inspection equipment, the equipment is designed with zero gravity. The weight of CRGT inspection equipment is about 80kg, which is inconvenient to operate and easy to collide with surrounding component of the nuclear power plant. Therefore, buoyancy parts are designed to offset the weight of the equipment (see Fig. 8). Personnel can easily operate the equipment on the temporary bridge, and carry out equipment positioning, installation, position replacement and other actions.

Fig. 8. Safety measures for the equipment

In order to prevent the risk of foreign material introduction, the CRGT opening plugs is designed to block the opening with a diameter of 58.5mm. Only the opening of the CRGT to be inspected is open before each inspection by the replacement of the plugs, which effectively reduces the risk of foreign material introduction into other CRGTs during the inspection process; In addition, the equipment is also designed with a protective cover, so that while it has the function of connection, and even if there are loose parts falling from the equipment, it is also contained inside of the equipment to prevent foreign materials from being introduced into the CRGT to be inspected. In order to prevent the equipment from being uncontrolled under extreme conditions, for example, when the power source or the air source is cut off, the inspection system can use the cylinder with spring to automatically lock the equipment in the safe state,

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and the motor brake device is used to keep the equipment in the current state without being uncontrolled.

4 Application Since 2020, CRGT inspection equipment has been used in 3 units of nuclear power plant of in-service inspections. Many guide plates with ligament of 0mm have been found, mostly concentrated in the lower area of CRGT. The most serious case is that there are two CRGTs with more than three consecutive layers of guide plates, and the slot width is larger than the diameter of control rod (Ø9.7mm). There is a risk of control rod falling out or sticking, and the relevant 6 CRGTs are finally replaced.

5 Conclusion Through the analysis of the inspected object, this paper puts forward a conservative and stable visual inspection scheme. In order to ensure the safe and reliable operation of the equipment, a variety of safety measures have been taken, which have been successfully applied to the in-service inspection of nuclear power plant and achieved good outcome. The inspection results guide the replacement of CRGTs with serious wear, and make a positive contribution to the safety of nuclear power plant.

References 1. Chen, B., Liu C.: Light Water Reactor Fuel Element. Chemical Industry Press, Beijing, China (2007) 2. Montero, M., Mickaël, G., Blachon, P.: Guide tube inspection, 10th International Conference on NDE, 810–816 (2013) 3. Hydeman, J.E., Smith, E.H.: Control rod guide tube inspection system. United states patent 5078955 (1992) 4. Cartry, J.P.: Device and method for checking the guide elements of a guide tube for the upper internals of a pressurized water nuclear reactor. United states patent 5544205 (1996)

A Potential Threat Risk Evaluation Method for Nuclear Facilities Chenliang Yuan(B) , Liang Ma, Pin Zhang, Yutong Liu, Xiaocong Zhang, Ziyi Li, Duoyi Zhang, and Xiaoyuan Wan Nuclear Security Technology Innovation Center of Hebei Province, The Fourth Research and Design Engineering Corporation of CNNC, Shijiazhuang, China [email protected]

Abstract. Nuclear facilities are critical infrastructures worldwide for their incapacitation or destruction would have a debilitating effect on security, national economic security, national public health or safety, or any combination thereof. Therefore, nuclear security plays an important role for the normal operation of nuclear facilities. Design basis threat (DBT), which is one of the basic subjects of nuclear security, represents the attributes and characteristics of potential insider and/or external adversaries, who might attempt unauthorized removal of nuclear material or cause destruction of nuclear facilities. It is the important foundation for the design, upgrading and renovation of physical protection system (PPS). Generally, the design basis threat is formulated through qualitative analysis methods, which are affected greatly by subjective factors of evaluation experts. This paper proposes a quantitative method for the risk evaluation of potential adversaries of nuclear facilities to guide the design basis threat formulation. The method considered the factors of the threat type, motivation, incident and the facility type. Each factor consists of several indices with a value evaluated based on the specific circumstance. The final potential threat risk is calculated according to the logic restriction analysis strategy and the risk level of different threats is confirmed. The method provides an intuitive result to demonstrate the design basis threat formulated is more reasonable. Keywords: Design basis threat · Risk evaluation · Hierarchical holographic model · Quantitative analysis

1 Introduction The nuclear technology, which is an essential cornerstone of national safety, plays an important role to the national development and domestic stability. However, the security of nuclear facility and materials is a critical issue of the international community, which has naturally attracted the world’s attention as well. Once a nuclear facility is sabotaged deliberately or some nuclear materials are suffered unauthorized removal, the radioactive consequence of the event will has serious negative effects on personal health, public safety, national economy and society opinion.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 419–427, 2023. https://doi.org/10.1007/978-981-19-8780-9_42

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According to the recommendations of IAEA in [1, 2] and regulations requirements of China, a Physical Protection System (PPS) should integrate detection, delay and response functions for the protection and prevention of nuclear facilities and materials against illegal actions, such as sabotage, theft, robbery, illicit transfer and other malevolent human attacks. While, Design Basis Threat (DBT), as a tool that provides a common basis for physical protection system, should be developed or revised before the planning or upgrading of PPS, so that the PPS is targeted in the design or maintenance to respond to challenges of the DBT effectively. DBT is a description of the attributes and characteristics of potential insider and outsider adversaries who might attempt a malicious act. Two main stages are undertaken in the development of a DBT for the design of physical protection in [3]: the first is threat assessment; the second is the evaluation and decision-making process. The information about motivation, intention, ability and other attributes of the potential threats should be collected and listed during the threat assessment period. The screened list is translated into a statement of representative attributes and characteristics of the postulated adversary and is modified considering the degree of conservatism, the cost-benefit-consequence tradeoffs and political factors during the second stage. Generally, the development of DBT is a process of risk perception and assessment that is widely applied in majority engineering management. The measures of risk assessment mainly include qualitative approaches and quantitative analysis approaches. Qualitative analysis is mostly executed based on the experience of professors and the instruction of industrial standards, such as FEMA and HAZOP in [4]. Quantitative analysis is commonly utilize a fixed liner model to calculate the risk value such as PRAT and FTA in [5, 6], which cannot handle the uncertainty and complexity in the society. In practice, the DBT is usually formulated based on qualitative analysis that affected greatly by subjective judgement of experts in the group, and lacks of quantitative data to support the assessment result. This paper proposed a hierarchical holographic model (HHM) of the potential threat evaluation, considering the factors of the threat type, motivation, incident and the facility type. Each factor consists of several indices with a value evaluated based on the specific circumstance. The final potential threat risk is calculated according to the logic restriction analysis strategy and the risk level of different threats is confirmed as a result. Structures of this article are as follows: it is mainly concerned the study background, content, method and value of this article in Sect. 1; the hierarchical holographic model of the potential threat risk analysis is designed in Sect. 2; the logic analysis algorithm is formulated and the values of different factors of the are set considering various conditions in Sect. 3; a simulated case is displayed to demonstrate the efficiency application of the proposed risk evaluation method in Sect. 4; the research conclusion and the future work is described in Sect. 5.

2 Modeling Hierarchical holographic model (HHM) is a systematic and omnidirectional identification model of risk sources, which has been gradually recognized and applied in the field of risk evaluation in recent years. It decomposes a system into subsystems, each of which can be decomposed continually according to the inherent characteristics and relations.

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This work established a HHM of the potential threat risk evaluation for nuclear facilities based on the DBT development framework, shown in Fig. 1. The framework contains four factors: threat, motivation, incident, facility. Potential threat risk evaluation Threat

Incident

Motivation

Adversary Type

Motivation Type

General Adversary

Political

Advanced Adversary Collusive Adversary

Facility

Intention

Attack Ability

Potential Consequence

Facility Attraction

Facility Sabotage

Group Sise

Information Stealing

Information Secrecy

Financial

Regional Environment

Nuclear Material Theft

Expertise

Operation Failure

Operation Stability

Ideological

Social Environment

Cause Social Panic

Insider Threat Status

Facility Damage

Facility Security

Personal

Political Situation

Vibrate Politics

Equipment

Radiation Leak

Material Security

Motivation Heat

Facility Ability

Transportation Skill

Strategy

Fig. 1. The HHM framework of potential threat risk evaluation

The threat section represents the adversary type that is categorized according to the speciality of potential adversaries. For example, the general adversary may represent mammonist, sociopath, or psychopath, of whom the attack pattern is relatively weak. The advanced adversary may represent violence and terrorism personnel, who is quite offensive to nuclear facility. The motivation section includes two factors: motivation type and motivation heat. The motivation type factor represents the reason that potential adversaries decide to conduct an attack, such as political, economic, psychiatric, and personal reason. The motivation heat factor means the attraction of the facility itself and the surrounding environment to threats, and includes four factors: facility attraction, regional, social and political environment. The four factors were subdivided into several indices, shown in Fig. 2. The facility attraction rests with the indices of facility type and management department. The regional environment factor consists of culture, nation, and religion indices. The social environment factor is affected by economic, social security and stability assessment indices. The political situation factor is comprised with international environment, special events and terrorism tendency. The incident section consists three factors: intention, attack ability and potential consequence. The intention factor means the purpose of threats chose to attack, such as sabotage, theft, arousing social panic or vibrate politics. The attack ability factor is decomposed into seven indices that describe the features of potential threats, which include group size, expertise, insider threat status, equipment, transportation, skill and strategy, shown in Fig. 3. Especially, the insider threat status considers the political accomplishment, personality, 6P indices, decision relationship, social relationship and authority of employees in nuclear facilities. The factor is critical for the evaluation of inner threats [7, 8]. The potential consequence factor includes information stealing,

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Facility Attraction

Management Department Culture

Regional Environment

Nation

Religion

Motivation Heat

Economic

Social Environment Social Security

Stability Assessment

International Environment Political Situation Special Events

Terrorism Tendency

Fig. 2. The sub-framework of motivation heat factor

operation failure, facility damage and radiation leak, which represents the possible result of the attack caused by threats.

Spy Group Sise

Armed Attack

Expertise

Smuggle

Insider Threat Status

Political Accomplishment

Attack Ability Equipment

Personality

6P indexes Transportation

Skill

Decision Relationship

Strategy

Social Relationship Authority

Fig. 3. The sub-framework of motivation heat factor

The facility section mainly considered the anti-risk ability against threats, such as information secrecy, operation stability, facility security and material security.

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3 Logic Analysis The overall of the potential threat risk is calculated as the following equation: R=

T ×M ×I F

(1)

where R represents the risk value, T means the threat value, M means the motivation value, I means the incident value and F means the facility value. The value of T is decided by the adversary type factor which is graded according the specific feature. The value of M is calculated by the algebraic operation of its factors according to Eq. (2).   M = Mt × Mh = Mti × Mhi (2) where Mt represents the motivation type factor and Mti is the the i-th index of it. Mh represents the motivation heat factor and Mhi is the i-th index of it. The value of I is calculated by Eq. (3).    I = In × Ia = Ini × Iai × Ipi (3) where In represents the intention factor, Ia is the attack ability factor and Ip is the potential consequence factor. The variables with subscript i are their indices. The value of F is calculated by Eq. (4).  F = Fa = Fai (4) where Fa represents the facility ability factor and Fai is the the i-th index of it. It should be noted that the indices of different factors are not independent, and some of them are restricted each other. When the motivation type is “political”, the value of social environment index is 0, and when the motivation type is not “political”, the value of political situation index is 0. Here are some values of different indices shown in Table 1, and they can be corrected according to the evaluation of experts. Owing to space constraints, the value range is provided instead of detailed values of some factors. Based on the HHM framework and the logic analysis, the risk value of a certain potential threat can be calculated. For the intuitive understanding, the risk level of the potential threat is decided according to Table 2. The procedure of the potential threat risk evaluation is shown in Fig. 4. Firstly, the thereat HHM of a certain facility is established based on its specific information and data collected by various means. Secondly, the values of the factors and indices of the HHM is assigned for the evaluation. Thirdly, the logic analysis algorithm is conducted and the risk level is calculated as an output result of the risk evaluation. Finally, the related department of the facility or regulators decides whether to take actions to decrease the threat risk.

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Factors

Indices

Values

Adversary type

General adversary

5

Advanced adversary

10

Collusive adversary

20

Political

20

Financial

5

Ideological

1

Motivation type

Personal Motivation heat

Facility attraction

Regional environment

Social environment

Political situation

Intention

Facility sabotage Material theft

2 Type

Nuclear plant

25

Nuclear material storage 10 Nuclear fuel element plant

15

Research reactor

5

Reprocessing factory

10

Management department

Civil

5

Military

10

Culture

Folk custom

0~5

Nation

Minority percent

0~5

Religion

Believers percent

0~5

Economic

Development level

0~5

Social security

Crime rate

0~5

Stability assessment

Disapproval rate

0~5

International environment

Negative factor

0~5

Special events

Self-interest

0~5

Terrorism tendency

Activity

0~5 20 10 (continued)

4 Case Description A potential threat risk evaluation case is described in this section. The detailed information of the HHM framework is shown in Tables 3, 4 and 5. The total threat risk evaluation value is calculated as 1392188, and the risk level is MEDIUM.

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Table 1. (continued) Factors

Indices

Attack ability

Values

Social panic

15

Vibrate politics

30

Group size

1 ~ 20

Expertise

0 ~ 20

Insider threat status

0 ~ 10

Equipment

0 ~ 30

Transportation

0 ~ 40

Skill

0 ~ 20

Strategy

1 ~ 10

Table 2. Risk level Risk value

Risk level

0 ~ 106

Low

106 ~ 2 × 106 2 × 106 ~ 3 × 106

Medium

3 × 106 ~ 4 × 106

Very high

High

MODELING

RISK EVACULATION

RISK LEVEL

FACILITY IMPROVEMENT

BASIC INDICES

Fig. 4. The flow chart of the potential threat risk evaluation method

5 Conclusion In this paper, a quantitative method for the risk evaluation of potential adversaries of nuclear facilities is developed. By establishing the hierarchical holographic model of potential threat risk, a logic analysis of the model is discussed with quantitative values of different factors and indices. Then, the risk level of the threat is decided according to the range of risk values. A sample case is described to illustrate the method provides an intuitive result to demonstrate the risk level of a certain threat. It is useful to guide the development of design basis threat.

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Description

Indices

Values

Nuclear plant

type

25

Civil use

Management department

5

Well-off life

Culture

1

Minority percent 15%

Nation

1.5

Believers percent 30%

Religion

3

South-east coastal city

Economic

1

Crime rate low

Social security

1

Disapproval rate medium

Stability assessment

3

Contradiction between western countries

International environment

2

Partial war

Special events

2

Sporadic terrorism activity

Terrorism tendency

3

Confidentiality medium

Information security

2

Operation redundancy

Operation stability

2

Defense in depth

Facility security

4

Material easy to move

Material security

1

Table 4. Threat description Description

Indices

Values

Mammonist

Adversary type

5

5 person

Group size

5

Driver, hacker

Expertise

10

UAV, forcible entry toll, cellphone

Equipment

12

Truck

Transportation

6

Network intrusion, equipment operator

Skill

8

Illegal intrusion, malicious insiders

Strategy

7

Key position, flabby and undisciplined, negative 6P test evaluation, domestic calamity, high authority, society relationship complex, lack of money

Insider threat status

7

The logic restriction analysis and the quantitative method of threat factors will be optimized in the future work.

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Table 5. Threat events Description

Indices

Values

Smuggling nuclear material

Motivation type

5

Grab nuclear material

Intention

15

Damage facility and material stolen

Potential consequence

15

References 1. IAEA, The physical protection of nuclear material and nuclear facilities, INFCIRC/225/Rev.5, Vienna 2. IAEA, The convention on physical protection of nuclear facilities and nuclear material as amended, INFCIRC/274 3. IAEA, Nuclear security series no.10: Development, use and maintenance of the design basis threat, Vienna (2011) 4. Rausand, M.: Risk Assessment: Theory, Methods and Applications (2013) 5. Kumara, S., Tripathib, B.K.: Modelling of threat evaluation for dynamic targets using Bayesian network approach. In: International Conference on Emerging Trends in Engineering, Science and Technology (2015) 6. John, A., Paraskevadakis et al.: An integrated fuzzy risk assessment for seaport operations. Saf. Sci. 68, 180–194 (2014) 7. Bunn, M., Sagan, S.D.: A Worst Practices Guide to Insider Threats: Lessons from past mistakes. American Academy of Arts and Sciences, MA (2014) 8. Healey, A.N.: The insider threat To nuclear safety and security. Secur. J. 29(1), 23–38 (2016). Nearing, Anne G. ASME Style Guide 2018

An Improved Hazop Method Was Used to Analyze the Safety of Hydrogen Production System in Nuclear Power Plant Jianfeng Yang(B) , Bingcheng Feng, Hangding Wang, and lihuang huan Suzhou Nuclear Power Research Institute, Shenzhen, China [email protected]

Abstract. With the increasing attention to industrial safety issues, application of HAZOP Technology has become an inevitable trend in China because of its comprehensive, systematic and in-depth advantages. In the nuclear power plant, there are chemical production and storage related systems such as hydrogen production station, sodium hydrogen production workshop, strong acid and strong alkali storage workshop. HAZOP analysis can improve the safety management level of hazardous chemicals related process system in the nuclear power plant. Because the traditional HAZOP analysis mainly relies on the experience and knowledge of experts, it can only qualitatively describe the relationship between the causes and consequences of deviation, but not quantitatively reveal the evolution process of system accidents, so its analysis results can not provide accurate decision-making basis for safety management. Based on the idea of HAZOP analysis, this project combines the advantages of LOPA technology and PSA technology to develop an improved HAZOP analysis method. In this paper, An improved HAZOP analysis method is used to carry out quantitative safety analysis on hydrogen production system of a nuclear power plant in China. The medium risk scenarios in the system are pointed out and the improvement measures are put forward. Relevant technical methods can be extended to other nuclear power plant hydrogen production station, sodium hydrogen production workshop, strong acid and strong alkali storage workshop and other system safety analysis work, which has guiding significance for the follow-up similar analysis work and improves the safety management level of chemical production related system in nuclear power plant. Keywords: Improved Hazop · Hydrogen · Safety · Nuclear power plant

1 Introduction The full name of HAZOP is hazard and operability analysis, which is a systematic risk analysis method developed by British Imperial Chemical Industries (ICI) started in the 1960s. HAZOP analysis can comprehensively find potential safety hazards in a timely manner and improve the safety level of the device. At present, the HAZOP method has become a widely used hazard analysis method in the process of chemical plants abroad. As the country pays more and more attention to safety issues, HAZOP has become an © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 428–435, 2023. https://doi.org/10.1007/978-981-19-8780-9_43

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inevitable trend in China due to its comprehensive, systematic and in-depth advantages. Because HAZOP analysis requires experts in multiple fields to form an analysis team, it requires a high level of professional knowledge of experts. At the same time, it can only qualitatively describe the relationship between the causes and consequences of deviation, but not quantitatively reveal the evolution process of system accidents, so its analysis results can not provide accurate decision-making basis for safety management. Based on the disadvantages of traditional HAZOP analysis method, this paper integrates the advantages of LOPA technology and PSA technology, and studies an improved HAZOP analysis method, which is applied to the safety analysis of hydrogen production systems in domestic nuclear power plants.

2 Development Process of Improved Hazop Analysis Method Based on HAZOP technology, this paper integrates the advantages of LOPA technology and PSA technology to achieve rapid quantitative evaluation of accident scenarios, and fully consider its uncertainty, so as to meet the needs of increasingly complex process industry safety management. The technical route of improved HAZOP analysis method is shown in Fig. 1,and The analysis steps are described in detail below [2, 3]:

Fig. 1. Technical Route of improved HAZOP Analysis Technology

Step 1: Analytical Definition The scope and objectives of the analysis should be determined when the analysis is defined. When determining the scope of the analysis, factors such as the boundary area of the analysis object, the available data, degree of detail and accuracy, and the scope of

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the process hazard analysis that have been carried out should be considered. The purpose of the analysis should be considered when determining the objective of the analysis. Step 2: Divide Nodes According to the production equipment used in the production process, the entire production process is divided into several nodes, and then each node is analyzed one by one. Step 3: Determine guide words, production parameters and deviations For separate nodes, Using HAZOP guide words and production parameters to assume the possible deviation, and then analyze the mode of error caused by deviation. Step 4: Complete the initial event analysis based on the cause of the deviation The reasons for each deviation are analyzed, such as pipeline leakage, hardware failure, program missing, human error, external interference, etc. These reasons will be used as the initial events of LOPA analysis. The integrity of the analysis is ensured by performing deviation and cause analysis on all nodes. At the same time, they are classified according to the severity of their consequences and the mitigation process of protective measures, and similar initial events are grouped into an initial event group. These initial event groups were used as initiating events for PSA analysis, and subsequent event tree analysis was carried out. Step 5: Event response analysis According to the method of LOPA layer, analyze the protection measures in the mitigation process of the above steps. At the same time, it is no longer strictly in accordance with the differentiation method of independent protection layers. Each protection layer can be associated with each other, and its relationship can be expressed in the way of fault tree and its transfer gate. According to the accident development scenario and the role of various protection measures, the event tree is used to analyze the effectiveness of the event response scenario and the existing safety mitigation measures. These protection layers act as the functional event header of event tree. Trigger event and header act as boundary conditions to carry out event response analysis. At this time, the consequence of event tree includes various configurations, which may be leakage, explosion, casualties or economic losses, depending on the deterministic effect of actual deviation on system devices. In engineering practice, the reliability of the safety system is very high, so the probability of chain is approximately treated as follows: Pi = P(I ) · P(Fi ) = P(I ) · Qi (t) Step 6: Support analysis of the protection layer system As the first event in the event tree, the protection layer system may contain different functional mitigation systems or personnel’s operation behaviors. In order to quantitatively simulate the failure probability of mitigation systems or personnel’s operation behaviors [1], system analysis, personnel response analysis, correlation analysis, data analysis, and common cause analysis need to be carried out, which determine the accuracy and detail of the protection layer system. These supporting analysis elements will be introduced in the following 1. System analysis System analysis uses the method of fault tree analysis to define the state that the system does not want to occur, that is, top event. Then analyze the system to

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find out all the ways that can lead to the top event. In this paper, the FUSSELL method is used to start from the top event and proceed from the top to the bottom. In fault tree, And Gate only increases the capacity of cut set, while Or Gate increases the number of cut sets. Each step is arranged from top to bottom according to the above principle until all logic gates are replaced by basic events. 2. Data analysis Equipment data analysis takes the modeling of equipment failure, equipment maintenance, equipment test and maintenance as the analysis goal. The random failure characteristics of the equipment in the system can be used to estimate the probability that the equipment can not perform its intended function. When there are enough equipment failure data, for example, the frequency of occurrence is more than 5 times/year, the classical estimation method is used. If the sample space of equipment reliability data is small and there are too few equipment failure data, then the Bayesian estimation method is used to introduce the general data, and the reliability data of the same type of equipment is used as the prior data, and the Bayesian estimation method is combined with the specific data of the specific system to obtain the post test distribution data for analysis. 3. Human factor analysis Human factors play an important role in the accident situation, which can play a positive role, but may also play a negative role. On the one hand, it is very important for personnel to implement appropriate procedures and remedial strategies to mitigate the consequences of accidents; On the other hand, wrong judgment and improper actions may also enlarge the consequences of the accident. 4. Correlation analysis Common cause faults may occur to the equipment, mainly manifested in the same functional parts operated, maintained and debugged by the same person under the same environment due to the same design or the same manufacturing process. The performance of equipment is often weakened by correlation, and the failure probability of redundant components or equipment is significantly higher than the predicted value under the independent failure assumption. Step 7: risk modeling and quantitative analysis According to the above basic work, such as initiating event analysis, event tree analysis, fault tree analysis, personnel reliability analysis, data analysis, correlation analysis, etc., the corresponding software is used to carry out model construction, accurately simulate the multi-level structure cascade response and failure mechanism among them, and complete quantitative calculation [1]. In this quantitative calculation, Boolean algebra operation is used to solve the minimum cut set, and limit approximation method and Monte Carlo sampling method are used to quantitatively calculate the point estimation value and uncertainty analysis of the minimum cut set. The calculation formula of MCUB is: F ≈ 1 − (1 − F(MCSi )) where, it represents failure probability of accident sequence and the minimum cut set. The event sequence or the total risk frequency value is equal to the sum of the frequency values of all the minimum cut sets included.

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Step 8: Importance Analysis The important influence analysis can be used to identify and confirm the factors that have important contributions to the consequences, that is, to confirm the initiating events, accident sequences, component failures and personnel error events that have important contributions to the consequences. The importance analysis is of great significance to reveal the weak links in the safety of system, reveal the important components and events in risk, and point out the ways to improve the safety of system and reduce the risk of system. Step 9: analysis of improvement measures and risk insights Through the risk modeling and quantitative analysis in step 7, we can calculate the risk frequency of the system under the existing protection layer measures. Compared with the corresponding technical standards, if the risk exceeds the standards, we need to propose corresponding improvement measures, add or modify the protection layer, expand the protection layer analysis, recalculate the system risk until the risk is reduced to an acceptable level. At the same time, it can also increase the dimension of cost analysis, form the three-dimensional matrix of improvement measures, cost and risk income, comprehensively analyze the cost and risk income of improvement measures, optimize risk management and obtain the best option. Using the existing computer computing engine, the quantitative analysis can be completed in a few minutes after completing the above risk modeling work. Therefore, the analysis method can realize the rapid quantitative assessment of system risk and optimize risk management.

3 Application Case of Improved Hazop Method In this paper, the improved HAZOP analysis method mentioned above is applied to the hydrogen production system in a nuclear power plant in China. According to the process flow of the water electrolysis hydrogen production system, the P&ID drawing is divided into five analysis nodes, including water adding alkali distribution system, hydrogen production electrolysis and vapor-liquid separation system, purification and drying system, hydrogen storage and distribution system, and blowdown system. According to the above divided nodes, HAZOP analysis shall be carried out in each node, considering the causes and consequences of deviation, existing safety measures and recommended safety measures, etc. Through HAZOP analysis, the risk level statistics of each node after reduction are shown in Table 1. There is one item of medium risk level, one item of low risk level, and the others are extremely low risks. According to the HAZOP analysis results of the hydrogen production system, 0SHY422VY or 0SHY429VY is normally open after 0SHY424VY fails, so it is likely to cause siphon effect, leading to external air entering the dryer and explosion. LOPA analysis is carried out for the identified key deviation, and semi-quantitative method is used to determine whether there is enough protection layer to make the process risk meeting national or industrial standards. Through LOPA analysis, it is concluded that SIS system with SIL 1 level should be added to the hydrogen generation system at least. In view of the current system

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Table 1. Risk level statistics of each node of hydrogen generation system after reduction Node name

Very low risk quantity (E)

Low risk quantity (L)

Medium risk quantity (M)

High risk quantity ( H)

Water and alkali distribution system

4

0

0

0

Hydrogen production electrolysis and vapor-liquid separation system

21

1

0

0

Purification drying system

7

0

1

0

Hydrogen storage and distribution system

3

0

0

0

Blowdown system

1

0

0

0

configuration of hydrogen production system, a simplified and least cost improvement scheme is proposed as shown in the following figure 2:

Fig. 2. Schematic diagram of improvement scheme of hydrogen production system

1. The local pressure gauge 0SHY403LP can be added for remote transmission to the DCS system in the control room, 2. Add pneumatic stop valve 0SHY423VY before back pressure valve 0SHY450VY, 3. Add configuration to control loop: when the pressure of 0SHY403LP is equal to or slightly higher than a certain value of atmospheric pressure 0.05Mpa, interlock and close the valves 0SHY423VY and 0SHY429VY.

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The SIL level of the above improvement measures is analyzed by the fault tree method of PSA technology. The construction fault book is shown in Fig. 3.

Fig. 3. Fault tree diagram of SIL checking calculation of improved scheme

Through the fault tree calculation, the estimated failure probability of the improved measures is 5.27e-03, and the dominant cut set is shown in the Table 2. From the above results, it can be seen that the SIL level is 2, and the reconstruction results meet the expectations. Table 2. Cut set of fault tree calculation No

Probability

%

Event 1

Event 2

1

4.26E-03

80.79

0SHY001FUM-UF

2

9.36E-04

17.75

0SHY403LPN-FT

3

4.09E-05

0.78

0SHY423/429VY-ALL

4

2.94E-05

0.56

0SHY001FUM-DF

5

9.96E-06

0.19

POWER1-FT

6

9.96E-07

0.02

POWER23-FT-ALL

7

1.35E-07

0.00

0SHY423VYN-FT

0SHY429VYN-FT

8

3.30E-09

0.00

0SHY423VYN-FT

POWER3-FT

9

3.30E-09

0.00

0SHY429VYN-FT

POWER2-FT

10

8.04E-11

0.00

POWER2-FT

POWER3-FT

4 Conclusions It can be seen from the above that the improved HAZOP analysis method proposed in this paper starts from the improvement direction of HAZOP quantitative analysis and

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integrates the advantages of LOPA technology and PSA technology. Based on the qualitative safety analysis of traditional HAZOP analysis method, Improved HAZOP analysis technology can carry out multi-level topological structure analysis and protection system action process analysis of the system, and use the deductive reasoning method of event tree and fault tree to determine the quantitative risk limit and risk management action matrix of the system, so as to provide technical support for the safety control of complex industrial system devices.

References 1. ASME/ANS RA-Sa-2009, Standard for level 1/large early release frequency probabilistic risk assessment for nuclear power plant applications, Addenda to ASME/ANS RA-S-2008, ASME/ANS, New York (2009) 2. International Standard, Functional safety-safety instrumented system for the process industry sector-Part2: Guidelines for the application of IEC61511–1IEC 61511–2 (2003) 3. International Standard, Functional safety-safety instrumented system for the process industry sector-Part2: Guidance for the determination of the required safety integrity levels. BS IEC 61511–3 (2003)

Research on the Optimal Management of Very Low Level Radioactive Waste Zhang Li(B) , Liu Jianqin, Qin Xiang, Gao Kai, and Guo Xiliang Institute for Radiation Protection, Taiyuan 030006, Shanxi, China [email protected]

Abstract. With the operation and decommissioning of large nuclear facilities and the implementation of environment improvement projects, the treatment and disposal of very low level radioactive waste (VLLW) with the properties of low radioactivity and large volumes has become a hot issue in the field of radioactive waste management. If VLLW is managed as low level radioactive waste, it will not only increase the operation cost, but also occupy the resources of the repository, which is unfavourable to the minimization management of radioactive waste. Based on the investigation of the supervision requirements and management status of VLLW in the USA, the UK, France and Germany, combined with the management status of VLLW in China, the paper proposes to achieve the overall goal of volume reduction, and promote the optimal management of VLLW in China by establishing rapid characterization and measurement technology and targeted treatment and disposal technology. Keywords: VLLW · Waste minimazation · Optimal management

1 Introduction 1.1 A Subsection Sample The nuclear safety guidance “Minimization of Radioactive Waste from Nuclear Facilities" [1] issued in 2016 by National Nuclear Safety Administration requires that the amount and radioactivity of radioactive waste should be kept as low as reasonably achievable through practical design and management measures. Minimization of radioactive waste is one of the key factors for sustainable development of nuclear industry, which can reduce the burden of radioactive waste treatment and disposal in NPPs and ensure human health and good ecological environment [2–4]. In order to achieve the safety management of radioactive waste minimization, while continuously improving the regulations and standards on radioactive waste management, China has paid more attention to the management of intermidiate and high level radioactive waste(HILW) from generation, measurement, treatment, conditioning and disposal, and has accumulated many good management practices [5–7]. Except HILW, some slightly radioactive contaminants generated from the operation of NPPs, which are characterized by low radioactive level and large amount, include APG resins (the steam © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 436–443, 2023. https://doi.org/10.1007/978-981-19-8780-9_44

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generator drainage system), filter and tritium containing molecular sieve (heavy water reactor) and so on. Among the technical wastes generated during the overhaul of Daya Bay nuclear power plant, plastics, paper and fibers account for 91.1% of the total waste (mass fraction) [8]. Since the promulgation and implementation of the Classification of Radioactive Wastes, the significance of VLLW management for waste minimization has been concerned by NPPs. Due to the lack of systematic methods to optimize VLLW management, at present, VLLW is mostly managed as low level waste, which increases the operating cost of NPPs and the pressure on the temporary storage, which is unfavourable to the minimization management of radioactive waste. Therefore, based on the international management experience and good practice of VLLW, according to the characteristics of VLLW from NPPs in China, the optimal management suggestions are proposed for VLLW classification collection, rapid identification, volume reduction treatment and disposal, and waste clearance, so as to achieve volume reduction, safe and economic management of radioactive waste.

2 Management Status of VLLW Abroad 2.1 USA The USA doesn’t have regulatory definition about VLLW. In some previous NRC guidances, VLLW has been referred to as “low-activity waste (LAW)” and is generally considered to be the least radioactive part of Class A waste. In the USA, VLLW can be disposed in 10 CFR 61 licensed LLW disposal facilities; If the licensee chooses to dispose in other facility (such as hazardous or solid waste disposal facilities), the relevant disposal procedures can also be authorized in accordance with 10 CFR 20.2002 [9]. Since 2000, the NRC has approved several 10 CFR 20.2002 applications for the disposal of VLLW. In March 2020, the proposed interpretation of the radioactive waste disposal regulations issued by NRC will permit licensees to dispose of waste by transfer to persons who hold specific exemptions for the purpose of disposal [10]. 2.2 UK In the UK, radioactive wastes are classified as higher activity waste and lower activity waste1 [11]. VLLW is a subclass of low-level waste that can be divided into two categories according to the definition [12]: One is small-volume waste packages, i.e., radioactive waste don’t have a designated disposal purpose and can be disposed with municipal, commercial or industrial waste. The total radioactive activity in 0.1m3 waste is less than 400kBq, or the total radioactive activity in single package is less than 400kBq; the other is bulk waste package, that is, radioactive waste needs to be disposed through a specific landfill and the maximum total activity concentration is 4MBq/t. For the waste containing 3 H, the activity concentration limit is 40 MBq/t tritium. According to Guidance on the Scope of and Exemptions From the Radioactive Substances (2011) [13], VLLW can carry out restricted clearance, and the goal after clearance is recycle, centralized management or disposal; When the activity concentration of VLLW decays to the exemption limit, exemption management is carried out.

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2.3 France France radioactive waste classification include VLLW, its activity concentration is generally less than 100Bq/g. Cires, a special VLLW near surface disposal facility, is responsible for long term management of VLLW; Metal can be sorted, sheared, smelted, recycled and reused in the nuclear field; Some VLLW (oil, resin and so on) that have been generated and have no treatment path, EDF has set up a special control area to storage for further disposal. At present, France is carrying out relevant research on optimization of Cires disposal space, establishment of new disposal capacity, disposal of specific waste after incineration, development of materials suitable for recycling, and exemption and clearance. 2.4 Germany Germany divides radioactive wastes into two categories: heat release wastes and non heat release wastes. Compared with the classification of IAEA radioactive wastes, it can be seen that [14]: certain types of waste referred to as VLLW according to the IAEA already exceed the current German clearance levels for management as conventional waste and therefore have to be disposed of in the Konrad repository, and the remaining VLLW should be cleared for disposal (conventional landfill). The Radiation Protection Act and the revised Radiation Protection Regulations [15] regulate that waste generated during the operation, decommissioning and dismantlement of nuclear facilities can be cleared and utilised, recycled, disposed of, possessed or transferred to third parties as non-radioactive material as long as it is proven to meet the clearance levels in Appendix 4 of § 35 (unrestricted clearance) and § 36 (restricted clearance) of the Radiation Protection Regulations. The management of clearance materials is shown in Fig. 1 [14].

3 Management Status of VLLW in China The definition of VLLW in the classification of radioactive waste is: the activity concentration of radionuclides is close to or slightly higher than the exemption level or clearance level, and the activity concentration of long-lived radionuclides should be very limited, and only limited containment and isolation measures are required, and they can be disposed in surface landfills, or in industrial solid waste landfills in accordance with the national solid waste management regulations. The lower limit value of VLLW activity concentration is the clearance level, and the upper limit value is generally 10 ~ 100 times of the clearance level. Article 9.2.3 of the Regulations for Radioactive Waste Management (GB14500– 2002) [16] regulates that VLLW should be sorted out from the waste stream as far as possible to reduce the amount of waste treatment and disposal; Article 20.2.11 regulates that VLLW shall be disposed according to the management limit and implementation project approved by the regulatory authority, but it is not necessary to send VLLW to the low and intermediate level waste(ILW) disposal site. In addition, the basic requirements

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Fig. 1. The management of clearance materials

for VLLW landfill disposal are regulated in the Landfill Disposal forVLLW (GB/T 28178– 2011) [17]. At present, NPPs either treat VLLW as ILW, or temporary storage for decay to the clearance level. Although China has regulated that VLLW can be disposed in surface landfills or industrial solid waste landfills, the VLLW special landfills currently in operation don’t receive VLLW from NPPs. Since the promulgation and implementation of the Classification of Radioactive Wastes, NPPs has begun to pay attention to the management of VLLW. However, due to NPPs don’t have a clear classification method for VLLW, and lack relative characterization and measurement technology, VLLW can’t be separated separately [18, 19]. In addition, the waste from NPPs is temporarily stored for a period of time before disposal. Many wastes may decay to the clearance level during the temporary storage, causing great trouble for the NPPs to gather statistics the amount and activity of the existing VLLW. Therefore, based on the good technology and practical experience of international VLLW management, this paper proposed the optimal management route of VLLW suitable for China.

4 Optimal Management of VLLW With the overall goal of achieving volume reduction, by establishing rapid characterization and measurement technology and targeted treatment and disposal technology, the optimal management route of VLLW as shown in Fig. 2 is proposed, which is carried out in four steps:

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Fig. 2. The optimal management route of VLLW

Firstly, source term analysis. The type, source, production, nuclide composition and storage status of VLLW are determined by consulting the historical data of waste management and combining with the site investigation. The sampling and site measurement project is proposed by investigating and analyzing the site operation conditions of waste measurement and conditioning. Secondly, radioactivity characterization. The radioactivity characterization can be carried out in the laboratory after sampling, or on the site. On the site, the waste shall be integratedly measured to obtain the surface contamination, dose rate level or possible nuclide composition. Subsequently, according to the measurement results, the "hot spot" samples shall be collected, and the key nuclide composition and activity concentration of the "hot spot" samples shall be detected and analyzed in the laboratory. Then, the wastes are classified according to the radioactivity characterization results. Wastes whose radioactivity level meets the clearance limit of the Activity Concentration for Material not Requiring Radiological Regulation (GB 27742–2011) shall be cleared directly; Wastes with radioactivity level between 100 times of the clearance limit and the clearance limit are considered as VLLW. Finally, based on the analysis of recycle and reuse, economy and safety, combined with waste property, the final outlet of waste is determined. Restriced clearance of VLLW can be considered and relevant limit can be studied. VLLW that meets the restriced clearance limit can be restriced cleared. VLLW that can’t be restriced cleared can be storaged and decayed, or landfilled in VLLW landfills or near surface disposal according to the Landfill Disposal for VLLW (GB/T 28178–2011). 4.1 Rapid Classification Measurement 1. Investigation of Waste Property. Before radioactivity measurement, the detailed information of waste properties shall be collected, including time of generation, types

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and activity level of radionuclide, radiological, physical, chemical, radiochemical and other characteristics. If the information is unknown, the step 2) should be followed. Otherwise, follow step 3). 2. Radionuclide Analysis. Rradionuclide in new waste stream or legacy waste is generally unknown. It is necessary to take some samples to identify radionuclide by radiochemical analysis. The scaling factor method will be used for the judgement of activity concentration of DTM nuclides, and the nuclide vector method the judgement of activity concentration of other nuclides except key nuclide in the waste stream. Note: before sampling for radiochemical analysis, a sampling procedure shall be designed to ensure that the samples taken are representative. 3. Theoretical Calculation and Surface Scanning. Dose rate limit can be calculated following Equation: L1 = L2 × (d/a)

(1)

Here, Limit1 (L1) is dose rate limit; Limit2 (L2) is the summation of activity concentration lower limit of γ radionuclide for VLLW in waste stream, the activity concentration limit shall be in accordance with the National standards on classification of radioactive waste and radiation protection, Bq/kg或 Bq/m3 ; d is theoretical dose rate, Gy/h; a is theoretical total activity concentration ofγradionuclide is calculated according to the method given in iso16966:2013, expressed in Bq/kg or in Bq/m3 . Surface scanning includes surface contamination and dose rate detection which is used to prevent unnecessary contamination and radiation exposure during radioactivity measurement, and shall be carried out following the requirements given in Clause 4.2.1 of this standard and the procedures given in ISO 7503–1: 2016, ISO 7503–2: 2016. The dose rate distribution can be used to identify hot spots, and the dose rate value can be used to calculate the ratio value of activity concentration to dose rate following the Eq. (1). If the measured result exceeds L1, the waste shall be classified as low level or higher level radioactive waste. If the results is less than or equal to L1, the step 4 shall be followed. 4. Activity Measurement. Measurement of total activity or activity concentration of waste by gamma spectrometer. If the measured activity concentration exceeds L2 of VLLW, waste shall be regarded as low level or higher level radioactive waste. Otherwise, the waste shall be VLLW. 4.2 Restriced Clearance For restriced clearance, VLLW can be recycled or reused after treatment. For example, slightly contaminated metal can be used as cask or waste packaging container in the nuclear industry after smelting. Some practices on VLLW will be encouraged, such as traditional incineration, disposal in industrial solid waste landfills or hazardous waste landfills.

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4.3 Landfill Disposal VLLW without reuse value (such as construction waste and sludge, etc.) can be simply packaged and centrally disposed in special VLLW landfills or special landfills within NPPs, or in hazardous waste landfills or industrial waste landfills. Wastes that meet the unrestriced clearance can be directly disposed in hazardous waste landfills or industrial waste landfills. Wastes that meet the restriced clearance are disposed in hazardous waste landfills or industrial waste landfills, it is necessary to research the limit for landfill disposal or consider case by case dose assessment. Wastes that need to be landfilled in the special VLLW landfills, more realistic and feasible method is dividing a small part of ILW disposal site for the construction of VLLW landfill, which can not only solve the disposal outlet of VLLW, but also greatly reduce the construction cost of the disposal site and the cost of radioactive waste disposal.

5 Suggestion Based on the international good practices and the status of VLLW in China, the following suggestions are proposed to achieve the optimal management of VLLW: 1. Carry out source term analysis. Collect the detailed information of VLLW from different nuclear facilities, including time of generation, types and activity level of radionuclide, radiological, physical, chemical, radiochemical and other characteristics. 2. Establish VLLW rapid classification measurement method to separate VLLW for further treatment and disposal. 3. Carry out research on VLLW restriced clearance, including limit for VLLW recycle and reuse after treatment, the limit for incineration, and the limit for landfill disposal in industrial solid waste landfills or hazardous waste landfills. 4. Promote the landfill disposal of VLLW, including the requirements for landfill disposal in industrial solid waste landfills or hazardous waste landfills, and the establishment of regulatory procedures such as application approval.

References 1. Principles of minimization of radioactive waste in nuclear facilities. https://www.mee.gov.cn/ gkml/sthjbgw/haq/201611/W020161104359304183010.pdf. Accessed 2016 Nov 1 2. Yao, Z.M., Zhu, J.R., Feng, J.C.: Practice and exploration of radioactive solid waste minimization in Yangjiang nuclear power plant. Journal 41(02), 151–156 (2021) 3. Xian, J.H., Zhang, X.W., Yang, R.: Minimization practice and management of radioactive wastes in a uranium enrichment plant. Journal 39(01), 61–66 (2019) 4. Liu, F.G., Liu, L.P., Dong, F.F.: Discussion on the minimization of radioactive waste from nuclear power plants in China. Journal 02, 39–44 (2018) 5. Deng, C.Y., Luo, G.: Status and major problems of radioactive waste management in NPPs of China. Journal 38(06), 11–16 (2018)

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6. Xiong, X., Zhang, G., Ren, L.L.: Research on radioactive waste management path of nuclear power plant. Journal 20(04), 1–6 (2021) 7. Tian, F.: Progress and challenges of radioactive waste management in nuclear power plants. Journal (10): 262–263+108 (2018) 8. Guo, X.L., Xu, C.Y., Feng, W.D.: Clearance of slightly radioactive contaminats from NPPs. Journal 34(02), 74–80 (2014) 9. US NRC. 10 CFR§20.2002-method for obtaining approval of proposed disposal procedures. https://www.law.cornell.edu/cfr/text/10/20.2002 10. Proposed interpretive rule: Transfer of Very Low-Level Waste (VLLW) to exempt persons for disposal. https://www.federalregister.gov/documents/2020/12/17/2020-27565/tra nsfer-of-very-low-level-waste-to-exempt-persons-for-disposal. Accessed 2020 Mar 6 11. The United Kingdom’s Seventh National Report on compliance with the obligations of the joint convention on the safety of spent fuel and on the safety of radioactive waste management. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_d ata/file/1006999/The_United_Kingdom_s_Seventh_National_Report_on_Compliance_w ith_the_Obligations_of_the_Joint_Convention.pdf. Accessed 2021 Sept 29 12. The United Kingdom’s Sixth National Report on compliance with the obligations of the joint convention on the safety of spent fuel and radioactive waste management. https:// assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/ 672640/20171020_-_UK_Sixth_National_Report_to_the_Joint_Convention.pdf. Accessed 2018 Feb 09 13. Guidance on the scope of and exemptions from the radioactive substances legislation in the UK. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/att achment_data/file/731733/RSL_Guidance_update_BEIS_format_v5_180803.pdf. Accessed 2018 08 14. Borrmann F.: Management of very low level radioactive waste in Europe-application of clearance (and the alternatives). http://www.doc88.com/p-3327742325576.html. Accessed 2015 Nov 26 15. Report of the federal government for the seventh review meeting in May 2021 on the fulfilment of the obligations of the joint convention on the safety of spent fuel management and on the safety of radioactive waste management. https://www.bmuv.de/fileadmin/Daten_BMU/Dow nload_PDF/Nukleare_Sicherheit/jc_7_bericht_deutschland_en_bf.pdf. Accessed 2021 05 16. Regulations for radioactive waste management. https://openstd.samr.gov.cn/bzgk/gb/newGbI nfo?hcno=4AAED4E2A3E852BE8365A3794B807727. Accessed 2003 Apr 1 17. Landfill disposal for very low level radioactive waste. https://openstd.samr.gov.cn/bzgk/gb/ newGbInfo?hcno=BF263C2E608ABCE3BF69E6A0EBA01710. Accessed 2012 June 1 18. Wang, Q.H., Jia, H.H., Liu, Y.: Analysis of methods using in disposal of very low-level radioactive waste. Journal 20(02), 62–65 (2009) 19. Wang, Y.L., Zhang, C.J., Duo, T.H.: Some problems about very low level radioactive waste disposa. Journal 31(01), 108–111 (2012)

Research on Contact Response of Active Compliant Assembly of Nuclear Power Maintenance Robot Lele Duan(B) , Xi Wang, and Jianwen Chen Research Institute of Nuclear Power Operation, Wuhan, Hubei, China [email protected]

Abstract. Peg-in-Hole assembly is the basic operation for robot to overhaul nuclear power equipment. Aiming at the possible stress damage of peg-in-hole contact in the process of active compliant assembly. The contact response of pegin-hole assembly of robot active compliance control is analyzed by elastic-plastic numerical method. The effects of position error and angle error on the contact stiffness and bearable pressure of peg and hole in different contact states are studied. It is observed that in the elastic range, the maximum contact stress and the maximum elastic deformation of the contact state outside the hole are proportional to the pressure. The smaller the position error is, the smaller the contact stiffness of the peg and hole is and the smaller the pressure can bear. The stiffness of the two-point contact outside the hole is larger than that of the single point contact outside the hole, and it can bear more pressure. For the single point contact outside the hole, the larger the angle error is, the smaller the contact stiffness is, and the smaller the pressure is. It is pointed out that in the process of peg-in-hole assembly of nuclear power maintenance robot, the position error should be reduced as much as possible and the position error should be controlled within the chamfer range. Minimize the angle error in order to increase the bearable pressure of the peg-in-hole assembly. Keywords: Peg-in-hole assembly · Active compliance · Nuclear power maintenance · Contact stiffness · Bearable pressure

1 Introduction In order to ensure the safe operation of nuclear power plants, nuclear power equipment must be inspected and maintained regularly. Due to the radiation inside the nuclear power plant and the narrow working environment, the use of robots for nuclear power equipment maintenance is a trend. Peg-in-hole assembly is the basic work of nuclear power robot equipment maintenance. Automatic assembly and disassembly of bolts and nuts, automatic replacement of underwater parts such as pins and screws, sealing of main nozzle of evaporator and hoisting of reactor internal components are all related to the peg-in-hole assembly of the robot. Due to the large contact stiffness of the peg and hole, the small position error in the assembly process of the robot will bring large interaction force, which cause a great © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 444–458, 2023. https://doi.org/10.1007/978-981-19-8780-9_45

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threat to the safety of the equipment. In order to reduce the contact force of peg-inhole assembly and prevent the equipment surface from damage caused by extrusion, the assembly robot needs to have enough flexibility [1]. Remote Compliance Center (RCC) is installed at the end of the robot as a passive compliance device for peg-in-hole assembly. The assembly force is buffered and absorbed by spring and damping to realize the passive compliance of peg-in-hole assembly [2]. However, the passive compliant device is difficult to meet the stiffness requirements of different assembly operations. And it is difficult to control the force accurately. Passive compliant devices are difficult to be used in high-precision assembly tasks. In 1985, Neville Hogan proposed the use of impedance control for active compliance control of peg-in-hole assembly [3]. With the rapid development of artificial intelligence, many compliance control methods based on intelligent algorithms have been developed on the basis of classical active compliance control algorithms [4–8]. The compliance control of the robot greatly reduces the risk of damage to the equipment during the peg-in-hole assembly, but the evaluation index of compliance remains to be studied. The active compliance control parameters are related to the contact stiffness of the peg and hole. The contact stiffness between the peg and the hole needs to be input in the simulation of peg-in-hole assembly, and the method of determining the contact stiffness remains to be studied. For the contact problem in the peg-in-hole assembly, in order to prevent the peg from getting stuck in the hole in the assembly process, resulting in the failure of the peg-inhole assembly. From the point of view of rigid body dynamics, Huang et al. studied the problem of wedge tightening and clamping of peg-in-hole assembly, and deduced the limiting conditions to avoid wedging and sticking [9]. Whitney deduces the relationship between the contact force and the assembly position of the peg-in-hole assembly by means of theoretical mechanics, and gives the maximum allowable deflection angle that the peg can be loaded into the hole based on the geometric relationship of the pegin-hole assembly [10]. The assembly conditions derived from the rigid body dynamics method can effectively solve the problem of whether the peg can be loaded into the hole. However, whether structural damage occurs in the process of peg-in-hole assembly needs to consider the elastoplasticity of the structure. According to the relative position relationship between peg and hole, peg-hole contact can be divided into out-of-hole contact and in-hole contact. The assembly clearance of nuclear power equipment is generally small, and the main difficulty lies in the control of the contact state outside the hole, which makes the peg enter the hole from the contact state outside the hole. For the contact state in the hole, it is mainly necessary to meet the conditions to avoid wedging and sticking, and apply vertical downward pressure to complete the assembly task of the peg and hole. In the process of adjusting the contact state outside the hole, it is easy to cause plastic deformation of the peg and the hole due to excessive pressure, or because the impedance parameters are not set properly, the adjustment range is unreasonable and the adjustment goal can not be achieved. According to the different contact characteristics, the assembly state of the peg and hole is subdivided. The stress and deformation response of the contact state outside the hole is calculated by numerical method, and the effects of different types of external contact state, position error and angle error on the contact response are studied. The bearable pressure is defined as the maximum assembly force within the range of peg

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and hole elastic deformation. The trend of contact stiffness and bearing pressure vary with peg-in-hole position error and angle error is studied to provide guidance for active compliance control of peg-in-hole assembly.

2 Contact Response of Peg-in-Hole Assembly 2.1 Peg-in-Hole Assembly Models As shown in Fig. 1, according to whether the bottom surface of the peg enters the hole, the contact state of the peg and hole is divided into in-hole contact and out-of-hole contact. According to the contact angle, position and the number of contact points, the out-of-hole contact and in-hole contact state can be divided into many different contact types.

Out-of-hole contact

In-hole contact Fig. 1. Contact types of peg-in-hole assembly

The assembly clearance of the peg and hole in the nuclear power field is generally small, and the contact state in the hole mainly overcomes the friction. As long as the conditions of avoiding wedge tightening and blocking are met, the contact stress caused by the peg-in-hole assembly is small. In contrast, the out-of-hole contact state of the peg-in-hole assembly is directly subjected to extrusion pressure, and the contact stress is large, the out-of-hole contact state of the peg-in-hole assembly is selected for contact response analysis. Because the three-point contact state outside the hole is the special case of two-point contact state when the deflection angle is large, the position and attitude control error of the existing robot is small, and the probability of three-point contact state outside the hole is very small, the four types of out-of-hole contact in Table 1 are mainly analyzed. 53 working conditions with maximum angle error of 10°, maximum position error of 4mm and pressure range from 0.01N to 100N are analyzed. The contact mechanics model of peg-in-hole assembly is shown in Fig. 2. The bottom surface of the hole is fixed and the pressure is applied on the upper surface of the peg. At

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Table 1. Working conditions for contact response analysis of peg-in-hole assembly. Contact type

Skew angle

Position error

Pressure

Surface contact outside the hole

0°

2mm 4mm

0 ~ 100N

Single point contact outside the hole

2° 5° 10°

4mm

0 ~ 100N

Two-point contact outside the hole

2° 5° 10°

4mm

0 ~ 100N

Chamfer contact

2° 5° 10°

1mm

0 ~ 100N

the same time, the rotation of the peg is constrained. The contact stress and deformation of the peg and hole are calculated. The angle error θ of the peg and hole represents the relative angle of the axis of the peg and the hole. The position error e represents the distance between the axis of the peg and the axis of the hole. F

F

e

Single point contact outside the hole

e

Two-point contact outside the hole

Fig. 2. Schematic diagram of force analysis of peg-in-hole assembly

In order to reduce the amount of calculation, the height of the peg and hole is 20mm. The peg adopts hollow structure. The chamfer of peg and hole is 1mm. The geometric dimensions are shown in Table 2. The material of the peg and hole is 235 steel. It is assumed to be an ideal elastic-plastic material model. The material parameters are shown in Table 3.

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Model

Inner diameter

Outside diameter

Height

Chamfer

Peg

34 mm

50 mm

20 mm

1 mm, 45°

Hole

50 mm

80 mm

20 mm

1 mm, 45°

Table 3. Peg and hole material model Density

modulus of elasticity

yield strength

7850kg/m^3

206GPa

235MPa

2.2 Convergence Analysis The mesh convergence is analyzed under the condition of surface contact outside the hole, position error 4mm and pressure 10N. The mesh sizes are 4mm, 2mm, 1mm and 0.8mm respectively, and the corresponding grid numbers are 9789, 70781, 485911 and 925493 respectively. The peg-in-hole assembly model is divided by different sizes of mesh, the peg-in-hole assembly stress and deformation are calculated.

Fig. 3. The maximum assembly stress varies with the number of meshes

Figures 3 and 4 show the curves of maximum assembly stress and maximum deformation varying with the number of meshes, respectively. As can be seen from the figure, for the surface contact outside the hole of peg-in-hole assembly, when the number of meshes is small and the mesh size is large, the maximum assembly stress and maximum deformation increase rapidly with the increase of the grid number. The growth trend slows down when the grid size reaches 2mm. When the mesh size is less than 1mm, the maximum assembly stress and maximum deformation basically remain stable. Therefore, for the numerical study of peg-in-hole assembly contact problem, the mesh size of 1mm is used to divide the model.

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Fig. 4. The maximum assembly deformation varies with the number of meshes

3 Results Figure 5 shows that the maximum contact stress and maximum deformation of the surface contact state outside the hole vary with the pressure when the assembly position error of the peg and hole is 2mm. In the elastic range, the maximum contact stress and maximum deformation of the peg and hole are positively proportional to the pressure. When the contact stress reaches the yield stress of the material, the maximum deformation increases rapidly with the increase of pressure.

Fig. 5. Variation of maximum stress and deformation with pressure in surface contact state outside the hole

Figure 6 shows that the maximum contact stress of the surface contact state outside the hole varies with the pressure when the position errors are 2mm and 4mm respectively. When the position error is 2mm, the pressure of about 3N can make the maximum contact stress reach the yield stress. While when the position error is 4mm, the maximum stress caused by 100N pressure is still much less than the yield stress of the material. For the surface contact state outside the hole, the position error has a great influence on the

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contact stress. Under the same pressure, the smaller the position error is, the greater the contact stress is.

Fig. 6. Variation of maximum stress with pressure in surface contact state outside the hole

Figure 7 shows that the maximum deformation of the surface contact state outside the hole varies with the pressure when the position errors are 2mm and 4mm respectively. Combined with Fig. 6, it can be seen that in the range of elastic deformation, the maximum deformation of different position errors is proportional to the pressure. The surface contact outside the hole of the position error 2mm appears plastic deformation when the pressure is small, and the maximum deformation is non-linear with the pressure. Under the same pressure, the maximum deformation of the position error of 2mm is much larger than that of 4mm. The main reason for this result is that the smaller the error is, the smaller the contact area between the peg and the hole is, and the deformation of the model with smaller contact area is larger under the same pressure. Figure 8 shows that the maximum contact stress of the single point contact outside the hole varies with the pressure when the position error is 4mm and deflection angle is 2°, 5° or 10°. When the contact stress is in the elastic range, the maximum stress is proportional to the pressure, and the proportional coefficient increases with the increase of the angle. The contact stress corresponding to 10° first reaches the yield stress, and the contact stress corresponding to 2° reaches the maximum pressure required for the yield stress. This phenomenon can be explained by the sharpness of the contact between the peg and the hole surface. When the angle is less than 10°, the larger the relative angle of the peg and hole is, the sharper the contact area is, and the larger the stress under the same pressure is, the larger the deformation is. It can also be inferred that for the single point contact outside the hole, the smaller the angle is, the more beneficial to prevent plastic deformation. Figure 9 shows that the maximum deformation of the single point contact state outside the hole varies with the pressure when the position error is 4mm and the angle error is 2°, 5° or 10°. When the contact stress is in the elastic range, the maximum deformation is

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Fig. 7. Maximum deformation of surface contact state outside hole with pressure

Fig. 8. Variation of maximum stress of single point contact outside hole with pressure

proportional to the pressure, and the proportional coefficient increases with the increase of the angle. When the contact stress reaches the yield stress, the maximum deformation increases with the increase of pressure. For the same pressure, the smaller the angle error is, the smaller the maximum deformation is. When the deflection angle is 0°, it is the surface contact state outside the hole. Compared with the surface contact deformation outside the hole in Fig. 7, it is also in accordance with this trend. Figure 10 shows the variation of the maximum contact stress with pressure when the position error is 4mm and the angle is 2°, 5° or 10°. When the contact stress is in the elastic range, the maximum stress is proportional to the pressure, and the proportional coefficient decreases with the increase of the angle error. The contact stress corresponding to 2° is the first to reach the yield stress, and when the pressure is 100N, the contact stress of 10° is still much smaller than the yield stress. The main reason for this phenomenon is

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that for the two-point contact outside the hole, the larger the angle error is, the farther the distance between the two contacts is, and the sharper the contact point is due to the existence of the chamfer. However, compared with Fig. 7, it can be found that when the angle is small enough up to 0°, that is, when the hole is in surface contact, the maximum stress under the same pressure decreases, which is due to the increase of the contact area when the deflection angle is small enough to contact the surface. Therefore, for the two-point contact outside the hole with angle error larger than 2°, the larger the angle error is, the more beneficial to prevent plastic deformation is.

Fig. 9. Variation of maximum deformation of single point contact outside hole with pres-sure

Fig. 10. Maximum contact stress with pressure in two-point contact state outside the hole

Figure 11 shows that the maximum deformation of the two-point contact state outside the hole varies with the pressure when the position error is 4mm and the angle error is 2°,

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5° or 10°. When the contact stress is in the elastic range, the maximum deformation is proportional to the pressure, and the proportional coefficient decreases with the increase of the angle. For the same pressure, the smaller the angle is, the larger the maximum deformation is. However, when the angle is 0°, it is the surface contact state outside the hole. Compared with Fig. 7, The maximum deformation decreases when the angle is 0°. For the two-point contact state outside the hole, the above observed trend is valid only when the angle error is larger than 2°. When the angle error increases gradually from 0°, under the same pressure, the maximum deformation increases at first and then decreases with the increase of the angle error.

Fig. 11. Maximum deformation of two-point contact outside the hole with pressure

Figure 12 shows that the maximum contact stress in the chamfer contact state outside the hole varies with the pressure when the position error is 1mm and the angle error is 2°, 5° or 10°. When the contact stress is less than the yield stress, the maximum stress is proportional to the pressure, and the proportional coefficient increases with the increase of the angle, the contact stress corresponding to the 10° angle is the first to reach the yield stress. The pressure required for the contact stress of 2° angle to reach the yield stress is the largest. This phenomenon can also be analyzed from the sharpness of the contact point. Because both the peg and the hole are chamfered by 45°, when the angle error is 0°, the peg-hole chamfer contact is actually face-to-face contact. The sharpness of the contact point increases with the increase of the angle. Therefore, for the chamfer contact outside the hole, the smaller the angle is, the more beneficial to prevent plastic deformation is. Figure 13 shows that the maximum deformation of the chamfer contact state outside the hole varies with the pressure when the position error is 1mm and the angle error is 2°, 5° or 10°. When the contact stress is less than the yield stress, the maximum deformation is proportional to the pressure, and the proportional coefficient increases with the increase of the angle. For the same pressure, the larger the angle error is, the

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Fig. 12. Maximum stress of chamfer contact outside the hole with pressure

larger the maximum deformation is. This is also in line with the trend and explanations observed in Fig. 12.

Fig. 13. Maximum deformation of chamfer contact with pressure outside the hole

4 Discussion In a macroscopic sense, the contact stiffness represents the ratio between force and deformation of the structure. In the case of peg-in-hole assembly, it represents the ratio between the pressure exerted on the peg and the deformation of the peg and hole. Through the analysis of the assembly stress and deformation of the peg-in-hole assembly in different contact states outside the hole, it is found that the assembly stress

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and deformation are directly proportional to the pressure in the elastic range. The contact stiffness in different assembly states can be obtained by linear fitting the deformation and pressure curves of the elastic zone. When the contact stress is equal to the yield stress, the corresponding pressure is used as the maximum bearable pressure for peg-in-hole assembly. Figure 14 shows the variation of the maximum bearing pressure and contact stiffness of the peg-in-hole assembly with the position error of the assembly. The maximum bearing pressure and contact stiffness increase exponentially with the position error. The smaller the position error, the smaller the assembly pressure should be applied. Otherwise, plastic indentation may appear at the edge of the peg and hole.

Fig. 14. Maximum bearing pressure and contact stiffness with position error

Figure 15 shows the variation of the maximum bearable pressure with the angle error in the single point contact state, two-point contact state and chamfer contact state. The maximum bearing pressure of two-point contact outside the hole is larger than that of single-point contact and chamfer contact outside the hole. The larger the angle error is, the greater the maximum pressure-bearing capacity of two-point contact outside the hole than that of single-point contact and chamfer contact outside the hole. The main reason is that the contact area of two-point contact outside the hole is larger than that of singlepoint contact and chamfer contact. Therefore, it can withstand more pressure when the same yield strength is required. On the other hand, the maximum bearing pressure of single point contact and chamfer contact outside the hole decreases with the increase of angle error. The maximum bearing pressure of the two-point contact outside the hole is large when the deflection angle is close to 0°, then decreases with the increase of the angle, and increases with the increase of the angle when the angle error is larger than 2°. In order to improve the pressure-bearing capacity and avoid scratches on peg and hole in the assembly process, the angle error should be reduced as much as possible in the assembly process. Figure 16 shows the variation of contact stiffness with angle error in the single point contact state, two-point contact state and chamfer contact state. The angle error and

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Fig. 15. Maximum bearing pressure with angle error

Fig. 16. Variation of contact stiffness with angle error

contact state have a great influence on the contact stiffness. For these three kinds of out-ofhole contact states, the contact stiffness of two-point contact outside the hole is the largest and that of single-point contact outside the hole is the smallest. For single point contact and chamfer contact outside the hole, the contact stiffness decreases exponentially with the angle. For the two-point contact outside the hole, when the angle is close to 0°, the contact stiffness is relatively large. When the angle is larger than 2°, the contact stiffness increases rapidly with the increase of the angle. The contact stiffness mainly reflects the overall rigidity of the peg, and the rigidity of different pegs is different, which mainly depends on the bending or shear stiffness of the peg itself. The rigidity of the solid peg is much greater than that of the hollow peg.

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5 Conclusions The main difficulty of robot assembly is to make the peg enter into the hole from outof-hole contact. The out-of-hole contact state of typical peg-in-hole assembly includes surface contact, single-point contact, two-point contact and chamfer contact. The stiffness and the maximum bearable pressure in the elastic range of the four out-of-hole contact states are studied. The effects of position error and angle error on the deformation stiffness and bearable pressure are analyzed. In the elastic range, the maximum stress and maximum deformation of the contact state outside the hole are positively proportional to the pressure. After reaching plasticity, the deformation increases with the increase of pressure. For the surface contact outside the hole, the smaller the position error is, the smaller the contact area is, the smaller the contact stiffness is, and the smaller the maximum bearable pressure is. In order to prevent indentation, the assembly pressure should be reduced as much as possible when the position error is small. Because the contact area of the two-point contact state outside the hole is larger than that of the single-point contact state and the chamfer contact state, the contact stiffness and the maximum bearable pressure of the two-point contact state outside the hole are also the largest in these three states. The larger the angle error is, the sharper the contact area is in the single point contact state and chamfer contact state outside the hole. Therefore, the contact stiffness and maximum bearable pressure of these two states decrease with the increase of angle error. In order to prevent plastic deformation, the angle error should be reduced as much as possible. When the angle error is close to 0° in the two-point contact state outside the hole, which is equivalent to the surface contact, the contact area is large. With the increase of the angle, the contact area becomes smaller and the sharpness of the contact area becomes larger. Therefore, the stiffness and maximum bearable pressure of the two-point contact outside the hole decrease at first and then increase with the increase of the angle error. For the out-of-hole contact, the most likely to cause indentation is the surface contact outside the hole with small position error. In order to avoid the occurrence of this state, the position error should be reduced as far as possible and the position error should be controlled within the chamfer range. At the same time, minimize the angle error to increase the bearable pressure in the single point contact and chamfer contact outside the hole.

References 1. Suarez-Ruiz, F., Pham, Q-C.: A framework for fine robotic assembly. IEEE International Conference on Robotics and Automation (ICRA), pp. 421–426. Sweden (2016) 2. Whitney, D.E.: Quasi-static assembly of compliantly supported rigid parts. J. Dyn. Syst. Meas. Contr. 104(1), 65–77 (1982) 3. Hogan, N.: Impedance control: an approach to manipulation. I-theory. II-implementation. III-applications. J. Dyn. Syst. Meas Contr 107 (1985) 4. Xu, W., Cai, C., Zou, Y.: Neural-network-based robot time-varying force control with uncertain manipulator-environment system. Trans. Inst. Meas. Control. 36(8), 999–1009 (2014)

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5. Yi, R., Yang, Z., Liu, Y., et al.: Adaptive object impedance control of dual-arm cooperative humanoid manipulators. 11th World Congress on Intelligent Control and Automation (WCICA), pp. 3333–3339 (2014) 6. Suomalainen, M., Calinon, S., Pignat, E., et al.: Improving dual-arm assembly by master-slave compliance. IEEE International Conference on Robotics and Automation (ICRA), pp. 8676– 8682. Montreal, QC, Canada (2019) 7. Kim, T., Yoo, S., Kim, H.S., et al.: Position-based impedance control of a 2-DOF compliant manipulator for a facade cleaning operation*. IEEE International Conference on Robotics and Automation (ICRA), pp. 5765–5770. Paris, France (2020) 8. Abu-Dakka, F.J., Saveriano, M.: Variable impedance control and learning—A review. Front Robot AI 7 (2020) 9. Huang, X., Wang, M., Hu, J.: Geometric and mechanical analysis of unchamfer peg in hole assembly process. ROBOT 18(2), 7 (1996) 10. Whitney, D.E.: Mechanical assemblies: their design, manufacture, and role in product development. Oxford University Press, New York (2004)

Digital Design and Improvement of PWR Rod Position Measurement System Huang Yuan1(B) , Chang Zhengke3 , Liu Yiqian2 , Lu Jian2 , and Huang Jing2 1 CNNC Equipment Technology Development Co., Ltd, Shanghai, China

[email protected] 2 CNNC Nuclear Power Operation Management Co. Ltd, Haiyan, Zhejiang, China 3 Nuclear Power Operation Research (Shanghai) Co. Ltd, Pudong, Shanghai, China

Abstract. Control rods can control the progress of reactor reactions in pressurized water reactor (PWR) nuclear power plants, Therefore, the rod position test system for monitoring the real-time position of control rods and controlling the operation is an important system to guarantee the safety of nuclear power plants. The commonly used analog rod position measuring equipment in China has some problems, such as high power consumption, high calorific value, and difficult threshold setting. In this paper, the overall structure of the device, signal acquisition, and program algorithm is independently designed and developed, and unique and ingenious designs such as algorithm compensation, interference filtering, and intelligent threshold setting are adopted to digitally design and improve the control rod testing system. Results show that the all-digital rod position measuring device can effectively reduce the circuit complexity, power consumption, and reliability of rod position equipment. Moreover, its intelligent threshold setting function can simplify the operation and maintenance test and shorten the main line time of overhaul, thus ensuring economical performance. Keywords: Rod position · Digitization · Intelligence · Improvement

1 Introduction The control rod is the most important control equipment [1] to realize normal start-stop, power operation, and emergency shutdown of nuclear power plant reactors, and it is also the only movable part [2–5] in the core structure. The normal operation of control rods in the core of a pressurized water reactor (PWR) nuclear power plant is an important means to ensure the safe and stable operation of nuclear power plants [6–8]. Therefore, having a set of control rod position measurement and indication systems with reliable operation, accurate measurement, and simple structure is necessary to monitor the correct execution of the sequential movement of control rods [9–11]. The rod position measurement system is an important system that enables nuclear power plants to monitor the operation and the actual position of control rods [12–15]. Most domestic PWR nuclear power plants adopt analog rod position measuring equipment based on the electromagnetic induction principle. The existing rod position detectors mainly include a primary coil, measuring © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 459–469, 2023. https://doi.org/10.1007/978-981-19-8780-9_46

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coil, auxiliary coil, coil skeleton, sealed shell, and outer sleeve [16]. The primary coil is used to generate the alternating magnetic field, the measuring coil is used to form the bar position code, and the auxiliary coil is used to adjust the primary current. To address the problems of a complex circuit, high power consumption, and difficulty in setting the Gray code threshold voltage of analog rod position measuring equipment, a set of all-digital rod position measuring equipment is developed. With the use of unique and ingenious designs such as algorithm compensation, interference filtering, and intelligent threshold setting, the above problems are improved and solved, the reliability of the equipment is improved, and the critical path of unit overhaul is shortened.

2 Measurement Method and Defects of Analog Rod Position At present, the analog rod position measurement method is mainly used in domestic nuclear power plants. Generally, two kinds of analog measuring equipment exist: the rod position measurement equipment controlled by a pulse width modulation (PWM) amplifier produced by China Nuclear Control System Engineering Co., Ltd. And the rod position measurement equipment controlled by a transformer produced by Rolls Royce plc. Figure 1 shows a block diagram of the excitation regulation circuit of the primary coil of the control rod position equipment of CNNC. The equipment adopts a high-power audio amplification circuit based on PWM amplifier SA01. The amplitude of the 50 Hz sine wave is regulated by a digital potentiometer, which is controlled by the frequency signal formed by the difference between the given value and the measured value of the auxiliary coil voltage.

Fig. 1. Block diagram of excitation regulation circuit of primary coil of control rod position equipment of CNNC

Figure 2 presents a block diagram of the excitation regulation circuit of the primary coil of the rod position equipment of Rolls Royce plc. A large resistor is connected in series in the primary excitation circuit. The two kinds of equipment for signal processing of the side measuring coil are roughly the same, mainly using filtering, shaping, and threshold comparison circuits for processing. Figure 3 shows the processing process. Over nearly 20 years of application practice, the following problems with the analog measurement method have been found:

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Fig. 2. Block diagram of excitation regulation circuit of the primary coil of R&R rod position equipment

Fig. 3. Signal processing process of the 2nd measuring coil

1. The circuit is relatively complex, and many components exist. Both the excitation circuit of the primary coil and the processing circuit of the secondary coil contain many analog components, and the circuit is complicated, especially the excitation circuit controlled by a PWN amplifier. This condition is not conducive to the improvement of equipment reliability and hinders operation and maintenance personnel from mastering core skills and quickly dealing with on-site defects. 2. High power consumption and high calorific value. High-power PWM and the series connection of large resistors in the loop cause the power consumption of the whole loop to increase greatly. The calorific value also increases, thus necessitating higher requirements for ventilation and dustproof ability of the cabinet. 3. Difficulty of setting the Gray threshold voltage. In the measurement signal processing circuit, analog components play a limited role in filtering the interference signal and shaping the waveform, which poses difficulties in finding the setting point of the threshold voltage with a small error

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and good smoothness, and without flash jumps. The threshold voltage needs to be adjusted by repeatedly lifting the control rod when the reactor is started, which takes a long time to occupy the critical path of refueling overhaul. The following are the core causes of the above problems: 1. A large number of components are introduced for voltage stability. The position of the control rod affects the mutual inductance coefficient of the coil, resulting in a great change in the load impedance. The introduction of alternating current regulation circuit can adapt to this change. Connecting a large resistor in series in the loop can resist this change, that is, the impedance change caused by the position change of the driving shaft and the ambient temperature change of the detector are small enough in the total impedance. Both methods can ensure the accuracy and stability of the loop output, thus ensuring the stability of the induced voltage. 2. Filtering out the influence of the high current of control rod drive mechanism on rod position signal by using the analog measurement method is difficult. When the control rod moves in each step, the CRDM lifting coil will pass a 40 A current, and the lifting coil is close to the rod position detector coil, which will cause a great interference to the detector coil, as shown in Fig. 4. However, filtering out this interference is difficult for analog components; this interference easily causes the rod position to flicker and increases the difficulty of setting the Gray code threshold at the same time. Generally, the analog measurement method involves increasing the power of the exciting circuit to alleviate the interference problem, but this approach causes other problems, such as high power consumption of equipment.

Fig. 4. CRDM action interference waveform

The above problems are the limitations of analog equipment, and breaking through these issues by using the original analog measurement method is difficult.

3 All-Digital Rod Position Measurement System The rapid adjustment of the reactor power in a PWR nuclear power plant is mainly achieved by controlling the lifting and lowering of the rod bundle. Control operations such as the lifting and lowering of the rod bundle are completed by the rod control system. However, existing rod control systems by themselves do not contain a feedback

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mechanism for verifying that the rod bundle control commands have been executed correctly. Therefore, a rod position measurement system needs to be set up to obtain the actual position of the rod bundle, monitor the operation status of the rod control system, and complete the positioning of the control rod bundle through the rod position measurement system. The rod bundle control assembly and its drive shaft are located in the hightemperature and high-pressure environment of the nuclear reactor. The measurement of its position generally uses the principle of electromagnetic induction and is conducted by rod position detectors. The drive shaft is made of magnetic material, and the magnetic permeability of the detector sealing shell, skeleton, outer casing, and other media in the detector is very low. Thus, the voltage induced by whether a drive shaft is present in the measurement coil varies greatly. The voltage induced by the measuring coil at a certain position can be used to determine whether the top of the drive shaft is above or below it. As long as a sufficient number of measuring coils are set and the induced voltage signals of each coil are monitored, the position of the drive shaft and the control rod can be roughly determined. A sufficient number of measurement coils must be provided to roughly determine the position of the control rods. The number and spacing of the measuring coils should be determined according to the length of the drive shaft stroke and the desired resolution. To reduce the number of wiring between the detector and the signal processing channel, and the number of signal processing equipment, the measurement coils must also be grouped. The induced voltage signals of the measuring coils of each detector group change with the lifting stroke of the control rod. The induced voltage of the measuring coils is processed in the rod position measuring cabinet, and the processed voltage is compared with a shaping threshold voltage, thus forming the rod position code of switching value, as shown in Fig. 5.

Fig. 5. Formation of switch bar bit code bit

With the development of digital and intelligent technology, Qinshan Nuclear Power Company began to develop an all-digital rod position measuring device in 2016 to reduce the complexity of the excitation circuit of the primary side and the signal processing circuit of the secondary side of the detector, simplify the threshold setting method of rod position processing, and improve the reliability and measurement accuracy of rod position processing equipment. At present, the R&D device has taken the lead, being

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applied to Unit 4 of Qin No. 2 Plant, and it runs stably and reliably for more than one fuel cycle. The module structure of the all-digital rod position measuring device is shown in Fig. 6, and it includes an exciting power supply, integrated interface board, and general signal processor. The excitation power supply provides working power to the primary coil of the rod position detector located in the containment. The general signal processor collects the output signals of the rod position detector, including the primary voltage, current signal, and voltage signals of each group of measuring coils and auxiliary voltage signals, and outputs the calculated control rod position, device failure, channel test, and other signals. The excitation power supply is an AC transformer, and the general signal processor is the Compact RIO platform of National Instruments. The field diagram of the device is shown in Fig. 7, and a single device contains nine available rod position measurement channels.

Fig. 6. Module structure of all-digital rod position measuring device

Fig. 7. Field diagram of all-digital rod position measuring device

4 Improvement Ideas and Implementation To break through the limitations of analog rod position measurement and maximize the advantages of digitalization, the fully digital rod position measurement device is mainly designed based on the following ideas:

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a. The high-power current regulating loop and the large resistor connected in series in the loop are eliminated, and the circuit complexity is reduced. b. The excitation voltage and power consumption are reduced. c. The threshold setting is intelligent and the setting process is simplified. 4.1 Compensation and Resistance Adjustment by the Algorithm The algorithm instead of the current regulating circuit and the resistor connected in series is used to compensate. Whether it is a high-power current regulating loop or a series resistor, the purpose of the algorithm is to ensure the stability of the magnetic field in the detector by ensuring the stability of the induced voltage of the auxiliary coil, that of the induced voltage of the secondary coil, and finally the stability and accuracy of rod position measurement. Under the condition without a regulating circuit, the voltage and current of the exciting power supply will change as a result of the change of load impedance, and the induced voltage of the secondary side will also follow the downward or upward trend, as shown in Fig. 8. At this time, combined with the fixed Gray code threshold, the boundary point drift phenomenon shown in Fig. 5 will occur, resulting in a large error in the rod position measurement.

Fig. 8. Induced voltage waveform of the secondary side of control rod step 5–225

In this digital design, the primary coil voltage, current, and other parameters are collected and introduced. The secondary induced voltage can be compensated by the algorithm by tracking the primary coil voltage and current parameters. Thus, the compensated induced voltage is stable and the boundary point drift phenomenon is avoided. As shown in Fig. 9, the induced voltage of the group A coil stabilizes after compensation.

Fig. 9. Compensation of induced voltage of group A coil

Therefore, the algorithm compensation replaces the function of the current regulation loop and series connection of large resistors in analog rod position measurement, which greatly reduces the complexity of the circuit.

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4.2 Interference Reduction by the Algorithm The algorithm filters out interference and reduces the excitation voltage. To resist the interference caused by CRDM action, analog measuring equipment generally adopts a higher excitation voltage, that is, a high-power alternating current regulating loop. Otherwise, the interference caused by CRDM action causes the rod position indication to jump easily. In this digital design, as shown in Fig. 10, the avoidance interval AB that needs to be avoided due to the interference of control rod action is searched in the collected signal interval by using a software algorithm according to the voltage signal of the auxiliary coil. The end point B of the avoidance interval is taken as the starting point of the calculation interval and sometime after the endpoint of the avoidance interval as the end point C of the calculation interval. Fast Fourier transform or average peakto-peak calculation method is used to calculate the average value of the measured coil voltage amplitude in the calculation interval BC to thoroughly filter out the interference caused by CRDM action. The interference filtering makes the higher excitation voltage unnecessary, thereby greatly reducing the power consumption of the whole measuring device.

Fig. 10. Interference filtering algorithm

4.3 Intelligent Integration The threshold voltage setting is intelligent, which replaces manual settings and shortens the time of overhauling the main line. After being processed, the induced voltage of the rod position measuring coil is compared with the threshold voltage, thus forming the rod position code of switching value. The setting of the threshold value directly affects the rod position measurement result, which is why it is important to work in the debugging process before the rod position measurement system is put into operation, and it is also important to work in the test process before the power operation after each overhaul. At present, the threshold setting is all performed manually. The whole process takes a long time and the accuracy cannot be guaranteed by constantly moving the bar and adjusting the parameters until the channel is qualified. In this digital design, an intelligent selftuning module and a self-tuning operation mode are set. In this mode, the operation and maintenance personnel lift and insert the control rods of each subgroup, starting from five steps to the top of the reactor and then inserting them back to five steps. In this

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process, the intelligent setting module automatically captures the control rod movement of each measurement channel, calculates the detector output record, processes the data, and calculates the threshold voltage. The process includes the following steps: a. Intelligent capture control rod action signal. The voltage of the continuous auxiliary coil is monitored, and when the voltage of the auxiliary coil has changed significantly for two consecutive periods, then the control rod starts to act. b. The current of the primary coil of the detector, the voltage of the auxiliary coil, and the voltage of each measuring coil are calculated and recorded after each action when the control rod is judged to be active. According to the CRDM coil current sequence, when the control rod moves, the control rod movement direction is further determined by the auxiliary coil voltage waveform. On the basis of the interference filtering algorithm, the primary coil current, auxiliary coil voltage, and each measuring coil voltage of the detector are calculated, and the number of control rod movement steps is accumulated after the calculation. c. Judging whether the automatic setting process is finished or not. Whether the setting process is finished or not is judged by whether the total number of control rod action steps or the latest action time has exceeded the set value; if so, then it is transferred to the threshold calculation link. d. Intelligent threshold calculation. For each group of measuring coils, all the recorded voltages of the corresponding code bits are sorted from small to large. The number of code bit changes, and the mean square of the corresponding measurement errors is verified when the threshold value is a certain voltage. For the four groups of coils A, B, C, and D, if a minimum threshold with a measurement error within seven steps and a minimum threshold with a measurement error greater than this value of more than seven steps exist, then the middle value of the two is used as the setting threshold. If this minimum threshold does not exist, then the threshold with the minimum mean square of measurement error is used as the set threshold. For group E coils, the available threshold with the smallest mean squared measurement error is found, and the middle value of the two available thresholds with the largest difference is used to set the threshold. Three percent of the set threshold is taken as the hysteresis difference, that is, when the current measured voltage is greater than the set threshold or the last code bit is 1 and the current measured voltage is greater than the set threshold minus the hysteresis difference, then the current code bit is 1; otherwise, it is 0. The calculation process is shown in Fig. 11. With the help of the intelligent threshold setting method, the threshold setting time of the all-digital rod position measuring device is shortened from about 5 h to 2 h, thereby greatly shortening the critical path time of overhaul.

5 Conclusions This paper analyzes the structure and algorithm problems of the traditional analog rod position measuring device and independently designs and develops solutions to address

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Fig. 11. Threshold calculation flow

the existing problems. The digital design and improvement of the control rod test system are performed by using a unique and ingenious design, and the following conclusions are obtained: 1. The traditional analog rod position measuring device has some problems, such as complicated circuits, high power consumption, high calorific value, and difficult threshold setting. 2. The core reasons for the above problems are that the analog measurement method needs to ensure the stability of induced voltage and that it has difficulty filtering out the influence of the high current of the control rod drive mechanism on the rod position signal. 3. The all-digital rod position measuring device can effectively reduce equipment circuit complexity, reduce equipment power consumption, and improve rod position equipment reliability. Its intelligent threshold setting function can simplify operation and maintenance tests, as well as shorten the overhaul mainline time. Overall, the device has good economy.

References 1. Xin, P.H., Yao, L., Shen, Y., et al.: Design of control rod position measurement system for nuclear power plant. Plant Maint. Eng. S2, 2 (2015) 2. Weifeng, L., Xiong, J., Chen, X., Tang, S., Liu, J.: Research and improvement of diagnosis method of fuel failures for pressurized water reactor nuclear power plant. Nuclear Power Engineering (2019) 3. Zhao, L.: Design and research of rod position measurement system without compensating coil in nuclear power plant. University of Nanhua (2012) 4. Lin, E.L.: Cause analysis and improvement of high temperature at reactor top of PWR nuclear power plant. Mech. Des. Res. (2021) 5. Chen, J.W., Gong, C.J.: Fault analysis and treatment measures of domestic rod position system. Appl. Electron. Techn. 47(S01), 6 (2021)

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6. Muniglia, M., Verel, S., Pallec, J., Do, J.M.: A fitness landscape view on the tuning of an asynchronous master-worker ea for nuclear reactor design (2021) 7. Huang, Y., He, B.Y., Zhang, H.W.: Design and reliability analysis of processing cabinet of rod position indication system in nuclear power station. Automation and Instrumentation 12, 6 (2020) 8. Zhang, J., Gao, F., Liu, M., Shengmao, L.I., Sun, B., Luo, H. et al.: Nuclear power plant reactor control rod addressing apparatus and method. EP3836163A1 (2021) 9. Zhou, Z.J.: Research on control rod action detection system of reactor. Harbin Institute of Technology (2019) 10. Yu, J.: Present situation and development of control rod drive mechanism in PWR nuclear power station. Sci. Technol. Innov. Herald 14(22), 3 (2017) 11. Ibrahim, Y.V., Abaleni, J.I., Simon, J., Ibikunle, I.K.: Experimental and analytical determination of irradiated control rod worth of a low power research reactor. Ann. Nucl. Energy 166, 108724– (2022) 12. Li, H.L., Qin, B.K., Bo, H.L.: Research on static characteristics of multi-electrode capacitive rod position measurement sensor based on rotary electrode. At. Energy Sci. Technol. 56(4), 8 (2022) 13. Guo, W., Ma, J.C.: Research and implementation of measurement method for static rod position linearity of control rod in nuclear power plant. Instrument and Meter for Automation 43(3), 5 (2022) 14. Guang, H.U., Liu, Q., Hanliang, B.O.: Application of capacitance sensor for control rod position measurement system in NHR-200. The Proceedings of the International Conference on Nuclear Engineering (ICONE) 2019(27), 1375 (2019) 15. Gallais, L., Burla, R., Martin, F., Richaud, J.C., Volle, G., Pontillon, M., et al.: An experimental platform for real-time measurement of the deformation of nuclear fuel rod claddings submitted to thermal transients. Rev. Sci. Instrum. 89(1), 013110 (2018) 16. Li, Y., Qin, B., Bo, H.: Research on multi-electrode capacitance rod position measurement sensor for Nhr-200. Ann. Nucl. Energy 166, 108730- (2022)

Numerical Study on Radiative Heat Transfer Performance of ACP100 Passvie Containment Air Cooling System Yu Feng(B) , Mingrui Yu, Hongliang Wang, Zhuo Liu, Xu Han, and Yidan Yuan China Nuclear Power Engineering Co., Ltd., Beijing, China [email protected]

Abstract. Passive containment Air cooling System (PAS) is an important Engineering Safety Feature of the small modular pressurized water reactor ACP100, designed by China National Nuclear Corporation (CNNC). The function of the PAS for ACP100 is to remove the heat inside the containment continuously through the natural circulation of air cooling during hypothetical accidents in order to keep the internal temperature and pressure of the containment within the design limits. Therefore, the heat transfer performance of the PAS has been widely concerned. Related heat transfer phenomena include conductive heat transfer, convective heat transfer, and radiative heat transfer. The radiative heat transfer has played an important role on the overall heat transfer performance of the PAS. In this paper, ANSYS Fluent 19.0 was used to build a CFD model for the PAS, investigating the effects of wall emissivity of the steel shell and the concrete shell on radiative heat transfer performance of the PAS under accident scenarios at steady state. The numerical study results show that 874.96 kW increase in the radiative heat transfer power when wall emissivity of the steel shell is from 0 to 1 at wall emissivity of the concrete shell of 0.92 at steady state, the proportion of the radiative heat transfer in the total heat transfer increases from 0% to 42.41%. 712.51 kW increase in the radiative heat transfer power when wall emissivity of the concrete shell is from 0 to 1 at wall emissivity of the steel shell of 0.84 at steady state, the proportion of the radiative heat transfer in the total heat transfer increases from 9.44% to 41.58%. Therefore, wall emissivity of the steel shell and the concrete shell has a significant influence on radiative heat transfer performance of the PAS. The above numerical study results can provide some necessary data reference and support for the PAS design and optimization, which has some practical engineering significance. Keywords: PAS · Radiative heat transfer · CFD · Steel shell · Concrete shell · Wall emissivity

1 Introduction Passive containment Air cooling System (PAS) is an important Engineering Safety Feature of the small modular pressurized water reactor ACP100, designed by China National Nuclear Corporation (CNNC) [1]. The function of the PAS for ACP100 is to remove the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 470–480, 2023. https://doi.org/10.1007/978-981-19-8780-9_47

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heat inside the containment continuously through the natural circulation of air cooling during hypothetical accidents in order to keep the internal temperature and pressure of the containment within the design limits. Therefore, the heat transfer performance of the PAS has been widely concerned. Related heat transfer phenomena include conductive heat transfer, convective heat transfer and radiative heat transfer. The radiative heat transfer has played an important role on the overall heat transfer performance of the PAS. The radiative heat transfer is also of interest. A number of experts and academics have carried out research work on the passive containment cooling system [2–8]. Computational fluid dynamics (CFD) codes can be applied to study on radiative heat transfer performance of the PAS for ACP100. In this paper, ANSYS Fluent 19.0 was used to build a CFD model for the PAS, investigating the effects of wall emissivity of the steel shell and the concrete shell on radiative heat transfer performance of the PAS under accident scenarios at steady state. The numerical study results can provide some necessary data reference and support for the PAS design and optimization, which has some practical engineering significance.

2 Pysical Model 2.1 Structure and Composition The ACP100 is designed with double containment, the inner shell is steel shell and the outer shell is concrete shell that connect to the shielded plant. The PAS consists of an air inlet, a cooling gallery between the inner shell and the outer shell, four air ducts connecting the air inlet to the cooling gallery, reinforced ribs and an air outlet. The general configuration of the PAS is shown in Fig. 1 [9].

Fig. 1. General configuration of the PAS

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2.2 Working Principle The working principle of the PAS is mainly to use the outer surface of the inner shell as the heat transfer surface, and the air in the cooling gallery is continuously heated, resulting in higher air density at the air inlet than in the cooling gallery. The air inlet enters the cooling gallery along the four air ducts, which can continuously extract the heat in the inner shell, and finally flows out from the air outlet and returns to environment, forming a natural circulation. In this process, the heat transfer methods involved are conductive heat transfer, convective heat transfer and radiative heat transfer.

3 Cfd Model 3.1 Governing Equations The governing equations for conservation of mass, momentum and energy have to be solved. Additionally, transport equations for turbulence that are required to provide closure for evaluation of turbulent viscosity also have to be solved. The turbulence model used in this study is the well-known k-ε model and the radiative model used in this study is the DTRM model. ANSYS Fluent 19.0 solver is used for solving these governing equations. The governing equations solved by ANSYS Fluent 19.0 are: 1. mass conservation:  → ∂ρ + ∇ · ρ− v = Sm ∂t 2. momentum conservation:   →−    ∂ − − → → ρ→ v +∇ · ρ − v→ v = −∇p + ∇ · τ + ρ − g + F ∂t 3. energy conservation:   ∂(ρT ) λ + div(ρvT ) = div gradT + ST ∂t cp

(1)

(2)

(3)

4. gas state conservation: ρ=

RT

p 

Yj Mj

(4)

5. heat transfer conservation: qt = cp fm (Tout − Tin )

(5)

→ In the upper equations, ρ is gas density (kg/m3 ), t is time (s), − v is gas flow velocity − → 3 2 (m/s), Sm is mass source term (kg/m ·s), τ is stress (N/m ), g is gravitational acceler− → ation (m/s2 ), F is lateral volume force (N/m3 ), T is gas temperature (K), λ is thermal conductivity (W/(m·K)), cp is constant pressure specific heat capacity (J/(kg·K)), ST is viscous dissipative term ((kg·K)/(m3 •s)), R is molar gas constant (8.3145 J/(mol·K)), Yj is mass fraction of component j, Mj is relative molecular mass (kg/mol), qt is total heat transfer power (W), fm is gas mass flow (kg/s), Tout is outlet gas temperature (K), Tin is inlet gas temperature (K).

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3.2 Simulation Boundary Conditions The setting of simulation boundary conditions needs to consider some conservatism and envelopment. Since ACP100 is built in Changjiang, Hainan Province, it is necessary to investigate the climatic conditions there in order to set the simulation boundary conditions. After investigation and analysis, the maximum ambient temperature is about 313.2 K from 2012 to 2021 in Changjiang, Hainan Province. Therefore, the setting of the ambient temperature in the simulation boundary conditions is 318.2 K in this paper, ensuring that the simulation results have some conservatism and envelopment. Under the accident conditions, a large amount of steam can generate inside the inner shell, which continuously heat the inner wall surface. It can make the temperature of the inner wall surface increase. Therefore, the setting of the temperature of the inner wall surface in the simulation boundary conditions is 403.2 K in this paper. The setting of simulation working conditions is shown in Table 1. The setting of simulation boundary conditions is shown in Table 2. The material of inner shell and outer shell is carbon steel and concrete respectively. The setting of carbon steel property parameter is shown in Table 3. The setting of concrete property parameter is shown in Table 4. Table 1. Setting of simulation working conditions Simulation working conditions

Wall emissivity of the inner shell Wall emissivity of the outer shell

I-1

0

I-2

0.2

I-3

0.4

I-4

0.6

I-5

0.8

I-6

1

O-1

0.84

0.92

0

O-2

0.2

O-3

0.4

O-4

0.6

O-5

0.8

O-6

1

3.3 Mesh Schemes Irrelevance Analysis The 3-dimensional CFD geometrical model of the PAS is shown in Fig. 2. There are three mesh schemes set in this paper, they are 1 (coarse, about 4,000,000 cells), 2 (medium, about 6,000,000 cells) and 3 (fine, about 8,000,000 cells), as shown in Fig. 3. The numerical simulation working condition is selected I-6. As for the numerical simulation

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Ambient temperature (K)

Ambient pressure (Pa)

Air relative humidity (%)

Inner shell wall roughness (μm)

Outer shell wall roughness (μm)

318.2

101325

0

20

25

Table 3. Setting of carbon steel property parameter Thermal conductivity (W/(m·K))

Specific heat capacity (J/(kg·K))

Density (kg/m3 )

Wall emissivity

43

440

7800

0.84

Table 4. Setting of concrete property parameter Thermal conductivity (W/(m·K))

Specific heat capacity (J/(kg·K))

Density (kg/m3 )

Wall emissivity

1.28

970

2400

0.92

result of average outlet air mass flow at three mesh schemes, the mesh scheme 2 is finally adopted.

Fig. 2. A 3-dimensional CFD geometrical model of the PAS

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Fig. 3. Mesh schemes irrelevance analysis

4 Results and Analysis 4.1 The Effect of Wall Emissivity of the Steel Shell on Radiative Heat Transfer Performance of the PAS The simulation working conditions I-1, I-2, I-3, I-4, I-5 and I-6 are selected for numerical simulation in this section.

Fig. 4. Variation of heat transfer power with wall emissivity of the steel shell at steady state

The data graph of variation of heat transfer power with wall emissivity of the steel shell at steady state is shown in Fig. 4. As is shown in Fig. 4, the radiative heat transfer power and the total heat transfer power both increases with wall emissivity of the steel shell. 874.96 kW increase in the radiative heat transfer power, 709.4 kW increase in the

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total heat transfer power, the proportion of the radiative heat transfer in the total heat transfer increases from 0% to 42.41%. The data graph of variation of radiative heat transfer power with wall emissivity of the steel shell at steady state is shown in Fig. 5. As is shown in Fig. 5, the radiative heat transfer power in straight section and upper head of the steel shell both increases with wall emissivity of the steel shell. 691.07 kW increase in the radiative heat transfer power in straight section, 183.89 kW increase in the radiative heat transfer power in upper head. Therefore, the radiative heat transfer power in straight section increases more obviously.

Fig. 5. Variation of radiative heat transfer power with wall emissivity of the steel shell at steady state

The data graph of variation of average outlet air temperature and average outlet air mass flow with wall emissivity of the steel shell at steady state is shown in Fig. 6. As is shown in Fig. 6, the average outlet air temperature and average outlet air mass flow both increases with wall emissivity of the steel shell. The average outlet air temperature increases 6.14 K, the average outlet air mass flow increases 6.12 kg/s. 4.2 The Effect of Wall Emissivity of the Concrete Shell on Radiative Heat Transfer Performance of the PAS The simulation working conditions O-1, O-2, O-3, O-4, O-5 and O-6 are selected for numerical simulation in this section. The data graph of variation of heat transfer power with wall emissivity of the concrete shell at steady state is shown in Fig. 7. As is shown in Fig. 7, the radiative heat transfer power and the total heat transfer power both increases with wall emissivity of the concrete shell. 712.51 kW increase in the radiative heat transfer power, 573.74 kW increase in the total heat transfer power, the proportion of the radiative heat transfer in the total heat transfer increases from 9.44% to 41.58%.

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Fig. 6. Variation of average outlet air temperature and average outlet air mass flow with wall emissivity of the steel shell at steady state

Fig. 7. Variation of heat transfer power with wall emissivity of the concrete shell at steady state

The data graph of variation of radiative heat transfer power with wall emissivity of the concrete shell at steady state is shown in Fig. 8. As is shown in Fig. 8, the radiative heat transfer power in straight section and upper head of the steel shell both increases with wall emissivity of the concrete shell. 565.14 kW increase in the radiative heat transfer power in straight section, 147.37 kW increase in the radiative heat transfer power in upper head. Therefore, the radiative heat transfer power in straight section increases more obviously. The data graph of variation of average outlet air temperature and average outlet air mass flow with wall emissivity of the concrete shell at steady state is shown in Fig. 9.

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Fig. 8. Variation of radiative heat transfer power with wall emissivity of the concrete shell at steady state

Fig. 9. Variation of average outlet air temperature and average outlet air mass flow with wall emissivity of the concrete shell at steady state

As is shown in Fig. 9, the average outlet air temperature and average outlet air mass flow both increases with wall emissivity of the concrete shell. The average outlet air temperature increases 4.24 K, the average outlet air mass flow increases 5.35 kg/s.

5 Conclusions This paper carried out a numerical investigation on the effects of wall emissivity of the steel shell and the concrete shell on radiative heat transfer performance of the PAS under

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accident scenarios at steady state. After analyzing the numerical data, the conclusions are drawn as follows: (1) 874.96 kW increase in the radiative heat transfer power when wall emissivity of the steel shell is from 0 to 1 at wall emissivity of the concrete shell of 0.92 at steady state, the proportion of the radiative heat transfer in the total heat transfer increases from 0% to 42.41%. Wall emissivity of the steel shell has a significant influence on radiative heat transfer performance of the PAS. (2) 712.51 kW increase in the radiative heat transfer power when wall emissivity of the concrete shell is from 0 to 1 at wall emissivity of the steel shell of 0.84 at steady state, the proportion of the radiative heat transfer in the total heat transfer increases from 9.44% to 41.58%. Wall emissivity of the concrete shell has a significant influence on radiative heat transfer performance of the PAS. These are our current research results. They can provide some necessary data reference and support for the PAS design and optimization. The radiative heat transfer power of the PAS can be increased by using of high emissivity materials to increase the total heat transfer power, especially the radiative heat transfer power in straight section of the steel shell.

Nomenclature CFD ρ t − → v Sm τ − → g − → F T λ cp ST R Yj Mj qt fm Tout Tin

computational fluid dynamics gas density, kg/m3 time, s gas velocity, m/s mass source term, kg/(m3 •s) stress, N/m2 gravitational acceleration, m/s2 lateral volume force, N/m3 gas temperature, K thermal conductivity, W/(m·K) constant pressure specific heat capacity, J/(kg·K) viscous dissipative term, (kg·K)/(m3 •s) molar gas constant, 8.3145 J/(mol·K) mass fraction of component j relative molecular mass, kg/mol total heat transfer power, kW gas mass flow, kg/s outlet gas temperature, K inlet gas temperature, K

References 1. Danrong, S., Zhong, Q., Huiping, C., et al.: Research and development for ACP100 small modular reactor in China. China Nucl. Power 10(2), 172–177+187 (2017)

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2. Wang, Y.: Experimental research on heat transfer characteristic of SMR’s passive containment cooling system. North China Electric Power University, Beijing (2017) 3. Wang, H., Mingrui, Y., Li, Y., et al.: Experimental study on wind load performance of ACP100 passive containment air cooling system. Nucl. Power Eng. 43(2), 175–180 (2022) 4. Wang, B., Zhuang, Y., Yao, J., et al.: ASTEM material B209 used in PCS of third generation nuclear plant. Mat. Heat Treat. 43(2), 175–180 (2012) 5. Liu, Y., Sun, M.: Effects of outlet Height on passive containment cooling system. High Power Laser and Particle Beams 27(12), 273–278 (2015) 6. Li, L., Li, C., Zhang, Y., et al.: Preliminary analysis of AP1000 PCCS and its enhanced performance. Nucl. Power Eng. 38(1), 36–40 (2017) 7. Pan, X., Xiang, W., Song, C.: Study on characteristics and sensitive factors of PCS air flow path resistance. Nucl. Power Eng. 37(2), 122–126 (2016) 8. Sun, Y., Zheng, Y., Ma, X., et al.: Sensitivity analysis of pressure response in containment cooled by natural circulation. Nucl. Power Eng. 40(3), 66–69 (2019) 9. Wang, H., Mingrui, Y., Feng, Y., et al.: Research on the heat exchange capacity of ACP100 passive containment air cooling system. Nucl. Sci. Eng. 41(1), 271–273 (2021)

Influence of Zirconia Addition on the Leaching Stability of Borosilicate Glass Xiaoyang Zhang, Fan Yang, Jiangjiang Mao, Shikun Zhu, Xu Chen, Kemian Qin, Haibo Peng(B) , and Tieshan Wang Lanzhou University, Lanzhou, Gansu, China {penghb,tswang}@lzu.edu.cn

Abstract. Immobilization of high-level radioactive wastes (HLW) in borosilicate glass for deep geological disposal has been widely used in the treatment of HLW around the world. The chemical durability of borosilicate glass, especially ability of resisting aqueous corrosion, is a crucial property, since the vitrification is carried out to prevent the dissipation of radioactive elements towards the biosphere. The addition of zirconia (ZrO2 ) to nuclear waste glasses could affect chemical properties such as chemical durability. In this work, borosilicate glasses with different composition were fabricated and leaching properties and the corresponding microstructures of borosilicate glasses in deionized water were investigated using the MCC-1 static leaching method at 90 °C. The leaching rate was characterized by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and the microstructure of the glass was analyzed by Fourier transform infrared spectroscopy (FTIR) and grazing incident X-ray diffraction (GIXRD). The results of FTIR and GIXRD spectra showed that the strength of the glass network improved with the addition of zirconia, which might lead to an increase in the hydrolytic resistance of the glass. The addition of zirconia decreased the dissolution rate of the borosilicate glasses. The normalized mass losses of the borosilicate glasses with the addition of zirconia were much less than that of the ternary sodium borosilicate glasses. The changes in structure and leaching rate of quaternary glass also suggest that the addition of ZrO2 could obviously affect the leaching stability of the borosilicate glasses as well. Zirconia will strengthen the borosilicate glass network and improve the hydrolytic durability of glass. Finally, the changes in microstructure and leaching rates of borosilicate glasses undoped and doped with ZrO2 were obtained, which is of great significance for revealing the zirconia addition effect on the leaching process of borosilicate glass. Keywords: Borosilicate glass · Zirconia · Leaching behavior · Microstructure

1 Introduction Borosilicate glasses have been selected as the vitrification materials for solidifying radioactive wastes in many countries. The hydrolysis resistance of borosilicate glass is a major parameter for the long-term safety of geological disposal. For the last several

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 481–489, 2023. https://doi.org/10.1007/978-981-19-8780-9_48

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decades, therefore, a large number of studies have been carried out on many different types of nuclear glasses dissolution processes [1–3]. Zirconia plays a significant role in glass technology, and it is also an important oxide in nuclear waste vitrification [4, 5]. Zirconium is usually used as a cladding material to enclose the UO2 fuel in the fuel cans. During reprocessing of spent fuel, it would produce a high level liquid waste product (HLLW) containing Zr leached from the fuel cans [6]. Zirconia is widely concerned for its application in nuclear waste vitrification due to the presence of Zr as a fission product [5]. As the substrate materials of vitrification, the borosilicate glasses doped with zirconium have been studied extensively [7–9]. Indeed, ZrO2 addition tends to result in changes in the physical properties of alkali silicate glasses, such as increase the glass transition temperature and density, and decrease the thermal expansion coefficient [10, 11]. However, ZrO2 has a low solubility in alkali borosilicate glasses and considerably increases glass viscosity. Watson [12] suggested that the solubility of ZrO2 was correlated with the amount of alkali oxide available for charge compensation. Knowledge of the glass structure, including the local environment of the various components, is important for a deeper understanding of the long-term stability of the glass form. The local environment of Zr has been extensively studied. In fact, Galoisy et al. [13] used X-ray absorption spectroscopy to investigate Zr coordination and the results presented that Zr coordination were approximately octahedral-like coordination within borosilicate glass, although ZrO7 and ZrO8 (VII Zr and VIII Zr) species have been reported as well [14]. Furthermore, the positive influence of zirconium on borosilicate glasses chemical durability has been reported. Bergeron et al. [15] studied the effect of substituting ZrO2 for silica in borosilicate glass. The addition of ZrO2 decreased the initial dissolution rate of glass. However, recently studies showed that Zr would influence physicochemical properties of the gel and inhibit the formation of gel protective layer, which led to an increase of the dissolution rate of vitrification in the long term [16, 17]. Whereas a lot of studies have been performed on the zirconium-containing waste glasses throughout the world, few studies investigate the relationship between glass structure and dissolution rates by addition of ZrO2 in boron-containing nuclear waste glasses. Two types of borosilicate glass were fabricated. The aqueous dissolution tests of the glasses were performed using the MCC-1 static leaching method at 90 °C. The changes in the glass structure and leaching rate were characterized by Fourier transform infrared spectroscopy (FTIR), grazing incident X-ray diffraction (GIXRD), and inductively coupled plasma optical emission spectrometry (ICP-OES). The purpose of this work is to investigate the influence on glass structure and dissolution of borosilicate glass induced by ZrO2 .

2 Methods Two types of glasses were fabricated, one without zirconium content, named NBS10, and another with zirconium content, named ZNBS3. Table 1 lists the compositions of the borosilicate glasses. The powders were melted and stirred for 21 h at 1300 °C in an alumina crucible and then annealed at 500 °C for 24 h. The samples were cut into a size of 10 × 10 × 1 mm3 and polished on both sides for experiments.

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Table 1. The chemical compositions of borosilicate glasses mol%

SiO2

B2 O3

Na2 O

ZrO2

NBS10

55.00

19.64

25.36

/

ZNBS3

50.00

16.45

29.05

4.5

The dissolution test of the glasses was performed by the MCC-1 static leaching method at 90 °C. The cleaned specimen was suspended in a PTFE bottle with nylon thread and the bottle was filled with 36 mL deionized water. The ratio of water volume to sample surface area is 15 cm. The experiments were conducted over 28 days to access the alteration of the glass. The nuclide contents in water were measured by ICP-OES. The leaching rate was calculated as follows [18, 19]: Qi = NRi =

Ci fi · (SA/V )

(1)

Ci fi · (SA/V ) · t

(2)

where Qi is the normalized mass losses of i element, g/m2 ; NRi is the normalized leaching rate of i element, g/(m2 ·d); C i is the concentration of element in the leachate, g/L; f i is the weight fraction of element in original solid sample; SA is sample surface area, m2 ; V is the volume of leaching agent, L; t is leaching time, d. Fourier transform infrared spectroscopy (FTIR) was used to analyze the microstructure of the leached glasses with a PerkinElmer Spectrum Two spectrophotometer. The FTIR spectra absorption measurements were done using the mode of attenuated total reflection. The infrared spectra ranged from 400 cm−1 to 2000 cm−1 with a resolution of 4 cm−1 . The phase development was characterized by the grazing incident X-ray diffraction (GIXRD) with Rigaku SmartLab instrument. The measurement used Cu Kα radiation (1.54 Å) at 1° incident angle. The typical test angle ranged from 10° to 60°.

3 Results and Discussion 3.1 FTIR Spectra FTIR was used to investigate the structural units involved in the glass network of the NBS10 and ZNBS3 glasses. The IR absorbance spectra of the NBS10 and ZNBS3 glass samples between 400 and 2000 cm−1 are presented in Figs. 1 and 2. The peak located around 460 cm−1 can be assigned to the Si-O-Si asymmetric bending vibrations [20, 21]. There is a weak peak around 700 cm−1 , which is assigned to the bending vibration of the B-O-B bond in borate networks (BO4 group) [22]. The peak near 800 cm−1 is attributed to the O-Si-O symmetric bond stretching [23], which is associated with the motions of silicon against its O cage. The region ranges from 850 to 1200 cm−1 , which is led by vibrations of Qn species(where n = 0,1,2,3,4 is the number of bridging oxygen atoms

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Fig. 1. FTIR spectra of NBS10 glasses before and after leaching

Fig. 2. FTIR spectra of ZNBS3 glasses before and after leaching

connected with a network former cation) [24]. The 850 cm−1 peak can be assigned to the vibration of Q0 [25, 26]. The peak located around 950 cm−1 might be due to a vibration of Q2 [25]. After leaching, the intensity of Q2 increased, indicating the structural changes in ZNBS3 glass.The band at 1030 cm−1 is generally attributed to the Si-O symmetric bond stretching motions of the Q3 species [27]. The band at 1150 cm−1 is generally attributed to the Si-O symmetric bond stretching motions in Q4 structure [28]. Furthermore, the bands located in the region between 1250 and 1500 cm−1 are attributed to the B-O stretching vibration of [BO3 ] units [29]. The peak located near 1250 cm−1 corresponds to the stretching vibration of the B-O bond in the [BO3 ] structural unit connected to the [BO4 ] structural unit [29]. The peak of 1390 cm−1 is attributed to antisymmetric

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stretching vibration of [BO3 ] units [29], and its intensity gradually dropped after 1 d of leaching, which inferred that [BO3 ] structural unit in the both leached glass decreased. After leaching of 14 d, the peak at 700 cm−1 , which is attributed to the B-O-B bond, disappeared on IR spectra. Furthermore, for ZNBS3 glass, the peak at 1390 cm−1 also vanished on IR spectra, which indicated the boron network of ZNBS3 glass disorganized. Comparing with ZNBS3 glass, the anti-hydrolysis ability of NBS10 glass was worse and the boron network of NBS10 glass disappeared after leaching for 3 d. 3.2 GIXRD

Fig. 3. GIXRD patterns of NBS10 glasses with and without leaching

The recorded X-ray diffraction patterns of the NBS10 and ZNBS3 glasses are shown in Figs. 3 and 4, respectively. The diffraction peaks of the pristine glass of ZNBS3 and NBS10 exhibit broad hump characteristics of amorphous nature of the samples. With increasing leaching time, the main peaks of ZNBS3 and NBS10 gradually become sharp. When the leaching time is more than 3 d, a slightly sharp diffraction peak appeared at 21° in the GIXRD pattern of NBS10 glass, indicating that the short range and medium range orders of NBS10 glasses began to increase. The diffraction peaks at 26° of ZNBS3 glass stared to appear after 1 d of leaching and became narrow with increasing leaching time, while other diffraction peaks (20° and 30°) gradually reduced. In conjunction with this shift, the shape of the peak changed systematically, which suggested the bonding environment changed during the leaching process. When glass was corroded in the water, the soluble sodium and boron elements disappeared in the glass network, and the glass network degenerated to the silicon-oxygen network structure. 3.3 Leaching Rate The normalized mass losses of Si, B, and Na of ZNBS3 and NBS10 glasses are presented in Fig. 5. When the normalized mass losses of ZNBS3 increased with the increase of

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Fig. 4. GIXRD patterns of ZNBS3 glasses with and without leaching

Fig. 5. The normalized mass losses (Qi ) of leached glasses during 28 d

leaching time, the normalized mass losses of NBS10 reached stable. The mass losses of NBS10 were about 2 orders of magnitude higher than that of ZNBS3. The hydrolysis resistance of ZNBS3 glass is better than NBS10. Figure 6 displays the normalized leaching rate of the ZNBS3 and NBS10 glasses during 28d. For ZNBS3 and NBS10 glass, the normalized leaching rates show a decreasing trend generally. The normalized leaching rate of Na of NBS10 is higher than two other elements Si and B, while the normalized leaching rate of B is the highest in ZNBS3 glass. Si and B atoms are the former of glass network and Na atom works as network modifier. The bond energy of Na is weak. Therefore, Na element is easier to hydrolyze. The alkali metal ions (Na+ ) that combine with [BO4 ] unit are replaced with H+ from water, which

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Fig. 6. The normalized leaching rate (NRi ) of leached glasses during 28 d

may result in rupturing the bond of B-O structure. The boron network is broken down and the element of B is released in leachate. Then, the major networks of borosilicate glass, such as silicon network, are dissolved by network hydrolysis reaction. Hence, the normalized leaching rate of Na usually is higher than the network former elements Si and B. Zirconium ions are mostly six-fold coordinated [30]. Six-coordinated zirconium is inserted in the silicate network by Si–O–Zr bond and the amount of Si–NBO structure units shows a downward trend, which will strengthen the silicon network [31]. Addition of ZrO2 can polymerize glass network in borosilicate glasses by attracting alkali cations to charge compensate six-fold coordinated zirconium. Some of the Na cations become Zr charge compensators and the proportions of three-coordinate boron would increase with increasing Zr content. Zirconium compensation takes preference over boron compensation [32]. Therefore, the normalized leaching rate of B is the highest in ZNBS3 glass and different from NBS10 glass.

4 Conclusions In this work, ternary borosilicate glass and zirconium-containing borosilicate glass were fabricated. The leaching properties and the corresponding structures of the borosilicate glasses leached by deionized water were investigated using the MCC-1 static leaching method at 90 °C. The experimental results revealed the following conclusions. The addition of ZrO2 could obviously enhance the leaching stability of the borosilicate glass. Six-coordinated zirconium would link with silicon tetrahedral, which could increase the polymerization of the borosilicate glass network. Zirconium and boron compete for charge compensate Na cations and zirconium compensation takes preference over boron compensation, which results in the drop of the proportions of four-coordinate boron atoms. [BO3 ] units are easily attacked by water, and the normalized leaching rate of B showed an increase.

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Acknowledgments. This work was supported by the National Natural Science Foundation of China (Grant Nos. U1867207, 12175092) and the Fundamental Research Funds for the Central Universities (Grant No. Lzujbky-2021-kb11).

References 1. Shin, H.S., Kim, I.T., Yoo, J.H., Shon, J.S., Kim, J.H., Seo, Y.C.: Leaching of glass components and surrogate nuclides from glassy waste forms for radioactive incineration ash. J. Radioanal. Nucl. Chem. 253, 121–128 (2002) 2. Bourcier, W.L.: Affinity functions for modeling glass dissolution rates, Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States) (1998) 3. Juoi, J., Ojovan, M., Lee, W.: Microstructure and leaching durability of glass composite wasteforms for spent clinoptilolite immobilisation. J. Nucl. Mater. 372, 358–366 (2008) 4. Connelly, A.J., Travis, K.P., Hand, R.J., Hyatt, N.C., Maddrell, E.: Composition–structure relationships in simplified nuclear waste glasses: 2. the effect of ZrO2 additions. J. Am. Ceram. Soc. 94, 137–144 (2011) 5. Donald, I.W., Metcalfe, B.L., Taylor, R.N.J.: The immobilization of high level radioactive wastes using ceramics and glasses. J. Mater. Sci. 32(22), 5851–5887 (1997). https://doi.org/ 10.1023/A:1018646507438 6. McKeown, D.A., Muller, I.S., Buechele, A.C., Pegg, I.L.: X-ray absorption studies of the local environment of Zr in high-zirconia borosilicate glasses. J. Non-Cryst. Solids 258, 98–109 (1999) 7. Plodinec, M.: Borosilicate glasses for nuclear waste imobilisation. Glass Technol. 41, 186–192 (2000) 8. Advocat, T., Jollivet, P., Crovisier, J., Del Nero, M.: Long-term alteration mechanisms in water for SON68 radioactive borosilicate glass. J. Nucl. Mater. 298, 55–62 (2001) 9. St-Pierre, J., Tran, H., Zikovsky, L.: Immobilization of radioactive wastes: Leachability of glasses containing zirconium. J. Nucl. Mater. 107, 286–289 (1982) 10. Karell, R., Kraxner, J., Chromcikova, M.: Properties of selected zirconia containing silicate glasses. Ceram. Silikáty 50, 78 (2006) 11. Fisher, J.G., James, P.F., Parker, J.M.: Soda lime zirconia silicate glasses as prospective hosts for zirconia-containing radioactive wastes. J. Non-Cryst. Solids 351, 623–631 (2005) 12. Watson, E.B.: Zircon saturation in felsic liquids: experimental results and applications to trace element geochemistry. Contrib. Miner. Petrol. 70, 407–419 (1979) 13. Farges, F., Ponader, C.W., Brown, G.E., Jr.: Structural environments of incompatible elements in silicate glass/melt systems: I. Zirconium at trace levels. Geochimica et Cosmochimica Acta 55(6), 1563–1574 (1991) 14. Hazen, R.M., Finger, L.W.: Crystal structure and compressibility of zircon at high pressure. Am. Miner. 64, 196–201 (1979) 15. Nkurunziza, G., Debaiky, A., Cousin, P., Benmokrane, B.: Durability of GFRP bars: A critical review of the literature. Prog. Struct. Mat. Eng. 7, 194–209 (2005) 16. Cailleteau, C., et al.: Insight into silicate-glass corrosion mechanisms. Nat. Mater. 7, 978–983 (2008) 17. Cailleteau, C., Devreux, F., Spalla, O., Angeli, F., Gin, S.: Why do certain glasses with a high dissolution rate undergo a low degree of corrosion? J. Phys. Chem. C 115, 5846–5855 (2011) 18. Raman, S.V.: Microstructures and leach rates of glass ceramic nuclear waste forms developed by partial vitrification in a hot isostatic press. J. Mater. Sci. 33, 1887–1895 (1998) 19. Sheng, J.W., Luo, S.G., Tang, B.L.: The leaching behavior of borate waste glass SL-1. Waste Manag. 19, 401–407 (1999)

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20. Devine, R.: Ion implantation-and radiation-induced structural modifications in amorphous SiO2 . J. Non-Cryst. Solids 152, 50–58 (1993) 21. Rokita, M., Handke, M., Mozgawa, W.: Spectroscopic studies of the amorphous SiO2 –AlPO4 materials. J. Mol. Struct. 511, 277–280 (1999) 22. Kamitsos, E., Karakassides, M., Chryssikos, G.: Cation-network interactions in binary alkali metal borate glasses. A far-infrared study, J. Phys. Chem. 91(22), 5807–5813 (1987) 23. Wan, J., Cheng, J., Lu, P.: The coordination state of B and Al of borosilicate glass by IR spectra, Journal of Wuhan University of Technology-Mater. Sci. Ed. 23, 419–421 (2008) 24. Macdonald, S.A., Schardt, C.R., Masiello, D.J., Simmons, J.H.: Dispersion analysis of FTIR reflection measurements in silicate glasses. J. Non-Cryst. Solids 275(1–2), 72–82 (2000) 25. Merzbacher, C.I., White, W.B.: The structure of alkaline earth aluminosilicate glasses as determined by vibrational spectroscopy. J. Non-Cryst. Solids 130, 18–34 (1991) 26. Matson, D.W., Sharma, S.K., Philpotts, J.A.: The structure of high-silica alkali-silicate glasses. A Raman spectroscopic investigation. J. Non-Cryst. Solids 58, 323–352 (1983) 27. Sanders, D.M., Person, W.B., Hench, L.L.: Quantitative analysis of glass structure with the use of infrared reflection spectra. Appl. Spectrosc. 28, 247–255 (1974) 28. Tenney, A., Wong, J.: Vibrational spectra of vapor-deposited binary borosilicate glasses. J. Chem. Phys. 56, 5516–5523 (1972) 29. Cormier, L., Domingos, D.S.M., Neuville, D.R., Echegut, P.: In situ evolution of the structure of alkali borate glasses and melts by infrared reflectance and Raman spectroscopies, physics and chemistry of glasses. Eur. J. Glass Sci. Technol Part B 47, 430–434(435) (2006) 30. Farges, F., Calas, G.: Structural analysis of radiation damage in zircon and thorite: An X-ray absorption spectroscopic study. Am. Miner. 76, 60–73 (1991) 31. Montorsi, M., Leonelli, C., Menziani, M., Du, J., Cormack, A.: Molecular dynamics study of zirconia containing glasses. Phys. Chem. Glasses 43, 137–142 (2002) 32. Angeli, F., Charpentier, T., De Ligny, D., Cailleteau, C.: Boron speciation in soda-lime borosilicate glasses containing zirconium. J. Am. Ceram. Soc. 93, 2693–2704 (2010)

Best Estimate Plus Uncertainty Analysis of a Pressurizer Surge Line Break LOCA on China’s Advanced PWR Cuiting Peng1 , Yao Yao2,3,4 , Ye Yang1 , Chengcheng Deng1 , and Jun Yang1(B) 1 Department of Nuclear Engineering and Technology, School of Energy and Power

Engineering, Huazhong University of Science and Technology, Wuhan 430074, China {pengcuiting,yang_jun}@hust.edu.cn 2 Department of Nuclear Science and Technology, School of Energy and Environment, Southeast University, Nanjing 210096, China 3 Institute of Nuclear Thermal-Hydraulic Safety and Standardization, Jinan, China 4 National Engineering Research Center of Power Generation Control and Safety, Nanjing 210096, China

Abstract. The Pressurizer Surge Line Break (PSLB) Loss of Coolant Accident (LOCA) is one of the representative design basis accidents (DBAs) which leads to the discharge of the primary coolant into the containment atmosphere. In this paper, the RELAP5 best-estimate code was used to perform a PSLB LOCA on China’s Advanced Pressurized Water Reactor (PWR). According to the phenomenon identification and ranking table (PIRT), the parameters that contribute significantly to the reactor safety standard were determined. Then, simple random sampling was performed using DAKOTA software and a set of 59 calculations were performed based on the Wilks’ formula. The uncertainty bands of key output parameters were obtained with 95/95 criterion, and the upper band of maximum containment pressure did not exceed the safety limit. Lastly, important uncertainty contributors for the maximum containment pressure were identified through sensitivity analysis. Keywords: PSLB LOCA · Relap5 · China’s advanced PWR · BEPU · Sensitivity analysis

1 Introduction In 1988, the U.S. Nuclear Regulatory Commission (U.S. NRC) revised the Emergency Core Cooling System (ECCS) guidelines in 10CFR 50.46 [1] and allowed the use of “ best estimate plus uncertainty “ (BEPU) methodology on the plant safety analysis. The BEPU adopts the best-estimate code, such as RELAP5, TRACE, CATHARE, etc. [2], which can perform more realistic and accurate calculations, avoid excessive conservatism, and bring more economic benefits to nuclear power. However, regulations require that the use of best-estimate codes must be coupled with uncertainty quantitative analysis. Therefore, the regulatory guidance RG1.157 [3] was issued in 1989, which sets the standard for quantitative evaluation, specifying that the best estimate must have at © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 490–505, 2023. https://doi.org/10.1007/978-981-19-8780-9_49

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least a 95% confidence level within the safety limit. From the perspective of statistics, it is required that the results of the uncertainty calculation meet the 95/95 criterion, that is, the probability that at least 95% of the concerned parameters fall below the safety limit is 95%. Among statistical methods, Wilks’ formula is often considered for sampling uncertainty analysis. To support the revised ECCS guidelines, the U.S. NRC and its contractors developed the code scaling, applicability, and uncertainty (CSAU) methodology to demonstrate the feasibility of BEPU [4]. Gen III reactors promise lower costs and simpler structures compared to conventional Gen II reactors without sacrificing safety and stability [5]. The AP600 and AP1000 are representative Gen III PWRs developed by Westinghouse and received final design approval from the U.S. NRC in 1999 and 2005 [6, 7], respectively. Based on the design of AP1000, the China’s Advanced PWR has been proposed and developed. Several LOCA studies have been performed for this type of reactor such as SBLOCA [8, 9], LBLOCA [10] and Main Steam Line Break (MSLB) [11]. The PSLB LOCA refers to the break at the pressurizer (PZR) surge line, which usually leads to the water of the PZR quickly draining into the containment system. The schematic diagram is shown in Fig. 1, At the same time, since many reactors rely on the relevant parameters of the PZR to trigger the subsequent safety signal or start the setting of coolant injection, the PSLB LOCA should be analyzed. Similar accidents have been investigated for other reactors, such as Krištof et al. [12] evaluated the PH4-SLB experiment from PMK-2 integral test facility of VVER-440/213 using the RELAP5/Mod3.2 code and the CIAU method for PZR Surge Line Break accident, also Huang Zhigang et al. [13] obtained the accident response characteristics of the passive safety system and the parameter variation of the primary loop system through the experimental study of double-ended break of Small Modular Reactor (SMR) PZR Surge Line. The RELAP5 code is one of the best estimate system codes that were developed by the U.S. NRC for light water reactors safety analysis [14]. It is a generic code that can be used for the simulation of a wide variety of hydraulic and thermal transients in both nuclear and nonnuclear systems involving mixtures of steam, water and no-condensable gas [15–18]. In this paper, the RELAP5 best-estimate code was used to perform a PSLB LOCA on China’s Advanced PWR, and BEPU analysis was performed. The accident transient process was analyzed. In addition, the uncertainty evaluation and the sensitivity analysis were carried out.

2 Accident Simulation 2.1 Description of Relap5 Model The code input model of China’s Advanced PWR was established with the RELAP5 code, based on the technical information acquired from public literature and design information [5, 8, 19]. As shown in Fig. 2, the primary system consists of a reactor pressure vessel (RPV), 2 hot legs, 4 cold legs, 1 PZR, 2 steam generators (SG) and 4 coolant pumps. The RPV downcomer is connected with 2 direct vessel injection (DVI) pipes, 4 cold leg nozzles and 2 hot leg nozzles through nozzles, so the downcomer is evenly divided into 8 vertical channels in the circumferential direction. The passive safety

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Fig. 1. The break position of the PSLB LOCA

system includes 2 core make-up tanks (CMT), 2 accumulators (ACC), 1 in-containment refueling water storage tank (IRWST) and passive residual heat removal (PRHR) system, four-stage automatic depressurization system (ADS) and associated pipes and valves. The CMT, ACC and IRWST are connected to the RPV through common DVI pipes and provide injection water to the core. A double-ended break of the PZR Surge Line is selected as the transient condition, and the main model node diagram of the transient setting is shown in Fig. 3. Based on the fact that the double-ended break is 200% of the surge line break, two trip valves are set up to connect the two ends of the surge line respectively, and both lead to the containment. 2.2 Results and Analysis of Basecase The main goal of the transient simulation in this study is to use the safety mechanism of the passive reactor to successfully alleviate the accident and ensure the safety of the reactor. At present, since the accident sequence data have not been found specifically for the PSLB LOCA of the China’s Advanced PWR. Therefore, the main accident sequences are referred from other similar LOCA tests performed at scaled facilities, shown in Table 1 [20]. Since there is no open literature about the PSLB LOCA of the China’s Advanced PWR, in order to verify the reliability of the transient simulation, the PSLB accident of VVER-440/213 is selected for qualitatively comparative analysis. Gen II VVER440/213 reactor was designed between 1970 and 1980 with a power of 440 MW, which has six primary loops, and the PZR is connected to hot leg with two approx. 22 m long lines with 207 mm inner diameter.

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Fig. 2. Reactor nodalization for RELAP5 code [20]

Fig. 3. The main model node diagram of the transient

Figure 4 shows the comparison between China’s Advanced PWR and VVER440/213 results for the system pressure. In the double-ended break LOCA, due to the larger size of the break, the rate of coolant loss to the containment is faster, so the system pressure is rapidly decreased to atmospheric pressure. Figure 5 shows the PZR pressure. Since the break occurs at the PZR, the pressure of the PZR decreases faster. Figure 6 shows the injection mass flow rate of CMT, ACC and IRWST. In the passive safety injection system, as the system pressure decreases, the safety signal is triggered, and CMT injection begins at first. Then the system pressure decreases continuously, and ACC injection begins when the injection point of ACC is reached. Because the injection of CMT is suppressed by the ACC when the three major safety injection systems are injected into the core through the DVI pipeline, the CMT continues to be injected after the ACC is emptied. After the CMT is emptied, there is a gap period. When the system pressure is decreased to atmospheric pressure, the IRWST begins to be injected by gravity. Figure 7 shows the comparison between China’s Advanced PWR and VVER440/213 results for the core collapsed level trend. Due to the scale difference of two reactors, only the trend of level is compared. There are two lowest points in RPV liquid

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Event

Set point

Actual time(s)

Break opens

–

0

Reactor SCRAM

13.2 MPa

2

Safety signal

12.8 MPa

2

SG main feed water valves start to close

After ‘Safety’ signal

4

RCPs trip

After ‘Safety’ signal

9

PRHRS isolation valve starts to open

After ‘Safety’ signal

17

CMT injection starts

After ‘Safety’ signal

17

ACC injection starts

4.83MPa

100

ACC empty

–

612

ADS1 valves start to open

30 s after 67.5% liquid volume in any CMT

900

ADS2 valves start to open

48 s after ADS1 actuation

1012

ADS3 valves start to open

120 s after ADS2 actuation

1125

ADS4a valves start to open

120 s after ADS3 & CMT low-low level

1663

ADS4b valves start to open

60 s after ADS4a actuation

1723

IRWST injection valve starts to open

RCS pressure less than 0.5 MPa

1824

End of transient

4500

4500

Fig. 4. Comparison of the system pressure

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Fig. 5. The PZR Pressure

level under the double-ended break of the PZR Surge Line. The reason for the first lowest point is mainly as follows: (1) The transient condition is a double-ended break, with a large break area. After the break, the flash phenomenon is more obvious and coolant loss is faster. However, the CMT supply is not large, and ACC has just started to inject at this time. (2) The break is close to the hot leg, where the pressure and temperature are higher than the cold leg, so the coolant is lost faster when the break occurs. (3) This break position is directly connected to the core channel, and the liquid level in the core decreases more obviously when the coolant is lost. The reason for the second lowest point is mainly as follows: There is a gap period between the end of CMT injection and the start of IRWST injection. At this time, there is almost no coolant to supply the primary side, so the RPV liquid level drops again. After the IRWST injection starts, the liquid level continues to rise to a stable level.

Fig. 6. The injection mass flow rate of CMT, ACC and IRWST

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Fig. 7. Comparison of the core collapsed level trend

Fig. 8. The mass flow rate of ADS

During the whole accident process, the three major safety injection systems mainly replenished the primary side coolant loss due to the break to avoid the core exposure. At the same time, the role of the four-stage ADS can ensure the smooth reduction of the system pressure, so that the passive safety injection systems can be smoothly injected. The mass flow rate of ADS1–4 are shown in Fig. 8. The containment pressure during the entire accident is shown in Fig. 9. The pressure of the containment vessel rises rapidly when the break is opened in the process of a transient accident. Then the pressure decreases without exceeding the safety limit-0.443 MPa [5] due to the cooling of the containment vessel by the thermal components outside the containment vessel, which imitating the passive containment cooling system(PCCS) and spray system. The release of mass and energy to the containment through the break are shown in Fig. 10.

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Fig. 9. The containment pressure

Fig. 10. Mass and Energy Release

3 Uncertainty Quantification and Sensitivity Analysis 3.1 Selection of Uncertain Parameters Due to the lack of complete uncertainty analysis research related to the PSLB LOCA on China’s Advanced PWR, the distribution and range selection of the uncertainty input parameters are selected basing on experience and engineering judgement. At the same time, the preliminarily selected accident condition in this study is the LOCA caused by the double-ended break of the PZR Surge Line. Therefore, the uncertainty analysis of AP1000 [21–23] and China’s Advanced PWR [10, 24] LBLOCA are investigated, and combined with the special break position of the PZR Surge Line [25], the uncertainty input parameters and their distribution ranges are preliminarily determined in Table 2.

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Parameter Category

Parameter

Distribution

Range

Surge Line Parameter

Internal Pressure

Normal

μ = 15.5、σ = 0.25

Normal

μ = 8.2e-3、σ = 5.0e-6 [0.99,1.01] [25]

Initial Pressure

Normal

μ = 15.5、σ = 0.05 [0.935,1.065] [25]

Friction form loss in the surge line

Log-normal

[0.5,2] [25]

Air Gap Dimension

Uniform

[0.939,1.67] [22, 24]

Fuel Thermal Conductivity

Uniform

[0.9,1.1] [24, 25]

Pressurizer Parameters Initial Level [25]

Local Hot Rod Parameters [10]

Plant Initial Conditions RCS Pressure Parameters [10] ACC Initial Pressure ACC Initial Liquid Volume

Core Power Distribution Parameters [10]

Global System Thermal-Hydraulic Parameters [10]

Uniform

[0.98,1.02] [21, 24]

Uniform

[0.941,1.059] [24]

Uniform

[0.975,1.025] [24]

ACC Initial Temperature Uniform

[0.88,1] [24]

CMT&IRWST Initial Temperature

Uniform

[0.97,1.03] [24]

ACC Injection Friction Coefficient

Uniform

[0.5,2] [24, 25]

Decay Heat

Uniform

[0.92,1.08] [24, 25]

Heat Flux Hot Spot Factor (FQ)

Uniform

[0.936,1.064] [21, 22]

Power Peaking Factor (FH)

Normal

μ = 0.038643、σ = 2.43% [0.941,1.059] [22–24]

Core Power

Uniform

[0.99,1.01] [21, 22, 24]

Break Discharge Coefficient

Uniform

[0.6,1.4] [24]

Break Resistance Coefficient

Same as the Break Discharge Coefficient (continued)

3.2 Wilks Nonparametric Statistics The Wilks nonparametric statistical method was first developed based on the Wilks theory [26] as an ordered tolerance limit method, which can establish tolerance confidence intervals for random samples of unknown distribution. In 1985, the GRS method first applied the Wilks method to the uncertainty analysis of the best estimate [27]. The significant advantages of applying the Wilks method to uncertainty analysis are: the

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Table 2. (continued) Parameter Category

Parameter

Distribution

Range

Other Parameters [24]

CMT Injection Friction Coefficient

Uniform

[0.5,2] [24]

Containment Initial Pressure

Uniform

[0.85,1.15] [24, 25]

Containment Outside Temperature

Uniform

[0.88,1.12]

number of calculations required by the code is independent of the number of input parameters, and all input parameters can be sampled and calculated at the same time, which greatly simplifies the process of quantitative uncertainty. Based on the pioneering work, the well-known Wilks’ formulas are expressed as follows [28]: One-side tolerance interval: β = 1 − γN Two-side tolerance interval: β = 1 − Iγ (N − 1, 2) = 1 − N (1 − γ )γ N −1 − γ N In this paper, the upper limit of the uncertainty band of the output parameter is determined according to the wilks one-side tolerance interval. To achieve 95% tolerance with 95% confidence (95%/95%), 59 sets of input variables were sampled according to the Wilks’ formula. The input sets were automatically generated by Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) [29] software of code using simple random sampling. 3.3 Uncertainty Analysis Figure 11 shows the uncertainty results of the PZR pressure. From the perspective of uncertainty bandwidth, although the accident is a doubleended break LOCA, the pressure variation is not significant. Due to the large break area, and the break position occurs in the PZR Surge Line, which is connected to the hot leg, and the temperature and pressure are higher. After the break occurs, the PZR pressure decreases rapidly, so the uncertainty band changes less. Figure 12 shows the uncertainty results of the break mass flow rate. The uncertainty of the discharge coefficient causes different break mass flow, which leads to differences in the system coolant inventory loss and depressurization rate. The uncertainty of the break mass flow, combined with the uncertainty of the system pressure, will also cause the difference in the trip time of the passive safety system. Figure 13 shows the uncertainty band of the CMT injection mass flow rate. The uncertainty of the CMT injection mass flow rate is caused by the uncertainty of the CMT loop friction coefficient. The uncertainty results of the IRWST injection mass flow

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rate is shown in Fig. 14. After the ADS4 valves are opened, the IRWST activation time is different resulting from the difference in the depressurization rate of the primary side. In this study, the upper limit of the containment pressure is considered as the key output parameter. Figure 15 shows the uncertainty results of the containment pressure. The uncertainty of the outside temperature of PCCS heat structure directly causes different heat removal from the steel containment shell, which leads to the difference of the pressure and temperature inside the containment. In all input conditions, there is a large uncertainty in the containment pressure due to the changes of various input parameters. However, the maximum is 0.379 MPa, which does not exceed the safety limit.

Fig. 11. The uncertainty results of the pressurizer pressure

Fig. 12. The uncertainty results of the break mass flow rate

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Fig. 13. The uncertainty results of the CMT mass flow rate

Fig. 14. The uncertainty results of the IRWST mass flow rate

3.4 Sensitivity Analysis Sensitivity analysis measures the influence of various uncertain input parameters on the output parameters to identify key input parameters that have important influences. It can be performed based on the data results of uncertainty analysis. In statistics, the degree of sensitivity can be measured by the correlation coefficient. In this paper, the Spearman rank correlation coefficient is selected as the sensitivity measurement. The spearman rank correlation coefficient is calculated based on the ranked values of the parameters rather than the actual data. The specific description of the coefficient is given below [30]: Spearman = rs

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Fig. 15. The uncertainty results of the containment pressure

  n  1 n 1 n i=1 Ri − n i=1 Ri Qi − n i=1 Qi = 2 n  2 n  1 n 1 n R − R i=1 i i=1 i i=1 Qi − n i=1 Qi n The correlation coefficient ranges from − 1 to + 1. Its absolute value reflects the sensitivity of the input parameter to the output parameter, and the sign indicates the positive and negative correlation. Figure 16 shows the Spearman correlation coefficients between input uncertainty parameters and containment pressure. When the absolute value of the parameter is greater than 0.2, the statistical significance of the parameter is considered. The main findings are given below: 1. Containment initial pressure: The containment initial pressure is positively correlated with the containment pressure. The input change of this parameter will directly affect the change of the maximum containment pressure. 2. Containment outside temperature: The containment outside temperature is positively correlated with the containment pressure. The temperature of the containment will affect the heat transfer conditions. The higher the temperature is, resulting in the slower the heat transfer rate and the steam condensation rate, which leads to the higher temperature and pressure of the containment.

4 Conclusions The code model of China’s Advanced PWR reactor was established with the RELAP5. Within the used model, a PSLB LOCA was simulated. The uncertainty analysis was performed, and the importance of each input parameter on the object safety parameter was evaluated by the spearman rank correlation coefficient. By comparing the code simulation results with the PSLB accident on VVER440/213, it was found that the established code model has certain reliability. The 95/95

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Fig. 16. The Spearman correlation coefficients between input uncertainty parameters and containment pressure

uncertainty bands of key output parameters were obtained, and the maximum of the upper band of containment pressure was 0.379 MPa, which did not exceed the safety limit, thus it was considered safe. According to the spearman rank correlation coefficient, the input uncertainty parameters that have a greater impact on the containment pressure were identified. The most influential variable was the containment initial pressure, which directly affected the change of the maximum containment pressure. And the containment outside temperature also had a great impact on the containment pressure.

References 1. U.S. NRC. 50.46 Acceptance criteria for emergency core cooling systems for light-water nuclear power reactors. NRC Regulations 10 (2012) 2. Lin, C., Liu, Z., Zhao, R.: Study on realistic best estimate methodology of PWR LOCA. Nucl. Saf., 1–12 (2010) 3. U.S. NRC. Best-estimate calculations of emergency core cooling system performance. Regulatory Guide 1.157, May, Washington, DC (1989) 4. Boyack, B., Catton, I., Duffey, R., et al.: Quantifying reactor safety margins part 1: an overview of the code scaling, applicability, and uncertainty evaluation methodology. Nucl. Eng Design 119(1), 1–15 (1990) 5. Zheng, M., Yan, J., Jun, S., et al.: The general design and technology innovations of CAP1400. Engineering 2(1), 97–102 (2016) 6. Schulz, T.L.: Westinghouse AP1000 advanced passive plant. Nucl. Eng. Design 236(14–16), 1547–1557 (2006)

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7. Matzie, R.A.: AP1000 will meet the challenges of near-term deployment. Nucl. Eng. Design 238(8), 1856–1862 (2008) 8. Li, Y.Q., Chang, H.J., Ye, Z.S. et al.: Analyses of ACME integral test results on CAP1400 small-break loss-of-coolant-accident transient. Progr. Nucl. Energy 88(375–397) (2016) 9. Li, Y.Q, Chang, H.J., Shi, Y. et al.: Analytical studies of long-term IRWST injection core cooling under small break LOCA in passive safety PWR. Ann. Nucl. Energy 88(218–236) (2016) 10. Qi, Z., Lu, L., Wang, W.: Preliminary analysis of Influence of uncertainty Parameters for CAP1400 LBLOCA accident. In: Proceedings of the 2017 25th International Conference on Nuclear Engineering, F. V006T08A053 (2017) 11. Zhuang, S-x., Sun, W., Jing, J-p. et al.: Break spectrum analysis and transient analyses of limited condition for CAP1400 MSLB. China Nucl. Power 12(1), 41–45 (2019) 12. Krištof, M., Kliment, T., Petruzzi, A., et al.: RELAP5 simulation of surge line break accident using combined and best estimate plus uncertainty approaches [J]. Nucl. Eng. Design 239(11), 2500–2513 (2009) 13. Zhigang, H., Yan, Z., Chuanxin, P., et al.: Experimental study on pressurizer surge line doubleended break of small reactor. Nucl. Power Eng. 42(6), 82–86 (2021) 14. Nuclear Regulatory Commission. RELAP5/MOD3 code manual: User’s guide and input requirements. Volume 2. Nuclear Regulatory Commission (1995) 15. NRC U. Relap5/Mod3. 3 Code manual volume I: Code structure, system models, and solution methods (2001) 16. Song, J.H., Bae, K.H.: Evaluation of analytically scaled models of a pressurized water reactor using the RELAP5/MOD3 computer code. Nucl. Eng Design 199(3), 215–225 (2000) 17. Jonnet, J., Stempniewicz, M., De with A., et al.: RELAP5 analysis of PKL, main steam line break test. Nucl. Eng. Design 265(755–764) (2013) 18. Yang, Z., Shan, J., Gou, J.: Preliminary assessment of a combined passive safety system for typical 3-loop PWR CPR1000. Nucl. Eng. Design 313(148–161) (2017) 19. Deng, C., Chen, L., Yang, J., et al.: Best-estimate calculation plus uncertainty analysis of SBLOCA transient for the scale-down passive test facility. Progr. Nucl. Energy 112(191–201) (2019) 20. Yang, Y., Deng, C., Yang, J.: Best estimate plus uncertainty analysis of a small-break LOCA on an advanced Generation-III pressurized water reactor. Int. J. Energy Res. 45(8), 11916–11929 (2021) 21. Zhang, S.X.: Uncertainty analysis of the effect of plant state parameter on LBLOCA in AP1000. Shanghai Jiao Tong University (2013) 22. Ni, C.: Modeling of AP1000 nuclear power plant LB-LOCA best estimate analysis and uncertainty study. Shanghai Jiao Tong University (2011) 23. Queral, C., Montero-Mayorga, J., Gonzalez-Cadelo, J., et al.: AP1000 ® large-break LOCA BEPU analysis with TRACE code. Ann. Nucl. Energy 85 (2015) 24. Chang, Y., Wang, M., Zhang, J., et al.: Best estimate plus uncertainty analysis of the China advanced large-scale PWR during LBLOCA scenarios. Int. J. Adv. Nucl. React. Design Technol. 2 (2020) 25. Perez, M., Reventos, F., Batet, L., et al.: Uncertainty and sensitivity analysis of a LBLOCA in a PWR Nuclear Power Plant: Results of the Phase V of the BEMUSE programme. Nucl. Eng. Design 241(10) (2011) 26. Wilks, S.S.: Determination of sample sizes for setting tolerance limits. Ann. Math. Stat. 12(1), 91–96 (1941) 27. Glaeser, H., Hofer, E., Kloos, M., et al.: Uncertainty and sensitivity analysis of a postexperiment calculation in thermal hydraulics. Reliab. Eng. Syst. Saf. 45(1–2), 19–33 (1994)

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28. Guba, A., Makai, M., PáL, L.: Statistical aspects of best estimate method—I. Reliab. Eng. Syst. Saf. 80(3), 217–232 (2003) 29. Adams, B., Bohnhoff, W., Dalbey, K., et al.: Dakota, a multilevel parallel object-oriented Framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: Version 6.13 User’s Manual. Sandia National Lab.(SNL-NM), Albuquerque, NM (United States) (2020) 30. Macklin, J.: An investigation of the properties of double radio sources using the Spearman partial rank correlation coefficient. Mon. Not. R. Astron. Soc. 199(4), 1119–1136 (1982)

Fault Diagnosis of Nuclear Power Plants Based on 1D-CNN with Dual Attention Mechanism Gensheng Qian(B) and Jingquan Liu Department of Engineering Physics, Tsinghua University, Haidian District, Beijing 100084, China [email protected]

Abstract. A nuclear power plant (NPP) is a large, integrated, complex, safetycritical industrial system. A small failure, such as a small loss of coolant accident (LOCA) caused by a pipe rupture, can evolve into a serious accident that threatens the safety of the plant. Thanks to the development of advanced sensor, communication and database technologies, a large amount of monitoring data is connected to the main control room in real time, providing operators with adequate information about the plant’s status. However, when a fault occurs, massive alarm information enters the main control room, which usually exceeds the operator’s analysis ability. Under great psychological pressure, it is difficult to make the right response, leading to increased risk of human error. Deep learning methods, such as convolutional neural network (CNN), are widely used in image recognition, computer vision and fault diagnosis fields. CNN achieves pattern recognition by extracting abstract features of the input data layer by layer through a deep network architecture. When facing big data scenarios, CNN may have difficulty in capturing important information, leading to long training time or non-convergence problems and low diagnostic accuracy. In this paper, we propose a fault diagnosis model for nuclear power plants based on one-dimensional CNN and dual-attention mechanism (CNN-BiAM). The parameter attention module and feature attention module are introduced to help the CNN model focus on key information and improve the feature extraction capability. Data samples of 10 types of fault conditions such as LOCA, main steam line break (MSLB) and steam generator tube rupture (SGTR), are obtained using the pressurized water reactor simulator software called PCTRAN, and the case study shows that the diagnostic accuracy of CNN-BiAM model is better than that of traditional methods, such as CNN, support vector machine (SVM) and random forest (RF) models. Keywords: Data-driven · Convolutional neural network · Attention mechanism · Fault diagnosis · Nuclear power plant

1 Introduction Nuclear power is a clean energy resource that can meet the growing demand for energy worldwide while reducing carbon emissions. However, the nuclear fission reaction generates radioactive materials, the release of which would endanger the health of occupational personnel and the public. Therefore, safety is a key issue for the production of nuclear energy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 506–514, 2023. https://doi.org/10.1007/978-981-19-8780-9_50

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A nuclear power plant (NPP) is a large, integrated and complex industrial system, whose condition monitoring relies on the real-time analysis of a large number of status parameters. Under abnormal conditions, lots of alarm messages enter the main control room, which is likely to exceed the analysis capability of the reactor operator. Under great psychological pressure, it is difficult to make the right response, which leads to increased risk of human error. A small fault, such as a small loss of coolant accident (LOCA) caused by a pipe rupture, can evolve into a severe accident if not handled in time or with the right emergency operating procedures. Hence, intelligent fault diagnosis techniques are critical to the safe operation of NPPs. Deep learning methods in artificial intelligence, such as convolutional neural network (CNN), are widely used in image recognition, computer vision and fault diagnosis fields [1, 2]. Deep learning algorithms achieve pattern recognition by extracting abstract features of the input data layer by layer through a deep network architecture. Peng et al. [3] proposed a deep belief network (DBN) model for fault diagnosis in NPPs. Their experiment results proved that DBN outperforms traditional back-propagation neural network (BPNN) and support vector machine (SVM) method. Li et al. [4] proposed a novel ensemble learning models for fault diagnosis. This method obtains fault diagnosis results by integrating diverse base models with plurality voting or weighted voting, and it fuses the advantages of multiple classifiers such as SVM, random forest (RF), k-nearest neighbor (KNN) and fully-connected neural network (FCNN). Lee et al. [5] processed NPP monitoring parameters into two-channel two-dimensional images, and then used CNN for anomaly diagnosis to achieve good accuracy and reliability. However, a real NPP system contains a large amount of sensor monitoring data. CNN model is likely to have difficulty capturing important information, leading to long training time or non-convergence problems and degraded diagnostic accuracy. The attention mechanism is a new deep learning technique inspired by the human brain’s processing mechanism for visual information. Humans usually focus on some local areas in the visual region at first and ignore other relatively unimportant parts in order to get the key information quickly. Recently, attention mechanism has been introduced into the fields of bearing fault diagnosis [6], diesel engine fault diagnosis [7], etc., but it is still in its infancy in the field of NPP fault diagnosis. Inspired by the above research, this paper proposes a fault diagnosis method for NPPs based on CNN and dual attention mechanism. The parameter attention module and feature attention module are introduced to help the CNN model focus on key information for realizing better diagnostic performance. The remaining sections of this paper are organized as follows: Sect. 2 presents the proposed fault diagnosis method. Section 3 introduces the dataset and the experiment setup. Section 4 gives the experiment results and discusses them. Section 5 concludes the research work of this paper.

2 The Proposed Method CNN was first proposed by LeCun [8] for processing structured data such as 1dimensional (1D) signals or 2D image data. CNN has four key innovations, i.e., local connections, shared weights, pooling and use of many layers [1]. Multiple convolutional kernels (also called filters) are used to extract local features of the input data. Pooling

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layer reduces the dimensionality of feature maps and merges semantically similar features by down-sampling. Usually, multiple convolutional and pooling layers are stacked to increase the depth of the network. In this paper, we introduce dual attention modules to conventional CNN model and propose a new NPP fault diagnosis model, called CNN-BiAM, as shown in Fig. 1. Attention module 1 is the parameter attention module, which processes the input parameters directly. Attention module 2 is the feature attention module, which handles the output features. The attention modules are all composed of vectors with the same dimensionality as their input data, which are dedicated to enhance the model’s attention to important information and optimize the feature extraction process by means of weighting calculation (see Eq. [1]). They participate in the whole model training by adaptively updating the attention weights values through back propagation and gradient descent algorithm.   (1) f  = f ⊗ A = f1 A1 , f2 A2 , · · · , fd Ad where f is the initial feature vector with d-dimension, f ’ is the feature vector after processing by the attention module. A is the attention vector. ⊗ denotes the element-wise product operation. 1D-CNN network is selected here, which is more suitable for processing sequential signals compared to 2D-CNN [2]. The top layer of the network is processed using a fully-connected layer and softmax function (see Eq. [2]). Other relevant details, like how to implement and train a CNN model, can be found in many publications, like reference [1]. exp(yi ) y  = N j=1 exp(yj )

(2)

where yi , yj are the output values of the i-th and j-th nodes of the output layer, respectively. yk ’ indicates the probability that the sample belongs to the k-th class. The final model diagnosis result is argmaxk yk ’.

Fig. 1. Schematic diagram of the proposed CNN-BiAM model in this paper

3 Case Study 3.1 Dataset Information In this paper, Personal Computer Transient Analyzer (PCTRAN) is used to simulate the fault data of a 3-loop 1000MWe pressurized water reactor NPP [9, 10]. The user

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interface of the simulator is shown in Fig. 2. PCTRAN can simulate many types of NPP transients and faults, and the derived data are used by scholars for NPP fault diagnosis studies [3, 4]. We simulated the data under normal and 9 fault conditions such as LOCA and MSLB, with 10 types of operating conditions in total. 250 s of 92 types operating data are collected for each condition. The sampling frequency is 1 Hz.

Fig. 2. Nuclear power plant simulator: PCTRAN

Two-step pre-processing was performed on the simulation data. First, 18 types of parameters that are invariant under fault conditions are removed, so the remaining 74 types of parameters are finally selected as model inputs. The specific selected parameter list is shown in Table A1 of our previous publication [11]. Then, to eliminate the effects of different parameters’ physical scales, min-max normalization (see Eq. [3]) is used. Finally, the dataset is built, as shown in Table 1, with each condition containing 250 samples, and each sample dimension is 74. x =

x − xmin xmax − xmin

(3)

where x’ is the pre-processed data, x is the initial data. X max and x min represent the maximum and minimum values of the parameter records, respectively. 3.2 Experiment Setup The open-source Python framework Pytorch [12] was used for the development of the network model. After extensive trying and debugging, the structure parameters of the CNN model are set as follows: Conv layer-1: 8@5 × 1 (8 filters with size 5 × 1), stride = 1 (moving step size is 1); MaxPool: 2 × 1, stride = 2 (Maximum pooling layer); Conv layer-2: 16@5 × 1, stride = 1; MaxPool: 2 × 1, stride = 2;

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G. Qian and J. Liu Table 1. Information of the fault dataset

Condition type

Abbreviation

Label

Normal

H

0

LOCA in cold leg

LOCA1

1

LOCA in hot leg

LOCA2

2

MSLB inside containment

MSLB1

3

MSLB outside containment

MSLB2

4

Steam generator tube rupture

SGTR

5

Control rod withdrawal

WT

6

Load rejection

LR

7

Turbine trip

TT

8

Loss of flow

LF

9

Flatten; FC-240 (fully-connected layer with size of 240). The widely used ReLU function, ReLU(x) = max(0, x), is used as the activation function after convolutional layers. The two attention modules are trainable vectors with dimensions 74 and 240, respectively, initialized with all values of 1. During the model evaluation process. The dataset is divided into training set, validation set and test set with division ratio of 5:2:3. The model is trained on the training set. The validation set is used to mark the model performance during training and select the best model. Finally, the model accuracy on the test set is used for comparison. The accuracy can reflect the comprehensive classification ability of the model for each type of samples and is calculated as follows: N acc =

i=1 I (pi

= yi )

N

× 100%

(4)

where I(.) is the indicator function, which return 1 if condition is valid and 0 otherwise. N is the number of samples in the test set. pi and yi are the predicted label and true label of the i-th sample in the test set, respectively.

4 Results and Discussion 4.1 Analysis of the Model Training Process During of the training process, the accuracy and loss function value of the CNN-BiAM and CNN model on the training set at the end of each epoch are recorded. Figure 3 shows the loss function curve. As the model is training, the loss function decreases and the model prediction results gradually approach the true distribution of the samples. From the 4-th training epoch until the end, the loss function value of the CNN-BiAM model is obviously smaller than that of the CNN model. This means that the prediction

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results of the CNN-BiAM model are closer to the true distribution of the training set. The CNN-BiAM model has better fitting ability than conventional CNN model. Figure 4 shows the training accuracy of the 2 models, which gradually increases as the model is training, from the initial 20% to over 95%. In most epochs, the accuracy of the CNN-BiAM model is apparently higher than that of the CNN model. This implies that the CNN-BiAM model has higher accuracy and better performance. Through the above analysis, it can be recognized that the introduced attention module significantly improves the performance of the CNN model, enabling it to better fit the distribution of samples and get more accurate diagnosis results.

Fig. 3. Loss function curve on the training set

Fig. 4. Model accuracy curve on the training set

4.2 Comparison with Baseline Models Due to the randomness of dataset division, evaluation experiments in each case were conducted 10 times in order to reduce the influence of randomness. The average accuracy of 10 experiments is taken as the final model performance index. Besides CNN,

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the classic machine learning models SVM and RF were also selected as the baseline models. For SVM model, radial basis function was chosen as the kernel function, and the regularization parameter C was taken as 50. For RF model, the number of decision trees was taken as 100, and the maximum depth of a tree was taken as 4. The comparison results of the models are shown in Table 2. All four models achieve a very good accuracy of more than 98%, and the highest accuracy of 99.61% is achieved by CNN-BiAM. It is comparable to the accuracy reported in related studies, such as Peng achieved 99.3% accuracy [3] and Li achieved 99.45% accuracy [4]. In conclusion, the proposed CNN-BiAM model can achieve the state-of-the-art accuracy. Table 2. Model average performance on the test set Model

Test Accuracy [%]

Standard deviation

CNN

98.71

0.0132

SVM

99.24

0.0016

RF

99.33

0.0042

CNN-BiAM

99.61

0.0028

4.3 Anti-noise Ability Since the dataset used is simulated data generated by the PCTRAN simulator, the data variation is smooth, while real sensor data is inevitably with ambient noise. Gaussian noise is added to the dataset to simulate the real noise environment and to increase the difficulty of model learning. The method of adding noise is shown in Eq. (5):   (5) x∗ = x + x · t, t N 0, a2 where x* is the data after adding noise, x is the initial data. t is the noise coefficient, reflecting the adding noise level. a is the standard deviation of the Gaussian distribution, taken as 0.02 ~ 0.1. The larger the value of a, the stronger the noise introduced to the dataset, the more contaminated the dataset, and the increased difficulty of model training. Figure 5 shows the test accuracy of the models under different noise levels. With the enhancement of the introduced noise, the accuracy of the models all show a downward trend, e.g., the accuracy of the CNN-BiAM model decreases from 98.48% to 92.77%, with a decrease of about 5.7 percentage points. Overall, the accuracy of the models in the order of superiority is CNN-BiAM > RF > CNN > SVM. When the noise level is greater than 8%, the accuracy of the RF model is slightly higher than that of the CNN-BiAM model. In all other cases, CNN-BiAM is the best model. The RF is an ensemble learning method. Combining the results of multiple base models (i.e., decision tree models) helps to improve the robustness of the RF model, so it is more resistant to noise than other models in many cases, which is consistent with the results of reference [13]. Overall, the noise robustness of CNN-BiAM is good. Compared with the initial CNN model, the attention module introduced significantly improves its anti-noise ability.

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Fig. 5. Model performance at different noise levels

5 Conclusions In this paper, the attention mechanism is introduced into the CNN to build the CNNBiAM model for NPP fault diagnosis. A NPP simulator software PCTRAN is used to simulate 10 types of operating data for method verification. The experiment results show that: 1) the introduced attention modules can optimize the performance of the original CNN model, improve the model learning ability and achieve better performance; 2) the accuracy of the proposed CNN-BiAM model is higher than that of the CNN model and classic SVM and RF model. It also has good anti-noise ability. Subsequent research will further promote the application of CNN-BiAM model in NPP fault diagnosis by using real datasets.

References 1. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https:// doi.org/10.1038/nature14539 2. Zhang, W., Li, C., Peng, G., Chen, Y., Zhang, Z.: A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech. Syst. Signal Process. 100, 439–453 (2018). https://doi.org/10.1016/j. ymssp.2017.06.022 3. Peng, B.-S., Xia, H., Liu, Y.-K., Yang, B., Guo, D., Zhu, S.-M.: Research on intelligent fault diagnosis method for nuclear power plant based on correlation analysis and deep belief network. Prog. Nucl. Energy 108, 419–427 (2018). https://doi.org/10.1016/j.pnucene.2018. 06.003 4. Li, J., Lin, M.: Ensemble learning with diversified base models for fault diagnosis in nuclear power plants. Ann. Nucl. Energy 158, 108265 (2021). https://doi.org/10.1016/j.anucene.2021. 108265 5. Lee, G., Lee, S.J., Lee, C.: A convolutional neural network model for abnormality diagnosis in a nuclear power plant. Appl. Soft Comput. 99, 106874 (2021). https://doi.org/10.1016/j. asoc.2020.106874

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6. Li, X., Wan, S., Liu, S., Zhang, Y., Hong, J., Wang, D.: Bearing fault diagnosis method based on attention mechanism and multilayer fusion network. ISA Trans. (2021). https://doi.org/ 10.1016/j.isatra.2021.11.020 7. Jiang, J., et al.: A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis. Sci. Rep. 12(1), 675 (2022). https://doi.org/10.1038/s41598021-04545-5 8. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791 9. Cheng, Y.-H., Shih, C., Chiang, S.-C., Weng, T.-L.: Introducing PCTRAN as an evaluation tool for nuclear power plant emergency responses. Ann. Nucl. Energy 40(1), 122–129 (2012). https://doi.org/10.1016/j.anucene.2011.10.016 10. Micro-Simulation Technology: PCTran PWR 3LP Version 6.0.1. http://www.microsimtech. com/pctran/. Accessed May 27 May 2022 (2010) 11. Qian, G., Liu, J.: Fault diagnosis based on conditional generative adversarial networks in nuclear power plants. Ann. Nucl. Energy 176, 109267 (2022). https://doi.org/10.1016/j.anu cene.2022.109267 12. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA (2019) 13. Zhong, X., Ban, H.: Crack fault diagnosis of rotating machine in nuclear power plant based on ensemble learning. Ann. Nucl. Energy 168, 108909 (2022)

Experiments of Polydisperse Aerosol Transport Progress in Horizontal and Vertical Pipes Zhichao Gao, Lili Tong, and Xuewu Cao(B) Shanghai Jiao Tong University, Minhang, Shanghai 200240, China [email protected]

Abstract. The retention of aerosol particles in pipes during severe accidents is one of the important ways to reduce fission products. To reveal the aerosol transport mechanisms in horizontal and vertical pipes, the ATRAP (Aerosol TRAnsport phenomena in Pipe) test facility has been built. The experimental pipe is an Lshaped stainless-steel pipe with four aerosol sampling points, which are arranged in different positions of horizontal and vertical pipes. Polydisperse SiO2 particles with diameters ranging from 0.3 to 17 μm are used in the experiment. Aerosol deposition velocity in the horizontal and vertical pipe are obtained by comparing aerosol concentrations at different positions. Some experiments have been carried out at Re ranging from 4500 to 10,000. Experimental results indicate that coagulation occurs in the aerosol transport progress. In addition, when τ + < 0.01, the particle deposition velocity becomes larger as the Re increases and particles diameter decreases. The calculation is performed with model for the experiment results, where two polydisperse particle size treatment methods are used. The calculated results by using the particle size classification calculation method are closer to the experimental results than using the average particle diameter calculation method. Keywords: Aerosol transport · Polydisperse aerosol · Aerosol deposition velocity · Diffusion regime

Abbreviations Cup Cdown Co Cc dp d+ D Db f g g+ J k

Aerosol concentrations at upstream sampling points (No. 1 or No. 3), P/cm3 Aerosol concentrations at downstream sampling points (No. 2 or No. 4), P/cm3 Aerosol concentration over the deposition surface, P/cm3 Cunningham slip correction factor Particle diameter, m ∗ Dimensionless particle diameter, d + = uυ dp Pipe diameter, m Brownian diffusion coefficient, cm2 /s Fanning friction factor Gravitational acceleration, 9.8m/s2 Dimensionless gravitational acceleration, g + = uυ∗3 g Particle flux to the deposition surface, P/cm2 /s Roughness scale of a surface, m

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 515–529, 2023. https://doi.org/10.1007/978-981-19-8780-9_51

516

kg k+ L L+ P Pi Ptotal Re S Sc u u∗ vd xi λ ρp μ υ τ+ τ σP σCup σCdown

Z. Gao et al.

Boltzmann constant, 1.38·10−23 J/K Dimensionless roughness of a surface The distance between two sampling points upstream and downstream, m   Dimensionless Saffman lift forces coefficients L+ = 3.08/ Sd + Particle penetration fraction The particle penetration fraction of ith interval particles The overall penetration coefficient of polydispersed aerosol particles Reynolds number Particle density to gas density ratio Particle Schmidt number Average velocity of carrier gas in pipe, cm/s Friction velocity. Particle deposition velocity, cm/s The proportion of ith interval particles in total particles Mean free path of air molecules, 0.068 μm Particle density, kg/m3 Dynamic viscosity of air, Pa·s Kinematic viscosity of air, m2 /s ∗2 Particle relaxation time, τ + = τ uυ Dimensionless particle relaxation time Measurement error of particle penetration fraction Measurement error of aerosol concentration at upstream Measurement error of aerosol concentration at downstream

1 Introduction During the containment depressurization venting process, the aerosol particles in the containment will be discharged from the containment through the exhaust pipe. The particles will be affected by the turbulence, gravity, thermal forces, or other mechanisms, deviate from the original streamline, and deposit on the inner surface of the exhaust pipe. Turbulent deposition is an important deposition mechanism of particle during this process due to the large Reynolds number of discharge gas [1]. Therefore, the study of aerosol deposition under turbulence mechanism is of great significance for assessing aerosol release during the containment depressurization venting process. Since the 1960s, a series of experimental studies of turbulence deposition mechanism had been carried out. Table 1 summarizes the existing aerosol turbulent deposition experiments in circular straight pipes [2–12]. The Re, particle diameter and pipe diameter are considered as the influencing factors of turbulent deposition. In addition, the experimental measurement method is also one of the factors affecting the experimental results. The existing experimental measurement method of particle deposition velocity can be divided into three types. The first method is to measure the aerosol concentration over the deposition surface and particle flux to the deposition surface and the deposition velocity is calculated by Eq. 1. vd =

J Co

(1)

5.4/13/25

12.7

5.33/15.7/29.3/71.4

12.7

12.7

41.8

50.8

6

6.22–7.87

102/52/26/13

Friedlander (1957) [2]

Wells (1967) [3]

Sehmel (1968/1971) [4, 5]

Farmer (1970) [6]

Liu(1974) [7]

Lee(1982) [8]

Shapiro (1993) [9]

Shimada (1993) [10]

Lee (1994) [11]

Muyshondt (1996) [12]

NR Not reported

Pipe diameter (mm)

Investigator

5.5/2.7

11.67–9.94

6

0.1

NR

1.02

1.52

4.57–15.24

0.15–0.5

0.24–0.8

Pipe length (m)

Vertical

Vertical

Horizontal

Horizontal

Vertical

Vertical

Vertical

Vertical

Vertical

Vertical

Pipe direction

Liquid

Liquid

Solid

Solid

Solid

Liquid

Liquid

Solid

Solid Liquid

Solid

Particle type

AMMD

CMD

NR

NR

Monodisperse

Monodisperse

NR

Monodisperse

Monodisperse

MMD

Expression of particle diametera

Table 1. Summary of particle deposition experiments

5–20

0.035–1.3

2.5–50

1.8–15.6

0.12–18

2.9–87

L = 50 D = 0.4–2 0.01–0.04

17

10/50

13.9–28

3–40

2–40

7.2–40

Re (103 )

100/200/400/800

1.4–21

93–262

2–25

0.65–5

3/5/30

Particle diameter (μm)

Method 2

Method 1

Method 2

Method 1

Method 3

Method 1

Method 1

Method 1

Method 1

Method 1

Measuring methods

Experiments of Polydisperse Aerosol Transport 517

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The particle flux is obtained by measuring the amount of particles deposited on the deposition surface in a certain period of time. However, in order to facilitate the measurement of particles on the deposition surface, it is usually necessary to remove the pipe. In order to reduce the loss of particles on the deposition surface during pipe disassembly and transportation, a tacky coating is usually coated on the deposition surface [2, 4, 5, 10]. However, this approach changes the wall boundary layer and affects aerosol deposition [4]. Aerosol concentrations are usually measured by sampling or by setting up a filter downstream of the experimental pipe. The second method is to measure upstream and downstream aerosol concentrations by sampling and calculate the deposition velocity by Eq. 2. This method minimizes the impact of measurements on the aerosol deposition process The third method is to use optical instruments to directly measure the deposition velocity of particles, but this method is limited by the optical measurement technology, which is difficult to effectively measure the turbulent deposition of micron and submicron particles.   Cup Du (2) ln vd = 4L Cdown Due to different particle generation mechanisms and measurement methods, the expression of particle diameter are different in existing experiments. Monodisperse particles are usually used in experiments or median diameters represent particle diameter used to analyze experimental results, which lacks analysis of particle size distribution change of polydispersed particles during deposition. Based on the above experiments, some empirical formulas had been developed, such as Liu model [7], Wood model [13], Fan and Ahmadi model [14], Muyshondt model [12], etc. Liu model [7] considers the effects of particle relaxation time and Wood model[13] considers the effects of particle relaxation time and Schmidt numbers. Fan et al. believed that more factors affected the turbulent deposition of particles. The Fan and Ahmadi model [14] also considered the influence of gravity on gas flow in vertical pipes and the influence of wall roughness. While Muyshondt model [12] is an empirical relation based on a large number of experimental data. However, the above models do not consider the influence of particle size distribution, so whether they are applicable to the calculation of turbulent deposition of multi-dispersed aerosol particles needs to be verified by experiments. The present paper describes the results of aerosol deposition experiments which has been carried out in ATRAP (Aerosol TRAnsport phenomena in Pipe) test facility. The experimental phenomenon is analyzed and the experimental results are used to verify Wood model, Fan and Ahmadi model and Muyshondt model.

2 Method 2.1 Experimental Setup The ATRAP test facility is designed to study the aerosol deposition mechanism and resuspension mechanism in the pipe during the containment depressurization venting process. The experimental pipe is an L-shaped stainless steel pipe with an inner diameter

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of 72 mm and a wall roughness of 0.4 μm. As shown in Fig. 1, the experimental pipe consists of a horizontal section and a vertical section. The horizontal section is provided with two aerosol sampling points with an interval of 1.8 m, and the vertical section with two aerosol sampling points with an interval of 2.3 m. Air has been used as aerosol carrier gas and polydisperse silicon dioxide (SiO2 ) particles have been used in these experiments. Particles are generated by the solid aerosol generator and then passed into the premix and mixed with air. Then the uniformly mixed aerosol is fed into the experimental pipe. An Electrostatic neutralizer be installed downstream of the aerosol generator. Pressure sensors, temperature sensors and flow meters are set up on the test facility, which are used to measure thermal-hydraulic parameters in the experimental process. In this paper, the measurement method 2 is used to obtain the deposition velocity of particles. The aerosol parameters are measured by the aerosol spectrometer which can measure the particle diameter and concentration of aerosol online. Each aerosol spectrometer has two probes. During the experiment, the two probes were used alternately with an interval of 60s. As shown in Fig. 1, in order to ensure the synchronization of data, an aerosol spectrometer is shared by sampling points 1 and 3, and the another aerosol spectrometer is shared by sampling points 2 and 4. 1800mm T

600mm

P

470mm

No. 1

No. 2 No. 3

Aerosol Spectrometer System

T P

Down

2300mm

Aerosol Spectrometer System

Temperature sensor Pressure sensor Aerosol sampling port No. 4

Fig. 1. Schematic diagram of experimental pipe of ATRAP

2.2 Aerosol Condition Due to its good sphericity and geometric stability, the SiO2 particles have been used to simulate fission product aerosol in this test, Fig. 2 is the scanning electron microscope (SEM) image of aerosols used in the experiment. It can be seen that most particles have good sphericity. The size distribution of solid particles used in the test is a log-normal distribution. The count median diameter (CMD) of aerosol is 0.5 μm and the GSD is 1.26. The particle size distribution used in the experiment is similar to that in containment under severe accidents [15].

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Fig. 2. SEM image of experimental aerosols

Some aerosol deposition experiments under different Re conditions have been carried out. Figures 3 and 4 show the particle size distribution at the entrance of horizontal pipe and vertical pipe. It can be seen that the particle size distribution has a great consistency under different experimental conditions. 0.16 Re=4500 Re=7500 Re=10000

No.1

Frequency

0.12

0.08

0.04

0.00 0.1

1 10 Particle diameter(µm)

100

Fig. 3. Particle size distribution at the inlet for horizontal pipe

2.3 Instrument Calibration In order to reduce the instrument measurement error, the aerosol spectrometers were calibrated in the calibration loop before the experiment. As shown in Fig. 5, the calibration loop consists of two aerosol spectrometer sensor, a filter membrane, a flow meter and a regulating valve in series. The aerosol concentration measured by the filter membrane and flow meter is used to calibrate the aerosol spectrometer. Figures 6 and 7 show the aerosol number concentration measurement results of two aerosol spectrometer sensors in series after calibration respectively. After calibration, the relative error of concentration between two spectrometer sensors is less than 10%.

3 Results Analysis and Discussion The experiments have been performed at the Reynolds numbers of 4500, 7500, 10,000. Aerosol data at the horizontal and vertical sections have been measured alternately with

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0.16 Re=4500 Re=7500 Re=10000

No.3

Frequency

0.12

0.08

0.04

0.00 0.1

1 10 Particle diameter(µm)

100

Fig. 4. Particle size distribution at the inlet for vertical pipe

No.1/No.3

Aerosol spectrometer sensor

No.2/No.4 Filter membrane Flow meter

Regulating valve Fig. 5. Aerosol spectrometer calibration loop

an interval of 60 s and a total experiment duration of 12 min. Each experiment will be repeated three times. 3.1 Influence of Flow Direction Taking the experimental group at Re = 7500 as an example, the deposition of particles in horizontal and vertical pipes is analyzed. Figure 8a, b show the changes of aerosol concentration at the two sampling points in the horizontal and vertical sections, respectively. Aerosol concentration was relatively stable during the experiment, and aerosol concentration decreased during the transport of both the horizontal and descending sections. Figure 9a, b show the changes of particle size distribution at the two sampling points in the horizontal and vertical sections, respectively. The experimental result shows the proportion of small particles decreases and the CMD increases. The CMD of particles at

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Concentration(p/cm^3)

105 No.1 No.2 104 103 102 101

0

300

600

900

1200

Time(s)

Fig. 6. Particle number concentrations measured by two probes at the horizontal pipe

Concentration(p/cm^3)

105 No.3 No.4 104 103 102 101

0

300

600

900

1200

Time(s)

Fig. 7. Particle number concentrations measured by two probes at the vertical pipe

Nos. 1–4 sampling points measured by aerosol spectrometer are 0.475, 0.588, 0.446 and 0.592, and the GSD of particles are 1.249, 1.281, 1.227 and 1.297, respectively. Because of the deposition of particles during the transport process in the pipe, the median diameter of particles becomes larger and the distribution of particles becomes more dispersed. The plots of vd+ versus τ+ have historically been often used to represent experimentally measured aerosol deposition velocities. The dimensionless deposition velocity is used to express the deposition velocity of particles. vd+ = where u∗ is the friction velocity.

 ∗

u = f =

vd u∗

f u 2

0.316 , Re < 105 4Re0.25

(3)

(4) (5)

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Concentration (P/cm^3)

105

523

(a)

104

103

102

No.1 No.2 0

200

400

600

800

Time(s)

Concentration (P/cm^3)

105

(b)

104

103

102

No.3 No.4 0

200

400

600

800

Time(s) Fig. 8. Aerosol concentration at different sampling points (a horizontal, b vertical)

The dimensionless particle relaxation time is used to express the particle diameter. According to the division method of Wood model [13], all experimental data can be divided into three regimes according to different τ + values: the diffusion regime (τ + < 0.1), the diffusion-impaction regime (0.1 < τ + < 10), and the inertia-moderated regime (τ + > 10). τ+ = τ= Cc = 1 +

τ u∗2 υ

ρp dp2 Cc 18μ

  2λ  1.257 + 0.4 exp −1.1dp /2λ dp

(6) (7) (8)

Table 2 shows the results of three repeated experiments at Re = 7500. It can be seen from the Table 2 that the repeatability of the experiment is acceptable.

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0.16

(a)

No.1 No.2

Frequency

0.12

0.08

0.04

0.00 0.1

0.16

1 10 Particle diameter(µm)

(b)

100

No.3 No.4

Frequency

0.12

0.08

0.04

0.00 0.1

1 10 Particle diameter(µm)

100

Fig. 9. Particle size distribution at different sampling points (a horizontal, b vertical)

The aerosol particle diameter is divided into 57 groups, and the dimensionless deposition velocity and dimensionless particle relaxation time of each group particle has been calculated. Figure 10 shows that aerosol dimensionless deposition velocity for different dimensionless particle relaxation time and dimensionless particle relaxation time distribution of particles. According to the dimensionless relaxation time of the particles, most of them are in the diffusion regime. The deposition of particles is mainly affected by Brownian diffusion and turbulent diffusion. The smaller the particle is, the faster the deposition rate is. For large particles, the gravity deposition mechanism is dominant. Large particles are more easily affected by the gravity deposition mechanism, and the deposition velocity increases with the increase of particle diameter in the horizontal pipe, while there is almost no particle deposition in the vertical pipe. In addition, no matter in horizontal pipe or vertical pipe, there are some particles with negative deposition velocity. This indicates that aerosol coagulation occurs during the transport process in the pipe. A similar phenomenon was found in the experiments of Barth et al. [16].

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Table 2. Repeated experiments results at Re = 7500

Concentration (P/cm3 )

vd+

Position

First

Second

Third

No. 1

6062

7987

9030

No. 2

5197

7259

8073

No. 3

8006

9796

11,097

No. 4

5126

6800

7582

Horizontal

0.023

0.015

0.018

Vertical

0.054

0.045

0.047

Fig. 10. Aerosol dimensionless deposition velocity for different dimensionless particle relaxation time

3.2 Influence of Re Figure 11 shows that particle deposition velocity with different Reynolds numbers in vertical pipes. Experimental results indicates that the particles deposition velocity does not increase linearly with the increase of Reynolds number. With the increase of Re, on the one hand, the turbulence becomes more obvious and promotes the migration of particles to the wall; on the other hand, the increase of Re leads to the increase of Stk and inertia of particles and inhibits particle deposition. Figure 12 shows that dimensionless particle deposition velocity with different particle diameters and Reynolds numbers in the vertical pipes. The dimensionless particle relaxation time is used to express the particle diameter. As can be seen from the Fig. 12, for small particle, the dimensionless particle deposition velocity becomes larger as the Re increases. This indicates that the particle deposition is affected by the diffusion intensity of airflow. When τ + < 0.01, diffusion is the main mechanism of particle deposition in diffusion region. Smaller particles are more likely to be carried to the wall by airflow and the higher the dimensionless particle deposition velocity is. When τ + > 0.01, the inertia of large particles is strong, the turbulent kinetic energy is not enough to change the original trajectory of particles, and the deposition velocity is low.

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vd(cm/s)

0.1

0.01

0.001 4500

7500 Re

10000

Fig. 11. Particle deposition velocity in vertical pipe at different Reynolds numbers

Fig. 12. Dimensionless particle deposition velocity with different particle diameters and Reynolds numbers in the vertical pipe

3.3 Model Validation Wood model [13], Fan and Ahmadi model [14] and Muyshondt model [12] are the three models with wide applicability among the existing models. Considering gravity and Brownian diffusion mechanism, the expression of deposition velocity in Wood model is: vd+ = 0.057Sc−2/3 + 4.5 × 10−4 τ +2 + τ + g +

(9)

where the Sc is particle Schmidt number. For vertical pipes, g + = 0 in Eq. 9. Sc =

3π μ2 dp μ = ρg Db ρg Cc kg T

(10)

Fan and Ahmadi model [14] considers the influence of gravity on gas flow in vertical pipe. For a horizontal pipe, g + = 0 in Eq. 11 and the gravitational sedimentation velocity

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needs to be added. ⎧⎧ ⎤1/1+τ +2 L+  ⎫ ⎡  +2 g + L+ ⎪ + 2 ⎪ ⎪ τ ⎪ d ⎪ + ⎪ ⎪ ⎪ ⎪ 0.64k + 2 + 0.01085 1+τ +2 L+ ⎪ ⎪ ⎪ ⎪ ( ) ⎪ ⎪ ⎥ ⎢ −2/3 ⎪ ⎪ ⎪ ⎪⎪ 0.084Sc + 0.5 ⎪ ⎦ ⎣ ⎪ +2 + + ⎨ ⎬ τ g L ⎪ ⎪ 3.42 + ⎨ 0.01085(1+τ +2 L+ ) + ⎪ vd = ⎪ ⎪  ⎪  ⎪ ⎪ 0.037 + −10 2 /32 ⎪ ⎪ ⎪ + − τ ⎪ ( ) ⎪ ⎪ ⎪   × 1 + 8e < 0.14 , v ⎪ ⎪ ⎪ ⎪ ⎪ d + ⎪ g ⎩ ⎭ +2 + ⎪ 1 + 1 − τ L ⎪ 0.037 ⎪ ⎪ ⎪ ⎩ 0.14, vd+ > 0.14 (11) Muyshondt model [12] is an empirical relation with a wide range of applicability. For a horizontal pipe, the gravitational sedimentation velocity needs to be added. 

vd+ = a1 e

−0.5



 Re−a2 2 a3





+ a4 e



−0.5

( )

ln τ + −ln a5 a6

2 

(12)

where a1 = 0.0226; a2 = 40300; a3 = 15330; a4 = 0.1394; a5 = 49.0; a6 = 1.136. For the theoretical calculation of polydispersed aerosols, it is necessary to consider the large difference in deposition velocity of particles with different particle diameters, and the calculation of particle deposition velocity by using CMD of polydispersed aerosols will lead to a large error. Therefore, the influence of particle size distribution on calculation results should be considered. There are two ways to consider the particle size distribution. The first method is to calculate the deposition velocity using the average diameter. dp = NMD × e0.5 ln

2 GSD

(13)

The second method is the particle size classification calculation method. The particle diameter is divided into n different intervals, and the particles in each interval account for x i of the total particle number. The aerosol penetration fraction under the particle diameter in the ith interval can be calculated by Eq. 14. Pi =

Cdown xi,down Cup xi,up

Sum the particle penetration fraction of n intervals. n Pi i=1   Ptotal = n xi,down i=1

(14)

(15)

xi,up

The particle penetration fraction of ith interval particles can be calculated by Eq. 16. Pi = e−

4Lv + u∗ d Du

(16)

Figures 13 and 14 show the comparison results of experimental and theoretical values of aerosol penetration fraction in horizontal and vertical pipes, respectively. It can be seen that compared with the average particle diameter calculation method, the calculation results using the particle size classification calculation method are closer to the experimental results.

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Fig. 13. Comparison of experimental and theoretical values of aerosol penetration fraction in a horizontal pipe

Fig. 14. Comparison of experimental and theoretical values of aerosol penetration fraction in a vertical pipe

4 Conclusion Polydispersed aerosol transport experiments in horizontal and vertical pipes have been carried out in this study. Experimental results show that coagulation occurs in the aerosol transport progress. When τ + < 0.01, diffusion is the main mechanism of particle deposition in diffusion region. The particle deposition velocity becomes larger as the turbulence intensity of carrier gas increases and particles diameter decreases. Two treatment methods of polydispersed particle size have been used to calculated aerosol penetration fraction, by using Wood model, Fan and Ahmadi model and Muyshondt model. The comparison of experimental and theoretical values shows that the calculated results by using the particle size classification calculation method are closer to the experimental results than using the average particle diameter calculation method.

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References 1. Allelein, H.J., Auvinen, A., Ball, J., et al.: State of the art report on nuclear aerosols. NEA/CSNI/R (2009)5 2. Friedlander, S.K., Johnstone, H.F.: Deposition of suspended particles from turbulent gas streams. Ind. Eng. Chem. 49(7), 1151–1156 (1957) 3. Wells, A.C., Chamberlain, A.C.: Transport of small particles to vertical surfaces. Br. J. Appl. Phys. 18(12), 1793 (1967) 4. Sehmel, G.A.: Aerosol deposition from turbulent airstreams in vertical conduits. BattelleNorthwest, Richland, Wash. Pacific Northwest Lab (1968) 5. Sehmel, G.A.: Particle deposition from turbulent air flow. J. Geophys. Res. 75(9), 1766–1781 (1970) 6. Farmer, R., Griffith, P., Rohsenow, W.M.: Liquid droplet deposition in two-phase flow (1970) 7. Liu, B.Y.H., Agarwal, J.K.: Experimental observation of aerosol deposition in turbulent flow. J. Aerosol Sci. 5(2), 145–155 (1974) 8. Lee, S.L., Durst, F.: On the motion of particles in turbulent duct flows. Int. J. Multiph. Flow 8(2), 125–146 (1982) 9. Shapiro, M., Goldenberg, M.: Deposition of glass fiber particles from turbulent air flow in a pipe. J. Aerosol Sci. 24(1), 65–87 (1993) 10. Shimada, M., Okuyama, K., Asai, M.: Depostition of submicron aerosol particles in turbulent and transitional flow. AIChE J. 39(1), 17–26 (1993) 11. Lee, K.W., Gieseke, J.A.: Deposition of particles in turbulent pipe flows. J. Aerosol Sci. 25(4), 699–709 (1994) 12. Muyshondt, A., Anand, N.K., McFarland, A.R.: Turbulent deposition of aerosol particles in large transport tubes. Aerosol Sci. Technol. 24(2), 107–116 (1996) 13. Wood, N.B.: A simple method for the calculation of turbulent deposition to smooth and rough surfaces. J. Aerosol Sci. 12(3), 275–290 (1981) 14. Fan, F.G., Ahmadi, G.: A sublayer model for turbulent deposition of particles in vertical ducts with smooth and rough surfaces. J. Aerosol Sci. 24(1), 45–64 (1993) 15. Powers, D.A., Burson, S.B.: A simplified model of aerosol removal by containment sprays. NUREG/CR-5966 Report, 83 (1993) 16. Barth, T., Lecrivain, G., Hampel, U.: Particle deposition study in a horizontal turbulent duct flow using optical microscopy and particle size spectrometry. J. Aerosol Sci. 60, 47–54 (2013)

Calculation of the Decommissioning Radiation Field of Nuclear Power Plants Based on the Coupling of MC and Point Kernel Integration Yufei Guo1(B) , Yanfang Liu1,2 , Li Wang1,2 , Shuiqing Liu1 , and Hangzhou Zhang1,2 1 Nuclear Power Institute of China, Chengdu 610005, China

[email protected] 2 Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste

Management, Chengdu 610005, China

Abstract. The 3D simulation system for the decommissioning of nuclear power plants is supposed to provide real-time display of the radiation field level of the decommissioning site, which requires its module for radiation field calculation to perform quickly and accurately. However, regions with complex geometry and regions with simple geometry coexist in the site, and a single method of radiation field calculation has its own advantages and disadvantages, which cannot meet the requirements of accuracy and calculation time simultaneously. Therefore, the coupling method of Monte Carlo and point kernel integration is proposed. Using the coupling method to calculate the radiation field of the single source model, the results show that the coupling method has reasonable accuracy and a greatly shortened calculation time compared with the single MC method. On this basis, the influence factors of the calculation accuracy and time of the coupling method are explored. Computational experiments show that the coupling method has better accuracy in the calculation of high source energy; when the mesh is fine, the calculation time of the coupling method becomes longer. In general, the coupling method is stable. Finally, the decommissioning radiation field of Qinshan I is calculated by the coupling method. The results indicate that the MC-PK coupling method has reasonable accuracy and fast calculation speed, which can cover the demands for the decommissioning radiation field calculation of nuclear power plants and provide a basis for the development of the calculation module in the simulation system. Keywords: Monte Carlo · Point Kernel integration · Coupling · Decommissioning · Radiation field

1 Introduction All calculations aim to produce accurate results fast. In the three-dimensional simulation system for the decommissioning of nuclear power plants, the radiation field level Guo Yufei (1995–), female, mainly engaged in research on radiation shielding calculation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 530–539, 2023. https://doi.org/10.1007/978-981-19-8780-9_52

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of the decommissioned site needs to be displayed in real time with the decontamination and demolition of the nuclear facilities, which requires the radiation field calculation module to calculate the radiation field level quickly and accurately. However, in the decommissioning sites of nuclear power plants, geometrically simple regions and geometrically complex regions both exist, and the three basic methods of radiation field calculation—discrete ordinate method, point kernel integration method (PK) and Monte Carlo method (MC), which have their own applicable regions and deficiencies [1–3], can not solve the space problem and then can not meet the fast and accurate requirements for calculation. Therefore, this paper proposes to couple Monte Carlo and point kernel integration to form the MC-PK coupling method, and then apply it to the calculation of decommissioning radiation field in Qinshan I.

2 The MC-KP Coupling Method The basic idea of the MC-PK coupling method is to use Monte Carlo method in geometrically complex regions, to use point kernel integration in geometrically simple regions, and to transform particle parameters on the coupling surface. Based on this, the facilities in the decommissioning site of the nuclear power plant are divided into source facilities and shielding facilities: a source facility is a device with radioactive source items; a shielding facility is a device without radioactive source items, and only has shielding effect. Every source facility is classified into a region with complex geometry, that is, a region of Monte Carlo calculation; the shielding facilities and the space outside all the facilities in the boundary are classified into a region with simple geometry, that is, a region of point kernel integration calculation; the coupling surfaces are the external surfaces of source facilities (i.e. the boundaries of MC regions), as shown in Fig. 1.

Fig. 1. Decommissioning site division of a nuclear power plant

Assuming that the MC region is a pure emitter, it can be derived from Boltzmann equation: jn+ (rs , E, ) = SA (rs , E, )

(1)

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where, jn+ (rs , E, ) is the γ flow rate at rs , which is on the coupling surface, with energy E and direction of motion , and in the positive direction of the coupling surface; SA (rs , E, ) is the strength of equivalent surface source at rs with energy E and direction of motion . It can be observed that the γ flow rate through the external surface of a source facility is a surface source term of the PK region. In the calculation process, the external surface of each source facility is meshed firstly, and then the γ flow rate through each panel is calculated by MC method. Next, a small sphere source is set for each panel, the strength of which is equal to the photon flow rate at the panel, the center of which is located at the center of the panel, and the diameter of which is equal to the shortest side length of the panel. Then the sphere source can be used as one of the input sources of PK calculation, as shown in Fig. 2. At last, the dose rate contribution of a sphere source to each point in the site space is calculated by the point kernel integration formula shown in (2), and then sums the dose rate contributions of all sphere sources to obtain the dose rate at each point in the space.  K(E, r) = F V

BS(E, r )e−μ|r−r | dV  2 4π r − r  

(2)

where, S(E, r ) is the γ strength at r with energy E; K(E, r) is the dose rate at r produced by the source; μ is the linear attenuation coefficient of the γ ray with energy E; B is the build-up factor; F is the conversion factor from γ photon flux rate to dose rate.

Fig. 2. A source on the coupling surface is converted into a sphere source (the length of the arrow in the figure is proportional to the γ energy)

In this paper, MCNP5 and QAD-CG are used as computing kernels of MC and PK respectively. And the coupling process is realized by programming in C#.

3 Single Source Calculation Model 3.1 Calculation Model Description Firstly, a calculation model of a single source is established to verify the MC-PK coupling method. As shown in Fig. 3, a cylindrical shell source made of Portland cement with energy of 0.662 meV and a strength of 1.0E6Bq is located in a large shielding room; the

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room boundary ranges − 800–1000 cm (x), − 800–800 cm (y), and – 10–290 cm (z); the center coordinate of the bottom surface of the source is (0, 0, − 10), the inner and outer radius are 130 cm, 150 cm, and the height is 200 cm; the shielding wall is located at 480–500 cm (x) and made of ordinary concrete.

Fig. 3. Single source calculation model

3.2 Analysis of Calculation Results 10 calculation points are selected in the room, and the γ energy flux rates at the calculation points are calculated by the MC-PK coupling method and MC method. The calculation results and ratios of the two methods are shown in Table 1. For the coupling calculation, the energy range is 0–1.4 meV and divided by 0.2 meV into 7 groups; the cylindrical shell source is divided into 32 grids angularly, and the axial and radial grid spacings are both 2.5 cm, that is, its axis is divided into 80 grids, and its radius is divided into 8 grids. For MC method, MCNP5 is used as the calculation software. It can be seen from Table 1 that for the selected calculation points, the results of the coupling method are 3–5 times of that of MCNP5, which agrees with the conservatism of point kernel integration in the coupling method, and is acceptable. In terms of calculation time, on a personal computer with a memory of 2 GB and a CPU whose frequency is 2.9 GHz, MCNP5 exported energy flux rates of the 10 calculation points in 4 h, and the relative errors of the calculation results on most points are less than 1%, several more than 5%; the coupling program worked out flow rates on every panel in 30 min with relative errors that are about 1.5%, and the PK calculation lasted about 40 min, reducing calculation time greatly. In summary, the MC-PK coupling method has reasonable accuracy and short calculation time, which can meet the needs of radiation field calculation in the decommissioning of nuclear power plants. 3.3 Influencing Factors of Calculation Accuracy and Time In order to facilitate the selection of grid spacings and other parameters in the engineering application of the coupling method and the evaluation of the calculation results, it

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Table 1. Result comparison of the coupling method and MC method in the single source model γ energy flux rate (MeV/(cm2 ·s))

Calculation point number

Coordinates x (cm)

y (cm)

z (cm)

Coupling method

MCNP5

Ratio

1

510

− 290

130

4.6630E−03

1.4678E−03

3.177

2

530

350

120

3.5499E−03

1.1497E−03

3.088

3

570

630

120

8.3030E−04

2.9278E−04

2.836

4

610

110

120

6.4362E−03

2.0831E−03

3.090

5

650

− 450

120

2.2223E−03

6.3462E−04

3.502

6

690

200

120

4.5223E−03

1.2367E−03

3.657

7

730

360

120

2.8517E−03

7.2289E−04

3.945

8

770

− 460

120

2.0303E−03

4.9189E−04

4.128

9

810

130

120

3.8058E−03

9.6218E−04

3.955

10

850

220

120

3.1389E−03

7.3675E−04

4.260

is necessary to explore the factors affecting the calculation accuracy and time of the coupling method and their influence characteristics. Modifying a single parameter in the single source model, including γ energy, the thickness of the shielding wall, grid numbers and the division spacing of energy groups, a series of calculation experiments were conducted. Tables 2, 3, 4 and 5 shows the ratios between the results of the coupling method and MC method. It can be drawn that the accuracy of the coupling method increases with photon energy, while the other three factors have no significant effect on it. In terms of calculation time, for two grid divisions, ➀ ➁, the coupling calculation spent ~70 min and ~230 min respectively, indicating that the finer the meshing, the longer the calculation time; but the other three factors almost have no effect on calculation time. Take together, the calculation accuracy and time of the coupling method are affected little by the parameters and have good stability.

4 Calculation of the Radiation Field of Qinshan I 4.1 Calculation Model of Qinshan I In this section, Qinshan I is taken as an example of practical application of the MCPK coupling method. The reactor of Qinshan I after unloading is simplified without considering the radioactive source items of the fuel assemblies. The simplified model is shown in Fig. 4, in which, the material of the pressure vessel (PV) head, shell and bottom head is 645-III steel, and the material of the reactor internals is 0Cr18Ni9Ti steel. The input source terms are the activation products after the overhaul in 2018, obtained from the literature [6]. From the conclusions drawn from the influence factors of the coupling method in the previous section, in this calculation, the angular grid number is set to 32, the axial and radial grid spacings are set to 2 cm, and the energy group division spacing is set to 0.2 meV, for the purpose of relatively short calculation time, and reasonable

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Table 2. Calculation accuracy of the coupling method for different photon energies in the single source model Calculation point number

The ratio of the calculation results of the coupling method and MC method 0.662 meV

0.83 meV

1.17 meV

1.33 meV

1

3.177

2.509

1.792

1.847

2

3.088

2.857

1.947

1.879

3

2.836

2.667

1.913

1.964

4

3.090

2.885

2.017

1.948

5

3.502

3.247

2.270

2.168

6

3.657

3.229

2.232

2.137

7

3.945

3.464

2.326

2.279

8

4.128

3.685

2.534

2.370

9

3.955

3.556

2.441

2.298

10

4.260

3.726

2.524

2.396

Table 3. Calculation accuracy of the coupling method for different shield thicknesses in the single source model Calculation point number

The ratio of the calculation results of the coupling method and MC method 5 cm

20 cm

40 cm

1

2.521

3.177

5.362

2

2.712

3.088

2.765

3

3.268

2.836

0.897

4

2.672

3.090

3.821

5

3.137

3.502

3.017

6

2.939

3.657

4.106

7

3.176

3.945

3.932

8

3.351

4.128

4.023

9

3.139

3.955

4.709

10

3.245

4.260

4.912

accuracy. To adapt to the fact that Qinshan I has a large number of source facilities and a great total number of panels, and to further save computer time, the MC calculation part and the PK calculation part of the coupling program are run in parallel to take full advantage of the computing threads of the CPU. This calculation was performed on a workstation with a CPU of 4.6 GHz and a memory of 64 GB.

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Table 4. Calculation accuracy of the coupling method for different grid numbers in the single source model Calculation point number

The ratio of the calculation results of the coupling method and MC method ➀ Angular grid numbers: 32 Axial grid numbers: 80 Radial grid numbers: 8

➁ Angular grid numbers: 64 Axial grid numbers: 200 Radial grid numbers: 20

1

3.177

3.178

2

3.088

3.088

3

2.836

2.837

4

3.090

3.091

5

3.502

3.503

6

3.657

3.658

7

3.945

3.946

8

4.128

4.129

9

3.955

3.956

10

4.260

4.262

Table 5. Calculation accuracy of the coupling method for different energy group spacings in the single source model Calculation point number

The ratio of the calculation results of the coupling method and MC method 0.2 meV

0.05 meV

1

3.177

2.968

2

3.088

2.877

3

2.836

2.600

4

3.090

2.913

5

3.502

3.260

6

3.657

3.443

7

3.945

3.696

8

4.128

3.855

9

3.955

3.731

10

4.260

4.014

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Fig. 4. The simplified model of Qinshan I

4.2 Analysis of Calculation Results Four calculation points are selected in Qinshan I, as shown in Fig. 4. Among them, the point No. 1 is located 1m above the highest point of the PV; the points No. 2–No. 4 are 20cm away from the outer surface of the PV, and their spacing is 150cm. The results of the coupling method and MC method and their ratios are shown in Table 6. It demonstrates that at all calculation points, the results of the two methods differ by no more than 7 times; at most of the calculation points, the results differ by no more than twice, which meets the requirements. On the aspect of calculation time, the calculation time of MCNP5 took 8 h and the relative errors are less than 3%; the coupling program completed the MC part in 1 h, and then finished the PK calculation part in about 5 min, leading to a massive saving of time. In summary, the calculation accuracy and time of the MC-PK coupling method are both satisfactory, and the method can solve the space problem in the calculation of decommissioning radiation field of nuclear power plants.

5 Conclusion Based on the requirements of computing time and accuracy of radiation field calculation in the three-dimensional simulation system for decommissioning of nuclear power plants, combined with the spatial characteristic of decommissioning sites, the MC-PK coupling method is proposed and a coupling program has been coded. First of all, the radiation field of the single source model is calculated by the coupling method and MC

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Table 6. The comparison between the results of the coupling method and MC method in Qinshan I Calculation point number

Coordinates x (cm)

y (cm)

z (cm)

Coupling method

Air-absorbed dose rate. (mSv/h) MCNP5

Ratio

1

0

0

783.4

4.3988E+02

7.3010E+01

6.02

2

206.2

0

− 150

4.1344E+03

2.7059E+03

1.53

3

206.2

0

0

6.3208E+03

6.4018E+03

0.99

4

206.2

0

150

4.1344E+03

6.5739E+03

0.63

method. The results reveal that the coupling method combines the advantages of both MC and point kernel integration, and the calculation accuracy is reasonable and the computing time is short meanwhile. Then a series of calculation experiments are carried out. It is found that the higher the γ photon energy is, the better the accuracy of the coupling method is; The larger the total number of coupling panels is, the longer the calculation time is; the thickness of the shielding wall and the quality of energy group division do not impact the accuracy and computing time of the coupling method appreciably. The conclusions above have valuable significance in the practical application of the coupling method and the coupling program. Finally, a simplified model of the reactor of Qinshan I is built, and then calculated by the coupling method and MC method respectively. It is verified that the MC-PK coupling method is adequate for the requirements of the accuracy and calculation time of radiation field calculation in decommissioning, and it can be applied to the development of the radiation field calculation module of the decommissioning simulation system and has certain meaning in engineering.

References 1. Guo, Y., Fu, M., Zhang, Z., Song, Y.: Study on the rapid calculation method of radiation field. In: Proceeding of the 2nd Nuclear Power Plant Radioactive Waste Management Symposium of China Nuclear Energy Industry Association, pp. 69–75 (2018) 2. Davision, B.: Neutron Transport Theory, 174. Oxford University Press (1957) 3. Xi, F., Fang, B.: A Monte Carlo method of the point kernel integration for gamma-rays calculation. Nucl. Power Eng. 8(2), 66–74 (1987) 4. Song, Y., Liu, H., Li, W.: Monte Carlo Simulation of Particle Detectors, pp. 4–10. Chongqing University Press, Chongqing (2016) 5. Ouyang, Y.: Design and construction of Qinshan nuclear power plant. Nucl. Power Eng. 6(6), 481–489 (1985) 6. Zhu, M., Xu, T.: Modeling Analysis and Estimation Report of Radioactive Retention. Nuclear Power Institute of China, Chengdu (2018) 7. Attix, F.H.: Introduction to Radiological Physics and Radiation Dosimetry, 41. Atomic Energy Press, Beijing 8. Yang, D., Xue, N., Cheng, H., Mao, Y.: MCNP and DORT couptation method. Sci. Technol. Rev. 31(18), 53–56 (2013) 9. Han, J., Chen, Y., Shi, S., Yuan, L., Lu, D.: Development of three-dimensional coupled code system based on discrete ordinates and Monte Carlo method. Chinese J. Nucl. Sci. Eng. 32(2), 160–164 (2012)

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10. Bi, Y., Yang, H., Guo, J., Luo, Z., Li, C., Chen, L.: Study on fast radiation transport calculation methodology in decommissioning of research reactor. Atomic Energy Sci. Technol. 49(S1), 470–474 (2015) 11. Bi, Y., Guo, J., Li, C., Luo, Z.: Development of rapid calculation software system of threedimensional radiation field. Annual report of China Institute of Atomic Energy, 190 (2017) 12. Bi, Y., Yang, H., Luo, Z., Guo, J., Li, C.: Coupling method for fast calculation of threedimensional radiation field. Annual report of China Institute of Atomic Energy, 246 (2013) 13. Zhang, Y., Hu, Y., Liu, M., Zhang, K., Dai, B., Zhang, H.: Study of nuclear reactor decommissioning simulation key technologies, pp. 64–70. Atomic Energy Press (2015) 14. Zhang, Y., Hu, Y., Liu, M., Dai, B., Zhang, H., Zhuang, Q.: Real-time computation and visualization of 3D radiation field for nuclear reactor decommissioning scene. Rad. Protect. 1(38), 19–25 (2018) 15. Luo, S., Zhang, Z., Zhang, H.: Decommissioning of nuclear facilities and radiation facilities. China Environmental Science Press, Beijing (2011) 16. Wang, J., Wang, S., Liu, K., Zhang, T.: Decommissioning of nuclear facilities. Atomic Energy Press, Beijing (2013) 17. Xiao, X.: One of the important tasks throughout the decommissioning of nuclear facilities— the characteristics survey. 65–68 (2004)

Influence Analysis of Dirt on PAS Heat Transfer Performance of ACP100 Hongliang Wang(B) , Yu Feng, Mingrui Yu, Zhuo Liu, Xu Han, and Yidan Yuan China Nuclear Power Engineering Co., Ltd, Beijing, China [email protected]

Abstract. During the operation of ACP100, the dirt accumulated on the surface of steel containment in the passage of passive containment air cooling system (PAS) has a significant impact on the normal operation of PAS after accident. In this paper, ANSYS fluent is used to simulate the ACP100 original field model to explore the influence of dirt area, location and thickness on the heat transfer performance of PAS. The results show that the PAS heat transfer power decreases with the increasing of the dirt area on the top of the steel containment. When the dirt area on the top of the steel containment accounts for 1/9, the corresponding PAS heat transfer power decreases by 9.87%, and when the top of the steel containment is completely covered with dirt, the corresponding PAS heat transfer power decreases by 22.53%. When studying the influence of dirt position on PAS heat transfer performance, it is found that the vertical section has the greater influence on PAS heat transfer, followed by the top position, and the influence of orientation angle on PAS heat transfer power can be ignored. With the increase of the dirt thickness in the upper area of the vertical section, the PAS heat transfer power tends to fluctuate steadily. It is found that the contact thermal resistance is the main reason for the decrease of PAS heat transfer power. Keywords: ACP100 · Passive containment air cooling system (PAS) · Dirt · Heat transfer power

Nomenclature Rcd Rct  A Q T λ

Thermal resistance of dirt, K/W Contact thermal resistance of dirt, K/W Thickness of dirt, m Area of dirt, m2 Heat transfer power, W Temperature, K Thermal conductivity, W/(m·K)

1 Introduction In April 2017, China National Nuclear Corporation officially announced “Linglong-1”, which is called the multi-functional modular small pressurized water reactor (ACP100) © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 540–548, 2023. https://doi.org/10.1007/978-981-19-8780-9_53

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as well. In July 2021, ACP100 was officially started construction in Changjiang, Hainan, becoming the world’s first onshore commercial modular reactor. In the meantime, the experimental research [1] and numerical simulation [2–5] of ACP100 had attracted widespread attention. PAS is an important special safety facility of ACP100. Its heat exchange capacity has great significance for the safety of nuclear power plants [6]. During the 60 years’ design life of ACP100, it is inevitable that dust will accumulate in the PAS channel. The dust adsorbed on the surface of the steel containment causes thermal resistance, which affects the heat exchange efficiency of PAS. In this paper, ANSYS fluent is used to calculate the influence of dirt area, location and thickness on the PAS heat transfer performance of ACP100, in order to provide a powerful reference for its design and optimization.

2 Computational Model ACP100 is located in Changjiang, Hainan, where the annual average ambient temperature is about 23–29 °C and the air average humidity is about 80%. Due to the sea, there is less dust and impurities in the air, so the dirt accumulated in the PAS channel mainly comes from the nuclear power plant. Moreover, concrete and stainless steel are mostly used as the main construction materials in the nuclear power plants. Therefore, this paper assumes that the main component of dirt is light-density concrete with the thermal conductivity of 0.38 w/(m·K), the density of 1200 kg/m3 , and the Specific heat of 1000 J/(kg·K) to calculate the heat exchange capacity of PAS under different dirt conditions qualitatively. PAS flow channel is similar to J-shaped pipe in structure. The influence of dirt parameters on PAS heat transfer capacity will be calculated based on PAS original model [7] in this paper, including dirt area, location and thickness. The calculation models mainly include dirt area model on the top of steel containment, dirt location model and dirt thickness model on the upper part of the vertical section. 2.1 Model of Dirt with Different Areas Divide the ellipsoidal top area of the steel containment into 18 equal parts, and a piece of dirt can be attached to each part of the area. For each piece of dirt, its center angle α is 20°, with a thickness of 5 mm, a surface area of 58.16 m2 , and a perimeter of 43.27 m. As shown in Fig. 1, it is a schematic diagram of dirt models with different areas. When studying the influence of dirt area on PAS heat transfer performance, different numbers of dirt are set on the top of steel containment by even numbers symmetrically arranging. 2.2 Model of Dirt with Different Locations The steel containment mainly includes the ellipsoidal part at the top and the cylindrical part of the vertical section, as shown in Fig. 2. The cylindrical vertical section is divided into the upper part of the vertical section and the lower part of the vertical section by the stiffener. When studying the effect of dirt location on PAS heat transfer, it is assumed that

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Fig. 1. The dirt models with different areas diagram

the dirt area projection is circular, and the dirt area of each part of the steel containment is same. The dirt area at each position is 58.68 m2 , with a perimeter of 27.16 m and a thickness of 5 mm as well.

Fig. 2. The dirt models with different locations diagram

When studying the influence of the dirt orientation angle on PAS heat transfer performance, the upper part of the vertical section is selected as the research object. As shown in Fig. 3, the dirt model of the steel containment is arranged by an interval of 60°. In addition, the dirt size parameters remain unchanged. 2.3 Model of Dirt with Different Thickness When studying the influence of dirt thickness on PAS heat transfer performance, the upper part of the vertical section with more obvious influence on PAS heat transfer power than the top ellipsoid position is selected. As shown in Fig. 4, the dirt thickness is changed to explore its influence on PAS heat transfer performance.

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Fig. 3. The dirt models with different orientation angles diagram

Fig. 4. The dirt models with different thickness diagram

3 Results and Analysis The dirt influence calculation on the PAS heat transfer performance still follows the conservative principle [5]. The ambient air temperature is assumed to be 45 °C and the steel containment outer surface is set at 130 °C under the constant wall temperature condition. The effects of external wind field, air humidity and radiation heat transfer are ignored in the calculation, and the dirt is assumed to be made of lightweight concrete. The structured grid and adaptive grid are combined to divide the grid. Through the grid sensitivity analysis, it is determined that the distribution range of the total number of grids corresponding to different dirt calculation conditions is 12–20 million. 3.1 Influence Analysis of Dirt Area on PAS Heat Transfer During the operation of ACP100, the projection of the top of the steel containment is parallel to the horizontal plane, which causes dirt more likely to accumulate. Therefore, studying the influence of dirt area on PAS heat transfer performance, the top of the steel containment is selected as the research object. The number of dirt pieces is set as 2, 4,

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6, 8, 10, 12, 14, 16 and 18. When the number of dirt pieces reaches 18, the top of the steel containment will be covered by dirt. Under different dirt area conditions, the air velocity and temperature distribution trend in PAS channel are similar. The following takes two pieces of dirt as an example to illustrate the parameter distribution in PAS channel affected by dirt under accident conditions.

Fig. 5. The PAS current velocity and temperature distribution diagram of two pieces of dirt

Compared with the calculation results of the original model [5], the difference between PAS flow rate and air temperature is mainly reflected in the dirt attachment position, as shown in Fig. 5. The driving pressure difference between the inlet and outlet of PAS is 379.2 Pa, and the average temperature of the air at the top outlet is 74.08 °C. The calculation results of PAS heat exchange power, corresponding air flow and chimney outlet air temperature under different dirt areas are shown in Fig. 6.

Fig. 6. The PAS heat transfer power changing with dirt areas diagram

With the increasing of dirt area, the PAS heat transfer power decreases gradually, the air flow decreases gradually, and the top outlet temperature decreases gradually. When

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the dirt piece number is 2, the corresponding PAS heat exchange power is 1143.57 kW, which decreases by 9.87%. When the dirt area is covered by 18 pieces, the corresponding PAS heat exchange power is 983.03 kW, which is reduced by 22.53%. During ACP100 operation, the dirt accumulation on the top of steel containment is common. However, the effect of dirt at the top of steel containment on PAS heat transfer can not be ignored. 3.2 Influence Analysis of Dirt Locations on PAS Heat Transfer The influence of dirt position on PAS heat transfer performance mainly includes the longitudinal height position and horizontal orientation angle. The ellipsoidal part at the top of the steel containment, the upper part of the vertical section and the lower part of the vertical section are selected in sequence in the longitudinal height. The horizontal orientation angle takes the upper part of the vertical section as the object, and sets the dirt with an interval of 60° in turn.

Fig. 7. The PAS heat transfer power changing with dirt positions diagram

As shown in Fig. 7, the calculation results of PAS heat transfer power at different dirt positions in the longitudinal height are as follows: with the change of dirt position in the longitudinal height, the PAS heat transfer power decreases compared with that under the condition of no dirt. The PAS heat exchange power corresponding to dirt at the top is 1139 kW, which is 10.2% less than that without dirt. The PAS heat exchange power corresponding to the dirt at the upper and lower parts of the vertical section is 1071 kW and 1078 kW respectively, which is reduced by 15.6% and 15.1% respectively. The heat exchange power of the vertical section corresponding to the dirt at each part is roughly the same while and the change trend of the top heat exchange power is the same as that of the PAS heat exchange power. The calculation in this paper adopts the assumption of constant wall temperature. In fact, under severe accident conditions, the temperature at the upper position of the steel

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Fig. 8. The PAS heat transfer power changing with dirt orientation angles diagram

containment is greater than that at the lower position. Therefore, the influence of the upper position of the vertical section on the PAS heat transfer power is greater than that of the lower position of the vertical section on the PAS heat transfer power. When studying the influence of dirt horizontal orientation angle on PAS heat transfer performance, the upper part of the vertical section is chosen as the research object with a circumferential interval of 60°. As shown in Fig. 8, the calculation results of PAS heat exchange power at different dirt positions in the horizontal direction are as follows: in the horizontal direction, the changing range of PAS heat exchange power, vertical section heat exchange power and top heat exchange power at all orientation angles is very small, which can be ignored. Although the structure of the four air inlet channels of PAS are different, the dirt that distributed at different orientation and angle positions of the vertical section of the steel containment has no significant impact on the PAS heat transfer power. Therefore, it is concluded that the effect of dirt orientation on PAS heat transfer performance can be neglected. 3.3 Influence Analysis of Dirt Thickness on PAS Heat Transfer As the steel containment is large in size, it is difficult to carry out fine surface treatment. In this paper, the surface roughness of steel containment is set to be 20 μm. In terms of manufacturing process, the 20 μm roughness surface is clearly visible tool marks, such as rough turning and rough milling. When dirt is attached to the steel containment surface, it is difficult to fully contact with the steel containment surface. The resulting thermal resistance includes contact thermal resistance in addition to the dirt itself. The contact thermal resistance is mainly affected by the surface roughness. There is a gap between the contact parts that is filled with air. In order to explore the details of steel containment surface heat transfer, the upper position of the vertical section is still taken as the research object, and the dirt thickness is selected as 0.1, 0.2, 0.3, 0.4, 0.5, 1, 2, 10 and 20 mm.

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Fig. 9. The PAS heat transfer power changing with dirt thickness diagram

As shown in Fig. 9, the PAS heat transfer power changing with dirt thickness: the heat exchange power changing range of PAS, vertical section heat exchange power, top heat exchange power and dirt surface heat exchange power under each dirt thickness is very small and can be ignored. Because the dirt curvature is small, it can be regarded as flat wall heat conduction, and the calculation formula of dirt heat conduction thermal resistance is: Rcd =

δ Aλ

Accordingly, the contact thermal resistance between dirt and steel containment wall can be calculated by the following formula: Rct =

∇t Q

Taking the dirt thickness of 0.5mm as an example, the inner surface wall temperature of dirt is 108.15 °C, and the thermal conductivity can be calculated Rcd = 2.24 × 10–5 (K/W), its contact thermal resistance Rct = 1.93 × 10–3 (K/W), Rct is about 86.2 times of Rcd . In conclusion, the reduction of PAS heat exchange power is mainly affected by the contact thermal resistance. In order to ensure the best PAS heat exchange effect in the project, the surface roughness should be reduced as much as possible to reduce the impact of contact thermal resistance.

4 Conclusion In this paper, the effects of PAS dirt area, location, thickness on PAS heat transfer performance are qualitatively calculated, and the main conclusions are as follows:

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(1) With the increasing of the dirt area on the top of the steel containment, the heat transfer power of PAS decreases gradually. When the dirt completely covers the top of the steel containment, the corresponding PAS heat transfer power decreases by 22.53%; (2) When studying the influence of dirt position on PAS heat transfer, it is found that in the longitudinal height, the vertical section has a greater influence on PAS heat transfer, followed by the top position. In the horizontal direction, the variation range of PAS heat transfer power corresponding to dirt at each orientation angle is very small, and the influence of orientation angle on PAS heat transfer power can be ignored; (3) With the increase of the dirt thickness in the upper area of the vertical section, the PAS heat transfer power tends to be stable, and the contact thermal resistance is the main reason for the decrease of PAS heat transfer power. Through the calculation and research in this paper, it is found that the influence of dirt on PAS heat transfer can not be ignored. In the future, ACP100 can be improved in the following two aspects: 1. Clean the steel containment surface regularly to reduce the adhesion of dirt; 2. Improve the surface treatment level of steel containment to make it as smooth as possible, so as to weaken the influence of contact thermal resistance on PAS heat transfer.

References 1. Wang, H., Mingrui, Y., Li, Y., et al.: Experimental study on wind load performance of ACP100 passive containment air cooling system. Nucl. Power Eng. 43(2), 175–180 (2022) 2. Sun, Y., Zheng, Y., Ma, X., et al.: Sensitivity analysis of pressure response in containment cooled by natural circulation. Nucl. Power Eng. 40(3), 66–69 (2019) 3. Gao, Y., Shen, Y., Zeng, W., et al.: Research of effects of accumulator on loss-of-coolant accident for small modular PWRs. Nucl. Power Eng. 36(3), 45–49 (2015) 4. Zeng, W., Song, D., Chen, Z., et al.: Strategy for loss of coolant accident in ACP100+. Nucl. Power Eng. 38(6), 72–75 (2017) 5. Wang, H., Mingrui, Y., Liu, Z., et al.: Influence of PAS channel structure on the heat exchange capacity of ACP100. Nucl. Sci. Eng. 41(1), 295–297 (2021) 6. Liu, J., Liu, C., Zhu, J., et al.: Design overview on passive containment cooling system of SMR. Nucl. Sci. Technol. 7(4), 123–132 (2019) 7. Wang, H., Mingrui, Y., Feng, Y., et al.: Research on the heat exchange capacity of ACP100 passive containment air cooling system. Nucl. Sci. Eng. 41(1), 271–273 (2021)

An Unsupervised Learning-Based Framework for Effective Representation Extraction of Reactor Accidents Chengyuan Li(B) , Meifu Li, and Zhifang Qiu Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China [email protected]

Abstract. Compared to knowledge-based diagnostic systems, data-based methods tend to perform better in terms of speed and accuracy in diagnosing reactor accidents, and have significant advantages in terms of scalability of models. With the increasing use of high-precision system analysis programs in nuclear engineering, the number of high-fidelity computational data for accident simulation is exploding. Therefore, an algorithm that can achieve both automatic extraction of low-dimensional features from the data and guarantee the validity of the features is needed to improve the performance and confidence of the accident diagnosis system. This study proposes an autoencoder-based autonomous learning framework, namely Padded Auto-Encoder (PAE), which is able to automatically encode accident monitoring data that has been noise-added and with partially missing data into low-dimensional feature vectors via a Vision Transformer-based encoder, and to decode the feature vectors into noise-free and complete reconstructed monitoring data. Thus, the encoder part of the framework is able to automatically infer valid representations from partially missing and noisy monitoring data that reflect the complete and noise-free original data, and the representation vectors can be used for downstream tasks for accident diagnosis or else. In this paper, LOCA of HPR1000 was used as the study object, and the PAE was trained by an unsupervised method using cases with different break locations and sizes as the dataset. The encoder part of the pre-trained PAE was subsequently used as the feature extractor for the monitoring data, and several basic statistical learning algorithms for predicting the break locations and sizes. The results of the study show that the pre-trained diagnostic model with two stages has a better performance in break location and size diagnostic capability with an improvement of 41.62% and 80.86% in the metrics respectively, compared to the diagnostic model with end-to-end model structure. Keywords: Accident diagnosis · Representation learning · Padded auto-encoder · Feature extraction · LOCA

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 549–564, 2023. https://doi.org/10.1007/978-981-19-8780-9_54

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1 Introduction The reactor accident diagnostic system plays an essential role in the accident management process, as it largely influences which accident mitigation measures should be taken by the operator to bring the reactor from abnormal condition to a safe state. Since data-based accident diagnosis systems have strong advantages in scalability and speed of inference, an increasing number of diagnostic systems are being built using deep learning (DL) techniques [1]. However, while DL-based diagnostics can automatically capture features of the raw data through deep non-linear mapping to enable an end-to-end inference approach, this black-box model is very prone to overfitting the network by capturing unnecessary features and can make unexpectedly severe inference errors for small input disturbances such as generation of adversarial signals or missing information, which can be fatal in reactor accident diagnosis can be fatal. To overcome these problems in DL-based diagnostic systems, additional means are needed to ensure the validity of the raw data features extracted by the model, and the most effective means currently available is the use of autoencoder (AE) algorithms for feature extraction. Li et al. [2] proposed a method for feature extraction and clustering of transients, using CNN as the backbone network of AE. Kim et al. [3] used Variational Auto Encoder to extract transient features and perform anonymous recognition. Naito et al. [4] proposed a two-stage AE for feature extraction of detection parameters within a time window to achieve abnormality recognition. However, problems with the method applied by previous researchers remain as follows. First, the robustness of feature extraction is hardly analyzed for different levels of noise. On the one hand, noise needs to be added as the data applied for model training is usually based on full-stack simulators or system analysis programs, which are smoother compared to real scenarios; on the other hand, the noise level of the detection parameters changes significantly under different electromagnetic noise disturbances, so noise of different levels needs to be added. Secondly, the effect of missing data on feature extraction is unconsidered. As there is a degree of probability of failure of reactor monitoring instrumentation during anomalous reactor transients, the effectiveness of feature extraction in the absence of some instrumentation data needs to be accounted for. Finally, the low-dimensional representation of operational states relies on a large scale of operational monitoring parameters and requires a model backbone network with a global view and capable of high-speed computation for feature extraction. However, previous work has focused more on the downstream tasks after feature extraction, with little discussion of the theory of feature extraction model construction. Based on the above insight, inspired by the Vision Transformer, which is entirely based on a self-attentive mechanism in machine vision tasks [5], and its good performance in the Masked Auto-Encoder task [6], a network structure named Padded Auto-Encoder for reactor monitoring parameter feature extraction is proposed, taking into account the actual needs in reactor accident diagnosis. This method first patches the monitoring parameters that have been sampled over time with noise of varying signalto-noise ratios; then randomly zeroes the data within the patches in a certain proportion; subsequently adds the sequence information of the patches that can be retained by gradient descent through parameter learning, and appends a learnable class token to the head

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of the patch sequence. In the encoder section, the sequence passes through several Transformer blocks containing multiple self-attentive layers and fully connected residuals, and then undergoes an LSTM encoding towards the head of the patch sequence, where the obtained state variables ct are connected by feedforward to get the encoded vector. In the decoder stage, the encoded vector is mapped into the patch sequence dimension through a fully connected network, and after several layers of Transformer is then de-patched and reduced to the dimension of the original input monitoring parameters. The contributions of this paper are presented as follows: 1. An idea is proposed to treat reactor monitoring data as a special two-dimensional image with time-series features and to capture the global effective features of monitoring parameters in a parallel manner using the Vision Transformer with self-attention mechanism; 2. A padded autoencoder model is proposed that not only reduces the noise of monitoring data with different signal-to-noise ratios, but also automatically completes monitoring data with up to 40% random missing; 3. For diagnostic tasks for LOCA accidents, the downstream diagnostic model utilizing statistical learning algorithms with the features extracted by the encoder from monitoring data as input outperformed several other end-to-end diagnostic models. The paper is organized as follows: we first introduce our proposed model in Sect. 2; in Sect. 3, we detail the experimental methodology and discuss the experimental results; and finally, in Sect. 4, we conclude the text and provide an outlook for future research.

2 Proposed Method This paper begins with a brief introduction to the task. Reactor accident transients are caused by an initiating event and are reflected in a number of thermophysical monitoring parameters. The task of reactor accident diagnosis is to determine the type and severity of the initiating event based on the changes in the monitored parameters. As each parameter ud is sampled as a discrete sequence of analogue signals, the monitoring data over a period of time is a two-dimensional tensor X made up of multiple vectors, and the ordering of the vector elements contains time series information. ud (t) =

∞ 

u(nT )δ(t − nT )

n=1

ud (n) = ud (nT ) ∈ Rp×1 , n ∈ {1, 2, . . . , p}   X = ud,1 ; ud,2 ; . . . ; ud,l ∈ Rp×l

(1)

where T is the sampling period; p is the number of samples; and l is the number of monitoring parameters. The tensor X contains tens of thousands of elements, so if a low-dimensional vector X → latent ∈ Rd can be used to characterize the monitoring data over this period of time and satisfy d  p × l, the low-dimensional vector can be used directly for further identification of anomalous transients or for other types of downstream tasks.

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The overall framework of our approach is shown in Fig. 1, which is an encoderdecoder type of feature extraction process. First, we add Gaussian noise with different signal-to-noise levels for each of the parameters X n = Noise(X ; snr). The parameters are then patched at the parameter level.   patchi,1 ; patchi,2 ; . . . ; patchi,m = ud,i patchi.m ∈ R(p/m)×1

(2)

where i indicates the serial number of the monitoring parameter. Each patch contains a segment of local variation of the monitoring parameters, so that the original twodimensional tensor is transformed into a sequence of word vectors with the patch size as the vector dimension. Xp =

m l  

ei ⊗ ej ⊗ patch i,j

i=1 j=1

⎡

⎤ [patch1,1 ; patch1,2 ; · · · patch1,m ] ⎢ [patch ; patch ; · · · patch ] ⎥ 2,1 2,2 2,m ⎢ ⎥ =⎢ ⎥ .. .. .. .. ⎣ ⎦ . . . . [patchl,1 ; patchl,2 ; · · · patchl,m ] N =l·m

∈ R(l·m)×(p/m) −−−−→ RN ×D D=p/m

(3)

where ei denotes the unit vector with the i-th element being 1; and ⊗ is the Kronecker product operator. To improve the robustness of feature extraction against partially missing data, the word vector sequence is masked by a randomly selected proportion, i.e. the data in that patch region is zeroed out. The word vector sequence is added with a layer of positional encoding containing learnable parameters Epos ∈ R(N +1)×D , to ensure that the sequence’s sequential information is retained. A learnable parameter of the same size as the patch xclass ∈ R1×D is added to the header of the sequence to enable the representation of the class information of the word vector sequence through multi-layer encoding.   X0 = xclass ; Xp + Epos ∈ R(N +1)×D

(4)

Subsequently, in the encoder stage, the sequence of word vectors is encoded through multiple layers of Transformers containing multi-headed self-attention, capturing remotely relevant information between patch blocks through a highly parallelised process. For each layer of Transformer passed through, the encoding is done as follows.

 Xq = MSA LN Xq−1 + Xq−1    Xq = MLP LN Xq + Xq , q ∈ {1, 2, . . . , Q} (5) where Q is the number of Transformer layers passed; MSA is the Multi-headed SelfAttention calculation; LN is the Layer Normalization calculation; and MLP is the

Fig. 1. Structural diagram of padded auto-encoder

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Multi-Layer Perceptron. The computation process of the multi-headed self-attentive mechanism is as follows.   q, k, v = X U qkv , X ∈ RN ×D , U qkv ∈ RD×3Dh    A = softmax qk / Dh SA = Av  k   MSA = ei ⊗ SAi (X ) U msa ∈ RN ×D

(6)

i=1

where Dh is the dimensionality of the word vector mapping; and U msa ∈ R(k·Dh )×D is the multi-headed self-attentive dimensionality transformation matrix. As the data after Transformer encoding is still a sequence of word vectors of the same length, a layer of LSTM is used to encode the sequence in order to compress the encoded vector sequence. ⎧ ⎪ it = σ (W ⎪

ii Xt + bii + Whi ht−1 + bhi )  ⎪ ⎪ ⎪ f = σ Wif Xt + bif + Whf ht−1 + bhf t ⎪ ⎪  ⎨ gt = tanh Wig Xt + big + Whg ht−1 + bhg t ∈ {1, 2, . . . , N } (7) ⎪ Ot = σ (Wio Xt + bio + Who ht−1 + bho ) ⎪ ⎪ ⎪ ⎪ ct = ft ct−1 + it gt ⎪ ⎪ ⎩ ht = Ot tanh(ct ) where N is the length of this vector sequence; W, b are the weights and biases to be learned; XN = xclass . The LSTM kernel moves in the direction of scanning from the tail of the sequence to the class token, due to the fact that the state variables within the LSTM retain the most information about the last input data. The state variables of the LSTM are then projected through a three-layer fully connected network to the lower dimensional features, i.e., latent = MLP(cN ). In the decoder stage, the hidden variables are first transformed directly through the fully connected network to the same dimension of the word vector sequence as in the = MLP(latent) ∈ RN ×D . At the same time, the class token in the encoder stage Xde,0 encoder stage is directly head of the initial word vector sequence in the  copied to the decoder stage Xde,0 = xclass ; Xde,0 ∈ R(N +1)×D , which can include sufficiently rich category information in the decoder’s word vector sequence. The word vector sequence is then encoded by a multi-layer Transformers. The encoded result is transformed to the original monitoring parameter dimension by an inverse patching, i.e., a two-dimensional tensor Xre is reconstructed. After a number of training episodes, if the autoencoder is able to not only reconstruct the missing data but also filter out the artificially added noise to some extent, the model can be considered to be able to learn low-dimensional hidden variables that reflect the overall characteristics of the transient from the partial trends in the monitored data and can therefore use this vector as input for downstream diagnostic tasks, such as the identification of categorical or floating-point accident labels. labelclass = classifier(latent)

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= classifier(encoder(Xn )) = regressor(latent) = regressor(encoder(Xn ))

(8)

3 Experiment and Analysis In this section, we evaluate the proposed method through the LOCA diagnostic task of the HPR1000. First, the data generation method, the evaluation metrics, and the experimental details will be briefly described; then, some benchmark models will be used for comparison; finally, the experimental results will be analyzed and discussed. 3.1 Dataset Acquisition The training data used in data-based reactor accident diagnosis models are generally generated from model-based system analysis programs or full-scale reactor simulators. The data set used in this paper is simulated by Advanced Reactor System Analysis Code, known as ARSAC, developed by the Nuclear Power Institute of China. ARSAC is based on a vapor-liquid two-phase non-uniform flow and non-equilibrium fluid dynamics model to solve heat transfer problems for vapor-liquid two-phase flows with noncondensable gases in a non-equilibrium thermal state [7]. A large number of general component models and special process models are built into the program for the construction of simple or various complex system loops. It covers the thermal-hydraulic transients and accident spectra of all nuclear power plants and has a powerful calculation capability and scope of application. Compared to other leading international system analysis software, such as CATHARE GB, WCOBRA/TRAC and S-RELAP5, the main features of ARSAC are: (1) Advanced matrix solving algorithm NRLU based on RCM (Reverse Cuthill-Mckee) chunk rearrangement technique; (2) A more refined wall heat transfer models; (3) A more refined re-inundation analysis module; (4) Advanced physical analysis module IAPWS-97. Similar to RELAP, ARSAC constructs complex systems by writing input cards that define generic components and their connections. In this paper, the system boundaries for transient processes are modified on the basis of the HPR1000 steady-state card to form a transient card for calculating LOCA. In order to obtain the transient response of the system for different break locations and break sizes in the LOCA, after 500 s of steady state calculation, a break of different sizes in the cold or hot leg is inserted as the initiating event and 38 parameters in one loop are obtained as monitoring objects according to the instrumentation inspection system catalogue and the parameters are sampled at a frequency of twice per second for 100 s. A total of 346 LOCA accident transients were obtained, ranging from small breaks of 0.1 cm to large breaks of 35.5 cm in size, and including cold and hot leg breaks. 3.2 Optimization Objectives and Evaluation Metrics The objectives are divided into optimization objectives for the autoencoder part and optimization objectives for the application of hidden variables to downstream diagnostic tasks.

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In the autoencoder part, the most critical task is the ability to obtain an effective representation of transients based on the ability to fill in missing data and reduce noise. In this section, the degree of suppression of noise and the ability of the model to fill in missing data based on different ratios of noise and missing data will be evaluated. In order for the encoder-derived hidden variables to characterize the anomalous transients, it is necessary to make the reconstructed data as close as possible to the original, noisefree, non-missing monitoring data. The MSE is used here as the optimization objective for the network parameters, and is expressed as follows 2

(x, y) = {l1 , . . . , lN } , ln = Xn − Xre,n arg min (x, y; θPAE ) θPAE

(9)

where N is the batch size; X is the original monitoring data without noise; Xre is the reconstructed monitoring data; θPAE is the parameters to be optimized in Padded AutoEncoder. In addition, in order to understand the substitution capacity of the hidden variables compared to the original data, the relative spatial distribution of the samples will be visualized in low-dimensions using a manifold learning method based on t-SNE. t-SNE is an outstanding method of dimensionality reduction for non-linear mapping of high-dimensional data to a low-dimensional space, implemented in two steps [8]. In the first step, t-SNE constructs a probability distribution over the high-dimensional data such that if two data points are more similar, they have a higher joint probability. In the second step, t-SNE constructs a probability distribution in the low-dimensional space such that the probability distribution in the low-dimensional space has a low KL divergence from the probability distribution in the high-dimensional space. As a result, the distribution of data in the lower dimensional space reflects the distribution of data in the higher dimensional space in a good way. In the downstream diagnosis task, optimization is targeted at classifiers and regressors that use low-dimensional features for diagnosis. The goal of the classifier and regressor is to better (1) determine the location of the break and (2) calculate the size of the break in the LOCA transient, respectively. Since the location of the break is a categorial label, as for an MLP classifier, a cross-entropy loss function is used as the optimization objective cl (x, y) = sum{l1 , . . . , lN },

 exp xn,c  yn,c  ln = − log C exp x n,i c=1 i=1 C 

(10)

where C is the number of categories, and C = 2 in the identification of hot and cold leg breaks; xn,c is the predicted logical value for category c; yn is the one-hot code vector of data category labels; and N is the batch-size. The size of the break is a numerical variable, so the mean square error is used as the objective loss function for the optimization re (x, y) = sum{l1 , . . . , lN },

ln = (xn − yn )2

(11)

where xn is the size of the break predicted by the regressor; yn is the actual size of the break as a data label; and N is the batch size. Thus, the joint loss function as the

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optimization objective is  = αcl + (1 − α)re

(12)

where α is the attentional weight of the loss function, chosen α = 0.5 here because the break location and the size of the break are almost equally important in diagnosis. In terms of metrics for diagnostic task, for the classification problem of determining the location of the break, we will use the Mcro-F1 of the classification result as the evaluation metrics; for the regression problem of predicting the size of the break, we use the Root Mean Squared Error (RMSE) as the evaluation metrics. The evaluation metrics are calculated as Macro − F1 =  RMSE =

C 1  F1c C

(13)

c=1

T t=1

yˆ t − yt T

2 (14)

where F1c is the F1 value for category c; C is the number of categories; yˆ t is the predicted break size; yt is the real break size label; and T is the number of samples in the test dataset. 3.3 Experiment Conditions During the construction of the autoencoder, since there are a total of 200 sampling points for each parameter, the size of each patch block is set to 40, and the length of the patched word vector sequence is N = 190, and the word vector dimension is D = 40. The dimensionality of the transient reactor feature vector after encoding is latent ∈ R128×1 . The number of Transformer layers in the encoder and decoder parts are depthenc = depthdec = 4. The number of heads in the encoder and decoder parts of the multi-headed self-attention is headsenc = headsdec = 4. Dimensionality reduction ratio of intermediate hidden layers of feedforward neural networks is ratiomlp = 0.8. The dropout ratio for the fully connected layer is set to 0.1 to improve the overfitting of the network as a means of regularization. For autoencoder and downstream diagnostic models built using deep learning models, the optimization method used is gradient descent, and the Adam algorithm with the addition of Nesterov Momentum is selected as the optimization tool [9]. It has a more flexible dynamic learning rate, which can improve the problem of falling into local minima in the process of parameter optimization. For the selection of the optimizer parameters, the initial learning rate is set to lr = 1.0 × 10−3 , the coefficients used to calculate the running average of the gradient and its square are β1 = 0.9 and β2 = 0.999, the smoothing coefficient is  = 1.0 × 10−8 , and the momentum decay coefficient is 4.0 × 10−3 . In each training epoch, for the same raw monitoring data, the autoencoder needs to learn separately for interference data with different signal-to-noise ratios and missing ratios. In the setting of the learning method, the autoencoder first learns the interference

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level in order from high to low, and then repeats the learning for the interference level of focus. The benefits of this training method are: (1) the autoencoder first learns for data with high interference levels during which the parameters are updated faster due to larger reconstruction errors; (2) when reconstructing data with low interference levels, the parameters can be updated more gently due to smaller reconstruction errors; and 3) finally, when training for specific interference levels, the parameters can be optimized more purposefully for the actual scenarios in which they may be applied. In the work of this paper, within each iteration step, the noise signal-to-noise ratio is added in the order of [20.0, 30.0, 40.0, 35.0, 35.0] and the data masking is added in the order of [0.40, 0.25, 0.10, 0.20, 0.20]. During the training process, the convergence is slower and the loss function fluctuates locally due to the stochastic masking process. The 1000th iteration is set as the time to stop training. The GPU device used for training was a single GT730 with 2GB of video memory. The total training time was 154.6 h (Fig. 2).

Fig. 2. Loss function descent during training autoencoder

3.4 Auto-Encoder Performance In order to demonstrate that the pre-trained model is able to effectively restore noisy and missing monitoring data information, we selected parameters that are vital to the monitoring for visualization. Hence, pressurizer pressure, pressure vessel water level and hot leg water level are used as example. As shown in the Figs. 3, 4 and 5, although noise is added to the monitoring data and some of the data are missing, the pre-trained autoencoder is able to fill in the missing parts of the monitoring data and suppress the noise to a certain extent. To ensure that effective features can be extracted using the encoder section of the autoencoder and can replace the original monitoring data, the spatial distribution of the high-dimensional data is mapped to two dimensions by the t-SNE manifold learning algorithm, which observes

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Fig. 3. Pressurizer pressure (SNR of 35, padding ratio of 0.2)

Fig. 4. PV water level (SNR of 35, padding ratio of 0.2)

the distribution of the three datasets. The three datasets are (1) the original noiseless and non-missing monitoring data, as shown in Fig. 6; (2) the noisy and missing monitoring data, as shown in Fig. 7; and (3) the features extracted from the noisy and missing monitoring data, as shown in Fig. 8. Since the visualization of high-dimensional data using t-SNE manifold learning suffers from the randomness of the learning process, attention is mainly focused on the relative positions of individual data points rather than their absolute positions in the graph. It can be seen that the distribution of the data in the high-dimensional space is already a chaotic mess after noise addition and partial padding by zeros. But after

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Fig. 5. Hot leg water level (SNR of 35, padding ratio of 0.2)

Fig. 6. Distribution of original noiseless and non-missing monitoring data

extracting features from it using the encoder of Padded Auto-Encoder, the data features reappear as clusters and the relative distribution between data points has similar characteristics to the smooth and complete data. 3.5 Diagnosis Performance The goal of this paper is to form input data that is useful for downstream diagnostic tasks by extracting valid features from monitoring data, so two types of diagnostic methods are compared: (1) the low-dimensional representations extracted from monitoring data containing noise and missing data are first used as input data, and then the diagnosis of break location and break size is performed, as shown in Fig. 9a; (2) the original monitoring data subjected to noise addition and partial missing are directly used as input

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Fig. 7. Distribution of noisy and missing monitoring data

Fig. 8. Distribution of extracted features

to the diagnosis model, as shown in Fig. 9b. In the first class of methods, the performance of different classifiers and regressors will be compared, including SVM [10], Random Forest [11], XGBoost [12] and MLP. In the second type of methods, the previously proposed TRES-CNN method by the author, as well as the end-to-end models proposed by previous researchers, will be used as classifiers and regressors, and the diagnostic performance will be compared with the performance of the first type of methods. The diagnostic experiment uses monitoring data containing noise with a ratio of 0.1 missing and SNR of 35. If a multi-layer perceptron containing three hidden layers and one output layer is used as the classifier and regressor after feature extraction, and Eq. (12) is used as the loss function for optimization, then after 256 iterations, the precision of cold leg breakage diagnosis is 90.5%, and the precision of hot leg breakage is 93.4%,

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Fig. 9. Differentiated diagnostic framework

with Macro − F1 = 0.919 and RMSE = 0.322. The experimental results compared to other diagnostic methods are shown in Table 1. Table 1. Diagnosis performance of different methods Type

Method

Cold leg precision

Hot leg precision

Macro-F1

RMSE

Upstream and downstream

MLP

0.905

0.934

0.919

0.312

SVC/SVR

0.898

0.917

0.908

0.649

End to end

XGBoost

0.911

0.946

0.927

0.564

Random Forest

0.858

0.871

0.864

0.982

TRES-CNN

0.682

0.646

0.662

2.945

BPNN [13]

0.612

0.588

0.598

3.623

CNN [14]

0.641

0.675

0.656

3.259

As the data in the Table 1 shows, the diagnosis method by feature extraction followed by downstream diagnosis has a significant improvement compared to the direct endto-end learning method. In particular, the use of end-to-end diagnostic models shows significant distortion in the case of interference from noise and missing data. At the same time, in since the dimensionality of the extracted effective features is already low enough, the use of classical statistical learning models, such as SVM and XGBoost, is also able to achieve similar or better performance than connectionist-centered deep learning algorithms. This means that with a suitable pre-trained feature extraction model,

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it is possible to build effective and robust accident diagnosis models for noisy and missing data at the user side at a lower cost.

4 Conclusion In this paper, a Padded Auto-Encoder model is proposed first. The autoencoder not only learns the effective low-dimensional features of the model during the compression and reduction of the data, but also complements the missing data and reduces the noise level. Then, a method is proposed to perform diagnosis using the encoder part of the pre-trained autoencoder. This method first uses the encoder to extract the effective lowdimensional features of the original data disturbed by noise and missing data, and then uses a simple learning algorithm to classify and regress the features. This two-step approach to diagnosis has significant benefits over direct end-to-end diagnosis methods, receiving an improvement of 41.62 and 80.86% in the classification and regression task metrics respectively, in that the extracted features are more robust to the diagnosis of disturbed data, and the training cost of downstream classifiers and regressors is reduced.

References 1. Zhao, X., et al.: Prognostics and health management in nuclear power plants: an updated method-centric review with special focus on data-driven methods. Front. Energy Res. 9, 294 (2021) 2. Li, X., Fu, X.-M., Xiong, F.-R., Bai, X.-M.: Deep learning-based unsupervised representation clustering methodology for automatic nuclear reactor operating transient identification. Knowl.-Based Syst. 204, 106178 (2020) 3. Kim, H., Arigi, A.M., Kim, J.: Development of a diagnostic algorithm for abnormal situations using long short-term memory and variational autoencoder. Ann. Nucl. Energy 153, 108077 (2021) 4. Naito, S., Taguchi, Y., Kato, Y., Nakata, K., Miyake, R., Nagura, I., Tominaga, S., Aoki, T.: Anomaly sign detection by monitoring thousands of process values using a two-stage autoencoder. Mech. Eng. J. 8(4), 20-00534 (2021) 5. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16 x 16 words: transformers for image recognition at scale. arXiv:2010.11929, arXiv (2021) 6. He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. arXiv:2111.06377 [cs] (2021) 7. Zhou, J., Wu, D., Ding, S., Jiang, G.: Research on analysis and modeling methods for LBLOCA in nuclear power plants based on autonomous LOCA analysis platform ARSAC (2020) 8. Van der Maaten, L., Hinton, G.: Visualizing data using T-SNE. J. Mach. Learn. Res. 9(11) (2008) 9. Dozat, T.: Incorporating Nesterov Momentum into Adam (2016) 10. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995) 11. Breiman, L.: Random Forests. Mach. Learn. 45(1), 5–32 (2001) 12. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 785–794. Association for Computing Machinery, New York, NY, USA

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13. Basu, A., Bartlett, E.B.: Detecting faults in a nuclear power plant by using dynamic node architecture artificial neural networks. Nucl. Sci. Eng. 116(4), 313–325 (1994) 14. Lee, G., Lee, S.J., Lee, C.: A convolutional neural network model for abnormality diagnosis in a nuclear power plant. 99, 106874 (2021)

TAC-1 Project Quality Management Research Based on Configuration Management Xu Hanjie(B) , Dong Cuicai, and Han Wei China Nuclear Power Engineering Co., Ltd., Beijing, China {xuhja,dongcc}@eu.cnpe.cc

Abstract. Introduced in 1950s, Configuration Management (CM) is more and more applied in large and complex system projects, organizations including NASA, CERN, Airbus and Crossrail have established relevant rules and methods to control quality by using configuration management. ITER project, as the first and the largest experimental fusion nuclear reactor in the world, consists of a wide range of complex systems and interfaces which needs to be controlled over the lifetime of the project. The international cooperation mechanism of ITER makes quality management even more difficult. In order to control the changes in this project, the ITER Organization (IO) is using CM in Quality Management (QM). As the supplier of ITER TAC-1, CNPE C is trying to embrace the CM to well control agile changes and keep pace with the ITER project baseline. This paper studies the improvement process of the configuration management system of CNPE Consortium (CNPE C) in ITER TAC-1 project, and verifies the important role of configuration management in project quality management. Through the comparison of documents, progress and construction quality data in different stages of the project, this paper compares and analyzes the effects of different configuration management implementation methods, such as on-site change application system, non-conformance report system, early warning system and so on. Compared with the guidance and requirements of ISO1007 and ITER organization, this paper tries to put forward a comprehensive scheme of configuration management which meets the requirements of all stake holders, and also provides a reference and basis for the sound development of other oversea projects. Keywords: ITER TAC-1 · Configuration management · Quality management

1 Introduction Quality management is very comprehensive and practical. It applies the achievements and methods of management, technology, mathematics and other disciplines. With the rapid development of industrialization, based on quality inspection and statistical control, configuration management came out in the United States in the 1950s. After continuous development and improvement, it has been increasingly integrated into the quality management system of large and complex projects. Including NASA, CERN, Airbus, Crossrail and other organizations or companies, this management approach has since been widely applied around the world. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 565–573, 2023. https://doi.org/10.1007/978-981-19-8780-9_55

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According to “Quality Management—Guidelines for Configuration Management (ISO10007:2017)”, China has issued its equivalent version GB/T 19017–2020, which has been implemented on June 1, 2021, aiming to enhance the understanding of configuration management, and help organizations for application to improve their performance. Configuration management is the process of identifying and documenting the characteristics of the overall process, systems and links, ensuring that changes in these characteristics are properly developed, evaluated, approved, published, implemented, validated and documented. Utilizing inspection and monitoring, configuration change information is collected and analyzed which served to predict and find the functional failure as early as possible. Hence the overall process could be kept in a good configuration status with adjustment in time, and eventually, get the specified functional and physical features. To reach the milestone of the first plasma discharge in 2025, ITER has established a configuration management system to record all functional features during the project process, maintaining the consistency between the requirements of the research project and the actual situation, and protecting the deliverables from unauthorized changes. ITER has adopted the Product Lifetime Management (PLM) system as the configuration management system since 2015, using a single platform to manage engineering data, documents and drawings. This implementation is evaluating proposed changes, tracking change status, and maintaining system inventories and supporting documents (for example, engineering dossiers). After years of design and development, the Tokamak Installation phase officially began in the second half of 2019, due to the different maturity of various systems and the procurement progress of components, changes are always happening. CNPE C, as the supplier of The Tokamak TAC-1 contract, follows the ITER project management and applies the configuration management approach to improve the quality and efficiency of TAC-1 project management. This paper will refer to the management guide GB/T 19017-2020 (ISO10007:2017) and adapt requirements for ITER TAC-1 project from aspects of configuration identification, baseline, statistics, control and audit, to study and analyze the improvement effect of configuration management on ITER TAC-1 quality management.

2 TAC-1 Project Configuration Identification and Baseline Configuration Identification (CI) is the foundation of configuration activities and the basic unit that forms the baseline of recording, control and management. CI varies widely in projects’ complexity, size or type. The ITER organization has established 4-level baselines (level 0–3) for the technology. Level 0 technical baselines consist of ITER Project Specifications (PS) and documents from ASN. The Level 0 baseline serves as the control baseline for the ITER Council and the nuclear regulatory authority. The Level 1 technical baseline consists of the ITER project requirements relating to nuclear safety and the ITER research program and is the control baseline for the General Director of ITER. Level 2 technical baseline controls the integration and unification of different systems, structures and components and is the control baseline for the Deputy General Director of ITER. Level 3 technical baseline describes and controls the specific system of the ITER device, and is the baseline for the functional realization of the

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ITER device. According to the on-site situation, CNPE C will add two additional levels based on the 4-level baselines of ITER organization as the control baselines for TAC1 project. Following ITER baseline number, the Level 4 baseline is the installation package (CWP). Each CWP contains compulsory installation information about one or more tokamak components and their attached tools, which are provided by IO owner as the input for the realization of the project, including the general technical description, relevant drawings, technical specifications, three-dimensional model of equipment, etc., and is the baseline for the project department to control the technical state. The Level 5 is the work breakdown structure (WBS). Through the control of WBS, the implementation of each CWP realized the configuration control of small processes ensuring that the installation process is strictly under the technical requirements, which is the control baseline of the CWP leader. For different WBS, CNPE C identified the following types of construction documents and compiled them as the construction control baseline, as shown in Table 1. Table 1. Documents type and configuration identification The file type

Technical Lifting Components Installation ITP Welding Schedules specifications plan drawings procedure book

File DDN identification

UMH

WED

UAS

PTI UWL

ZDS

3 Configuration Status Statistics 3.1 Physical Configuration Status For the physical configuration status of the real object, according to the characteristics of the installation project, CNPE C applied special tools and machine addition table (PSR) and Preservation Plan for statistics and tracking. PSR is a comprehensive list of information status from multiple departments. According to the construction requirements of TAC-1, the demand for special tools and custom machining is massive. The procurement information of various tools and machining, such as the person in charge of corresponding installation packages and contract information are tracked separately by the procurement team, contract department and control department through the list. Thus the configuration status of a tool is recorded from launching demand till arriving at the site (Fig. 1). The Preservation Plan is applied for the protection and inspection of finished and semi-finished products in the construction phase of the TAC-1 project contract. CNPE C issued relevant procedures to control its physical configuration status. Preservation Plan also involved multiple departments, stages and information status: the CWP department is responsible for the preparation of process documents, procurement and other relevant document status records; the engineering department is responsible for on-site maintenance and recording; the quality department is responsible for quality supervision and

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Fig. 1. ITER level 0–3 baselines and TAC-1 level 4–5 baselines

completion of the MCD documents according to the protection records; the owner’s representative (IO/CMA) is responsible for guiding and supervising the occurrence of non-conformance. Preservation Plan shall be divided into detailed requirements to cope with general or emergencies under different processes, such as infrastructure protection measures, equipment storage protection measures, etc. Preservation Plan weekly meeting is hosted by the engineering department, involved departments and IO counterparts are also invited, the preservation situation is reported and corresponding measures are discussed. Thus, the configuration status is tracked and managed from micro to macro scale, from internal to external perspectives. 3.2 Information Management System The statistics of configuration status are also managed by the information system. In large projects, a digital information platform is implemented as common practice for the submission of applicable documents, EoMR and supplier-owner communication system. ITER requires suppliers to use SmartPlant system to manage installation, construction and commissioning. As an EAP system, this platform not only manages workflow, provides data and synchronizes information, but also performs important functions of controlling equipment and maintenance, coordinating contractors, and managing changes. To prevent unauthorized use of materials on site, for example one chemical product required for a CWP that has not been reviewed by the HSE system, the material should not be used on site, even if requested by the owner. Through this system, the technical documentation, materials, equipment, and activities configuration status for a CWP are determined. The configuration baseline is established. In the TAC-1 project, CNPE C applied EDMS, a Level 4 baseline configuration management platform. The technical documents to be published will be reviewed online in the EDMS system. Once the document is uploaded, it will be assigned to the corresponding logical point; by searching configuration identification, it can be linked to the corresponding information storage

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place and assigned to the corresponding reviewer. Any change in the configuration of the document needs to be modified in the EDMS system, and the relevant process will be automatically recorded. The new version of the technical document will cover the original logical point information, and the original information will be automatically archived.

4 Configuration Control For TAC-1, configuration changes were primarily caused by the Level 5 baseline change described above. Mechanism of Early Warning (EW), Field Change Request (FCR) and Non-conformity Report (NCR) is applied to control the upgrade of relevant technical documents. 4.1 EW Mechanism and Process During contract execution of TAC-1, on-site changes are triggered in the following ways: oral instructions from TRO (Owner’s technical director); defect of owners’ goods or absence of relevant supporting documents; loss made by the owner on the materials which have been handed over; early arrival of materials provided by owner than the planned date; supplier delivery delay; delay in owner’s issued drawings and process documents; delay or block of construction caused by other suppliers or other partners of the owner. In these complex and changing situations, the EW alert mechanism is a configuration management method for TAC-1 as a non-fault party. The EW is usually initiated by the CWP leader and coordinated by the contract department and the control department. By analyzing the above-described situation in terms of quality, scope, frequency and cost, the contract department finally sends the EW to inform the owner of the impact on technology, cost and construction period in advance. Internally, the control department will adjust the personnel, material allocation and construction schedule of relevant CWP to make the configuration conform to the actual situation on site, improving the installation quality and reducing the occurrence of non-conformance items, reducing or avoiding the occurrence of work suspension, and controlling the actual expenditure. The baseline management of TAC-1 project is adjusted. Generally, adjustment for one CWP affects at least two CWP installation packages, the subsequent one and the one in parallel. So far, the project has sent more than 370 EW letters. The application of EW mechanism can communicate in time with the owner in simple fact-based English within 1–2 working days, by taking initiative to acquire the reply, the extra labor hours for additional construction are justified. 4.2 FCR Mechanism and Process When changes have been triggered, FCR is used to schedule changes during the onsite installation. CNPE C classified changes into two categories: major changes and minor changes. Major changes involve changes in regulatory requirements (regulations, standards), nuclear safety, environmental impact, operation and maintenance, geological

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and geographic impact, and project functional performance, while other changes are minor changes. TAC-1 project is in the assembly stage, and the changes involved are minor changes. FCR process is based on the TAC-1 on-site changes characteristics including identifying and analyzing project changes. After approval by the owner, modification of technical baselines and upgrading of the engineering documents related to FCR requirements will be launched. By evaluating the changes in construction technology, process and schedule, adaptive modifications will be applied to the corresponding documents. During implementation, the FCR involved parts are recorded and inspected separately to ensure the correct implementation of FCR. 4.3 NCR Mechanism and Process Nonconformance refers to a product or process that does not meet technical baseline requirements. The nature of international cooperation of ITER leads to a large number of project interfaces, such as interfaces between owner IO and DA offices (participation countries’ offices), and the interface between DA and suppliers, as well as the differences in technical level and relevant requirements of various countries, resulting in a large possibility of non-conformance items of ITER products. In order to effectively deal with nonconformities, NC in TAC-1 is divided into two categories: major nonconformities and minor nonconformities. Nonconformities related to nuclear safety, regulations, policies, functional impact and other aspects shall be considered as major nonconformities, while other categories shall be considered as minor nonconformities. The related management process of NCR is divided into two stages. The first stage is NCR initiation stage: after nonconformance is found, the installation shall be stopped immediately, and the nonconformance shall be registered and the treatment process initiated; at the same time, the evaluation of non-conformance items is carried out, performing a preliminary root cause analysis and providing corrective action suggestions and got approved by the owner. The second stage is the closing stage of NCR: after completing the detailed root cause analysis, upgrading the configuration of the corresponding nonconformance items and completing corrective measures one by one, the closing process of NCR can be carried out with the approval of the owner. The processing results in this stage may include: use as is, correction, etc. The impact on the configuration status shall be assessed according to the final processing results. Timely through FCR and other modifications of the corresponding physical and document status.

5 Configuration Review Configuration review of TAC-1 project mainly involves functional and physical configuration review. For the functional configuration, the review basis is the approval rate of each configuration file and FCR/NCR closing rate. Approval of the configuration file indicates that the documentary preparation meets the performance and functionality specified in the configuration baseline. Concluded from the above chart, the approval rate of IO owners for configuration documents is gradually rising, reaching about 75% by the first half of 2022, and the closing rate of FCR/NCR is maintained at about 80%, indicating that the current functional

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and configuration of TAC-1 project basically meets the requirements of owners. For the examination of the physical and configuration of the real object, CNPE C provides inspection reports, such as metrology and testing, for the examination of the physical configuration of the completed work. Through a variety of forms of a comprehensive inspection, the physical configuration is reviewed and audited, and the approval of the inspection report means the physical configuration met the requirements of the project. It can be seen from Table 2 that the physical configuration of TAC-1 project basically meets the project requirements (Figs. 2 and 3). Table 2. Report status Review the types

Inspection report

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6 Conclusions and Recommendations It can be seen from the statistical results in the above sections that the approval rate of configuration documents by owners and the closing rate of NCR/FCR have increased significantly since 2021. By using root cause analysis and returned experience to correct and adjust the configuration management method, the project is making continuous improvements. CNPE C is the first one to undertake the construction of an international nuclear large scientific project. It lacks relevant experience and technical preparations, and needs to learn and adapt to European laws and regulations, construction habits and culture shock. After nearly a year of running-in and adjustment, the cooperation between the consortium and the owner management has been significantly improved.

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Fig. 3. FCR/NCR status statistics

At the same time, the configuration management of the TAC-1 project still has challenges, such as the long FCR\NCR shutdown cycle on site. This long cycle of working flows may cause tracking fatigue and delay, affecting the site progress and completion price settlement. We suggest that for such change tracking, the main configuration parameters should be adjusted, and the change classification status, management interface status, processing cycle status, file flow status and other parameters should be considered comprehensively to adjust the problem handling strategy in time. If the information of the management interfaces personnel changes, the status should be updated immediately and the new counterpart should be connected in time to avoid the failure of the tracking chain for solving problems. If the status of the processing cycle is stagnant, reminders through emails or phone calls are strongly recommended to urge the problem to be solved. The corresponding measures need to be synchronized in the EDMS system, with clear rights and responsibilities for transparency and traceability. Configuration management is a part of quality management. By selecting the appropriate technology according to the project characteristic configuration parameters, using statistical techniques with the right tools and information system, adjusting technical parameters of project baselines, recording and archiving during the whole project cycle and preventing unauthorized changes and reviewing the actual state of technology constantly can help an organization manage the project correctly and help the owner to reach the project milestone. As a supplier of the international project, we need to follow the owner’s management method to facilitate the communication, and to protect our interests, meanwhile we need to customize the approach based on the characteristic of the organization to adapt to the applicability. Using a method without adaptation will increase the cost of management, only operational and practical methods can improve project management efficiency.

References 1. Chiocchio, S., Martin, E., Barabaschi, P., et al.: System engineering and configuration management in ITER. Fusion Eng. Des. 82(5–14), 548–554 (2007)

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2. Ingo, K.: Configuration management of ITER. Fusion Sci. Technol.: Int. J. Am. Nucl. Society 64(2), 103–110 (2013) 3. ITER Organization.: Working Instruction for processing Non-conformity Reports issued work supervised by construction. U8VPSS (2021) 4. ITER Organization.: Working instruction for construction field change request (FCR). EBUK3B (2020) 5. ITER Organization.: ITER Baseline Diagram. 27LHHE (2019) 6. Kuehn, I., et al.: Management of the ITER buildings configuration for the construction and installation phase. IEEE Trans. Plasma Sci. 46(1), 194–200 (2018) 7. Lindkvist, C., Stasis, A., Whyte, J.: Configuration management in complex engineering projects. Procedia CIRP 11, 173–176 (2013). ISSN 2212-8271 8. Kuehn, I., et al.: Management of the ITER configuration towards construction phase. In: 2013 IEEE 25th Symposium on Fusion Engineering (SOFE), 2013, pp. 1–6 9. Whyte, J., Stasis, A., Lindkvist, C.: Managing change in the delivery of complex projects: configuration management, asset information and ‘big data’. Int. J. Project Manage. 34(2), 339–351 (2016). ISSN 0263-7863 10. Ali, U., Kidd, C.: Barriers to effective configuration management application in a project context: an empirical investigation. Int. J. Project Manage. 32(3), 508–518 (2014). ISSN 0263-7863

Research on Diffusion of Oceanic Radionuclides Deposited from Atmosphere Under Nuclear Leakage Accidents Zichao Li1(B) , Rongchang Chen1 , Zheng Wang1(B) , Chen Liu1 , and Tao Zhou2 1 China Waterborne Transport Research Institute, Beijing 100088, China

[email protected], [email protected] 2 School of Energy and Environment, Southeast University, Nanjing 211096, China

Abstract. After a nuclear leakage accident, some radionuclides diffuse through the atmosphere and some radionuclides diffuse through the ocean. Under the action of gravity, rainfall and other factors, some radionuclides in the atmosphere fall into the ocean and carry out a series of complex movements with the ocean current. It is of great significance to study the diffusion of oceanic radionuclides deposited from atmosphere under nuclear leakage accidents for the emergency response after a nuclear leakage accident. Based on Haiyang nuclear power plant, oceanic radionuclides deposited from atmosphere was taken as an area source. Taking oceanic radionuclides deposited from atmosphere as an area source, a radionuclide diffusion model in the ocean was established, and the radionuclide concentration in the ocean was calculated. The results show that taking the area source as the center, radionuclides diffuse about 40 km eastward and about 37 km southward on the 7th day, and radionuclides diffuse about 56 km eastward on the 14th day in summer. Radionuclides diffuse about 60 km eastward on the 7th day, and radionuclides diffuse about 90 km eastward on the 14th day in winter. There is a good corresponding relationship between surface concentration and vertical concentration. It shows the accuracy of the calculation results. Keywords: Nuclear leakage accidents · Oceanic radionuclides deposited from atmosphere · Area source · Diffusion in the ocean

1 Introduction The development and utilization of nuclear energy has brought new impetus to human development, but it is also accompanied by risks and challenges. We must deal with various nuclear security challenges and maintain nuclear security. On March 11, 2011, an earthquake with a magnitude of 9.0 on the Richter scale occurred in the northeast Pacific of Japan, followed by a tsunami. A serious nuclear leakage accident occurred at Fukushima Daiichi nuclear power plant. A large number of radionuclides were released [1, 2]. The amounts of 131 I and 137 Cs directly released into the atmosphere were about 100PBq–500PBq and 6PBq–20PBq respectively. After atmospheric diffusion, 80% of radionuclides in the atmosphere were deposited into the ocean [3, 4]. At present, all units © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 574–583, 2023. https://doi.org/10.1007/978-981-19-8780-9_56

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in operation and under construction in China are distributed in coastal areas. At the same time, the first 60MW floating reactor is under construction and will be used in the first planned floating nuclear power plant in China. Once a nuclear leakage accident occurs, radionuclides will diffuse through the atmosphere and deposit into the ocean, polluting the oceanic ecological environment. It is of great significance to study the diffusion of oceanic radionuclides deposited from atmosphere under nuclear leakage accidents for the emergency response after a nuclear leakage accident. After a nuclear leakage accident, some radionuclides diffuse through the atmosphere and some radionuclides diffuse through the ocean. Under the action of gravity, rainfall and other factors, some radionuclides in the atmosphere fall into the ocean and carry out a series of complex movements with the ocean current. Scholars at home and abroad have done a lot of research on radionuclide transport in the atmosphere and in the ocean. After the Fukushima nuclear leakage accident, Buesseler et al. [5] analyzed the radionuclide concentration in the ocean near the nuclear power plant and found that the amount of 137 Cs was nearly 10000 times higher, four months after the accident, than that before the accident. Nakano and Dietze et al. [6, 7] simulated the long-term diffusion of radionuclides in the ocean with a global transport model after the Fukushima nuclear leakage accident and analyzed the migration path of radionuclides. Wang et al. [8] simulated and predicted the transportation of radionuclides leaked into the ocean from the Fukushima nuclear plant for ten years. Kanai Y et al. [9] pointed out that radionuclides in the atmosphere transported rapidly after the nuclear leakage accident, and radionuclides can be rapidly reduced due to rapid diffusion and deposition within a few months. At present, there have been many studies on the long-term migration of radionuclides in the ocean after nuclear leakage accidents [10], but few studies on oceanic radionuclides deposited from atmosphere and their diffusion in the ocean. Therefore, taking oceanic radionuclides deposited from atmosphere as an area source, a radionuclide diffusion model in the ocean was established, and the radionuclide concentration in the ocean was calculated. It provides a theoretical reference for emergency response of nuclear leakage accidents.

2 Research Object Haiyang nuclear power station plans to build six pressurized water reactor with million kilowatt. And the unit capacity is 1250 MW and the thermal power is 3415 MWt. The coastal areas of the nuclear power plant are mostly tourist and residential areas. Therefore, the Haiyang nuclear power plant was selected to research. The nuclear power plant and its surrounding environment is shown in Fig. 1.

3 Calculation Model Based on the ROMS model [11], the hydrodynamic model and radionuclide diffusion model were established. The momentum equation is shown in formulas (1), (2) and (3), and the continuity equation is shown in formula (4). The diffusion equation is shown in formula (5), and the state equation is shown in formula (6). The dynamic pressure equation is shown in formula (7). ∂ϕ ∂ ∂u ∂u + V · ∇u−fv = − − (u w − ν ) + Fu + Du ∂t ∂x ∂z ∂z

(1)

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Fig. 1. Location of Haiyang nuclear power plant

∂v ∂v ∂ϕ ∂ + V · ∇v + fu = − − (v w − ν ) + Fv + Dv ∂t ∂y ∂z ∂z

(2)

∂ϕ −ρg = ∂z ρ0

(3)

∂u ∂v ∂w + + =0 ∂x ∂y ∂z

(4)

∂C ∂C ∂ + V · ∇C = − (C  w − νθ ) + Fc + Dc ∂t ∂z ∂z

(5)

ρ = ρ(T , S, P)

(6)

ϕ(x, y, z, t) = p/ρ0

(7)

where, u is the velocity component in x direction, m/s; v is the velocity component in y direction, m/s; w is the velocity component in z direction, m/s; V is the average velocity vector, m/s; f is the parameter of coriolis force, rad/s; ϕ is the kinetic pressure, Pa·m3 /kg; ν is the kinematic viscosity, m2 /s; ρ is the seawater density, kg/m3 ; ρ0 is the seawater reference density, kg/m3 ; g is the gravitational acceleration, m/s2 ; T is the temperature, °C; S is the salinity; P is the pressure, Pa; C is a scalar which represents temperature, salinity or pollutant concentration; Fu and Fv are forced terms; Fc is the source term; Du , Dv and Dc are horizontal diffusion terms; u w and v w are Reynolds stress terms; C  w is the flux of turbulent particles.

4 Boundary Conditions 4.1 Boundary Condition of the Ocean The terrain data of the hydrodynamic model in coastal waters of the nuclear power plant was selected from the GEBCO terrain. Initial temperature and salinity fields were obtained from the results of the dynamic fields in coastal waters of China. The wind

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speed, pressure, temperature, rainfall and other data in NCEP-DOE meteorological data set were used for ocean surface boundary conditions. The open lateral fields adopted the calculation results in coastal waters of China. All 10 tidal components were selected to calculate the open boundary water level. 4.2 Boundary Condition of Area Source The deposition area was set as 121.25° E–121.375° E and 36.5° N–36.6° N. The deposition range was about 11 km from east to west, and about 11 km from north to south. The area center was about 10 km away from the nuclear power plant. If the wet deposition flux is set as 425Bq/(s·m2 ) and the rainfall lasts for two hours, then the total amount of radionuclides deposited into the ocean is 3.7 × 1014 Bq.

5 Conclusion 5.1 Surface Diffusion in Summer The radionuclide diffusion during two weeks (from June 8, 2014 to June 21, 2014) was calculated. Under the wet deposition action of atmospheric radionuclides, surface diffusion of radionuclides during two weeks in summer is shown in Fig. 2. It can be seen from Fig. 2 that oceanic radionuclides deposited from atmosphere mainly migrate and diffuse with the ocean current. In summer, the area source migrate slowly with the current. On the 14th day, the area source center migrate about 30 km eastward, basically showing a circumferential diffusion. Taking the area source as the center, radionuclides diffuse about 40 km eastward and about 37 km southward on the 7th day, and radionuclides diffuse about 56 km eastward on the 14th day in summer. Due to the influence of wind, radionuclides migrate and diffuse quickly in the atmosphere, and the wet deposition flux is large. Therefore, it is very important to accurately calculate the atmospheric radionuclide flux into the ocean and select the appropriate area source. 5.2 Vertical Diffusion in Summer The radionuclide diffusion during two weeks (from June 8, 2014 to June 21, 2014) was calculated. Under the wet deposition action of atmospheric radionuclides, vertical diffusion of radionuclides during two weeks in summer is shown in Fig. 3. It can be seen from Fig. 3a that radionuclides diffuse both horizontally and vertically. Because of the shallow water depth in coastal waters, radionuclides with low concentration diffuse to the bottom in the first hour in summer. It can be seen from Fig. 3b–d that radionuclides in the upper and lower ocean diffuse with consistent concentrations. Comparing with Fig. 2, there is a good corresponding relationship between surface concentration and vertical concentration in summer. On the one hand, it shows that radionuclides mix quickly in the vertical direction. On the other hand, it also shows the accuracy of the calculation results.

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(a)The 1th day

(b)The 7th day

(c)The 10th day

(d)The 14th day Fig. 2. Surface diffusion of radionuclides during two weeks in summer

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(a)The 1th day

(b)The 7th day

(c)The 10th day

(d)The 14th day Fig. 3. Vertical diffusion of radionuclides during two weeks in summer

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5.3 Surface Diffusion in Winter The radionuclide diffusion during two weeks (from January 8, 2014 to January 21, 2014) was calculated. Under the wet deposition action of atmospheric radionuclides, surface diffusion of radionuclides during two weeks in winter is shown in Fig. 4. It can be seen from Fig. 4 that area source radionuclides migrate rapidly in winter. Taking the area source as the center in winter, radionuclides diffuse about 60 km eastward on the 7th day, and radionuclides diffuse about 90 km eastward on the 14th day in winter. In winter, a warm water tongue comes from Jeju Island in the south and enters the South Yellow Sea in the north. Under the action of the warm water tongue, water in the south of the nuclear power plant flows to the shore. So radionuclides at the nuclear power plant diffuse to both sides, and then rapidly diffuse to the south. 5.4 Vertical Diffusion in Winter The radionuclide diffusion during two weeks (from January 8, 2014 to January 21, 2014) was calculated. Under the wet deposition action of atmospheric radionuclides, vertical diffusion of radionuclides during two weeks in winter is shown in Fig. 5. It can be seen from Fig. 5a that radionuclides with high concentration diffuse to the bottom in the first hour in winter. The cold water mass of Yellow Sea makes the upper and lower water mix slowly in summer, while the upper and lower water mix faster in winter under the north strong wind. Comparing with Fig. 4, there is also a good corresponding relationship between surface concentration and vertical concentration, and it also shows the accuracy of the calculation results.

6 Conclusion Based on Haiyang nuclear power plant, oceanic radionuclides deposited from atmosphere was taken as an area source. Taking oceanic radionuclides deposited from atmosphere as an area source, a radionuclide diffusion model in the ocean was established, and the radionuclide concentration in the ocean was calculated. (1) In summer, the area source migrate slowly with the current. On the 14th day, the area source center migrate about 30 km eastward, basically showing a circumferential diffusion. Taking the area source as the center, radionuclides diffuse about 40 km eastward and about 37 km southward on the 7th day, and radionuclides diffuse about 56 km eastward on the 14th day. (2) Taking the area source as the center in winter, radionuclides diffuse about 60 km eastward on the 7th day, and radionuclides diffuse about 90 km eastward on the 14th day. (3) There is a good corresponding relationship between surface concentration and vertical concentration. On the one hand, it shows that radionuclides mix quickly in the vertical direction. On the other hand, it shows the accuracy of the calculation results.

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(a)The 1th day

(b)The 7th day

(c)The 10th day

(d)The 14th day Fig. 4. Surface diffusion of radionuclides during two weeks in winter

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(a)The 1th day

(b)The 7th day

(c)The 10th day

(14)The 14th day Fig. 5. Vertical diffusion of radionuclides during two weeks in winter

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Acknowledgements. This study is supported by the Fundamental Research Funds of Ministry of Finance (Grant 62215, Grant 62216 and Grant 62223), and the National Key Project of Research and Development Plan (Grant 2016YFC1402501).

References 1. Mimura, N., Yasuhara, K., Kawagoe, S., et al.: Damage from the Great East Japan earthquake and Tsunami-a quick report. Mitig. Adapt. Strat. Glob. Change 16(8), 943–945 (2011) 2. Xiegu, X., Zhen, B., Yang, X., et al.: Experience and lessons learned from emergency disposal of Fukushima nuclear power station accident. Milit. Med. Sci. 36(12), 889–892 (2012) 3. Stohl, A., Seibert, P., Wotawa, G., et al.: Xenon-133 and caesium-137 releases into the atmosphere from the Fukushima Dai-ichi nuclear power plant: determination of the source term, atmospheric dispersion, and deposition. Atmos. Chem. Phys. 12(5), 2313–2343 (2012) 4. Morino, Y., Ohara, T., Nishizawa, M.: Atmospheric behavior, deposition, and budget of radioactive materials from the Fukushima Daiichi nuclear power plant in March 2011. Geophys. Res. Lett. 38(7), 136–147 (2011) 5. Buesseler, K., Aoyama, M., Fukasawa, M.: Impacts of the Fukushima nuclear power plants on marine radioactivity. Environ. Sci. Technol. 45(23), 9931 (2011) 6. Nakano, M., Povinec, P.P.: Long-term simulations of the 1 37 Cs dispersion from the Fukushima accident in the world ocean. J. Environ. Radioact. 111(3), 109–115 (2012) 7. Dietze, H., Kriest, I.: 137Cs off Fukushima Dai-ichi, Japan—model based estimates of dilution and fate. Ocean Sci. 8(3), 319–332 (2012) 8. Wang, H., Wang, Z.Y., Zhu, X.M., et al.: Numerical study and prediction of nuclear contaminant transport from Fukushima Daiichi nuclear power plant in the North Pacific Sea. Chin. Sci. Bull. 57(26), 3518–3524 (2012) 9. Kanai, Y.: Monitoring of aerosols in Tsukuba after Fukushima nuclear power plant incident in 2011. J. Environ. Radioactivity 111 (2012) 10. Lin, W., Kefu, X., Jinqiu, D., et al.: Consequences of marine ecological environment and our preparedness for Fukushima radioactive wastewater discharge into the ocean. Chin. Sci. Bull. 66(35), 4500–4509 (2021) 11. Chen, X., Wan, X., Ma, W.: Southwestern Yellow sea circulation and its influence on the distribution of Enteromorpha Prolifera. Period. Ocean Univ. China 52(4), 1–11 (2022)

An Overview of R&D Activities on High Level Liquid Waste Partitioning at Tsinghua University, China Chao Xu(B) and Jing Chen Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China [email protected]

Abstract. Safe management of radioactive waste is of great importance to ensure the sustainability of nuclear energy. The reprocessing of spent nuclear fuel generates huge quantities of High-Level Liquid Waste (HLLW) that need to be further treated to minimize its long term hazards prior to the final geological disposal. Partitioning of HLLW into different groups not only would reduce the waste burden, but also could benefit the implementation of transmutation technologies and the utilization of the abundant nuclide resources in HLLW. China has a long history working on the partitioning of HLLW. An overview of recent R&D activities on this subject at Tsinghua University in China are presented in this paper. Keywords: Nuclear waste · HLLW · Partitioning · Actinide · Extraction

1 Introduction High level liquid waste (HLLW) is produced from the plutonium uranium extraction (PUREX) process for the reprocessing of spent nuclear fuel (SNF). Although a majority of U and Pu has been recovered, HLLW still contains a moderate amount of U/Pu, and transuranium (TRU) actinides such as Np, Am, as well as other highly radioactive nuclides such as 90 Sr and 137 Cs. Due to the high radiotoxicity and long-term hazard, HLLW imposes a potential hazard to the environment and human health. A so-called partitioning and transmutation (P&T) strategy has been proposed for the treatment of HLLW by first partitioning the waste into different groups and then converting the long-lived nuclides into short-lived or stable nuclides [1]. Obviously, partitioning of HLLW is one of the critical steps for the implementation of the P&T strategy. A variety of processes based on solvent extraction have been developed for the partitioning of HLLW worldwide [2]. China is one of the fast-growing countries in nuclear energy production and has adopted a closed fuel cycle policy [3]. A huge amount of spent fuels has been and will be continuously discharged from nuclear power plant in China. There will be a great pressure for the safe management of HLLW if these spent fuels are processed through PUREX process. Therefore, there is an urgent demand to develop efficient techniques for the treatment of HLLW. To solve this problem, research groups in China has conducted R&D work on the partitioning of HLLW for more than 30 years. This paper will give an overview of the recent progresses on this issue at Tsinghua University, China. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 584–588, 2023. https://doi.org/10.1007/978-981-19-8780-9_57

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2 Technology and Key Progresses HLLW partitioning at Tsinghua University, China is mainly based on solvent extraction technology by using a variety of functional extractants to selectively extract targeted components from HLLW. This is also the preferred technology for HLLW partitioning worldwide considering the maturity of this technology in nuclear industry and its easy coupling with the PUREX process. The longtime R&D work on HLLW partitioning in China focuses on the recovery of minor actinides, strontium and cesium from HLLW and the subsequent trivalent actinide and lanthanide separation. 2.1 Chemical Fundamentals Recovery of minor actinides is a primary task in HLLW partitioning. A TRPO process was developed at Tsinghua University in 1980s [4]. The functional extractant TRPO is trialkyl phosphine oxides with the alkyl group contains 6–8 carbons. TRPO is commercially available and is well soluble in the preferred solvent such as kerosene in nuclear industry. TRPO has very strong affinity to actinides in III, IV and VI oxidation states, thus could be used for the recovery of actinides such as Am(III), Pu(IV) and U(VI) from HLLW. The best performances are usually achieved with a nitric acid concentration of around 1 M in the feed solution. It should be noted that the fission product lanthanides are coextracted with the trivalent actinides by TRPO, and therefore subsequent lanthanide/actinide separation is required to obtain the desired actinide product for future transmutation. 90 Sr and 137 Cs, with half-lives of 28.9 y and 30.1 y, respectively, contribute a large part of the heat load and radioactivity in HLLW. Removal of 90 Sr and 137 Cs from HLLW can greatly reduce the waste volume and thereby save the repository capacity by eliminating most of the heat load and radioactivity. Removal of 90 Sr and 137 Cs can also facilitate the handling and transportation of HLLW, either for subsequent treatment or for disposal. Moreover, the recovered 90 Sr and 137 Cs are useful isotopes that can be used as radiationsource for radiation therapy or radioisotope power generator. For the removal of 90 Sr, crown ethers (DCH18C6 or DtBuCH18C6) are used as the functional ligands at Tsinghua University, China [5]. For the removal of 137 Cs, the KTiFC (potassium titanium ferrocyanide) ion exchanger was first used to selectively adsorb Cs+ . But this method was abandoned soon due to safety issues and the large amount of secondary waste produced. Currently, solvent extraction by a calix crown ether (bis(2propyloxy)calix [4] crown-6, abbreviated as BPC6) is adopted as the preferred technique to fulfill this purpose. Both the deliberately selected crown ethers and calix crown ethers show high selectivity to Sr2+ and Cs+ , respectively, through accurate supramolecular recognition. As mentioned earlier, subsequent trivalent actinide/lanthanide (An(III)/Ln(III)) separation is required after minor actinide recovery by TRPO process. Such a separation task is very challenging due to the chemical similarity between the two groups of felements. Soft donor ligands containing N or S atoms are suggested to show selectivity to An(III) over Ln(III). The research group in Tsinghua University first demonstrated the effectiveness of dithiophosphinic acids in An(III)/Ln(III) separation in 1990s [6]. The purified Cyanex 301 (bis(2,4,4-trimethylpentyl)dithiophosphinic Acid, abbreviated

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as HC301 hereafter) could achieve an extremely An(III)/Ln(III) high separation factor (> 5000) in one single solvent extraction contact. The molecule structures of the functional ligands (TRPO, DCH18C6, DtBuCH18C6, BPC6, HC301) currently used for HLLW partitioning at Tsinghua University, China are shown in Fig. 1.

Fig. 1. Structures of functional ligands for HLLW partitioning at Tsinghua University, China

2.2 Key Progresses TRPO process was first proposed in 1980s. Since then, a large amount of work has been conducted to test and demonstrate this process [4]. A hot test of the TRPO process was first carried out with HLLW of WAK in the Institute for Transuranium Elements (ITU) at Karlsruhe, Germany in 1993. The hot test was conducted on 24 stages of miniature centrifugal contactors in a hot cell. The decontamination factors (DF) for actinides such as 239 Pu, 237 Np and 241 Am were generally above 103 , but the value for 241 Am was not very satisfied. The process was then optimized and a multistage counter current cascade experiment with simulated HLLW spiked with 241 Am was carried out in a glove box. A DF of 241 Am as high as 106 was obtained [7]. Very recently, TRPO process were further demonstrated in Tsinghua University on 30 stages of miniature centrifugal contactors using simulated HLLW spiked with 237 Np, 239 Pu and 241 Am. The total DF for alpha elements was above 105 , meeting the requirements to produce non-alpha raffinate. Further hot tests to demonstrate the TRPO process for the treatment of genuine Chinese HLLW have been planned in the next few years. TRPO process was also employed for the treatment of legacy defense HLLW in China. These legacy HLLW contains relatively low level of actinides as compared to HLLW generated from the reprocessing of commercial spent fuel [4]. In 1996, a multistage hot test was conducted with genuine legacy HLLW in the hot cell at Tsinghua University. The hot test lasted for 6 h and a total DF of 588 was achieved for all the actinides. Such a DF value is sufficient to transfer the legacy HLLW into non-alpha waste. In 2009, a much larger scale hot test using multistage miniature centrifugal contactors was further conducted to process genuine legacy HLLW in China. The test lasted for

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160 h and more than 300Ci of HLLW was successfully processed. The DFs for actinides such as Np, Pu and Am all exceeded 103 , well above the required values. Moreover, TRPO process was also tested on semi-industrial scale pulsed column with simulated HLLW. Satisfied results such as good hydraulics and high DFs were obtained. Currently, the construction of a pilot scale facility is under consideration for the treatment of legacy HLLW in China. For the removal of 90 Sr from legacy HLLW, the crown ether DCH18C6 is employed as the extractant. Octanol is selected as the diluent. The process based on this extraction system was first demonstrated in the hot cell at Tsinghua University in 1996. A DF > 2500 for 90 Sr was achieved. The process was further demonstrated in a hot test in 2009 and a DF > 10000 for 90 Sr was achieved. For the removal of 90 Sr from commercial HLLW in China, the crown ether DtBuCH18C6 is employed as the extractant [8]. DtBuCH18C6 shows a higher extractability and selectivity toward Sr2+ . The DtBuCH18C6 based process has been demonstrated on multistage centrifugal contactors using simulated HLLW and a DF > 1000 is well achievable. For the removal of 137 Cs from HLLW in China, currently the calix crown ether BPC6 is employed as the extractant and octanol is the diluent. A DF > 10000 for 137 Cs was achieved in a hot test in 2009. Most importantly, processes for the removal of 90 Sr and 137 Cs have been coupled together with the TRPO process to form a total-partitioning process. The aforementioned hot test for the treatment of legacy HLLW in 2009 is a successful example of this totalpartitioning process [9]. The hot test of this combined process was tested on 72 stages of centrifugal contactors (40 stages for TRPO, 16 stages for Sr, and 16 stages for Cs). No operational problems have been encountered during the hot test and high DFs were achieved for all the targeted nuclides. For the separation An(III) from Ln(III) by purified Cyanex 301 (HC301), two tests have been conducted at Tsinghua University using 14 stages of miniature centrifugal contactors. In the 2011 test, 99.95% Am was separated from lanthanides and only 0.1% lanthanides were extracted together with Am [10]. But problems such as large fluctuations of pH were encountered during this test, which has somewhat lowered the mutual separation of An(III) and Ln(III). The process was thus been improved by introducing a buffer reagent and a new test was conducted recently [11]. In this test, 99.995% Am was separated from lanthanides and less than 1% lanthanides were extracted together with Am. Moreover, a novel technique for the separation of Am from the lanthanides and Cm based on the oxidation of Am has been developed very recently at Tsinghua University [12, 13]. In this technique, pentavalent Am was prepared and stabilized in a biphasic extraction system by incorporating the oxidative Bi(V) reagents into an organic solvent, leading to ultraefficient separation of Am from the lanthanides and Cm (separation factors > 104 through a single biphasic contact).

3 Conclusion Tsinghua University has a long history working on the partitioning of HLLW to minimize its long-term hazard and ensure the sustainability of nuclear energy. Partitioning processes based on functional ligands and solvent extraction technology have been developed to recover nuclides such as minor actinides, 90 Sr and 137 Cs from HLLW in China.

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Some of these processes have been successfully demonstrated with genuine HLLW. Further optimization and industrialization of these processes are currently underway at Tsinghua University.

References 1. Salvatores, M., Palmiotti, G.: Radioactive waste partitioning and transmutation within advanced fuel cycles: achievements and challenges. Prog. Part. Nucl. Phys. 66, 144–166 (2011) 2. Veliscek-Carolan, J.: Separation of actinides from spent nuclear fuel: a review. J. Hazard. Mater. 318, 266–281 (2016) 3. Coping with growth: China’s spent fuel management strategy, IAEA Bull. 60(2), 16 (2019) 4. Chen, J., Wang, J.C.: Overview of 30 years research on TRPO process for actinides partitioning from high level liquid waste. Progr. Chem. 23(7), 1366–1371 (2011) 5. Xu, C., Wang, J.C., Chen, J.: Solvent extraction of strontium and cesium: a review of recent progress. Solvent Extr. Ion Exch. 30, 623–650 (2012) 6. Zhu, Y.J., Chen, J., Jiao, R.Z.: Extraction of Am(III) and Eu(III) from nitrate solution with purified Cyanex 301. Solvent Extr. Ion Exch. 14, 61–68 (1996) 7. Song, C.L., Xu, J.M.: Recent progresses on partitioning study in Tsinghua University. In: 6th Information Exchange Meeting on Actinide and Fission Product Partitioning and Transmutation, Madrid, Spain, December 2000, pp. 673–680 8. Xu, C., Ye, G., Wang, S.W., Duan, W.H., Wang, J.C., Chen, J.: Solvent extraction of strontium from nitric acid medium by Di-tert-Butyl Cyclohexano-18-Crown-6 in n-Octanol: extraction behavior and flowsheet demonstration. Solvent Extr. Ion Exch. 31, 731–742 (2013) 9. Duan, W.H., et al.: Application of annular centrifugal contactors in the hot test of the improved total partitioning process for high level liquid waste. J. Hazard. Mater. 278, 566–571 (2014) 10. Chen, J., Wang, S.W., Xu, C., Wang, X.H., Feng, X.G.: Separation of americium from lanthanides by purified Cyanex 301 countercurrent extraction in miniature centrifugal contactors. Procedia Chemistry 7, 172–177 (2012) 11. Xu, C., et al.: Improving the robustness of trivalent actinides/lanthanides separation by bis(2,4,4-trimethylpentyl) dithiophosphinic acid: batch extraction and process demonstration. Solvent Extr. Ion Exch. 39, 290–304 (2021) 12. Wang, Z.P., et al.: Ultra-efficient americium/lanthanide separation through oxidation state control. J. Am. Chem. Soc. 144, 6383–6389 (2022) 13. Wang, Z.P., Dong, X., Yan, Q., Chen, J., Xu, C.: Separation of americium from curium through oxidation state control with record efficiency. Anal. Chem. 94, 7743–7746 (2022)

Analysis of the Economic Prospect of Nuclear Heat Supply by Megawatt-Class Nuclear Power Units from the Perspective of Carbon Peaking and Carbon Neutrality Xiao-lei Song, Xin Li(B) , and Song-kun Jiao China Nuclear Power Engineering Co., Ltd., Hebei Branch, Shijiazhuang, Hebei, China [email protected]

Abstract. Under the vision of carbon peaking and carbon neutrality, clean heat supply as one of the important ways to realize low-carbon transition, will face a broad market, and replacing some conventional heat sources with nuclear energy is an effective means to reduce pollutants and a priority choice to optimize energy structure. Based on the current situation of heat supply in China, this paper first analyzes the necessity of nuclear heat supply (NHS) by megawatt-class nuclear power units from the demand side and supply side respectively. The paper then constructs an economic model and based on the opportunity cost theory, uses the power loss method to carry out financial calculation after apportioning the cost of thermal power. It will analyze the impact of nuclear feed-in/cost tariff, heating load, and distance of heat supply network on the cost-effectiveness of NHS by megawatt-class nuclear power units in the case of low-pressure steam supply, as well as the range of heat price variation caused by related factors within a certain controllable range. The study shows that the cost-effectiveness of NHS is most sensitive to feed-in/cost tariff, and that at the current level of nuclear feedin/cost tariff, the price of NHS is significantly lower than the price level set by the Development and Reform Commissions in most regions of China. Therefore, NHS by megawatt-class nuclear power units has a very good market prospect in terms of both price competitiveness and policy support. Keywords: Nuclear heat supply (NHS) · Low-pressure steam · Heat price · Feed-in/cost tariff · Cost-effectiveness

1 Introduction The proposal of carbon peaking and carbon neutrality provides a great opportunity for the development of nuclear energy. As a low-carbon, safe and stable localized non-fossil energy, nuclear energy can not only provide electricity, but also replace a part of fossil energy such as natural gas and coal through nuclear heating, so nuclear power can reduce emissions on a massive scale. In November 2019, with the successful implementation of the China’s first nuclear power civil central heating commercial project in Haiyang, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 589–595, 2023. https://doi.org/10.1007/978-981-19-8780-9_58

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Shandong province, which means that the technology of cogeneration of nuclear power units has achieved a breakthrough, and it has become economically competitive with coal heating because that the heating price of single square meter is 1 yuan lower than in previous years. Meanwhile, the safety of using nuclear power units to provide heating for residents has also been recognized. Since CNNC Tianwan steam energy supply project officially started, China started its first industrial nuclear energy steam supply project research and exploration. The main factors affecting the economy of nuclear heating retrofit project is different from the normal heating project, to study and explore the competitiveness of nuclear energy supply industrial steam projects in the market is conducive to promoting the coupling development of active nuclear power units and high energy consumption industries, and further realizing the coordinated development of energy, economy, and ecological environment.

2 Great Opportunities for Nuclear Heating Under Carbon Peaking and Carbon Neutrality Targets 2.1 Analysis of China Heating Status From 2011 to 2020, China’s heating area keeps growing, and the hot-water heating capacity and total heating volume keep rising. In contrast, the steam heating capacity and total heating volume are relatively small, in which the annual steam heating volume only accounts for about 16% of the total urban heating volume and presents a downward trend. On the demand side, with the acceleration of China’s urbanization process and the rapid development of industry, the area of central heating keeps increasing, and the demand for industrial heat also rises every year. High energy consumption industries such as petrochemical and steel mainly rely on fossil energy such as natural gas and coal for a long time, which inevitably leads to high carbon emissions. Therefore, it is appropriate to develop the central heating mode based on nuclear waste heat utilization, which is a new way to solve the petrochemical industry’s demand for steam, reduce comprehensive energy consumption and reduce environmental pollution. According to experts’ forecasts, the use of waste heat from coastal nuclear power is expected to meet the winter heating demand of nearly 7 billion square meters of buildings within 200–300 km from the coast to the hinterland. 2.2 The Government Issued Relevant Policies, the Development of Nuclear Heating Sustains Positive In recent years, China has gradually increased the policy support for nuclear heating and issued several policies. In 2017, the Plan for “Clean Winter Heating in Northern China (2017–2021)” which issued by ten ministries and commissions, made it clear that nuclear, geothermal, and solar energy are important forms of clean heating. In 2018, the National Energy Administration pointed out in the “Guidance on Energy Work in 2018” and the “Five-year Action Plan for Nuclear Energy Heating in Northern China” (draft for comments) that the pilot work of nuclear energy heating in northern China

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should be actively studied and promoted, and the demonstration project of nuclear energy heating should be promoted as a key project. “The Guidance Catalogue for Industrial Restructuring (2019 Edition)” which took effect in January 2020, also includes the comprehensive utilization of nuclear energy in heating, steam supply and other fields in the “encouraged category” of the catalogue. In September 2020, as CPC General Secretary clearly proposed the ambitious goal of “striving to achieve carbon peak by 2030 and carbon neutral by 2060” at the general debate of the seventy-fifth Session of the United Nations General Assembly, the development of nuclear energy has once again become the focus of local government arrangements. In 2021, “Opinions of the CPC State Council on Fully, accurately and comprehensively Implementing the New Development Concept to achieve carbon peak and Carbon Neutral Work” proposed that “Actively and prudently carry out nuclear waste heat for heating”. In that year’s government work report, it was clearly stated that “Nuclear power should be actively and orderly developed on the premise of ensuring safety”. Nuclear energy heating has been listed by many local governments as an important expansion direction of the “14th Five-year Plan” and the medium- and long-term comprehensive utilization of nuclear energy, nuclear energy heating has a promising future.

3 The Main Factors Affecting the Economy of Nuclear Heating Retrofit Project The economy of nuclear heating retrofit project should consider heating mode, matching relation of heat load, distance of heating pipe network and parameters optimization, etc. Therefore, it is necessary to optimize and analyze the whole system including heating scale, heat source and heat network, and analyze the economics of nuclear heating retrofits based on the investment and cost of different heating scales [1]. The main factors affecting economy are as follows: (1) Heating scale: It is related to the demand of heat users, the demand of heat users further determines the heating load, heating parameters, heating pipe network parameters and the loss of reactor power generation, etc. The short-term heat load requirements of heat users should be determined based on the heat requirements from existing, under construction and approved industrial projects. (2) Heating parameters: It is related to the industrial type of heat users. Due to the difference of raw materials, technology and products, different industrial enterprises have different requirements for steam heating parameters. Generally, 1.6 MPa can meet the heating requirements of machinery, pharmaceutical, while petrochemical enterprises need pressure grade parameters such as 2.2, 3, 5 and 4.0 MPa to meet the heating requirements [2]. (3) Distance of heating pipe network: It is related to the distribution characteristics of nuclear power units and chemical industrial parks. Nuclear power units in China are all distributed in developed coastal areas in the east and south, and chemical parks are also generally far from central cities. As the larger the heating radius is, the higher the cost required by burying pipes is, and the greater the heating loss is

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[3]. There may have problems that the steam parameters cannot meet the needs of the user when the steam is transported over a long distance of more than 35 km. Therefore, we should consider the reasonable distance of heating network to select hot users. Meanwhile, we can also further optimize the initial parameters of steam, the diameter of heating pipe, the thickness of pipe insulation and the layout of pipe to reduce the heat loss in steam transportation [4]. (4) Reactor generation losses: It is related to the demand of heat users and the operating status of nuclear power plants. Because nuclear energy heating needs to extract steam from the second loop of the main steam pipe of the original nuclear power unit, the generation of electricity is reduced. From the analysis of the opportunity cost theory of economic, the less electricity produced here should be the cost of heating. Therefore, we need to determine the loss rate of power generation caused by steam extraction heating and then obtain the reduced power generation of nuclear power units, and the consumption of reactor power, etc. (5) Feed-in/cost tariff: The type of electricity price is related to the operation of nuclear power units in service. If the nuclear power unit reactor works in TMCR(turbine maximum continuous rating) condition, that is, the annual power generation is at full capacity, then the reduction of generating capacity due to steam extraction will only affect the Feed-in generation of the unit, and the electricity price should be considered according to the Feed-in tariff. If the reactor of a nuclear power unit doesn’t work in TMCR condition, that is, the annual power generation isn’t at full capacity, then the effect of main steam extraction on the reactor will be divided into two parts: if the reduction of electric quantity affects the Feed-in generation part of the unit, the cost should be considered according to the Feed-in price; if the reactor is consumed because of the additional work done by the extraction of steam, according to the opportunity cost theory of economic theory, the cost of additional consumption of reactor should be compensated based on cost tariff (Table 1).

4 Calculate the Financial Benefit of Different Heating Demands in the Case of Low-Pressure Steam Supply Pressurized water reactor (PWR) has good operation stability and large thermal production capacity. Taking an active million-kilowatt pressurized water reactor nuclear power units as the model, the units were put into a hypothetical low-pressure industrial steam supply system, the steam conversion scheme is adopted to produce industrial steam by using the main steam of the second loop of the original nuclear power unit through the steam conversion device, and to provide low pressure industrial steam to the thermal users of the industrial park through the long transmission pipeline. At this time, the original pure condensing generator unit is transformed into a combined heat and power supply unit, and the former nuclear power plant is transformed into a nuclear thermal power plant. The modified nuclear thermal power plant consists of a nuclear power generation system and a steam extraction heating system, Since the nuclear power unit in service is already in economical operation state, the cost-effectiveness of the transformed nuclear thermal power plant will depend on the extraction steam heating system.

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Table 1. Overview of the main parameters of nuclear energy heating renovation project Main factors

Related factors

Conformation method

Heating scale

It is related to the demand of heat users

Based on the heat requirements from existing, under construction and approved industrial projects

Heating parameters

It is related to the industrial type of heat users

The enterprise type of heat users determines the steam pressure level

Distance of heating pipe network

It is related to the distribution Consider the reasonable characteristics of nuclear power distance of heating network to units and chemical industrial select hot users parks

Reactor generation losses

It is related to the demand of heat users and the operating status of nuclear power plants

Determine the loss rate of power generation caused by steam extraction heating and then obtain the reduced power generation of nuclear power units, and the consumption of reactor power, etc.

Feed-in/cost tariff

The type of electricity price is related to the operation of nuclear power units in service

The nuclear power unit reactor works in TMCR condition

This paper take a study based on the nuclear heating retrofit project. Taking the extraction point as a cut-off point, The steam extraction heating system on the steam supply side as a nuclear energy heating renovation project, which includes heat source engineering and heat network engineering. The heat source engineering includes steam energy supply system, chemical water treatment system, instrument and control system, electrical system, etc. the cost of a ton of steam is about 120 yuan/t; Heat network engineering includes thermal system, thermal control system and ancillary production engineering, etc. The investment cost is about 25,000 yuan/m. Other main parameters can be seen as follows (Table 2). Based on 《Economic Evaluation Methods and Parameters of Construction Projects 》 (The Third Edition) to make economic calculations, the impact of nuclear feed-in/cost tariff, heating load, and distance of heat supply network on the cost-effectiveness of NHS by megawatt-class nuclear power units in the case of low-pressure steam supply was analyzed one by one. The study shows that the cost-effectiveness of NHS is most sensitive to feed-in/cost tariff, followed by heating load, and the distance of long-distance transmission network has a relatively small impact on the cost-effectiveness (Fig. 1). The main reason is that in the process of heating project transformation, the loss cost of reactor power generation is relatively high, accounting for about 80–90% of the total operating cost. The change of Feed-in/cost tariff price will directly cause the change of the loos cost, and then it affects the heat price.

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Main parameters

Unit

Quantity

Heating scale

t/h

600

Heating hours

h

8000

Distance of heating pipe network

km

27

Loss of reactor power

MW

175

Feed-in tariff

¥/kWh

0.43

Heating parameters

–

Low-pressure steam

Internal rate of return of capital

%

10

Other fees

¥/t

20.8 × 104

Operation Hours of reactor

H

7000

Cost tariff

¥/kWh

0.21

Fig. 1. Sensitivity analysis

In the most unfavorable case, the nuclear power unit reactor works in TMCR condition, that is, the annual power generation is at full capacity. At this time, the cost of making up for the loss of reactor power generation is calculated according to the benchmark electricity price of 0.43 yuan/kWh (the highest value) approved by the National Development and Reform Commission in June 2013. Refer to “Guidelines for economic evaluation of fossil-fired power plant” (DL/T5435-2019), the heat price was calculated based on 10% internal rate of return of capital, and the heat price is about 172.83 yuan/t on the user side. If the nuclear power unit reactor does not work in TMCR condition, Cost tariffs will be applied to the reactor loss caused by the extraction of main steam, and the average cost per kWh will reduce 0.21 yuan/kWh, and the heat price to the user side will be further reduced. According to the Interim Measures for The Administration of Urban Heating Price (No. 1195 [2007]), the heat price shall be fixed or guided by the government in principle. At present, Jiangsu, Zhejiang and Shandong are the three provinces which have the largest number of national economic development zones in China, those provinces have many industrial heat users. The average price of low-pressure steam published by the National Development and Reform Commission is

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about 176–222 yuan/t. The calculated heat price of the project is lower than the guiding price level of the National Development and Reform Commission in most regions of China, indicating good market economic competitiveness.

5 Summary At present, there is no nuclear energy heating project for industrial steam in China, Retrofitting the existing million-kilowatt pressurized water reactor nuclear power unit to Provide low pressure steam for thermal users in industrial parks, to satisfy China’s growing energy demand, and to realize the energy and coordinated development between economy and ecological environment. The calculation results show that the biggest influence on heat price is the Feed-in/cost tariff of nuclear power units. Based on the benchmark electricity price of 0.43 yuan/kWh (the highest value) verified by NDRC, heat price is still lower than the guiding price level of NDRC in most regions of China. To sum up, no matter in terms of price competitiveness or policy support, NHS by megawatt-class nuclear power units has a very good market prospect in terms of both price competitiveness and policy support. Later we will use post evaluation model constructed by this paper to demonstrate the construction achievements of the example project [5], and then put forward the evaluation results.

References 1. Tian, J., Yang, F.: Economic analysis of low-temperature nuclear district heating. Nucl. Power Eng. 15(06) (1994) 2. Wang, C., Sun, H., Jiang, J.: Brief analysis of centralized heat-supply system in the industrial zone. In: CPCESDA 2013, pp. 80–83 3. Lv, T., Wang, Z.: Discussion on heating range of thermal power plant. J. Shenyang Inst. Eng. (Nat. Sci.) 4(04) (2008) 4. Zhao, X., Wei, C., Xin, G.: Present situation of heat supply in Zhejiang and study on steam heat supply distance. Energy Res. Inf. 24(03) (2008) 5. Mastoroudes.: Selecting projects for ex post evaluation. Eval. Pract. 14(3) (2003)

The Study on Monitoring of Different Chemical Forms of Tritium and Tritium-Induced Radiation Dose Mengjie Zhou1,2(B) , Yao Wu1,2 , Li Wang1,2 , Tao Jin1,2 , Ting Shen1,2 , Jianqiang Wang1,2 , and Min Pei1,2 1 The Reactor Operation and Application Research Sub-institute, Nuclear Power Institute of

China, Chengdu 610005, China [email protected] 2 Sichuan Engineering Laboratory for Nuclear Facilities Decommissioning and Radwaste Management, Chengdu 610005, China

Abstract. [Background] During the operation of nuclear facility, tritium will be produced when the fission of 235 U and the activation of the primary circuit coolant. Besides, 2 H, 6 Li, 10 B and other nuclides can also produce tritium after being bombarded by neutrons. The tritium-containing radioactive gas effluent produced during the operation of nuclear facility and the reprocessing of spent fuel cannot be processed through the conventional three-waste system, and it will be discharged into the environment in the form of HTO and OBT through scavenging and leakage. [Purpose] Tritium has strong mobility and circulation in the biosphere, it can rapidly exchange with hydrogen atoms in all substances and enter the human body through breathing inhalation, skin absorption, and ingestion of contaminated food or water. Once inhaled, tritium is easily absorbed and utilized by the human body, causing a certain period of homogeneous internal exposure hazards and seriously affecting human health. Tritium is an important nuclide in the radioactive gas effluents, and its different chemical forms will have different degrees of impact on professionals and resident around nuclear facility. Therefore, monitoring, managing and controlling the discharge of different chemical forms of tritium is crucial to reducing internal exposure of professionals, reducing radiation level, and ensuring the health and safety of surrounding resident. [Methods] In this paper, the sampling and monitoring experiment of HTO and OBT is designed based on the composition difference of tritium in gas effluents during the operation of nuclear facility. The MARC 7000 tritium sampler is used to collect tritium. And the HTO and OBT are sampled by condensation bubbling method and combustion bubbling method, respectively. Then the tritium samples are obtained by the distillation condensation method. And the concentration of HTO and OBT are measured and analyzed by the liquid scintillation. Finally, dosimetry models are established to estimate the radiation dose of professionals caused by different chemical forms of tritium. [Results] The results show that the emission of tritium in the gas effluents is dominated by HTO, and the breath inhalation contributes more to the intake of tritium. However, in the skin absorption route, the dose conversion factor of OBT is two orders of magnitude higher than that of HTO and

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 596–606, 2023. https://doi.org/10.1007/978-981-19-8780-9_59

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OBT in other routes. Therefore, skin absorption is the main way to cause tritiuminduced internal exposure of professionals, and the contribution to the dose mainly comes from OBT. Keywords: Gas effluent · Organic tritium · Inorganic tritium · Monitoring · Radiation dose

1 Introduction During the operation of nuclear reactor, there are two main ways to produce tritium. One is that part of tritium produced by ternary fission of the fuel element 235 U will diffuse into the primary circuit through the cladding. The second is the neutron activation reaction of nuclides such as trace elements 2 H, 6 Li and 10 B in the primary circuit coolant to produce tritium, including 2 H (n, γ) T, 6 Li (n, α) T, 10 B (n, 2α) T [1–6]. The tritiumcontaining radioactive gas effluent produced during the operation of nuclear reactor and the reprocessing of spent fuel cannot be processed by the conventional three-waste system. Instead, it will be discharged into the environment in the form of tritiated water vapor (classified as inorganic tritium (HTO) in this paper) and other chemical forms of tritium except HTO (such as T2 , HT and CH3 T, classified as organic tritium (OBT) in this paper) through scavenging and leakage. Tritium, as the low-energy beta radionuclide, will not cause external exposure hazards to the human body. However, due to its long half-life and high isotope exchange rate and oxidation rate, it will cause internal exposure hazards to human tissues and organs, so it is necessary to monitor, manage and control its emission in the environment [7–11]. For example, the “Regulations for environmental radiation protection of nuclear power plant” (GB 6249-2011) specifies the emission limitation of tritium in the gas effluent and liquid effluent of nuclear power plants, and “Technical specification for radiation environmental monitoring” (HJ 61-2021) puts forward more stringent emission control and supervision requirements for effluent discharge. With the development of nuclear energy, the enhancement of human’s awareness of nuclear safety and the increasing demand for environmental safety monitoring, the discharge of tritium to the environment during the operation of nuclear facility has attracted more and more attention. Many scholars have also conducted in-depth analysis and research on the radiation effects of tritium from nuclear power plants on the surrounding environment, the public and professionals [12–21]. Therefore, the monitoring of different chemical forms of tritium in radioactive gas effluent and the research on the tritium-induced radiation dose are key topics worthy of discussion in the field of tritium radiation protection. It can provide reference value for accurately estimating the amount of tritium emission, improving the supervision ability of tritium emission, further improving the dose evaluation model of tritium, and effectively reducing the radiation dose received by professionals. The research content of this paper is arranged as follows: Sect. 2 introduces the experimental setup, including the collection of tritium, the preparation of tritium samples to be tested, and the measurement of tritium. Section 3 establishes the dosimetry model of HTO and OBT, and estimates the radiation dose of professionals caused by breathing inhalation and skin absorption. The results and discussion are described in Sect. 4. The conclusions are given in Sect. 5.

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2 Experiment 2.1 Tritium Collection The tritium in the gas effluent is mainly monitored by catalytic oxidation, and the MARC 7000 tritium sampler is used for sampling and collection. The MARC 7000 tritium sampler is mainly composed of the heating furnace, sampling bottle, sampling circuit and cooling circuit, as shown in Fig. 1. The basic working principle is: For HTO, the condensation bubbling method is used to collect the No. 1 and No. 2 bottles by continuously bubbling the air flow in the absorption medium, and the capture efficiency is more than 99%. For OBT, combustion bubbling method is used. The method converts it into HTO through the high-temperature catalytic oxidation furnace, which is also collected by bubbling in the No. 3 and No. 4 bottles, and the capture efficiency is more than 96%. Among them, 110–160 ml of deionized water is used as the tritium bubbling absorption medium of the 4 sampling bottles. The collected liquid loaded in each sampling bottle shall not exceed the Max line of the bottle body. Weigh the total weight of the sampling bottle and water for later use. Set the sampling parameters, and replace the filter paper after each sampling. When the air particle pollution is serious, it should be replaced frequently. After starting the air pump, the bubbling state in the sampling bottle is 4 → 3 → 2 → 1 in sequence. If there is no bubbling or uneven bubbling of the sampling bottle, it indicates that there is air leakage in the device. Turn the handle clockwise, raise the heating furnace and reposition the bottles, and put down the heating furnace until the bubbling is normal. Start the heating furnace and reach the preset temperature of 450 °C after 2–3 min. Start the cooling system, the coolant reaches the preset temperature after 20–30 min, and then the coolant temperature will be maintained between 3 and 7 °C. At this point, the total sampling volume and time are reset to zero, and sampling begins. After the sampling process is completed, turn off the heating furnace/cooling system/air pump in sequence, raise the heating furnace, remove the sampling bottles, seal the sampling bottles and send them to the chemical laboratory for sample preparation and analysis.

Fig. 1. The MARC 7000 tritium sampler

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2.2 Tritium Sample Preparation The laboratory sample preparation method of tritium is called distillation condensation method, and the specific process is as follows: Weigh the No. 1 and No. 2 sampling bottles, and pour them into the clean empty beaker, add 0.5 g of sodium peroxide, and place it on the magnetic stirrer to stir and mix. After fully stirring and mixing, the solution is transferred to the distillation flask; 0.3 g of potassium permanganate is added to the distillation flask, and zeolites are added to prevent bumping of the solution during distillation; then the distillation flask is shaken slightly to make it fully mixed with potassium permanganate. Put the distillation flask on the constant temperature heating jacket and connect it with the condensing pipe, turn on the switch of the cooling water and the heating jacket, and use the end with the liquid receiver. Pay attention to discard the initial 40 ml of condensed water. The No. 3 and No. 4 sampling bottles are treated in the same way as above. The distillation process is shown in Fig. 2.

Fig. 2. Distillation to prepare tritium condensate

2.3 Tritium Measurement Liquid scintillation is the common method for measuring the low-energy beta radionuclide tritium [22, 23]. After the distillation reaction, pipette 8 ml of condensed water into the liquid scintillation counting bottle, add 12 ml of the Gold Star Quanta tritium special scintillation solution to make 20 ml of tritium sample to be tested, as shown in Fig. 3. Before the measurement, wipe the outer wall of the counting bottle with the cotton ball dipped in alcohol, and then put them into the LSC-LB7 low-background liquid scintillation measuring instrument (as shown in Fig. 4) to measure for 24 h. This process needs to be carried out under low light condition. The Lower Limit of Detection (LLD) of the instrument refers to the minimum amount or minimum concentration of the analyte that the analytical instrument can detect, also

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Fig. 3. Prepare 20 ml tritium sample to be tested

Fig. 4. LSC-LB7 low-background liquid scintillation measuring instrument

known as the lower detection limit. Under the condition that the sample volume and the performance of the measuring instrument remain unchanged, the LLD of the instrument (this paper refers to the low-background liquid scintillation measuring instrument) is determined by the measured net count. If the net count of the sample is less than the LLD, it can be judged that it is not detected. At this time, the LLD should be used as the net count of the sample to calculate the activity concentration of the sample. The calculation formula is as formula (1).  Nb Nb 2.71 + 3.29 + (1) LLD = Ts Ts Tb

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where, LLD represents the lower limit of detection of the instrument, cpm; Nb represents the background count rate, cpm; Tb represents the background measurement time, min; Ts represents the sample measurement time, min. After the count rate of the sample is obtained, the activity concentration of tritium is calculated by formula (2). C=

(Ns − Nb )(m2 − m1 ) 60 × E × m × V

(2)

where, C represents the activity concentration of tritium, Bq/m3 ; (Ns − Nb ) represents the net count rate of the sample, cpm; m1 represents the weight of the empty sampling bottle before sampling, g; m2 represents the weight of the sampling bottle (containing tritium sample) after sampling, g; (m2 − m1 ) represents the net weight of the sample before and after sampling, g; E represents the detection efficiency of the low-background liquid scintillation measuring instrument, %; m represents the volume of tritium condensed water required for measurement, g; V represents the cumulative volume of air passing through the tritium sampler, m3 .

3 Dosimetry Model of Tritium “The International Basic Safety Standards for Protection from Ionizing Radiation and the Safety of Radiation Sources”, published by the IAEA, emphasizes the evaluation of the nature, extent and possible consequences of exposure from radioactive sources. Report ICRP-60 calls for the comparison of the transport of radioactive waste in the environment and dose estimation. Therefore, in the radiation protection evaluation and dose modeling work, the dosimetry model is very necessary to evaluate the radiation dose caused by radioactive substances entering the human body. The main ways for professionals to ingest tritium are breath inhalation and skin absorption. Due to the different chemical forms of tritium emission, and the different transformation forms of different chemical forms of tritium, the estimation of the internal exposure dose is also different. At present, the evaluation standards for internal dose of tritium used include GB 18871-2002, EJ/T 287-2000, GB/T 16148-2009 and GBZ 1292016, etc., which stipulate the methods and parameters for the estimation of internal exposure dose to radionuclides, including tritium. 3.1 Dosimetry Model for Breath Inhalation Calculated in terms of 365 d of one year, a total of 8760 h. Among them, the annual working time is 2085.71 h (about 261 d), the annual sleep time is calculated as 8 h per day, a total of 2920 h, the rest time is rest time, a total of 3754.29 h. It is assumed that professionals work 8 h per day, of which 7.5 h are light manual labor and 0.5 h are heavy manual labor, the concentration of tritium in the air is C Bq/m3 , and the rest and sleep time are based on the atmospheric background tritium concentration X Bq/m3 (the concentration of HTO is 0.94 Bq/m3 and the concentration of OBT is 0.02 Bq/m3 ) for estimation. For the reference person who breathes air at the rate of A m3 /h, the annual intake of tritium by professionals through the breath inhalation route is calculated by

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referring to the typical value of adult breathing rate in GB/T 17982-2018, the breathing rate during sleep, rest, light manual labor, and heavy manual labor are: 0.45 m3 /h, 0.54 m3 /h, 1.5 m3 /h, 3.0 m3 /h, respectively. The annual intake of HTO is calculated as formula (3), and the annual intake of OBT is calculated as formula (4). BHTO = A × CHTO × T1 + A × XHTO × T2

(3)

BOBT = A × COBT × T1 + A × XOBT × T2

(4)

where, BHTO represents the annual intake of HTO by breath inhalation, Bq; BOBT represents the annual intake of OBT by breath inhalation, Bq; A represents the breathing rate of the reference person, m3 /h; CHTO represents the concentration of HTO in the air, Bq/m3 ; COBT represents the concentration of OBT in the air, Bq/m3 ; XHTO represents the ambient background concentration of HTO, Bq/m3 ; XOBT represents the ambient background concentration of OBT, Bq/m3 ; T1 represents the annual working time, h; T2 represents the annual rest and sleep time, h. Referring to the breath inhalation route, the Dose Conversion Factor (DCF) of HTO is 1.8 × 10–11 Sv/Bq, and the DCF of OBT is 4.1 × 10–11 Sv/Bq, the radiation dose caused by HTO and OBT through breath inhalation can be obtained, as shown in formula (5) and formula (6), respectively. DB-HTO = BHTO × DCFB-HTO

(5)

DB-OBT = BOBT × DCFB-OBT

(6)

where, DB-HTO represents the radiation dose caused by breath inhalation of HTO, Sv; DB-OBT represents the radiation dose caused by breath inhalation of OBT, Sv; DCFB-HTO represents the dose conversion factor of breath inhalation of HTO, Sv/Bq; DCFB-OBT represents the dose conversion factor of breath inhalation of OBT, Sv/Bq. 3.2 Dosimetry Model for Skin Absorption When the human body is exposed to the tritium-polluted environment, if the concentration of tritium in the air is C Bq/m3 , the absorption rate through intact skin is (0.6·C) Bq/h [24], so as to calculate the annual intake of tritium by professionals through the skin absorption route. The annual intake of HTO is calculated as formula (7), and the annual intake of OBT is calculated as formula (8). SHTO = (0.6 · CHTO ) × T1 + 0.6 × XHTO × T2

(7)

SOBT = (0.6 · COBT ) × T1 + 0.6 × XOBT × T2

(8)

where SHTO represents the annual intake of HTO by skin absorption, Bq; SOBT represents the annual intake of OBT by skin absorption, Bq. Similarly, referring to the skin absorption route, the DCF of HTO is 1.8 × 10–11 Sv/Bq, and the DCF of OBT is 4.5 × 10–9 Sv/Bq, and the radiation dose caused

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by HTO and OBT through skin absorption can be obtained, as shown in formulas (9) and (10), respectively. DS-HTO = SHTO × DCFS-HTO

(9)

DS-OBT = SOBT × DCFS-OBT

(10)

where DS-HTO represents the radiation dose caused by skin absorption of HTO, Sv; DS-OBT represents the radiation dose caused by skin absorption of OBT, Sv; DCFS-HTO represents the dose conversion factor of skin absorption of HTO, Sv/Bq; DCFS-OBT represents the dose conversion factor of skin absorption of OBT, Sv/Bq.

4 Results and Discussion In order to obtain accurate measurement results and effectively reflect the impact of radiation dose on professionals, the sampling sites are selected as representative places where the professionals stay for a long time and the professionals are relatively concentrated. The annual intake of tritium by professionals through breath inhalation and skin absorption are shown in Table 1. The tritium-induced radiation dose of professionals through breath inhalation and skin absorption are shown in Table 2. Table 1. Annual intake of tritium by professionals through breath inhalation and skin absorption Sampling point

Tritium concentration in air (Bq/m3 )

Annual intake of tritium through breath inhalation (× 104 Bq)

Annual intake of tritium through skin absorption (× 104 Bq)

HTO

OBT

HTO

OBT

HTO

OBT

1

8.34

0.28

3.09

0.10

1.42

0.04

2

8.87

0.32

3.26

0.11

1.49

0.05

3

9.12

0.35

3.35

0.12

1.52

0.05

Table 1 shows the annual intake of tritium by professionals through breath inhalation and skin absorption. Among them, breath inhalation contributes more to tritium intake, accounting for about 69% of the annual intake; and HTO is the main intake, accounting for about 97% of tritium intake through breath inhalation. Table 2 shows the tritium-induced radiation dose of professionals through breath inhalation and skin absorption. Among them, the contribution of skin absorption to tritium intake is small, accounting for about 31% of the annual intake. However, in the skin absorption route, the DCF of OBT is two orders of magnitude higher than that of HTO and OBT in other routes. Therefore, skin absorption is the main way to cause tritiuminduced internal exposure of professionals, and the contribution to the dose mainly comes from OBT. Under normal working conditions, the annual cumulative equivalent

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Table 2. Tritium-induced radiation dose of professionals through breath inhalation and skin absorption Sampling point

Tritium-induced radiation dose through breath inhalation (μSv/a)

Tritium-induced Annual cumulative radiation dose through equivalent dose (μSv/a) skin absorption (μSv/a)

HTO

OBT

HTO

OBT

1

0.56

0.04

0.25

1.94

2.79

2

0.59

0.05

0.27

2.16

3.07

3

0.60

0.05

0.27

2.33

3.25

dose of tritium for professionals is far below the annual dose limitation required for ionizing radiation protection, indicating that the radiation level caused by radioactive tritium released during the operation of the nuclear facility is relatively low, and it will not cause ionizing radiation hazards to professionals.

5 Conclusion Tritium, the gas effluent produced during the operation of nuclear facility, cannot be eliminated through the three-waste system, and is mainly discharged into the environment in the form of HTO and OBT, which is one of the main nuclides that cause internal exposure of professionals. In order to evaluate the radiation dose impact of the tritium released by the nuclear facility on professionals, this paper establishes the dosimetry model of HTO and OBT based on the measurement results of tritium activity concentration in the workplace obtained by liquid scintillation method, and estimates the annual intake of tritium and tritium-induced radiation dose of professionals through breath inhalation and skin absorption. The results of the study show that breath inhalation is the main way of tritium intake, and HTO is the main intake. Although the contribution of skin absorption to tritium intake is small, since the DCF of OBT is two orders of magnitude higher than that of HTO and OBT under other routes, skin absorption is the main way to cause tritium-induced internal exposure of professionals, and the contribution to dose mainly comes from OBT. Due to the discharge of tritium from nuclear facility, the concentration of tritium in some workplaces is slightly higher than that in other areas. However, under normal working conditions, the tritium-induced radiation dose received by professionals is at a low level, which meets the radiation protection limitation. The monitoring of different chemical forms of tritium in gas effluents can provide the data source for accurately estimating tritium emission and evaluating whether nuclear facility meet specified emission limitation. By establishing dosimetry models of different chemical forms of tritium, it can provide guidance for accurately estimating the tritium-induced radiation dose of professionals, effectively reducing the radiation level around nuclear facility, and ensuring the safety of ionizing radiation for professionals, the public and the surrounding environment.

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References 1. Nie, B., Fang, S., Jiang, M., et al.: Anthropogenic tritium: inventory, discharge, environmental behavior and health effects. Renew. Sustain. Energy Rev. 135, 110188 (2021) 2. Son, S., Lee, S., Kim, K.: Tritium production, recovery and application in Korea. Appl. Radiat. Isot. 67, 1336–1340 (2009) 3. Peterson, H., Jr., Baker, D.: Tritium production, releases and population doses at nuclear power reactors. Fusion Technol. 8, 2544–2550 (1985) 4. Hou, X.: Tritium and 14 C in the environment and nuclear facilities: sources and analytical methods. J. Nucl. Fuel Cycle Waste Technol. 16, 11–39 (2018) 5. Yang, D., Chen, X., Li, B.: Tritium release during nuclear power operation in China. J. Radiol. Prot. 32, 167–173 (2012) 6. Yang, J., Li, Y., Wang, Y., et al.: Benchmarking study on radioactive effluents released by nuclear power plants of the world. Environ. Sci. Manag. 42, 127–132 (2017) 7. Eyrolle, F., Ducros, L., Dizes, S., et al.: An updated review on tritium in the environment. J. Environ. Radioact. 181, 128–137 (2018) 8. Kim, H., Kong, T., Lee, G., et al.: Analysis of metabolism and effective half-life for radiation worker’s tritium intake at pressurized heavy water reactors. Progr. Nucl. Sci. Technol. 1, 545–548 (2011) 9. Galeriu, D., Melintescu, A., Takeda, H.: Risk from tritium exposure. IRPA Region. Congr. Central Eastern Europe 9, 24–28 (2007) 10. Momoshima, N., Okai, T., Kaji, T., et al.: Distribution and transformation of various chemical forms of tritium in the environment. Radiochim. Acta 54(3), 129–132 (1991) 11. Koranda, J.J., Anspaugh, L.R., Martin, J.R.: The significance of tritium releases to the environment. IEEE Trans. Nucl. Sci. 19, 27–39 (1972) 12. Shinohara, K.: Environmental monitoring and public dose assessment around the Tokai reprocessing plant. J. Radioanal. Nucl. Chem. 260(3), 563–577 (2004) 13. Jang, Y., Park, E.: Social acceptance of nuclear power plants in Korea: the role of public perceptions following the Fukushima accident. Renew. Sustain. Energy Rev. 128, 109894 (2020) 14. Kong, T., Kim, S., Lee, Y., et al.: Radioactive effluents released from Korean nuclear power plants and the resulting radiation doses to members of the public. Nucl. Eng. Technol. 49, 1772–1777 (2017) 15. Son, J., Kim, H., Kong, T., et al.: Radiological effluents released and public doses from nuclear power plants in Korea. Radiat. Protect. Dosim. 155, 517–521 (2013) 16. Wang, K.Z., Sun, L., Xiong, K.H., et al.: Monitoring of Tritium internal exposure doses of heavy-water reactor workers in third Qinshan nuclear power plant. Dose-Response 17(4) (2019) 17. Kim, C.K., Han, M.J.: Dose assessment and behavior of tritium in environmental samples around Wolsong nuclear power plant. Appl. Radiat. Isot. 50(4), 783–791 (1999) 18. Paunescu, N., Cotarlea, M., Galeriu, D., et al.: Evaluation of environmental tritium level in preoperational period of Cernavoda CANDU Nuclear Power Plant. J. Radioanal. Nucl. Chem. 239(3), 465–470 (1999) 19. Paunescu, N., Galeriu, D., Mocanu, N.: Environmental tritium around a new CANDU nuclear power plant. Radioprotection 37(C1), 1253–1258 (2002) 20. Palomo, M., Penalver, A., Aguilar, C., et al.: Tritium activity levels in environmental water samples from different origins. Appl. Radiat. Isot. 65(9), 1048–1056 (2007) 21. Jean-Baptiste, P., Baumier, D., Fourre, E., et al.: The distribution of tritium in the terrestrial and aquatic environments of the Creys-Malville nuclear power plant (2002–2005). J. Environ. Radioactivity 94(2), 107–118 (2007)

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22. Kapulla, H., Kraemer, R., Heine, R.: Tritium inventory measurements using calorimetry. Fusion Technol. 21(2), 412–419 (1992) 23. Varlam, C., Stefanescu, I., Doliu, O.G., et al.: Applying direct liquid scintillation counting to low level tritium measurement. Appl. Radiat. Isot. 67, 812–816 (2009) 24. Osborne, R.V.: Absorption of tritiated water vapour by people. Health Phys. 12, 1527–1537 (1986)

Improvement and Realization of AGC Strategy in Nuclear Power Plants Jing Chuanxiang(B) and Zhao Yuntao China Nuclear Power Engineering Co., Ltd., Shenzhen, Guangdong, China [email protected]

Abstract. China raises not specific requirements of AGC (Automatic Generation Control) for Nuclear Power Plants (NPP). With the development of power supply structure towards clean energy, NPP will be deemed to contribute their parts for grid stabilization. This paper mainly focuses on EPR™ 1750MW NPP, and comprehensively states the improvement and realization of AGC strategy considering nuclear safety, unit relay protection, grid management automation, power system stabilizer (PSS) and speed regulation system. Keywords: AGC · Frequency regulation · NPP · EPR™ · Nuclear safety · Reactor control · Automatic mode

1 Introduction AGC is also named Secondary (or Remote) Frequency Regulation (FR), and is to realize zero-error regulation of grid frequency. When AGC is in-service, the specific units will receive target load commands separately from the grid dispatching center by telephone communication or wiring to the Economic Dispatch Control System (EDCS). There are four performance coefficients to characterize the frequency regulation capability of a power plant, they are Weighting Rate (K1), Response Time (K2), Regulating Accuracy (K3), and Integrated index (K). Examples are in Table 1. As Table 1 illustrates, the nuclear power plants have relatively sufficient capacities for FR while it is actually difficult to find a balance between the requirements for nuclear safety and economic benefits. At present, in China, AGC and Primary Frequency Regulation (PFR) performance of nuclear power plants are exempted from assessment by the grid administration parties during daily operation. However, with the large-scale replacement of the existing conventional thermal power by nuclear power in the future, it is believed to become a mandatory requirement for nuclear power plants to participate in daily grid frequency regulation (like France). And it will raise much more appropriate requirements for plant’s own primary frequency regulation and the automatic realization of AGC function. Taking the EPR™ 1750MW nuclear power plants as an example, this paper discusses how to improve the nuclear AGC control strategy.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 607–615, 2023. https://doi.org/10.1007/978-981-19-8780-9_60

14,998

6120

Nuclear

Total

770

6268

Gas

Green energy (hydroelectric)

1840

Installed capacity (MW)

Coal

Power plant

Shenzhen City (2015)

100

40.8

5.1

41.8

12.3

Proportion (%)

3.64

5.00

3.20

3.00

1.50

Governing rate (%/min)

1.37

0.88

0.82

0.41

Weighting coefficient (K1)

0.99

0.93

0.85

0.75

0.99

0.65

0.85

0.75

Response time Regulating coefficient (K2) accuracy coefficient

Table 1. AGC performance in Shen Zhen City

1.12

0.82

0.84

0.64

Integrated coefficient (K)

0.43

0.31

0.79

0.45

Electrovalency (¥/kW*h)

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2 AGC Strategy 2.1 AGC of Conventional Thermal Power Plants China has stipulated a detailed standard for thermal power AGC functions, as shown in Table 2. Table 2. Thermal power AGC capacity No.

AGC capacity

Criteria for conventional thermal power plant

1

Real regulation rate

≥ 1.5% rated power /min

2

Response delay time

≤ 90s

3

Unit actual reverse load change delay

≤ 180s for supercritical plants

4

Unit actual reverse load change percent

≮ 60% within 60s

5

Regulating error

≯ 3%

6

Function availability time

Above 98% of grid operation time

7

Function input time

Above 90% of grid operation time

8

Qualified electricity quantity (kW*h)

For the AGC activated plant, = (1 − AGC qualification rate) × 0.1%

9

Prerequisites

Under CCS (Coordination Control System) AGC command available AGC command within upper and bottom limits None Runback situation Grid frequency 50 ± 0.5Hz P < |±10%|

10

I/O channel accuracy

Analog accuracy ≤ 0.1%

11

Other critical parameters

Available load range for AGC function Upper limit of AGC target load Bottom limit of AGC target load Allowable maximum gradient of AGC command Allowable maximum load deviation before AGC on Allowable maximum step of load change Frequency thresholds for AGC auto off Unit average response delay time Unit critical shake area

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2.2 AGC of EPR™ Nuclear Power Plant It is known that China, Britain and Finland have EPR™ nuclear power plants, among which Finland has no AGC function because it is mainly powered by Russia and EU while AGC for China and Britain is exactly the same, as shown in the following (Fig. 1).

AGC On

MCR TGC Stress Hold AGC On T=120s

MCR

S E L E C T

Actual Load Setpoint

AGC

Fig. 1. AGC control strategy for EPR™

The prerequisite conditions of AGC are listed in the following: (1) Manual command from MCR to enable AGC; (2) The gross power (P) is controlled in automatic mode where if any of the following conditions is not met, it will be disabled automatically; • • • • • • • • • •

Manual switch-off by the Operator Unavailability of gross measurement signals P > 0% Pe f > 200 MHz Over 98% of rated turbine speed, the speed acceleration is greater than 4.84 %/s (overspeed protection is active) Any RB exists and the gross load limit is reached House load or idling operation; 2 out of 3 live steam pressure measurement transmitters failed 2 out of 3 of HP inlet pressure measurement transmitters failed Turbine trip

(3) AGC command signal failed or disturbed. Compared with AGC function of thermal power plants, it is clear that the AGC control strategy of EPR™ nuclear power plant is relatively simple. For example, it is forbidden to activate AGC function until to all the boiler control, turbine control and CCS are put into automatic control mode, while it only requires for turbine control without fully consideration of the impact introduced by AGC mode on the reactor and other NI systems in EPR™; At the same time, there is no step, upper and bottom limit justification for AGC in EPR™. Obviously, the existing AGC control strategy can not inhibit human failures of the grid dispatching centers, and fully guarantee nuclear safety.

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3 Major Constraints for AGC Function in EPR™ 3.1 Operating Range for AGC The capacities for scheduled load variations are the results of a reasonable compromise between maximum grid requirements and constraints on the plant design. This is the reason why these capacities are formulated considering different ranges of power or burnup situations. The normal operating range of the plant is between 60 and 100% NP where the mean temperature is maintained constant. In this range, the reactivity effects and fatigue contributions to the most solicited parts of the primary circuit are minimized. Nevertheless, load follow is also possible in the range of power between 25 and 60% NP. 3.2 Load Change Limit 3.2.1 Reactor Load Change Limit The plant is capable of step load changes of +10% of Rated Thermal Output (RTO) with a rate of 1% NP/sec within a range of 25% to 90% NP and step load changes of – 10% of RTO with a rate of 1% NP/min within a range of 35–100% NP without tripping the reactor or opening the primary and secondary system relief valves, and without the use of steam dump or bypass systems. The plant is also capable of power ramps with maximum rates of 5% NP/min, in the range 25–100% NP, without tripping the reactor or opening the primary and secondary system relief valves, and without the use of steam dump or bypass systems. In the range 0–15% NP, manual control is mandatory. Without consideration regarding strategy to apply at the low power level, this requirement is fulfilled up to 80% of the natural fuel cycle length. Throughout the above processes, the Operator can use “HOLD” function to enable and disable load regulation. The design capacity of turbine bypass system can meet 85% of the maximum steam flow before the gross load is reduced to the house load level, and at the same time, it can still provide enough feed water to the Steam Generators. 3.2.2 Turbo-Generator Limitation (1) Turbine thermal Stress limitation After generator synchronized, the load escalation and reduction rate is determined by the minimum of manual setting value by the Operator and the rising gradient corresponding to the center temperature of the IP inlet flange, which slightly differs between different turbo-generator manufacturers. (2) Gross Load limitation There are four types of gross load limitation in EPR™, they are live steam pressure limitation, HP inlet pressure limitation, maximum inlet steam and fast unloading limitation, which is limited by the minimum gross load set values of 11 runback cases.

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3.3 Frequency Regulation Dead Band The turbo-generator applied for EPR™ can operate safely and continuously in the frequency range of 48.5 to 50.5 Hz without any restriction on the output of the unit. When |f| is above 0.2 Hz (0.4%), the gross load control will be disabled. In China, the allowable deviation of local power system with large capacity above 3GW is ± 0.2 Hz, and that of medium and small capacity power system is ± 0.5 Hz. It is easy to tell that the control deviation of AGC function of EPR™ meets these requirements.

Fig. 2. PFR performance in EPR™

As shown in Fig. 2, the default f dead band for primary frequency control after grid synchronization is [− 2.5%, − 0.3%] and the target dead band is [− 0.1%, 0.1%]. Obviously, if the accuracy of primary frequency control function is sufficiently high, there is no any need to enable AGC in EPR™. 3.4 Reactor Response to Power Fluctuation Under Normal Operating Conditions The capability of EPR™ plants to perform load follow transient is ensured by the core control system. Reactivity feedback effects resulting from fuel temperature (Doppler reactivity), coolant temperature, fission products (particularly xenon) and fuel burnup are mainly compensated for in the short term by changes in the RCCA bank positions and in the long term by changes in the boron concentration of the reactor coolant. Besides the necessary boronization and dilution, the reactor coolant needs to be treated in order to meet the requirements of chemical specifications and reactivity, and the corresponding liquid volume shall be recovered. Meanwhile, the load fluctuation introduced by AGC performance will also cause the fluctuation of other critical parameters involved in reactor protection, such as Steam Generator liquid level, Pressure of the primary loop, steam turbine bypass flow, etc. Thus, in order to minimize the disturbance and ensure the nuclear safety, most of control loops, such as Chemical and Volume Control System, Reactor Boron and Water Makeup System, RCCA shall be put into automatic control mode as early as possible.

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3.5 Inherent Low Frequency Oscillation of Gross Load As the low-frequency oscillation of gross load exists in most power plants, which presents that the rotor angle, speed, related gross load and bus voltage, oscillate with approximately equal or increased amplitude at a frequency between 0.1 and 2.5 Hz. PSS (power system stabilizer) is applied, which takes the terminal voltage and gross power as inputs, and adds a damping to the grid system when real gross power oscillates, working together with the primary frequency regulation function, to minimize the low-frequency oscillation as soon as possible and stabilize the grid frequency. During the commissioning phase of the first EPR™ nuclear power plant, it was found that 1 Hz gross load oscillation exists no matter under open or automatic control mode, and the oscillation amplitude tends to increase with the load escalation. It was about 5 MW at 10% Pe and 33 MW (alarm threshold for grid administration is 35 MW) at 80% Pe. As the low frequency oscillation of gross load can be obviously alleviated but never completely eliminated by PSS parameter optimization. Obviously, before PSS is qualified, there is little significance to enable AGC.

4 Evolution of AGC Strategy for EPR™ Based on the above comparison and analysis, the following evolution are proposed for AGC strategy of EPR™ 1750 MW nuclear power plant (Fig. 3).

MCR TGC

S E L E C T

Stress Hold

AGC On

Actual Load Setpoint

MCR

AGC

+ S E L E C T

ABS

S E L E C T

S E L E C T

Fig. 3. Optimized diagram of AGC function in EPR™

4.1 Newly Added the Justification of Available Load Range for AGC According to the operating technical specification, the Moisture separator-reheater, high/low pressure feedwater heaters shall be put into service between 30 and 50% Pe, and follows their switchover from emergency draining to normal draining. Thus, the

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operating range [60% Pe, 80% Pe] seems to be the best choice for AGC, where the operating performance of the Reactor is much better and the thermal efficiency between the primary loop and secondary loop is comparatively high (2.43). That is, the upper limit of AGC target load is ≯ 80% Pe and the bottom limit is ≮ 60% Pe. 4.2 Newly Added P Limitation As known that the characteristic of grid load P and grid frequency f are approximately a linear function. P/P = K ∗ f/f

(1)

Assuming that the total installed capacity P in Shenzhen is 15 GW and K is 1.5, then if f = 0.2 Hz, P(f) = 90MW. As the target load change is ± 5% Pe (±87.5 MW) during PFR test of EPR™), and the test results are qualified. Hence, the upper/bottom limit of AGC target load (allowable P range) set by Operator manually can be [− 87.5 MW, 87.5 MW], which is extremely close to the load change requirement for ± 0.2 Hz frequency deviation in 15 GW grid like Shen Zhen. 4.3 Newly Added Step Limitation for AGC Command The allowable max step for AGC command is mainly decided based on the following data: • EPR™ Reactor can bear + 10% Pn at 1% Pn/s and − 10% Pn at 1% Pn/s. • The PFR test results with load step change ± 5% Pe meet the requirements of grid assessment where the delay time of load regulation is simply 1 s, the actual peak time of frequency regulation is 35 s and the actual change of primary frequency regulation command within 60 s reached over 93%. • Among the 11 Runback conditions in EPR™, the Reactor Runback gradient is the highest (200% Pe/min) and the test results are qualified. • According to the EPR™ Operation Technical Specification (OTS), during normal operating conditions, the power change rate shall not exceed 5% NP/min from 25 to 100% NP. And then if the average thermal efficiency coefficient of the primary and secondary circuits is 2.0, the equivalent power rate of the unit shall not exceed 2.5% Pe/min. Therefore, judging from a nuclear safety perspective, the maximum step of AGC command should not exceed 2.5% Pe/min (i.e. 48.0 MW/min), which can be used to alleviate the potential frequency fluctuation of 0.10 Hz in Shenzhen grid. 4.4 Prerequisites for AGC As the gross load shall be controlled in automatic mode ahead, it means that the allowable maximum load deviation before AGC in-service is simply zero.

Improvement and Realization of AGC Strategy in Nuclear

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Besides the prerequisites referred in Sect. 2.2, the control loop for primary loop average temperature, nuclear island Pressurizer pressure, evaporator water level, live steam pressure, RCCA, turbine bypass system and all other critical parameters as many as appropriate should be in automatic mode ahead of AGC in-service.

5 Conclusion Obviously, there are many defect of AGC function in nuclear power plants. The perspectives and requirements of nuclear safety and frequency regulation capacity are fully considered in the proposed AGC strategy in this paper, and the method for AGC value setting is also given based on real test results/performance. It’s believed that this improved AGC strategy would provide some reference for nuclear power plants to participate in secondary frequency regulation in the future.

References 1. South China Energy Regulatory Office of National Energy Administration.: Notice on printing and distributing the detailed rules for the implementation of grid operation management of power plants in South China and the detailed rules for the implementation of auxiliary service management of grid-synchronized power plants in South China (2020 Edition) [EB/OL]. http://120.31.132.37:16001/portal-oa/unauth/readPdf?filePath=/biz/DOC_ SEND/888.Pdf, 2022-06-13 2. GB/T 30370-2013.: Guide of primary frequency control test and performance acceptance for thermal power generating units. China National standardization Committee, Beijing (2013) 3. EPRTM Taishan Nuclear Power Joint Venture Co., Ltd.: Construction and innovation of Taishan nuclear power plant phase I, the first project in the world, 599. Atomic energy Publishing House, Beijing (2020) 4. Generator operation and maintenance manual. Shanghai Electric Machinery Co., Ltd. Limited (2010)

Study on Simulation Method of CRUD Deposition Behavior on Nuclear Fuel Xiao-han Liu(B) , Kai-yuan Wang, Yong Lu, Ya-Ni Liu, and Xin Jin China Nuclear Power Technology Research Institute Shenzhen, Guangdong, China [email protected]

Abstract. The corrosion products released from the materials of primary circuit in PWR (pressurized water reactor) will deposit on the fuel surface, potentially increasing the safety risks of nuclear fuel and reactor. In this paper, the deposition process and principle of curd in PWR primary circuit are studied. we analyzed the sources of main elements in curd and the deposition process. A curd deposition model on PWR fuel surface is established, and it has been applied in CAMPSIS (a fuel curd calculation software developed by CGN). it can perform the quantitative calculation of curd. The software validated with the measured data in the power plant, and the results fit the measured data well. Keywords: PWR · Fuel crud · Model · Validation

1 Introduction The materials used in the primary circuit, as bare material, are not stable in the water and dissolve by corrosion. In normal operating conditions, the corrosion is under control and is not a problem for the plant. However, to some small extent, the materials dissolve in the coolant and are transported as corrosion products often called the CURD to the reactor core. Furthermore, the corrosion and release of primary circuit elements and their subsequent deposition on the fuel clad remain a problem in nuclear reactor systems. In PWR units operating, the problem has been most recently emphasised by the occurrence of CIPS (crud induced power shifts) or (AOA)axial offset anomaly problems, because of the plant high boiling duties [1, 2]. The crud deposited on the nuclear fuel surface captures boron which can lead to neutron flux depression in the top of fuel assemble [3]. The detailed principle for these processes has been discussed elsewhere [4] and will be discussed. In addition to CIPS, crud on the fuel clad will increase activity levels in the primary circuit since the residence time of elements, as Ni-58 and Co-59 (subsequently activated to Co-58 and Co-60) increase. Another important problem is the crud induced localized corrosion (CILC). It also leads to fuel degradation and fuel failures in extreme cases. CILC may be a consequence of the clad temperature increases discussed above, but may also be impacted by local chemistry conditions.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 616–628, 2023. https://doi.org/10.1007/978-981-19-8780-9_61

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For these reasons, it is valuable to understand the mechanism of material release, transport, and deposition in nuclear plant and to have some form of tool that can be used to assess the effect of changes in plant operations and fuel design on these phenomena. The CAMPSIS computer code was developed with this purpose in mind, the code focused on calculating the extent of CIPS and CLIC phenomena. The boron uptake material transport had already been updated for CPR and HPR. The CAMPSIS software had passed the Generic Design Assessment (GDA) from the British Office for Nuclear Regulation (ONR). This paper discusses the ideas and models about the most important crud deposition and presents some of the preliminary results from calculations using the program.

2 Model 2.1 CAMPSIS Software CAMPSIS is a crud behavior and activated corrosion product analysis code. Essential information, such as corrosion release rate and thermal-hydraulic parameters from input cards are input into the code. The crud behaviors and radionuclide qualities are calculated by solving corresponding mass balance equations, then, the results are output for further reactor safety analysis and radiation shielding calculations. CAMPSIS is used to perform a fuel crud calculation for a PWR reactor core. It can be applied in PWR reactors with nickel-based and iron-based structural materials for the main equipment, under normal operational conditions. The schematic diagram of Transport Process is shown in Fig. 1. CAMPSIS can perform a crud behavior and activated corrosion product simulation of the Pressurized Water Reactor (PWR). The main outputs are listed as below: (a) (b) (c) (d)

Distributions of crud on fuel surfaces. Deposited boron distributions in fuel crud. Distributions of crud in Steam Generator (SG) and main pipes. Amounts of activated corrosion products on the surface of the fuel, primary main pipe, and the SG. (e) Amounts of activated corrosion products in the primary coolant.

2.2 Brief Conclusion of CAMPSIS Theory In CAMPSIS, the key phenomena of crud formation are very complex. The models implemented in CAMPSIS to simulate the phenomena are presented and discussed [5]. The Structure of CAMPSIS Models is shown in Fig. 2. (a) Thermal-hydraulics of fuel assemblies: thermal-hydraulic parameters are calculated with a thermal-hydraulic code, LINDEN, as input for CAMPSIS. (b) Corrosion release model: The corrosion released rates are obtained with experiments that provide the corrosion release data of the materials adopted in CPR. The release rates are determined in consideration of conservatism.

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Fig. 1. Transport process of corrosion products

(c) Water chemistry model: The water chemistry model includes the key chemical reaction formulas in the primary coolant. The chemical equilibrium constants in each chemical reaction formulas are functions of temperature obtained from the test date. (d) Crud Formation Model: In the crud formation model, the deposition rate of each species with/without boiling has been considered. The transport of corrosion products in the primary circuit is calculated with the mass balance equation of the system. (e) Boron deposition model: The hideout return phenomenon of boron elements inside fuel crud is simulated with the boron deposition model. Mechanisms of boron deposition have been discussed, including physic-adsorption and precipitation. (f) Activated corrosion product calculation model: Activated corrosion release products, including Cr-51, Mn-54, Mn-56, Fe-55, Fe-59, Co-58, Co-60, and Ni-63, have been considered in the activated corrosion product calculation model. (g) Zinc injection model: The zinc injection model quantifies zinc effects on corrosion release rate, fuel crud physical parameters, and fuel crud behaviors. (h) To conclude, CAMPSIS is applicable in the simulation of crud behaviors, CIPS, and activated corrosion activated corrosion product calculation, fulfilling the design requirements for CPR1000.

2.3 Crud Deposition Model 2.3.1 Diffusion and Crud Formation The following mechanisms of crud formation are suggested: Particles are deposited on the clean fuel surface, causing soluble species to precipitate inside and forming fuel crud. In the boiling area, as the thickness of fuel crud thickens, boric acid and lithium concentration elevate, and local pH value will be affected, inducing some soluble species to continue to precipitate. Local thermal-hydraulic conditions will affect the composition and physical parameters of fuel crud.

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Fig. 2. Structure of CAMPSIS models

A simplified model to simulate the formation of fuel crud has been developed and implemented in CAMPSIS. Based on the diffusion layer model [6], the effects of turbulent mixing and boiling on crud formation are the key factor of crud formation.

Fig. 3. Crud formation process based on diffusion layer model

Figure 3 shows the crud formation process based on the diffusion layer model. Mass transfer during turbulent mixing can be divided into two processes: the mass transfer between bulk coolant and diffusion layer; and the mass transfer between diffusion layer and surface. Mass transfer during SNB is determined by mass evaporation rate. The process of turbulent mixing has an impact on crud formation as mass transfer from the bulk coolant to the diffusion layer, and then from the diffusion layer to the surface.

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For soluble species, the mass flux from the bulk coolant to the diffusion layer and from the diffusion layer to the surface is calculated as following: Wms = kms ∗ (Cs − Cws ) ∗ 109

(1)

Ws = ks ∗ (Cws − Cs0 ) ∗ 109

(2)

where, Wms : soluble species mass flux from the bulk coolant to diffusion layer, g/(cm2 · s); Ws : soluble species mass flux from diffusion layer to the surface, g/(cm2 · s); kms /ks : soluble species mass transfer coefficient, g/(cm2 · s); Cs : soluble species concentration in bulk coolant, ppb; Cws : soluble species concentration in diffusion layer, ppb; Cs0 : soluble species concentration on surface, ppb. Soluble species mass transfer coefficient between bulk coolant and diffusion layer (kms ) is derived as following. Since the wall layer is an assumed region, it is reasonable to assume that soluble species mass transfer coefficient between diffusion layer and surface (ks ) is equal to kms. According to the Chilton-Colburn formula [7]: kms f = *Sc−2/3 (3) mw 2 υ (4) Sc = Ds f = 0.023*Re−0.2 Re =

ρw*V*l μ

where, mw : mass rate of coolant per area, g/(cm2 · s); f: friction factor, non-dimensional; Sc: Schmidt number, non-dimensional; υ: kinematic viscosity, m2 /s; ρw : coolant density, g/ m3 ; v: coolant velocity, cm/s; l: characteristic length, cm; μ: dynamic viscosity, g/(cm · s); Ds : diffusion coefficient, calculated by Stokes-Einstein formula [8]

(5) (6)

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DAB =

κ*T 6π *rA ∗ μB

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(7)

where, DAB : species A’s diffusion rate in B solution, m2 /s; κ: Boltzmann Constant, 1.381 * 10−23 J/K; T: absolute temperature, K; rA : radius of species A, m; μB : viscosity of B solution, Pa · s. An important difference between particles and solubles is their radius. Mass transfer of particles to a surface depends on the particle size. Small particles transfer like molecules and so standard mass transfer correlations can be used. Large particles on the other hand are impacted by inertial effects and standard mass transfer correlations don’t work. The smaller the radius, the higher the mass transfer coefficient, which means more corrosion products could deposit. For particles, the turbulent mixing mass transfer coefficient’s derivation process is similar to that of soluble species, except for the difference of radius compared to soluble species. 2.3.2 Boiling Effect on Crud Formation In the absence of fuel crud, a clean core, heat is removed from the fuel pins by forced convection and SNB. Several empirical heat transfer correlations are used to determine the forced convective heat transfer coefficient (e.g. Dittus-Boelter [9]), which applies for forced convection in the absence of boiling. Likewise, various sub-cooled nucleate boiling equations are utilized (e.g. Thome correlation [10]), which apply when boiling is happening in the absence of forced convection. These heat transfer equations are combined in a non-linear fashion (see Steiner-Taborek [11]) to get the total heat flux and predict reasonable surface temperatures. This approach, used in most thermal-hydraulics codes, predicts in a clean reactor core that where SNB happens most of the heat is removed from the pins by forced convection, and only a very small fraction goes into sub-cooled nucleate boiling. The presence of SNB in parts of the core will lead to a boiling deposition velocity that enhances deposition of soluble and particulate corrosion products in those regions. This is simply because water is converted to steam at the pin surface must be replenished by incoming water that contains soluble and particulate material. However, the deposition enhancement is small because the deposition velocity is proportional to the sub-cooled boiling heat flux, which as stated, is a small fraction of the total heat flux. Once crud starts to form though heat is now removed from the fuel pin by wick boiling, in the case of wick boiling heat is transferred across the crud by conduction and boiling in the steam chimneys present. At the coolant-crud interface, the conducted heat is then removed by forced convection. The heat transfer models for wick boiling predict most of the heat is removed by boiling in the chimneys. The actual extent will depend on

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crud properties, including its thickness, porosity, thermal conductivity, evaporative heat transfer coefficient, etc. The change in heat transfer mechanism, low boiling and large forced convection, to high boiling combining low conduction (forced convection), means a large increase in the deposition velocity in regions of significant crud. This process has two effects, it may change the temperature map of the core and it focuses deposition where thick crud has started to accumulate. The boiling mass flux can be calculated as: we = me *C*109

(8)

where, we : boiling mass flux of corrosion product, g/(cm2 · s); me : mass evaporation rate, g/(cm2 · s); For soluble species, C corresponds to saturated concentration in bulk coolant; for particle, C corresponds to equilibrium concentration in bulk coolant. 2.3.3 Deposition Rate of Corrosion Product It is assumed that the deposition process of corrosion release product can reach a steady state in the diffusion layer instantaneously in CAMPSIS. Considering both the effect of turbulent mixing and that of SNB, the deposition rate of soluble species and particles can be derived: ws =

˙e ks ∗ m ks *kms Cs + (Cs − CS0 ) ks + kms ks + kms

(9)

wp =

˙e kp ∗ m kp *kmp Cp + (Cp − Cp0 ) kp + kmp kp + kmp

(10)

where, ws : deposition rate of soluble species, g/(cm2 · s); wp : deposition rate of particle, g/(cm2 · s); Cp0 : particle concentration on surface, assumed 0; CS0 : soluble concentration on surface, determined by local conditions, ppb. 2.3.4 System Mass Balance If the Chemical and Volume Control System (CVCS) system is not taken into account, PWR primary circuit can be regarded as a closed loop, therefore the mass balance of corrosion products in the PWR primary coolant can be established. Corrosion products are released, transported, and removed by the following mechanisms: 1. The release rate of corrosion product is determined by pH value and behaviors of metallic materials.

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2. Corrosion product will deposit on the fuel surface and out-core surface, forming fuel crud and out-core crud. 3. Corrosion products may redeposit or drop off from fuel surface as well as the out-core surface. 4. A specific portion of the corrosion product will be removed via the CVCS system. Based on the principle of mass balance, for the primary coolant system, following equation can be derived: Mbulk ∗ 106 ∗

dmrel dmic dmoc dmcvcs dC = − − − dt dt dt dt dt

(11)

where, Mbulk : water load of primary coolant, ton; dC dt : rate of change of corrosion product concentration, g/s; dmrel dt : release rate of corrosion products, g/s; dmic dt : deposition rate of corrosion product on fuel surface, g/s; dmoc dt : deposition rate of corrosion product on the out-core surface, g/s; dmcvcs dt : removal rate of CVCS system, g/s. (a) Release rate of corrosion products Release rates of corrosion products are obtained from experiments. The impacts of pH and DH concentration on release rate are also considered and quantified. Differentiating corrosion and release rate of Inconel 690TT and 304/316L Stainless Steel, the formula of release rate can be derived:  dRi  dmrel = *Ai (12) dt dt i

where, i: corresponding to Inconel 690TT and 304/316L Stainless Steel; R: release amount of metallic materials, mg/dm2 ; A: area of metallic materials, dm2 . (b) Deposition rate of corrosion products Equations (2–9) and (2–10) show the equations to calculate the deposition rate of soluble and particle species. These two equations are applicable for boiling and non-boiling area. For the boiling area (fuel surface), the value of mass evaporation rate is calculated by CGN’s sub-channel analysis code LINDEN [12]; for the non-boiling area (SG and main pipe surfaces), the value of mass evaporation rate is zero.

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Summarizing the product of deposition rate and deposition area:   dmic = ws,j + wp,j *Aj dt

(13)

  dmoc = ws,k + wp,k *Ak dt

(14)

i

i

where, subscript j/k: corresponding to in-core and out-core sections respectively; subscript s/p: corresponding to soluble and particle species respectively. For soluble species, the change of saturated concentration in the coolant is considered in CAMPSIS. The change of coolant temperature will cause soluble species under-saturated or over-saturated, depending on the type and solubility of the soluble species. If soluble species become under-saturated, particles will dissolve to have soluble species saturated again; on the contrary, soluble species will precipitate and becomes particles. The transform process between soluble species and particles is assumed to happen instantaneously. (c) The removal rate of CVCS system Different removal efficiencies can be defined for soluble and particle species. Normally, the efficiency for soluble species will be 1.0, since these materials can be considered part of the coolant, and for particle, species will be less than 1.0, since particles will not move along the same paths as the coolant. The mass of removal rate via the CVCS system can be defined as:   dmcvcs = Cp ∗ ηp + Cs ∗ ηs *Vletdown ∗ 2.78 ∗ 1011 dt

(15)

where, subscript s/p: corresponding to soluble and particle species respectively; C: concentration, ppb; Vletdown : flow rate of CVCS system, ton/h; η: removal efficiency of CVCS system. 2.3.5 Carry-Over of Corrosion Products From cycle to cycle, the carry-over of fuel crud from old assemblies will affect the total release amount of corrosion product. Carry-over efficiency has been implemented in CAMPSIS to simulate this phenomenon. For each element, the carry-over efficiency may be different. For a specific reactor, with or without Ultrasonic Fuel Cleaning (UFC), the carry-over efficiency may be different. The determination of carry-over efficiency values depends on multiple measurement data.

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3 Calculations Result 3.1 Validation Case The main goal of code validation is to justify the capability to model or simulate problems in real-world by comparison with operation data. For real plant operation data, it depends on the plant measurement state. Currently, there are 22 reactors in CGN operating as CPR1000. Some of them have been operating for nearly 30 years. The key parameters affecting fuel crud for CPR1000 plants are core design parameters (listed in Table 1), material choice, and chemical control strategy. More detailed information on CPR1000 has been illustrated in reference [13]. Table 1. Main parameters of CPR1000 Parameters

Values

Parameters

Values

Thermal capacity (MW)

2905

Fuel cycle length (month)

12/18

Number of loops

3

Let-down flow in chemical and volume control system (CVCS) (t/h)

13.6

Average primary coolant pressure (MPa)

15.5

Material of SG tube

Alloy 690

Geometry of fuel assemblies

17 × 17

pH value

6.9–7.2

Cladding material

M5

Expected hydrogen concentration (cm3 /kg)

25–35

Coolant water volume (m3 )

243

Alloy 690 wetted surface area (m2 )

16,300

Inlet coolant temperature at hot full power (HFP) (°C)

293.0

Stainless steel wetted surface area (m2 )

2800

Average temperature at hot full power (HFP) (°C)

310

\

\

Regrettably, there is no measured fuel crud data from CPR1000. We just choose one of them which is Unit A. Following measured data of Unit, A will be used: (a) The radioactivity level of Co-58, Co-60, Cr-51, Mn-54, and Fe-59 in primary coolant during normal operation. (b) Radioactivity levels of crud in out-core regions, mainly SG and main pipe. (c) Shutdown nickel release amount in several reactors. The Radioactivity levels in the primary circuit are closely related to the curd model. Therefore, these operation data can indirectly prove the reliability of the CAMPSIS calculation results. The relationship between curd and radioactivity is shown in Fig. 4.

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Fig. 4. Closed-Loop validation process of CPR1000

3.2 Result of CGN Unit A The comparison between measured coolant activity data and calculated values by CAMPSIS in CGN Unit A from 2004 to 2018 are shown in Fig. 5. Note that both the measurement and calculation results have been non-dimensionalised. For nuclide Co-58, Co-60 and Mn-54, the data samples are plenteous though the data are scattered during several periods. The simulated values by CAMPSIS are at the same magnitude as the measured data in CGN Unit A during the continuous thirteen cycles. The comparison between the measured dose rate in the main pipe and calculated values by CAMPSIS in CGN Unit A from cycle 1 to cycle 13 are shown in Fig. 6. The simulated dose rates in the cold leg and hot leg are at the same magnitude as the measured dose rates in the CGN Unit A. Among all the radionuclides, Co-58 and Co-60 are the main contributors to the dose rate in the primary pipes of the primary circuit. Shutdown nickel release amount of CGN Unit A was measured in cycle 13, and the measurement value is about 0.3 kg. The simulated value of shutdown nickel release amount is 0.26 kg, which fits well with the measurement value.

4 Conclusions This paper has outlined the main models of CAMPSIS and give some detailed description of crud deposition. The basis of the models is a combination of fundamental chemistry and physics as well as plant and laboratory experience. It calculates all plant data on every assembly basis for the core, while ex-core surfaces at present are modeled in less detail. then we choose the classic plant case and make validation. The results of validation show that the code can predict the trends correctly and the results match well with the operation data. It can further prove that CAMPSIS code is applicable for crud behavior and source term analysis of commercial nuclear power plant.

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Fig. 5. Coolant Co-58, Co-60, Cr-51, Mn-54 activity values in CGN unit A

Fig. 6. Hot and cold leg dose rate in CGN unit A

5 Funding category Supported by Shenzhen Science and Technology Research and Development Fund (JSGG20210629144537005)

References 1. PWR Axial offset Anomaly (AOA) Guidelines, Revision 1, EPRI Report 1008102 (2004)

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2. Henshaw, J., McGurk, J., Sims, H., Tuson, A., Dickinson, S., Deshon, J.: A model of chemistry and thermal hydraulics in PWR fuel crud deposits. J. Nuc. Materials 353, 1 (2006) 3. Meng, S., Yan, Y.: Research of thermal hydraulic conditions effect on PWR CIPS Risk. Front. Energy Res. (2022) 4. J.T. Bosma, Deshon, J., Epperson, K.R., Kennamore, P., Secker, J.R., Young, M.Y.: A comprehensive method of assessing fuel performance risks due to crud deposition. In: Proceedings of the 2004 International Meeting on LWR Fuel Performance, Orlando, Florida (2004), paper 1061 5. CNPRI.: China General Nuclear Power Group Co. Ltd. China General Nuclear Power Co. China: Ltd. CRUD behavior analysis software [CAMPSIS] V1.0 [CP]. 2021 SR0623899 (in Chinese) (2021) 6. Kang, S, Sejvar, J.: The CORA-II model of PWR corrosion-product transport, EPRI-NP-4246, EPRI (1985) 7. Bird, R.B., Stewart, W.E., Lightfoot, E.N.: Transport Pheomena, 2nd edn. University of Wisconsin, Department of Chemical Engineering (2002) 8. Lee, C.B.: Modeling of Corrosion Product Transport in PWR Primary Coolant, MIT (1990) 9. Collier, J.G., Thome, J.R.: Convective Boiling and Condensation, 3 edn. Clarendon Press (1994) 10. Thom, J.R.S., Walker, W.M., Fallon, T.A., Reisling, G.F.S.: Boiling in Subcooled Water During Flow up Heated Tubes or Annuli, Symposium on Boiling Heat Transfer (1965) 11. Steiner, D., Taborek, J.: Flow boiling heat transfer in vertical tubes correlated by an asymptotic model. Heat Transfer Eng. 13(2), 43 (1992) 12. CGN, LINDEN.: A Subchannel Analysis Code: Verification and Validation Report, GHX00600142DRAF02TR, Rev. D (2020) 13. CGN.: Assessment of Fuel Crud for UK HPR1000, GHX00100061DRAF03GN, Rev B, (2021)

Evaluation on the Release of Key Nuclides in Cemented Waste Form in a Rock Cavern Disposal Site Ren Yaqing(B) , Xie Wenzhang, Li Kunfeng, Lin Peng, Du Yingzhe, Liu Xiajie, and Li Li China Nuclear Power Technology Research Institute Co., Ltd., Shenzhen, Guangdong Province, China [email protected]

Abstract. It is an effective and feasible way to dispose cemented waste form from nuclear power plant in the rock cavern disposal site. Simulation work with Ecolego software was conducted in this paper to better quantify the transfer process of nuclides in cemented waste form in the rock cavern disposal site over a long time scale. A rock cavern disposal site of low and intermediate radioactive waste was taken as an example. For the transfer process of the nuclides crossing the metal steel drums to the concrete disposal container, 3 sets of compartment models were established respectively. One was for the convective transfer process, and the other two were for the diffusion process in the not deteriorated or partially deteriorated cemented waste form. Release activities of Co-60, Sr-90, Cs-137, Ni-63 and I129 were calculated with a period of 1000 years beginning at the closing point of the disposal site. In the case of convective transfer, the total peak release time occurs in 120 years after the closure of the disposal site, and the dominant nuclide is Cs-137. During the 1000 years, almost all of the I-129 is released into the concrete disposal container. In the case of diffusion transfer, the total peak release rate occurs in 103 years after the closure of the disposal site, and the dominant nuclide is still Cs-137. During the 1000 years, the total release ratio of I-129 head the list. The total release ratio of Co-60 is almost zero in both processes. Results show that Cs-137 wins top places in the peak release rate and total release activity in both convective and diffusion transfer cases, indicating that Cs-137 is the most concerned nuclide in the release of cemented waste form. The peak release rate and total release activity of each nuclide under diffusion process were lower than those under convective transfer process, regardless of whether the cemented waste form was degraded or not, indicating that the release of transfer under convection transfer was more significant. The total release activity of all nuclides in the diffusion process of the partially deteriorated cemented waste form is 161.434% higher than that of the non-degraded cemented waste form, which shows that the deterioration of the cemented waste form has a significant impact on the diffusion process. This paper is the very first domestic evaluation study on the transfer process of nuclides in the cemented waste form and the metal barrel under the condition of rock cavern disposal. The results can provide an effective basis for the safety assessment and the engineering scheme design of the disposal site.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 629–642, 2023. https://doi.org/10.1007/978-981-19-8780-9_62

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R. Yaqing et al. Keywords: Rock cavern disposal site · Cemented waste form · Release of nuclides · Ecolego

1 Introduction For low-and medium level radioactive waste, the main way of disposal is surface and nearsurface disposal or intermediate depth disposal after solidification. Cement solidifying technology has been widely used as a solidifying method of low and medium radioactive waste in the world due to its advantages of simple process, no need for high temperature, less one-time investment, less secondary pollution and stable hydration products. At present, rock cavern disposal is an effective and feasible way of disposal of cemented waste form. It has been successfully operated in Sweden, Finland, South Korea and other countries. The preliminary work of Rock-Cavern-type low and medium-level radioactive waste disposal has been carried out in Fujian, Zhejiang, Guangdong and other provinces in China. The method has a positive application prospect [1]. However, in the process of long-term disposal and storage of radioactive waste, when the barrier failure occurs in the disposal site, the cemented waste form will come into contact with groundwater. At this time, due to the structural characteristics of cement such as porous, large specific surface area, the cured radionuclides are easy to migrate into the environment through the pores. On the one hand, radionuclide leaching diffusion occurs, and on the other hand, cement-based materials may also undergo a variety of degradation processes, thus affecting the radionuclide transfer process. The transfer of radionuclides to the environment will cause harm to human health and environmental safety. Therefore, in order to better quantitatively evaluate the release of nuclides in cemented waste form in the rock cavern disposal site over a long-time scale, a rock cavern disposal site of low and intermediate radioactive waste was taken as an example. Three sets of compartment models with Ecolego software were conducted. The case 1 was for the convective transfer process in not-deteriorated cemented waste form. The case 2 and case 3 was for the diffusion process in not-deteriorated and partially deteriorated cemented waste form respectively. Release activities of Co-60, Sr-90, Cs-137, Ni-63 and I-129 were calculated with a period of 1000 years. And the effect of the deterioration of the cemented waste form under the action of groundwater on the safety performance index of the disposal site was observed. The results can provide an effective basis for the safety assessment and the engineering scheme design of the disposal site.

2 Computing Software and Model Building Ecolego software is a computing software for the safety evaluation and dynamic simulation of radioactive waste disposal. It was developed by Facilia AB Sweden with support from the Swedish Radiation Protection Agency (SSM). Using MATLAB/Simulink as the calculation engine, the software contains a complete database of radionuclides and decay chains to simulate the release and transfer process of radionuclides [2]. At present, the software has been widely used in the field of safety evaluation of nuclide transfer at

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home and abroad [1, 3–5]. When simulating a disposal system, Ecolego divides the system into a series of compartments (the compartment model). The compartment model is based on the finite difference method of pollutant migration in medium, and each compartment is mathematically equivalent to a single finite difference grid. The following two basic conditions must be met when using the compartment model for mathematical simulation: (1) Pollutants are rapidly and evenly mixed after entering the compartment. In other words, the concentration of pollutants is uniform in each compartment. (2) The transfer of pollutants in each compartment is characterized by the transfer coefficient, which represents the transfer ratio of pollutants per unit time. 2.1 Conceptual Model According to the completed preliminary design scheme of low and medium level radioactive waste rock cavern disposal site, waste disposal tunnels are arranged in parallel in the granite bedrock mountain. The steel drums are placed in concrete disposal containers, and the concrete disposal containers are layered by the crane to the disposal unit. In fractured bedrock, the groundwater is usually dominated by structural fissure water, and there is no uniform groundwater level. When calculating the release rate of key nuclides at the boundary between the steel drum and the concrete disposal container under normal scene, the following calculation assumptions are made. (1) The calculation time starts from the closing time of the disposal site. During the period from the closure of the disposal site to the closure of 100 years, all engineering barriers of the disposal site except the steel drum maintain their expected barrier function, and there is no groundwater in the disposal tunnel. And the steel drum has been corroded and failed when the disposal site was closed. (2) After the closure of the disposal site for 100 years, the function of each barrier project in the disposal site gradually became invalid. Rainwater infiltrates into the disposal tunnel, then collects from top to bottom to the underground water layer located below the disposal unit, and migrates outward along the direction of the underground water flow. (3) Rainwater seeps into the cemented waste form, causing the radionuclides in the cemented waste form to leach and dissolve in the groundwater, and then migrate with the groundwater. The following two scenarios are considered. In the first case, the fluidity of groundwater is very small and the convection effect is not obvious. The release of radionuclides from cemented waste form through steel drum to concrete disposal containers mainly depends on diffusion. In the other case, groundwater has good fluidity and radionuclides mainly transfer vertically downward with groundwater. (4) The corrosion products of steel drum may have an impact on the transfer of nuclides. (5) The released radionuclides are completely soluble in water, regardless of solubility constraints. The effects of nuclide concentration, near-field REDOX reaction and water chemical reaction on nuclide migration were not considered. (6) The geological environment in the whole evaluation period is consistent with the present condition, without considering the future evolution.

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2.2 Mathematical Model According to the law of conservation of mass, the rate of activity change of radionuclides in compartment is as follows.   dNi = λji Nj + λM Mi + Si (t) − λij Ni − λN Ni dt j=i

(1)

j=i

Among them, M is the activity of the parent radionuclide m, the unit is Bq; N is the activity of the daughter radionuclide n, the unit is Bq; λM , λN are the decay constants of the parent and daughter radionuclides, the unit is a−1 ; λi, i +1 , λi + 1, i are the transfer coefficient of radionuclides between compartment i and compartment i + 1, the unit is a−1 ; S I (T) is the other external source leakage term of the radionuclide n, the unit is Bq/a. For the nuclide transfer process based on convective transfer, the adsorption of the nuclide by the corrosion products of the steel drum should be considered, as well as the decay process of the radionuclide itself. The vertical downward transfer coefficient λinf of the nuclide caused by the infiltration is as follows. λinf =

q LϕR

R = 1 + ρKD/ϕ

(2) (3)

Among them, q is the Darcy flow rate of groundwater through the compartment, that is, the amount of rainwater infiltration, the unit is m/a; L is the transfer distance of nuclide with a unit of m; ϕ is the porosity of the compartment material; R is block coefficient; ρ is the solid particle density of the medium in the compartment with a unit of kg/m3 ; K d is the solid-liquid distribution coefficient with a unit of m3 /kg. For the diffusion-based nuclide transfer process, the adsorption of the nuclide by the corrosion products of the steel drum and the decay process of the radionuclide itself should be considered simultaneously. So, the transfer coefficient λij of the radionuclide N from the compartment i to the compartment j is as follows. 1 0.5(ri + rj )Ci

(4)

L , C = V [ϕ + (1 − ϕ)Kd ρ] = V ϕR ADe

(5)

λij = r= Among them,

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R is the diffusion resistance of a compartment, the unit is a·m−3 ; C i is the capacity of nuclide N in compartment i, the unit is m3 ; L is the transfer distance in the calculated direction, the unit is m; A is the cross-sectional area of the compartment perpendicular to the transfer direction, the unit is m2 ; De is the effective diffusion coefficient of the porous medium in the compartment, the unit is m2 /a; V is the volume of the compartment, the unit is m3 ; ϕ is the porosity of the compartment material; K d is the solid-liquid distribution coefficient, the unit is m3 /kg; ρ is the solid particle density of the medium in the compartment, the unit is kg/m3 ; R is the block coefficient. The deterioration of cemented waste form has a great influence on the effective diffusion coefficient. The convective transfer process has nothing to do with the effective diffusion coefficient, so this paper mainly analyzes and explores the influence of deterioration on the process of diffusion. 2.3 Compartment Model This paper establishes the compartment model of the following 3 cases. In case 1, considering the radionuclide transfer process dominated by convection in a single steel drum, and the cemented waste form is not deteriorated, the schematic diagram of the compartment division is shown in Fig. 1. The decay, adsorption and convective migration of nuclides are considered in the migration of nuclides in each compartment. 1 2

3 4 5 6

The direction of convective transfer

7 8

1~6-non-degraded cemented waste form 11- steel drum 12-concrete disposal container

Fig. 1. Schematic diagram of compartment division in case 1

In case 2, considering the diffusion-based nuclide transfer in a single steel drum, and the cemented waste form is not deteriorated, the schematic diagram of the compartment division is shown in Fig. 2. The transfer of nuclides in each compartment takes into account the vertical and horizontal diffusion transfer of nuclides, decay and adsorption. In case 3, considering the partially deterioration of cemented waste form and the diffusion-oriented nuclide transfer in a single steel drum, the schematic diagram of the compartment division is shown in Fig. 3.

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The direction of diffusion transfer

1

6 7

4

5 1 -- cemented waste form 2-the top cover of steel drum 3- the top of concrete disposal container 4- the bottom of steel drum 5- the bottom of concrete disposal container 6- the side wall of steel drum 7- the side wall of concrete disposal container

Fig. 2. Schematic diagram of compartment division in case 3

4 3 2

5

6

7

1

The direction of diffusion transfer 8 9 10

1-undegraded cemented waste form 2-Top of degraded cemented waste form 3-top of Waste Bucket 4-top of concrete disposal container 5-deteriorated cemented waste form side 6-waste bucket side wall 7-concrete disposal container side Wall 8-degraded cement curing body bottom

Fig. 3 Schematic diagram of compartment division in case 4

2.4 Parameter Value During the 100 years after the disposal site is closed, the engineering barriers of the disposal site except the steel drum maintained their expected barrier functions. There is no groundwater in the disposal tunnel and no transfer of nuclides will occur. Therefore, the release of radionuclides will not occur during this period, but only the decay of nuclides will be considered. After 100 years of closure of the disposal site, the barrier function of the disposal site failed, rainwater was infiltrated into the disposal tunnel, and then gathered from top to bottom into the groundwater layer below the disposal unit. The Darcy velocity of groundwater is 3.511E-02 m/a. The activity of radionuclides in the cemented waste form is shown in Table 1. It is believed that the size of the cemented waste form is the same as the internal size of the steel drum. According to EJ 1042-2014 [6], the values of the parameters of each engineering barrier are obtained as shown in Table 2, in which the partially deteriorated cemented waste form has a deterioration depth of 0.15 m. The adsorption distribution coefficients and effective diffusion coefficients of cemented waste form, corrosion products of steel drum and concrete disposal containers are shown in Tables 3 and 4, respectively.

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Table 1. Total activity of nuclide in cemented waste form Nuclide

Activity (Bq)

Co-60

1.92E+12

Cs-137

6.75E+11

I-129

1.01E+08

Ni-63

5.75E+11

Sr-90

2.30E+10

Table 2. Geometrical dimensions, effective porosity and density of engineering barriers Name

Effective porosity

Density (kg/m3 )

Inner diameter (m)

Thickness (m)

Height (m)

Undeteriorated cemented waste form

0.2

1816

0.35

/

1.083

Partially deteriorated cemented waste form

0.42

1317

0.35

/

1.083

Steel drum

0.80141

848

0.35

0.0015

1.132

Concrete disposal container

0.2

2000

0.353

0.1

1.232

Table 3. Adsorption distribution coefficient of engineering barrier Kd (m3 /kg) Nuclide

Cemented waste form [7]

Steel drum

Concrete disposal container

Co-60

0.1

9.081E-03

2.600E-03

Cs-137

0.001

2.006E-02

3.563E-02

I-129

0.001

1.238E-02

1.000E-03

Ni-63

0.1

9.178E-03

3.020E-03

Sr-90

0.005

9.130E-03

1.000E-01

3 Calculation Result Analysis 3.1 Release Rate Within 1000 years after the closure of the disposal site, the release rate of nucleon from the boundary of steel drum to the concrete disposal container in the nuclide transfer

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Nuclide

Undeteriorated cemented waste form

Partially deteriorated cemented waste form

Steel drum

Concrete disposal container

Co-60

2.239E-06

1.959E-05

9.081E-03

1.100E-04

Cs-137

2.839E-05

2.839E-05

2.006E-02

5.396E-03

I-129

1.276E-06

1.024E-04

1.238E-02

1.104E-04

Ni-63

2.516E-07

7.445E-06

9.178E-03

4.976E-06

Sr-90

5.045E-06

1.087E-03

9.130E-03

2.525E-06

process of different cases is shown in Figs. 4, 5 and 6. The transfer coefficient of each nuclide in the cemented waste form and steel drum is shown in Tables 5, 6 and 7. The results show that, except for the I-129 in case 2, the release rate of each nuclide showed a trend of first rise and then fall, the peak release time appeared in 100–150 years after the disposal site was closed. The total peak release time in the process of convective transfer is about 120 years after the closure of the disposal site, and the total peak release time in the diffusion transfer process is about 103 years after the closure of the disposal site. In any case, the dominant nuclide is Cs-137. In the case 1, after 800 years of closure of the disposal site, Ni-63 become the key nuclide to determine the total release of the disposal site. In the case 2 and case 3, when the disposal site was closed for 1000 years, I-129 and Ni-63 became the key nuclides to determine the total release of the disposal site. The change of release rate is the result of the joint action of convective transfer or diffusion transfer, radioactive decay and adsorption process. For the steel drum, the nuclides in the cemented waste form transfer to the steel drum with the groundwater, which is the ‘increase term’ of the unclean in the steel drum compartment. While the radioactive decay of nuclides in steel drum and their migration to concrete disposal containers are the main ‘reduction items’ of nuclides in steel drum compartment. After the disposal site was closed for 100 years, the nuclides began to transfer from the cemented waste form to the steel drum. The activity of nuclide in the steel drum increased rapidly from zero, so the release rate curve showed a rapid upward trend after 100 years. The activity of nuclide in the cemented waste form is reduced due to radioactive decay and outward transfer, which means that the ‘increase term’ in the steel drum compartment is gradually decreasing. As a result, the release rate curve begins to decline immediately after reaching an equilibrium position where the ‘increasing term’ equals the ‘decrementing term’ (i.e., rising to a peak position). I-129 barely decreased after peaking in Case 2. Because it has almost no decay during the 1000 years of closure of the disposal site, the transfer coefficient in the cemented waste form is also small, but the transfer coefficient in the waste barrel is large, resulting in its ‘increase term’ equal to ‘decrease term’ to reach a balance. For the diffusion transfer situation, the Cs-137 reached the peak release rate almost at the time when the nuclide release occurred at the disposal site (103.5 a), which was 17 years earlier than the convective transfer process. Because the transfer coefficient of Cs-137 from steel drums to concrete disposal containers is much

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higher than that of cemented waste form to steel drums (and higher than that of other nuclides). In the case of convective transfer, the two are of the same order of magnitude.

Fig. 4. Release rate of nuclides from boundary of steel drum to concrete disposal containers in case 1

Fig. 5. Release rate of nuclides from boundary of steel drum to concrete disposal containers in case 2

3.2 Total Release Activity and Release Ratio The total release activity and release ratio of nuclides under different nuclide transfer conditions are shown in Tables 8 and 9 respectively within 1000 years after the closure of the disposal site. For the convective transfer process, compared with the initial activity of each nuclide of the cemented waste form, the total release proportion of I-129 is the

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Fig. 6. Release rate of nuclides from boundary of steel drum to concrete disposal containers in case 3 Table 5. The transfer coefficient of each nuclide in the cemented waste form and the steel drum in case 1 (a−1 ) Compartment

Co-60

Cs-137

I-129

Ni-63

Sr-90

Cemented waste form

1.067E-03

9.618E-02

9.618E-02

1.067E-03

2.090E-02

Steel drum

1.372E-01

6.550E-02

1.032E-01

1.359E-01

1.366E-01

highest, reaching 100%, followed by Cs-137. The total release ratio of Ni-63 and Sr-90 is similar, and the total release ratio of Co-60 is almost 0. The total release activity of Cs-137 is the highest, followed by Ni-63, Sr-90 and I-129. While the total release activity of the Co-60 is more than 5 orders of magnitude than other nuclides. For the diffusion transfer process, the total release ratio of the I-129 is the highest as well, and the total release ratio of the Co-60 is almost 0. The total release activity of the Cs-137 is the highest. The total release activity of the Co-60 is the lowest, and in case 3, the total release ratio of the Co-60 is even on the order of single digits. The total release ratio of nuclides in the process of convective transfer is mainly affected by half-life and adsorption, and they are also affected by diffusion during the process of diffusion transfer. Since the nuclide is released 100 years after the disposal site is closed, and the half-life of Co-60 is only 5.27 years, most of the Co-60 has already decayed. Therefore, the total release ratio is close to 0 and the total release activity is the lowest. For I-129 with a very long half-life, the decay variable is very small throughout the calculation period. In addition, the transfer coefficient in the cemented waste form and the steel drum is relatively high, so its release ratio is always the highest. The variation of total release is different from the total release ratio, because the total release is also affected by the initial activity of each nuclide in the cemented waste form. Due

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Table 6. The transfer coefficient of each nuclide in the cemented waste form and the steel drum in case 2 (a−1 ) Compartment

Co-60

Cemented waste form to top cover of steel drum

5.248E-08 5.857E-05

Cs-137

I-129

Ni-63

Sr-90

2.632E-06 5.897E-09 2.305E-06

Top cover of steel drum to the 4.957E-02 1.000E+00 4.005E-02 3.091E-03 1.136E-03 top of concrete disposal container Cemented waste form to top bottom of steel drum

5.248E-08 5.857E-05

2.632E-06 5.897E-09 2.305E-06

The bottom of steel drum to the bottom of concrete disposal container

3.136E-02 7.971E-01

2.535E-02 1.958E-03 7.197E-04

Cemented waste form to side wall of steel drum

5.025E-07 5.608E-04

2.521E-05 5.647E-08 2.207E-05

The side wall of steel drum to 8.054E-01 1.000E+00 6.504E-01 5.013E-02 1.842E-02 side wall of concrete disposal container

to the relatively high initial activity of Cs-137, although its total release ratio is always smaller than that of I-129, its total release activity remains the highest. In the process of convective transfer, the I-129 is basically all released with the convection of groundwater, which also confirms that the barrier material has a weak adsorption and retention effect on it. For Ni-63 with an equally long half-life (100 years), the transfer coefficient in the cemented waste form is very small (in other words, the adsorption and retention effect of cemented waste form on the Ni-63 is greater), so its total release ratio is very low. The half-life of the Cs-137 and Sr-90 is similar, but the release ratio of Cs-137 is nearly 6 times that of the Sr-90. The transfer coefficient of the Sr-90 in the cemented waste form is lower than that of the Cs-137, that is, the adsorption and retention effect on Sr-90 of the cemented waste form is stronger than that of the Cs-137. So that the release ratio of the Sr-90 is smaller than that of the Cs-137. In the diffusion transfer process, the total release ratio of I-129 is the highest, but much less than the convective transfer. Because the transfer coefficient of the I-129 in the diffusion transfer process of the cemented waste form is much smaller than that in the convective transfer process. Compared with the convective transfer, the peak release rate and total release activity of each nuclide in the case of diffusion transfer are much lower than the former. It indicates that the nuclide transfer under convection is more significant. The radionuclide diffusion and release laws of undeteriorated cemented waste form and partially deteriorated cemented waste form are not completely consistent. In the process of diffusion transfer of the partially deteriorated cemented waste form, the total release activity of the Sr-90 is 3 orders of magnitude higher than that in the process of undeteriorated cemented waste form. It shows that the transfer resistance of the Sr-90 in the cemented waste form decreases as the deterioration degree deepens. In

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Table 7. The transfer coefficient of each nuclide in the cemented waste form and the steel drum in case 3 (a−1 ) Compartment

Co-60

Cs-137

I-129

Ni-63

Sr-90

Undeteriorated cemented waste 9.622E-08 8.104E-05 form to the top of partially deteriorated cemented waste form

5.014E-06 1.114E-08 4.403E-06

The top of partially 2.274E-05 2.138E-03 deteriorated cemented waste form to top cover of steel drum

7.709E-03 8.641E-06 2.277E-02

The top cover of steel drum to the top of concrete disposal container

4.957E-02 1.000E+00 4.005E-02 3.091E-03 1.136E-03

Undeteriorated cemented waste 1.473E-06 1.232E-03 form to the side of partially deteriorated cemented waste form

7.682E-05 1.707E-07 6.746E-05

The side of partially deteriorated cemented waste form to side wall of steel drum

2.267E-05 2.132E-03

7.688E-03 8.616E-06 2.277E-02

The side wall of steel drum to the side wall of concrete disposal container

8.054E-01 1.000E+00 6.504E-01 5.013E-02 1.842E-02

Undeteriorated cemented waste 9.622E-08 8.104E-05 form to the bottom of partially deteriorated cemented waste form

5.014E-06 1.114E-08 4.403E-06

The bottom of partially deteriorated cemented waste form to bottom of steel drum

2.274E-05 2.138E-03

7.708E-03 8.641E-06 2.271E-02

The bottom of steel drum to the 3.136E-02 7.971E-01 bottom of concrete disposal container

2.535E-02 1.958E-03 7.197E-04

fact, with the deepening of deterioration degree, the diffusion coefficient of I-129 and Ni-63 in the cemented waste form increases significantly. However, the initial activity of I-129 is low, so the following phenomenon occurred -- the proportion of total release of I-129 is significantly increased while its total release activity is only higher than that of Co-60. The total release activity of Ni-63 increases by two orders of magnitude due to its high initial activity. In addition, the peak release rate, total release ratio and total release activity of all nuclides in the diffusion transfer process of partially deteriorated cemented waste form are higher than those of the undeteriorated cemented waste form. And the total release activity of all nuclides in the diffusion transfer of partially deteriorated

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cemented waste form is 161.434% higher than that of non-deteriorated cemented waste form. It notes that the diffusion transfer process of nuclides in partially deteriorated cemented waste form is more significant. The peak release rate and total release activity of each nuclide under the condition of diffusion transfer are lower than those under convection, regardless of whether the cemented waste form is deteriorated or not. This shows that the nuclide transfer under convection is more significant in the two transfer processes. Table 8. Total release ratio of each nuclide (%) Nuclide Co-60 Cs-137 I-129

Case 1

Case 2

0.000000135

0.000000000682

3.719

0.279

100.0

2.688

Case 3 0.0000000203 0.643 77.8

Ni-63

1.220

0.000380

0.0382

Sr-90

1.096

0.00364

1.29

Total

1.017

0.0591

0.155

Table 9. Total release activity of each nuclide (Bq) Nuclide

Case 1

Case 2

Case 3

Co-60

2.596E+03

1.310E+01

3.894E+02

Cs-137

2.510E+10

1.882E+09

4.339E+09

I-129

1.010E+08

2.715E+06

7.860E+07

Ni-63

7.014E+09

2.186E 06

2.195E+08

Sr-90

2.522E 08

8.369E+05

2.978E+08

Total

3.247E+10

1.888E+09

4.935E+09

4 Conclusions From the simulation results of Ecolego software, it is found that whether it is convective transfer or diffusion transfer, the peak release rate and total release activity of Cs-137 are the largest. Cs-137 belongs to the most important nuclide release of cement. At the Darcy flow rate of 3.511E-02 m/a, regardless of whether the cemented waste form is deteriorated, the radionuclide transfer under convection is more significant than that under diffusion. It is also found that that the deterioration of the cemented waste form has a great influence on the diffusion transfer. Therefore, in the subsequent safety analysis and evaluation of the disposal site, it is necessary to focus on the release of Cs-137, and comprehensively consider the release of nuclides in the convection transfer process and diffusion transfer process after the deterioration of cemented waste form.

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References 1. Li, X., Liu, X., Wang, X., et al.: Research on nuclide transport compartment model for low and intermediate level waste disposal in rock cavern type. Ind. Constr. 48(04), 18–22 (2018) 2. Avilar, R., Broer, R., Pereira, A.: Ecolego—a toolbox for radioecological risk assessment. In: Proceedings of the International Conference on the Protection from the Effects of Ionizing Radiation, IAEA-CN-109/80. International Atomic Energy Agency, Stockholm, pp. 229–232 (2003) 3. Krisanangkura, P., Itthipoonthanakorn, T., Udomsomporn, S.: Environmental dose assessment using Ecolego: case study of soil from Japan. J. Radioanal. Nucl. Chem. 297(3), 443–450 (2013) 4. Zhao, Y., Li, Y., Gu, Z.: Calculation of nuclides transfer at solid ILRW disposal facility using Ecolego software. Radiat. Prot. 33(4), 230–234 (2013) 5. Liu, X., et al.: Uncertainty analysis of nuclide transfer in surface radioactive waste disposal sites based on compartment model. J. Eng. Geol. 27(s1), 334–339 (2019) 6. EJ 1042-2014, Containers for low and intermediate level radioactive solid waste. Steel drum 7. IAEA-TECDOC. Derivation of activity limits for the disposal of radioactive waste in near surface disposal facilities. Vienna, IAEA (2003)

Research and Validation of RCP Temperature Automatic Control During Cold Shutdown Stage in a GEN-III Nuclear Power Plant Fei Song1(B) , Hang Liu1 , Zhenhua Luan2 , Jianfeng Qiao1 , and Peng Liu1 1 China Nuclear Power Engineering Co.,Ltd, Shenzhen, Guangdong, People’s Republic of China

[email protected] 2 State Key Lab of Industrial Control Technology, College of Control Science and Engineering,

Zhejiang University Hangzhou, Zhejiang, People’s Republic of China

Abstract. In the start-up and shutdown stage of the nuclear power plant, the Steam generator has not been put into operation, and the core residual heat and temperature control are realized by the residual heat removal system. Traditional GEN-II nuclear power plants use RRA system to control the primary temperature. Operators manually control the regulating valve on the pipeline where Heat exchanger is located, adjust the primary coolant flow through Heat exchanger, so as to control the primary temperature and its gradient. In the start-up and shutdown stage of a GEN-III nuclear power plant, when the steam generator has not been put into operation, the waste heat removal function is completed by RIS lowpressure safety injection system. And the primary loop can automatically heat up and cool down in a temperature gradient and target temperature which set by the operators, hereinafter referred to as RHR temperature control system. The RHR temperature control system should not only avoid the thermal shock to the low pressure injection pipeline during the automatic heat-up and cool down process, but also maintain the stable flow rate of the RIS low pressure injection pipeline. The design of RHR temperature control system is complicated. During the first test of Validation, there were some anomalies such as temperature setpoint fluctuation and full closing of actuator. Finally, through design modification and parameter optimization, the test Validation for the control system was completed. And the automatic heat-up and cool down control of primary loop temperature in the cold shutdown stage of nuclear power plant has been realized, which improves the intelligent level of nuclear power plant. Keywords: Residual heat removal · Automatic heat up and cool down · Primary temperature control · Cold shutdown stage · Low pressure safety injection · RHR

1 Introduction The guidance of nuclear fuel heat release is one of the main measures to ensure the safety of the reactor. Under normal operation conditions, the heat generated by the reactor is mainly transferred to the secondary loop through the Steam generator to be released. Under the shutdown condition of the plant, the waste heat of the reactor is initially © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 643–653, 2023. https://doi.org/10.1007/978-981-19-8780-9_63

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transferred to the secondary loop by the steam generator for release. With the decrease of the primary temperature, when the secondary loop has no thermal conductivity, the waste heat removal system is connected to the RCP loop to export the reactor heat through the heat exchanger to ensure the core cooling. When the Steam generator is not put into operation in the start-up and shutdown stage in the traditional GEN-II nuclear power plant, the RRA system is used to adjust the primary temperature and export the heat [1]. RCP coolant from loop 2 is inhaled by the RRA pump, and returns to the cold section of loop 1and loop 3 through two RRA heat exchangers. The operator manually controls the regulating valve of the pipeline where the heat exchanger is located and adjusts the water flow through the RRA heat exchanger to achieve the purpose of controlling the cooling or heating gradient and controlling the primary temperature [2]. The flow diagram of RRA system of GEN-II nuclear power plant is as shown in Fig. 1.

Fig. 1. Temperature control flow diagram of RRA system

In the start-up and shutdown stage of a GEN-III nuclear power plant, When the Steam generator has not been put into operation, the waste heat removal function is completed through the low-pressure safety injection pipeline of each RCP loop, and each loop can realize the waste heat removal function independently, which can be redundant with each other [3]. The GEN-III nuclear power unit has three loops, all of which can realize the waste heat removal function independently. Compared with the RRA system of the GEN-II nuclear power plant, the availability and safety of the waste heat removal function are greatly improved. The waste heat removal function is realized by RIS system, hereinafter referred to as RHR, which is different from the RRA system of the GEN-III nuclear power unit. The flow diagram of RHR system of one loop is as shown in Fig. 2. RHR primary temperature control should not only avoid the thermal shock to the low-pressure injection pipeline in the automatic control process, but also maintain the flow stability of the low-pressure injection pipeline, and ensure that the primary loop automatically heat up and cool down with the temperature gradient and target temperature set by the operator at the same time. And each loop can complete the access and exit of RHR temperature pipeline without disturbance. RHR temperature control logic is complex, including the temperature control, mode detection and flow control and so on. During the first commissioning test, there were abnormalities such as temperature

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Fig. 2. RIS-RHR temperature control flow diagram for one RCP loop in a GEN-III nuclear power plant

fluctuation and full closing of actuator. Finally, through parameter optimization and logic correction, the test of the control system was completed, and the full-automatic control of primary loop heat up and cool down in the cold shutdown stage of nuclear power plant was realized, which improved the intelligent level of nuclear power plant.

2 Primary Temperature Control Principle 2.1 General Introduction of Primary Temperature Control RHR temperature control can not only realize the automatic heat up and cool down during start-up and shutdown stage of nuclear power plants, but also control the primary temperature at the setpoint during primary loop water filling and drainage stage. RHR temperature control mainly includes two control loops: flow control and temperature control. The structure of flow control is simple, and the structure of temperature control is complex. The control principles of two control loops are briefly introduced below. 2.2 Flow Control Flow controllers controls the total flow rate of low-pressure safety injection pipeline, ensure the flow rate of low pressure safety injection line is consistent with the flow rate setpoint set by operator, and maintain the stable flow rate of low pressure safety injection line. As shown in Fig. 3, the flow rate of low-pressure safety injection pipeline is divided into two parts, Heat exchanger pipeline flow rate and bypass pipeline flow rate, the flow of these two pipelines is controlled by flow control valve and temperature control valve respectively. The total flow of low-pressure safety injection pipeline is mainly controlled by flow control valve. When the flow control valve has no adjustment ability, it is controlled by temperature control valve. Flow control structure is shown in the Fig. 3, the deviation between the flow rate setpoint and the actual measured flow rate is calculated by flow control PID, then the PID output command is sent to flow control valve and temperature control valve through two limiters respectively. Normally the output of flow controller is greater than 0, the flow control valve receives the PID instruction and control the total flow of the low-pressure safety injection pipeline. When the flow control

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valve is fully closed, if the total flow rate of the low-pressure safety injection pipeline is still greater than the setpoint, then the output of flow controller becomes negative and the temperature control valve receives the PID instruction to bring the total flow rate back to setpoint. The above control system structure ensures that the flow controller has a higher priority than the temperature controller.

Fig. 3. RHR flow control for one RCP loop in a GEN-III nuclear power plant

2.3 Temperature Control The structure of temperature Control is complex, which includes three parts: temperature gradient control PID part, limit condition judgment part and valve position switching part (Fig. 4).

Fig. 4. Structure of temperature control

The temperature gradient control mainly calculates the opening of temperature control valve according to the temperature gradient setpoint by the operator and the actual

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temperature gradient as input deviation, including PID controller under the condition of no Primary pump operation and PID controller under the condition of Primary pump Primary pump. The limit condition judgment part mainly limit the actual gradient setpoint which participate in actual PID deviation calculation, product preprotection action to prevent temperature and temperature gradient exceed limits and make sure that the temperature control meets the requirement of technical specifications of equipment and plant. The main purpose of the valve position switching part is to prevent the thermal shock of RCP pipeline inlet and RIS system caused by the valve opening too quickly when one loop’s RHR temperature control is put into use, perform the preprotection action and generate temperature control valve opening command. 2.4 Mode Detection Primary temperature control has three control mode, heat up, cool down and maintain a constant value for primary temperature. The control mode is decided by the deviation between actual measured primary temperature and target temperature set by operator. If the deviation is within the dead zone, the primary temperature is kept consistent with the target value set by operator. If the deviation is not within the dead zone and the target temperature set by operator is greater than the actual primary temperature, it indicates that the unit should be heated up and the control system heats up the unit at a gradient calculated by limit condition judgement module. If the deviation is not within the dead zone and the target temperature set by operator is smaller than the actual primary temperature, it indicates that the unit should be cooled down and the control system cool down the unit at a gradient calculated by limit condition judgement module (Fig. 5).

Fig. 5. Mode detection of temperature control

3 Test Validation and Optimization The test validation of RHR primary temperature control system mainly includes two parts: the stability of RHR primary temperature control system and the automatic heat

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up and cool down capacity of nuclear power plants through RHR primary temperature control system. 3.1 Stability of RHR Primary Temperature Control System The main contents of RHR primary temperature control system stability test are as follows. Put the temperature control valve and flow control valve of the selected loop in automatic mode, Keep the low pressure safety injection pump of the selected loop in running, and then start the primary pump of the same loop to validate the RHR primary temperature control system stability of this loop. Perform the preceding operations one loop by one loop. During the test of one loop RHR primary temperature control system, the temperature control valve and flow control valve of this loop are put in automatic, the temperature control valve of other loops is put in manual mode. The target temperature is set as the current actual measured primary temperature by operator. During the test, flow control and temperature control should have no effect on each other, primary temperature should be maintained around the setpoint and the damping ratio of temperature response should meet the test requirements. If the above requirements are no met, the control system parameters must be optimized. 3.2 Temperature Response in Stability Test The following Fig. 6 shows the test curve of RHR primary temperature control system stability in one certain loop. When the primary pump of the selected loop is started, the coolant of this loop starts to mix with coolant of other loop. During the mixing process, the primary temperature drops first, and after the coolant is fully mixed, the primary temperature starts to rise, as shown in the green curve in the figure. As shown in the figure above, the detailed process of temperature response is as follows. When the coolant just started mixing after Primary pump started, the primary temperature dropped, the actual temperature gradient of the primary loop dropped steeply, the temperature gradient setpoint was 0 °C/h. The input deviation of temperature gradient controller decreased, and the controller turned the temperature control valve down to reduce the flow rate through the RHR Heat exchanger, reduced the heat transfer and bring the temperature gradient back to the setpoint. With the stable operation of the primary pump, the coolant was fully mixed, the primary temperature increased gradually and the actual primary temperature gradient increased due to the heat of primary pump. The temperature gradient setpoint was kept at 0 °C/h during the whole stability test. The input deviation of temperature gradient controller increased and the controller turned the temperature control valve up to increase the flow rate through the RHR Heat exchanger, increased the heat transfer and bring the temperature gradient back to the setpoint. After starting the primary pump for a period of time, under the action of RHR primary temperature control, the heat generated by the primary pump and the heat transfer of the RHR heat exchanger reached equilibrium, and finally the actual primary temperature gradient approached 0 °C/h.

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Fig. 6. Response curve of a loop RHR temperature control system started in Primary pump

3.3 Flow Response in Stability Test In the process of starting the primary pump, the RHR primary temperature control maintains the primary temperature and the actual primary temperature gradient around the setpoint by changing the opening of the temperature control valve. The change of the opening of the temperature control valve will affect the flow rate of the RIS low-pressure safety injection pipeline. In order to maintain the flow rate of the RIS low-pressure safety injection pipeline at setpoint, flow controllers compensates the change of the flow rate by changing the opening of the flow control valve. The following figure shows the variation curves of temperature control valve, flow control valve and flow rate of low-pressure safety injection pipeline during the stability test. In the whole process of stability test, when the temperature control valve is turned down, the flow control valve is turned up. And when the temperature control valve is turned up, the flow control valve is turned down, so that the flow rate of the low-pressure safety injection pipeline is always kept around the setpoint (Fig. 7). 3.4 Automatic Cool Down The following Fig. 8 shows the automatic cooling process through RHR primary temperature control when all loop primary pumps are in operation. During the whole automatic cool down test, temperature control valves for all loops are in automatic mode. The primary temperature is cooled from 90 °C to 60 °C, and the temperature gradient setpoint is set to the maximum value by the operator. According to the limit condition judgment part, the actual temperature gradient setpoint is limited to −40 °C/h when the primary loop temperature is greater than 70 °C, and −15 °C/h when the primary loop temperature

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Fig. 7. Flow control response curve when one loop Primary pump is started

is less than 70 °C, as shown in the yellow-green curve in the Fig. 8. The RHR temperature control system increases the opening of the temperature control valve, increases the heat transfer through the RHR Heat exchanger, and gradually makes the actual primary temperature gradient consistent with the actual temperature gradient setpoint, as shown in the red curve in the figure below. In the whole cooling process, the opening of the temperature control valve is increased firstly, and then part of the opening is adjusted back. The flow control valve always compensates the opening change of the temperature control valve, so as to maintain the flow rate of low-pressure safety injection pipeline around the setpoint. When the nuclear power plant starts to cool down, the temperature gradient setpoint is 40 °C/h, the temperature control valve needs to increase a large opening to maintain the actual primary temperature around −40 °C/h, and the flow control valve needs to reduce a large opening to compensate for the flow rate change caused by the temperature control valve and keep the total flow rate unchanged. When the primary temperature drops below 70 °C, the temperature control valve needs to adjust the partial opening to reduce the heat transfer through RHR Heat exchanger, ensure that the actual primary temperature gradient changes from −40 °C to −15 °C. And the corresponding flow control valve increase the opening to keep the total flow rate of low-pressure safety injection pipeline unchanged. The opening change process of temperature control valve is shown in the cyan curve above, and the opening change process of flow control valve is shown in the blue curve above.

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Fig. 8. Automatic cooling curve of primary circuit through RHR temperature control system

3.5 Automatic Heat up The following figure shows the automatic heating process through RHR primary temperature control when all loop primary pumps are in operation. During the whole automatic heat up test, only two loops’ temperature control valve are in automatic mode, and the other loop’s low-pressure safety injection pipelines are isolated from RCP pipelines. The temperature gradient setpoint is 30 °C/h during the whole automatic heat up test. The main heat sources of primary loop are core waste heat and three Primary pump, while the cold source is RHR Heat exchanger. There is no core waste heat during the commissioning stage of nuclear power plants, so only the three primary pumps serve as heat sources. Under the test condition, the three primary pumps do not have enough heat to heat the primary coolant at temperature gradient 30 °C/h. Therefore during the automatic heat up test, the temperature control valves are kept closed, and the actual maximum primary temperature gradient is 23 °C/h. The change of primary temperature is shown in the black curve, the change of temperature gradient is shown in the red curve, the gradient setpoint is shown in the yellow-green curve, the opening change of the temperature control valve is shown in the cyan curve, and the opening change of the flow control valve is shown in the blue curve (Fig. 9). During the whole automatic heat up test, the increase of the opening of the flow control valve compensates the flow fluctuation caused by the change of the opening of the temperature control valve, and keeps the flow rate of the low-pressure safety injection pipeline around the setpoint (Fig. 10).

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Fig. 9. Automatic temperature rise curve of primary loop through RHR temperature control system

Fig. 10. Variation curve of low pressure injection flow during automatic heating

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4 Problem Analysis and Treatment During commissioning stage of a nuclear power plants, the RHR primary temperature control system of loop 1 is in manual mode, and RHR primary temperature control systems of the other loops are in automatic mode. After the loop 1 RHR primary temperature control system is put into automatic, all loops’ temperature control valves are suddenly fully closed. Then the primary temperature rises, and the temperature control gradually returns to normal. After analysis, the cause of the problem is the tracking logic problem of gradient PID controller, after optimizing the PID tracking logic, this problem is solved. During the automatic heat up test of a GEN-III nuclear plant, the temperature rise from 60 °C to 90 °C, after the primary temperature reached 90 °C, a large negative temperature gradient suddenly appeared and the primary temperature decreased by 2 °C. After analysis, the cause is the problem of the mode detection module. Even though it can be solved by optimization of the mode detection logic, but there is an easier way, which is to set the gradient to 0 when the temperature reaches the target value.

5 Summary This paper introduces the Validation of RHR control system under the conditions of Primary pump startup disturbance, primary loop automatic heating and primary loop automatic cooling, etc. It proves that the primary loop can automatically heat up and cool down through RHR temperature control system at the temperature change rate and target temperature set by the operator, and realizes the automatic control of primary loop heat up and cool down during the cold shutdown stage of nuclear power plant, thus improving the intelligent level of nuclear power plant.

References 1. Jizhou, Z., Jianqiang, S., Bin, Z.: Operation of Pressurized Water Reactor Nuclear Power Plant. Atomic Energy Press, Beijing (2008) 2. Guozhen, M., Jisheng, Q.: Commissioning and Start-Up. Atomic Energy Press, Beijing (2000) 3. Taishan Nuclear Power Joint Venture Co., Ltd.: Design Principle of Intelligent Control for EPR nuclear plants. Atomic Energy Press, Beijing (2020)

Application Status and Prospect of Two-Dimensional Graphene for Hydrogen Isotope Separation Ruixi Gao(B) , Li Lin, Zhenchen Li, Yi Liang, Wenlu Gu, Jingjie Yang, Jiabing Yan, and Jiheng Fan Nuclear Power Institute of China, No. 328, Section 1, Changshun Avenue, Shuangliu District, Chengdu, Sichuan Province, China [email protected]

Abstract. For some countries, the installed capacity of coastal nuclear power alone will not be able to meet the electricity demand of some countries, so the development of inland nuclear power is of great significance to meet future electricity demand. Because of the limited capacity of the receiving water bodies at inland sites, there is still a lot of work to be done to optimize the control of effluent discharge from nuclear facilities according to the principle of optimal radiation protection. Tritium is chemically very similar to hydrogen, especially for tritiumcontaining wastewater, which is difficult to remove by conventional filtration and reverse osmosis methods. The separation of the three isotopes of hydrogen, protium, deuterium and tritium, is the key to ensure the tritium supply and reduce the tritium content in liquid effluent. The design and preparation of new materials to improve the separation efficiency of hydrogen isotopes is one of the current challenges. Since their introduction, graphene materials have been successfully applied to supercapacitors, separation and other fields due to their high specific surface area and excellent electrical, thermal, optical and mechanical properties. Compared with traditional separation methods, graphene separation membranes have the advantages of low energy consumption, good economy and simple equipment for hydrogen isotope separation. At present, the theoretical calculation of graphene for hydrogen isotope separation is relatively mature, but the experimental research is late, and most of them are at the stage of non-radioactive ‘protiumdeuterium’ separation. This paper firstly introduces the hydrogen isotope separation technologies commonly used in the industrial market, and then proposes the future separation of hydrogen isotopes through graphene separation membranes by summarizing the current status of theoretical calculations and experimental research in the field of hydrogen isotope separation with two-dimensional material graphene separation membranes. This not only provides a new technology in the field of hydrogen isotopes, but also provides a new prospect and solution idea for the future purification of tritium water. Keywords: Graphene · Hydrogen · Ion · Isotopes · Tritium · Separation

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 654–667, 2023. https://doi.org/10.1007/978-981-19-8780-9_64

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1 Introduction Heavy isotopes of hydrogen, namely deuterium and tritium, have important applications in thermonuclear weapons, nuclear reactors, isotope tracing, and spectroscopy, making them crucial resources for national defense and national production. In addition, these heavy isotopes are also involved in the effective purification and treatment of tritium-containing wastewater, thus proving valuable for future inland nuclear power plants to reduce the tritium concentration of the liquid effluent and increase their public acceptability [1]. Due to its scarcity and toxicity, tritium is either recovered from water deuteration systems or diluted with large amounts of seawater and discharged into the sea, resulting in great waste [2, 3]. However, efficient and economic tritium extraction and heavy water separation remain great challenges due to the ultra-low abundance of tritium and the same physicochemical properties of heavy water with water. Despite the extreme rarity of naturally occurring tritium, the growing global demand has accelerated the development of tritium production technologies. Currently, hydrodistillation [2], cryogenic distillation [4], liquid-phase catalytic exchange [5], and combined electrolysis catalytic exchange (CECE) [6] have been reported to achieve large-scale tritium removal from water. Typically, deuterated liquid treatment procedures with multi-stage cascades are relatively complex, energy-intensive, and inefficient [7]. Therefore, the cost-effective separation of hydrogen isotope-containing water is urgently needed for nuclear energy development. Other than the existing hydrogen isotope separation methods, a recently developed method based on two-dimensional (2D) materials overcomes the high energy consumption and achieves a high separation factor and a wide application range, making it the most promising solution. Since the discovery of mechanically exfoliated graphene in 2004, research on ultrathin 2D nanomaterials has grown exponentially in condensed matter physics, materials science, chemistry, and nanotechnology. This paper first introduces the hydrogen isotope separation techniques commonly used in industry. Secondly, the R&D status of graphene, a 2D material, in hydrogen isotope separation is summarized. Finally, the potential development and improvement of the graphene-based separation technology are summarized, and graphene membranes for hydrogen isotope separation are proposed.

2 Hydrogen Isotope Separation Technologies 2.1 Cryogenic Distillation In recent years, various hydrogen isotope separation methods such as chromatography, thermal diffusion, and laser separation have been developed and applied in heavy water production, CANDU reactor heavy water tritium removal and upgrading, fusion reactor deuterium-tritium fuel cycle, and weapon-grade tritium production [8]. Cryogenic distillation is a well-established hydrogen isotope separation method that separates the six hydrogen isotope molecules (H2 , HD, HT, D2 , DT, T2 ) based on the subtle differences in their boiling points [8]. A traditional cryogenic distillation-based hydrogen isotope separation plant is relatively complex with a feed gas purification system, a cryogenic distillation column system, a vacuum and refrigeration system, a purification and treatment system, a measurement and control system, and a safety protection system [9].

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The hydrogen isotope separation is achieved in the cryogenic distillation column system based on the different saturation vapor pressure (or boiling point) characteristics of each component in the gas mixture. Such a cryogenic distillation column system consists of the distillation column, reboiler, condenser, inlet and outlet pipes, and temperature and pressure measuring elements. The column has stainless steel plates of specific sizes and shapes inside. The reboiler is equipped with heating elements of different powers to control the evaporation of liquid hydrogen. The condenser condenses the rising vapor. Phase equilibrium can be established in the distillation column to match the reboiler heating power with the condenser cooling capacity and maintain a certain pressure in the column. The high boiling point components tend to enrich in the liquid phase, while the low boiling point components tend to enrich in the gas phase. Since the rising vapor is rich in low boiling point components and the liquid below is rich in high boiling point components, high purity low boiling point components such as H2 can be collected at the top of the column, and high purity high boiling point components such as HD and D2 can be collected at the bottom [9].

Fig.1. Application of cryogenic distillation system in ITER [8]

Cryogenic distillation has long been adopted for hydrogen isotope separation. ITER in Cadarache, France, is currently the largest international fusion energy development project with members including China, India, the European Union, South Korea, Japan, Russia, and the United States. As part of ITER’s D-T fuel cycle, the process design of the cryogenic distillation unit is a joint effort of several countries, as shown in Fig. 1. The cryogenic distillation has a large capacity and is operated in two columns at the Savannah River Site (SRS), USA, which consists of a 20-foot tower maintained at a temperature close to that of liquid hydrogen and a pressure of 900 Torr (Fig. 2) [10]. A reboiler at the bottom of the tower provides hydrogen vapor, and a condenser at the top condenses the vapor into liquid hydrogen. The concentration of isotope molecules is

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equal between the counter-current phases, with the liquid phase flowing downward and the gas phase flowing upward [10]. Table 1 Pilot-scale system experiment parameters of CECE method [12] Temperature/o C Processing Application capacity/kg Height/m Inner · h−1 diameter/cm

Country

Catalyst

Reaction bed

America

Pt/C/PTFE 7.5

2.5

26–33

14.4

Lightwater tritium off

Canada

Pt/C/PTFE 8.3

6.3

22–27

36–45

Tritium removal with heavy water, tritium recovery

Belgium

Pt/C/PTFE 2.0

3.0

20–80

1.66

Lightwater tritium off

Germany Pt/C/PTFE 6.2

44.0

88

180

Lightwater tritium off

Russia

Pt/SDB

6.9

10.0

40–80

2.5

Lift heavy water

Japan

Pt/SDB

12

7.0

70

30

Lift heavy water

Shaw and Butler [11] designed a model to assess the basic feasibility and approximate size of a cryogenic distillation system and concluded that the cryogenic distillation system is suitable in terms of energy use and isotope separation, but a single system size is not suitable for handling a range of different components. Their evaluation of the suitability of cryogenic distillation for isotope re-equilibration and protium removal in the European DEMOnstration (DEMO) reactor can serve as a comparison basis for other hydrogen isotope separation technologies in terms of size, power requirements, and safety. Shaw and Butler also pointed out that gas chromatography (GC) and thermal cyclic absorption processes (TCAPs) can be alternatives to cryogenic distillation systems [11]. Both TCAPs and GC rely on temperature fluctuations in the separation, which can lead to significant energy costs, whereas cryogenic distillation systems rely on cryogenic cooling. GC and TCAP technologies have a lower risk of tritium release because tritium is stored in solid form under atmospheric conditions. Despite the lack of flexibility in terms of isotope rebalancing and profession, cryogenic distillation is a viable option for isotope re-equilibration and deprotonation at DEMO [11]. The advantages of cryogenic distillation include high separation coefficient, low energy consumption, high production capacity, short start-up time, design flexibility, and small molecular mass of the working substance. However, it is not without disadvantages, such as large one-time investment, high retention of tritium in the system, and strict

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Fig. 2. Schematic of the SRS cryogenic distillation process between 1967 and 2004 [10]

measurement and control safety requirements. Effective measures should be taken to strictly control the retention of high-grade tritium.

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2.2 Combined Electrolysis Catalytic Exchange CECE is a composite process integrating hydrogen-water isotope exchange and electrolysis. In the catalytic exchange tower, liquid water flows from top to bottom, forming convection with the inflowing hydrogen at the bottom, where the isotope exchange reaction occurs, as shown in Fig. 3 [13]. Because the recombinant fraction collects in the water vapor and liquid water, deuterium and tritium are gradually concentrated in the liquid water flowing from top to bottom and depleted in the hydrogen gas on the way up [13]. In that process, electrolysis plays the role of phase conversion and hydrogen isotope separation, with the heavy isotopes enriched in the liquid phase and the light isotopes in the gas phase [14]. The catalytic exchange process pre-concentrates a certain hydrogen isotope, which enhances the taste of heavy water containing gas or the concentration of heavy water containing neon. Therefore, the whole CECE plant is similar to a distillation unit, with the exchange tower resembling a distillation column and the electrolysis cell equivalent to a reboiler. Characterized by the simultaneous concentration and deuteration of tritium-containing wastewater, CECE has become a widely adopted method for treating tritium-containing wastewater due to its high separation coefficient and approximate ambient temperature operating conditions [15]. The main parameters of pilot CECE tests in each country are listed in Table 1 [12].

Fig. 3. CECE Process [13]

2.3 Crystalline Materials The conventional separation technologies mentioned above usually have high energy consumptions, which contributes to problems such as global climate change and environmental disruption [16]. Alternative separation technologies with low energy consumption will reduce the global energy demand and thus protect the environment in an economically-friendly manner [17]. The development of adsorption and membrane technologies has attracted more and more attention and shown great potential in industrial processes due to their high efficiency, ease of operation, low energy consumption, and

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environmental sustainability [17]. Among them, crystalline materials, especially MOFs and COFs, often show superior separation performance over non-crystalline materials (e.g., PAFs) due to their ordered structure, tunable pore size, high surface area, etc. [17, 18]. In addition, the crystalline nature of the porous skeleton can be directly identified by pore size and structure using single-crystal X-ray diffraction (XRD) or refined by Rietveld using powder XRD, which is very useful in systematically studying the relationship between pore structure and isotopic separation [19]. Quantum sieving (QS) is another hydrogen isotope separation mechanism. Recent studies have considered the separation of hydrogen isotopes by confining the spatial structure of the material based on QS and tested various porous materials [20–22]. However, almost all materials show low separation factors. The QS effect is evident in systems where the difference between pore size and molecular size is comparable to the de Broglie wavelength. With this limit, the zero-point energy of the gas molecules overcompensates for the interaction potential, thus producing a larger diffusion potential for the lighter isotopes [23]. Therefore, the pore size is crucial in determining the overall separation. Kim et al. [24] chose MIL-53(Al), a representative flexible MOF, as the host material and demonstrated a reversible structural change of hydrogen adsorption from narrow pores (np) to large pores (lp). During the dynamic np-to-lp phase transition, MIL-53(Al) can produce an intermediate pore size with an effective QS effect (Fig. 4). Their experiments showed that the selectivity of D2 for H2 was closely related to the pore structure state of MIL-53(Al). The highest selectivity (SD2 /H2 = 13.6 at 40 K) was obtained by optimizing the exposure temperature, pressure, and time to systematically tune the pore state of MIL-53(Al).

Fig. 4. Schematic diagram of D2 separation in MIL-53(Al) of 1D channel during the breathing propagation [24]

Covalent organic frameworks (COFs), a new generation of crystalline framework materials, can be considered sister materials to MOFs, and their well-defined crystal structure, low density, good chemical stability, large surface area, and easy customization grant them unique properties and great potential for applications [17]. Compared with MOFs and other crystalline skeletal materials, COFs have superior membrane properties and selectivity. Therefore, the pore size of COFs can be tuned in

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various ways to separate different gas molecules [17]. Oh et al. [23] experimentally characterized and theoretically demonstrated Py@COF-1 by QS separation of hydrogen isotope mixtures with pyridine molecules (Py) doped in the pore wall of COF-1 (C3 H2 BO; Fig. 5) in combination with a reverse topology approach and genetic algorithm.

Fig. 5. Schematic diagram of the pore channel of COF-1 [23]

Py@COF-1 exhibits varying degrees of hysteresis in the low-pressure isotherm with temperature, suggesting the presence of low-temperature flexible pore size. In addition, the molar ratio of 1:1 D2 /H2 isotope mixture is significantly more selective than that of pure gas, which is attributed to the quantum isotope effect of low-temperature flexibility (Fig. 6). SD2 /H2 (ratio of D2 to H2 desorption) increases with pressure, reaching a maximum of 9.7 at 26 mbar and 22 K, which is much better than that of commercial cryogenic distillation processes (SD2 /H2 ≈ 1.5 at 24 K) [23].

Fig. 6. a Thermal desorption spectroscopy (TDS) of H2 and D2 at 26 mbar with a ramp-up rate of 0.1 K · s−1 (1:1 H2 /D2 mixture), b selectivity of equimolar mixtures as a function of pressure at different temperatures, c temperature dependence of the maximum selectivity and adsorption D2 amount [23]

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2.4 2D Materials Although water distillation [2], cryogenic distillation [4, 25], liquid-phase catalytic exchange [5, 26], and CECE [6] can separate hydrogen isotopes for large-scale applications, these tritiated liquid processing procedures with multi-stage cascades are relatively complex, energy-intensive, and inefficient. In the laboratory research stage, QS-cased separation with MOFs or COFs materials requires high-temperature or low-temperature equipment, which greatly increases the energy consumption. Ways to separate hydrogen isotopes from water are urgently needed for nuclear energy development [27]. A recently developed hydrogen isotope separation method based on 2D materials overcame the disadvantages of other separation methods in terms of high energy consumption and had the advantages of high separation factor and wide applicability. The research on the hydrogen isotope separation based on 2D materials has two main directions: the behavior of protons through 2D materials and the hydrogen isotope effects. The former focuses on how protons pass through 2D materials, the influencing factors of such process, and the potential barriers, while the latter studies the hydrogen isotope effect of the process based on the former. The former corresponds to the study of the potential barrier, while the latter corresponds to the study of isotopic effects. Recent studies have shown that graphene can be a potential hydrogen isotope separation material with a high separation coefficient.

3 Hydrogen Isotope Separation by Graphene Graphene is a novel 2D material with excellent optical properties [28], excellent mechanical properties [29, 30], high surface area [30], high electrical conductivity, and high chemical and thermal stabilities. Since its introduction in 2004 [31], graphene has gradually been adopted in electronics, new displays, sensors, composites, energy storage and conversion, biomedicine, environmental protection, and thermal management. Unlike conventional separation materials, monolayer graphene has subatomic selectivity, i.e. [32–37], graphene without structural defects is permeable to protons [38] and electron [39] but completely impermeable to larger atomic species [33]. The transport of protons through graphene is a thermally activated process, and the energy barrier for a single layer of graphene to penetrate the electron cloud density of protons is 0.8 eV [38]. 3.1 Theoretical Calculation Theoretical calculations for graphene-based hydrogen isotope separation have been conducted for over a decade. As mentioned in the previous section, QS theoretically demonstrated that microporous graphene could be used for gaseous hydrogen isotope separation. Density functional theory (DFT) calculations showed that protons could easily pass through graphene sheets with significantly lower tunneling effects [34]. In addition to graphene membranes, porous graphene membranes were also designed by doping with nitrogen, which showed high selectivity for hydrogen at low temperatures in DFT calculations [35]. Conventional theoretical calculations suggested that although microporous graphene had good D2 /H2 selectivity, its pore size is too small for a sufficient gas separation flux at

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low temperatures. Nevertheless, Hankel et al. [36] conducted asymmetric modification of nitrided porous graphene with lithium metal or titanium and eliminated the design problems associated with precise control of cavity size by eliminating the cavity itself. Their solution has a very high selectivity for deuterium and achieves the necessary gas separation flux at low temperatures (Fig. 7).

Fig. 7. Schematic diagram of nitride microporous graphene [36]

3.2 Experimental Research In 2014, Lozada-Hidalgo’s group [38] designed and built an electrical measurement device to study the proton transmission through MoS2 , graphene, and hexagonal boron nitride (hBN). The fabrication process of the electrical measurement device is shown in Fig. 8a. Firstly, the 2D crystals of low-defect monolayer graphene, monolayer hBN, and monolayer MoS2 were prepared on micron-sized holes etched by Si wafers. Then, the 2D crystals were coated with Nafion films on both sides. Finally, palladium hydride (PdHx) electrodes were attached to the Nafion from both sides of the 2D crystals. The current-voltage (I-V) curves of the composite structure were plotted. The conductivity of protons through the three 2D materials was calculated according to the I-V curves, which were hBN, graphene, and MoS2 in descending order, where the conductivity of MoS2 is zero. Therefore, protons cannot pass through MoS2 but can pass through hBN and graphene, especially hBN. The reason is that MoS2 is a double sublayer structure, while graphene and hBN are planar structures. Thus, protons are trapped between the sublayers of MoS2 but pass directly through the planar structures of graphene and hBN. According to the in-plane electron distribution in Fig. 8c, the electron density in the center of the six-element ring is smaller because the B and N atoms of hBN are strongly polarized, and electrons are bound around the nucleus. In contrast, graphene is composed entirely of C atoms with a weaker binding effect on electrons, and the electron density in the center of the six-element ring is larger. Since the two sublayers of MoS2 overlap, the electron density in the center of the six-element ring is larger. The transport energy barrier E for different 2D crystals as a function of temperature T was calculated, as shown in Fig. 8b. For monolayer graphene, the calculated energy barrier E = 0.78 ± 0.03 eV, while the energy barrier E = 1.25–1.40 eV was obtained through simulations by molecular dynamics methods. The difference between the theoretical and experimental energy barriers is because the protons in Nafion and water move along the hydrogen bonds

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in the experiment instead of in vacuum as assumed in the theoretical calculations. This study confirms that monolayer graphene and hBN constitute a class of proton conductors, i.e., a class of 2D materials with high proton conductivity, high chemical and thermal stabilities, and impermeability to H2 , water, and other substances.

Fig. 8. a Schematic diagram of the transport of protons through two-dimensional materials, b energy barrier diagram of protons crossing 2D materials, c electron cloud density in two-dimensional materials [38]

In 2016 [33], to further investigate whether deuterium nuclei can pass through atomiclevel thin-film crystals and elucidate the proton transport mechanism, Lozada-Hidalgo’s group improved their previously developed device and built a ‘protium-deuterium’ separation device with combined electrical and mass spectrometric measurements. In that study, they explored the selectivity of 2D atomic crystals for deuterium nuclei (D+ ) and hydrogen nuclei (H+ ) by using conductivity measurements and mass spectrometry to detect airflow. The results showed that all hydrogen isotopes could pass through graphene by inputting a series of mixed solutions with different ratios of H+ and D+ . However, the output hydrogen atom fraction was out of proportion to the input H+ (Fig. 9), and the separation factor was calculated to be about 10, confirming that graphene could indeed separate hydrogen isotopes efficiently. This high selectivity is actually attributed to the isotope effect, where the 2D atomic crystals have different transport energy barriers for hydrogen and deuterium nuclei. In this system, the hydrogen and deuteron nuclei form hydrogen bonds with groups in the solution and water before passing through the 2D atomic crystals, and the zero-point energy of the hydrogen-oxygen bond is different for both, exhibiting an energy barrier difference of 60 meV. Therefore, if the proton conduction medium has stronger hydrogen bonds, the separation factor will be larger, which is more favorable for the efficient separation of hydrogen isotopes. αH/D =

σH = exp(−ED-H /KB T) σD

(1)

A theoretical model for calculating the hydrogen isotope separation factor of graphene, i.e., the Arrhenius formula in Eq. (1), was proposed based on experimental

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Fig. 9. a Mass spectrometry test schematic, b the relative fractions of H2 , HD and D2 in the output for the different components of the input electrolyte [33]

conductivity measurements and mass spectrometry detection by Lozada-Hidalgo et al. Although the model calculation results agreed well with the experimental results, the finger front factor was neglected. Nevertheless, the separation factor 12 of Zhang et al. [40]. Through theoretical calculation of the zero-point energy according to Eq. (1) was slightly higher than the result of Lozada-Hidalgo et al.10. The difference in the separation factor indicates that the model has a great influence on the results, where the calculated results differ even with the same model. In addition, Bukola et al. [41] obtained a higher separation factor14 than Lozada-Hidalgo et al. by improving the experimental setup and derived an inverse push-out energy barrier of − 77 meV, both of which indicate that there are still some errors in the calculation of the Arrheius model adopted by LozadaHidalgo et al. that ignores the finger front factor, which also requires a more appropriate theory to describe the process of hydrogen isotope separation and a more systematic and detailed study of the hydrogen isotope separation mechanism. The experiments of Lozada-Hidalgo et al. [16] also concluded that the hydrogen isotope separation factor was independent of the 2D material. However, the calculations of Zhang et al. [40] indicated that the separation factor correlated with the 2D material. Bukola et al. [41] concluded that the separation factors of graphene and hBN were also different and varied widely, indicating that the separation mechanism proposed by Lozada-Hidalgo et al. was not suitable for all systems or had certain deficiencies. The above analysis shows that the hydrogen isotope separation mechanism of graphene requires more in-depth research.

4 Conclusions Graphene, as a new material, has been gradually applied in hydrogen isotope separation, which has the advantages of low energy consumption and high efficiency when used to separate heavy aqueous solutions. It is estimated that [42] with a separation voltage of 0.5 V, the flux provided by the electrochemical pump is 0.8 mmol/h · cm2 . With a graphene separation membrane of 30 m2 , about 40 tons of heavy water could be produced per year at standard temperature and pressure, requiring only 20 GJ of energy to produce 1 kg of 20% concentrated heavy water, 10 GJ/kg less than the Girdler Sulfide (GS) and NH3 /H2 processes reported by the Heavy Water Board of India. Previous experimental and computational studies suggest that the experimental and computational separation factors, a major concern in hydrogen isotope separation, differ to some extent. Further

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research on the hydrogen isotope separation mechanism of graphene, separation device optimization, and comprehensive improvement of the separation device preparation process can improve the hydrogen isotope separation ability of graphene. Graphene-based hydrogen isotope separation undeniably provides more efficient, economical, and environmentally friendly solutions than the traditional methods and has a broader prospect of application in the future.

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22. Teufel, J., et al.: MFU-4–A metal-organic framework for highly effective H2 /D2 separation. Adv. Mater. 25, 635–639 (2013) 23. Oh, H., et al.: A cryogenically flexible covalent organic framework for efficient hydrogen isotope separation by quantum sieving. Angew. Chem. Int. Ed. 52, 13219–13222 (2013) 24. Kim, J.Y., et al.: Selective hydrogen isotope separation via breathing transition in MIL-53 (Al). J. Am. Chem. Soc. 139, 17743–17746 (2017) 25. Iwai, Y., et al.: HDT cryogenic distillation experiments at TPL/JAERI in support of ITER. Fusion Eng. Des. 61, 553–560 (2002) 26. Li, P., et al.: Separation process study of liquid phase catalytic exchange reaction based on the Pt/C/PTFE catalysts. Chin. J. Chem. Eng. 27, 1837–1845 (2019) 27. Yan, X.L.; Hino, R.: Nuclear hydrogen production handbook (2016) 28. Tan, C., et al.: Recent advances in ultrathin two-dimensional nanomaterials. Chem. Rev. 117, 6225–6331 (2017) 29. Zhao, Y., et al.: Two-dimensional material membranes: an emerging platform for controllable mass transport applications. Small 10, 4521–4542 (2014) 30. Perez-Page, M., et al.: Single layer 2D crystals for electrochemical applications of ion exchange membranes and hydrogen evolution catalysts. Adv. Mater. Interfaces 6, 1801838-n/a (2019) 31. Novoselov, K.S., et al.: Electric field effect in atomically thin carbon films. Science 306, 666–669 (2004) 32. Bunch, J.S., et al.: Impermeable atomic membranes from graphene sheets. Nano Lett. 8, 2458–2462 (2008) 33. Lozada-Hidalgo, M., et al.: Sieving hydrogen isotopes through two-dimensional crystals. Science 351, 68–70 (2016) 34. Zhu, X., et al.: Membranes prepared from graphene-based nanomaterials for sustainable applications: a review. Environ. Sci. Nano 4, 2267–2285 (2017) 35. Hauser, A.W., et al.: Helium tunneling through nitrogen-functionalized graphene pores: pressure-and temperature-driven approaches to isotope separation. J. Phys. Chem. C 116, 10819–10827 (2012) 36. Hankel, M., et al.: Asymmetrically decorated, doped porous graphene as an effective membrane for hydrogen isotope separation. J. Phys. Chem. C 116, 6672–6676 (2012) 37. Qu, Y., et al.: Highly efficient quantum sieving in porous graphene-like carbon nitride for light isotopes separation. Sci. Rep. 6, 19952 (2016) 38. Hu, S., et al.: Proton transport through one-atom-thick crystals. Nature 516, 227–230 (2014) 39. Britnell, L., et al.: Electron tunneling through ultrathin boron nitride crystalline barriers. Nano Lett. 12, 1707–1710 (2012) 40. Zhang, Q., et al.: Differential permeability of proton isotopes through graphene and graphene analogue monolayer. J. Phys. Chem. Lett. 7, 3395–3400 (2016) 41. Bukola, S., et al.: Selective proton/deuteron transport through Nafion| graphene| Nafion sandwich structures at high current density. J. Am. Chem. Soc. 140, 1743–1752 (2018) 42. Lozada-Hidalgo, M., et al.: Scalable and efficient separation of hydrogen isotopes using graphene-based electrochemical pumping. Nat. Commun. 8, 15215 (2017)

Research on Two Power Reduction Strategies of Cpr1000 Unit and Its Influence on Axial Power Deviation Delta I Zhenhua Zhang, Bo Zhang(B) , and Chaoying Zheng Nuclear and Radiation Safety Center, MEE, Beijing, China [email protected]

Abstract. Usually, nuclear power plants operate at 100% full power level, but in some special circumstances, the power level needs to be adjusted to meet the needs of power grid dispatching and other situations. For example, implement long-term low-power operation and extended low power operation; Due to typhoon, test and equipment maintenance etc., nuclear power plant decreases power to some specific power level and stays several hours. As well as, the plant reduces power to zero power (hot shutdown) for overhaul at the end of life (EOL). There are limitations when the power compensation rod (G Rod) is inserted into the reactor in the technical specification. Therefore, when the unit reduces the power, there are two options of power reduction strategy, one is to follow the power reduction through the G Rod inserted, and the other is to reduce the power through boronation. Operations of power reduction by G rod following or boronation were performed in fully configured simulation platform. The similarities and differences between the two power reduction strategies and the different effects of the two strategies on axial power deviation I (Delta I/I) of CPR1000 units were studied. Based on the core characteristics of the reactor, the main factors affecting the axial power deviation I are analyzed, and the strategy of controlling the reactor axial power deviation I is proposed. When the operator performs operations of power reduction by G rod following or boronation, it provides different reference suggestions for the effective control of axial power deviation. Keywords: CPR1000 · Decrease power · Delta I · G rod following · Boronation

1 Introduction In normal operation of power plant, in addition to full power operation, power reduction operation is very common. The common operation conditions requiring power reduction are mainly the following. Such as one of the CRF pump trips when the power is higher than 60%FP (full power) or An APA pump trips at full power, but the standby pump cannot be started. During full power operation, only one pump of CEX operates and the unit of power plant needs to reduce the power to operate. According to the requirements of the power grid, nuclear power plant decreases power from 100% full power to a lower power level in order to implement extended low power operation (ELOP) or stretch-out © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 668–678, 2023. https://doi.org/10.1007/978-981-19-8780-9_65

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operation [1]. Due to typhoon, test and equipment maintenance etc., nuclear power plant decreases power to some specific power level and stays several hours. As well as, the plant reduces power to zero power (hot shutdown) for overhaul at the end of life (EOL). For the above operation conditions, there are two different strategies to decrease power in nuclear power plant. One is to decrease power by inserting control rod, that is the G rod following, and another one is to decrease power by boron injection. Which one should be adopted depends on the current unit operating conditions, power reduction rate requirement, power reduction reason, and target conditions and so on.

2 Power Reduction Strategy and Influence Factors of the Axial Power Deviation 2.1 Power Reduction Strategy (1) The strategy of power reduction by G rod following The principle of reactor power control system is shown in Fig. 1. The reactor power regulation system selects the power to be tracked according to the secondary conditions and control mode. The selected power plus the correction factor serves as the power setting value, which is then converted to the set point of G rod positon by the function generator. Comparing the actual positon of G rod with the set point, the polarity and size of the rod position deviation determine the movement direction and speed of the power-regulating rod group. The power control rod follows the stepping procedure until the rod deviation enters the dead zone. In fact, the power correction factor provides a means for the operator to temporarily change the power adjustment rod position. The control rod operates, uses flexibly, and moves quickly and the accuracy of the control reactivity is high. The control rod is mainly used to quickly control the reactivity change of the reactor. The reactor power regulation system is an open-loop regulation system [2]. During the automatic control, the reactor power can quickly track the secondary power. (2) The strategy of boronation power reduction Boronation power reduction, that is power reduction by boron injection, is to inject boron into the reactor core to keep the G rod on the top of reactor core by using the power correction factor. Boronation power reduction provides the operator with the means to temporarily change the power compensation rod (G rod) position. According to the reactivity balance calculation, the change of boron concentration is calculated from power loss, Xenon Poison effect and rod position change etc., and then the boronation rate is calculated according to the requirements of power reduction rate. 2.2 Impact Factors of Axial Power Deviation During Power Reduction It is quite important to control the axial power deviation during the reactor power reduction. The control of I is one of the most difficult technical problems. The main factors

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Turbine power

The final power setpoint

Turbine load reference value A

Max

Turbine opening reference value M

Correction factor Load limit

The power setpoint

demanded position

GD +

MIN

Control rod location setpoint Σ

GD Control rod speed

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Fig. 1. Principle of power control rod

of the axial power deviation (I) are moderator average temperature, boron concentration of primary, control rods position, Xenon poison, reactor power level and so on. How the various factors affect I is introduced as follows [3]. (1) Moderator average temperature Moderator temperature effect is negative feedback. The average temperature of the core moderator decreases with the core power. Because the inlet temperature change of the core is very small, the outlet temperature change of the core is more, so the reactivity change of the upper part of the core is larger than that of the lower part of the core. When the power decreases, the core moderator temperature decreases. Compared to the lower part of the core, moderator temperature of the upper part of the core changes greatly. Therefore, the upper part of the reactor core releases more reactivity, the upper power share of the reactor core increases, and I shifts in the positive direction. (2) Boron concentration of primary Boron can be considered to be well-distributed in the reactor core. Differences in neutron energy spectrum between upper and lower core lead to different boron differential value. The moderator density of the upper core is lower than the lower core, so in the under slowed core, the neutron energy spectrum of the upper core is harder than the lower core, resulting in a greater boron differential value. The core power is tilted toward the lower core, and the rising boron concentration will cause I to shift to the negative direction.

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(3) Rod position During the reactor is in operation, the power distribution is affected by the rod position, so the I is also affected by the rod position. When the control rods are above the middle plane of the core, the insertion of rod will cause the I moving to the negative direction. On the contrary, the I moves to the positive direction. When the control rod groups pass through the middle plane of the core and enter into the lower half of the core, the insertion of rod will cause the I moving to the positive direction. On the contrary, the I moves to the negative direction. (4) Xenon poison For the CPR1000 unit, there is Xenon poison and Samarium poison in the reactor. The influence of Xenon on reactivity is analyzed in this paper. The distribution of Xenon is related to the neutron flux density. Where the neutron flux density is high, the Xenon poison is high. Where the neutron flux density is low, the Xenon poison is low. Due to the distribution of neutron flux is not uniform in reactor, so the distribution of Xenon poison is not uniform. Core power decreases, the increase of Xenon poison introduces negative reactivity. In addition, due to the moderator effect, I shifts to the right. The increased neutron flux in the upper core can bias the Xenon poison distribution towards the lower core, exacerbating the I moves t to the right.

3 Analysis of Parameter and Axial Power Deviation Trend During Power Reduction 3.1 Parameter Analysis During Power Reduction by G Rod Following Take the unit power reduction from full power to hot shutdown as an example. Standard hot shutdown condition requires that R and G rods to be inserted in 5 steps. Shutdown rods SB, SC, SD are in the top of core (the 225th step), SA rod is inserted in 5 steps, and the primary boron concentration reaches the hot shutdown boron concentration specified in technical specifications. Since the G rod is finally required to reach the hot shutdown condition, the strategy of inserting G rod to reduce power is adopted. In the simulation test, the first step was to decrease power to 300 MW without any human intervention. Initial state and target state were as follows. Initial state: Electric power is 1089 MW. Nuclear power is 100% Pn. Boron concentration is 775 ppm. G rod position is the 615th step. R rod position is the 190th step and regulation band of R rod is from the 178th step to the 202nd step. Target state: Electric power is 300 MW. Nuclear power is 29% Pn. Boron concentration is 775 ppm. G rod position is the 358th step. R rod position is the 165th step and regulation band of R rod is from the 178th step to the 202nd step. The trend of each curve can be seen in Fig. 2. The curves are nuclear power, turbine power, the temperature deviation and the axial power deviation.

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The axial power deviation turbine power

Nuclear power

The temperature deviation

Fig. 2. Simulation curves of power reduction by G rod following

(1) Analysis of the reasons f of R rod movement In the process of load reduction, some disturbance will be caused to the primary temperature, and certain measures must be taken to control, otherwise the R rod may be inserted below the low-low limit or raised to the 225th step to generate C11 signal. Due to the following reasons will cause a certain disturbance to the temperature of the primary, certain measures must be taken to control. Otherwise it is possible to insert the R rod below the low-low-low limit or withdraw R rod to the 225th step to generate the C11 signal. ➀ Under the automatic load control, the primary power change generated by G rod following has a certain lag effect relative to the turbine load change, which makes the primary and secondary power unbalanced. This leads to a trend of overheating in the primary during the initial load reduction, which causes the R rod to lift up, even reach the 225th step. ➁ During the process of power reduction, Xenon poison gradually accumulates and introduces negative reactivity, leading to a undercooling trend in the primary. This trend causes the R rod to move down and even reach low limits [4].

(2) Analysis of the control of power reduction rate In the process of power reduction, the power reduction rate should be adjusted according to the cold and hot status of the primary, or appropriately borated or diluted to maintain the temperature change of the primary and the power change of the secondary to prevent undercooling or overheating in the first passing through. Or the appropriate amount of

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boronation or dilution is adopted to maintain the temperature change of the primary and the power change of the secondary to achieve a balance to prevent undercooling or overheating. 3.2 Trend Analysis of Axial Power Deviation of Power Reduction by G Rod Following The change trend of I during power reduction by inserting G control rod can be seen in Fig. 3. The change trend of I is divided into four stages in Fig. 3. In the first stage, I shifts to negative direction due to the stepwise operation of the power regulating rod. The movement of the G rod within the core is performed by stepwise procedure. During the power reduction process, the G1 rod is first inserted. When the G1 rod is inserted in a certain position, the G2 begins to be inserted down, followed by the N1 rod and finally N2 rod. Therefore, when reducing the power with the G rod, the power reduction of the upper core precedes the lower core, and I shifts to the negative direction. Further, the reduction of upper power precedes the reduction of lower power, which results in the increase of Xenon poison in the upper precedes that of the lower. Thus, I shifts further to the negative direction. This trend can be restrained until the control rod reaches the lower part of the core. In the second stage, when the power is reduced, the outlet temperature of the core decreases, and the inlet temperature of the core does not change much. Due to the negative temperature effect of the moderator, the power increase of the upper core is greater than that of the lower core, so I shifts to the positive direction. In the third stage, in the process of power reduction without any intervention, the core overheats and causes R rod insertion [4]. Upper core power drops more than that of the lower core, so I continues to shift to the negative direction. In the fourth stage, when the G rod moves in the lower part of the core, the trend of I moving towards the negative direction is intensified. 3.3 Parameter Analysis of Boronation Power Reduction Simulation of boronation power reduction based on fully configuration simulation platform: Due to the blockage of APA filter, CPR1000 unit needs to reduce power to 80% Pn for filter replacement. The replacement time is about 2 days, and the residence time of the unit on the 80%Pn platform is more than 12 h. Due to the limitation of the time of G rod in the reactor core specified in the technical specification, the boronation power reduction strategy is selected (The R rod position is maintained unchanged during power reduction.). (1) Unit state data Initial and target states are as follows. Initial state: Electric power is 1089 MW. Nuclear power is 100% Pn. Boron concentration is 775 ppm. G rod position is the 615th step; R rod position is the 190th step.

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The first stage

The second stage

The third stage The fourth stage

Fig. 3 Ladder diagram of axial power deviation during power reduction by G rod following

Target state: Electric power is 870 MW. Nuclear power is 80% Pn. Boron concentration is 800 ppm. G rod position is the 615th step. R rod position is the 190th step. (2) Reactivity balance calculation ➀ Power loss: Ppower = 280 pcm. ➁ Control rod: The R rod position is remained in the same position in this simulation. The G rod position is kept at the top of reactor core. ➂ Xenon poison effect: Xenon poison effect can only be estimated and cannot be accurately calculated. The appearance of Xenon poison effect changes with time, so in the process of boronation power reduction, the boronation rate will be adjusted in time to achieve the compensation of Xenon poison effect [3]. ➃ The total amount of introduced reactivity is calculated as a power loss: 280 pcm. Which was only calculated by the power loss and Xenon poison was not taken into account. ➄ The differential value of boron is − 11.5 pcm/ppm. ➅ The boron concentration required to be changed is − 24.35 ppm. To sum up, the calculated volume of boric acid injection: 0.75 m3 . The turbine load reduction rate is 2 MW/min, and a total power reduction is about 200 MW, which takes about 100 min. Boron injection rate is 0.45 m3 /h.

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(3) Power reduction process The correction factor is 50 MW and then boron injection is started. When the temperature deviation e of the R rod is about 0.3 °C, the turbine power reduction rate of 2 MW/min is selected to start the power reduction. It is important to pay attention to the temperature deviation e of R rod during power reduction. And it is best to keep it between − 0.4 and + 0.4 °C to keep the R rod motionless. There are two ways to maintain temperature deviation e within the ideal range. One is to adjust the power reduction rate; the other is to regulate the boron injection rate. Considering the effect of the turbine rate change on the turbine equipment, the latter is adopted in this operation. When the power approaches 1045 MW, the correction factor is added to 100 MW, similar operation and so on until the correction factor is added to 200 MW. The trend of each curve could be seen in the Fig. 4. (1) In the process of boronation power reduction, the G rod is always kept at the top of the reactor by adding the correction factor. The correction factor is added successively to prevent the G rod from being quickly inserted into the bottom of the pile when the unit is transient. (2) The borization rate or power reduction rate is adjusted according to the change of temperature deviation e to keep the R rod motionless. (3) Similarly, when the power decreasing, Xenon poison gradually accumulates, and the borization rate or power reduction rate should be adjusted according to the change of temperature deviation e. In addition, the borization rate needs to match the power reduction rate to maintain the core temperature change matched with the secondary power change.

Boron flow

Boron injection valve opening

Correction factor The total boron injection

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Fig. 4. A Simulated curves of boronation power reduction

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3.4 Trend Analysis of Axial Power Deviation of Boronation Power Reduction The trend of I during boronation power reduction can be seen in the Fig. 5. Due to the influence of the different differential value of boron between the upper and lower of the core and the influence of temperature effect, the decrease in the upper power of the core is less than the lower core during boronation power reduction, which shifts I in the positive direction. At the same time, due to the inconsistent power reduction rate of the upper and lower parts of the core, the Xenon poison change rate of the upper and lower parts of the core is inconsistent [5]. The accumulation rate of Xenon poison in the upper core is smaller than that in the lower core, which shifts I further in the positive direction during the accumulation process. This makes subsequent I control more difficult.

Fig. 5. Ladder diagram during boronation power reduction simulation

4 Conclusion and Suggestion The two power reduction strategies are applied to different power reduction condition, and the correct power reduction strategy should be selected according to the specific working conditions and target working conditions of the unit. In addition, the power reduction should be prepared in advance in order to control the unit status easily. As well as a good preparation can also make the control of the axial power deviation after power decreased easily. Based on the above analysis, the following suggestions are given.

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(1) In the process of power reduction, close attention should be paid to the key equipment, especially the R and G rods, and to monitor and control the overall condition of the unit. (2) Therefore, the change trend of I caused by the power reduction by inserting the G rod makes the I control easier only from the control of I in the power reduction process. However, if appropriate methods are not taken to compensate, the G rod insertion is too deep and the I changes too much [6]. This makes it difficult to extract the G rod from the reactor core within the specified time and subsequently control of I. Therefore, in the progress of decreasing power by inserting control rod, R rods should be extracted from reactor core gradually according to the change of I. And Xenon Poison accumulation and appropriate boron injection should be also used to control I in a specific range that is Iref2 ± 3% (of course in the initial I should also be within this range) and reduce the insertion of G rod. On one hand, it reduces the difference between the upper and lower power changes of the core in the power reduction process so as to reduce the inconsistency caused by the change of Xenon poison on the core of upper and lower, and reduces the difficulty of subsequent I control; On the other hand, because the R rod position is high and the G rod insertion is not very deep, it is not difficult to extract the G rod from the core within the specified time. At the same time, enough R rod margin is left to compensate for the change of I during the lifting of the G rod. (3) According to the analysis to the change trend of I during decreasing power by boron injection, the operating point should be placed within a range of Iref2 ± 3% in one or two days (or earlier) before the planned power reduction, and the R rod should be placed in a high position (The two should balance each other). During the power reduction process, I is controlled in the range ofIref2 ± 3% by the timely insertion of the R rod according to the change of I, which can reduce the inconsistency of the power changes in the upper and lower parts of the core. Thus, the inconsistency of Xenon poison changes in the upper and lower core is reduced, and the subsequent I is easy to control [7]. When the reactor power reaches the target power and runs for a period of time (about 6–8 h), the amount of Xenon poison begins to decrease, and then I will shift to the negative direction. At this time, I can be controlled by the appropriate lifting R rod, but it should be noted that the R rod should not be raised too high, so as to leave sufficient margin for the future power increase.

References 1. Bai, C., Fei, Wang, X., Cai, D.: Extended low power operation justification of two typical first cycle for CPR1000. Nucl. Power Eng. No.S2, 1–3 (2014) 2. Guo, J., Chen, Q.: Research of reactor control strategy. Sci. Technol. Vision. 000(023), 87–88 (1999) 3. Song, J.: Axial power deviation and operation control of the reactor. Sci. Technol. Vision. 000(011), 199–203 (2015) 4. Duan, C., Wu, X.: Analysis of power decrease and axial power deviation control. Shandong Ind. Technol. 000(017), 276 (2016)

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5. Liao, Y., Xiao, M., Li, X., Zhu, M.: Axial power difference control strategy and computer simulation for GNPS during stretch-out and power decrease. Nucl. Power Eng. 25, 4, 297–300 (2004) 6. Chen, Z., Chen, C.: Axial power deviation control of PWR nuclear power plant in the end of lifetime. Chinese J. Nucl. Sci. Eng. 32(A01), 63–70 (2012) 7. Zhou, J., Cai, J., Zhao, L., Wen, S.: Analysis of axial power deviation I control during power rise and decrease process. China Electr. Power. 000(009), 54–57 (2013)

Analytical Displacement Analysis for an Inverted U-Shaped Tunnel Excavated in Anisotropic Rock Mass Shaojie Wang(B) , Xinli Zhao, Dongmei Wang, and Di Jiang Structural Technology Research Center of Nuclear Engineering, China Nuclear Power Engineering Co., LTD., Beijing, China [email protected]

Abstract. Tunnel is constructed in nuclear industry for multiple functions, such as for cooling water of nuclear power plants, spent fuel storage and so on. The structural property of the tunnel is closely related to the property of surrounding rock mass. Rock mass generally possesses the obvious anisotropy due to structural planes and the directional alignment of soil particles in rock mass formation. Based on the conformal mapping and complex variable method, the displacement analysis for the non-circular tunnel excavated in anisotropic rock mass is derived. The displacement solution on excavation boundary of tunnel is acquired, where the tunnel is in inclined sedimentary rock mass. Compared with the numerical solution, the results are in good agreement. Due to the anisotropy of the rock mass, the deformation of tunnel is asymmetric. Considering different in-situ stresses and elastic moduli along orthogonal directions, the displacements of tunnel are acquired and discussed. Keywords: Anisotropic rock mass · Inverted U-shaped tunnel · Complex variable method · Conformal mapping · Asymmetric deformation

1 Introduction As a common structure constructed in rock mass, tunnel is broadly used in nuclear industry for multiple functions, such as for cooling water of nuclear power plants, spent fuel storage and so on. In nature, the anisotropy is widespread in rock mass due to the two main factors: the structural planes (joints, fissures, faults et al.) and the directional alignment of soil particles in rock mass formation. Based on the assumption of continuous and homogeneous medium and the global anisotropic property acquired from the material property and the joints information, the constitutive relations of anisotropic linear-elasticity in various conditions could be established [1–11]. In constitutive relation, it is considered that the joints are continuous and the distribution of joints is regular. Specially, the joint surfaces are parallel and equidistant; the joint surfaces are orthogonal when multi-joints exist. Kulatilake et al. [12] provide an constitutive relation of linear elastic orthotropy which is based on the arbitrary combination of through or non-through joints in rock mass. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 679–688, 2023. https://doi.org/10.1007/978-981-19-8780-9_66

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When the constitutive relation of the orthotropic body is built, the mechanical analysis of the orthotropic rock mass can be investigated. Lekhnitskii [13, 14] obtained the analytical stress solution of the elliptical hole in an anisotropic plate through the complex variable method. Owing to the complication of anisotropic materials, the investigators usually use the perturbation method [15] to acquire stress and displacement distribution of non-circular holes. However, the application of perturbation method only gets the approximate solution when the hole is close to elliptical and the plate gets weak anisotropy. Lu et al. exhibit the analytical method of stress analysis for orthotropic rock mass with an arbitrary-shaped tunnel [16]. The practice has proved that the displacement information of surrounding rock can not only be used in the stability analysis, but also for other studies, such as the back analysis and the deformation prediction [17]. This paper will investigate the displacement of the non-circular tunnel in anisotropic rock mass, based on the following assumptions: ∞ ) and the cross The tunnel is buried deep enough. The in-situ stresses (σx∞ , σy∞ , τxy section of the tunnel are not changed along the axis of tunnel (Fig. 1). The surrounding rock mass is always in the elastic state after tunnel excavation. The tunnel is unlined or lined with a thin lining. The axial strain εz is equal to zero which can be considered as the elastic plane strain problem in infinite domain. σ∞[

τ∞[\ τ∞\[

[ (\ಬ

([ಬ

\ಬ [ಬ

σ∞\

φ

\

σ∞\

R

τ∞\[

σ

∞ [

τ∞[\

Fig. 1. An inverted U-shaped tunnel excavated in anisotropic rock mass

2 Theories 2.1 Conformal Mapping The conformal mapping method in complex variable is an effective tool to solve the problem of hole. For the isotropic media, only a set of mapping function z = ω(ζ ) is involved which transforms the outer region of the tunnel in physical plane (z plane, z = x + iy, where i2 = −1) into the outer or inner region of the unit circle in image plane (ζ

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plane, ζ = ρeiθ ). But for the orthotropic media, three Cartesian coordinates z = x + iy, z1 = x 1 + iy1 , and z2 = x 2 + iy2 are involved. It is not enough if only one mapping function is introduced. So we need to use three mapping functions z = ω(ζ ), z1 = ω1 (ζ1 ) and z2 = ω2 (ζ2 ) to transform the outer regions of the tunnel in z, z1 and z2 planes into the outer or inner region of unit circle in ζ , ζ1 and ζ2 planes. Three corresponding complex variables are defined as ζ = ρeiθ , ζ1 = ρ1 eiθ1 and ζ2 = ρ2 eiθ2 [16]. The general form of transforming the outer region of the tunnel in z plane into the outer region of the unit circle in ζ plane is:   ∞  Ck ζ −k (1) z = ω(ζ ) = R ζ + k=1

where R and C k are complex constants which reflect the size and shape of the tunnel respectively. When k is big enough, Eq. (1) can represent the various shapes. Three Cartesian coordinates z, z1 and z2 are mathematically related as follow: z1 = x1 + iy1 = x + μ1 y = γ1 z + δ1 z

(2)

z2 = x2 + iy2 = x + μ2 y = γ2 z + δ2 z

(3)

where γ1 = (1 − iμ1 )/2, γ2 = (1 − iμ2 )/2, δ1 = (1 + iμ1 )/2, δ2 = (1 + iμ2 )/2 μ1 = α1 + iβ1 , μ2 = α2 + iβ2

(4) (5)

where αj and βj (j = 1, 2) are real constants relevant to the anisotropic properties and β1 > 0, β2 > 0, and i2 = −1. The expression of z1 = ω1 (ζ1 ) and z2 = ω2 (ζ2 ) are given by Lu et al. [16]:     n n   1 −k k Ck ζ1 + C k ζ1 + δ1 R (6) z1 = ω1 (ζ1 ) = γ1 R ζ1 + ζ1 k=1 k=1     n n   1 −k k Ck ζ2 + C k ζ2 + δ2 R (7) z2 = ω2 (ζ2 ) = γ2 R ζ2 + ζ2 k=1

k=1

2.2 Basic Equations When the anisotropy of rock mass is orthogonal, and the coordinate axis z is perpendicular to a plane of elastic symmetry, the compatibility equation of plane strain problem in elastic body without considering the body forces can be given as [13–15]: β22

∂ 4F ∂ 4F ∂ 4F ∂ 4F ∂ 4F − 2β26 3 + (2β12 + β66 ) 2 2 − 2β16 + β11 4 = 0 4 3 ∂x ∂x ∂y ∂x ∂y ∂x∂y ∂y

(8)

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where βjk are real constants calculated by the anisotropic material properties. The solutions of Eq. (8) are determined by the roots of following equation: β11 μ4 − 2β16 μ3 + (2β12 + β66 )μ2 − 2β26 μ + β22 = 0

(9)

which can be proved that these 4 roots are conjugate complexes, i.e., μ1 , μ1 , μ2 and μ2 . The solution of Eq. (8) can be equivalent to the solution of two analytic functions 01∗ (ζ1 ) and 02∗ (ζ2 ). For the infinite domain problem, two analytic functions have the following forms: 01 (z1 )

=

01 [ω1 (ζ1 )]

=

01∗ (ζ1 )

=

n 

ak ζ1−k

(10)

bk ζ2−k

(11)

k=0

02 (z2 ) = 02 [ω2 (ζ2 )] = 02∗ (ζ2 ) =

n  k=0

In this study, the compressive stress is positive, and the tensile stress is negative. The stress boundary condition of the tunnel can be expressed by the boundary value of the analytic function 01∗ (σ ) and 02∗ (σ ).         Re 01∗ (σ ) + 02∗ (σ ) = −Re B∗ ω1 (σ ) + B ∗ + iC ∗ ω2 (σ ) (12)         Re μ1 01∗ (σ ) + μ2 02∗ (σ ) = −Re μ1 B∗ ω1 (σ ) + μ2 B ∗ + iC ∗ ω2 (σ ) 

(13)



where the real constants B∗ , B ∗ , C ∗ can be calculated by the in-situ stresses σx∞ , σy∞ , ∞ and the parameters of orthotropic material α , β , α and β [13–15]: τxy 1 1 2 2 B∗ = 

∞ σx∞ + (α22 + β22 )σy∞ + 2α2 τxy

2 (α2 − α1 )2 + (β22 − β12 )

∞ (α12 − β12 − 2α1 α2 )σy∞ − σx∞ − 2α2 τxy

2 (α2 − α1 )2 + (β22 − β12 ) 

B∗ =

 ∞ (α1 − α2 )σx∞ + α2 (α12 − β12 ) − α1 (α22 − β22 ) σy∞ + (α12 − β12 ) − (α22 − β22 ) τxy ∗   C = 2β2 (α2 − α1 )2 + (β22 − β12 )

(14)

(15) (16)

2.3 The Solution of Complex Functions At the edge of the tunnel, ζ1 = ζ2 = ζ = σ = eiθ and σ −k = cos(kθ ) − i sin(kθ ). We substitute the boundary value of formulas (6), (7), (10), (11) into the (12), (13). Owing to the formulas (12) and (13) must be satisfied for the any value of θ , the linear equations of ak , bk can be acquired by comparing the coefficients of cos kθ and sin kθ in (12) and (13). It can be proved that ak , bk are complex constants when k = 1, …, n and ak = 0,

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bk = 0, when k > n. Then the complex functions 01∗ (ζ1 ), 02∗ (ζ2 ) can be determined. The stress components in Cartesian coordinate system is calculated by   σx = 2Re μ21 1 (z1 ) + μ22 2 (z2 ) (17)

σy = 2Re 1 (z1 ) + 2 (z2 )

(18)



τxy = −2Re μ1 1 (z1 ) + μ2 2 (z2 )

(19)

where 1 (z1 ) = B∗ z1 + 01 (z1 ) = B∗ z1 +

n 

ak ζ1−k

(20)

k=0 n      ∗ 0 ∗ 2 (z2 ) = B + iC z2 + 2 (z2 ) = B + iC z2 + bk ζ2−k



∗

∗

(21)

k=1

3 Results and Discussion 3.1 Basic Formulas for Displacement U and v represent the displacement at x and y direction respectively in global Cartesian coordinate system

(22) u = 2Re p1 1 (z1 ) + p2 2 (z2 )

v = 2Re q1 1 (z1 ) + q2 2 (z2 )

(23)

where: p1 = β11 μ21 + β12 − β16 μ1 p2 = β11 μ22 + β12 − β16 μ2

(24)

q1 = β12 μ1 + β22 /μ1 − β26 q2 = β12 μ2 + β22 /μ2 − β26

(25)

Before the excavation, the displacement which has already been produced by the ∗ in-situ stresses  before excavation are the first parts in Eqs. (20) and (21), i.e. B z1 and   ∗ ∗ B + iC z2 . The displacement caused by excavation are the rest parts of Eqs. (20)   and (21), i.e. nk=0 ak ζ1−k and nk=1 bk ζ2−k . In reality, only displacement caused by excavation can be measured.

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3.2 The Process of Displacement Solution It is assumed that the tunnel lies in an inclined sedimentary rock mass, which exhibits transverse isotropy. The relation between the global coordinate system xoy and the local coordination system x o y is shown in Fig. 1, where the x  -axis is consistent with the rock inclination and the ϕ is the angle between x  -and x-axis. z-axis is the tunnel axis and the z -axis shares the same direction with the z-axis and the x  o z is an isotropic plane. For the transversely isotropic problem, Ex = Ez and Ex , Ey , Ez are the elastic modulus at the direction of x , y , z  respectively. The Poisson’s ratios should satisfy: vz y = vx y , vy z = vy x , vz x = vx z , vy x = vx y Ey /Ex . The shear moduli satisfy the following relations: Gy z = Gx y , Gx z = Ex /[2(1 + vx z )]. For the plane stress problem, the material parameters which are not equal to zero  = 1/E  , a  = −v   /E  , a  = in local Cartesian coordinate system x o y are: a11 x yx y 12 13     = 1/G   . In global −vz x /Ez , a22 = 1/Ey , a23 = −vz y /Ez , a33 = 1/Ez , a66 xy  : Cartesian coordinate system xoy, the material parameters ajk can be calculated by ajk     a11 = a11 cos4 ϕ + (2a12 + a66 ) sin2 ϕ cos2 ϕ + a22 sin4 ϕ

(26)

    a22 = a11 sin4 ϕ + (2a12 + a66 ) sin2 ϕ cos2 ϕ + a22 cos4 ϕ

(27)

     a12 = a12 + (a11 + a22 − 2a12 − a66 ) sin2 ϕ cos2 ϕ

(28)

     a66 = a66 + 4(a11 + a22 − 2a12 − a66 ) sin2 ϕ cos2 ϕ

(29)

      a16 = a22 sin2 ϕ − a11 cos2 ϕ + (a12 + 0.5a66 ) cos(2ϕ) sin 2ϕ

(30)

      a26 = a22 cos2 ϕ − a11 sin2 ϕ − (a12 + 0.5a66 ) cos(2ϕ) sin 2ϕ

(31)

  a13 = a13 cos2 ϕ + a23 sin2 ϕ

(32)

  a23 = a13 sin2 ϕ + a23 cos2 ϕ

(33)

  a36 = (a23 − a13 ) sin 2ϕ

(34)

 a33 = a33

(35)

3.3 Example Analysis By determining the coefficients C k in conformal mapping function [19], the shape of the horseshoed-tunnel and the inverted U-shaped tunnel can be presented. Two shaped ∞ = 0 MPa and tunnels are investigated in 3 kinds of in-situ stresses: σx∞ = 10 MPa, τxy

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σy∞ = 5 MPa, 10 MPa, 15 MPa. We define the lateral pressure coefficient λ = σy∞ /σx∞ and λ = 0.5, 1.0, 1.5. An inverted U-shaped tunnel is symmetric with respect to x-axis, so the parameters of conformal mapping function are real constants: R = 1.9297, C1 = 0.0850, C2 = 0.0766, C3 = −0.0970, C4 = 0.0385, C5 = 0.0046, C6 = −0.0091, C7 = 0.0068, C8 = −0.0053, C9 = 0.0011, C10 = 0.0020, C11 = −0.0036, C12 = 0.0013, C13 = −0.0001 and C14 = 0.0001, which describes approximate a 4.0m high and 3.5m wide tunnel shown in Fig. 2.

Fig. 2. Model of Finite element method

The parameters of transverse isotropic rock mass are: ϕ = 45◦ , Ey = 100 MPa, G = 80 MPa, vx y = 0.2, vx z = 0.2. Ex = 200 MPa and 1000 MPa which means Ex /Ey = 2 and Ex /Ey = 10. In order to compare with solution of other method, finite element simulation is introduced into the analysis. In modeling, the coordinate of tunnel is determined based on the conformal mapping function (Eq. (1)). The anisotropic surrounding rock mass is represented by a plane of 40 × 40m size. The region of surrounding rock mass is meshed into 138003 42#PLANE elements and 138942 nodes. The model is constrained by simple support, shown in Fig. 2. We simulate the model under λ = 1.5 condition and results are in good agreement with the analytic outcomes (Figs. 3 and 4). Figures 3 and 4 exhibit the solutions of the inverted U-shaped tunnel under the different conditions of Ex /Ey and λ. The deformations exhibit the asymmetry in orthotropic rock mass. Compared with the isotropic rock mass, the deformation is symmetric under the condition. The tunnel shape and the in-situ stresses are symmetric with respect to x-axis. In figures, the deformation is clearly not symmetric and the asymmetry is more obvious for higher-degreed anisotropic rock. The reason caused this phenomenon is that the inclined structural plane (ϕ = 45◦ ) and the different elastic moduli in two directions. The variety of Ex /Ey , which indicates the degree of anisotropy, is one of the main factors determining the displacement. The smaller elastic modulus in y direction is, the x y

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Y

2

1

Ex'/Ey'=2 Excavation Boundary

λ=0.5 Deformation Boundary

' -2

-1

0 λ=1.0 Deformation Boundary 0 1

% 2

λ=1.5 Deformation Boundary

X

λ=1.5 Ansys Analysis -1

& -2

Fig. 3. The convergent deformation of the inverted U-shaped tunnel in the different lateral pressure coefficients when Ex /Ey = 2

$

Y

2

1

Ex'/Ey'=10 Excavation Boundary

' -2

-1

λ=0.5 Deformation Boundary 0 λ=1.0 Deformation Boundary 0 1 λ=1.5 Deformation Boundary λ=1.5 Ansys Analysis

% 2

X

-1

& -2

Fig. 4. The convergent deformation of the inverted U-shaped tunnel in the different lateral pressure coefficients when Ex /Ey = 10

greater deformation in y direction is, and the more obvious asymmetry of the tunnel is. Therefore, for the anisotropic rock mass, the symmetry of the displacement is no longer irrelevant to the properties of the material like isotropic problem. The horizontal deformation is influenced by the horizontal in-situ stress. The horizontal deformation gets larger as the horizontal in-situ stress is greater (λ is bigger). A, B, C and D represent the edge points on each coordinate axis in inverted U-shaped tunnel. From the Table 1, the displacement of the points on x-axis is increased from 0.0573 m to 0.3539 m and from 0.0608 m to 0.3567 m when λ is from 0.5 to 1.5 on the condition of Ex /Ey = 2.

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Table 1. Displacement of the points on x-axis of the inverted U-shaped tunnel λ

Displacement (m) of Ex /Ey = 2

Displacement (m) of Ex /Ey = 10

B

D

B

D

0.5

0.0573 m

0.0608 m

0.0531 m

0.0606 m

1.0

0.2056 m

0.2088 m

0.1550 m

0.1611 m

1.5

0.3539 m

0.3567 m

0.2565 m

0.2604 m

On opposite, the vertical displacement is of difference with the horizontal displacement. In Table 2, The vertical displacement is reduced from 0.1652 m to 0.0450 m and from 0.2327 m to 0.1317 m as the λ is from 0.5 to 1.5 on the condition of Ex /Ey = 2. This phenomenon can be considered that owning to the larger degree of the horizontal compress, the vertical deformation is restricted. Table 2. Displacement of the points on y-axis of the inverted U-shaped tunnel λ

Displacement(m) of Ex /Ey = 2

Displacement(m) of Ex /Ey = 10

A

C

A

C

0.5

0.1652 m

0.2327 m

0.1276 m

0.1642 m

1.0

0.1062 m

0.1802 m

0.0999 m

0.1401 m

1.5

0.0450 m

0.1317 m

0.0722 m

0.1169 m

Although increasing the elastic modulus in one direction can bring in the more apparent asymmetric deformation for the whole tunnel, the value of displacement is reduced. From the Tables 1 and 2, the displacements of Ex /Ey = 10 are all smaller than the corresponding points of Ex /Ey = 2. And when λ is from 0.5 to 1.5, the range of the displacement on the condition of Ex /Ey = 10 is also smaller than the corresponding points of Ex /Ey = 2. For example, the displacement range of the A position of Ex /Ey = 10 is 0.1276–0.0722 = 0.0554 m and of Ex /Ey = 2 is 0.1652– 0.0450 = 0.1202 m; for the D position of Ex /Ey = 10 is 0.2604–0.0606 = 0.1998 m and of Ex /Ey = 2 is 0.3567–0.0608 = 0.2959 m. This appearance indicates that increasing the elastic modulus in one direction could improve the ability of resisting the deformation for the whole rock mass.

4 Conclusion An analytical solution of the displacement field for an anisotropic rock mass with an arbitrary-shaped tunnel subjected by the in-situ stresses is acquired. The analytical method is accurate and reliable in the derivation and the solutions are in good agreement with the finite element results obtained by ANSYS numerical software.

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The results show that the displacement could be asymmetric and the asymmetry is more significant for the higher-degreed anisotropic rock mass. The symmetry of convergent deformation in orthotropic rock mass is not only determined by the tunnel shape and the in-situ stresses, but also orthotropic property, especially the inclined structural plane and the variant elastic moduli in different directions. Increasing the elastic modulus in one direction could improve the ability of resisting deformation for the whole rock mass.

References 1. Amadei, B.: Rock Anisotropy and the Theory of Stress Measurements. Springer-Verlag, Berlin (1983) 2. Barla, G.: Rock anisotropy, theory and laboratory testing. Rock Mech. 131–169 (1974) 3. Salamon, M.D.G.: Elastic moduli of a stratified rock mass. Int. J. Rock. Mech. Min. 5, 519–538 (1968) 4. Singh, B.: Continuum characterization of jointed rock masses. Int. J. Rock. Mech. Min. 10, 311–349 (1973) 5. Morland, L.W.: Elastic anisotropy of regularly jointed media. Rock. Mech. 8, 35–48 (1976) 6. Stephansson, O.: The Nasliden project—rock mass investigations. In: Proceedings of Conference on Application of Rock Mechanics to Cut the Fill Mining, pp. 145–161. Lulea, Sweden (1987) 7. Amadei, B., Goodman, R.E.: A 3D constitutive relation for fractured rock masses. In: Proceedings of International Symposium on Mechanical Behavior of Structured Media. Ottawa, Canada (1981) 8. Gerrard, C.M.: Equivalent elastic moduli of a rock mass consisting of orthohombic layers. Int. J. Rock. Mech. Min. 19, 9–14 (1982) 9. Gerrard, C.M.: Elastic moduli of rock masses having one, two and three sets of joints. Int. J. Rock. Mech. Min. 19, 15–23 (1982) 10. Gcrrard, C.M.: Joint compliances as a basis for rock mass properties and the design of supports. Int. J. Rock. Mech. Min. 19, 285–305 (1982) 11. Fossum, A.F.: Effective elastic properties for a random jointed rock mass. Int. J. Rock. Mech. Min. 22, 467–470 (1985) 12. Kulatilake, P.H.S.W., Wang, S., Stephansson, O.: Effect of finite size joints on the deformability of jointed rock in three dimensions. Int. J. Rock. Mech. Min. 30, 479–501 (1993) 13. Lekhnitskii, S.G.: Theory of elasticity of an anisotropic body. Mir Publishers, Moscow (1981) (Translated from the revised 1977 Russian edition.) 14. Lekhnitskii, S.G.: Anisotropic Plates. Gordon and Breach, New York (1968) 15. Chen, Z.Y.: Analytical Method of Rock Mechanics Analysis. China Coal Industry Publishing House, Beijing (1994). (in Chinese) 16. Lu, A.Z., Zhang, N., Zhang, X.L., Li, W.S.: Analytic method of stress analysis for an orthotropic rock mass with an arbitrary-shaped tunnel. Int. J. Geomech. 15, 04014068 (2015) 17. Huang, Q.X., Deng, J., Su, P.Y., Wang, D., You, P.: Displacement characteristics analysis of surrounding rock underground powerhouse chambers at pubugou hydropower station during construction. China J. Rock. Mech. Eng. 30(suppl 1), 3032–3041 (2011) (in Chinese) 18. Muskhelishvili, N.I.: Some Basic Problems of the Mathematical Theory of Elasticity. Noordhoff, Groningen (1963) 19. Lu, A.Z., Zhang, L.Q.: Complex Function Method on Mechanical Analysis of Underground Tunnel. Science Press, Beijing (2007). (in Chinese)

Calculation of Cs Migration Behaviorin Argillaceous Material Heng Zhang(B) and Li Hong Hui China Institute for Radiation Protection, Taiyuan, Shanxi, China [email protected]

Abstract. Cs is an important element in high-level radioactive disposal. In this paper, the migration behavior of Cs in clay is studied. The existence of Cs in groundwater is simple and mainly exists in the form of monovalent cations. The main factors affecting the diffusion of nuclides are seepage, generalized adsorption, diffusion, etc. The diffusion models mainly include macro-scale models and micro-scale models. The macroscopic model is used in this paper, considering the adsorption, seepage, and diffusion of groundwater in the clay rock, and assuming one-dimensional diffusion, the adsorption is isothermal adsorption, and the model equation is obtained according to the mass conservation equation. Parameters include half-life, specific activity, effective diffusion coefficient, distribution coefficient. The effective diffusion coefficients use values from the literature, and the distribution coefficients are derived from the laboratory. The adsorption mechanism of Cs is mainly ion exchange, in which K+ and NH4 play the main competitive adsorption. Keywords: Argillaceous · Migration · Adsorption · Simulation · Diffusion

1 Introduction In the process of human development and utilization of nuclear fission energy, a large amount of high-level radioactive wastes are produced. Since high-level radioactive wastes contain nuclides with strong radioactivity, high calorific value, high toxicity and long half-life, they must be isolated from the human environment for a long time and reliably; The safe disposal of high-level radioactive waste has become a strategic task to ensure the sustainable development of nuclear energy, protect human health and safety, and protect the environment. At present, Our country has temporarily stored a certain amount of military high-level radioactive waste liquid, and with the development of nuclear power, the production of high-level radioactive waste such as spent fuel is also increasing year by year. Obviously, how to dispose of these high-level radioactive waste in a timely and safe manner is an important issue for my country’s nuclear energy industry. The major problems and challenges faced by the world and the defense science and technology industry. Among the many disposal schemes currently proposed, the deep geological disposal of high-level radioactive waste is the most promising disposal

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 689–699, 2023. https://doi.org/10.1007/978-981-19-8780-9_67

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scheme for practical application. Natural barrier) to achieve containment and blocking of radionuclides. The high-level radioactive waste geological disposal system is composed of highlevel radioactive waste solidified body, waste container and its outer packaging, buffer/backfill material, and surrounding rock of the repository. The first three constitute an artificial barrier, and the surrounding rock is a natural barrier. The migration process of nuclides in the natural barrier mainly includes the seepage of the solution containing nuclides in the surrounding rock, the adsorption of radioactivity by the surrounding rock, and the diffusion of nuclides in the surrounding rock. The important evaluation method is the nuclide in the surrounding rock. The maximum migration distance or the maximum dose after migrating to the biosphere, this paper organizes and analyzes the research results of the physical, chemical properties, seepage, adsorption and diffusion of nuclides in the mudstone, and establishes a description of the migration of nuclides in the mudstone. Theoretical model. Solve and analyze the nuclide migration flux in mudstone on long time scale.

2 Argillaceous Material 2.1 Basic Conception Argillaceous refer to sedimentary rocks dominated by clay minerals (content > 50%). Loose or unconsolidated diagenesis is called clay. Claystones are sedimentary rocks consisting mainly of fine clastic material (> 50%) with particle sizes < 0.01 mm. The particle size components of clay rocks are very small, mainly because of the small particle size of clay minerals. The particle size of clay minerals is generally < 0.005 mm. It is mainly composed of SiO4 , Al2 O3 and H2 O, followed by oxides of Fe, Mg, Ca, Na, K and some trace elements. The main clay rocks include kaolinite, montmorillonite, illite and chlorite, The main physical properties of clay rock include: plasticity, clay rock is crushed and added with appropriate moisture, and can be molded into a certain shape after being pressed, and the shape will remain unchanged after the pressure is removed; refractory, it is not easy to melt at high temperature; sintering, clay Minerals are partially melted at the low temperature of hell refractory, so the particles are bonded to each other to form hard ceramic stones; cohesiveness, clay and sand are bonded to form a good mud body, which is dried to form a hard green body; dry Shrinkage, after the clay deposits are air-dried or heated, the volume shrinks due to the evaporation of water bound on the particle surface; Adsorption, the ability of clay particles to adsorb various gases, liquids and organic pigments from the surrounding medium; Water-absorbing, clay has a strong ability to absorb water, while producing volume expansion. 2.2 Argillaceous Texture The structure of clay minerals consists of two basic structural layers, namely siliconoxygen tetrahedron layer and aluminum-oxygen octahedron layer or magnesium-oxygen octahedron layer tetrahedron: It is a tetrahedron (SiO4 ). Octahedron: It is an octahedron

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composed of one Al3+ (or Mg2+ ) and six oxygens (or OH− ) closely packed. The structural layer is a crystal structure unit layer formed by connecting the tetrahedral layer and the octahedral layer according to different rules. A unit structure layer composed of a tetrahedron sheet and an octahedron sheet is called a 1:1 structure layer or a doublelayer structure; if an octahedron sheet is sandwiched between two tetrahedron sheets, it is called a 2:1 structure layer or Three-layer structure. The basic structure of clay minerals is very similar. In nature, with the change of physical and chemical environment, clay mineral A can become B, and in the process of this transformation, there is a transition stage, the structural state of this stage, we call it Transform transition structures. This transition structure mainly includes (1) 2:1 to 1:1 transition transition structure, such as illite to kaolinite transition transition structure. (2) The transition structure between 2:1 and the adjacent 2:1, such as the transition structure of montmorillonite to illite. (3) The layered chain structure is transformed into a transitional structure, such as the transitional structure of sepiolite to talc. No matter how this kind of transition structure is adjusted, the process of transforming A mineral into B mineral is the emergence of a “B” domain in the mineral “A”, with the expansion of the “B domain” and the reduction of the “A” domain until All transformed into the mineral “B”. 2.3 Tamusin Argillaceous Properties The mineral composition is dominated by dolomite, analcite and clay minerals (chlorite), with an average content of 70% to 80% of the three minerals. Among them, the content of analcite and clay minerals with strong adsorption capacity for radionuclides reaches 36%. % to 58%. Taking TZK-2 drill hole as an example, the mineral content of mudstone in the upper and lower layers is shown in Table 1. Table 1. Tamusin argillaceous parameters Upper argillaceous 33–527 m (%)

Mean of upper argillaceous (%)

Lower argillaceous 527–720 m (%)

Mean of lower argillaceous (%)

20.1–61.5

33.93

2.7–40

19.39

Clay mineral 12.8–42.3

25.46

9.7–23.9

17.23

Carbonate minerals

18.03

26.1–62.1

42.54

Analcite

4.5–40.6

3 Migration of Nuclides 3.1 Seepage The flow of fluid in a porous medium is called seepage. This flow process is seriously affected by the porous (void) structure of the porous medium itself. Water flow and

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nuclides move in the voids of the porous medium. Flowing borders. Porous media The geometry of the skeleton and voids is very complex, and it is difficult to describe the geometry of the medium and the trajectory of water flow in the pores on a microscopic scale. In order to describe the motion law of fluid in the porous medium by mathematical means, a medium that continuously fills the study area is assumed to replace the actual multiphase porous medium, that is, the continuum. A multiphase continuous medium contains at least two phases, a solid phase and a liquid phase of the porous medium. The parameters of the medium and water flow can be defined at any point in the study area. The medium parameters studied by the continuum method are its macroscopic average values. It does not need to study the particle migration law of single-pore fluid, and can obtain the same state as the actual fluid. Parameters, and then can well meet the actual needs. Darcy’s law (Darcy, 1856) is the basic law of seepage. The seepage velocity in Darcy’s law is not the real velocity of fluid motion, it is an imaginary flow rate assuming that the fluid fills the entire seepage area (including the skeleton and space), and the water flows through the void section outside the range occupied by the skeleton and bound water. The actual flow rate is the ratio of seepage velocity to effective porosity. Darcy’s law has the form: q = Q/A = −K dh/dl

(1)

The permeability coefficient depends entirely on the characteristics of the porous medium and the fluid itself. The permeability coefficient of the porous medium can be further described as: K = ρg/μk = ρg/υk

(2)

For the porous media system with complex pore structure, such as argillaceous, the permeability coefficient must be measured through experiments. The permeability coefficient of mudstone in Tamusu area is between 10−10 m /s and 10−11 m/s. 3.2 Adsorption of Cs by Argillaceous Adsorption is the reversible accumulation and attachment of ions on the surface of oxides/viscid minerals. The adsorption mechanism of cations on the oxide surface corresponds to surface coordination, while the adsorption of cations on the clathrate minerals includes two mechanisms, ion (cation) exchange and surface coordination. The two mechanisms correspond to two different types of charges on the surface of the sticky mineral. When in contact with water, the clay king minerals are thought to have two types of charges, permanent negative charges and pH-dependent charges. Isomorphic substitutions that occur during the formation of clay minerals are the source of permanent negative charges, e.g., Al3+ in the aluminum-oxygen octahedral layer is replaced by Mg2+ , and Si4+ in the silicon-oxygen tetrahedral layer +) is replaced by Al3+ . For some clay kings (e.g., kaolinite) and metal oxides, the charge is mainly pH dependent, and these charges originate from the fractured Al–O and Si–O around the mineral to generate surface functional group through hydroxylation. The positive and negative nature of these pH-dependent charges depends on the cation of the suspension due to the two-sided nature of surface functional groups. Under acidic conditions, minerals tend to have a

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positive charge due to protonation of functional groups. Correspondingly, deprotonation of functional groups under alkaline conditions will lead to negatively charged minerals, and the excess negative charge of the minerals will attract cations such as Na+ , K+ and Ca2+ in this solution to neutralize them. This charge. Typically, these cations are located on the basal plane of the clay (i.e., the layer site), and a small fraction of cations will be adsorbed on the end surface of the clay (i.e., the edge site). The residence time of contaminants in water-saturated porous media is determined by their interactions with particle surface, surfaces, including ion exchange, surface coordination precipitation or structural infiltration of individual clay minerals. The mechanisms of these retentions have been extensively investigated through batch experiments using pure mineral phases in the dispersed state, and have generally been successfully fitted by mechanistic and/or empirical models in laboratory systems. However, it is questionable whether these models can be used to predict the adsorption of radionuclides under field conditions in natural systems. This is due to at least three reasons: (1) the summation rule (the overall partition coefficient (Kd) of radionuclides on mineral mixtures can be predicted from Kd measured on individual mineral components); (2) from dispersion Effectiveness of model conversion of system to compacted/intact system; (3) Adsorption equilibrium under long-term conditions. For radioactive Cs, although these issues have been extensively studied for more than two decades, there is still no unified conclusion. The chemical form of Cs in aqueous solution is simple, and its interaction with soil is known to be dominated by mudstone components through ion-exchange reactions. The model can calculate the retention of trace radioactive Cs in various environments (< 10−7 M). At this concentration level, most of the Cs uptake in soil is at a specific adsorption site, which is defined as the “wear edge site” (FES) on the illite trace mineral. Provided that the composition of the aqueous solution is known, and the FES capacity can be measured experimentally, in this case the solid/liquid distribution coefficient of Cs can be calculated by a single formula. At higher concentrations, FES sites saturated Cs adsorption may occur at other surface sites, and researchers have developed a “generalized Cs adsorption” (GCS) model to calculate Cs adsorption in viscous-rich rocks adsorption on, where the concentration range of Cs is relevant for radioactive waste management. In this model, illite is the only adsorbed phase, and two additional cationexchange sites are considered (another unique site for Cs is called “Type II”, and one on the surface of the illite particle substrate, site). The model can simulate the concentrationdependent uptake of Cs in a viscous-rich argillaceous system (up to 10−3 mol/L Cs). The GCS model has been successfully applied to many argillaceous rock systems . The following are the experimental results obtained by static experiments under laboratory conditions. The results show that the adsorption equilibrium time of Cs in tamsu mudstone is about 4 days. The figure shows the Cs+ concentration of 0.1 g mudstone in 20 mL 50 mg/L is the adsorption condition (Fig. 1). 3.3 Diffusion The radionuclide diffuses with the solution in your mudstone. Since the compacted mudstone is a porous material, the diffusion behavior of Cs+ is affected by the distribution of the pore structure and the degree of compaction of the mudstone. The diffusion of nuclides is different from the free diffusion in water. It is affected by factors such as

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Fig. 1. The adsorption performance of tamsuin clay rock varies with pH and concentration

the shape, size and tortuosity of the pores in the bentonite. Therefore, the diffusion coefficient Dp is commonly used to describe the diffusion in the pores. Contributing are those pores that allow nuclides to pass from one end of the porous material to the other, so the effective diffusion coefficient De is defined as: De = φDp

(3)

where φ is called the effective porosity, and this parameter needs to be obtained experimentally when the diffusion reaches a steady state. However, considering that it takes

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a very long time to reach a steady state in the actual experimental process, the apparent diffusion coefficient Da is often measured in general experiments. Da = De/α

(4)

The one-dimensional diffusion process that occurs in porous materials can be described using Fick’s second law: ∂ 2C ∂C = Dα 2 ∂t ∂x

(5)

where C is the nuclide concentration in pore water, t is the diffusion time, and x is the one-dimensional diffusion direction. The migration process of nuclides in bentonite is not only affected by diffusion, but also affected by various chemical and physical factors such as ion exchange, precipitation reaction, adsorption, complexation, etc. If all the influences in the migration process of nuclides are attributed to In the case of diffusion, only the apparent diffusion coefficient of the nuclide in the migration process can be obtained through the regression of the experimental results. Obviously, this apparent diffusion coefficient is the result of the comprehensive influence of various chemical and physical effects of the nuclide in the bentonite, not the nuclide. The intrinsic mechanism of diffusion in bentonite is reflected, so this parameter will inevitably bring relatively large errors in the simulation study of the migration of nuclides in bentonite. In order to obtain a relatively more accurate effective diffusion coefficient value, the current practice is to decompose the migration of nuclides in bentonite into two parts, and some nuclides are under the action of generalized adsorption (ion exchange, precipitation reaction, adsorption, complexation), stay in the pores of bentonite, and the remaining nuclides migrate out of the bentonite under the action of diffusion. Based on this decomposition, a relatively more accurate diffusion coefficient value can be obtained.

4 Migration Simulation 4.1 Migration Model The main function of the geological barrier is to block the migration of radionuclides to the biosphere. The following figure is a conceptual diagram of the geological disposal of high-level radioactive waste (Figs. 2, 3 and 4). When the packaging container of the high-level glass solidified body is damaged, and the groundwater enters the gap between the bentonite buffer material and the glass solidified body, under the erosion and dissolution of groundwater, the nuclides contained in the glass solidified body are leached and migrated into the groundwater in the gap, and the nuclide in the groundwater in the gap diffuses into the bentonite. On the surface, under the action of seepage and diffusion, the groundwater solution containing nuclides enters the pores of the bentonite, and the nuclides are also affected by the pore wall in the process of seepage, diffusion and migration in the pores of the bentonite. Migration away from the bentonite buffer material. Then it migrates from the pores to the pores of the surrounding rock. Basic assumptions of the model.

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Fig. 2. Concept of high-level radioactive Waste disposal repository

Fig. 3. Radial structure and geometry of the barrier system

(1) (2) (3)

(4) (5)

The starting time of the simulation calculation of the nuclide migration process is the time when the bentonite is completely saturated (about 20,000 years), There is a gap between the mudstone and the bentonite and this space is always filled with water. The concentration of nuclides in interstitial groundwater is uniformly distributed, which is taken as the maximum concentration of nuclides leached from bentonite (700 Bq/m3 ) Ignoring the effect of gas generation on the migration process due to radiation, chemical reactions, etc., it is assumed that the generated gas has been exhausted. Since the migration along the radial direction of the cylindrical mudstone is the most typical nuclide migration scenario, a one-dimensional radial model is used to describe the migration process, assuming that the radial direction of the mudstone is isotropic, without axial and angular gradients.

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Fig. 4. Symmetric model geometry dimensions

(6)

Based on the needs of theoretical simulation calculation, the research object is a single disposal unit, (7) The migration of nuclides in mudstone mainly depends on the solution seepage and the diffusion of nuclides in the pore solution, regardless of the adsorption on the surface of bentonite materials and the diffusion of complexed nuclides along the surface of solid phase materials (8) In the basic calculation, it is assumed that the retention of nuclides by bentonite is a linear, reversible and equilibrium process. The isotherm linear adsorption equation is used to describe it, and the specific retention mechanism of different nuclides and the complex geochemical reactions occurring in the pores of bentonite are no longer distinguished in detail. (9) Due to the small pores and complex pore structure in bentonite, the promotion effect of colloids on the migration of nuclides can be neglected. (10) Due to the small pores and complex pore structure in mudstone, the promotion effect of colloids on the migration of nuclides can be ignored. The basic equation for Cs migration in argillaceous is    2  ∂ qci ∂ ci 1 ∂ci ∂ci = De − − Rηli ci + ηR ∂t ∂r 2 r ∂r ∂r where R is the delay factor, and its expression is R=1+

ρS Kd η

The seepage velocity q is calculated as q = −K

∂h ∂r

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where h is the hydraulic head along the radial direction of the bentonite, which can be obtained by solving the differential equation of saturated groundwater flow: ∂ 2h ∂h =K 2 ∂t ∂r The boundary conditions of the equation are given below Sw

ci (∞, 0) = 0 h(∞, 0) = h0 ci (0, t) = 700 The parameters related to Cs in your mudstone are that the effective diffusion coefficient is 6 × 10−10 , the partition coefficient is 15mL/g, and the specific activity is 4.26 × 107 The following table shows some parameters of tamusin clay. Moisture content

Natural bulk density g/cm3

Particle density g/cm3

Porosity

Permeability coefficient m/s

4%

2.445

2.62

4.89%

0.5 × 10−10

The simulation results are as follows (Fig. 5).

Fig. 5. Nuclide migration simulation

5 Conclusions The activity concentration at 68 m from the nuclide release point reached a maximum of 23,000 Bq/L at 80,000 years, and the activity concentration at 132 m from the nuclide release point reached a maximum of 9 Bq/l at 180,000 years, while the activity concentration at 242 m from the release point of the nuclide is always close to 0 Bq/L, indicating that the nuclide did not migrate here. From the above data analysis, it can be seen that in the time scale of one million years, the migration distance of radionuclides in mudstone is quite small, and the maximum migration range does not exceed 242 m, which is mainly due to the low permeability of mudstone.

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References 1. Zhao, S., Liu, X., Li, H., Mao, L. : Investigation and research on the preselected Lot of Kuta Lignin for clay disposal of high radioactive waste. Environ. Sci. Manage. (2021) 2. Chen, Z. : Adsorption of Several Radionuclides on Bentonite and Callovo-Oxfordian Clay. Lanzhou University (2015) 3. Mao, L. et al. : Migration simulation study of key nuclides for geological disposal of high-level radioactive waste in clay preselected area. Environ. Sci. Manage (2016) 4. Guo, Z. et al. : Adsorption of Se(IV) on Beishan granite. Acta Phys. (2011) 5. Davis, J.A., Meece, D.E., Kohler, M., Curtis, G.P.: Geochim. Cosmochim. Acta 68, 3621 (2004) 6. Brown, P.L., Haworth, A., Sharland, S.M. : Modelling Studies of the Sorption of Radionuclides in the Far Field of a Nuclear Waste Repository

Radiological Consequences and Risk Analysis for On-Site Workers During PSA Fault Sequences Wenwang Ran(B) , Jing Zhou, Weifeng Lv, Quan Gong, and Jun Xiong China Nuclear Power Design Co. Ltd., Shenzhen, Guangdong, China [email protected]

Abstract. Radiological consequences and risk of death for members of the public from accidents resulting in exposure to ionizing radiation are often mentioned for pressurized water reactor (PWR) nuclear power plants. At the same time, for onsite workers, dose control is preferable. From the perspective of total radiological risk control for PWR nuclear power plants, radiological risk and frequency–dose acceptance criteria are established based on international guidance and practices in this research. The related evaluation methodology is also developed, including the fault sequences grouping principles, workers grouping principles, the calculation method for different exposure pathways, and the assessment of the risk of death, to accurately reflect the dose evaluation for on-site workers. Based on the inputs from a typical third-generation nuclear power plant, the above risk acceptance criteria and evaluation methodology are verified by analyzing radiological consequences from PSA fault sequences. The sensitivity and uncertainty analysis of L1 PSA modeling is also considered in the verification. The individual risk of death for different groups of on-site workers, the risk of death in each nuclear island building, and the risk of death from different fault sequences can be obtained from the established methodology and the verification results can guide and improve the radiological consequences analysis and radiological protection for on-site workers during accident conditions. The acceptance criteria and evaluation methodology presented can also assist in the risk control and As Low As Reasonably Practicable demonstration of nuclear facilities. Keywords: Accident conditions · Radiological consequences · On-Site worker · PSA · Risk of death

1 Introduction Radiological protection design under accident conditions is a crucial part of the radiological protection design for nuclear power plants. It aims at limiting radioactive releases and radiological consequences to workers and members of the public to an acceptable level and protecting intervention workers when accidents occur. The current Chinese radiological protection design under accident conditions focuses on controlling the off-site consequences for members of the public. At the same time, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 700–711, 2023. https://doi.org/10.1007/978-981-19-8780-9_68

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less attention is paid to radiological design for on-site workers under accident conditions. Similarly, regulations focus more on members of the public; for example, EUR sets out the dose limits for members of the public during normal operations and accident conditions [1], while for on-site workers, only a dose control value for intervention workers can be found in ICRP-103 and the value is independent of the accident frequencies [2]. However, the radiological consequences and risks for on-site workers are generally higher than for members of the public during accident conditions; it is necessary to establish radiological consequences and risks acceptance criteria to enhance further the radiological safety level of new-build nuclear power plants.

2 Acceptance Criteria of Radiological Risk Control for On-Site Workers Safety goals are the key indicators of the safety level for nuclear power plants. To quantify the safety objectives, the U.S. Nuclear Regulatory Commission (NRC) has proposed two safety goals: (a) The risk to an average individual in the vicinity of a nuclear power plant of prompt fatalities that might result from reactor accidents should not exceed one-tenth of one percent (0.1%) of the sum of prompt fatality risks resulting from other accidents to which members of the U.S. population are generally exposed; (b) The risk to the population in the area near a nuclear power plant of cancer fatalities that might result from nuclear power plant operation should not exceed one-tenth of one percent (0.1%) of the sum of cancer fatality risks resulting from all other causes [3]. The Health and Safety Executive (HSE) of the U.K. discusses the tolerability of risks from the nuclear power plant in Reducing Risks, Protecting People (R2P2). Based on the statistics of the actual risk of death per annum for the British public and workers, an individual risk of death of one in a million is recommended as the boundary between the ‘broadly acceptable’ and ‘tolerable’ regions and the boundary between the ‘tolerable’ and ‘unacceptable’ regions for risk entailing fatality is one in a thousand for workers and one in ten thousand for members of the public [4]. There aren’t similar quantitative safety goals for risk of death in Chinese nuclear regulations nor specific dose limits for on-site workers under fault and accident conditions. In order to establish acceptance criteria for radiological risk control for on-site workers, this paper investigated international regulations, guidance, and statistics. It proposed two quantitative targets of radiological risk control for on-site workers based on probabilistic safety analysis. 2.1 Acceptance Criteria of Overall Individual Risk of Death for On-Site Workers Under Fault Conditions Table 1 [5] gathers the risk of death for the British population from industrial accidents to employees for various industry sectors. The actual fatality rate for workers is below the

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upper limit of risk of death of one in a thousand recommended by R2P2. The equivalent risk of death for workers with ionizing radiation who receive 20 mSv in a calendar year is larger than 1.0E-04 y−1 . In the Safety Assessment Principles for Nuclear Facilities (SAPs) [6]. Published by Office for Nuclear Regulation (ONR), 1.0E-04 y−1 is used as the boundary of the ’unacceptable’ region. In consequence, 1.0E-04 y−1 is chosen conservatively as the basic limit for the acceptance criteria of individual risk of death for on-site workers under radiological fault conditions. Table 1. Annual risk of death from industrial accidents to workers for various industry sectors Industrial sector

Risk of death (y−1 )

Agriculture, forestry and fishing

1.2E-04

Construction

1.9E-05

Manufacturing

7.4E-06

Transportation and storage

6.5E-06

Wholesale, retail, motor repair; Accommodation and food

2.6E-06

Waste and recycling

7.5E-06

Administrative and support service

2.5E-05

For the higher goal, the annual risk of death from cancer for the England in the year of 2018 is 2.66E-03 [7]. According to the ‘one-tenth of one percent’ of NRC’s safety goal, the higher goal should be around at 2.66E-06 y−1 , which is also in consistent with R2P2 and SAPs. Hence, 1.0E-06 y−1 is chosen conservatively as the higher target, named Radiation Protection Targets 5 (RPT5), c.f. Table 2. Table 2. Acceptance criteria of RPT5 BSL

BSO

Overall individual risk of death to a person on the site, from accidents at 1 × 10–4 1 × 10–6 the site resulting exposure to ionizing radiation (y−1 )

2.2 Acceptance Criteria of Frequency-Dose Target for Any Single Accident Generally, a worker could not be exposed in more than one accident in any single year according to the occupational exposure management regulations. In this case, Radiation Protection Targets 6 (RPT6) has been set according to the dose control limit in IAEA principles [8], the numerical targets in SAPs and the value of RPT5 to control the frequency of the accidents which cause a certain level of dose to on-site workers, c.f. Table 3.

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Table 3. Acceptance criteria of RPT6 The frequency-dose target for any single accident, which could give doses to a person on the site (mSv)

Predicted frequency per annum (y−1 ) BSL

BSO

2–20

1 × 10–1

1 × 10–3

20–200

1 × 10–2

1 × 10–4

200–2000

1 × 10–3

1 × 10–5

> 2000

1 × 10–4

1 × 10–6

There are two sets of values for both RPT5 and RPT6; one is Basic Safety Limit (BSL), representing the mandatory limit of the design; the other is Basic Safety Objective (BSO), describing the safety goal of the design. The design should be improved if the BSL is not met, and the ALARA demonstration should be provided if the BSO is not met.

3 Radiological Risk Assessment Methodology for On-Site Workers The radiological risk assessment for on-site workers during radiological fault and accident conditions consists of the following steps: (a) (b) (c) (d) (e) (f)

Selection of fault sequences that could lead exposure to on-site workers; Grouping of the accidents identified; Categorization of on-site workers during accident conditions; Calculation of dose for all categories of workers for each group of accidents; Calculation of risk of death for all categories of workers for each group of accidents; Comparison of the results with the RPT5 and RPT6.

3.1 Selection and Grouping Principles for Fault Sequences The principles used to select the fault sequences which could lead exposure to on-site workers in this paper are as follows: (a) All Level 1 PSA (reactor core and spent fuel pool), Level 2 PSA (reactor core and spent fuel pool), waste route Postulated Initiating Events (PIE), and expert and additional review sequences should be considered; the duration of the accidents mentioned in this paper indicates the period from the initiation of a postulated accident to the period when the plant is returned to a safe condition (no more than 30 days); (b) Accidents that were identified but that have been assessed as resulting in a negligible (< 0.1 mSv) dose to workers are not included;

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(c) The following fault sequences might lead to non-negligible dose to workers: fault sequences requiring workers to perform on-site mitigation operations; fault sequences with radiological material released to the atmosphere inside the Nuclear Island Buildings, Radioactive Waste Treatment Building, or Turbine Generator Building; Fault sequences with radiological material released to systems that typically contain low or negligible radioactivity; Fault sequences with loss or degradation of shielding effectiveness (d) For fault sequences that result in worker doses larger than 2000 mSv, the detailed calculation of the worker dose caused by the fault sequence is not carried out. The compliance against RPT6 in these cases is demonstrated mainly by minimizing the frequency of the relevant fault sequences. Based on the above principles, the fault sequences selected can be grouped by the following principles to simplify the calculations: (a) Fault sequences can be grouped according to the quantity of radioactive release and resulting worker dose; (b) Fault sequences can be grouped according to their causal or functional similarity; (c) Fault sequences can be further grouped according to the location of the radioactive release or the location of the loss or degradation of shielding effectiveness; (d) The fault sequences with loss or degradation of shielding effectiveness are selected and grouped separately. 3.2 Categorization of On-Site Workers During Accident Conditions The on-site workers during fault and accident conditions are categorized into the following four groups according to their duties, the locations, and possible exposure routes. The description and assumptions of the exposure route for these workers are listed in Table 4. It should also be noticed that the location of workers varies for different fault sequences, and specific assumptions should be used. For a certain fault sequence, all the four groups of workers could get exposed; the following two principles are considered to simplify the calculation: (a) The dose for all four groups of workers are calculated for a certain accident and the maximum dose is taken conservatively as the dose for this accident when comparing to RPT6; (b) The risk of death for all four groups of workers is calculated for a certain accident based on the dose received, the location of workers, and the working hour ratio in each building. The maximum value is taken conservatively as the risk of death when calculating RPT5.

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Table 4. Grouping of on-site workers Worker groups

Exposure route

Main control room (MCR) workers

• The external exposure by submersion and internal exposure by inhalation from airborne radioactivity caused by ventilation in the MCR • The direct radiation to the MCR

Local accident intervention workers: workers • The direct radiation and external exposure who would participate in local accident by submersion during the path • The direct radiation and external exposure intervention operations by submersion at the operating region Accidentally involved workers: workers who • Direct radiation before evacuation are present in the area that is directly affected • External exposure by submersion and by the accident when it occurs, and may be internal exposure by inhalation caused by involved in work that is directly related to the airborne radioactivity • The exposure time is assumed to be 10 min occurrence of the accident, such as the fuel considering that these areas usually are handling operators in the event of a fuel designed with radiation monitoring, which is handling accident in line with U.K.‘s practice Other workers: workers who are present in the plant but belong to none of the above categories

• Direct radiation of the radioactive source • External exposure by submersion and internal exposure by inhalation caused by airborne radioactivity • The exposure time is assumed to be 10 min if the work is in the release room and 30 min if the worker is not in the release room according to U.K.‘s practice

3.3 Calculation Methods of Worker Dose During Accident Conditions (1) Calculation Methods for Direct External Exposure The methods and codes used for the calculation of dose rate from direct radiation are based on point kernel and Monte Carlo methods: (a) MicroShield (7.02): The MicroShield code is developed by Grove Software Inc based on the point-kernel method and is widely used for the shielding design of nuclear power plants. MicroShield code can only be used for the shielding of gamma source terms and simple geometry modeling. For neutron source terms or complex geometry modeling, or several source terms, MicroShield code is not applicable. (b) SuperMC (2): The SuperMC code is developed by the Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences/The Team for Frontier Development of Science (FDS) based on Monte Carlo methods. With excellent geometry interface, radiation field visual interface and precise neutron/photon translation simulation ability, SuperMC code has been used for the nuclear design and safety

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evaluation of nuclear systems. The SuperMC code can be applied for shielding and criticality analysis with complex geometry modeling and neutron/gamma source terms [9, 10].

(2) Calculation Method for Airborne Activity The internal exposures by inhalation and external exposure by submersion are calculated through dose conversion factors and empirical formulas, which is the common practice in the nuclear industry. The committed dose to the workers from inhaled radionuclides during 1 h of retention in the building is defined as: D=

n 

3.6 × 106 × BR × Ci × (DCF)i

(1)

i=1

The external exposure dose rate to on-site workers by submersion in the finite cloud is defined as: ˙ = D

n  3.6 × 106 i=1

GF

× Ci × (DCF)Ei

(2)

where, D: Committed dose from inhaled radionuclides during 1 h of retention in the building, mSv. ˙ External exposure dose rate by submersion in the finite cloud, mSv/h. D: Ci : Activity concentration of the nuclide i in the building atmosphere, Bq/m3 . BR: Breathing rate of the workers, i.e., 3.5 × 10–4 m3 /s [11]. (DCF)i : Conversion factor for committed effective dose or committed dose equivalent to the thyroid from inhalation of the nuclide i [12], Sv/Bq. (DCF)Ei : Conversion factor for external exposure by submersion in i th nuclide [13]. GF: Geometric correction factor of the compartment, 352/V 0.338 [11], with no dimensions, and V is the free space volume of the compartment, m3 .

(3) Calculation Methods for Risk of Death RPT 5 characterizes the total risk to a worker on the site during fault and accident conditions, not only from a single accident but from a collection of all accidents at all potential locations. Moreover, the worker evaluated by RPT5 does not refer to a specific worker on site, but refers to all the workers on site. Therefore, a generic worker model is considered as the primary worker group for RPT5 to represent the possibility that workers receive exposure from accidents at all potential locations. The worker’s risk of

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death is determined by the frequency of the fault sequences, the worker dose evaluation results, and the worker’s occupancy factor. The summated risk for the most exposed on-site worker, for comparison against the RPT5 criteria, is calculated using the following formula:  Ri = (Fn × Dn × CFn × Oi ) (3) n

where, Ri : Overall individual risk of death for exposure i, y−1 ; F n : Frequency of fault sequence n, y−1 ; Dn : Dose of fault sequence n, Sv; CF n : Dose-risk conversion factor which is appropriate for a dose uptake of Dn, 0.05 Sv−1 [2]; O i : Occupancy factor of worker for the location of exposure i. The occupancy factor is used to express approximately: (a) The proportion of time the worker will spend on-site per year,generally taken as 2000 h/8760 h = 0.228; (b) When on-site, the average time spent in different buildings where faults can occur.

4 On-Site Worker Radiological Risk Assessment for a Typical Third-Generation NPP 4.1 Assessment Scope and Results The above acceptance criteria and radiological risk assessment methodology were applied to a typical third-generation PWR nuclear power plant to validate the design, all fault sequences which could lead exposure to on-site workers were considered, as detailed in Table 5. The frequency of each group of fault sequences was taken conservatively as the sum of all fault sequences grouped based on the principle described in Sect. 3.1. The uncertainty of Level 1 PSA by setting all parameters at the upper 95% confidence interval has been taken into consideration. The assessment results are shown in Fig. 1. From Fig. 1, it can be deduced that the worker dose of the majority of the 34 bounding accidents is below the BSO of RPT6. Only 7 of them are between BSO and BSL. The overall individual risk of death for on-site workers calculated by Eq. (3) is estimated to be just under 86.5% of the RPT5 BSO target of 1E-06 y−1 . The radiation protection design of the typical third-generation nuclear power plant can provide good protection for the on-site worker during fault and accident conditions.

708

W. Ran et al. Table 5. The scope of accidents for RPT5 and RPT6 assessment

The scope of radiation risk analysis

Numbers

PIEs

Waste route PIEs and Fuel route PIE, such as TEU tank failure

14

Level 1 PSA (reactor core)

Sequences caused by internal events, internal fire hazards, internal flooding hazards and external hazards, such as SB-LOCA

243

Level 1 PSA (spent fuel pool)

Spent fuel pool level drop sequences caused by internal events, internal fire hazards, internal flooding hazards and external hazards

115

Level 2 PSA (reactor core and spent fuel pool)

Accident sequences with core damage and with spent fuel damage

16

Expert and additional review sequences

Auxiliary system failure sequences and 13 sequences not considered in PSA sequences list (such as RCCA ejection)

4.2 Results Discussion The top 10 contributing accident groups of the total RPT5 risk are shown in Table 6. Spent fuel pool related operations are the riskiest operations, and the radiation protection design and controls should be enhanced for these operations to reduce the risk. The overall risks of death for different groups of on-site workers are shown in Table 7; it can be found that: (a) The risk of death to MCR workers is significantly low thanks to the habitability design of MCR; (b) Although the working environment of local accident intervention workers is generally much riskier, the use of respiratory protection equipment (RPE) can significantly reduce the risk of internal exposure and further risk of death; (c) Accidentally involved workers and other on-site workers are the most vulnerable groups during fault and accident conditions as they are not generally equipped with RPE. The internal exposure contributes to nearly half of the total risk of death. The overall risks of death for on-site workers in different buildings are shown in Table 8. Workers in the fuel building have the highest risk of death from radiological accidents.

5 Conclusions This paper investigated international practice and guidance and proposed acceptance criteria of radiological risk control for on-site workers and the related assessment methodology for worker dose and overall individual risk of death, which is validated by the input

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Fig. 1. The dose calculation results of on-site workers for different accident groups

Table 6. Hierarchy of accidents by risk significance Rank

Description

Risk (y−1 )

% of total risk

1

SFP level drops to + 8.78 m-Internal Fire hazards

1.56E-07

18.22

2

Spent fuel assembly drop

1.47E-07

16.33

3

Cold overpressure of the primary system (CP)

1.37E-07

15.37

4

SFP level drops to + 8.78 m-internal event

9.20E-08

10.76

5

Small break-loss of coolant accident (SB-LOCA)

7.84E-08

9.18

6

Spectrum of RCCA ejection accidents

6.61E-08

7.73

7

Anticipated transient without scram (ATWS)

4.96E-08

5.80

8

Main steam line break (MSLB)

3.34E-08

3.91

9

Steam generator tube rupture (SGTR)

2.62E-08

3.06

10

Residual heat removal (RHR) System break (outside containment)

2.37E-08

2.77

of a typical third-generation nuclear power plant. From the assessment and validation of the typical nuclear power plant, we know that: (a) The overall individual risk of death for the on-site workers is 8.6E-07 y−1 , which is below the higher target and the risk of death from the other industrial accidents; (b) Fuel-related operations in the fuel building contribute the most risk; the radiation protection design and control measures should be considered to reduce the risk further;

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W. Ran et al. Table 7. Risk of death for each worker group

Worker groups

Risk of death (y)

Main control room (MCR) workers

2.43E-08

Local accident intervention workers

2.96E-07

Accidentally involved workers

3.87E-07

Other workers

5.40E-07

Table 8. Risk of death for workers in each building Worker’s location

Risk of death (y)

BRX

3.87E-08

BNX

1.34E-07

BFX

4.38E-07

BSA/BSB/BSC

3.17E-07

BWX

1.28E-08

Other areas

1.14E-08

(c) Accidentally involved workers and other on-site workers have the highest risk during fault and accident conditions; the use of RPE can significantly reduce this risk. The research results in this paper can provide a reference for the safety design of nuclear power plant to verify whether the radiological hazard is being adequately controlled. The acceptance criteria established can also help in the acceptance of PSA analysis and hence indicates the direction of further improvement of safety design.

References 1. EUR utilities.: European Utility Requirements for LWR Nuclear Power Plants, vol. 2 Chapter 1 Safety Requirements, Rev E 2. ICRP.: Recommendations of the international commission on radiological protection. ICRP Publication 103, Ann. ICRP 37 (2–4), 2007 3. USNRC.: Safety Goals for the Operation of Nuclear Power Plants; Policy Statement. 51 FR 28044 (1986) 4. HSE.: Reducing Risks, protecting people, HSE’s decision making process. HSE Books (2001) 5. HSE.: Workplace fatal injuries in Great Britain (2021) [EB/OL],(2021–12–16)[2022–06–27], https://www.hse.gov.uk/statistics/pdf/fatalinjuries.pdf 6. ONR.: Safety Assessment Principles for Nuclear Facilities. Edition, Revision 0 (2014) 7. UK Government.: Cancer registration statistics: cancer mortality in England, (2020–11–27) [2022–06–27], https://www.gov.uk/government/statistics/cancer-registration-statistics-can cer-mortality-in-england-2018/cancer-registration-statistics-cancer-mortality-in-england2018#authors

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8. IAEA.: Basic Safety Principle for the Nuclear Power Plant. INSAG-12 11 (1999) 9. Wu, Y., Team, F.: CAD-based interface programs for fusion neutron transport simulation. Fusion Eng. 84, 1987–1992 (2009) 10. Wu, Y., Song, J., Zheng, H., et al.: CAD-based monte carlo program for integrated simulation of nuclear system SuperMC. Ann. Nucl. Energy 82, 161–168 (2015) 11. U.S. Nuclear Regulatory Commission, Regulatory Guide 1.183, Alternative Radiological Source Terms for Evaluating Design Basis Accidents at Nuclear Power Reactors, p. 23 (2000) 12. United States Environmental Protection Agency, Federal Guidance Report No. 11, Limiting Values of Radionuclide Intake And Air Concentration and Dose Conversion Factors For Inhalation, Submersion And Ingestion, FPA-520/1–88–020 [S], Washington, Office of Radiation Protection Agency, pp. 127–158 (1988) 13. Environmental Protection Agency, Federal Guidance Report No 15, External exposure to radionuclide in air, water, and soil, EPA-402-R-19–002 [S], Washington, Office of Radiation and Indoor Air U.S. Environmental Protection Agency, pp. 186–216 (2019)

Study of the Capacity Building for Interim Storage of PWR Spent Nuclear Fuel in China Lei Shi1(B) , Chen Chen2 , Na Ma3 , Honglin Zhang1 , Yang Su1 , Jian Hu1 , and Chao Chen1 1 China Institute of Nuclear Industry Strategy, Beijing, China

[email protected] 2 CNNC Xinneng Nuclear Engineering Corporation, Taiyuan, China 3 China National Uranium Corporation, Beijing, China

Abstract. With the development of nuclear power in China, the amount of spent nuclear fuel is increasing rapidly. It is estimated that the PWR spent nuclear fuel will reach 33,000 tHM by 2035. However, the present interim storage and reprocessing capacity for spent nuclear fuel is not sufficient for the future demand. Therefore, it is necessary to suggest a solution regarding the capacity-building and the top-level management design for interim storage of spent nuclear fuel as soon as possible from an overall perspective. In this study, the interim storage requirement for China PWR spent nuclear fuel is calculated. And, it is suggested by the authors that a central wet interim storage facility of 9000 tHM capacity should be built in the near future. The legislative framework of spent nuclear fuel management could be optimized by proposing the Atomic Energy Law, specific regulations and rules for SNF management. It is also suggested that the policies and planning for the industrial and technological development of spent fuel management could be proposed each three or five years. Moreover, public communication and participation are the key factors for the effectiveness of spent nuclear fuel management. Keywords: Spent nuclear fuel · Capacity building · Management

1 Introduction Nuclear power has been considered as a potential option to secure China’s energy sustainability [1]. By the end of 2020, the total installed capacity of China’s nuclear power units is 51 million kilowatts, and nearly 6500 tons of spent nuclear fuel (SNF) have been generated from pressurized water reactors (PWRs). The government lately stated that the carbon dioxide emission will peak by 2030 and the carbon neutrality will be achieved by 2060. In order to realize this plan, nuclear power is expected to increase rapidly in the following decades [2–6]. It is predicted that nuclear power will account for 10% of China’s total generation output by 2035 [2]. It is estimated that the total installed capacity of nuclear power units in operation and under construction will reach about 200 million kilowatts by 2035 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 712–723, 2023. https://doi.org/10.1007/978-981-19-8780-9_69

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[3] (see Fig. 1). In order to achieve this goal, 8–10 “Hualong One” [7] or equivalent “million-kilowatt-class” nuclear power units should be constructed each year from 2020 onwards. At this development pace, the SNF generated from PWRs will surely increase rapidly. China’s annual amount of PWR SNF will reach 1222 tons by 2025, and will reach nearly 2800 tons by 2035 [8] (see Table 1 and Fig. 2). The total amount of PWR SNF is estimated to reach 33,000 tons by 2035. Considering this massive increase of spent fuel, the management of SNF becomes a prominent challenge to face to secure the sustainable development of nuclear power in China. For realizing the management of SNF, industry development planning, policy making and legislative framework optimization are essential prerequisites. In some previous studies [9–13], strategies before 2050 for the nuclear fuel cycle industries in China have been analyzed using various scenarios, and some roadmaps considering such as the development of SNF reprocessing and recycling were proposed. However, few studies were conducted with a focus on the interim storage in the nuclear fuel cycle, and it is widely known that this section is an inevitable process for the management of SNF. This study explores China’s long-term options for managing the nuclear spent fuel by examining the interim storage capacity. And, the study aims to provide an overview of SNF management in China to the general audience and policy implications on China’s long-term spent fuel management to Chinese policy makers.

China’s NPP in operation

Fig. 1. Estimate of total installed capacity of China’s NPP in operation from 2020 to 2040

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L. Shi et al. Table 1. Estimate of the amount of China’s PWR SNF from 2020 to 2035

China’s PWR SNF (tHM) Annual amount Total amount

2020

2025

992

1222

1919

2771

12,815

20,970

33,013

6500

2030

2035

China’s PWR spent nuclear fuel (tHM) Annual amount Total amount

Fig. 2. Estimate of the amount of China’s PWR spent nuclear fuel from 2020 to 2035

2 Interim Storage Capacity Building 2.1 Requirement for Interim Storage of SNF For both open and closed nuclear fuel cycles, interim storage is required in the industrial chain for SNF management (see Fig. 3). Therefore, interim storage is a key element of the fuel cycle management, regardless of whether the planned permanent option is reprocessing or direct disposal.

Fig. 3. Industrial Chain for the SNF management

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In order to calculate China’s SNF interim storage capacity, two scenarios are studied. The high scenario is to start to transfer the spent fuel that has been stored in the reactor pool for 8 years, and the low scenario is to start the transfer when 70% of the effective capacity of reactor pool is reached. Under the two scenarios, the annual and total SNF transfers for interim storage are calculated and shown in Table 2 and Fig. 4. Only PWRs are considered in this study. Table 2. Estimate of SNF transfer for interim storage in China from 2020 to 2035 China’s PWR SNF (tHM) Annual transfer (High) Total transfer (High) Annual transfer (Low) Total transfer (Low)

2020

2025

262

756

2363

5115

182 1745

2030

2035

1176

1631

10,417

17,276

337

897

1166

2948

6438

12,119

Under high scenario, it is estimated that nearly 800 tHM of SNF would be transferred from the reactor pool to interim storage facilities by 2025, 1200 tHM by 2030 and nearly 1600 tHM by 2035. Under low scenario, nearly 300 tHM of SNF would be transferred from the reactor for interim storage by 2025, 900 tHM by 2030 and 1200 tHM by 2035. The total transfer of SNF is the amount of SNF to be stored in interim storage facilities.

Fig. 4. Estimate of CHINA’s PWR SNF for interim storage from 2020 to 2035

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2.2 Capacity Vacancy for Interim Storage of SNF By the end of 2019, nearly 6500 tons of PWR SNF has been generated in China and almost all are stored in the reactor pool inside the NPP site [14]. No SNF has been disposed in China. As for the interim storage capacity, China has built some wet and dry storage facilities [3]. There is one wet interim storage facility of 1300 tHM, and two dry storage facilities of 550 tHM in total at the NPP site (see Table 3). In addition, China is constructing a 2nd wet interim storage facility of 1200 tHM for PWR SNF, and it is expected to be commissioned in 2025 [3]. The SNF interim storage capacities in the next 15 years are calculated and shown in Table 3. In conclusion, the SNF interim storage capacity in China is 1820 tHM in 2020, and that amount is estimated to reach 3050 tHM after 2025. Table 3. Estimate of China’s PWR SNF interim storage capacity from 2020 to 2035 SNF interim storage facility

Capacity (tHM)

2020

1st wet interim storage facility

1300

In operation

Daya Bay SNF dry storage facility

400

In operation

Tianwan SNF dry storage facility

150

In operation

2nd wet interim storage facility

1200

Total interim storage capacity (tHM)

2025

2030

Under construction

In operation

1850

3050

3050

2035

3050

It is known that the new generation of PWR pool can store the SNF generated for up to 20 years, greatly improving the storage capacity and leading to the reduction of demand for interim storage. However, even we calculate the demand of annual SNF transfer under lower scenario, there will still be capacity vacancy in China (shown in Table 4). It is estimated that the SNF interim storage capacity will not meet the demand by 2025. Under low scenario of SNF transfer, there will be vacancy of 3388 tHM by 2030. That vacancy will increase to about 9069 tHM by 2035. Therefore, interim storage facilities should be planed and constructed in the following decades. 2.3 Comparison Between Dry and Wet Storage The dry storage is mostly demanded by the NPP utilities facing saturation problems in the process of storing SNF in the reactor pool. And, it is known that the cost of dry storage is much less than that of wet storage. Considering the storage of 5000 tHM SNF, the total cost of dry storage is estimated to be 46% less than that of wet storage [15].

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Table 4. Comparison between capacities and requirements of China’s PWR SNF interim storage from 2020 to 2035 Year

2020

2025

2030

2035

SNF interim storage requirement under low scenario (tHM)

1745

2948

6438

12,119

Estimate of SNF interim storage capacity (tHM)

1850

3050

3050

3050

0

0

3388

9069

SNF interim storage capacity vacancy (tHM)

For example, the construction cost is 8% less, the operation cost is 17.4% less, and the decommissioning cost is 7.5% less. For the wet interim storage, the facility is mostly built next to the reprocessing plant to ensure the sustainable operation for SNF reprocessing. Both dry and wet approaches are acceptable, and either may be preferable in particular situations. In recent years, however, reactor operators are increasingly choosing dry storage. The authors hold that in case of open nuclear fuel cycle (without reprocessing), dry storage which is flexible, safety and low in long-germ cost will prove to be the preferable approach.

3 Management of SNF 3.1 Polices The spent fuel management policy in China is to implement the reprocessing of SNF and to extract and recover uranium and plutonium materials, so as to achieve maximum use of resources, reduce the generation of high level radioactive wastes (HLW), ensure the safety of SNF management and the public safety, and lower the risks to the future generations. During China’s early nuclear power development in 1980s, the policy on spent fuel reprocessing was defined [14]. On the basis of the demand for nuclear power expansion in the near and long-term future, China is making efforts to develop overall planning for spent fuel management capability building, encourage enterprises to participate in capability building and scientific research, improve the regulatory system, and train high qualified talents, so as to ensure the smooth implementation of the spent fuel management policy. As pointed out in the Outline of Thirteenth Five-Year Plan for National Economic and Social Development of the People’s Republic of China (OTNSPRC), the construction of large-sized commercial reprocessing plant will be demonstrated as soon as possible and pushed forward. In order to ensure the implementation of the spent fuel management policy, on the basis of the demand for nuclear power expansion in the near and long-term future, China is making efforts to develop overall planning for spent nuclear management capability building, scientific research, improvement of regulatory system, and high quality talent team training. The public acceptance of spent fuel treatment site has been promoted in the past few years. By the joint effort of several departments, the Policy Statement on Nuclear

718

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Safety Culture (PSNSC) was issued in December 2014 and the Methods of the Public Involvement in Environmental Protection (MPIEP) was issued in July 2015, which ensure the public to have the right of access to information, participation and supervision in many ways. Through opinion collection, questionnaires, open dialogue, expert meeting and public hearings, it may be possible for relevant departments to solicit the public comments and suggestions from citizens, legal persons and other organizations. These comments and suggestions shall be taken into account in decision-making, followed by giving timely feedback. 3.2 Legislative Framework Nuclear law is an essential prerequisite for realizing the safety management of spent nuclear fuel. China has established and maintained a legislative framework, which incorporates a comprehensive set of relevant national laws, administrative regulations, department rules, management guides and reference documents (see Fig. 5). The laws applicable to the safety of spent fuel management are developed and promulgated by the National People’s Congress Standing Committee (NPCSC); administrative regulations are developed and issued by the State Council as mandated by the National Constitution and the relevant laws; department rules are developed and issued by the environmental protection authority, nuclear facility authority and health and family planning authority, under the State Council, as mandated by the relevant national laws, regulations and responsibility assignment; the management guides are developed and issued by the relevant departments of the State Council. Reference documents are developed and issued by the State Council’s subsidiary departments or its mandated agencies.

Fig. 5. Legislative framework for SNF management

Considering top level, there are mainly five laws and State Council regulations in force applicable for safety management of spent fuel and radioactive waste, which are: • Law of the People’s Republic of China on Prevention and Control of Radioactive Pollution (LPCRP), enacted by the NPCSC in 2003. This law specifies the supervision

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•

•

•

•

719

and management responsibilities for the prevention and control of radioactive pollution. Low and medium level radioactive waste should be disposed near the surface in areas that meet the national regulations, and high level radioactive waste should be disposed in deep-geological formations. It is forbidden to dispose of radioactive waste in inland waters and oceans. Law of Nuclear Safety, enacted by the NPCSC in 2017. This law specifies that the company that generates, stores, transports and reprocesses the SNF shall be responsible for the nuclear safety of SNF. And, the mechanism for SNF treatment and disposal fees is specified in this law. Regulations of the People’s Republic of China on Safety Control of Civilian Nuclear Installations (HAF001), issued by the State Council in 1986. This regulation specifies that MEP/NNSA (Ministry of Environmental Protection, National Nuclear Safety Administration) is responsible for the supervision over the safety of nuclear facilities, including SNF storage and reprocessing facilities. Regulations on Safety of Radioactive Waste Management (RSRWM), issued by the State Council in 2011. This regulation is proposed according to the Law of the People’s Republic of China on Prevention and Control of Radioactive Pollution (LPCRP), and specifies the safety requirements for the treatment, storage and disposal of radioactive waste. Regulations of the People’s Republic of China on nuclear materials, issued by the State Council in 1987. This regulation states that MEP/NNSA is responsible for the safety supervision of uranium, plutonium and other civil nuclear materials, and is responsible for the approval of nuclear material licenses.

In China, the primary responsibility of spent fuel management safety rests with the operators of NPPs, research reactors and spent fuel storage facilities. According to the Regulations of the People’s Republic of China on Safety Control of Civilian Nuclear Installations (HAF001), the operators shall hold the overall responsibility for nuclear facilities they operate, including spent fuel management facility, and shall be subject to the supervision of the nuclear safety regulatory bodies. 3.3 Relevant Laws Specific for SNF Management Requirements and recommendations for the safety in management of spent fuel away from reactor are provided in Regulations on Civilian Nuclear Fuel Cycle Safety (HAF301) and Design Criteria for Spent Fuel Storage Pool away from Reactor (EJ/T878–2011), with special emphasis on the safety of dry storage or pool as follows: • to maintain the sub-criticality of spent fuel. The basic design objective of away-fromreactor spent fuel pool is to ensure spent fuel to be kept at sub-criticality in normal and accidental conditions. • to ensure heat removal. The design basis of pool water cooling system is aimed at keeping bulk water temperature within 40 °C degree. Once the cooling system fails to work, it shall be able to recover to normal conditions before the pool water temperature exceeds the design limit.

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• to ensure the amount of radioactive waste generated is kept at as low as actually achievable. There are two department rules proposed for the management of the SNF funds. One is the Interim Procedures on Collection, Utilization and Management of the Funds for Treatment and Disposal of Spent Fuel from Nuclear Power Plants (IPCUMFTDSFNPP), which is to standardize the collection, utilization and management of the funds for treatment and disposal of SNF. The other is the Project Management Methods of the Funds for Treatment and Disposal of Spent Fuel from Nuclear Power Plants (PMMFTDSFNPP), which is to standardize the project management of the funds for treatment and disposal of SNF in China and to ensure the rational and effective use of the funds. There have been some developments in the specific regulatory requirements for decommissioning of SNF management facilities. A plan was launched to prepare the laws and regulations on nuclear facility decommissioning management with respect to nuclear and radiation safety during the 13th Five Year Plan period. These include Rules on the Management of Civilian Nuclear Facilities Decommissioning (RMCNFD), Safety Guidelines on Decommissioning of Nuclear Power Plants and Research Reactors (SGDNR), and Safety Guidelines on Decommissioning of Nuclear Fuel Cycle Facilities (SGDNFCF). Currently, the relevant documents are under preparation on schedule. In addition, the initial draft of the Rules on the Management of Civilian Nuclear Facilities Decommissioning (RMCNFD) has been completed. This document is applicable to the management of the decommissioning of nuclear power plants, research reactors and other nuclear cycle facilities, encompassing decommissioning activity classification, policy/strategy and termination, and involving decommissioning planning, investigation, technology selection, process, waste management, as well as records and reports etc. 3.4 Challenges For the challenges in terms of the management of SNF, the top-layer law, Atomic Energy Law, has not been enacted yet in the legislative framework in China. As a result, the regulations and department rules for spent fuel management cannot be proposed systematically. The difficulty in making arrangements for ownership and liability has been a key factor in many past international discussions of storage or disposal approaches. In general, the long-term liability for the spent fuel or nuclear waste from customers is likely to exceed the difficulty of establishing a temporary site without liability transfer. Therefore, it is known that reactor operators are likely to be willing to pay higher prices for a service to take their spent fuel or nuclear waste off their hands. For the management of the SNF funds, more specific implementation rules or guides are needed. For example, the evaluation mechanism for the collection and use of funds has not yet been established in any regulations or rules. And, the value-added approach of funds has not yet been considered, since it is indeed an economic loss if the fund is treated as a “frozen account” while not used for SNF treatment. There is also a lack of cost risk control measures proposed in any regulations and rules.

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For a long time, the propaganda of nuclear science is insufficient all over the world, and the public understanding of nuclear science is generally inadequate. “Not in my back yard” still exists in the country having nuclear power.

4 Suggestions 4.1 Capacity Building In order to solve the problem of insufficient capacity in SNF interim storage, the SNF dry facility in NPPs could be useful in the near future, since it is a good solution to the NPPs having saturation problems in their SNF pools, and dry storage is a highly cost-effective approach to SNF management in general. However, China is planning to reprocess SNF in the following decades, and there will inevitably be wet storage of large capacity in or near the reprocessing facility. Therefore, building dry storage of large capacity to satisfy all the demand of SNF transfer shown in Table 4 is not an economical choice. The ideal option is to build a central SNF interim storage facility of capacity sufficient to satisfy the demand next to SNF recycling facilities, which would save the transfer cost when SNF needs to be reprocessed. It is suggested by the authors that one central SNF interim storage facility of 9000 tHM capacity should be built next to the reprocessing plant in 5–8 years, and the first section of 3000 tHM capacity should be in operation by 2030. Thus, the capacity would meet the SNF interim storage requirement until 2030. And, it is necessary to build the 2nd and the 3rd section of total 6000 tHM capacity during the five years between 2030 and 2035, so that the demand of interim storage by 2035 will be satisfied. Moreover, if the central SNF wet interim storage facility is not constructed as suggested above, some dry storage facilities of small capacities should be prepared as supplement and constructed during the five years between 2025 and 2030. The suggestions for the capacity building plan of PWR SNF interim storage in the next 15 years are shown in Fig. 6. Considering some uncertainties such as site selection of interim storage facilities, it is necessary to plan the construction in advance, and due to high risk issues of radioactive materials, the SNF interim storage facilities should be operated by a specialized company with relative qualifications approved by the regulatory bodies for SNF. 4.2 Management Optimizing the legislative framework of SNF. The Atomic Energy Law is suggested to be proposed as the “top level law” in the legislative framework, so that all the regulations, rules and guides for SNF management could be proposed systematically. In addition, a regulation specific for the SNF management could be proposed as well, so that the safety of SNF storage, transport and geological disposal of high-level radioactive waste generated in the reprocessing will be guided and ensured integrally, and the ownership and liability of SNF could be specified. Improving SNF management laws and rules. The management of the SNF fund could be improved. Specific department rules or guides are suggested to be proposed in order

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Fig. 6. Suggestions on China’s PWR SNF interim storage capacity building from 2020 to 2035

to specify the methods for the mechanism of increasing the value of the fund, the regular evaluation of the fund amount, and the proposal of the medium- and long-term use plan of the fund. Thus, the SNF could be managed with more sufficient financial support. The authors suggest that the policies and planning for the industrial and technological development for spent fuel management could be proposed each three or five years. It is known that nuclear power development is facing many challenges for any country in the world. There will inevitably be many uncertainties in the following years, such as the nuclear power increasing pace and technology development for SNF management, which could influence the capacity building and technical approach for SNF storage. Therefore, policies or national plans for SNF management could be proposed each three or five years, in order to ensure that all the SNF will be managed safely with reasonable approach in time. Improving public acceptance of nuclear facilities. Both the Policy Statement on Nuclear Safety Culture (PSNSC) and the Methods of the Public Involvement in Environmental Protection (MPIEP) have been issued, which specify the public involvement way in attempt to raising the public acceptance. Moreover, the authors suggest that public communication and participation is the key factor for the effectiveness of spent fuel management. With the development of national nuclear power and the capacity-building of all industries of nuclear fuel cycle, especially for the back-end, the science popularization, public awareness and participation, and public opinion response are becoming more and more important.

5 Conclusions For the capacity building, the interim storage industry development is important for the long-term management of China’s PWR SNF. At present, the capacity of interim storage could not satisfy the future need. It is necessary to consider building a central wet interim storage facility of 9000 tHM capacity next the commercial reprocessing facility in the near future, and dry storage of small capacities should be considered as supplement. One specific company or organization with qualifications should be selected to operate the interim storage facilities.

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For the management, there has been large improvement of policies and laws for SNF. The legislative framework of SNF management could be optimized by proposing the Atomic Energy Law, specific regulation for SNF, and the specific rules or guides for SNF fund. It is also suggested that the policies and planning for the industrial and technological development for spent fuel management could be proposed each three or five years. Moreover, public communication and participation is the key factor for the effectiveness of spent fuel management. Acknowledgements. This work was supported by the Institute Fund of CINIS. Furthermore, we are grateful for the cooperation from Division of Strategy and Division of Management in CINIS.

References 1. Ye, G.A.: Main options for China, towards LWR+GIF systems. In: Proceedings of International Conference on GLOBAL 2015, September 21–24, Paris, France (2015) 2. Zhang, T., et al.: Blue Book of Nuclear Energy Development: China Nuclear Energy Development Report (2019), pp. 144–145. Social Sciences Literature Press, Beijing (2019) 3. Zhang, T., et al.: Blue Book of Nuclear Energy Development: China Nuclear Energy Development Report (2020). Social Sciences Literature Press, Beijing (2020) 4. National Development and Reform Commission, National Medium and Long-term Development Plan for Nuclear Power (2005–2020) (2007) 5. National Development and Reform Commission, National Energy Administration, 13th FiveYear Plan for Energy Development (2016) 6. National Development and Reform Commission, National Energy Administration, 13th FiveYear Development Plan for Electric Power Development (2016) 7. Hualong One is the third-generation nuclear power plant of million-kilowatt-class newly developed in China 8. The annual amount of SNF generated from a “million-kilowatt-class” PWR is about 23 tHM 9. Zhou, Y.: China’s spent nuclear fuel management: current practices and future strategies. Energy Policy 39(7), 4360–4369 (2011) 10. Chen, J., et al.: Back-end of nuclear fuel cycle in China. Prog. Nucl. Energy 54(1), 46–48 (2012) 11. Cao, B., et al.: Preliminary study on nuclear fuel cycle scenarios of China before 2050. Energy Procedia 39, 294–299 (2013) 12. Ye, G.A., et al.: 20—development of closed nuclear fuel cycles in China. In: Reprocessing and Recycling of Spent Nuclear Fuel, Woodhead Publishing, pp 531–548 (2015) 13. Yue, Q., et al.: Fuel cycles optimization of nuclear power industry in China. Ann. Nucl. Energy 111, 635–643 (2018) 14. The People’s Republic of China National Report for the Seventh Review Meeting of the Joint Convention on the Safety of Spent Fuel Management and on the Safety of Radioactive Waste Management (2020) 15. Bunn, M. et al.: Interim Storage of Spent Nuclear Fuel, (2001)

The Study and Suggestions on the Civil Nuclear Safety Equipment Standards in China Ting Liu, Zhiyuan Liu, Meng Chang, Dahu Song, Yiman Dong, Bingchen Huang, Shujie Jiang, Yanxin Lv, and Li Huang(B) Nuclear and Radiation Safety Centre of MEE Beijing, Beijing, China [email protected]

Abstract. This paper studies current civil nuclear safety equipment standards situations in China with Chap. 2 “Standards” of Regulation on the Supervision and Management of Civil Nuclear Safety Equipment, which are the application in the nuclear safety review, the management of standards revision, the endorsement of industry standards and other standards by the National Nuclear Safety Administration (NNSA), and then analyzes current problems, at last proposes suggestions of the implement endorsement of standards mechanism in this field in China. Keywords: Nuclear safety equipment · Regulation · Application · Revision · Endorsement

1 Introduction Regulation on the Supervision and Management of Civil Nuclear Safety Equipment [1] (hereinafter referred to as “Equipment Regulation”) has come into force since January 1, 2008. It has played an important role in laying the legal foundation and providing legal safeguards for the supervision of civil nuclear safety equipment in accordance with the law. After more than 10 years of practice, China’s nuclear safety regulatory situation has undergone profound changes, while with the promulgation and implementation of the “Nuclear Safety Law of the People’s Republic of China”, some of the provisions of the existing “Equipment Regulation” cannot fully adapt of the new situation and new requirements of nuclear safety supervision. Chapter 2 “Standards” of the “Equipment Regulation” has a rich connotation and provides for the role of civil nuclear safety equipment standards, the standard system, revision management and standard endorsement. This paper discusses the role of civil nuclear safety equipment standards, the management and development of civil nuclear safety equipment standards by NNSA, and the endorsement of civil nuclear safety equipment standards by NNSA.

2 The Role of Civil Nuclear Safety Equipment Standards Article 8 of the “Equipment Regulation” [1] requires that “Civil nuclear safety equipment standards are the technical basis for civil nuclear safety equipment design, manufacture, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 724–728, 2023. https://doi.org/10.1007/978-981-19-8780-9_70

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installation and non-destructive testing activities.” This article defines the nature of the role of civil nuclear safety equipment standards. The current industry understanding of this article is unified, and the role and nature of civil nuclear safety equipment standards in departmental rules and guides under the “Equipment Regulation” are consistent with the provisions of this article in the “Equipment Regulation”.

3 The Management and Development of Civil Nuclear Safety Equipment Standards by NNSA Article 10(1) of the “Equipment Regulation” [1] states that “National standards for civil nuclear safety equipment involving the basic principles and technical requirements of nuclear safety shall be formulated by the nuclear safety regulatory authority of the State Council,” But NNSA as the nuclear safety regulatory authority of the State Council, currently has not developed or managed national standards for civil nuclear safety equipment for the time being. In the process of civil nuclear safety equipment review and supervision, the work is mainly based on foreign standards, endorsed industry standards and the requirements of regulatory documents issued by NNSA. The analysis of the reasons for this status is mainly based on the following objective factors: (1) The basic principles of nuclear safety and the definition of technical requirements are not clearly defined in the “Equipment Regulation”, so the scope of the NNSA’s national standards is unclear. Since the concept and interpretation of the basic principles of nuclear safety and technical requirements do not appear in any of the regulations, guides or institutional documents, the technical understanding and definition of the concept is very difficult; if for the only field of civil nuclear safety equipment, whether it is reasonable to establish a separate concept to be further demonstrated; if the definition from the perspective of top-level design and legal provisions to increase the need to organize technical demonstration. (2) At present, China’s nuclear power units are mostly introduced from abroad, the introduction of equipment technology at the same time also introduced foreign equipment standards. In the process of actual equipment supervision, mostly foreign standards such as ASME, RCC-M, RCC-E, IEEE, etc. are as the basis for regulatory review. The technical route and standard system of different reactor types are very different, and the industry has not yet reached a unified understanding of the standard system, which makes it difficult for the NNSA to prepare national standards for nuclear safety equipment. (3) The level of localization of nuclear safety equipment still needs to be improved, and the development of the domestic nuclear industry is not yet mature enough to have the ability and level to develop unified standards, resulting in low demand for the independent preparation of standards.

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Although there are difficulties in the preparation of standards by the NNSA due to the development of the industry and the lack of uniformity in industry awareness, as the industry develops and the ability to prepare standards improves, the NNSA as the national nuclear safety regulatory authority, will still have the ability to develop basic principles and technical requirements for nuclear safety in the future. The scope of civil nuclear safety equipment involving the basic principles and technical requirements of nuclear safety can be determined with reference to the list of regulatory equipment. For the subsequent development of national standards under the responsibility of nuclear safety regulatory authorities, the prescribed requirements for nuclear safety system equipment in Provision on Design Safety of Nuclear Power Plant (HAF102-2016) [2] and Provision on Operation Safety of Nuclear Power Plant (HAF103-2016) [3] can be used as the subsequent establishment of civil nuclear safety equipment national standard classification guidelines for reference. The nuclear safety regulatory authority has issued “Civil Nuclear Safety Equipment Catalogue (the first Bathes)” [4] and “Civil Nuclear Safety Equipment Catalogue (2016 Revised)” [5], based on the scope of equipment regulation and the requirements of specific nuclear safety guidelines. At the same time, the detailed classification of general nuclear safety equipment for nuclear power plants and research reactors, special nuclear safety equipment for reprocessing plants of nuclear fuel cycle facilities is provided in the “Explanation of the ” [5]. In the explanation, whether the relevant equipment is included in the licensing scope is also specified. Therefore, in the field of civil nuclear safety equipment regulation, nuclear safety regulatory authorities may consider combining the implementation of nuclear safety functions as clearly stated in the regulations and organizing the development and endorsement of national standards for equipment within the scope of the two batches “Civil Nuclear Safety Equipment Catalogue” [4, 5].

4 The Endorsement of Civil Nuclear Safety Equipment Standards by NNSA According to the requirements of Article 10(1) and (2) of the “Equipment Regulation” [1], other national standards and industry standards for civil nuclear safety equipment prepared by the competent nuclear industry department of the State Council should be endorsed by the NNSA for review. However, only the National Energy Administration and the NNSA have carried out endorsement pilot work for 30 nuclear power standards in the energy industry (NB) since 2013. During preliminary analysis, the nuclear safety endorsement pilot projects of NB related to civil nuclear safety equipment have 7 items.4 items have been endorsed by the NNSA and issued by the National Energy Administration, 2 items are on hold, 1 item is pending review letter (see Table 1 “ Statistics table of 7 items NB listed in the endorsement pilot program”). The main point of disagreement of 2 items nuclear safety equipmentrelated NB, which are on hold endorsed, is that the technical parameters reference from foreign standards, but domestic and foreign engineering practice projects are not used, the preparation unit cannot explain the background and reasons for the adoption of new parameters of foreign standards.

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Due to the current difficulties in the preparation of national standards for civil nuclear safety equipment by the NNSA, the endorsement of other national and industry standards prepared by the nuclear industry authorities would be a feasible and efficient solution to improve the standard system for civil nuclear safety equipment. But considered the characteristics of civil nuclear safety equipment, the endorsement mode of pilot work should be adjust. As China’s nuclear power units are mostly introduced from abroad, the introduction of equipment technology at the same time also introduced foreign standards. Current existed domestic equipment standards are mostly translations of foreign standards, not strong in use, lack of systemic. The number of equipment-related industry standards is large, not suitable for the whole process of tracking the development of standards in accordance with the endorsement pilot work model. Moreover, the variety of equipment types and characteristic parameters, it is not recommended for full-text endorsement. Table 1. Statistics table of 7 items NB listed in the endorsement pilot program No Standard name

Status

1.

Design guidelines of compressed air start-up systems for emergency diesel generators in nuclear power plants

Endorsed

2.

Design and test requirements of emergency diesel generator sets for nuclear power plants

Endorsed

3.

Fuel system design guidelines of emergency diesel generator sets for nuclear power plants

Endorsed

4.

Design guidelines of piping arrangement for nuclear island process systems in pressurized water reactor nuclear power plants

Endorsed

5.

Guidelines of the development of pressure-temperature limiting curves for On hold reactor pressure vessels in pressurized water reactor nuclear power plants

6.

Guidelines of assessment of reactor pressure vessel against rapid fracture in pressurized water reactor nuclear power plants

7.

Casing eddy test of core neutron flux measurement for nuclear power plant Pending reply

On hold

The NNSA can draw on the United States Nuclear Regulatory Commission to establish a special mechanism to commission a comprehensive technical support department for regulations and standards to analyze the nuclear safety relevance of other national and industry standards for equipment from the perspective of systematization and coordination and consistency with nuclear safety regulations and ecological and environmental standards. Each year, the system of relevant national standards and industry standards is identified from the overall perspective, screened in terms of maturity, urgency, and importance, and the scope of endorsement related to nuclear safety equipment is determined among other national standards and industry standards that have been issued, and the endorsed review of nuclear safety equipment series is conducted in a batch and centralized review mode, and recommendations for endorsement are made. For the specific circumstances of different equipment, the conditions of use are described in the

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endorsement criteria. The NNSA regularly publishes endorsed standards and conditions of use. For the field of civil nuclear safety equipment without national standards and industry standards, the NNSA should carry out the endorsement of other reference standards, such as ASME, RCC-M and other foreign standards, and clarify or develop a guiding approach to endorse foreign standards.

5 Conclusions Civil nuclear safety equipment standards are very important in nuclear safety regulatory and supervision activities. They are the technical basis for engaging in civil nuclear safety equipment-related activities. Due to the situation that there is no national standard for civil nuclear safety equipment developed or managed by NNSA now, NNSA may consider organizing the development and endorsement of national standards according to equipment performing nuclear safety functions that are clearly indicated in the regulations and equipment within the scope of the two batches of “Civil Nuclear Safety Equipment Catalogue” [4, 5]. However, it will take some time for NNSA to prepare national standards for civil nuclear safety equipment, and endorsement of other national standards and industry standards prepared by nuclear industry authorities will be a feasible and efficient solution to improve the standard system for civil nuclear safety equipment. According to the characteristics of civil nuclear safety equipment, NNSA will adjust the mode of endorsement pilot work, improve the system of civil nuclear safety equipment standards, play the role of standards, and provide a technical basis for the supervision and review of civil nuclear safety equipment.

References 1. State of Coucil.: Regulation on the Supervision and Management of Civil Nuclear Safety Equipment. (2007) 2. National Nuclear Safety Administration.: Provision on Design Safety of Nuclear Power Plant (2016) 3. National Nuclear Safety Administration. : Provision on Operation Safety of Nuclear Power Plant (2004) 4. National Nuclear Safety Administration.: Notice on the release of《Civil Nuclear Safety Equipment Catalogue (the first Bathes)》(2007) 5. National Nuclear Safety Administration.: Notice on the release of《Explanation of the 》(2016)

Effect of Corrosion Damage on Structural Failure Models Under Different Boundry Conditions Sheng Qian(B) , Yu Sun, Yuzhao Huangfu, and Ke Zhang Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China [email protected]

Abstract. Nuclear energy is an important means to deal with the energy crisis and global climate change, and the long-term safe operation of nuclear power plants strongly depends on the service behavior of structural materials. Nuclear power equipment is subject to complex multiple loads in actual situations, especially under compression, and even the buckling of tiny structures may have important safety hazards. Experience has shown that corrosion is an unavoidable result of nuclear power structures, and structures damaged by corrosion will cause internal stress redistribution under compression, resulting in changes in failure modes. This paper takes the most common uniform corrosion and pitting corrosion as the research objects, and explores the effects of different corrosion forms and corrosion parameters on the buckling failure of compression members under the same corrosion rate. Keywords: Corrosion damage · Corrosion form · Compression structure · Buckling failure · Critical stress

1 Introduction As a clean, reliable and efficient energy, nuclear power is an important pillar of the world’s energy and an important means for mankind to deal with the energy crisis and global climate change. The long-term, safe and stable operation of nuclear power plants strongly depends on the beneficial behavior of structural materials, experience It is shown that corrosion is one of the main reasons for the failure of nuclear power structural materials [1]. For metallic materials immersed in a solution medium, the driving force for the chemical reaction comes from the potential difference at the interface, where the potential difference at the metal/oxide interface controls the chemical reaction at the oxidation front, and the potential difference at the oxide/solution interface controls the reaction process in the outer layer of the oxide film, while the potential difference inside the oxide controls the transport of species inside the oxide. The specific reaction process of each interface is shown in Fig. 1.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 729–741, 2023. https://doi.org/10.1007/978-981-19-8780-9_71

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Fig. 1. Reaction in metal-oxide-solution system

The surface of the structural material forms an oxide film due to corrosion, and an electric double layer structure is formed on the solution side. The oxide film formed on the surface of the material is formed due to the electrochemical process of corrosion of the material, and this is also an obstacle to the further corrosion process of the material, thus playing a protective role. The formation of oxide film includes [2]: (1) establishment of oxide film; (2) dissolution of oxide film; (3) deposition of metal ions in solution. From the perspective of the form of corrosion, uniform corrosion and localized corrosion are the most common forms of corrosion. Uniform corrosion can be regarded as the uniform thinning of metal materials, while localized corrosion is the high concentration of corrosion in local parts of the structure. Among them, pitting corrosion is the most common representative [3]. In the nuclear industry system, due to different materials, environments and stress states, the nuclear power structure will be subjected to complex multiple loads. For the more dangerous compression conditions, even buckling of a tiny structure can cause the entire structure to lose its bearing capacity. In the high-temperature and high-pressure water environment of nuclear power, corrosion is an unavoidable result of the structure. Structures damaged by corrosion, especially pitting corrosion, will cause internal stress redistribution under pressure, which will lead to changes in the failure mode. Therefore, the study of nuclear power the transformation of structural failure modes is of great significance.

2 Theory of Acoutisc Damping Compressive load is one of the main loads of nuclear engineering structural components. The influence of local stress concentration and eccentric force on components caused by corrosion on the mechanical properties and failure modes of compressive components cannot be ignored, and will affect the safety of the overall structure. In terms of efficiency and stability, the effect of simulating corrosion by finite element is unacceptable in structural-level performance evaluation [4]. If these material models can be reasonably

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integrated into the commonly used fiber models, the related problems can be effectively solved. For uniform corrosion plates and pitting corrosion plates with typical localized corrosion characteristics, the response of the corroded plate can be simulated with a fiber element model, in which the plate is longitudinally discretized into several fiber elements, and each fiber element can be assign a uniaxial material model that correctly describes the effect of corrosion damage on the structure, as shown in Fig. 2.

Fig. 2. Analysis process for structure systems

For the structure with uniform corrosion, the section reduction method can be used to uniformly thin the rod, and the critical load can be determined by the Euler critical stress formula. For the structure damaged by pitting corrosion, it is no longer a homogeneous compression rod of equal section, but to determine the critical load of the compression rod of variable section, the Euler critical stress formula is no longer applicable, and the related stability analysis theory is not perfect, the differential equation obtained in the variable section instability problem encountered in the project is no longer constant coefficient, and it will encounter mathematical difficulties when solving. The structural instability generally includes two forms of branch point instability and extreme point instability [5]. For an ideal axial compression rod, when the axial pressure is lower than the critical value, the rod is in a stable equilibrium state; When the axial pressure is higher than the critical value, the rod will be unstable or buckling. There are certain criteria for judging the critical value, including the following methods: (1) Static criteria; (2) Dynamic criteria; (3) Energy criterion; (4) Initial defect criterion: Under the condition of linearity, the above four criteria are equivalent, and the critical loads obtained according to them are the same. At present, these four methods are commonly used for the calculation of the critical load of the variable cross-section compression rod: (1) Equal section method of minimum section. The minimum cross-sectional inertia moment I min of the variable-section compression bar is taken as the inertia moment of the entire bar, and then Euler’s formula is used to solve the critical load. However, this method is conservative, and the calculated critical load value is lower than the real value, which is likely to cause the structure to be too large. (2) Static analysis method. For step-type variable cross-section compression rod, this method divides it into segments according to the characteristics of the variable cross-section of the member, lists the continuous conditions of its deflection line under the bending equilibrium condition, solves the integral constant, and finally

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obtains the critical load value. Although this method can obtain accurate numerical values with high precision, it is difficult to establish differential equations under equilibrium conditions when the number of sections is large. (3) Finite element method. The finite element method is to discretize the variablesection compression rod into a finite number of rod elements that are only linked at the nodes according to the limit idea of curve segmentation. According to the equilibrium conditions, the stiffness equation is established to further obtain the overall stiffness equation to solve the critical load. This method is commonly used in finite element calculation software. (4) Energy method. The energy method is to establish a critical state equation between the increase of the external force potential energy and the increase of the structural strain energy to obtain the critical load. Generally, a satisfactory approximate solution can be obtained by the energy method. Under the axial pressure load, the structure will appear buckling and instability, so it is necessary to verify the stability of the structure. In this paper, a simplified physical model is used to deduce the critical stress formula of the pitting damage and variable section specimens and the parameter sensitivity analysis of the transition of the failure mode is carried out influences.

3 Theoretical Overthrow 3.1 Parameter Analysis of Fixed Support In order to gain a deeper understanding from a physical point of view, this chapter conducts a parameter sensitivity analysis to theoretically analyze the transition mechanism between the two failure modes of the corroded specimen subjected to the axial force P. The critical buckling theory of variable cross-section bars for pitting-damaged compression specimens is studied. Pitting corrosion will show different shapes during the initiation process. The scholar Huang [6] found through the finite element analysis of the pitting damage plate that when the pitting damage has the same corrosion volume, the difference in the shape of the pit has no obvious effect on the buckling of the structure. For the sake of simplicity, the pitting damage is simplified as a cuboid located in the central area of the axially compressed specimen, whose length and width are d, and the depth along the direction of the specimen is h, as shown in Figs. 3 and 4. For pitting corrosion damage specimens with fixed restraints at both ends. The deflection curve equation of the specimen can be expressed as:   2π x (1) y = δ 1 − cos l In the formula, δ is one-half of the maximum lateral displacement of the specimen; l is the length of the specimen, and x is the ordinate of any point on the specimen (as shown in Fig. 3). According to Timoshenko energy method, the increment W of external force work is: P W = 2

l  0

dy dx

2 dx

(2)

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Fig. 3. The instability of the compression rod under the constraint of fixed supports at both ends

Fig. 4. Schematic diagram of the cross-section of the pitting corrosion of the specimen

Putting formula (1) into (2), we can get formula (3): 2π 2 δ 2 P W = l2

l sin2

π 2 δ2 2π dx = P l l

(3)

0

The bending deformation energy U can be expressed as ⎛ ⎞ l 2 l1  ⎜ Mx2 Mx2 ⎟ U = 2⎜ dx + dx⎟ ⎝ 2EI1 2EI2 ⎠ 0

(4)

l1

where Mx is the bending moment of the section at any point of the compression specimen. E is the elastic modulus of the material, and I 1 and I 2 are the moments of inertia of the intact part and the pitted part of the specimen, respectively. l and l1 are the total length of the specimen and the distance from the pitting edge to the edge of the specimen respectively, that is, l = 2l1 + d. Since the section bending moment can be expressed as M x = P(δ−y), then ⎞ ⎛ l l1 2   ⎟ P2 δ2 ⎜ ⎜ cos2 2π x dx + I1 cos2 2π x dx⎟ U = ⎠ ⎝ EI1 l I2 l 0

l1

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=

     I1 l 4π l1 I1 P 2 δ 2 l I1 1 + sin 1− + l1 1 − EI1 4 I2 2 I2 8π l I2

(5)

According to W = U, the critical buckling load Pcr of the single-pitting damage compression specimen can be expressed as Pcr =

π 2 EI1 l2

1 I1 4 I2

+

1 l1 2 l

1−

I1 I2



1 +

1 8π

sin

4π l1 l

1−

I1 I2



where β 1 is the length coefficient, determined by the following formula     1 I1 1 l1 1 I1 4π l1 I1 β1 = + + 1− sin 1− 4 I2 2 l I2 8π l I2

(6)

(7)

The formula (6) can be expressed as Pcr =

π 2 EI1 β1 l 2

(8)

If the plate width of the test section is b, and other parameters are the same as the previous settings, Fig. 4 is a schematic diagram of the cross-section of the pitting corrosion part of the test piece, then for the single pitting corrosion test piece, I 1 and I 2 are the complete corrosion part and the single corrosion pit corrosion part respectively. Moment of inertia: bt 3 12

(9)

1 bt 3 dh3 + bte02 − − dh(t − h)2 − dhe02 − dhe0 (t − h) 12 12 4

(10)

I1 = I2 =

The eccentricity e0 of the center of the cross section where the pitting is located is e0 =

dht − dh2 2(bt − dh)

(11)

According to formulas (6)–(11), the influence of pitting corrosion and specimen parameters on the critical load of the compression specimen can be obtained. Assume the values of the basic parameters: t = 4.0 mm, l = 20.0 mm, b = 9.0 mm. d varies from 0 to 8 mm and h from 0 to 3.0 mm. Figure 5 shows the change of the length coefficient β 1 with the normalized pitting depth h/t. It can be seen that the increase of the pitting diameter will lead to a gradual increase of the length coefficient, the critical buckling stress of the compression specimen will be decrease and the probability of buckling failure increases. However, in the whole frequency band of h/t from 0 to 0.4, the amplitude of its change is small, its impression on the critical buckling stress is almost negligible, and it will not lead to the transition of structural failure mode;when h/t gradually exceeds the critical value, the change amplitude of the length coefficient begins to increase, and the critical load decreases rapidly.

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Fig. 5. Length coefficient versus normalized pitting depth (single pit)

Figure 6 shows the change in the normalized pitting diameter d/b of the length coefficient β 1 . Compared with the small change by the pitting depth, the increase in the pitting diameter leads to a significant increase in the length coefficient, which makes the critical buckling stress of the specimen under compression drop significantly. When the pitting diameter normalization parameter d/b exceeds 0.6, the nonlinear increase of the length factor leads to a rapid decrease in buckling strength. At this time, the increase of the pitting diameter will reduce the ability of the structure to resist buckling failure, so that the structure changes from strength failure to buckling failure.

Fig. 6. Length coefficient versus normalized pitting diameter

3.2 Parameter Analysis of Hinged Support When using the energy method to solve the critical load, it is assumed that the deformation curve during buckling is the most critical factor. Since the deformation curve cannot be completely accurate, the critical load is usually an approximate solution. The deformation

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curve of the rod is generally set according to the boundary conditions. Therefore, for the compressed rod, the constraint of the boundary conditions is extremely important to the Euler critical stress. Under different boundary conditions, the critical stress of the compression rod may change. On this basis, the critical stress of the compression rod for pitting damage with different boundary conditions is deduced (Fig. 7).

Fig. 7. The instability of the compression rod under the constraint of hinged support at both ends

For the compression pitting corrosion specimen constrained by hinges on both sides, as shown in 6, the deflection curve equation can be expressed as: y = δ sin

πx l

(12)

The increment W of external force work is P W = 2

l 

dy dx

2 dx =

Pπ 2 δ 2 4l

(13)

0

There are two types of U expressions for bending deformation energy: l U =

M2 dx 2EI

(14)

EI (y )2 dx

(15)

0

l U = 0

The bending deformation energy obtained by the expression of formula (14) has higher accuracy, while the bending deformation energy obtained by formula (15) is slightly less accurate, but the calculation process is relatively simple. When solving the critical load stress with Timoshenko method, the expression form of bending deformation energy can generally be calculated by selecting the appropriate bending deformation

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energy expression form according to different deflection curve equations. For the compression rods hinged at both ends, the bending deformation energy is generally derived by formula (15). ⎛ ⎞ l l 1 2   ⎜ ⎟  2 U = 2⎜ EI (y ) dx + EI2 (y )2 dx⎟ (16) 1 ⎝ ⎠ 0

l1

According to the energy method theory W = U, the expression of the critical buckling load Pcr of the single-pitting damage compression specimen can be obtained:   π 2 EI1 I2 l1 1 I2 2π l1 I2 + 2 (1 − ) + sin (17) Pcr = ( − 1) l2 I1 l I1 π l I1 Let β2 =

1 I2 l1 I2 1 I1 +2 l (1− I1 )+ π

sin

2π l1 I2 l ( I1 −1)

, the formula (17) can be expressed as

Pcr =

π 2 EI1 β2 l 2

(18)

Figures 8 and 9 show the variation curve of the length coefficient β 2 with the normalized pitting depth h/t and normalized pitting diameter d/b under the condition of hinged support at both ends. It can be seen from the figure that with the increase of the degree of pitting damage, the length coefficient β 2 is gradually increasing, and the critical stress of the specimen is gradually decreasing. Comparing the increase of the normalized pitting depth and the normalized pitting diameter on the length coefficient, it can be found that the influence of the pitting diameter on the length coefficient is much greater than that of the pitting depth on the length coefficient, which makes the specimen more prone to instability.

Fig. 8. Length coefficient versus normalized pitting depth

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Fig. 9. Length coefficient versus normalized pitting diameter

4 Comparsion of Corrosion Damage Forms The reasonable evaluation of the damage to the mechanical properties of steel by corrosion is very important for the safety of practical engineering. At present, corrosion depth [7, 8], corrosion density (DOP) [3], cross-sectional area loss rate (ALR) [9] and volume loss rate (VLR) [10] are often used as comprehensive indicators to evaluate the degree of corrosion damage. The corrosion depth only describes the depth of the corrosion pit, and has nothing to do with other corrosion pit parameters and the size of the structure. Considering factors are simple, it is suitable for evaluating the degree of uniform corrosion damage. DOP characterizes the degree of corrosion damage on the surface of the component, and can better measure the mechanical properties of the corroded tensile structure. However, DOP does not include the influence of factors such as pit depth and plate thickness, and does not pay enough attention to the damage degree of the fractured section. The section loss rate represents the damage degree of the section at the maximum corrosion part of the structure, and can better measure the mechanical properties of the pitting corrosion damage specimen, but it is insufficient for the evaluation of the overall performance of the structure. The volume loss rate includes more pitting parameters and plate geometric parameters, and is often used as the most favorable indicator for evaluating the degree of corrosion loss. Therefore, this section will use the volume loss rate as an index to compare the effects of uniform corrosion and pitting corrosion on the mechanical properties of the specimens under the same volume loss rate. It is assumed that the basic parameters of the test piece are taken as follows: the thickness is 4.0mm, the length is 20mm, and the width is 9mm; the pitting corrosion parameters take the values in Sect. 3, and the volume loss rate is shown in Table 1 below; Uniform corrosion can be regarded as uniform thinning of the specimen, and the corresponding corrosion loss rate is shown in Table 2. The pitting corrosion specimens and uniform corrosion specimens under the same corrosion loss rate were compared. In order to facilitate the observation, the critical stress (Ploss )under the different corrosion loss rates was compared with the critical stress(P0 )of the non-damaged specimens and then normalized, as shown in Fig. 10.

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Table 1. Pitting corrosion parameters Specimen number

Pitting diameter d/mm

Pitting depth h/mm

V loss /V 0/%

P1-T4-N20-d2-h3

2

3

1.67

P1-T4-N20-d4-h3

4

3

6.67

P1-T4-N20-d6-h3

6

3

15.00

P1-T4-N20-d8-h3

8

3

26.67

Table 2. Uniform corrosion parameters Specimen number

The thickness of the corrosion t loss /mm

V loss /V 0/%

T4-N20-d2-h3

0.067

1.67

T4-N20-d4-h3

0.267

6.67

T4-N20-d6-h3

0.600

15.00

T4-N20-d8-h3

1.067

26.67

It can be seen from the figure that under the two different boundary conditions, with the increase of the volume loss rate, the critical buckling stress of the pitting corrosion specimen and the uniform corrosion specimen are gradually reduced, and their ability to resist buckling failure is gradually reduced. Comparing the two corrosion forms of uniform corrosion and pitting corrosion, under the same volume loss rate, the decrease of critical buckling stress of uniform corrosion specimens is significantly larger than that of pitting corrosion specimens, so uniform corrosion is more likely to lead to structural instability than pitting corrosion.

5 Conclusion This paper mainly explores the relevant factors affecting the failure mode transition of the specimen under the action of axial pressure. Based on the energy method, the critical load of the variable section compression rod of the pitting damage specimen is deduced with a simplified physical model, and the parameter sensitivity analysis of the failure mode transition was carried out. On this basis, the effects of different corrosion forms on the critical buckling stress under the same corrosion loss rate were compared, and the following conclusions were obtained: (1) Based on the energy method, a simplified physical model is used to deduce the critical load formula of the variable section compression rod of the pitting damage specimen under two symmetrical boundary conditions. (2) For structures subjected to axial compression, the increase in the degree of corrosion damage will reduce the critical load of the structure and reduce its ability to resist buckling failure. Among them, the pitting damage parameters will lead to the

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Fig. 10. The relationship between critical stress and corrosion rate of uniform corrosion and pitting corrosion specimens

reduction of the mechanical properties of the structure, and the pitting diameter has a greater influence on the length coefficient of the compression specimen than the pitting depth. (3) At the same volume loss rate, compared with pitting corrosion damage, uniform corrosion weakens the critical load of the structure more seriously.

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References 1. Ahearne, J.F.: Prospects for nuclear energy. Energy Econom. 33(4), 572–580 (2011) 2. Leistner, K., Toulemonde, C., Diawara, B., et al.: Oxide film growth kinetics on metals and alloys: II. Numerical simulation of transient behavior. J. Electrochem. Soc. 160(6), C197– C205 (2013) 3. Yao, Y., Yang, Y., He, Z., et al.: Experimental study on generalized constitutive model of hull structural plate with multi-parameter pitting corrosion. Ocean Eng. 170, 407–415 (2018) 4. Samadani, S., Aghakouchak, A.A., Niasar, J.M.: Nonlinear analysis of offshore platforms subjected to earthquake loading considering the effects of joint flexibility. ASME Int. Conf. Ocean (2009) 5. Liu, G.D., Luo, H.Q.: Stability of rod system. People’s Communications Publishing House 6. Huang, Y., Zhang, Y., Liu, G., et al.: Ultimate strength assessment of hull structural plate with pitting corrosion damnification under biaxial compression. Ocean Eng. 37(17–18), 1503– 1512 (2010) 7. Nakai, T., Matsushita, H., Yamamoto, N.: Effect of pitting corrosion on local strength of hold frames of bulk carriers (2nd Report)—lateral-distortional buckling and local face buckling. Marine Struct. 17(8), 612–641 (2004) 8. Chapkis, D.T.: Simulation of pitting corrosion of hull plating under static loading. Trudy TSNIIMF 82, 34–50 (1967) 9. Paik, J.K., Lee, J.M., Ko, M.J.: Ultimate shear strength of plate elements with pit corrosion wastage. Thin-Walled Struct. 42(8), 1161–1176 (2004) 10. Zhang, Y., Huang, Y., Wei, Y.: Ultimate strength experiment of hull structural plate with pitting corrosion damage under unaxial compression. Ocean Eng. 130, 103–114 (2017)

Research on the Applicability of Experience Feedback of Multi-plants to Nuclear Safety Management of New Nuclear Power Plants Shuang Zhang(B) , Xiaoyan Sun, Wenwen Zhao, Chao Gao, and Yang Li Suzhou Nuclear Power Research Institute Co. Ltd., Equipment Management Center, Shenzhen, Guangdong, China [email protected]

Abstract. Experience feedback management of nuclear power plant includes event reporting, screening, analysis, corrective action management and assessment. In order to standardize the top-level design as well as operation process of experience feedback, and clarify the business responsibilities and roles of experience feedback among commercial nuclear power plants, under-construction power plants and specialized technical centers, the multi-plant experience feedback management mechanism covers both the commercial nuclear power plants and the under-construction nuclear power plants. Through setting a framework and a workflow of experience feedback system and taking advantage of intensification and specialization of multi plants, we can realize the functions of experience exchange, information sharing, resource allocation and co-construction as well as sharing among member plants, and it can be achieved to improve the effectiveness of multi-plant experience feedback, and avoid recurrence of an event, and reduce the consequences of nuclear security events. As an important element of nuclear safety management, multi-plant experience feedback focuses on the common technical events involving quality problems, safety hazards and functional defects of the unit, such as system failure, unusual equipment function, file or program error, human error, material defects of spare parts and non-conformance items. Converting from event management to performance improvement, taking into account the timeliness of information and the effectiveness of feedback action, the multi-plant experience feedback combines quantitative with qualitative, standards the management, centralizes the data, modularize the analysis, shares the information, and integrate the professions, functionalizes the system, and hence indicates the direction for the continuous improvement of nuclear safety culture construction. Keywords: Experience feedback · New nuclear plants · Effectiveness evaluation · Corrective actions · Closed-Loop management

1 Introduction After the Three Mile Island accident of America in 1979 and the Chernobyl accident of the former Soviet Union in 1986, the international nuclear power industry has begun to © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 742–752, 2023. https://doi.org/10.1007/978-981-19-8780-9_72

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comprehensively improve the safety management of nuclear power plant. Accordingly, the experience feedback management mechanism was established and has become an important management mode for nuclear power plants [1, 2]. The experience feedback mechanism is clearly defined in The Operation Safety Regulations of Nuclear Power Plant, which was approved and published by the National Nuclear Security Administration, and is mainly applied in commercial nuclear plants. In general, the construction of a new nuclear project is characterized by a long cycle, a large basic investment, complex technology and irreversible critical activities. The regulatory authorities ask high requirements for the safety and quality of commercial nuclear power plants, and the plants have always attached great importance to relevant experience feedback and corrective actions. With the in-depth development of nuclear safety management, the scope of management has completely covered the phases span from design, procurement, construction, installation, commissioning to retirement of nuclear power plants. At the present stage, the experience feedback management of commercial nuclear power plants has been in effect for many years, and has been equipped with mature organization, explicit operation mode and scientific process [1–3]. For lack of unified management standards and guidelines, plus different management capability of engineering construction companies, the experience feedback management of new nuclear projects is in a passive management state and in lack of a full closed-loop management process. Therefore, the mature experience feedback management mode of commercial nuclear power plants should be popularized to the construction period of new nuclear projects. Besides, it is of great significance to establish an effective experience feedback management similar to commercial plants for under-construction nuclear power plants. First of all, the establishment of multi-plant experience feedback management can realize the closed-loop event management of new nuclear projects from decision-making, execution, evaluation, feedback to optimization, which is of great significance for improving the performance of nuclear power engineering construction management and is beneficial to the continuous optimization of projects; Secondly, the establishment of experience feedback management of new nuclear projects also has important reference value for maintaining a healthy engineering construction and event management state for nuclear power plants in the future.

2 Research and Applicability of Multi-plant Experience Feedback Multi-plant experience feedback management standardizes event management criteria and the closed-loop operation process of corrective action. It also clarifies responsibilities and roles of both commercial nuclear power plant and professional technology center by building an accurate business system framework and workflow, so as to realize the functions of experience exchange, information sharing and corrective actions tracking of multi plants [4–6]. The summary of multi-plants experience feedback process is given in Fig. 1. This mode improves the effectiveness of multi-plants experience feedback, and avoids the recurrence of events. Besides, multi-plants experience feedback management embodies the advantages of intensification and specialization, and promotes the nuclear safety development of the commercial plant.

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Multi-plants experience feedback management

Organization system

Manage organizations Execution organizations Cooperative organizations

Document system

Management measures Management procedure Enforcement regulation

Information system

Information collection Filter and definition Relative analysis Corrective action

Evaluation system

Internal evaluation External evaluation

Fig. 1. Sketch of multi-plant experience feedback management

Taking the experience feedback mechanism of commercial plants as a reference, the experience feedback mechanism set up in under-construction nuclear power plants needs to include organization, document system and information system of experience feedback data, in order to carry out management towards quality events as well as effectiveness evaluation of experience feedback, and promote the development of experience feedback management in phases of design, manufacturing, procurement, civil engineering, installation and commissioning [7–10]. 2.1 Organization System The under-construction nuclear power plants need to build a systematic experience feedback organizational management structure to provide support for the daily operation of project management. The organizational structure consists of three organizational levels, including the experience feedback committee, the experience feedback management team and the experience feedback executive department. The experience feedback committee is a decision-making organization, responsible for reviewing the operation of experience feedback management, communicating and coordinating major nuclear safety events, summarizing and giving feedback, reviewing major event reports, undertaking the responsibility of top-level design and supervising the experience feedback. The experience feedback management team is a supervision organization, responsible for the business of daily processes and promoting the implementation of corrective actions. The team is composed of full-time experience feedback engineers, focusing on event distribution, actions coordination, and trend tracking, to ensure the quality of experience feedback. The organization of experience feedback is given in Fig. 2. As the executive organization of experience feedback management, the technical department of nuclear power plant is responsible for the practicing of technical work, such as the collection of unusual event, the implementation of corrective actions and the application of good practices, in order to ensure closed-loop management of experience feedback.

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Experience feedback committee

Experience feedback management team

Experience feedback implementation team

Fig. 2. Organization of experience feedback

2.2 Document System The document system of multi-plant experience feedback includes management methods, technical procedures, operation documents. It’s necessary for the under-construction nuclear power plant to establish and consummate a document mechanism of experience feedback. The management method of experience feedback clearly defines the overall requirements, basic principles, responsibilities and authorities of management. Experience feedback technical procedures make detailed descriptions about the routine issues, including information collection, screening, investigation, analysis, corrective actions, status tracking, good practice and effectiveness evaluation. Experience feedback operation documents mainly include instructions of technical methods, such as event investigation and analysis, report writing guidelines and preparation of trend status reports. 2.3 Information System The functions of the multi-plant experience feedback system are as follows: (1) Collecting the operation and maintenance information; (2) Recording the information of unusual events in nuclear power plants, such as good practices and management deviations [11, 12]; (3) Making the experience feedback business processing more informative and visualized; (4) Tracking the implementation of corrective actions; (5) Making the sharing information of experience feedback more comprehensive and timely. All in all, the target function the multi-plant experience feedback system is to promote the utilization value of events, and realize the closed-loop management of the events in nuclear power plants. The functions of information system is given in Fig. 3. In combination with the characteristics of new nuclear projects, in order to realize closed-loop management of the whole process of engineering construction, the experience feedback information system of the under-construction plant needs to have the functions like engineering construction event data query, corrective actions tracking, and supervision management, including quality event collection, screening, investigation, analysis, corrective action, information utilization, effectiveness evaluation.

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By means of the experience feedback system, the under-construction nuclear power plants can collect and display the typical events of new nuclear projects as well as the important events of commercial nuclear plants, and track the implementation of relevant corrective actions. In this case, the under-construction nuclear power plants can put the experience feedback process management into practice in phases spanning from design, manufacturing, civil engineering, installation to commissioning of new nuclear projects, so as to form an entire computerization process of experience exchange and information feedback and realize the closed-loop management of events [12, 13]. Experience feedback system functions

Event query

Evaluation

Trend status

Action tracking

Index display

Fig. 3. Functions of information system

2.4 Daily Operation of Experience Feedback The screening criteria of unusual events from multi plants are as follows: (1) Events that lead to serious threats or challenges to nuclear safety; (2) Events that lead to shutdown or power reduction; (3) Events that affect the availability of the unit or have the risk of reducing the availability; (4) Events that lead to personal injury; (5) Events that cause major equipment damage or have equipment damage risks; (6) Repeated events that lead to serious consequences or risks; (7) Other events that have great feedback value. It is suggested that the events screening criteria of new nuclear plants should include the contents: (1) Typical events resulted from equipment quality or human error; (2) Impacts of the event on project progress; (3) Impacts of the event on nuclear safety; (4) Events worthy of management attention. The engineers of new nuclear projects can identify serious unusual events or common problems based on the above-mentioned event screening criteria, and track the implementation of corrective actions by the experience feedback system. 2.5 Event Analysis Method As an important component of experience feedback management, event analysis includes direct cause analysis, apparent cause analysis, and root cause analysis [13–15]. Among them, the root cause analysis is a structured problem analysis method, which is applied to analyze the root cause of the unusual event and formulate effective corrective actions to solve the event that occurs repeatedly. The steps of event root cause analysis is given in Fig. 4. Root cause analysis is a systematic event analysis process which mainly includes determination of problems, analysis of causes, formulation of measures and evaluation

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of corrective actions. The event analysis technology of commercial nuclear plants should be introduced and absorbed by the under-construction nuclear power plants, so engineers can develop the event analysis methods of experience feedback that meets their construction activities, and the methods may be applied to the contractor activities management of new nuclear power projects [14].

Fig. 4. Main steps of event root cause analysis

2.6 Experience Feedback Effectiveness Evaluation The purpose of evaluation was to determine strengths and areas of the experience feedback management in which improvements could be made. New nuclear projects should regularly evaluate the effectiveness of experience feedback management to identify the weaknesses and good practices, so as to achieve common improvements and enhance the experience feedback management of the plants [15]. The scope of evaluation includes experience feedback organization, process, daily management, team building, information utilization and good practices. The evaluation was assembled from the reviews of documentation, discussions, interviews and observations conducted in the nuclear plants. During the evaluation, the experienced evaluation professionals will interview the plant personnel, evaluate the procedures, event analysis reports, corrective actions and event management database; It is also necessary to evaluate the implementation of some corrective actions on site. The evaluation criteria mainly include the following contents: (1) Whether the under-construction nuclear power plant has established a complete experience feedback system, which clearly defined the responsibilities of each department. (2) Whether a complete experience feedback organization has been established, or qualified personnel has been designated as coordinator of experience feedback.

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(3) Whether internal and external experience feedback management procedures that are aiming at standardizing the work process and can be continuously optimized has been formulated in new nuclear projects. (4) Whether the experience feedback personnel have the required knowledge, skills and can skillfully perform their duties. (5) Whether the closed-loop management of daily experience feedback has been developed. (6) Whether an experience feedback incentive mechanism has been established to ensure the effectiveness of experience feedback operation. (7) Whether the comparative analysis has been carried out in light of the external events, and special issues have been compiled on critical engineering construction activities, so as to reduce repeated events. Evaluation conclusion is given by the project team in the experience feedback effectiveness evaluation report. The elements of evaluation sub-item are presented, including current situation, the areas for improvement and suggestions, and the good practices found in the evaluation will also be demonstrated. The areas for improvement are the most important results of the evaluation. In the report the problems and possible consequences need to be pointed out concisely, as well as causes, contributors and suggestions. The corresponding facts should be included as supporting details. The recommended items are not limited to the actual evaluation content, but also include the good practices and achievements given by the evaluation project team after comparing with other nuclear power plants. 2.7 Application of Experience Feedback There have been many events concerned with cables and pipelines being cut accidentally during the construction period of new nuclear projects, causing degradation of the fire protection system, failure of systems or equipment, and the risk of electric shock to personnel. By searching the experience feedback system, it was found that there were 22 unusual excavation or drilling events from 2016 to 2020, including 10 pipeline cutting events and 12 electrical cable cutting events. Those events were mainly occurred in the peripheral area, and among the excavated pipelines, the number of fire-fighting pipelines were the most, followed by the power cables and domestic water supply pipelines. The number of civil engineering events is given in Fig. 5.

Fig. 5. Number of civil engineering events

In reference to the event screening criteria, the multi-plant experience feedback project team suggested that more attention should be paid to the events, the drawings and

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documents of the directly-buried pipelines should be checked, the pipeline inspection plan should be formulated, the skills of the personnel responsible for excavation or drilling should be strengthened, and the risk control capability of work leader should be improved. The following problems were found in the wake of the experience feedback evaluation: the joint investigation and analysis on the excavation or drilling events did not carried out by new nuclear projects and commercial nuclear plants. The corrective actions implemented and the good practices of commercial nuclear plants have not been extended to the plants under construction, so it is necessary to strengthen the experience feedback construction of new nuclear projects. After investigation and analysis, the event causes were as follows: (1) The construction personnel were not familiar with the underground equipment and adopted inappropriate construction methods in the process of driving excavators or drilling; (2) The requirements in the operation management and control measures were not implemented conscientiously; (3) The risk awareness of the personnel was weak, and the information about both excavating and drilling event was not learned; (4) During the engineering construction, the construction companies did not arrange the directly-buried pipelines based on the design drawings, and did not upgrade the drawings timely after changing the direction and route, resulting in incomplete or inaccurate drawings of the underground directly-buried pipelines. In response to the experience feedback management problems in civil engineering quality events, a comprehensive project team composed of multiple companies was established, and the event analysis was jointly carried out by engineering construction companies, design institute and nuclear power plants. After the depth analysis of the directly-buried pipelines disconnected by mistake, the group carried out a series of optimization measures in pipelines installation management, as-built drawing management, on-site acceptance, excavation risk control management and other contents. According to the analysis, the action items that need to be concerned by the plants are given, corrective actions include the following: (1) The design institute checks the distribution chart of the underground directlyburied pipelines, including fire-fighting pipelines, water supply main pipes and cables, completes the mapping and identification of the underground directly-buried pipelines, and compiles the underground comprehensive pipelines network diagram of the plant. The 3D model of underground corridor of nuclear power plant is given in Fig. 6. (2) The design institute should arrange the directly-buried pipelines network reasonably, and improve the protection measures of the pipelines network to avoid the unprotected directly-buried work. (3) The engineering construction companies should refine the work intervention management of civil engineering, and provide reasonable risk analysis report, excavation methods and emergency response plans for different pipelines. The on-site supervision of civil engineering is given in Fig. 7. (4) Engineering construction companies should reasonably set warning signs on ground for construction of underground pipelines, upgrade the drawings of underground pipelines in relevant areas and update the ground signs. A set of underground water

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supply directly-buried pipelines and the marking stake on the ground are given in Fig. 8. (5) For new nuclear projects, the departments should check the rationality of the setting of ground warning signs regularly. Before the civil engineering, the team should provide the technology instruction in written to ensure that the team members are aware of the risks. (6) The following requirements shall be take into consideration prior to the excavation of underground pipelines: Inspect the underground pipelines diagram; Mark the location of the pipelines; Apply for the excavation permit; Start the excavation or drilling work. (7) Commercial nuclear plants should purchase underground equipment such as metal pipelines detection instruments for pipelines detection before civil engineering. The civil engineering is given in Fig. 9.

Fig. 6. 3D model of underground corridor of nuclear plant

Fig. 7. On-site supervision of civil engineering

The implementation of actions on optimizing the underground pipeline network has achieved remarkable results and has good application and promotion value. The data shows that the number of power cable and pipeline cutting events in new nuclear projects have decreased significantly. Meanwhile, the reliability of on-site equipment and personnel safety have been improved effectively after the implementation of corrective actions.

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Fig. 8. Pipelines and stake a underground water supply directly-buried pipelines, b marking stake on the ground

Fig. 9. Civil engineering a metal detector used during the construction, b manual digging detection

3 Conclusions In the thesis, we systematically analyzed applicability of multi-plant experience feedback management among new nuclear power plants based on organization management, document management and information system construction of experience feedback. Both the effectiveness evaluation method of experience feedback and a typical example of experience feedback closed-loop management of optimizing the underground pipeline network were described in detail. We also researched some countermeasures aiming at improving the reliability of equipment and strengthening closed-loop corrective actions management to reduce the recurrence of unusual events and promote the nuclear safety management in construction.

References 1. Revuelta, R.: Operational experience feedback in the World Association of Nuclear Operators. J. Hazard. Mater. 111(1–3), 67–71 (2004) 2. Ruiz, P.P., Foguem, B.K., Grabot, B.: Generating knowledge in maintenance from Experience Feedback. Knowl.-Based Syst. 68, 4–20 (2014)

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3. Liu, H. : Application of IAEA safety standards and experience feedback in China. In: Effective Nuclear Regulatory Systems: Further Enhancing the Global Nuclear Safety and Security Regime. Proceedings of an International Conference (2010) 4. Sun, G., Zhu, L., Wang, X.: Research on operating experience feedback of US NRC. Nucl. Saf. 65–69, 73 (2011) 5. Rollenhagen, C., Westerlund, J., Näswall, K.: Professional subcultures in nuclear power plants. Saf. Sci. 59, 78–85 (2013) 6. Fauquet-Alekhine, P.: Safety and Reliability for nuclear production. Socio-Organizational Factors Safe Nucl. Oper. 1, 25–30 (2012) 7. Ranguelova, V., Bruynooghe, C., Noel, M.: European clearinghouse on nuclear power plants operational experience feedback. Kerntechnik 75(1–2), 7–11 (2010) 8. Gandhi, S., Kang, J.: Nuclear safety and nuclear security synergy. Ann. Nucl. Energy 60, 357–361 (2013) 9. Bouchet, J.L., Eichenbaum-Voline, C.: Case-based reasoning techniques applied to operation experience feedback in nuclear power plants. In: European Workshop on Advances in CaseBased Reasoning, pp. 497–511. Springer, Berlin, Heidelberg (1996) 10. Jabrouni, H., Kamsu-Foguem, B., Geneste, L., et al.: Continuous improvement through knowledge-guided analysis in experience feedback. Eng. Appl. Artif. Intell. 24(8), 1419–1431 (2011) 11. Qun, Y., Ling, L. : Experience feedback system of nuclear power plant under construction (2012) 12. Zou, Y., Xiao, Z., Zhang, L., et al.: A data mining framework within the Chinese NPPs operating experience feedback system for identifying intrinsic correlations among human factors. Ann. Nucl. Energy 116, 163–170 (2018) 13. Yu, Q., Lin, L.: Experience feedback system of nuclear power plant under construction. In: Progress report on nuclear science and technology in China (vol. 2). Proceedings of academic annual meeting of China Nuclear Society in 2011, No. 2--nuclear power sub-volume (Pt. 1) (2012) 14. Ziedelis, S., Noel, M.: Comparative analysis of nuclear event investigation methods, tools and techniques. European Commission, Joint Research Center (EC/JRC), EUR, 24757 (2011) 15. Zou, Y., Wang, W., Zio, E., et al.: An integrated framework for analysing operational events in China nuclear power plants. Ann. Nucl. Energy 130, 192–199 (2019)

Drying-into-salt Technology of High Salinity Radioactive Liquid Waste Fan Jiheng(B) , Luo Feng, Wu Guanghui, Li Zhenchen, Jia Zhanju, Zhang Hangzhou, Muo Shuangrong, Fan Chunxin, and Gao Ruixi Sichuan Provincial Engineering Laboratory of Nuclear Facility, Nuclear Power Institute of China, Chengdu, China [email protected]

Abstract. The radioactive waste treatment has been brought into focus with the rapidly developing of the nuclear industry. Due to the large proportion of volume and radioactivity of radioactive water in radioactive waste, the treatment of radioactive water draws people’s attention in the process of waste minimization. This research has systematically carried out mechanism study, engineering design, device development and performance verification surround the drying-into-salt technology of high salinity radioactive water. The results show that the engineering prototype runs steadily. The speed of drying is about 6-8L/h, and the product doesn’t contain free water. Meanwhile, the temperature of inside and surface of drum is 100 °C. The pressure inside the drum locates at 1–2 kPa. This work will provide important reference for the experimental study and device development of drying-into-salt technology of high salinity radioactive water. Also, it will be a great help to follow-up engineering application of this new technology. Keywords: Microwave drying · Radioactive waste liquid · Prototypedevice · Reliability

1 Introduction With the rapid development of China’s economy and the continuous depletion of fossil energy, the rapid development of nuclear energy has become an inevitable choice. It is also an important way to help China achieve carbon peak and carbon neutralization. At the same time, the amount of radioactive waste gas, liquid waste and solid waste generated during the production and operation of nuclear facilities is also increasing, and so is the damage. Among them, both the volume and the total radioactivity of radioactive waste liquid account for a large proportion of the “three forms of wastes”. At present, the cement solidification process is widely used for the “liquid to solid” treatment of radioactive waste liquid. However, the cement solidification process system is complex, covers a large area, and the volume of solid waste is significantly increased, resulting in higher waste disposal and management costs. At the same time, with the strengthening of public awareness of environmental protection and the improvement of radioactive waste management level and safety culture literacy, relevant management departments © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 753–761, 2023. https://doi.org/10.1007/978-981-19-8780-9_73

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have successively issued the nuclear safety law and the minimization of radioactive waste from nuclear facilities (had 401.08), in recent years, which clearly requires nuclear facilities to reduce the generation of radioactive waste from the source during the design, construction, operation and decommissioning stages. So the generation of radioactive waste should be reasonably as low as possible. The cement solidification process is contrary to the principle of radioactive waste minimization. As an emerging technology in the field of radioactive waste treatment, the drying and salifying technology of radioactive high salinity waste liquid has the advantages of large volume reduction ratio, small floor area and high treatment efficiency compared with the traditional cement solidification technology. At present, it has been successfully applied in nuclear power plants and research institutes abroad (Germany, France, etc.) [1–5] and has become a good practice of radioactive waste minimization. In order to break through the blockade of foreign technologies and realize the independent control of key technologies in the field of nuclear environmental protection, many domestic scientific research institutes have also successively carried out the research and development of this technology in recent years, and made a series of gratifying achievements in the direction of reaction mechanism, numerical simulation and principle verification [6–10]. However, most of the work at present is mainly limited to the laboratory stage, and there is still a lack of process optimization, system design and engineering verification oriented to the actual needs of the project, As a result, the technology has not yet achieved its engineering application in China. In this paper, according to the requirements of high-efficiency treatment of radioactive high salt waste liquid generated by new facilities, the technical route demonstration and process design are systematically carried out, and the equal proportion engineering prototype is developed to carry out verification and optimization, which provides an important reference for the engineering research of this technology and lays a solid foundation for subsequent application.

2 Process Planning 2.1 Source Item Overview According to the design input, the concentrated liquid produced by the evaporation process of a radioactive waste liquid treatment facility in our institute is used as the source item of drying and salifying technology. The main components of the waste liquid include Na+ , NO3 − , SO4 2− , Cl− , Ca2+ , F− etc. The concentration of the main chemical components is shown in Table 1, and the basic data of the concentrated liquid is shown in Table 2. Table 1. Chemical composition of liquid waste Component

Na+

NO3 −

SO4 2−

Concentration (mg/L) 12,400 70,300 1830

Cl−

Ca2+

F−

888

435

98.6 8.48

Fe3+

Mn2+

Zn2+

7.07

6.42

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Table 2. Basic data of liquid waste Project

Ph value

Activity concentration (Bq/L)

NaNO3 content (g/L)

Density (kg/m3 )

Parameter

6~8

≤ 4.0 × 108

≤ 50

≤ 1210

2.2 Preliminary Process Design The preliminary design of the process is carried out based on the technical investigation, and the formation process is as follows. Firstly, the drying barrel is sent to the drying station, and compressed and sealed. The radioactive steam residue liquid is transported to the waste liquid intermediate tank at the front end of the drum drying system through the pipeline for temporary storage, and the high-efficiency filter is connected to the vent of the storage tank. The vacuum unit at the end of the system is used to provide negative pressure for the whole system, and the steam residue in the intermediate storage tank is intermittently pumped into the drying barrel for drying; The secondary steam generated by the drying unit enters the secondary steam condensate storage tank after passing through the mesh demister and the condensate cooler in turn; After the inert gas is filtered through the high-efficiency filter, it enters the vacuum pump, and finally the tail gas is discharged into the ventilation system for centralized treatment; The secondary steam condensate shall be sampled and analyzed. Those whose specific activity reaches the standard can be directly diluted and discharged into the receiving water body. Those who do not reach the standard will be discharged into the corresponding waste liquid treatment system, such as ion exchange system and evaporation system, according to the specific activity value; The dry salt bucket is equipped with a cover removal device, and is transported through rails and professional slings; The salt bucket is hoisted into the shielding container by the numerical control crane or other lifting tools and then sent to the temporary storage. In the temporary storage, the salt bucket is put into the standard steel box for grouting and fixation. After temporary storage, it is transported out for disposal. The flow chart is shown in Fig. 1. 2.3 Process Parameter Calculation The process calculation of microwave heating drying salt formation is divided into two parts: the evaporation crystallization stage of waste liquid in the barrel and the crystallization drying stage. Using the open source modeling language Modelica, a flexible and extensible dynamic model is built in stages and regions. The main conclusions are as follows. Figure 2 shows the change of evaporation/drying rate with time in the evaporation and drying stages. In the evaporation stage, when the effective microwave power is 5 kW, the evaporation rate is about 7 kg/h, and the evaporation rate increases slowly with time. This is because the temperature of the lower material body increases with time, and the energy required to heat it to the boiling point decreases; From the evaporation stage to the drying stage, the drying rate decreases significantly, and the drying rate at the end of the drying stage is only about 0.05 kg/h. This is because the microwave heating

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Fig. 1. Process flow diagram of drying-into-salt technology (In the figure: 1—waste liquid intermediate tank; 2—metering pump; 3—drying device; 4—wire mesh demister; 5—condensate cooler; 6—condensate storage tank; 7—track; 8—sling; 9—high efficiency filter; 10—vacuum pump)

Fig. 2. Microwave power and evaporation rate changes with time

power needs to be significantly reduced to ensure that the temperature of the heated

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solid layer does not exceed the limit. If the mass fraction of water is less than 1%, it will take about 56 days to complete the crystallization and drying of a barrel of solid salt, including about 43 days in the evaporation stage and about 13 days in the drying stage. In the evaporation stage, the effective microwave power remains unchanged at 5 kW, and the evaporation rate remains about 7 kg/h; After entering the drying stage from the evaporation stage, the microwave power needs to be greatly reduced, and the drying rate will also be greatly reduced. 2.4 Key Equipment Design Through demonstration and comparison, the microwave heating device is the core equipment of the system. The detailed design of this study is carried out. The main results are as follows: the microwave heating device mainly includes microwave emission source, waveguide and other components. The total power of the device is designed to be 10kW according to the process requirements. The top of the device is equipped with feed inlet, discharge outlet, exhaust outlet and various process parameter measurement sensors. Figure 3 is the schematic diagram of the microwave heating device. Exhaust port

Waveguide

Infrared probe Discharge port

Pressure sensor and relief valve port Level gauge

Infrared probe Feed port

Fig. 3. The chart of microwave heating device

3 Prototype Development and Test 3.1 Prototype Development According to the functions of each part, the microwave drying salt forming engineering prototype is divided into four units, including feeding, microwave heating, condensation cooling and transmission. The main equipment includes feed tank, microwave heating device, condensing cooler, roller table, etc. the physical object of the engineering prototype is shown in Fig. 4.

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Fig. 4. Engineering prototype of microwave drying-into-salt technology

3.2 Test Process The feeding of the test process is divided into two stages. The first stage: initial feeding, conservative evaporation, and the second stage: intermediate feeding, stable evaporation. The feeding times and single feeding amount of the two stages are determined according to the salt content of the solution. After the drying barrel completes automatic transmission and cooperates with the microwave heating device, feed a certain amount of analog liquid into the drying barrel, turn on the vacuum equipment to keep the system in the micro negative pressure state, and turn on the microwave heating device for heating. When the single analog liquid completes the drying and salt forming process, the micro wave heating device will automatically close and start the next feeding. When the system completes all feeding as set, stop heating when the theoretical moisture content of the materials in the barrel meets the requirements, and then take samples and determine the moisture content in turn after cooling.

4 Results and Discussion The source term of this project prototype test is 200 g/l NaNO3 salt solution. Under the working condition of maximum microwave heating power of 10kW, the microwave drying test of 200L drying barrel is carried out. The test results show that the overall drying rate of the prototype is 6–8 l/h, the maximum temperature in the barrel and the barrel wall is about 100 °C, the pressure in the barrel is maintained between 1 ~ 2 kPa, and the steam temperature is maintained at about 98 °C. The variation curve of the liquid level parameter in the barrel with the drying time during the drying process is shown in Fig. 5. During the heating process, the liquid level in the barrel drops obviously, and the water evaporation rate increases rapidly at the initial stage of heating. After heating for about 10min, the evaporation capacity fluctuates slightly and is basically stable. When heated to about 100 min, the simulated liquid presents a solid salt cake and no liquid

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Fig. 5. Liquid level changes with time

is found. When heated to 120 min, no liquid (including droplets) is found in the barrel. The curve of the whole microwave drying process is consistent with the classical drying curve, including three stages: heating and preheating stage, constant temperature stage and heating and almost no drying stage. The corresponding evaporation rate curve also shows three stages: first rapid rise, basic stability and decline. In the first two stages, the main way of heat consumption is water vaporization and heat absorption. In the later stage, salt gradually becomes the main microwave absorption medium. At this time, if the drying end point is not grasped in time, the material temperature will rise rapidly, and the material will overheat, the internal structure will be damaged, resulting in the deterioration of product properties and heat waste. In general, the test results show that the change law of waste liquid in the barrel from evaporation crystallization stage to crystal drying stage is basically consistent with the process calculation conclusion (Fig. 6).

Fig. 6. Evaporation rate changes with time

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The dried products are sampled and the moisture content is measured. The microwave source of the microwave heating device is evenly arranged on the top and around the heating hood. The energy distribution of the rectangular microwave antenna under this structure is mainly related to the incident angle. Sampling and measuring the moisture content along the radial direction at the barrel wall and the barrel center can directly reflect the distribution of the moisture content of the products in the barrel. The sampling measurement results are shown in Table 3. The water content distribution results show that the water content at the barrel wall and bottom is high, and the water content at the barrel center is low. According to the principle of microwave field propagation, it is speculated that stainless steel has a strong reflection on microwave, resulting in low microwave field strength on the wall and bottom of the barrel, so the moisture content of the drying product is high. However, in general, the moisture content of the dried product is less than 5% on the whole, which basically meets the moisture content requirements. However, the newly added simulation liquid in the test process has little effect on the dried salt cake in the previous feeding cycle. The reason may be that the heating rate of microwave heating is very fast, and the evaporation rate of water in the concentrated liquid is greater than the decline rate of water penetration. Table 3. The result of moisture content and density of the salt cake Sample serial number

Moisture content (%)

Density (g/cm3 )

Wall sample 1

1.12

2.0

Wall sample 2

2.08

2.0

Bottom sample 1

1.52

2.0

Bottom sample 2

2.47

2.0

Sample 1 in the middle of barrel

0.76

2.0

Sample 2 in the middle of barrel

0.59

2.0

Through calculation, the volume reduction ratio of the dried product obtained from the test reaches 10. The volume reduction ratio of the dried product mainly depends on the salt content of the dried material. The lower the salt content of the same volume of the material liquid, the smaller the volume of the dried product, and the larger the corresponding volume reduction ratio. Under the background of increasingly severe environmental protection requirements and strong demands for core technology innovation, aiming at clarifying the engineering application requirements, this paper systematically completed the route demonstration, engineering design and device development of high salt waste liquid drying and salifying technology, and realized the localization of the technology, which is expected to realize the first engineering application of this technology in China. The operation process of the engineering prototype is stable, and the drying rate is about 6–8 l/h. The dried product does not contain free water, which is highly consistent with the principle of waste minimization. Compared with the conventional cement solidification technology, it is

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estimated that the capacity of final waste will be reduced by 5–10 times. With the increasing demand of old nuclear facilities for efficient treatment of waste liquid, the drying and salifying technology of radioactive high salt waste liquid has great application potential in different application scenarios brought about by the research and development of new nuclear facilities. The miniaturization, mobility and intelligent remote control of devices are the important directions for future development.

References 1. IAEA: Innovative Waste Treatment and Conditioning Technologies at Nuclear Power Plants, pp. 30–31. IAEA-TECDOC-1504 IAEA, Vienna (2006) 2. Process and Filling Adapter for the In-Drum Drying of Liquid Radioactive Waste . US Patent 5566727 (1996) http://www.google.com/patents/US5566727 3. Oldiges, O., Blenski, H.J.: A New Small Drying Facility for Wet Radioactive Waste and Liquids. WM’ 03 Conference (2003) 4. Dejan, K., Vladislav, K., Milan, J.: Krko NPP radioactive waste characteristics. In: International Conference Nuclear Energy for New Europe (2007) 5. Linn Therm High: Appratus for Concentrate Salt-containing Solution with Microwave Energy. US Pat 6080977 (2000) 6. Chao, G.A.O., Dahai, X.U.E., Meilan, J.I.A., et al.: Pilot study on microwave drum concentrate-drying technology. Radiat. Prot. Bull. 33(2), 48–51 (2013) 7. Meilan, J.I.A., Chao, G.A.O., Hongxiang, A.N.: Status of microwave in-drum drying for simulate borate waste solution. Environ. Sci. Manag. 40(4), 97–100 (2015) 8. Chao, G.A.O, Hongxiang. A.N., Dong, L., et al.: The pilot study on microwave drum drying technology for waste resin. Drying Technol. Equip. 10(3), 21–26 (2012) 9. Chao, G.A.O., Shuai, G.A.O., Hongxiang, A.N., et al.: Spent radioactive resin control study on microwave drum drying. Drying Technol. Equip. 12(4), 35–38 (2014) 10. Ding, Z., Yang, W.: Research and development status of microwave drying technology. Appl. Energy Technol. 256(04), 44–47 (2019)

Study on Key Technical Issues of Marine Environmental Safety Assessment of the Floating Nuclear Power Plant Yueping Xu(B) , Lianghui Liu, Xiaofeng Zhang, and Naigui Tao Suzhou Nuclear Power Research Institute, Xihuan Str. 1688, Suzhou, China [email protected]

Abstract. As a civil nuclear facility combining the organic innovation of marine engineering and nuclear engineering, the Floating Nuclear Power Plant (FNPP) is significantly different from the existing land-based fixed Nuclear Power Plant (NPP) because of the particularity of its shipborne platform and marine operating environment, although it has the general nuclear characteristics of the Small Modular Reactor (SMR). The existing national nuclear safety regulations and regulatory requirements are mainly based on onshore NPPs, so the applicability for the environmental safety assessment of the FNPP is limited. In this paper, through the study of the FNPP on the applicability of environmental safety supervision regulations and assessment requirements, the marine environmental characteristics and site suitability, the feasibility of radioactive waste treatment and discharge, the rationality of environmental monitoring scheme and implementation, the accident consequences and risk controllability, and the key technical issues of the marine environmental safety assessment of the FNPP are proposed, which can provide references for the environmental safety research and supervision of the FNPP. Keywords: FNPP · Transportable characteristics · Radioactive effluent · Marine environmental impact · Accident risk

1 Introduction In recent years, the energy demand for marine resources and island exploitation along with the development of marine economy in China is increasingly, and particular attention has increasingly been paid to the research of the FNPP in the nuclear energy industry [1–4]. The FNPP, with the advantages of flexible application, stable power supply, economic efficiency, can provide power or energy for islands development, oilfields exploitation, seawater desalination, etc., and can reach areas where large power grids are difficult to cover. Compared with the land-based fixed NPPs, FNPPs have great differences in the use purposes, performance requirements, design parameters, environmental impact, etc., so they are faced with economic, safety, environmental and other issues. There are no specific regulations or standards for the FNPP, and the related research is still deficient [5–8]. The IAEA said that, ship-mounted mobile NPPs may have some problems in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 762–772, 2023. https://doi.org/10.1007/978-981-19-8780-9_74

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specific technical fields, and it is necessary to review all standards in every field to determine whether the existing requirements are sufficient, and more safety guidelines are needed [9]. FNPPs can be mainly categorized into four types: Barge type, Gravity Based Structure(GBS) type, Spar type and Submerged type, with Russia, South Korea, the United States and France being the most representative [1, 2]. In May 2020, the Russian ‘Akademik Lomonosov’, operated by KLT-40S reactor was fully commissioned. As a civil nuclear facility that combines nuclear energy engineering and marine engineering, the FNPP will realize the promotion process of China’s nuclear power reactors from large to small, from fixed to mobile, and from land to sea. The national nuclear safety regulations and regulatory requirements are mainly based on onshore NPPs. However, the FNPP is a kind of the marine transportable SMR, so the existing environmental safety assessment regulations and technical methods are insufficient. According to the engineering characteristics and operation mode of the FNPP in China, the environmental safety impact of the FNPP operation is analyzed from five aspects: the requirements of regulations and standards, the environmental characteristics, the waste treatment and discharge, the implementation of environmental monitoring, and the accident risk control, and then the key technical issues are proposed.

2 Environmental Safety Regulatory Requirements and Applicability 2.1 General Regulatory Requirements of the FNPP The FNPP has the nuclear-related characteristics of the SMR. The reactor system is the fundamental guarantee and key basis to the safety operation of the FNPP, and the design of the reactor system has the strategy of defense-in-depth like the traditional large pressurized water reactor (PWR). However, the reactor power of the FNPP is smaller, the decay heat is lower, and the layout is more compact. The FNPP can be relocated, which may have different sites during site selection and ship construction to marine operation, for it can be built and loaded with fuel rod at onshore bases or shipyards, and then generate power at offshore site according to the power needs of marine users. The FNPP has high requirements for adaptability to the marine environment, because of the high requirements of nuclear reactor control and radiation protection under marine operating conditions, the compact layout of hull and cabin space, and the limited operation and maintenance conditions for long-term operation in the open sea. Therefore, the FNPP has the dual characteristics of nuclear related features of the SMR and the floating movability of the marine engineering. Considering that the marine environment characteristics and the wind, wave and current circumstances are complex and changeable, the marine environmental impact is obvious, and the nuclear emergency conditions and accident risks are prominent, so the environmental safety supervision for the FNPP should be carried out in full combination with the nuclear safety requirements of small reactors and the marine environmental protection requirements.

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2.2 Regulatory Requirements for the Relocation of the FNPP It is multiplicity in the environmental safety supervision of the FNPP. Generally, it must meet not only the nuclear safety regulations and standards for the nuclear facilities, but also the marine, maritime and other legal and regulatory requirements for the ship and marine engineering. The FNPP have different site locations and corresponding marine environment characteristics during different nuclear activities or operation scenarios throughout its whole lifetime, then the regulatory requirements are also different. The FNPP has the nuclear-related generality, so the existing nuclear safety regulations are applicable in principle. It is required to meet the environmental safety requirements and the nuclear safety activity licenses by the National Nuclear Safety Administration. As a special facility operating at sea, the supervision of the environmental protection, transportation, maritime authorities on the operation activities that may cause marine environmental pollution should be accepted. The FNPP shall comply with the three-stage environmental safety supervision requirements of nuclear power plants from site selection, construction to operation. Generally, the FNPP will complete the process of site selection, integrated assembly and construction, nuclear fuel loading and commissioning on onshore nuclear facility sites, and the environment impact is similar to a certain extent compared with the coastal NPP. When the FNPP is relocated to the offshore operation site for power generation, it shall be implemented in accordance with the National Nuclear Safety Authority’s licensing requirements for the relocation of nuclear facilities [10]. The application for the offshore operation site shall be proposed, the environmental safety assessment of the new site shall be conducted, and the relocation activities can be carried out only after the approval. 2.3 Requirements for Marine Environmental Safety Impact Assessment Although the design parameters of the FNPP are taken into account different operation scenarios and possible marine environment conditions, the potential users are highly uncertain, and the site location and marine environment changes with the relocation of the FNPP, then the marine environment protection requirements and waste discharge conditions are obviously different. The assessment should be carried out from the perspective of site selection requirements and potential marine environmental impact, especially focusing on the compliance of marine environmental management requirements, the acceptability of the impact of waste discharges, accident risks and effectiveness of preventive measures, and the conclusion should be given from the aspects of site selection suitability and environmental protection feasibility at last. The FNPP has a relatively fixed site location at sea during operation. As a nuclear facility, the state’s overall evaluation criterions and assessment methods for NPPs are applicable to the FNPP. When carrying out the impact assessment of the FNPP at the specific operation sea site, the requirements of GB/T19485 [11] for marine engineering should be taken into account on the basis of requirements of HJ808 [12] for the NPP according to the engineering characteristics of offshore operation and the site’s marine environmental protection requirements, and in accordance with the assessing requirements of the operation phase of NPPs.

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3 Marine Environment Characteristics and Site Suitability 3.1 Marine Environment Characteristics and Parameter Data Acquisition The FNPP does not occupy land resources and is far away from coastal land with dense population. It does not need to implement preparatory work such as land acquisition and resettlement or construction of supporting projects such as site leveling and access roads. The impact on the public and land environment around the site is small, and there are few restrictive factors affected by the terrestrial environment. The marine environment impact of pollutants discharged by the FNPP operating offshore is more sensitive and prominent because of the good quality of the offshore sea area water, the abundant marine biological resources, fewer human activity disturbance, and the complex ecological environment. At the same time, the marine environment, including the hydrological, meteorological, marine ecology, island status, sea utilization, environmentally sensitive areas, will change with the relocation of the FNPP. In order to obtain the data of marine environmental characteristic parameters of the operation site, it should carry out thematic studies such as marine investigation, meteorological investigation, and radioactive background survey, as shown in Table 1. 3.2 Environmental Requirements and Site Suitability The potential operation site of the FNPP is mainly determined according to marine power users such as oilfields, so the site suitability is one of the key factors in the environmental safety assessment during the site selection. The environmental quality and capacity of the site area are the basis for the waste treatment and pollutant discharge of the FNPP, and are also the basic data and input requirements for the environmental safety assessment and environmental monitoring. From the perspective of environmental protection, the environmental control restrictions and access requirements of marine environmental functional zones are the most important factors in site selection, and environmentally sensitive areas are the key environmental features and concerns in the assessment. Therefore, it is very important that, the marine environmental management requirements and control conditions around the operation site should be investigated in detail, and the site shall be kept as far away from ecological red lines, marine protected zones, and other possible marine ecologically sensitive areas, with full attention to the environmental compatibility of the site and marine functional zoning, as shown in Table 1.

4 Feasibility of Radioactive Waste Treatment and Discharge 4.1 Characteristics of Treatment and Discharge of Radioactive Waste The main difference between the FNPP with the marine engineering or ships is the discharge of radioactive effluents. Due to the complexity of the marine environment, the limitations of the hull space and the diversity of operating conditions, the Radwaste Treatment Control System of the FNPP is simplified compared with the onshore NPP,

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Table 1. The key issues in the investigation and research of marine environmental characteristics Environmental characteristics and research issues

Key research content

Marine environment and sea utilization

Investigation of seawater quality, marine ecology (including biological resources), sea utilization, survey of marine sediment, marine topography and silting, marine hydrodynamics

Meteorological characteristics and meteorological data

Regional climate and conventional meteorology, meteorological observation station siting and meteorological data acquisition

Environmental status and radioactive quality

Radiation environment investigation and radiation background

Environmental control requirements and site suitability

Conformity and compatibility verification with environmental functional zones and sensitive areas

and the treatment process and operation mode are different, so that the radioactive waste is treated and discharged in different ways. The FNPP uses Gaseous Radwaste System (WGS), Liquid Radwaste System (WLS), and Solid Radwaste System (WSS) to treat the different radioactive waste generated during the reactor operation. As the FNPP is far from the coast and with limited platform space and small amount of waste, a comprehensive scheme combining platform treatment and off-reactor treatment is used in the waste system design, that is, the waste gas is discharged directly from the chimney, the liquid waste which is treated to less than 1000Bq/L will be discharged to the local sea according the marine environment control requirements and pollutant discharge permits, and the solid waste is received and disposed by the land base during the FNPP’s return to port. The schematic diagram is shown in Fig. 1.

Fig. 1. Schematic diagram of the treatment and discharge of radioactive waste from the FNPP

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4.2 Discharge Conditions and Feasibility of Radioactive Waste 4.2.1 Discharge of Radioactive Waste Effluent The liquid radioactive effluent of the FNPP will be discharged after being treated to meet national standards. According to environment characteristics of the operating site and supporting conditions of the power user, three main discharge schemes can be considered for liquid effluent during operation: discharge from the FNPP platform directly, on-site discharge from the oil platform, and off-reactor transfer and coastal discharge. The characteristics of each scheme are shown in Table 2. The comparative analysis shows that the direct discharge from the FNPP is better with relatively small impact on the whole marine environment. However, it is important to do a good job in the demonstration of the rationality of the discharge outlet setting and the analyze of the dilution and diffusion conditions of the sea area. Table 2. Liquid effluent discharge scheme and discharge feasibility Discharge scheme

Discharge characteristics and discharge feasibility

FNPP platform discharge directly

The most simple, economical and reasonable scheme, with good dilution and diffusion in open sea, and low radiation dose to the public, but the receiving sea needs to meet the requirements for receiving the liquid radioactive effluents discharge

On-site discharge from the oil platform

Discharge effluents through the oil platform pollutant discharge outlet, instead of discharge with the circulating cooling water of the FNPP, no extra outlet is required, but the condition of blow-off connection is complicated, and may cause high radioactive concentration in local area around the oil platform

Off-reactor transfer and coastal discharge Transfer the liquid radwaste by the wastewater transfer ship frequently because of the small volume of the wastewater storage tank on the FNPP, with no radiation impact on the site sea, and this scheme is difficult to implement if the land base is far away

4.2.2 Feasibility of Waste Transfer and Disposal The FNPP waste system has the off-reactor treatment capability, and the liquid waste or solid waste can be transferred to the land base for discharge or disposal through the transfer ship, which has the advantages of flexibility and maneuverability and can transfer the waste generated by other FNPPs in the same marine area as a mobile supporting device. In addition, the FNPP can transfer and deposit wastewater and solid waste temporarily using the environmental protection equipment and site field resources of the oil platform.

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At present, there is no specific standard that requires total emission control or dose constraint requirements of the radioactive effluents from the FNPP in China. The standard GB13695 [13] provides the authorized limits for normalized releases of radioactive effluents from nuclear power reactors. However, the standard is currently being revised and will no longer requires authorized limits to nuclear power reactors. HAFJ0060 [14] specifies that the effluents control limits of the heating reactor should be at least 2.5 times lower than control requirements of effluents from PWR reactors in GB6249 [15]. According to the statistics of existing operating power plants, the correlation between the radioactive effluents and the power is not very good except for liquid H-3. Considering the power feature of the FNPP and the marine site characteristics, and referring to requirements of standards for the heating reactor, it is proposed to adopt 40% of the annual effluents control limits of a single reactor specified in GB6249 as its control value, and the radiation dose limits of radioactive effluents on the public and marine organisms are 0.1 mSv/a and 10 μGy/h as the evaluation criteria, respectively. According to the source term of the FNPP and the marine environment of the potential site in China, the air-borne effluents are discharged from the chimney and the liquid effluents are discharged directly from the ship platform outlet into the receiving sea, and the total discharge amount of radioactive effluents during operation is lower than the corresponding control limits (see Table 3). The maximum effective dose to the public is 3.15 × 10−6 Sv/a, and the maximum dose rate to marine organisms is 5.98 × 10−1 μGy/h, which are far less than the requirements of the proposed criteria. Table 3. Effluents control limits of the FNPP Radioactive materials

Radionuclides

Control value of discharge (Bq·a−1 )

Amount effluents (Bq·a−1 )

Airborne radioactive effluents

3H

6 × 1012

1.00 × 1011

1.67

14 C

2.8 × 1011

2.40 × 1010

8.57

Noble gas

2.4 × 1014

5.17 × 1013

21.54

Iodine

8 × 109

2.51 × 108

3.14

Particle (t1/2 ≥ 8d)

2 × 1010

2.52 × 108

1.26

3H

3 × 1013

9.00 × 1011

3.00

14C

6 × 1010

1.00 × 109

1.67

Other nuclides

2 × 1010

2.44 × 109

Liquid radioactive effluents

Proportion (%)

12.2

The atmospheric dispersion around the site is generally good during the operation of the FNPP, and the radiation dose from the liquid exposure pathway on the public can be negligible, so the radiation impact to the marine environment is a key concern. Therefore, it is needed to focus on the evaluation of the radiation impact on marine organisms and marine ecology on the basis of appropriate total amount control limits and radiation dose limits of radioactive effluents proposed.

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5 Rationality of Environmental Monitoring Plan 5.1 Source Term Characteristics and Monitoring Nuclides The purpose of radiation environment monitoring of nuclear facilities mainly includes: understanding and evaluating the surrounding radiation level and radiation impact, indicating the possible cumulative impact of long term discharge of effluents, and conducting the accident emergency monitoring. The monitoring scheme and system design of the FNPP should be implemented with the consideration of characteristics of its effluents source terms. In liquid effluents besides 3 H and 14 C, there are many kinds of nuclides with the proportion more than 1%. Except for some short-lived nuclides, 131 I, 60 Co, 137 Cs, 134 Cs, 58 Co, 124 Sb, 110m Ag and 51 Cr should be mainly considered. In gaseous effluents besides 3 H and 14 C, the proportion of noble gases 133 Xe and 85 Kr exceeds 90%. However, the monitoring of the γ radiation dose rate is mainly considered instead of the noble gases, and radioactive iodine elements are mainly 133 I and 131 I, which is accounting for about 63% in total. Based on the analysis of such source term characteristics, the monitoring of γ radiation dose rate, 3 H, 14 C and γ nuclide should be carried out around the operation site. 5.2 Monitoring Points and Monitoring Equipment The population distribution and sea utilization in the FNPP site are relatively simple, but the monitoring accessibility in the open sea is not good enough. The applicability of the existing monitoring standards and monitoring programs under the land-based environmental conditions is insufficient for the FNPP. Therefore, it is necessary to propose a monitoring plan that should be based on sensitive characteristics of the marine environment and the implementation conditions of on-site sampling, fully relying on the facilities of marine oilfield users. More consideration in the environmental γ radiation level investigation should be given to the sea area close to the site, while the entire 20km monitoring range can be appropriately reduced. The monitoring point layout should be mainly conducted focusing on the downwind direction near the platform, and the site ocean current, islands and reefs, nuclides migration pathways, radiation affected areas, and environmental sensitive areas. The gaseous pathways monitoring should be mainly concerned on the γ radiation and aerosol in air, while the liquid pathways monitoring should be mainly concerned on seawater, marine sediments, marine organisms, and species with indicative and cumulative radiation effects should be considered also. The monitoring frequency shall be determined according to the sampling conditions on site, and the seawater γ radiation and aerosol will be monitored online while the marine environmental medium will be monitored every half year or every year. The mobile monitoring scheme of the FNPP should be well studied. The monitoring facilities can be considered mainly movable and supplemented by the fixed equipment according to the mobile monitoring ability and emergency monitoring requirements. Mobile equipment will be used to monitor radioactive effluents and surrounding environment during normal operation and accident emergency. A mobile laboratory for effluents and environmental monitoring can be established in the form of an auxiliary cabin in the

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case of limited space on the hull, and a certain number of special unmanned monitoring ships and aircrafts for emergency monitoring during accidents can be configured.

6 Environmental Risk Controllability of Accident 6.1 Radioactive Accident Release and Radiation Effects The potential radioactive release of the FNPP and its accident consequences are one of the most important issues in the environmental safety assessment. With the analysis of the accident source term of the FNPP, the loss of coolant accident (LOCA) is used as the postulated siting accident, and the atmospheric dispersion factor given by the RG1.4 [16] is conservatively used for calculation. According to the analysis of accident consequences, the personal effective doses at 500 m and 1000 m are 1.72 × 10−2 Sv and 5.58 × 10−3 Sv respectively, and the dose beyond 800m area can be lower than the 10mSv control value limits proposed in the review principle for SMRs [17]. The radiation dose caused by the accident will be further reduced since the atmospheric dispersion factor used in the calculation is very conservative and the actual meteorological conditions in the marine site are far better, so the overall risk is expected to be small due to the lack of people at sea. However, the key problem to be pointed out is that, there is still a lack of suitable regulations and standards for accident analysis of SMRs in China, and the FNPP do not have a fixed site boundary for its mobility, so environmental safety assessment requirements such as accident source term analysis, accident consequence acceptance criteria, accident boundary control measures should be further studied and proposed. 6.2 Marine Platform Accidents and Risk Controllability Besides the radioactive accident of the reactor itself, the FNPP may also suffer from external safety threats that may lead to accidents. The FNPP can achieve reactor circulating cooling more easily using seawater as the coolant, and can avoid impacts from external events such as earthquakes and floods that may encountered by costal NPPs more effectively. However, it may encounter external events such as the collision of the floating platform, oil spills from ships and oilfields in the sea, or natural disasters such as harsh climate and extreme marine conditions, which may affect its safe operation directly. The envelope values of the wind, wave and current loads in the domestic sea area are taken into account as the design benchmark of the FNPP, and the platform is maritime movable and can be transferred in extreme cases. Studying with potential operation scenarios of the FNPP and existing accidents of offshore projects, oil spill accidents and ship collision accidents are important external factors that cause threats. The FNPP, which is an important supporting part of the power supply of the oilfield, is closely related to the production and activities of the oilfield. The FNPP should keep a certain safety distance from oilfields according to the general safety requirements of oil platforms, and attention should be paid to the sea conflicts and environmental compatibility risks that may result from the sea area exploitation and utilization of well groups in the oilfield. In general, the FNPP should investigate possible external event factors in detail following the guideline [18], and conduct strict demonstration of external events and their safety impacts at the site, then put forward corresponding accident risk prevention measures in the emergency plan.

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6.3 Emergency Plan and Feasibility of Implementing Emergency The SMR has lower environmental and public radiation effects than the large PWR plant due to relatively small amount of radioactive substances in the reactor core. The reactor of the FNPP is designed with engineered safety features, with passive features and severe accident mitigation measures. There will be no accidents such as large break loss of coolant, and it can reduce the radiation impact and facilitate the emergency evacuation after the accident. However, the FNPP operates offshore, far away from the land, and the accident emergency response and rescue is more difficult. Therefore, it is important to analyze the accident consequences and prepare the site emergency plan elaborately in accordance with the national nuclear safety regulations, and take risk prevention and control measures effectively. Besides, the attention should be paid to the offshore evacuation of oilfield personals. The tidal current of around marine site of the FNPP are diverse. Compared with the coastal areas, the distribution and aggregation of marine organisms such as algae, shrimp and larval fish in the sea area where the FNPP operates could be quite complex, and the diffusion condition of the thermal discharge is also different. The intake water amount of the FNPP is small, but the intake and outlet which are close to each other are located in the same small area under the bottom of the ship platform, and the FNPP do not have intake protection barriers and monitoring system like coastal NPPs now. Therefore, it is necessary to study well in the safety protection of cold sources against marine creatures and research the impact of seawater temperature rise of the intake water, so as to ensure the safety of the water intake.

7 Conclusions and Prospects As a special offshore nuclear reactor, the FNPP has the nuclear-related characteristics and mobility. The marine environment of the site and the operation mode of the power station are obviously different with coastal NPPs. The environmental safety supervision requirements caused by the FNPP are one of the most important issues. The study shows that the existing nuclear safety regulatory is applicable in principle. The FNPP should comply with the requirements of environmental safety supervision of nuclear facilities and the permission of nuclear safety activities of the national nuclear safety authorities. When the FNPP is relocated, the environmental safety assessment of the new site should be implemented, which can be carried out in combination with the technical and methodological requirements of the assessment for the NPP and with the consideration of the characteristics of the marine engineering. At the same time, the key issues of environmental safety assessment of the FNPP are proposed from the perspectives of the applicability of laws and regulations, environmental characteristics of the site marine area, waste discharge conditions, environmental monitoring plans, and environmental risks of accidents. In order to improve the environmental safety assessment of the FNPP further, the following suggestions are put forward: (1) Give full consideration to the requirements of technical standards in nuclear industry and marine engineering, make adaptive supplements and improvements to the existing nuclear safety supervision system according to the characteristics of the FNPP, accelerate the establishment of domestic laws and

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regulations, and clarify and implement the regulatory authorities, regulatory responsibilities and regulatory requirements, (2) During the design and construction of the FNPP, the requirements of the following offshore operation scenario shall be considered thoroughly, including marine characteristics and environment requirements of the site, the feasibility of operation and maintenance of environmental protection facilities at sea, the possibility of waste transfer and disposal, and the controllability of accident emergency and environmental risk, (3) Rely on facilities and site platform conditions of marine oilfield users as much as possible, and ensure the coordinated development and environmental safety with offshore power users.

References 1. Li, J., Liu, F., Zhao, F.: Development status of overseas offshore floating nuclear plant industry. Ship Eng. 39(4), 5 (2017) 2. Lee, K.-H., Kim, M.-G., Lee, J.I., et al.: Recent advances in ocean nuclear power plants. Energies 8, 11470–11492 (2015) 3. Li, J., Liu, F., Yang, L.: Key technology of offshore floating nuclear plant based on technology foresight method. Ship Eng. 39(4), 7 (2017) 4. Zhang, Y., Buongiorno, J., Golay, M., et al.: Safety analysis of a 300-MW offshore floating nuclear power plant in marine environment. Nucl. Technol. 203(2), 129–145 (2018) 5. Wang, L., Li, G., Rui, M., et al.: Research on Supervision Mode of Offshore Floating Nuclear Power Station with Small Marine Reactor. 中国核学会学术年会 (2017) 6. Shen, X.: Operating model and legal issues of transportable nuclear power plants. Global Sci. Technol. Econ. Outlook 31(6), 65–69 (2016) 7. Liu, F., Li, J., Liu, L., et al.: Policies and standards for overseas offshore floating nuclear plant. Ship Eng. 39(4), 4 (2017) 8. Lv, S., Liu, H., Cai, Q., et al.: preliminary study on nuclear safety supervision of offshore nuclear power platforms. Sci. Technol. Innov. Herald 13(34), 46–48 (2016) 9. IAEA: Legal and institutional issues of transportable nuclear power plants: a preliminary study, IAEA Nuclear Energy Series No. NG-T-3.5, Vienna (2013) 10. Ministry of Ecology and Environment. Regulations on safety licensing procedures for nuclear power plants, research reactors and nuclear fuel cycle facilities (2019) 11. Technical guidelines for Environmental impact assessment of marine engineering: GB/T 19485-2014 (2014) 12. Ministry of Ecology and Environment. Technical guidelines for environmental impact assessment format and content of Environmental impact for nuclear power plant: HJ808-2016 (2019) 13. The State Bureau of Quality and Technical Supervision. Authorized Limits for Normalized Releases of Radioactive Effluents from Nuclear Fuel Cycle: GB13695-1992 (1992) 14. Nuclear Regulatory Commission. Safety Criterion of Radiation Protection for Low Temperature Heating Reactor Operation: HAFJ0060 (1996) 15. Ministry of Ecology and Environment. Regulations for environmental radiation protection of nuclear power plant: GB6249-2011 (2019) 16. Nuclear Regulatory Commission. Assumptions Used for Evaluating the Potential Radiological Consequences of a Loss of Coolant Accident for Pressurized Water Reactors: RG1.4 (2017) 17. National Nuclear Safety Administration. Principles for Safety Review of Small Pressurized Water Reactor Nuclear Power Plants (Trial) (2016) 18. National Nuclear Safety Administration. External events selected in the design of floating nuclear power plant (2018)

Research on the Application of Tension Monitoring System of the Interception Net in Nuclear Power Plant Xiaolin Liu, Wei Meng(B) , Qian Huang, Jiecong Liu, and Shuai Wang Suzhou Nuclear Power Research Institute, Suzhou, Jiangsu, China [email protected], [email protected]

Abstract. In order to prevent marine organisms from invading cooling water system, lots of interception nets are generally installed in nuclear power plants. By connecting tension gauge on main net rope, the status of interception net can be effectively monitored. According to empirical analysis, there are two main influencing factors of tension: tide level and catch. Two hypotheses can be made: Hypothesis 1: Daily average tension is positively correlated with daily catch. Due to the repetitive law of tides, the contribution of daily tide level to the tension should be close to the same in a short period of time. Therefore, if Hypothesis 1 is true, it can be considered that catch is the major factor in tension. Hypothesis 2: Hourly average tension is significantly correlated with the time-sharing tidal trend. If Hypothesis 2 is true, it can be considered that the tide is the major factor in tension. If none of the Hypotheses hold, the effects of other factors need to be further considered. To verify Hypothesis 1: From the existing data sample plots, it can be seen that there is no correlation between them, and the hypothesis does not hold. To verify Hypothesis 2: OLS regression analysis is performed on the samples, and there is a significant negative correlation between the two, so Hypothesis 2 is true. To use tension and tide level to characterize the catch, in which the actual catch come from the results of daily cleaning. Process raw data at the same frequency, and all data are processed to 1 time/hour by averaging the tension and refining the catch to the cleaning time. It can be seen from the above analysis that for the tension y, tidal contribution f(t) and catch contribution f(c) in the same period, y = f(t) + f(c) + ε, where ε is the contribution of other environmental factors (such as waves, random wind, etc.) to the tension except for tides and catch, it is obvious that df(c) = dy − df(t) = dy− ∝, where ∝ is the correlation coefficient between tension and tide level established by OLS. Namely the variation of tension caused by the change of net catch can be characterized by the difference between the variation of tension and the component of the variation of the tension with tides (extra change in tension). Hypothesis 3 is drawn: Actual catches are related to the aforementioned df(c).Curvilinear regression on samples, the results show exponential model is optimal, with P = 0.458, P = 0.002, which means they are extremely significantly correlated, and the regression model needs to be optimized with the accumulation of data. The regression equation of catch c(kg) to the additional variation of tension force df(c)(tf) is: c = 227.994e22.163df(c) .

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 773–782, 2023. https://doi.org/10.1007/978-981-19-8780-9_75

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1 Introduction The core goal of cooling water system guarantee work of nuclear power plant is to preventing the large-scale invasion of marine organisms. To achieve this goal, interception net is the first and extremely important barrier. A large number of on-site practices have confirmed that a scientifically reasonable and well-maintained interception net system can effectively intercept and collect garbage and marine organisms. Therefore, each nuclear power base has set up multi-level, multi-quantity and targeted interception nets according to their own water intake characteristics [1–5]. In order to ensure the good working condition of the interception net, it is necessary to check the status of the interception net regularly. At present, the nuclear power bases mainly use divers with underwater cameras, multi-beam imaging sonar, etc. for underwater inspection, or direct underwater visual inspection. There are several drawbacks to such status checking and evaluation: 1. The risk of operators is high, and it is difficult to perform inspections under severe weather conditions, so there are obvious monitoring defects; 2. Interception network status data are all discrete data, and due to harsh operating conditions and long intervals, it is difficult to form effective trend tracking, which is not conducive to the realization of status management; 3. Existing inspections and evaluations are highly subjective and have no reliable quantitative standards. Different operators may describe and judge the same situation differently, which affects decision-making and has quantitative obstacles. In order to solve the above problems, some power plants have established online monitoring systems for the status of the interception network by adding tension gauges to the main rope of the interception network to provide guidance for on-site operations.

2 Design and Construction of Tension Online Monitoring System 2.1 Overall Design of Tension Online Monitoring System Referring to the design concept of Refs. [6–8], combined with the actual situation of the on-site work, the tension online monitoring system is designed into three parts: equipment terminal (local terminal), server and mobile user terminal. Its main structure is shown in Fig. 1. On-site wired transmission mainly refers to the output of analog quantity from the external cable of the tension sensor to the local control board. This cable contains the excitation and signal parts required by the tension sensor; the wireless transmission section is mainly used to convert analog to digital on the control board, then the data is uploaded through the wireless communication module (4G mode). Data processing is mainly done on the backend server. It mainly involves data verification, data screening, data storage, data analysis, result display and push, etc. Among

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Equipment box Local wired power supply

Battery

Tension sensor Transmission module

Wireless transmission

Wired transmission

Remote server

Tension measurement

Data processing Data display

Main rope of intercept net

Emergency response

Mobile APP

Over-limit alarm

Fig. 1. Monitoring architecture diagram

them, the user terminal is mainly used to display monitoring result and receive alarm push, which is realized through desktop client or mobile client (including real-time data display, graph trend tracking, historical data query, value overrun alarm, conventional parameter setting, etc.). In the case of multiple network, the centralized management mode of separate monitoring, separate setting, separate alarming, and aggregated display of multiple measuring points can be realized, and the inconvenience caused by high complexity can be avoided while fine monitoring. Considering the advantages and disadvantages of several sensors in terms of installation method, measurement accuracy, range, typical application scenarios, etc., the column tension sensor is considered as the core sensor in interception net tension online monitoring system, so as to make full use of its advantages of large range, sensitivity and high precision. In terms of power supply design, in order to enhance the applicability of the tension meter in various on-site environments, the system has designed two power supply modes: mains power supply mode and self-power supply mode. Among them, in the mains power supply mode, 220 VAC can complete the power supply to the 12 VDC tension meter through power conversion module; In the case of self-power supply, it is necessary to take the charge and discharge controller as the core, connect the power generation equipment such as fans and solar panels, and draw out the 12 VDC to power the tension meter. In terms of communication design, since there is no available network in most of the water intake area of the existing power plants, especially at the anchor block where the main rope of the interception network is hung, in order to meet the needs of sending data to the server, the design considers the use of 4G DTU to complete signal transmission.4G DTU converts serial port data of the downstream tension sensor and the upstream network signal bidirectionally, and completes the sending and receiving process. The server is located at the remote end, establishes a data sending and receiving relationship with the 4G DTU, and uses a virtual serial port to acquire and process each group of tension

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data at the same time. The system is designed to collect and process data in a 30s/time manner. 2.2 On-site Installation The connection method between the tension sensor and the main rope of interception net is mainly connected in series, using the shackle connection holes at both ends of the sensor, and the shackle is connected to the main rope and the anchoring ring respectively. After the connection is completed, the main rope is re-tightened and stressed. Then the tension sensor is used as the serial link of the force-bearing system. So the measured tension value has a representative significance for the actual tension of the entire main rope and the actual state of the network. The schematic diagram of the actual on-site installation is shown in Fig. 2.

Fig. 2. Schematic diagram of on-site tension meter installation

3 Influence Factor Analysis of Tension 3.1 Brief Description of Research Conditions The working conditions of interception nets are complex. On the one hand, the coastal open air environment is affected by multiple environmental factors such as wind, rain, waves, and tides. These working conditions will directly or indirectly act on the interception system formed by the shape of the network, the rope structure, and the anchoring mechanism, and have an impact on the force of the tension sensor connected to it. The online monitoring of interception net status requires the analysis of tension value to reverse the occurrence of these factors, that is, the key physical state and parameters of interception network are represented by tension value. The data involved in this study mainly come from two pocket-type interception nets (hereinafter referred to as net 1 and net 2) that are relatively close to each other on-site, with an average flow velocity of about 0.3m/s. The maximum net cleaning capacity on-site is 1 net/day, considering that there are multiple intercepting nets to be cleaned,

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the actual cleaning cycle of each net is about 1 net/week. There is no extreme weather such as typhoons and rainstorms during the research period, and there is no interception net replacement operation. 3.2 Determination of Main Influencing Factor of Tension There are two ways of thinking about the analysis of the factor contributing to the tension of intercepting network. One is to use mechanical analysis methods to establish the deterministic relationship between the tension value and each factor by means of force model simplification and finite element calculation. The advantage is that accurate numerical results can be obtained when each contributing factor is determined [9–14]. However, due to the complex working conditions and the actual force situation, the omission of force or the unmeasurable force has a great influence on the analysis results. In order to solve this problem, the method of probability theory is used in the research of this paper, and regression models are used to attempt to establish the corresponding relationship [15–17]. 3.2.1 Hypothesis of Main Influencing Factors In complex on-site conditions, the meteorological conditions under non-extreme weather are characterized by randomness. According to the viewpoint of probability theory, the total impact can be regarded as a normal distribution, and in a relatively long period of time, it can be considered that its expectation to be zero. Therefore, the research on the main influencing factors will consider contributing factors other than non-extreme meteorological conditions. According to empirical analysis, there are two main influencing factors: tide height and fish catch. Therefore, in the study, the following two hypotheses can be made: Hypothesis 1: Daily average tension is positively correlated with daily catch. Due to the repetitive law of tides, the contribution of daily tide level to the tension should be close to the same in a short period of time. Therefore, if Hypothesis 1 is true, it can be considered that catch is the major factor in tension. Hypothesis 2: Hourly average tension is significantly correlated with the time-sharing tidal trend. If Hypothesis 2 is true, it can be considered that the tide is the major factor in tension. If none of the Hypotheses hold, the effects of other factors need to be further considered. 3.2.2 Hypotheses Verification Take Net 1 as the research object to verify the hypothesis. Verification hypothesis 1: Select the date of cleaning and salvaging of net 1 as the research object, take the average value of the original tension data on a daily basis, and analyze the data by scatter trend graph. It can be seen from the figure that there is no obvious correlation between the two. Hypothesis 1 is false (Fig. 3). Verification Hypothesis 2: Take the hourly average tension values of net 1 to correspond to the frequency of tides heights, and obtain a total of 822 sets of valid data.

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400 300

1

200 0.5

100

0

0 12.3 12.1 12.15 12.23 12.3 1.5 1.11 1.19 1.25 Catch of Net 1(kg)

Daily average tension()

Fig. 3. Scatter plot of daily average pulling force and catch

OLS regression analysis is performed on the samples, and there is a significant negative correlation between the two (significant level is close to 0.001, as shown in Table 1), so Hypothesis 2 is true. Table 1. OLS regression analysis result b Constant Tide

Standard Error

Significant

0.696789

0.026886

1.1126E-108

−0.000572

0.000176

0.001161

Referring to the method of Ref. [6], perform FFT on the data, try to verify the consistency of the two amplitude-frequency characteristics. Since the FFT transformation requires the sampling amount to be 2n , the continuous 64 groups, 256 groups, and 1024 groups of data (with 0 to be filled in) are taken for FFT, and the change periods have strong consistency. That is, the tide height and the tension value change in the same period (the amplitude-frequency characteristic diagram of 64 sets of data is shown in Fig. 4), which can also support the hypothesis 2.

4 Characterization Methods of Catches For on-site work, there are two main network states that need to be paid attention to: one is the tension of main rope. If it is close to the breaking tension of the material used for the main rope, there is a risk of the rupture of the main rope, and a response action is required. The second is the amount of fish caught by the interception net. The amount of catch is related to the tension of main rope, but the more direct challenge is the bearing capacity of the net body. If the catch is too much, the response action should also be taken. Among them, the former data can be directly measured by the tension meter. Next, try to characterize the catch with the data of tension and tide height, so that the real-time monitoring of the catch can be realized and the on-site operation can be guided. As mentioned above, limited by the on-site operation arrangement and objective factors, its catch data is not continuous. The actual research data are the catch data (1

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Fig. 4. Amplitude-frequency characteristic diagram when sets amount is 64

time/day), the tension monitoring data (1 time/30 s), and the tide data (1 time/hour). However, this brings about the problem of different frequencies of data, which requires preliminary processing of data. The idea is as follows: It is known from the previous research that the tide height contributes significantly to the tension value. In order to filter out the influence of environmental factors such as occasional winds and waves, the tide data is usually taken hourly, so the tide data will not be further refined, and all other variables are taken as hourly. Considering that most of the process of clearing net to obtain the catch data can be completed within 1 h, the net clearing time can be used to correspond to the acquisition time of the catch data. The tension monitoring data refer to the tide calculation method, and the average is recorded on an hourly basis and recorded in this period. It can be seen from the above analysis that for the tension value y, the tidal contribution f(t) and the catch contribution f(c) in the same period, there are y = f(t) + f(c) + ε

(1)

where ε is the contribution of other environmental factors (such as waves, random wind directions, etc.) to the tension value except for tide and fish catch, then it is obvious that df(c) = dy − df(t) = dy− ∝

(2)

where ∝ is the correlation coefficient between the tension value and the tide established by OLS. Equation (2) can be understood as the variation of tension caused by the change of catch can be characterized by the difference between the variation of tension and the variation of the tension which is caused by tide height(the additional variation of tension force). Then hypothesis 3 is put forward: the actual net catch is related to the additional variation of tension force df(c). As mentioned earlier, the net catch data sample acquisition cycle is long, so in the process of data sample expansion, considering that Net 1 and Net 2 are located close to each other, the working conditions are similar, and hypothesis 3 involves each parameter corresponding to the respective difference, so it is considered that the models are similar, so the data of net 1 and net 2 are combined in this research. During the research period,

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net 1 and net 2 were salvaged and cleaned 18 times in total; for net 1 and net 2, the correlation coefficient ∝ was obtained by OLS respectively; calculate the corresponding dy and then derive df(c). Regression analysis is carried out on these 18 sets of data, and it can be seen from the scatter plot that these data have relatively obvious curve characteristics. Try to use linear model, quadratic model, cubic model, exponential model for regression analysis, the results are shown in Fig. 5, it can be seen that the fitting effect of exponential model is better.

Fig. 5. Regression analysis of catch and additional variation of tension force

As shown in Table 2, from the evaluation of model parameters, it also supports the conclusion that the exponential model has a better effect. Its R2 value is the highest, which is 0.458, which can explain 45.8% of the difference in catches, and the significance level is 0.002, reaching a significant level. Table 2. Regression model evaluation of catches Equation

R2

F

Sig

Linear

0.401188

10.719584

0.004773

Quadratic

0.419943

5.429770

0.016828

Cubic

0.420029

3.379709

0.048576

Exponential

0.457596

13.498328

0.002052

Considering the objective conditions of insufficient on-site catch measurement accuracy and limited data amount, this model still has certain guiding significance for on-site

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interception net catch estimates. Especially for the case that the catch is less than 200kg, the fitting effect is better. To sum up, hypothesis 3 is true, according to the fitting result of this model, the regression equation of the catch c(kg) to the additional variation of tension force df(c)(tf) is: c = 227.994e22.163df(c)

(3)

5 Conclusions For the state monitoring of interception network in nuclear power plants, it is an inevitable development trend to use monitoring equipment such as tension meters for real-time online monitoring. With the continuous accumulation of monitoring data, the monitoring algorithm will be continuously iterated and improved, and the accuracy of monitoring data and the degree of restoration of on-site events will be better. In particular, the comprehensive application of multi-sensors such as tension meter, netsonde, and current meter will greatly increase the expressibility of on-site events at the monitoring value level, so as to assist in better emergency response decisions.

References 1. Jianwen, L., Xiaolin, L., Jinfei, Z., Yahui, M.: Research on marine biological detection technology to improve the safety of nuclear power plants cold source. Electric Power Safety Technol 19(10), 40–45 (2017) 2. Rui, H., Yijun, Z., Ping, J., Zhanshan, K., Jiaxing, W.: Research on countermeasures for cold source risk of Binhai nuclear power plant. Water Supply Drainage 56(474), S1 27–30 (2020) 3. Wei, G., Bilu, X., Chunmei, C., Yufeng, G., Zhi, L.: Selection and arrangement of anti-sea biological barriers based on safety improvement of cold source in nuclear power plants. Water Supply Drainage 53(432), S2 4–6 (2017) 4. Chuan, X., Zhengchun, H.: Construction and research on cold source interception system of Binhai nuclear power plant. Electric Power Safety Technol 21(281), 09 53–56 (2019) 5. Tao, L.: Layout optimization of nuclear power plant cold source pollution blocking network in view of marine organism outbreak problem. Autom. Appl. 12, 96+135 (2017) 6. Zhihao, H.: Early warning system and application of marine biological blocking in Hongyanhe nuclear power water intake. Dalian University of Technology (2021) 7. Xin, M. Research on monitoring technology of high-speed railway contact line running state. Beijing Jiaotong University (2016) 8. Jian, W.: Design and implementation of overhead conductor tension monitoring system. North China Electric Power University (2013) 9. Yang, W.: Analysis and calculation of the resistance of the plane mesh of the sewage interception network in the open channel of the nuclear power plant. China Water Transp (the second half of the month) 21(11), 148–149+151 (2021) 10. Wenyun, H., Qingwei, W., Xinxin, Z.: Force analysis of arresting net under external load. Ship Eng. 42(280), 06 137–141 (2020) 11. Yuanjie, X., Jiandong, Y., Huichun, C., Jiang, G.: Calculation of the tension of the floating type of sewage interception and discharge at the water inlet of a hydropower station. J. Hydraul. Conservancy 03, 47–52 (2005)

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12. Fukun, G., Yucheng, L., Huaihui, Z.: Model similarity conditions for force test of net clothing. China Ocean Platform 05, 23–26 (2002) 13. Qi, Z.: Experimental study on hydrodynamic characteristics of cages under different diving depths. Dalian University of Technology (2017) 14. Junfeng, K., Dundun, F.: Monitoring and analysis of main rope tension of nuclear power plant pollution barrier net. Low Temp. Build. Technol. 39(227), 05 63–65+77 (2017) 15. Weiming, C.: Research on the principle of curve fitting and its application. Changsha University of Science and Technology (2018) 16. Jie, L., Zhiming, S.: Comparison of three commonly used software sample size calculation methods and results difference between SAS, PASS and Stata. China Med. Herald 12(368): 18 139–143 (2015) 17. Xinke, J., Lele, C., Yuanyuan, J., Tao, G., Shun, Z.: Research on coal consumption characteristic curve fitting algorithm of thermal power plants. Power Syst. Prot. Control 42(412): 10 99–104 (2014)

Study on Preparation Technology of UCL4 From CCl4 Qiaojuan Wei(B) , Fuchen Ma, Gangqiang Yang, Dan Xu, and Jianxin Feng The 404 Company Limited, CNNC, Lanzhou 732850, China [email protected]

Abstract. During the purification of the uranium metal, a large amount of radioactive waste salts will be produced, The main component of this kind of waste salt are mostly LiCl-KCl-UClx , which has high recovery value. To accurately simulate the composition of molten salt, the paper uses carbon tetrachloride (CCl4 ) as the chlorinating agent, to carry out the reduction of uranium trioxide (UO3 ) under different temperature (673K, 698K, 723K, 748K and 773K) researches on the technology of preparation of uranium tetrachloride (UCl4 ). Firstly, the reaction enthalpy and equilibrium constant are calculated by means of the basic thermodynamic equation, and it is found that the reaction is an endothermic reaction and that carried out under a certain temperature condition. At the same time, the content of tetravalent uranium and phase structure in UCl4 were tested. The results showed that with the increase of reaction temperature in the temperature range studied in the paper, The content of UCl4 in the product showed a trend of increasing first and then decreasing. When the reaction temperature was 723K, the content of UCl4 in the product was the largest. Keywords: Carbon tetrachloride · Uranium trioxide · Uranium tetrachloride

1 Introduction As an important part of the fuel cycle, reprocessing of spent nuclear fuel can be divided into two types: water reprocessing and dry reprocessing according to the different separation systems. The radiation resistance and high temperature resistance of solvents used in traditional wet reprocessing cannot meet the demand for high fuel consumption in the future. It is imperative to develop dry reprocessing of spent fuel using high temperature molten salt as medium. Dry reprocessing technology is an anhydrous and high-temperature chemical treatment process. Usually, a carrier molten salt are mixtures which composed of alkali and alkaline earth metal-based fluoride salts or chloride salts is used as the medium Under the conditions of hundreds of degrees or higher, techniques such as distillation, reduction extraction, electrolysis and precipitation are used to separate and recover metallic uranium from spent nuclear fuel. In the process, a large amount of uranium-containing halide salts that are difficult to handle and have radioactivity are produced, and the existing technology cannot recycle the metal uranium therein, resulting in waste of nuclear materials. Therefore, it is of great significance to carry out the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 783–792, 2023. https://doi.org/10.1007/978-981-19-8780-9_76

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research on volume reduction of radioactive molten salt and the recycling and reuse of metallic uranium. Uranium-containing chloride salt is a typical radioactive molten salt produced during electrolytic refining. For separation radionuclides and matrix salts, realizing volume reduction of waste salts and recycling of radionuclides, It is necessary to simulate the recovery and treatment experiment of electrolytic refining molten salt to determine its recovery route and process parameters. Studies have shown that uranium trichloride is difficult to exist stably, so uranium tetrachloride is often used as raw material for uranium electrolytic refining and recycling research. The literature survey found that the relevant research on the preparation of tetravalent uranium mainly focused on the reduction of hexavalent uranium by electrolysis or catalysis to prepare tetravalent uranium in solution, while the preparation process of solid uranium tetrachloride was rarely reported, there are many methods for preparing uranium tetrachloride in theory, For example, it is prepared by reacting chlorine-containing gas (Cl2 , CCl4 , SOCl2 , etc.) with a mixture of uranium, uranium hydride or uranium oxide plus carbon at 500 ~ 700 °C, Uranium tetrachloride can also be produced by direct combination of metallic uranium and chlorine, However, the reaction product is often a complex mixture of various uranium oxidation state chlorides, and the various valence chlorides are difficult to separate. Therefore, the method has poor practical operability, and the reaction process is difficult to control, which limits its application. In addition, the existing methods for preparing uranium tetrachloride need to be improved in terms of yield and safety, and it is urgent to develop a method for preparing uranium tetrachloride with high conversion rate, mild reaction conditions, and safety and reliability.

2 Experimental Section 2.1 Materials All chemicals were of analytical grade purity and used as received without further purification. 2.2 Experimental Device In this study, uranium tetrachloride was prepared by reacting carbon tetrachloride (CCl4 ) liquid with uranium trioxide (UO3 ) solid powder, The reaction needs to be carried out in the temperature range of 673 ~ 773 K, while the boiling point of CCl4 is about 349 K. Obviously, the real process is the reaction between CCl4 vapor and UO3 solid. Based on this, the CCl4 liquid is heated into steam by means of a constant temperature water bath, and a fixed tube furnace with quartz tube is selected as the reaction site, In addition, the experimental device should also include a condensation reflux part for collecting incompletely reacted CCl4 vapor, and the collected CCl4 will be reused in subsequent experiments. The reaction of CCl4 and UO3 will generate toxic gases such as chlorine (Cl2 ) and phosgene (COCl2 ), so a 5% sodium hydroxide solution (NaOH) is set up as a tail gas absorption device. The schematic diagram of the experimental device is shown in Fig. 1.

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Fig. 1. Diagram of experimental apparatus

2.3 Preparation of Uranium Trioxide Using U3 O8 as raw material and 6 mol/L HNO3 as solvent, add 130 mL HNO3 per 100 g U3 O8 and continuously stir at 60 ~ 90 °C to fully dissolve U3 O8 . After the solution is clarified, stop stirring and filter to obtain UO2 (NO3 )2 solution. Then add 30% H2 O2 solution in excess of 6–8 times the stoichiometric ratio, Slowly. Stir continuously at room temperature, age for 40–50 min after a large number of golden yellow precipitates appear in the solution, filter. The filter cake is placed in a muffle furnace at 370 ~ 390 °C and calcined to constant weight to obtain brick red UO3 powder. The purity of UO3 is greater than 99% by chemical analysis, which can meet the requirements of this experimental study. 2.4 Preparation of Uranium Tetrachloride It has been reported that uranium oxides such as U3 O8 , UO3 and UO2 can react with CCl4 to form UCl4 in the temperature range of 698 ~ 748 K [5]. However, in actual production, U3 O8 is mostly calcined from slag, cutting chips, etc., its reactivity is low, and the process of preparing UO2 is more complicated. Based on this, this study selected the reaction of self-made UO3 and CCl4 to prepare UCl4 . The specific experimental process is as follows: Measure a certain volume of CCl4 into a three-necked flask, place the three-necked beaker in a water bath for heating, and set the temperature with reference to the boiling point of CCl4 as required. Accurately weigh a certain mass of UO3 and evenly spread in a dry and clean monel reaction boat, and place it in the middle of the quartz tube. The left end of the quartz tube of the fixed tube furnace is connected with a three-necked flask, The right end of the quartz tube is connected to a condensation device, the lower end of the annular condenser is connected to a three-necked flask, and the three-necked flask is used to collect the condensed and refluxed CCl4 , After the device is connected, turn on the metering pump and water bath in turn, and set temperature program and target temperature (the target temperature is set to 673K, 698K, 723K, 748K and 773K, respectively) to start the reaction, keep it for 4 h, and observe the progress of reaction in real time. After the reaction is finished and cooled to room temperature naturally, the product is collected. Sampling and analysis of its purity, tetravalent uranium content and phase structure and other information. Theoretically, CCl4 and UO3 react according to the formula (1) to generate UCl4 , Cl2 , COCl2 and other substances. In the experiment, the materials are fed according to the stoichiometric ratio in the reaction (1): (1)

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2.5 Analytical Methods The phase structure of the prepared UCl4 was determined by X-ray powder diffraction method, The U4+ content was determined by potassium dichromate titration method (precision: 0.5%); The final experimental result is the average value after 3 parallel determinations.

3 Results and Disscussion 3.1 Thermodynamic Calculation 3.1.1 Reaction Enthalpy The reaction principle of UCl4 prepared in this paper is shown in reaction formula (1), thermodynamic basic equation is used to calculate the reaction enthalpy and the equilibrium constant of UCl4 prepared and the reaction enthalpy is calculated by the following formula (2):  r Hθm (298.15K) = vB f Hθm (B, 298.15K) (2) In the formula, vB represents the stoichiometric number of the product and the reactant, wherein the product vB takes a positive value, and the reactant vB takes a negative value. r Hm θ (298.15 K) and f Hm θ (B,298.15 K), Respectively represent the reaction molar enthalpy change and the molar formation enthalpy of each component in the reaction equation in the standard state (temperature is 298.15K, pressure is 100 kPa). For non-standard states, the molar formation enthalpy of each component in reaction Eq. (1) at different temperatures can be calculated according to Kirchhoff’s law using Eq. (3): f Hθm (B, T)

=

f Hθm (B, 298.15K) +

T CP dT +



Hi

(3)

298.15K

Cp = a + b · T + c · T2 + d · T−1 + e · T−2

(4)

In the formula, Cp is the molar heat capacity of the component, which is a function of temperature, that can be calculated by formula (4), Hi is the phase change enthalpy of the component. In this study, CCl4 changes from liquid to gas, the phase transition temperature is 349K ~ 350K, and its phase transition enthalpy is 32.54 kJ/mol. From the formula (3), the molar formation enthalpy of each component in the reaction formula (1) at the temperature T can be calculated, Using formula (2), the reaction enthalpy at the temperature T can be calculated. 3.1.2 Reaction Equilibrium Constant The equilibrium constant is used to measure the maximum limit that a chemical reaction can reach at a certain temperature. The larger the equilibrium constant is, the more

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thorough the reaction is. The chemical reaction equilibrium constant Kθ can be calculated from the Gibbs natural energy change(r Gm θ (T)) of the chemical reaction, as the following formula (5): ln Kθ =

r Gθm RT

(5)

in the formula r Gθm (T) = r Hθm (T) − T · r Sθm f Sθm = r Sθm (298K) +

T CP dlnT +

(6) 

Si

(7)

298.15K

In formulas (5–7), r Sm θ is the entropy change of the components, Si is the phase transition entropy of the components at a certain temperature, In this paper, there is only the phase transition entropy when CCl4 changes from liquid phase to gas phase, and its entropy of phase change is 92.97 kJ/mol, and the reaction equilibrium constant can be calculated according to the formulas (5–7). 3.1.3 Calculation Results of Reaction Enthalpy and Equilibrium Constant Table 1 lists the standard molar formation enthalpy and entropy of CCl4 , UO3 , UCl4 , Cl2 , COCl2 and their molar heat capacity-temperature parameters, among them, the standard molar formation enthalpy, entropy and molar heat capacity-temperature parameters of UO3 (s) and UCl4 (s) at 298K are from the literature [6], and the standard molar formation enthalpy and entropy of CCl4 , Cl2 , COCl2 and other substances are from literature [7]. The molar heat capacity-temperature correlation equation is obtained by fitting with Eq. (4) in reference [6–8]. Table 1. Standard molar formation enthalpy and entropy of each substance and their molar heat capacity-temperature equation parameters f Hm θ kJ·mol−1

f S m θ J·mol−1 ·K−1

Molar heat capacity parameters /J·mol−1 ·K−1 a

b

c

T/K

CCl4 (g)

− 139.3

214.43

151.43

5.18*10–3

− 1.62*10–5

298 ~ 1000

UO3 (s)

− 1223.8

96.11

88.10

0.017

0

298 ~ 850

− 1.5335

1.575*10–3

298 ~ 1000

0.027

− 7.34*10–6

298 ~ 1000

− 1.118

1.287*10–3

298 ~ 1000

Substance

UCl4 (s)

− 1019.2

197.1

Cl2 (g)

0

222.95

COCl2 (g)

− 108

284

487.5 9.37 285.87

Combining the thermodynamic parameters of each component in Table 1, ignoring the pressure change during the reaction process, according to Eqs. (2)–(7), the reaction enthalpy and equilibrium constant of the reaction at different temperatures can be calculated. The results are listed in Table 2.

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Table 2. Molar formation enthalpy (r Hm ), molar formation entropy (r Sm ) and Gibbs free energy change (r Gm ) at different temperatures T/K

r Hm /(kJ·mol−1 )

r Sm /(J·mol−1 ·K−1 )

r Gm /(kJ·mol−1 )

lnK

673

70.2

− 35.65

78.71

14.07

698

386.08

− 35.64

410.96

70.82

723

382.35

− 35.64

408.12

67.90

748

378.11

− 35.64

404.77

65.09

773

375.74

− 35.66

403.31

62.76

Figure 2 shows the relationship between the reaction enthalpy and equilibrium constant as a function of temperature. It can be seen from Fig. 2a the reaction is an endothermic reaction, and with the increase of temperature, the heat required to absorb gradually increases. In addition, after 723K, there is little change of reaction enthalpy, and the change of reaction enthalpy is within 2%, indicating that the temperature has no obvious effect on the reaction enthalpy in the range of 723 ~ 773K. It can be seen from Fig. 2b that the logarithm of the equilibrium constant (lnK), is greater than 0, indicating that the reaction is theoretically feasible. With the increase of temperature, lnK increases gradually and tends to be stable, indicating that the increase of temperature is conducive to the forward progress of the reaction. During the experiment, with the increase of temperature, the content of tetravalent uranium in the product first increased and then decreased, which was consistent with the thermodynamic results.

Fig. 2. The reaction enthalpies and lnK with temperature

3.2 Phase Structure Identification UCl4 was prepared by reducing UO3 with CCl4 at 673K, 698K, 723K, 748K and 773K, respectively. The prepared sample is shown in Fig. 3a. It can be seen that the samples prepared at each temperature are green, which is consistent with the color of UCl4 itself, but it can also be clearly seen that there are obvious differences in the colors of the samples prepared at different temperatures, which may be due to the different purity of the samples. The result is that the content of UCl4 in the samples prepared at different

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temperature is different, indicating that the temperature directly affects the reaction between CCl4 and UO3 . In order to verify whether this conjecture is reasonable, the research tested the tetravalent uranium content in the prepared UCl4 , and the results are shown in Fig. 3b. It can be seen that when the temperature is between 673K and 723K, the content of tetravalent uranium increases with the increase of temperature; when the temperature is between 723K and 748K, the content of tetravalent uranium decreases with the increase of temperature. Small; when the temperature is between 748K and 773K, the content of tetravalent uranium increases with the increase of temperature. However, some studies have pointed out that when the temperature is 773K, the byproduct UCl5 will be produced in the reaction, and with the decomposition of CCl4 , so it is difficult to obtain relatively pure UCl4 at 773K. The above results show that there is an optimal temperature range for the reaction of CCl4 and UO3 . Based on the existing experimental results, 723K is a more suitable reaction temperature.

Fig. 3. a Diagram of UCl4 at different temperatures, b Diagram of uranium tetravalent content changing with temperature

In this study, the XRD patterns of UCl4 prepared at different temperatures were tested, and the results are shown in Fig. 4. Compared with the standard XRD pattern of UCl4 (PDF#97–020-2331), it is found that the characteristic diffraction peak positions of UCl4 prepared at each temperature can roughly correspond to its standard card (Fig. 4a, c, e, g,

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and i). However, the intensity of each characteristic diffraction peak is different, which indicates that the main exposed crystal faces of UCl4 prepared at different temperatures are different. In addition, the characteristic peak intensity of UCl4 prepared at 723K is significantly weaker than other temperatures, indicating that the UCl4 prepared at this temperature has poor crystallinity (Fig. 4e). It can be seen from Fig. 4. That the peak positions of the UCl4 prepared at each temperature are slightly shifted except the diffraction peak intensities. After local enlargement of its XRD pattern (Fig. 4b, d, f, h, and j), it is obvious that the diffraction peaks shift to low-angle or high-angle directions. According to literature reports, this phenomenon can be attributed to the existence of macroscopic residual stress leading to lattice distortion [9, 10]. Specifically, macroscopic residual stress can cause lattice anisotropic shrinkage. When there is compressive stress, the interplanar spacing becomes smaller, so the diffraction peak shifts to the high-angle direction. On the contrary, when there is tensile stress, the inter-lattice distance increases, causing the diffraction peak to shift to the low-angle direction. In addition, it can be seen from Fig. 4b, d, and h that compared with its standard card, the diffraction peaks of UCl4 prepared at different temperatures have obvious broadening phenomenon, and one of the reasons for the broadening of the diffraction peaks is that the stress affects the lattice parameters, which can be mutually demonstrated with the shift of the diffraction peak.

4 Conclusions This study uses CCl4 to reduce UO3 to prepare UCl4 at different temperatures. It is found that UCl4 can be prepared at temperatures of 673K, 698K, 723K, 748K and 773K. The content of tetravalent uranium in the prepared UCl4 is affected by the temperature, and the specific rules are as follows: When the temperature is between 673K and 723K, the content of tetravalent uranium increases with the increase of temperature; when the temperature is between 723K and 748K, the content of tetravalent uranium decreases with the increase of temperature; When the temperature is between 748K and773K, the content of tetravalent uranium increases with the increase of temperature. It is worth mentioning that when the temperature rises to 773K, the reaction products are mixed with by-products (UCl5 ) and thermal decomposition products of CCl4 , so it is not recommended to prepare UCl4 at this temperature.

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Fig. 4. XRD patterns of uranium tetrachloride prepared at different temperatures, a 673K; b the local enlarged of (a); c 698K; d the local enlarged of (c); e 723K; f the local enlarged of (e), 748K, g 748K; h the local enlarged of (g); i 773K; j the local enlarged of (i)

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References 1. Shi, H., Liang, B.-L., Li, B., et al.: Application of heterogeneous catalysis in catalyticreduction of hexavalent uranium to tetravalent uranium. Modern Chem. Ind. 41(Suppl), 100–104 (2021) 2. Zhao, X., Hou, B., Shi, H., et al.: A fixed bed reactor for catalytic reduction of hexavalent uranium by hydrazine nitrate and its application. China 111249999 [P] 2020–06–09 3. Yuan, Z., Zheng, W., Yan, T., et al.: Device and method for preparing tetravalent uranium by dynamic membrane electrolysis. China 102534644[P] 2012–07–04 4. Yuan, Z., Meng, X., Yan, T., et al.: A kind of preparation device and method of tetravalent uranium. China 114000160[P] 2022–02–01 5. Process for Producing Uranium Tetrachloride. Patent Specification 1(3), 812121 (1944) 6. Grenthe, I., Fuger, J., Konings, R., et al.: Chemical thermodynamics of uranium, pp. 29–51. OECD Publications, France (2004) 7. Tian, D., Pang, A.: Handbook of temperature coefficients of thermodynamic functions. China Aerospace Press, Beijing, pp. 574–575+580, 707, 714 (2014) 8. Carling, R.W., Westrum, E.F.: Thermodynamics of the mono-hydrogen difluorides V. Melting thermodynamics of NH4 HF2 . J. Chem. Thermodyn. 8(03), 269–276 (1976) 9. Xiang, X., Chen, C., Jiang, C.: Effects of Fe and C doping on the lattice distortion of He ion implanted Al surface by XRD 10. Guo, Z., Eang, N., Fu, T., et al.: A simple XRD method for determining crystal orientation and its distribution. J. Inorganic Mater. 3, 460–464 (2002)

Development and Application of High-Accuracy Metal Fuel Performance Analysis Code Based on Fem Method Qihao Tao, Bo Zhang(B) , Yongbo Hui, and Jianqiang Shan Xi’an Jiaotong University, Xi’an, Shaanxi, China [email protected]

Abstract. Metal fuel is considered to be the future of the Sodium-cooled Fast Reactor due to its advantage. However, the lower melting point is limit for its application, accuracy prediction of the fuel behavior is urgent. In this paper, based on the principle of FEM, the coupled model of thermal conduction and stressstrain analysis is established. The two dimensional model is chosen considering the characteristic of fuel. The heat conduction and mechanical model of fuel rod pellet and cladding, and the fission product behavior model are fully investigated in this paper. Then, the model is analyzed, and the applicable fuel behavior model is obtained. The analysis code SSTFEM is developed and verified using the experimental data. In this paper, the fuel behavior of X425 metal fuel rod under the condition of wire-wrapped constraint is also analyzed. It is found that wirewrapped has little effect on the temperature distribution and the contact between slug and cladding. However, due to the mechanical constraints at contact points, wrapped wire will have a greater impact on the circumferential stress distribution of cladding. The peak stress appears near the contact point of wrapped wire, which threatens the safety of cladding. The code is used to analyze the effect of the asymmetry deformation which is caused by the asymmetry coolant temperature distribution in adjacent subchannels and the manufacture eccentricity between the slug and the cladding. The effect law of deformation and the stress is obtained after a systematic analysis. Keywords: Metal fuel · Fast reactor · Fuel performance analysis · Fem · Stress-strain analysis

1 Introduction Accurate stress and temperature distribution prediction of the nuclear fuel during operating conditions are important in the nuclear fuel and reactor safety design. And the azimuthally different deformation which will cause the stress and temperature concentration mainly caused by two reasons: different subchannels coolant temperatures and the manufacture issues. The concentration of stress and temperature will not only make the fuel melted but also make the cladding occur a weak point and much easier to break down. But this two-dimension temperature distribution is too complex to be solved by the unidimensional analysis code. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 793–808, 2023. https://doi.org/10.1007/978-981-19-8780-9_77

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There are already some codes are developed to analyze the fuel performance. The FREY [1] code was developed for both the steady-state and transient condition and it also can handle the gas mixture, but it used a two-node element to model the pelletcladding contact which is disable to solve the two-dimension asymmetry condition. And the ESCORE [2] code is developed for steady-state fuel performance, and ESCORE can handle both PWR and BWR condition, but the ESCORE is restricted to solve only some specific fuel design and can’t simulate the other fuel rod. Gaspar Jr, J.C.A. and Moreira et al. [3] also used FEM to simulate the eccentricity rod with error less than 3.6%. The code they used is effective for the analysis of fuel rods temperature distribution but they didn’t add the mechanical model to the code and it’s just a thermal analysis code. So the demand for a two-dimension thermal-stress analysis code is necessary. The FEM which has an advantage in solving this asymmetry deformation problem is used in SSTFEM code. Some fundamental steady-state models such as: thermal conductance, thermal expansion and mechanical models have already been built. Several simulations are used to verify both the thermal and deformation results of the SSTFEM. Then a further study of X425 fuel rod is carried out to analysis the effect of the eccentricity and the asymmetry subchannels [4, 5].

2 Analysis Model In SSTFEM code, a 2-D steady-state FEM model is built and the axial direction thermal conductivity which it’s very slight compared with radial direction is ignored. SSTFEM uses plane-strain model to deal with the axial stress. The X425 fuel is chosen as the analyzing object. 2.1 Finite Element Method Model Compared with the conventional finite difference method, FEM has an incomparable advantage in dealing with the asymmetry deformation for the triangle element which fit the complex boundary well. And the transformation of coordinates method will have a significant decrease in the calculate time, therefore the FEM is chosen in SSTFEM to solve the thermal and strain-stress problem. 2.2 Metal Fuel Model 2.2.1 Physical Properties of Metal Fuel Rods In the SSTFEM program, the fuel physical property calculation model of Feast-Metal program was used to set up each material。The physical properties of the U-19Pu-10Zr are related to the composition of each component. The physical properties of U-19Pu-10Zr in SSTFEM program are shown in Table 1. 2.2.2 Fission Gas Release and Swelling Model of U-Pu-Zr The fission gas release behavior of metal fuel in reactor is very complicated. Due to the finite element method is more limited than finite difference, The SSTFEM program uses

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the empirical relation calculation obtained from the experimental data of the EBR-II reactor [8]:   0 BUat  BUat < 0.8 at.% (1) fmetal = 0.8 1 − e− 1.8 BUat ≥ 0.8 at.% where f metal and BU at are fission gas release rate of metal fuel and fuel consumption / at.%. The comparison between the simulated value of fission gas release rate and the experimental value is shown in Fig. 1, which is in good agreement. Table 1. Physical properties of U-19Pu-10Zr Parameters

Data

Thermal conductivity of the core/W·m−1 ·K−1

3 + 1.334 × 10−2 T + 4.568 × 10−6 T 2 1.76 × 10−5

Thermal expansion coefficient of the core/K−1

T < 868.15 868.15 < T < 923.15 1.76 × 10−5 · T −868.15 55 T −868.15 −5 −4 T ≥ 923.15 1.76 × 10 + 2.5 × 10 · 55

Poisson’s ratio of the core

0.3 56000 − 115.8 · (T − 868.15)

Young’s modulus of the core/MPa

T < 868.15

20000 − 127.3 · (T − 868.15) 868.15 < T < 923.15 31000 − 80 · (T − 923.15)

T ≥ 923.15

 Creep rate of the core/% s−1

 −26170 4.5 · e T 5 × 103 · σeq + 6 · σeq T < 923.15 3 ·e 0.08 · σeq

Thermal conductivity of the sodium/W·m−1 ·K−1 Thermal conductivity of HT9 steel/W·m−1 ·K−1

−14350 T

T ≥ 923.15

107.985 − 0.0581T + 1.173 × 10−5 T 2 17.622 + 2.42 × 10−2 T − 1.696 × 10−5 T 2 T < 1030 12.027 + 1.218 × 10−2 T

T ≥ 1030

Thermal expansion −0.2191 + 5.678 × 10−4 T + 8.111 × 10−7 T 2 − 2.576 × −1 coefficient of HT9 steel/K 10−10 T 3 Young’s modulus of HT9 steel/MPa

2.137 × 10−5 − 102.74·T

When calculating fission gas swelling, the SSTFEM program uses the ideal gas state equation to calculate the size of the fission gas bubble: 4 3 = mbub RT Pbub · π rbub 3

(2)

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Fig. 1. The comparison between the simulated value and the experimental value of fission gas release rate

where Pbub , rbub and mbub are bubble surface pressure/Pa, bubble radius/m and amount of substance of fissile gas material in bubble/mol. The pressure of the bubble is calculated by the surface tension and hydrostatic pressure: Pbub =

2γ + σh rbub

(3)

where γ and σh are bubble surface energy/N/m and the hydrostatic pressure applied to the bubble /Pa。 At the same time, there is not only a single bubble in each unit and the bubble density is calculated by a temperature empirical relation calculation. The calculation is as follows [9]: Cbub = 1 × 103 e

3.1×104 T

(4)

where Cbub is the density of the bubble/m−3 . From what has been discussed above, the radius and density of bubbles can be calculated and the fission gas swelling can be calculated by bubble volume: εswellgasm = 1 × 102 ·

4π 3 r Cbub 3 bub

(5)

where εswellgasm is fission gas of the metal fuel swells. 2.2.3 Ht9 Steel Cladding Creep Model Cladding creep is another important model in metal fuel analysis, Irradiation creep [10] and thermal creep [11] are considered. The irradiation creep rate is calculated as follows:   −305299 1.3 εcrecmir = 1.83 × 10−4 + 2.59 × 1014 e RT × φneut σeq (6) where εcrecmir is the irradiation creep rate of HT9 steel cladding/% · s−1 .

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The thermal creep rate is calculated as follows:

Qcrecmc1 Qcrecmc2 Qcrecmc3 4 0.5 εcrecmth = Acrecmc1 e− RT σeq + Acrecmc2 e− RT σeq + Acrecmc3 e− RT σeq · Acrecmc4 Acrecmc3 e− + Acrecmc6 e−

Qcrecmc3 RT e−Acrecmc4 ttot

+ Acrecmc5 e−

Qcrecmc4 RT σ 2 eq

Qcrecmc5 RT σ 5 eq

(7)

where εcrecmth is the thermal creep rate of HT9 steel cladding /% · s−1 ; Acrecmc1 ~ Acrecmc6 , Qcrecmc1 ~ Qcrecmc5 are fitting parameters obtained by experimental data. ttol is the total creep time/s.

3 Solution Procedure The SSTFEM assumes that the volumetric heat source in pellet is uniform. There are two iteration procedures in SSTFEM for temperature and mechanical deformation calculation respectively. Here is the calculation flow chart in Figs. 2 and 3.

4 Validation of Metal Fuel for Sodium Cooled Fast Reactor Because the experimental operating parameters and data related to sodium cooled fast reactor are very few and most of them have not been published publicly. In the SSTFEM program, the metal fuel irradiation condition of FEAST- -METAL X425 sodium cooled fast reactor was used to carry out corresponding program of comparison, verification and the experimental data of cladding strain was compared. The relevant parameters of X425 fuel rod are shown in the table. The variation of X425 fuel operating conditions in the core is shown in the Figs. 4 and 5. The cladding axial strain distribution of X425 fuel rod was calculated by the program SSTFEM. Figures 6 and 7 show the comparison of X425 axial distribution of clad strain at burnup 15.8 at.% and 18.9 at.% calculated by SSTFEM program, FEAST-METAL and experimental values. Compared with the experimental results, the strain calculated by the SSTFEM program at the beginning position of the fuel rods is lower and the strain at the end position of the fuel rods is higher. This is mainly because the fission gas release rate in the SSTFEM program is only related to the average burn up and is not affected by the change of axial temperature and burn up. Although there is deviation among the results of SSTFEM program, the experiment and Feast-Metal program, it can provide us a better understanding about irradiation behavior of metal fuel.

5 Result and Analysis 5.1 The Influence of Metal Fuel Wire-wrap According to the operating characteristics of sodium cooled fast reactor, the SSTFEM program calculates the asymmetry condition when the rod surface is bound by wirewrap. Wire-wrap around the fuel rod will limit the displacement of the cladding outer

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start Input data

Node and element generaon

Coordinates calculaon

Temperature inializaon

Calculate pellet, gap and cladding thermal conducvity

Calculate boundary condion and volumetric heat source Global matrix generaon

No

Solve matrix

Temperature convergence

A

Yes

B

B Calculate Gap and coolant pressure

Calculate thermal expansion

Global sffness matrix generaon

Solve matrix and get displacement

A

Gap closed or not Yes

Update coordinates

Modified gap size

Displacement convergence Yes Calculate stress and strain

End Fig. 2. Calculation flow chart

No

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Fig. 3. Mesh of the fuel rod

Table 2. Fuel rod parameters of X425 Parameters The composition of fuel core

U-19Pu-10Zr

Cladding material

HT9

Fuel core radius/mm

2.16

Inner radius of cladding/mm

2.539

The outer radius of cladding/mm

2.92

Fuel effective density/%

72.4

Fig. 4. X425 Fuel rod power operation history and coolant outlet temperature history

surface and affect the stress distribution of the fuel cladding. The analysis object is still X425 fuel rod, which runs to 15at.% burnup under the line power condition of 40. The SSTFEM program analyzes the influence of the wire-wrap. In the analysis, it is assumed that the wire-wrap will limit the displacement of the contact point between the cladding and the wire-wrap, but will not have a significant effect on the heat conduction of the fuel rod (Fig. 8). As shown in the fig. 9, the contact pressure increases slowly before 6at.% burnup but increases rapidly after 6at.% burnup. This is mainly because when the contact pressure

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Fig. 5. Axial power distribution of X425 fuel rods

Fig. 6. X425 axial distribution of clad strain at burnup 15.8 at.%

Fig. 7. X425 axial distribution of clad strain at burnup 18.9 at.%

increases, the bubble of fission gas will be compressed and decrease with the increase of contact pressure at a low level of the burn up, thus alleviating the increase of contact pressure. However, the bubble effect will gradually decrease and the influence of incompressible solid fission products will gradually increase with the improve of the

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Fig. 8. Temperature distribution of metal fuel with wire-wrap at 15at.% burnup

burn up. And the wire-wrap has little effect on the calculation results of fuel rod temperature distribution. There is no significant difference about the contact time and contact pressure of core and cladding. This is because the wire-wrap has little influence on the heat conduction of the fuel rod and heat transfer of the coolant, so it has little influence on the swelling and creep of the core. And the wire-wrap has no significant effect on the core contact.

Fig. 9. The contact pressure of the core varies with the burnup

Figure 10 shows the change of cladding peak stress varies with the burnup in the two conditions of fuel rod with and without the wire-wrap. It can be seen that although the contact pressure does not change significantly, the peak value of the cladding stress has significantly increase under the condition of wire-wrap. The peak value of cladding stress increase about 10MPa. This is because the displacement of the element affected by the wire-wrap is limited, so the tension strain of the adjacent part of the cladding will be larger than the normal working condition. So the stress concentration point is generated and the peak stress appears on both sides of the constraint point. This makes the cladding fractures more easy at stress concentration points. This will affect the safety of the fuel rod. However, in practice, the wire-wrap is arranged in a spiral shape around the fuel rod which can alleviate the circumferential unevenness to a certain extent.

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Fig. 10. The cladding peak stress varies with the burnup

From the simulation results presented, the wire-wrap has no significant effect on the heat conduction of the metal fuel and the fuel-cladding contact. However, due to its mechanical restriction on the cladding, the cladding stress distribution in the circular direction will be changed. Stress concentration points are generated on both sides of the wire-wrap contact point, which increases the possibility of damaging the cladding at the stress concentration point and affects the safety of fuel rods. 5.2 The Asymmetry of X425 Fuel Rod In the operation of nuclear reactor, the eccentricity of pellets due to manufacturing problems and the temperature difference of the adjacent subchannel are two common two-dimensional asymmetry conditions. The X425 fuel rod is chosen as the object of the asymmetry research. The irradiation behavior of the fuel rod under these two twodimensional asymmetrical conditions is calculated and analyzed. After that, the sensitivity analysis of two dimensional asymmetric conditions effect on fuel performance was carried out and the effect laws of eccentric conditions and subchannel temperature asymmetry on fuel behavior were obtained. The basic fuel rod parameters of X425 are as Table2. 5.2.1 Pellet Eccentricity In case one a series of eccentricity displacement from 0.005 to 0.035 mm for the pellet is set. The coolant temperature is 700K, the coolant thermal transfer coefficient is 195452W/m2 K and the line power is 40kW/m. The change of central temperature is shown as the following figures. Figures 11 and 12 respectively show the temperature distribution of metal fuel under normal working conditions and at 0.03mm eccentricity. In this case, the calculation results are the fuel runs up to 15at.% burnup. According to the results, under normal working conditions, the maximum temperature of metal fuel pellets is 919.7K. Under the eccentric condition of 0.03mm, the highest temperature of metal fuel pellet is 920.1K.

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Fig. 11. Temperature distribution at 0mm eccentricity for metal fuel

Fig. 12. Temperature distribution at 0.03mm eccentricity for metal fuel

Under normal working conditions, the sodium gap pressure is 8.6 MPa, and the fuelcladding contact pressure is 11.83 MPa. Under the eccentric condition of 0.03mm, the sodium gap pressure is 8.6MPa, and the fuel-cladding contact pressure is 11.88MPa.

Fig. 13. Temperature distribution at 0.03mm eccentricity for UO2

Figure 13 show the temperature distribution of uranium dioxide fuel under eccentric condition of 0.03mm respectively. By comparison, it is obvious that when the uranium

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dioxide fuel is eccentric due to manufacturing problems, the fuel peak temperature point is significantly shifted to the pellet side. This is because wide air gaps have poor thermal conductivity, while narrow air gaps have better thermal conductivity, so the peak temperature is deviation. By comparison, it can be seen that when the pellets are eccentric due to manufacturing problems, the temperature of the metal fuel pellets does not change basically for the sodium cooled fast reactor due to the excellent thermal conductivity of sodium and there is no obvious pellet deviation. The safety of the fuel rods can hardly be affected. 5.2.2 Asymmetry Subchannel In the normal operation of the reactor, the temperature of subchannels adjacent to the fuel rods is not completely same affected by the radial power distribution of the reactor core, etc. The SSTFEM program simulates the influence of the asymmetric subchannel temperature on the behavior of the fuel rods. The connection relationship between the subchannel and the fuel rods is shown in Fig. 14.

Fig. 14 connection relation between rod and subchannel

In this calculation, the steady-state operating conditions of X425 fuel rods are also referred to, the parameters are shown in the above Table 2. The coolant temperature in subchannel 1 and 3 is set to a normal steady-state operation of 700K, the coolant in subchannel 2 is set to a higher temperature, and the coolant in subchannel 4 is set to a lower temperature. The temperature difference is set as 0 ~ 20 k. X425 fuel rod runs to 15 at.% burnup in 40 line power. The calculation results are as follows (Fig. 15). The temperature distribution of fuel rods under the condition that the temperature difference between adjacent sub-channels is 20K is shown in Fig. 17. It can be seen that the coolant temperature difference has little effect on the pellet temperature distribution, and the pellet peak temperature does not have obvious eccentricity. However, the temperature asymmetry of the subchannel has obvious effect on the temperature distribution of the cladding. It can be seen that the temperature of the cladding near No. 2 high temperature subchannel is obviously higher than that near No. 4 subchannel. The maximum temperature difference between the highest and lowest cladding temperatures

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Fig. 15. Temperature distribution at 20° difference

is about 40K. The temperature distribution of the cladding is asymmetrically along the circumferential direction.

Fig. 16 pellet peak temperature change under different subchannel temperature.

The Fig. 16 shows the change of pellet peak temperature under different subchannel temperature conditions. The peak temperature of pellet first decreased and then increased with the operating time, mainly because the width of the air gap gradually decreased with the swelling of pellets in the early stage. Then, with the improvement of thermal conductivity, the peak temperature gradually decreased. When the burn-up reaches 8at.%, the air gap disappears and the gap size does not change. Meanwhile, with the burn-up increasing, the thermal conductivity of the pellets decreases and the peak temperature of the pellets rises again. But the temperature increase has little effect. Figure 17 shows the difference peak creep of cladding under different temperature conditions at 15at.% burn-up. With the increase of temperature difference, the maximum creep of cladding is basically the same. This is because metal sodium has a high convective heat transfer coefficient and the convective heat transfer coefficient changes

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Fig. 17. The maximum creep of cladding under different temperature in the adjacent subchannel at 15at.% burnup

little with temperature. Therefore, in the difference of temperature in the adjacent subchannels, the maximum creep of cladding is basically not affected by the difference of temperature in the adjacent subchannels. In conclusion, compared with the normal symmetric working condition, although the influence on the fuel-cladding contact time and the temperature distribution of the pellet is small, the temperature distribution of the coolant has a greater influence on the temperature distribution of the cladding due to its direct influence on the heat transfer. However, under the combined action of liquid metal sodium and the cladding of HT9 steel, there is no significant difference in the maximum creep of fuel cladding under different subchannel temperature, which further demonstrates the excellent safety performance of sodium cooled fast reactor. 5.2.3 Blockage Accident Based on the above analysis of asymmetric working conditions, the SSTFEM program takes X425 fuel rod to carry out the analysis of subchannel blockage working conditions. At 40Kw/m line power, assuming that No.4 subchannel is blocked, the convective heat transfer coefficient of the coolant drops to 83856W/m2 K, the coolant temperature is 870.1K, and the rest are normal channels, with convective heat transfer coefficient of 166,000 W/m2 K (Fig. 18). According to the results, under normal working conditions, the maximum temperature of metal fuel pellets and the cladding is 1068.86K and 862.59K, respectively. Under the blockage condition, the highest temperature of metal fuel pellet and the cladding is 1074.19K and 891.48K. It can be seen from the calculated data that the temperature distribution and deformation of fuel rod are not significantly affected under the condition of blockage. Although the convective heat transfer coefficient of liquid metal sodium is greatly reduced, the fuel rod can still remove most of the heat due to the high thermal conductivity of sodium itself. Sodium cooled fast reactor has good safety performance when faced with blockage accident condition and fuel rod parameters are within the safety limit. It is also proved by the experiments that the blockage condition has little effect on the sodium cooled fast reactor.

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Fig. 18. Temperature distribution of the blockage condition

6 Conclusions and Perspective The SSTFEM is developed using the finite element method to predict the fuel performance. By comparing with FEAST-METAL and experimental data, the result of the temperature distribution is acceptable and can be used to make further study on the fuel rod. Metal fuels have many advantages, such as good thermal conductivity and good propagation. At the same time, due to the excellent convective heat transfer coefficient and thermal conductivity of sodium, fuel pellet eccentricity, the difference of temperature in the adjacent subchannels and blockage conditions have little influence on the fuel rod. The accident will not affect the safe operation of sodium cooled fast reactor. For the development of nuclear reactors, sodium cooled fast reactor is worthy of further research and development. This paper has completed the construction of the program framework and the selection of fuel irradiation behavior model to obtain a preliminary fuel performance analysis ability of the program. However, the idea of layer division is used in the meshing of the whole program is still worthy for improvement. In the following study, the quality of the mesh needs to be improved. We can encrypt the mesh locally in the outer surface of the pellet and reduce the mesh in the center of the pellet. It can ensure the accuracy of the calculation results and also to reduce the unnecessary calculation. A more accurate fuel-cladding contact model should be added to SSTFEM too. Furthermore, a subchannel analysis code will be coupled with the SSTFEM to have a more accurate result of the fuel performance in the reactor.

References 1. Rashid, Y.R., Dunham, R.S., Lu, Y.M.: “FREY-01: Fuel Rod Evaluation System, Volume 1: Theoretical and Numerical Bases” EPRI NP-3277-CCM, Project 1321–4, Computer Code Manual, Electric Power Research Institute, October 1983 2. Krammen, M.A., Freebum, H.R.: “ESCORE-the EPRI Steady-State Core Reload Evaluator Code: General Description” EPRI NP-5100-L-A, Project 2061–6–13, Final Report, Electric Power Research Institute, April 1991

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3. Johnson, C.: Numerical Solutions of Partial Differential Equations by the Finite Element Method. Cambridge University Press, Cambridge (1987) 4. Bentejac, F., Hourdequin, N.: TOUTATIS: an application of the CAST3M finite element code for PCI three-dimensional modeling. Proc. Pellet-clad Interact. Water Reactor Fuels 1(1), 9–11 (2005) 5. Repetto, G., Jacq, F., Barré, F., et al.: A new 3D thermal mechanical computer code to simulate LOCA transients on nuclear power plants. ICAPP, Tokyo, Japan, May 10–14 2009 6. Chandrupatla, T.R., Belegundu, A.D., Ramesh, T., et al.: Introduction to Finite Elements in Engineering. Prentice Hall, Upper Saddle River (1997) 7. 杨世铭, 陶文铨. 传热学(第4版). 高等教育出版社 (2010) 8. Lee, C.B., Kim, D.H., Jung, Y.H.: Fission gas release and swelling model of metallic fast reactor fuel. J. Nucl. Mater. 288(1), 29–42 (2001) 9. Ogata, T., Soo Kim, Y., Yacout, A.M.: 3.23—Metal Fuel Performance Modeling and Simulation, Comprehensive Nuclear Materials, vol. 1, issue 3. Elsevier, Oxford, pp. 713–753 (2012) 10. Toloczko, M.B., Garner, F.A.: Variability of irradiation creep and swelling of HT9 Irradiated to High Neutron Fluence at 400–600 C: Effects of Radiation on Materials: 18th International Symposium. ASTM International, Hyannis, Massachusetts, America (1999) 11. Ryu, H.J., Yacout, A.M., Kim, Y.S., et al.: Review of HT-9 cladding creep correlations for advanced liquid metal fast reactors. Argonne National Lab. (ANL), Argonne, IL (United States), No. ANL/NE/CP-118797 (2006) 12. IAEA Annual Report for 2017, IAEA (2018) 13. 于平安. 核反应堆热工分析. 原子能出版社 (1981) 14. Uffelen, V.P.: Oxide fuel performance modeling and simulations. Compr. Nucl. Mater. 1(2), 535–577 (2012) 15. 李宁. 快堆与核燃料循环的未来[J]. 中国核工业 10(1), 30–32 (2013) 16. Pahl, R.G., Porter, D.L., Crawford, D.C., et al.: Irradiation behavior of metallic fast reactor fuels. J. Nucl. Mater. 188(1), 3–9 (1992) 17. Kittel, J.H., Frost, B.R.T., Mustelier, J.P., et al.: History of fast reactor fuel development. J. Nucl. Mater. 204(1), 1–13 (1993) 18. Hofman, G.L., Walters, L.C.: Chapter 1 Metallic fast reactor fuels. Nucl. Mater. 1(1), 1–43 (1994) 19. Pahl, R.G., Porter, D.L., Lahm, C.E., et al.: Experimental studies of U-Pu-Zr fast reactor fuel pins in the experimental breeder reactor-II. Metall. Trans. A 21(7), 1863–1870 (1990) 20. 杜平安, 甘娥忠, 于亚婷. 有限元法--原理.建模及应用. 国防工业出版社 (2004) 21. 唐昌兵, 焦拥军, 陈平等. 燃料棒辐照-热-力耦合行为的精细化数值模拟研究. 核动力工 程 06, 180–184 (2017) 22. Todreas, N.E., Kazimi, M.S.: Thermal hydraulic fundamentals. CRC press (2011)

Site Condition Sensitivity Analysis Based on the Sub-structuring Method for a Base-Isolated Nuclear Building Xiaoying Sun1,2(B) , Yingying Gan2 , Jian Chen2 , and Dongyang Wang2 1 Key Laboratory of Earthquake Engineering and Engineering Vibration, China Earthquake

Administration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China [email protected] 2 China Nuclear Power Engineering Co. Ltd, Beijing 100084, China

Abstract. Taking a base-isolated nuclear reactor building as an example, the influence from the site condition on the base-isolation effectiveness is studied using three typical sites of hard rock (HR), medium hard rock (MHR) and soft soil (SS). The software ACS SASSI based on sub-structuring method is selected in baseisolation analysis in order to simulate the soil-structure interaction (SSI) properly. In ACS SASSI, linear and bilinear constitutive models are used to simulate the horizontal mechanical behavior of the isolation bearings respectively. The following conclusions can be drawn by comparing the in-structure response spectrum (ISRS) from three sites at the key locations in the reactor pool and the spent fuel pool: (1) both analysis using linear or bilinear constitutive model present similar sensitivity tendency that the variation of the site condition has negligible effect on the horizontal response spectra. The horizontal response spectra from the three sites are basically the same. However, in the vertical direction, the peak values and the predominant frequencies of the vertical response spectra increase gradually with the soil stiffness increases. In addition, the equivalent linear method used in ACS SASSI to simulate the bilinear behavior of the bearing is proved to be reasonable, given that the same base-isolation analysis conducted in ACS SASSI and SAP2000 respectively provide the similar hysteretic curve for the bearing at the same location. Keywords: Nuclear power plants · Base isolation · Site condition sensitivity · Equivalent linearization · Sub-structuring method

1 Introduction Swimming pool heating reactor (hereinafter referred to as swimming pool reactor), an important part of “Dual Carbon Emission Reduction” and “Green Energy Strategy”, can be used to replace fossil energy to alleviate the haze problem caused by heating. It has the advantages of normal pressure operation, high inherent safety, zero reactor melting, zero emission, easy decommissioning, etc. Moreover, its economic scale is only about © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 809–826, 2023. https://doi.org/10.1007/978-981-19-8780-9_78

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1/10 of that of nuclear power plants (NPPs). Therefore, it’s applicable to most area of China and has broad application prospects. The proposed construction site of the demonstration project of the swimming pool reactor is in a high seismicity area, the pipelines and equipment in the reactor building will therefore suffer from the excessive earthquake action, resulting in significant damages. The base-isolation technology, developed more than half century, is one of the effective options to protect the superstructures, equipment, and pipelines, which is also beneficial to guarantee the overall safety of reactor. In the demonstration project, the base isolation technology is adopted for the reactor building of the swimming pool reactor, which effectively reduces the response and the internal force of the superstructure and equipment. The benefit of adopting base isolation is that the design of the whole reactor type has better adaptability to various sites when the peak ground acceleration of the proposal site changes within a certain range. It only needs a slight adjustment to the base-isolation layer to meet the safety design requirement without changing the design of the superstructure, pipelines and equipment. In order to investigate the site adaptability of the base isolation design, three typical site conditions of NPPs, which are HR, MHR and SS, were selected to perform base isolation analysis for the isolated reactor building. The influence from the site condition on the base-isolation effectiveness is studied by comparing the response spectra at the key locations in the reactor pool and the spent fuel pool. The conclusion of this paper provides technical reference for improving the site applicability of the swimming pool reactor and promoting its standardization design.

2 Base Isolation Analysis Method Considering Soil-structure Interaction In the field of civil engineering, hard rock site is normally selected as the construction site when applying base isolation technology in order to keep the internal force in each isolator uniform, and keep their mechanical properties as consistent as possible throughout the in-service period. However, some scholars have carried out research on the SSI effect in base isolation design. Peng [1] explored the effect of SSI on the isolation effectiveness for an isolated containment of CNP1000 reactor type. The research showed that the SSI effect has slight effect on the maximum acceleration of the isolated structure. Zheng [2] conducted the base isolation analysis for bridges supported by flexible foundation. The study showed that the horizontal lateral displacements of the trough body and the top of the pier are slightly larger when considering SSI while the maximum base shear force is slightly larger. Yu [3] established a three-dimensional finite element model of the soilpile-foundation-isolated structure in ABAQUS considering the nonlinear characteristics of the soil. The research results showed that the seismic response of the isolated structure on rigid foundation is quite different with that on soil foundation. The floor acceleration magnification factor of the base isolated structure on the soil foundation is greater than that on the rigid foundation, and the difference increases with the increase of the peak acceleration value at the base. Song [4] took the base isolated structure located on a soft soil site as the research object to study the influence from parameters such as the structure-soil stiffness ratio on the horizontal base isolation coefficient. The research

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showed that SSI cannot be ignored in base isolation analysis. The softer the site is, the higher the base isolation efficiency of the system is. Tan [5] studied the influence from the soil parameters on the seismic response of the isolated structure. The research shows that the SSI effect reduces the isolation effect and prolongs the period of the original isolation structure system. Yu [6] pointed out that the horizontal base isolation coefficient of the multi-layer base isolation structure considering the SSI effect increases with the increase of the wave velocity, period ratio and aspect ratio, and the corresponding isolation effectiveness decreases. Luo [7] conducted time history analysis for a base isolated multi-layer structure under various sites. The study showed that the base isolation efficiency in the soft soil site is relatively lower than that in the hard soil site. Zhang [8] studied the influence from the SSI effect on the base isolation effectiveness based on the sub-structuring method. The research showed that the soft site condition may lead to an amplification effect on the seismic dynamic response of the base isolated structure. Zeng [9] investigated the effect from SSI on the isolation performance of a floor-isolated structures subjected to a long-period ground motion. The study showed that the base isolation efficiency of the base isolated structure decreases when the SSI effect is considered. The design of the floor-isolated structure on the sites of category III and IV subjected to long-period ground motion should consider the SSI effect. Kelley [10] conducted a 1/6 scale shaking table test for the base isolated nuclear facility on a soft soil site. The ground motion characteristics such as the site dominant frequency, embedment, SSI effect, etc. were considered in the test. Test results showed that the floor response spectrum at the main frequencies of the pipes and equipment are not amplified. According to the provisions of the Code for Seismic Design of Nuclear Power Plants (GB50267-2019), SSI effect is negligible only if the average shear wave velocity of the site is greater than 2400 m/s or the stiffness of the foundation is twice greater than that of the superstructure [11]. Therefore, SSI shall be taken into account when conducting base isolation analysis for structures on a SS site in order to broaden the applicability of the base isolation technology in the field of the nuclear industry. Hence, a numerical analysis method which can properly consider the effect of SSI and simulate the behavior of the bearing is also required. 2.1 Numerical Analysis Methods Many scholars have summarized the researches on SSI effect [12]. Two widely-used methods are direct method and sub-structuring method. The sub-structuring method is a frequency domain solution method. Its basic principle is to decompose the soilstructure interaction system into three parts: the super structure, the foundation and the excavated soil. The load acting on the structure is obtained under the condition of displacement coordination at the interface between soil and structure. Compared with the direct method, the sub-structuring method has less degree of freedom due to that the soil layer is assumed to be an analytical model. In addition, the damping adopted by the sub-structuring method remains unchanged in the entire frequency range and is more stable. The sub-structuring method is based on the superposition principle and is suitable only for linear analysis systems. Therefore, sub-structuring method is widely used in the field of nuclear engineering because the nuclear structure is required to maintain a linear state under the safe shutdown earthquake.

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In this paper, the ACS SASSI software based on the sub-structuring method is adopted, which can properly simulate the SSI effect and the mechanical behavior of the isolation bearing. 2.2 Simulation of Isolation Bearing Characteristics The lead rubber bearing (LRB) was used in the base isolation design of the swimming pool reactor. It shows stable bilinear restoring force characteristics in the horizontal direction within a certain range of compressive stress and shear strain. Therefore, a proper bilinear model can be used to represent the nonlinear constitutive relationship of the isolation bearing, as shown in Fig. 1.

Fig. 1. Hysteretic curve of a LRB

In ACS SASSI, the spring element is used to simulate the isolation bearing. Two constitutive models described below is adopted to describe the horizontal mechanical characteristics of the bearing. The stiffness of the vertical spring element is a relatively large value given that LRB cannot isolate the earthquake action in vertical direction. Linear Constitutive Model. The equivalent horizontal stiffness and equivalent damping ratio of the bearing can be obtained by the diagonal lines of the bilinear model in Fig. 1. In ACS SASSI, the stiffness of the linear spring used to simulate the isolation bearing is set to be the equivalent horizontal stiffness of the bearing. Bilinear Constitutive Model. In ACS SASSI, an approximate method based on the equivalent linearization theory is used to simulate the bilinear characteristics of the isolation bearing, which is similar to the equivalent linearization method used for soil. The main idea of the approximate method is to replace the variable amplitude vibration with a constant amplitude steady vibration. From the hysteretic curve of the bearing provided by the supplier, and the relationship of the stiffness of the bearing with the displacement of the bearing is obtained. Therefore, the nonlinear characteristic of the isolation bearing is simplified to a linear solution. Main steps of the approximate method are: (a) Input the backbone curve of the isolation bearing into ACS SASSI;

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(b) Perform the time history analysis to obtain the horizontal displacement using the initial stiffness G0 as the stiffness of the isolation spring element; (c) Obtain the equivalent displacement value using an equivalent amplitude coefficient based on the study of Seed [13] (0.65 in this study); (d) Determine the corresponding stiffness value G1 from the nonlinear property curve of the isolation bearing according to the equivalent displacement amplitude; (e) If the difference between G0 and G1 is lower than 5%, it is considered that the initial stiffness G0 is consistent with the practical displacement level. The calculation is convergent. Otherwise, repeat the process from (b) to (e) by replacing G0 with G1 until the stiffness difference is within 5%; (f) Calculate the response of the isolated structure based on the convergent stiffness and damping values in the last iteration. The above-mentioned approximate method used in ACS SASSI is a relatively new analysis method. In this paper, the software SAP2000 which is normally used in base isolation analysis to calibrate ACS SASSI before it is used in site sensitivity analysis. Two base isolation analyses were conducted using SAP2000 and ACS SASSI respectively without considering SSI. Figures 2 and 3 are the comparison of the hysteretic curve of the location bearing at the same location obtained by two software. 1500.0

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It can be seen that the hysteretic curves from two software present the similar tendency and magnitudes, which shows that the approximate method used in ACS SASSI is reasonable.

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3 Base Isolation Design Scheme 3.1 General Information The research object is the reactor building of the swimming pool reactor. The reactor building consists of three parts: the reactor pool, the spent fuel pool and the cleaning pool, as shown in Fig. 4. The building is 39.15 m long, 14.0 m wide and 30.0 m high. Two empty rooms are set below the spent fuel pool and cleaning pool to add the counterweight in order to make the center of mass and the center of rigidity of the isolated superstructure close as much as possible. The baseslab is 2 m thick and is cantilevered by 2 m on both sides along the Y axis in order to increase its resistance moment around the X axis. The top elevation of the baseslab of the reactor pool is −26.0 m, the bottom elevation of the spent fuel pool and the cleaning pool are −16.0 m, whose top elevation are 0.0 m, and the top elevation of the steel slab over the reactor pool is 2.0 m. The reactor pool and the spent fuel pool are always full of water. The liquid level elevation is −2.0 m.

Fig. 4. Section view of the isolated building (length in mm, elevation in m)

3.2 Isolation Bearing The final base isolation scheme is shown in Fig. 5. 28 LRB with an effective diameter of 1300 mm were used for the isolation layer of the reactor building. The mechanical parameters of the LRB1300 isolation bearings are shown in Table 1.

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Fig. 5. Base isolation scheme Table 1. Mechanical parameters of LRB1300 Parameters (unit)

LRB1300

Effective diameter (mm)

1300

Vertical stiffness (kN/mm)

6600

Vertical equivalent damping

0.05

Initial horizontal stiffness (kN/mm)

20.9

Yield force (kN)

350.0

Stiffness ratio after yielding

0.077

Equivalent horizontal stiffness (100% shear deformation) (kN/mm)

3.0692

Horizontal equivalent damping

0.295

Total thickness of rubber layer (mm)

240

3.3 Finite Element Model The finite element model of the isolated structure is shown in Fig. 6. The superstructure is simulated with solid elements connecting with the isolation baseslab by spring elements in three directions whose stiffness are presented in Sect. 2.2. In the analysis, the sloshing of the water in two pools is not considered from a conservative perspective given that the two pools are full of water all the year round. All the mass of the water is assumed as impulse mass and simulated with three-directional mass elements attached to the pool walls. After base isolation analysis, the ISRSs of the key positions shown in Fig. 6 are obtained for comparison, and detailed description of the key positions are provided in Table 2.

4 Analysis Parameters 4.1 Site Condition Parameters Three typical site conditions are selected: HR, MHR and SS, considering the proposed site of the swimming pool reactor demonstration project and the standard site condition of HRP1000. The parameters of the sites are shown in Table 3.

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Fig. 6. Key positions for output

Table 2. Description of the key positions Node number

Corresponding location

1

−26.0m (the bottom of the reactor pool)

2

2.0m (the top of the reactor pool)

3

−16.0m (the bottom of the spent fuel pool)

Table 3. Parameters of three typical site conditions Parameters (unit)

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MHR

SS

Shear wave velocity (m/s)

2166

1311

407

Compressive wave velocity (m/s)

3970

2613

1158

Horizontal damping

0.051

0.063

0.08

Vertical damping

0.0515

0.063

0.08

Density of the bed rock (kg/m3 )

2667

2383

1920

In ACS SASSI, the soil layer is assumed to be a horizontal layered foundation. The foundation parameters are uniform throughout the thickness range. Figure 7 shows an example model of the soil layer in ACS SASSI. 4.2 Earthquake Wave The design response spectrum used is site-specific response spectrum provided in seismic safety evaluation of the demonstration project site of the swimming pool reactor, and the input position is at the bottom of the baseslab of the isolated structure.

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Fig. 7. Soil layer model in ACS SASSI

In ACS SASSI, three acceleration time histories fitting the site-specific response spectrum were used. The duration of the acceleration time history is 40 s, the time step is 0.005 s, and the peak ground accelerations in the horizontal direction and the vertical direction are 0.53 g and 0.61 g, respectively. The acceleration time-history curves are shown in Fig. 8.

5 Sensitivity Analysis of Site Parameters 5.1 Linear Constitutive Model In this section, the base isolation analysis was conducted using the linear constitutive model mentioned in Sect. 2.2 and comparison of the ISRS of three nodes were presented in Figs. 9, 10 and 11. It can be seen that the horizontal ISRSs from three typical sites are nearly same, which means the variation of the site parameters has slight effect on the horizontal isolation effectiveness. This conclusion is consistent with the research conclusion of Peng [1]. The reason that horizontal isolation ISRSs is not sensitive to the variation of site parameters is mainly because the natural vibration frequency of the base isolation system is apart from the predominant frequency of the site. However, changing the stiffness of the site has obvious effects on the vertical isolation ISRSs. This mainly because that LRB cannot isolate the earthquake action in vertical direction. The tendency of the vertical ISRSs for isolated structure is similar to that for seismic structure. That is, the greater the site stiffness is, the greater the peak value of the vertical ISRSs is, and the higher the peak frequency is. 5.2 Bilinear Constitutive Model In this section, the bilinear constitutive model mentioned in Sect. 2.2 was used in the base isolation analysis for three typical sites. The comparison of ISRSs is shown in Figs. 12, 13 and 14.

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Fig. 8. The acceleration time-history curves of input ground motion

The comparison of ISRSs showed that the bilinear constitutive model presented a similar influence tendency with linear constitutive model in Sect. 5.1. That is, the change of the site parameters has slight effect on the horizontal ISRSs while has apparent effect on the vertical ISRSs. For the vertical direction, the peak value of the vertical ISRSs decreases with the decrease of the site stiffness and the peak frequency shifts to the left. Furthermore, the horizontal ISRSs of node 3 from two constitutive models were compared in Fig. 15. It can be seen from the comparison that the ISRSs obtained by the linear constitutive model analysis have higher peak frequencies and lower peak values than those obtained by the bilinear constitutive model analysis. This is because that the iteration convergent secant stiffness is used in bilinear constitutive model. The secant stiffness is slightly

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0.8 HR MHR SS

0.6 0.4 0.2 0.0 0.1

1

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Fig. 9. ISRSs of Node 1 from linear constitutive model

lower than the equivalent stiffness used in linear constitutive model, which leads to a lower dominant frequency of the whole isolation system.

6 Conclusions In this paper, the study on the site condition sensitivity of the base isolation design of the swimming pool reactor was conducted based on the sub-structuring method. Three typical site conditions (HR, MHR and SS) were selected. The sensitivity of site parameters in the base isolation design was discussed by comparing the ISRSs from three sites. In the analysis, both the linear constitutive model and the bilinear constitutive model were used to simulate the mechanical properties of the isolation bearing. The research reached the following conclusions:

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Fig. 10. ISRSs of Node 2 from linear constitutive model

1. ACS SASSI which is based on the sub-structuring method can properly consider the effect of SSI and reasonably simulate the mechanical properties of the isolation bearing. It can be used in the subsequent numerical analysis of base isolation for soil sites; 2. The approximate method based on the concept of equivalent linearization used in ACS SASSI to simulate the bilinear mechanical behavior of the isolation bearing is calibrated to be reasonable; 3. Both the linear constitutive model and the bilinear constitutive model present the similar sensitivity tendency: changing the site stiffness has negligible effect on the horizontal isolation ISRSs because the natural vibration frequency of the base isolation system is apart from the predominant frequency of the site; 4. For the vertical direction, the peak value of the vertical ISRSs decreases with the decrease of the site stiffness and the peak frequency shifts to the left. This mainly

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Fig. 11. ISRSs of Node 3 from linear constitutive model

because that LRB cannot isolate the earthquake action in vertical direction. The tendency of the vertical ISRSs for isolated structure is similar to that for seismic structure.

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References 1. Peng, Q.Y., Liu, Y.L., Wang, B.: Seismic response of base-isolated nuclear containment and optimization for the number of isolation bearings considering soil-structure interaction. J. Disaster Prev. Mitig. Eng. 40(3), 372–379 (2020) 2. Zheng, M.Y.: Study of dynamic response of simply supported isolated bridge subjected to earthquake load considering soil-structural interaction. China University of Geosciences, Wuhan China (2014) 3. Yu, X., Chen, Y.D.: Comparison between shaking table test and numerical simulation of isolated structure system considering SSI. World Earthq. Eng. 27(2), 100–106 (2011) 4. Song, J., Xiong, F., Lv, Y., et al.: Parametric analysis of seismic performance of base-isolated buildings considering SSI effects. Earthq. Eng. Eng. Dyn. 39(4), 236–244 (2019) 5. Tan, P., Song, X., Zhou, F.L.: Performance research on seismic isolated structure considering soil-sturcture interaction and rotation of isolation layer. Chin. Civil Eng. J. 49(S1), 78–83 (2016) 6. Yu, X., Zhuang, H.Y., Liu, S.: Investigation into the seismic reduction factor sturcture with SSI effect soft sites. World Earthq. Eng. 33(3), 183–191 (2017) 7. Luo, X., Dai, K.S., Lv, Y., et al.: Seismic response reduction of base-isolated buildings located on soft soil sites. Earthq. Eng. Eng. Dyn. 40(1), 213–222 (2020) 8. Zhang, X.Y., Ge, N., Fu, T., et al.: Influence of soil-structure interaction on seismic response of isolated structures. Build. Sci. 36(1), 26–33 (2020) 9. Zeng, J.X., Pan, Q.F., Fang, Y.W., et al.: Impact of soil-structure interaction on seismicreduction performance of interstory isolation structures under long-period ground motions. Ind. Constr. 48(11), 81–86 (2018) 10. Kelley, J.M.: Shake Table Tests of Long Period Isolation System for Nuclear Facilities at Soft-Soil Sites (1991)

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11. State Seismological Bureau of China: Standard for Seismic Design of Nuclear Power Plants. GB 50267–2019. China planning press, Beijing China (2019) 12. Lin, G.: Soil-structure dynamic interaction. World Earthq. Eng. 01, 4–21+36 (1991) 13. Seed, H.B., Idriss, I.M.: Simplified procedure for evaluating soil liquefaction potential. J. Soil Mech Found. Div. (1971)

Modelling Analysis of Non-uniform Flow and Heat Transfer in Parallel Rectangular Channels During Flow Blockage Condition Jiayue Chen1(B) , Huandong Chen1 , Xiaoyu Wang2 , and Zefeng Wang2 1 Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-Sen University,

Zhuhai 610213, China [email protected] 2 Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, China

Abstract. In order to establish the non-uniform flow and heat transfer modelling method in parallel rectangular channels, as an additional technique and tool for safety analysis of plate-type fuel, one dimensional two-fluid model coupled with two-dimensional heat conduction of fuel are used to develop a special code for non-uniform flow and heat transfer simulation during flow blockage condition. The flow distribution and fuel temperature field are obtained under different blockage conditions. The two-dimensional heat conduction model are testes under four different power distributions. The simulation results show that mass flow and heat transfer between parallel channels would redistribute after blockage. And the twodimensional heat conduction model predicted more uniform temperature profile of fuel plate cross-section. Thus, the modelling method used in this paper can be used in the parallel rectangular channels prediction in non-uniformly flow and heat transfer phenomenon. Keywords: Plate type fuel · Flow blockage · Rectangular channel · Parallel channels · Two-fluid model

1 Introduction In contrast with traditional rod type fuel assembly, the coolant flow channel inside the plate fuel assembly are disconnected from each other. This allows no cross flow interchange between each adjacent channels. Therefore, many situations can cause or enhance the flow non-unifom distributions, such situations include the geometry structure change, blockage, fuel swelling and so on. The flow non-uniform distribution leads to the asymmetric cooling of the fuel plate, and eventually cause an important impact on the system safety performance of the reactor core. Mainly there are two type of methods of predicting the system thermal-hydraulics for the plate type fuel assembly. One is based on the existing PWR codes, e.g. RELAP5, COBRA, SubChanFlow [1–3]. The other one is to develop specific purpose codes for plate type fuel, e.g. PARET, MERSAT, THAC-PRR [4–6]. In this paper, we developed © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 827–838, 2023. https://doi.org/10.1007/978-981-19-8780-9_79

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a transient simulation code for plate type fuel assembly. The one-dimensional twofluid two-phase model and a two-dimension fuel conduction model are employed. The physical models of the code are validated and then the code is used to perform the modeling analysis of the typical flow blockage in parallel plate channels.

2 Numerical Models 2.1 One-Dimension Two-Fluid Model The developed code uses the one-dimension two-fluid model, which is the most accepted two-phase flow resolution. The field equation type of two-fluid model adopted here follows the RELAP5 code [7]. However, new idea is to use a coupled solution for the equations. The sum mass balance equation: ∂(αg ρg + αf ρf ) ∂(αg ρg ug A + αf ρf uf A) + =0 ∂t A∂Z

(1)

The difference mass balance equation: ∂(αg ρg − αf ρf ) ∂(αg ρg ug A − αf ρf uf A) + = 2g ∂t A∂Z

(2)

The vapor energy balance equation:   ∂(αg ug A) ∂αg ∂(αg ρg eg ) ∂(αg ρg ug eg A) ∗ + = qwg + g hg − P + ∂t A∂Z ∂t A∂Z

(3)

The liquid energy balance equation:   ∂(αf ρf ef ) ∂(αf ρf uf ef A) ∂(αf uf A) ∂αf + = qwf − f h∗f − P + ∂t A∂Z ∂t A∂Z

(4)

The sum momentum balance equation: αg ρg

∂uf2 ∂ug2 ∂uf ∂ug ∂P 1 1 + αf ρf =− − αg ρg − αf ρf − ρm g ∂t ∂t ∂Z 2 A∂Z 2 A∂Z − αg ρg Cwg ug − αf ρf Cwf uf − g (ug − uf )

(5)

The difference momentum balance equation:   ∂uf2 ∂ug2 ∂uf ∂ug 1 1 1 1 ∂P − =− − αg ρg + αf ρf − Cwg ug − ∂t ∂t ρg ρf ∂Z 2 A∂Z 2 A∂Z   1 1 (ug − uf ) − Cwf uf − Ci (ug − uf ) − g − ρg ρf

(6)

The staged mesh format and the Gaussian elimination method are used to discretized and solved the field equation. More details can be found in previous work [8].

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2.2 Two-Dimension Heat Conduction Model Most of the existing codes are limited to one-dimensional heat conduction model. In our code, one- and two-dimensional heat conduction models are developed to prediction the temperature field of the plate cross-section. The Cartesian coordinates is used to describe the model equation: ρcp

∂T ∂ ∂T ∂ ∂T = (k )+ (k )+S ∂t ∂X ∂X ∂Y ∂Y

(7)

A fully implicit scheme is adopted to generate the discretization.

3 Selection and Validation of Flow and Heat Transfer Models 3.1 Test Cases This part will assess different heat and flow model correlations for plate type channel, and select the minimum deviation one as the target correlation. Three experiment tests are adopted for validation. The detail parameters and test conditions are concluded in Table 1. 3.2 Friction Model Figures 1 and 2 give the comparison results of friction coefficient. Different Re numbers are considered to include both laminar and turbulent regions. In Fig. 2, the Spiga correlation has the best agreement with the laminar friction experiment data, while both Troniewski and Darcy correlations lead to an under-estimate of the data. For the turbulent flow region, the Ghione correlation shows the minimum deviation from the experiment result. Therefore, the Spiga and the Ghione correlations are selected for the friction calculation model.

Fig. 1. Validation of laminar friction coefficient

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Fig. 2. Validation of turbulent friction coefficient

3.3 Single-Phase Heat Transfer Model Figures 3 and 4 show the validation results of wall surface temperatures predicting with different heat transfer correlations. The simulations are performed with one-dimensional heat conduction model. Both correlations for pipe channel and rectangular channel are adopted in calculations. In Fig. 3, the Sieder-Tate and Stephan correlations are rectangular-channel laminar heat transfer correlations, while Churchill-chun and Sellars are circle-channel laminar heat transfer correlations. It can be seen from the comparison result in Fig. 3 that the rectangular-channel correlations Sieder-Tate and Stephan achieve better agreements than those circle-channel correlations. Among them, the Stephan correlations predicted the best and it was selected as the laminar heat transfer model. For turbulent heat transfer situation, the Mayer correlation is the best prediction among the circle-channel correlations. Among the rectangular correlations, the Ghione correlation predicts the good agreement with the experiment data. In the developed code, Ghione correlation is selected. 3.4 Two-Phase Heat Transfer Model We selected the David-Anderson and Omar models for predicting the ONB point and performed the two-phase boiling heat transfer simulations. For typical subcooled boiling experiment, Fig. 5 presents the local wall temperature scatters along the heated length. As can be seen, the wall temperature increases linearly from the inlet of the heated channel. At 0.2 m distance from the inlet, this increment tendency slows down, which means the subcooled boiling heat transfer occurred. The ONB threshold level predicted using the David-Anderson correlation is lower than using the Omar correlation which comes a better agreement with the experiment data. In subcooled boiling, the heat transfer usually contains two mechanisms: the nucleation and the convection contributions. In this paper, the boiling heat transfer calculation is based on the Chen type and Kim-Mudawar type correlations. The calculations results are presented in Figs. 6 and 7 respectively. The circle-channel correlations used in Chen correlation are Dittus-Boelter and Meyer correlations. The rectangular-channel correlations of Ma, Jo and Ghione are used both in Chen and Kim-Mudawar correlations. From

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Table 1. Experiments for code model validation Authors

Channel geometry Length × wide × height (mm)

Boundary conditions

Testing models

Ma [9]

1143 × 40 × 2

Q = 4.8 kW; G = 875.8 kg/m2 s Tin = 29 °C; P = 0.1 MPa

Turbulent heat transfer model

Sudo [10]

750 × 50 × 2.25

Q = 2.0 kW; W = 179 kg/h Tin = 285.55 K; P = 0.1 MPa

Laminar heat transfer model

Ghione [11]

600 × 53 × 2.2

q = 2.0 MW/m2 ; G = 2642 kg/(m2 s) Tin = 335.55 K; P = 0.5 MPa

Onset of flow boiling and two-phase heat transfer models

Fig. 3. Validation of laminar heat transfer model

the comparison results, it can be concluded that the Chen correlation under-estimates the inherent heat transfer enhanced effect of the plate-channel. In conclusion, the Omar, Kim-Mudawar and the Ghione correlations are adopted in predicting the two-phase boiling behavior.

4 Blockage Modeling of IAEA’S 10mw Reactor 4.1 Boundary Conditions The developed code is used to simulate the blockage phenomenon of the IAEA’s 10MW reactor. Only the typical standard fuel assembly is considered in simulation. The geometry can be seen from Fig. 8.

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Fig. 4. Validation of turbulent heat transfer model

Fig. 5. Validation of ONB model

Fig. 6. Validation using Chen correlation

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Fig. 7. Validation using Kim correlation

Fig. 8. Cross-section of fuel assembly

4.2 Single Channel Blockage The single channel blockage is first considered in the analysis. Giving the symmetry of the fuel assembly, only 7 coolant channels (named as PIPE1 ~ PIPE7) are considered in the modeling. Moreover, the inlet and outlet plenums, the cladding and fuel pellet are also modeled. The computational model is shown in Fig. 9. Each channel and plate fuel are divided into 10 cells in the axial flow direction. In this section, only one-dimensional heat conduction model is conducted. The cross section of the fuel is divided into 12 cells, yielding a 13 temperature spots distribution, and 4 cells for the fuel pellet region and 8 cells for both adjacent cladding sides. In the calculation, the pipe 4 channel is selected as the blockage channel. The blockage is located in the channel inlet and the blockage area is 50%. The calculations are performed with blockage and no blockage boundaries, blockage with heat transfer and without heat transfer. Figure 10 shows the outlet mass flowrate distributions of each channel under different calculations. Comparison of the results between blockage and no blockage cases, it can be seen that the flowrate of each channel redistributed after blockage. The heat transfer effect seen to diminish the flow non-uniformity.

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Fig. 9. Computational model of single channel blockage

Figure 11 gives the temperature distribution of the cross-section at central height of fuel 3. Node 1 is adjacent to the blockage channel. The wall temperature is highest at the fuel center and decreases gradually at both sides. After blockage, all wall temperatures increase. The blockage side of the fuel shown a higher increment than the other side.

Fig. 10. Comparison of Predicted flow distributions

4.3 Two-Dimensional Heat Conduction To test the two-dimensional heat conduction model, the no blockage case is considered. Same boundaries are used as described in Sect. 4.1. Moreover, the fuel is divided into a

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Fig. 11. Fuel pellet temperature distribution

5x12 cell matrix along the height and wide dimensions of the cross-section to generate the two-dimensional mesh. Four non-uniform power distribution shapes in each dimensions are also employed as follows: (1) (2) (3) (4)

Shape-1: uniform in both height and wide direction; Shape-2: only non-uniform in height direction; Shape-3: only non-uniform in wide direction; Shape-4: non-uniform in both height and wide direction.

Figure 12 (1) and (2) present the fuel temperature contours with power shape-1 and shape-2 at the axial distance of 0.3m. Although the two-dimensional heat conduction model was applied, the temperature contour behaves as the one-dimensional case. Further introducing a non-uniform power distribution of shape-2, the temperature contour is gives in Fig. 12 (2). However, this non-uniform power has little effect on the temperature profile along the height direction. When introducing the non-uniform power distribution at the height direction, the temperature contours now show obvious temperature gradients in both height and wide directions as shown Fig. 12 (3) and (4). It is known that when the coolant properties vary only in axial direction, the fluid and flow properties are same in each cross-section. Under this condition, when power is uniform distributed along height direction, no two-dimensional non-uniformity temperature profile exists. For power shape-4 condition, calculations have been conducted in order to compare the one- and two-dimensional heat conduction models. Figures 13 and 14 show the calculated fuel pellet and cladding surface temperature comparison results. It can be seen that with the two-dimensional model, the fuel temperatures reduce at the central pellet while the cladding surface temperatures increase. The two-dimensional heat conduction model narrows the temperature difference.

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Fig. 12. Two-dimensional temperature contour

Fig. 13. Central fuel pellet temperature distribution

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Fig. 14. Cladding surface temperature distribution

5 Conclusions This paper develops a computer program to predict the thermal-hydraulic behavior of plate-type fuel assembly for research reactors. The code is used to model and study the blockage accident of the IAEA’s 10MW reactor. Several conclusions can be drawn: (1) Blockage in parallel plate channel will cause non-uniform distribution of mass flow. The coupled heat transfers between fuel and coolant can reduce the non-uniformity. (2) After blockage, the partition of power between each side of the fuel will change. The heat transfer from fuel to the blockage channel side will reduced, while more heat will transfer to the non-blockage channel side. (3) Compared to one-dimensional heat conduction model, the two-dimensional heat conduction model can moderate the fuel temperature field.

Acknowledgements. This work was financially supported by Science and Technology on Reactor System Design Technology Laboratory.

References 1. Hamidouche, T., Salah, A., Adorni, M., et al.: Dynamic calculations of the IAEA safety MTR research reactor Benchmark problem using RELAP5/3.2 code. Ann. Nucl. Energy 31, 1385–1402 (2004) 2. Aghaie, M., Zolfaghari, A., Minuchehr, A., et al.: Transient analysis of break below the grid in Tehran research reactor using the newly enhanced COBRA-EN code. Ann. Nucl. Energy 49, 1–11 (2012) 3. Juan, C.A., Victor, S., Uwe, I.: Extension and validation of the SubChanFlow code for the thermo-hydraulic analysis of MTR cores with plate-type fuel assemblies. Nucl. Eng. Des. 379, 111221 (2021)

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4. Obenchain, C.F.: PARET-A Program for the Analysis of Reactor Transients. Idaho National Engineering Laboratory, Report IDO-17282 (1969) 5. Hainoun, A., Ghazi, N., Alhabit, F.: Simulation of LOFA and RIA for the IEA-R1 research reactor using the Code MERSAT. Ann. Nucl. Energy 35, 2093–2104 (2008) 6. Lu, Q., Qiu, S., Su, G.: Development of a thermal-hydraulic analysis code for research reactors with plate fuels. Ann. Nucl. Energy 36, 433–447 (2009) 7. Idaho National Laboratory: RELAP-3D Code Manual Volume I: Models and Correlations, Report: INEEL-EXT-98-00834, Revision 4.0 (2012) 8. Chen, J.Y., Chen, H.D., Zhang, X.Y.: Implementation and validation of a one-step coupled solution method for the two-fluid model. Nucl. Eng. Des., 56–64 (2019) 9. Ma, J., Li, L., Huang, Y., Liu, X.: Experimental studies on single-phase flow and heat transfer in a narrow rectangular channel. Nucl. Eng. Des. (2011) 10. Sudo, Y., Usui, T., Kaminaga, M.: Heat transfer characteristics in narrow vertical rectangular channels heated from both sides. JSME Int. J. Ser. 2, Fluids Eng. Heat Transfer Power Combust. Thermophys. Prop. 33(4) (1990) 11. Ghione, A., Noel, B., Vinai, P., et al.: Assessment of thermal–hydraulic correlations for narrow rectangular channels with high heat flux and coolant velocity. Int. J. Heat Mass Transfer 99 (2016)

Research Progress of SCR Denitration Catalyst in NOx Exhaust Gas Treatment Tang Jinlong(B) , Xu Yan, and Wu Yuyong China Nuclear Power Engineering Co. LTD, BeiJing, China [email protected]

Abstract. Selective Catalytic Reduction (SCR) is an effective treatment technology for NOx in exhaust. It has the advantages of high denitration efficiency and stable operation performance. However, SCR-related catalysts are prone to poisoning and the operating costs are high. The intensive researches of SCR-related catalysts are important for further industrial application. This paper focuses on the SCR reaction principle and technological processes. According to the different catalyst compositions, we introduced the research progress of noble metal catalysts, metal oxide catalysts and molecular sieve catalysts. Among them, Pt and Pd noble metal catalysts are expensive and have low selectivity, and gradually eliminated from the market; V2 O5 -based metal oxide catalysts are the most successful in commercial applications, but there are still disadvantages such as catalyst poisoning and unfriendly environment because of vanadium metal toxicity; Ce-based metal oxide catalysts are classified as low-temperature catalysts, it is found that the blockage of the active center is mainly avoided by changing the structure of the support, and the catalyst activity is improved by adding other transition metals. Mn-based metal oxide catalysts have excellent performance at low temperature and have been the most studied in recent years, and their selectivity and stability are mainly improved by changing the crystal form and doping with other elements. Iron-based metal oxide catalysts are environmentally friendly and low-cost, and most recent studies have focused on improving the activity of catalysts by controlling the crystal planes and morphologies. In addition, molecular sieve catalysts have attracted the attention of scholars due to the large specific surface area and the special microporous structure, and recent researches are focused on the synthesis process of molecular sieves and element doping modification to improve the thermal stability and activity of catalysts. Finally, this paper points out the possible problems of catalyst replacement, radioactive waste treatment when SCR technology is applied in the field of spent fuel reprocessing, and forecasts the future development direction of SCR-related catalysts. Keywords: SCR · NOx · Denitration · Metal oxide · Molecular sieve · Catalyst

1 Introduction Most of the processes in the spent fuel reprocessing process are carried out under acidic conditions, and they will generate a large amount of acidic NOx waste gas. With the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 839–844, 2023. https://doi.org/10.1007/978-981-19-8780-9_80

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increasing attention of international community and domestic environmental protection and pollutant discharge requirements, it is becoming more and more important to find an effective treatment of pollutants. Selective catalytic reduction (SCR) is a treatment technology for NOx in exhaust gas, which has the advantages of high out-of-stock efficiency and stable operation. However, the researches of SCR-related catalysts are the focus and difficulty for further industrial application of SCR technology.

2 Scr Technology SCR technology refers to the selective reaction of reducing gas with NOx under the action of a catalyst to eventually generate N2 and H2 O. Its main reaction equation are as follows: 4NO + 4NH3 + O2 → 4N2 ↑ + 6H2 O 6NO + 4NH3 → 5N2 ↑ + 6H2 O 2NO2 + 4NH3 + O2 → 3N2 ↑ + 6H2 O 6NO2 + 8NH3 → 7N2 ↑ + 12H2 O NO + NO2 + 2NH3 → 2N2 ↑ + 3H2 O The main process flow diagram is as follows (Fig. 1).

Fig. 1. Schematic diagram of SCR treatment process

3 Catalyst System The most important part that affects the out-of-stock efficiency, operating cost and stability of the SCR system is the catalyst system. Catalysts are roughly classified into noble metal catalysts, metal oxide catalysts and molecular sieve catalysts according to their composition. 3.1 Noble Metal Catalysts Pt and Pd noble metal catalysts with carrier of AlO2 O3 are the earliest applied SCR catalysts. These catalysts have a low operating temperature and usually have high activity. However, noble metals in catalyst could cause part of the ammonia gas to be oxidized to nitrogen oxides, increasing the consumption of ammonia. In addition, their price are relatively high, which is not conducive to large-scale industrial application.

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3.2 Metal Oxide Catalysts Metal oxides, especially metal oxides of transition metals, have the property of changing valence, which has attracted the attention of researchers. At present, the most commonly used V2 O5 metal oxide catalysts which generally use TiO2 as the carrier have an active temperature window of 300 ~ 420 °C. When the reaction temperature is too high, the catalysts will be deactivated, and the toxicity of V has great harm to the environment. At present, the hotspots of transition metals catalyst are mainly concentrated on metal oxides such as Ce-based, Mn-based and Fe-based. 3.2.1 Ce-Based Catalysts Ce-based catalysts are mainly based on CeO2 which is often used as a carrier or an active component in the catalyst. However, the activity of CeO2 is very low, because it is prone to agglomeration and lacks acid sites that are conducive to NH3 adsorption. When CeO2 is used as a carrier, the interaction between CeO2 and active components can be regulated by electronic effects to improve the catalyst performance. He et al. [1] used the method of volume impregnation to support WO3 with different morphologies on CeO2 , which greatly improved the redox performance of the catalyst due to the interaction between W ions and Ce ions. When CeO2 is used as the active component, researchers tried to improve its stability and activity by adding different transition metals due to its instability at high temperature. Liu et al. [2] used co-precipitation method to add Mo atoms to increase the activity of the catalyst and the performance of water resistance and sulfur resistance. 3.2.2 Mn-Based Catalysts Mn-based catalysts which are considered to be one of the most promising catalysts have excellent performance at low temperature. Mn oxides mainly appear in the form of MnO2 , Mn2 O3 , Mn3 O4 , etc. MnOx can provide a lot of free electrons and oxygen vacancies to promote the SCR reaction, which led the oxides of Mn of different valences are converted into each other and a redox reaction occurs. Kapteijin et al. [3] found that MnO2 has the highest activity per unit surface area and Mn2 O3 has the best selectivity. Tang et al. [4] prepared manganese oxide catalysts by different methods and found that the lower the crystallinity of MnOx had the better SCR performance at low-temperature. Yao et al. [5] compared MnOx supported on various rigid carriers and found that MnOx/γAl2 O3 had the best activity which was related to its good dispersion, the most acid sites and Mn4+ . Although Mn-based catalysts exhibit excellent catalytic performance at low temperature, their practical applications are limited due to their low selectivity. Therefore, some researchers improve their selectivity by doping with certain elements. Kim et al. [6] found that the introduction of Fe contributed to the improvement of the low-temperature activity of Mn/TiO2 ; Qi et al. [7] found that the addition of Fe could not only improve the catalyst activity, but also improve the selectivity. In addition, Co, Sm, Cr, Ni, Sn and other elements are also used for compounding with MnOx, and most of them can achieve 100% NOx destocking efficiency within 150 °C.

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3.2.3 Fe-Based Catalysts Fe-based catalysts are environmentally friendly and low-cost. Among them, the control of crystal phase/plane and morphology of Fe2 O3 nanomaterials is the key research topic in the field of Fe-based catalysts. When Fe2 O3 is used as the active component, the interaction between Fe2 O3 and the support can be controlled by adjusting the crystal plane of the support to adjust the SCR activity of the catalyst. Liu et al. [8] prepared nanosheets and nano shaft-supported monolayer Fe2 O3 catalysts, which showed better low-temperature activity due to more acidic centers, oxygen defects, and reactive oxygen species in the nanosheets. When Fe2 O3 was used as a carrier, Xin et al. [9] used coprecipitation method to dope Fe2 O3 with acidic Mo and W atoms, which showed better SCR performance than pure Fe2 O3 in experiments. Other researchers used a microwaveassisted method to load phosphotungstic heteropolyacids on the surface of Fe2 O3 , and exhibited > 90% NOx conversion at 250 ~ 500 °C. 3.3 Molecular Sieve Catalyst Molecular sieve catalysts play an important role in SCR catalysts due to their large specific surface area and special microporous structure, and have the advantages of wide temperature window, strong selectivity, and good stability. Initially, Iwamoto [10] found that Cu-ZSM-5 (medium pore zeolite) could promote the catalytic decomposition of NO into N2 and O2 , then gradually entered the field of scientific researches, but its NOx conversion rate is insufficient, and thermal stability is poor and it is easy to deactivate. Later, beta molecular sieve (large-pore zeolite) became a research hotspot due to its good thermal stability, but it still could not maintain high activity. After that, researchers turned their attention to the study of microporous molecular sieves, mainly including Fe-based and Cu-based molecular sieves catalysts. Hu et al. [11] prepared Fe-Cu/ZSM-5 catalyst by impregnation method, when the Fe/Cu molar ratio was 1:4, the removal efficiency of NOx could reach 90% in the temperature range of 250 ~ 450 °C Zhu et al. [12] obtained Fe–CuOx/ZSM-5 by ultrasonic impregnation, since ultrasonic impregnation could promote the dispersion of active components in the pores of ZSM-5 and significantly improved the SCR performance, besides, the denitration rate could reach 95% in the range of 180 ~ 360 °C. Chen et al. [13] studied the de-stocking mechanism of Fe-ZSM-5 and found that NH3 and NO convert Fe3+ into Fe2+ through redox and generate H+ , H2 O and N2 , and H+ and acid sites adsorbed NH3 react to generate NH4 + , and NH4 + reacted with NO/NO2 to generate N2 and H2 O, and Fe2+ was converted into Fe3+ at the same time. Sultant et al. [14] and other studies found that the low-temperature denitration performance of Fe-ZSM-5 was mainly related to the reducibility of metal ions, and high-temperature denitration performance was mainly related to the acid site, and the addition of metal ions could improve the low-temperature activity of the catalyst. Jouinia et al. [15] et al. found that the ion exchange sequence of the Fe-Cu/ZSM-5 catalyst would change the aggregation state of the metal species, thereby affecting the catalyst performance. The surface modification of Cu/ZSM-5 zeolite catalyst was carried out by chemical liquid deposition method of tetraoxysilane. In the experiment, it was found that the hydrothermal stability of the catalyst was significantly improved due to the hindering effect of SiO2 on the detachment of Cu2+ . Gao

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et al. [16] synthesized Fe-SZZ-13 catalyst by ion exchange method, and the denitration efficiency was higher than 80% in the range of 190 ~ 310 °C. Niu et al. [17] directly synthesized Fe-SZZ-13 catalyst by hydrothermal method, and the results showed that the catalyst obtained by multi-stage calcination process reached nearly 100% destocking efficiency at 300 ~ 400 °C. Liu et al. [18] got the copper-modified SAPO-34 molecular sieve catalysts with different copper contents that obtained by dipping method and impregnation method. The results showed that no matter how the preparation was made, the obtained catalysts showed high catalytic activity at low temperature. Wang et al. [19] found that the use of Cu/Fe bimetallic support can better modify the activity and hydrothermal stability of SSZ-13 molecular sieve catalyst.

4 Catalyst Recycling The catalyst system is an important part of the SCR technology, accounting for more than 20% of the cost of the entire system. The regeneration of the catalyst will greatly reduce the operating cost of the entire SCR. With the widespread promotion of SCR technology in the future, the production and resource recovery market of denitration catalysts will expand rapidly. At present, multi-step offline regeneration processes are often used abroad, including ultrasonic cleaning, heat treatment and active impregnation. Domestic regeneration of SCR catalysts has a complete set of systems, including quality screening of spent catalysts, making regeneration plan according to the cause of deactivation, which mainly including soot blowing, cleaning, impregnation, roasting, quality inspection and other processes. Through the regeneration process, the mechanical properties of the catalyst can be restored to 85 ~ 90%, and the activity can be restored to 90%. Aiming at the collapse of the honeycomb structure of the bulk powder denitration catalyst, it cannot be used by regeneration. At present, the principal methods include blending and burning resources, blending industrial raw materials and resource extraction.

5 Conclusions To sum up, among the commonly used SCR catalysts, noble metal catalysts are gradually eliminated from the market due to their high price and low selectivity; metal catalysts such as Ce-based, Mn-based and Fe-based catalysts currently mainly improve its selectivity and stability through changing the crystal form and doping with other elements, among them, Mn-based metal oxide catalysts have been studied the most with excellent performance at low temperature, and they are considered to be the most promising catalysts. At present, the main research on molecular sieve catalysts focuses on the synthesis process of molecular sieve and element doping modification to improve the thermal stability and activity of the catalyst. For the selection of catalysts, different catalysts should be selected according to different working conditions based on the actual situation, and the advantages of different catalysts in catalytic effect, stability, service life and economy should be exerted. When the above catalysts are used in the spent fuel reprocessing plant to treat NOx exhaust gas, besides the catalytic effect and stability, it is also necessary to consider the operation method when the catalyst is replaced, such as the catalyst replacement without stopping; the compression and treatment of the generated radioactive solid waste. Due to the high cost of some catalysts, the cost should also be considered.

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References 1. He, J., Xiong, Z., Du, Y., et al.: Morphology effect of tungsten oxide on Ce/W catalyst for selective catalytic reduction of NO with NH3 : Influence of structure-directing agents. J. Energy Inst. 94, 85–95 (2020) 2. Liu, Z., Zhu, J., Zhang, S., et al.: Selective catalytic reduction of \\{NOx\\}by\\{NH3 \\} over MoO3 -promoted CeO2 /TiO2 catalyst. Catal. Commun. 46, 90–93 (2014) 3. Kapteijn, F., Singoredjo, L., Andreini, A., et al.: Activity and selectivity of pure manganese oxides in the selective catalytic reduction of nitric oxides with ammonia. Appl. Catal. B 3(2–3), 173–189 (1994) 4. Tang, X., Hao, J., Xu, W., et al.: Low temperature selective catalytic reduction of NOx with NH3 over amorphous MnOx catalysts prepared by three methods. Catal. Commun. 8(3), 329–334 (2007) 5. Yao, X., Kong, T., Yu, S., et al.: Influence of different supports on the physicochemical properties and denitration performance of the supported Mn-based catalysts for NH3 -SCR at low temperature. Appl. Surf. Sci. 402(APR.30), 208–217 (2017) 6. Kim, Y.J., Kwon, H.J., Nam, I.S., et al.: High deNOx performance of Mn/TiO2 catalyst by NH3 . Catal. Today 151(3–4), 244–250 (2010) 7. Qi, G., Yang, R.T.: Performance and kinetics study for low-temperature SCR of NO with NH3 over MnOx-CeO2 catalyst. J. Catal. 217(2), 434–441 (2003) 8. Liu, J., Meeprasert, J., et al.: Facet–Activity relationship of TiO2 in Fe2 O3 /TiO2 nanocatalysts for selective catalytic reduction of NO with NH3 : in situ DRIFTs and DFT studies. J. Phys. Chem. C 121(9), 4970–4979 (2017) 9. Xin, Y., Zhang, N., Li, Q., et al.: Active site identification and modification of electronic states by atomic-scale doping to enhance oxide catalyst innovation. ACS Catal. 8(2), 1399–1404 (2018) 10. Iwanto, M., Furukawa, H., Mine, Y., et al.: Copper(II)ion-exchanged ZSM-5 zeolites as highly active catalysts for direct and continuous decomposition of nitrogen monoxide. Chem. Commun. 5(16), 1272–1273 (1986) 11. 胡海鹏, 王学涛, 张兴宇,等. Fe-Cu/ZSM-5催化剂的NH3 -SCR脱硝性能[J]. 燃料化学学 报, 046(002), 225–232 (2018) 12. Zhu, L., Zhang, L., Qu, H., et al.: A study on chemisorbed oxygen and reaction process of Fe-CuOx/ZSM-5 via ultrasonic impregnation method for low-temperature NH3 -SCR. Mol. Catal. 409, 207–215 (2015) 13. Chen, P., Magdalena, J., Weide, P., et al.: Formation and effect of NH4+ intermediates in NH3 -SCR over Fe-ZSM-5 zeolite catalysts. ACS Catal. 6(11), 109–163 (1989) 14. Sultana, A., Sasaki, M., Suzuki, K., et al.: Tuning the NOx conversion of Cu-Fe/ZSM-5 catalyst in NH3 -SCR. Catal. Commun. 41, 21–25 (2013) 15. Jouini, H., Mejri, I., et al.: Characterization and NH3 -SCR reactivity of Cu-Fe-ZSM-5 catalysts prepared by solid state ion exchange: The metal exchange order effect. Microporous Mesoporous Mater. Official J. Int. Zeolite Assoc. 260, 217–226 (2018) 16. Gao, F., Kollár, M., Kukkadapu, R.K., et al.: Fe/SSZ-13 as an NH3 -SCR catalyst: a reaction kinetics and FTIR/Mssbauer spectroscopic study. Appl. Catal. B Environ. 164, 407–419 (2015) 17. Niu, K., Li, G., Liu, J., et al.: One step synthesis of Fe-SSZ-13 zeolite by hydrothermal method. J. Solid State Chem. 287, 121330 (2020) 18. 刘雪松. Modification of Cu/ZSM-5 catalyst with CeO2 for selective catalytic reduction of NOx with ammonia. 中国稀土学报:英文版, 34, 1009 (2016) 19. Wang, J., Fan, D., Yu, T., et al.: Improvement of low-temperature hydrothermal stability of Cu/SAPO-34 catalysts by Cu2+ species. J. Catal. 322, 84–90 (2015)

Laser Decontamination Experiments for Radioactive Contaminated Metals Wu Xiaojiang1(B) , Li Zhihua2 , Jiang He2 , Yu Dawan2 , Wang Shuai3 , Wen Xiaojun3 , Wen Jin3 , Liu Maoquan3 , Zhao Wan1 , and Cao Junjie1 1 Sichuan Provincial Engineering Laboratory of Nuclear Facilities Decommissioning and

Radwaste Management, Nuclear Power Institute of China, Chengdu 610213, China [email protected] 2 CNNP Nuclear Power Operation Management Co., Ltd, Haiyan 314300, China 3 Nuclear Power Institute of China, Chengdu 610213, China

Abstract. Laser decontamination is a competitive technology for decontamination treatment for radioactive contaminated metals. In order to research and test the application effect of laser decontamination, a series of no-radioactive experiments for stainless steel and carbon steel were conducted based on a highly effective laser decontamination equipment. According to the results, the precise ablation effects and efficiencies were proved. What’s more, radioactive experiments for typical radioactive contaminated metals conducted in a nuclear power plant have proved its good decontamination effects. For low level contaminated metals, a laser cleaning method can decontaminate the metals to the clearance level. While for higher lever contaminated metals, good decontamination results to clearance level were also achieved after laser ablation treatment. Keywords: Laser decontamination · Decontamination experiment · Radioactive contaminated metals · Clearance level

1 Introduction At present, a large number of early nuclear facilities in China have entered the decommissioning stage, during which a large number of radioactive metal wastes will be generated. At the same time, the nuclear facilities in operation are also continuously producing a large amount of radioactive metal wastes, and the pressure of radioactive waste disposal is increasing. The surface contaminated radioactive metal wastes generated during the operation and decommissioning of nuclear facilities can be clearance level or recycled through reasonable decontamination methods, which greatly reduces the amount of radioactive waste and saves the cost of radioactive waste disposal. Laser decontamination technology uses high-energy laser to focus the laser beam on the polluted metal surface through the optical path system, so that the attachments on the metal surface, the oxide layer or the shallow surface of the substrate can rapidly vaporize and generate plasma, so as to realize the removal of radioactive substances on the metal surface. Compared with traditional decontamination technology, laser decontamination has © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 845–852, 2023. https://doi.org/10.1007/978-981-19-8780-9_81

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the advantages of no-contact, high precision, high reliability, high automation level, less secondary waste generation and wide application range. It is considered as an important research direction to minimize radioactive metal waste. Conventional laser decontamination technology has been widely used in the industrial field, mainly for removing the rust layer, oxide layer, paint layer and other attachments on the metal surface. According to the research, about 98% of the radionuclides in the typical surface contaminated metal waste of PWR are in the deposits and oxide films on the metal surface; Not more than 2% of radionuclides are located at 0 ~ 10 µm depth of metal surface material. Therefore, on the basis of conventional laser decontamination technology, it is necessary to peel the metal substrate to a certain depth by high-energy laser, so as to reach clearance level or recyclable of the radioactive contaminated metals. Based on the self-developed high-efficiency laser decontamination equipment, this paper builds a platform for no radioactive stripping decontamination experiment and radioactive decontamination experiment. Systematic experimental research and verification on the performance and effect of high-efficiency laser decontamination are carried out to provide reference for the process improvement and engineering application of laser decontamination of radioactive surface contaminated metal wastes.

2 Laser Decontamination Experiment Equipment There are various types of surface contamination of radioactive metal wastes generated during the decommissioning and operation of nuclear facilities, which are generally complex no-standard structures with unknown structural parameters. In order to realize the wide application of laser decontamination and meet the optimization principle of radiation protection, it is necessary to not only realize the high-precision removal of metal surface at different depths, but also realize the adaptive motion control of complex wastes. In addition, in the laser decontamination process, radionuclides are converted into radioactive smoke and aerosol after being ablated and removed. The radioactive airborne waste generated in the laser decontamination process should be effectively collected and disposed to avoid harm to personnel and the environment. The high efficiency laser decontamination equipment is mainly composed of a laser system, an adaptive motion system and a radioactive airborne waste filtration system. The laser system consists of a nanosecond pulse laser, a laser transmission optical fiber, three high-speed oscillators, three high-power field mirrors and a laser control system. The laser beam generated by the laser is transmitted through the optical fiber to the focusing system, and then the scanning oscillator and field mirror of the focusing system are focused to the surface of the object to be decontaminated. The control system controls the scanning motion of the laser beam in a local range to complete the surface scanning at a fixed point on the object to be decontaminated. The adaptive motion system mainly takes the multiaxis manipulator as the hardware base and the adaptive control system as the control core. It obtains the surface structure parameters of the contaminated object by scanning, and automatically plans and controls the motion track and position of the laser head to achieve the automatic scanning and contamination of the contaminated object. Radioactive exhaust gas collection and filtration system mainly includes vacuum negative pressure collection structure and high-efficiency radioactive airborne waste

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filtration equipment, which can collect and filter the radioactive exhaust gas generated from the decontamination process and meet the discharge standards of nuclear power plant. The structure diagram of the efficient laser decontamination equipment is shown in Fig. 1.

Fig. 1. Schematic diagram of a highly effective laser decontamination equipment

The power of the laser decontamination equipment can reach up to 1050W, which can decontaminate the attachments and oxide layers on the surface of stainless steel and carbon steel. At the same time, it can realize the stripping of the substrate at different depths of 0 ~ 100 µm. Depending on the high automation level of the equipment, metal items can be decontaminated off-line, and mobile devices can be equipped to realize online decontamination. The equipment also has good stability. It can work continuously for 20 h at a time, and the service life of key equipment components can reach 50,000 working hours. Laser decontamination experiment evaluate the efficiency of different depths of decontamination, the quality of the metal surface after decontamination, and the level of radioactivity after decontamination. Therefore, micron-scale contamination depth, surface contamination level, radiation dose level, radionuclide activity concentration, aerosol concentration and other measuring devices are used to test the parameters of the process.

3 Experiment Scheme for Laser Decontamination The laser decontamination experiment mainly evaluates the decontamination results under different depths to study and verify the effect. The preparation of radioactive contamination samples is difficult, and a large number of process parameter experiments need to be carried out in the decontamination experiment process to optimize the results. In order to reduce the difficulty of radioactivity experiment and unnecessary impact on personnel and testing instruments. In this experiment, the decontamination depth and effect are tested by means of no radioactive stripping experiment, and then the process experiment is carried out for the radioactive surface contaminated metal waste of typical nuclear power plants through the solidified decontamination process, so as to verify and evaluate the effect. According to the research, about 98% of the radioactive nuclides contaminated by typical metal waste on the surface of PWR are located in the deposits and oxidation film

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on the metal surface. Less than 2% of radionuclides are in the 0 ~ 10 µm depth of the metal surface material. The remaining radionuclides at depths of 10–50 µm in the metal surface layer are less than 0.1%. Therefore, no radioactive decontamination experiment verifies and evaluates the removal effect and efficiency of 304 stainless steel and Q235 carbon steel by laser exfoliation at depths of 0 µm (only surface contamination and oxidation layer are removed, no metal substrates are removed), 5 µm, 10 µm, 20 µm, 50 µm and 100 µm, respectively. Three samples were tested for a single parameter, and the results were averaged for three samples. Radioactive process parameters experiment prepared typical surface contaminated radioactive metal wastes from nuclear power plant as samples. In the negative pressure isolation sheds (SAS sheds), the process parameters verified by no-radioactive peeling test were used to gradually deepen the experiment to a depth of 50 µm or to achieve sufficient effect. The main objects to be decontaminated include stainless steel pedals, carbon steel waste water storage tanks, scaffolding, control rod pool storage shelves. Stainless steel pedals and carbon steel waste water storage tanks are cut into about 200mm, scaffolds are cut into about 300 mm, and control bar pool storage shelves are cut into about 30 mm. The effect of the decontamination experiment was evaluated by surface radiation level, surface contamination level and typical radionuclide activity concentration before and after decontamination of the samples. The evaluation index mainly used the Decontamination Factor (DF). DF = activity of radionuclides before decontamination/activity of radionuclides after decontamination. In addition, the level of radioactive aerosol in the SAS shed will be monitored by the radioactive aerosol detection equipment during the experiment to verify and test the performance of the equipment’s radioactive waste gas treatment system.

4 Laser Decontamination Test Results 4.1 No-radioactive Process Experiment Results After peeling 304 stainless steel and Q235 carbon steel with different process parameters of decontamination depth, the macro-morphology of the sample surface was observed, and the contaminated pollutants were evenly and completely peeled off. The metal surface after decontamination is metallic white (e.g. Sample 12), and with the increase of decontamination time and depth, there is some oxidation phenomeno in the decontamination site which is yellow-black (e.g. Sample 1). The micro-morphology of the sample was observed by 3D profiler and SEM electron microscopy. The main decontamination areas were completely removed, and the surface of the sample was homogeneous without obvious melting layer. 304 stainless steel material is decontaminated as shown in Fig. 2. The results of no-radioactive experiments are shown in Table 1. Based on the experimental results, it can be seen that the effect and trend of laser decontamination for stainless steel and carbon steel are basically the same, the error of decontamination depth is small, and more accurate decontamination depth can be

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Fig. 2. Laser decontamination result of 304 stainless steel

Table 1. Results of no-radioactive experiments Metal material

304 stainless steel

Q235 Carbon Steel

Expected decontamination depth (µm) 0

Average decontamination depth (µm) 0.4

Decontamination efficiency (m2 /h) 17.55

5

5.2

2.07

10

10.2

1.65

20

20.1

1.02

50

52.4

0.6

100

103.3

0.3

0

0.6

17.55

5

5.6

2.7

10

10.4

1.83

20

20.7

1.18

50

53.2

0.69

100

102.4

0.38

achieved. When only surface attachments and oxides are removed, the removal efficiency is high, reaching 17.55 m2 /h. The decontamination efficiency decreases significantly at the beginning of the stripping process, and decreases linearly with the increase of the decontamination depth. Under the typical decontamination depth of 10 µm, the average decontamination efficiency of no-embroidered steel materials is about 1.65 m2 /h, and that of carbon steel materials is about 1.83 m2 /h. The curve of laser efficiency with depth is shown in Fig. 3. 4.2 Radioactive Process Experiment Results The stainless steel pedal is mainly used in the nuclear auxiliary plant, and the surface radiation level is low. The average surface radiation level before decontamination is about 0.96 Bq/cm2 . After using laser surface cleaning parameters to decontaminate the waste, the metal surface shows metallic luster, and the average surface radiation level is reduced to 0.02 Bq/cm2 , reaching clearance level.

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Fig. 3. The relationship between laser decontamination deficiency depth

Carbon steel waste water storage tank is mainly used to store clean water. After long-term use, a large amount of floating rust is generated on its surface, and the average radiation level on its outer surface is about 1.94 Bq/cm2 . Due to the large amount of floating rust on the surface, there is still a small amount of rust residue after decontamination with single surface cleaning parameters, but the surface radiation level is reduced to 0.1 Bq/cm2 . The decontamination depth was increased to 5 µm, the surface rust was completely removed, the carbon steel surface showed metallic luster, and the surface radiation level was reduced to 0.18 Bq/cm2 . The storage rack of control rod pool is immersed in the storage pool for a long time, and its surface is covered with corrosive products, forming a dense metal oxide layer, which has a high fixed radioactive pollution. After decontamination with different depths, the results are shown in Fig. 4. According to the decontamination results, the surface radiation level and the decontamination result characterized by the activity concentration of Co-60 showed the same trend, that is, the decontamination factor showed an overall upward trend with the increase of the decontamination depth, and the final residual pollution level showed a downward trend. After the metal substrate is decontaminated to a depth of 10µm, the decontamination effect of the sample is significantly improved, the surface contamination level of the sample can reach the clearance level, and the activity concentration of the sample can basically reach the clearance level. The scaffolds are made of carbon steel and the surface is galvanized. After surface laser decontamination, the adhesion on the surface of the galvanized layer is basically removed, but the galvanized layer remains intact and the surface radiation level is reduced from 19.45 to 7.96 Bq/cm2 on average. Increasing the parameters of decontamination depth to 5µm, some zinc plating layers been eliminated, but there are still more residual zinc plating layers, and the surface radiation level decreases from an average of 12.79– 1.79 Bq/cm2 . By further increasing the decontamination depth parameter to 10 ~ 20 µm, the surface galvanized layer was removed, but the surface radiation level was 1.50 ~ 179 Bg/cm2 , which could not be further reduced. The results show that when the depth of decontamination is 5µm, the radiation level of the outer surface has not changed with the increase of the depth of decontamination. It can be concluded that the radioactive contamination of the outer surface has been basically removed, and the residual contamination is mainly caused by the inner surface contamination. Due to the structural

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Fig. 4. Laser decontamination results of the support structure of control rod

characteristics of the scaffolds, it is not possible to remove the pollution on the inner surface of the scaffolds. However, it is still of great value to decontaminate the scaffolds with higher radiation levels (> 4 Bq/cm2 ) for unlimited use (< 4 Bq/cm2 ) in the control area. During the laser decontamination process, the maximum level of radioactive aerosol at 1.5 m of the decontamination work point in the SAS shed is 0.1 µGy/h, the average level of radioactive aerosol is lower than the detection limit of the equipment, and the secondary radioactive waste is well filtered.

5 Conclusion In this paper, a systematic process experiment has been carried out on the experimental platform built by the developed laser decontamination equipment. The results show that the equipment can achieve accurate and quantitative removal of radioactive contaminants and substrates from metal surface. Good decontamination results have been obtained for typical radioactive metal wastes from nuclear power plants. For stainless steel and carbon

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steel wastes with low radiation level, the level of clearance can be reached. For wastes immersed in radioactive liquid for a long time, such as control rod sink storage shelves, the level of clearance can basically be reached when the decontamination reaches 10µm depth. For scaffolds and other objects with surface coatings, coatings or contamination on the inner surface, it is more difficult to decontaminate, and it is more difficult to achieve the level of clearance, but it still has a higher decontamination factor, which can greatly reduce the radiation level. Therefore, the high-efficiency laser decontamination technology with surface peeling ability has good decontamination effect and application prospects in the field of radioactive surface radiation metal waste decontamination. It can be used for the clearance level of a large number of low-level metal waste or the decontamination and degradation of high-level contaminated metal waste. It can also be used for the decontamination of radioactive contamination tools, contaminated sites with metal coating, etc. Acknowledgements. Radioactive process validation tests carried out in this paper are mainly carried out in Qinshan Nuclear Power Plant. We sincerely thank CNE Nuclear Power Operations Management Co., Ltd. For the good test conditions. We sincerely thank all experts of CNE Nuclear Power Operations Management Company for their professional guidance and assistance in the formulation and implementation of test plans.

References 1. Nuclear Energy Agency: Recycling and reuse of materials arising from the decommissioning of nuclear facilities, radioactive waste management. OECD Publishing, Paris (2017) 2. Saul, D., Davidson, G., Wirendal, B., et al.: Treatment of Berkeley boilers in Studsvik project description and experiences. Symposium on recycling of metals arising from operation and decommissioning of Nuclear Facilities, Nykoping (2014) 3. Ma, P.: Research and Enlightenment of several decontamination technologies in Japan in recent years. Radiat. Prot. Commun. 04, 18–20 (2007) 4. Kai, F., Zhang, Y., Daibo, et al.: Application of high-energy laser decontamination technology in decommissioning of nuclear facilities. Nucl. Power Eng. 36(1), 0207–0211 (2015) 5. Luo, S., Zhang, Z., Zhang, H., et al.: Retirement of nuclear and radiation facilities. China Environmental Science Press, Beijing (2010)

The Optimization Design of the Reactor Coolant System Based on Optimus Yuan Yanli(B) and Ye Xianhui Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, Sichuan, China [email protected]

Abstract. Structural dynamic analysis of reactor coolant systems is an important means of evaluating the safety of nuclear power plants under seismic loads. The seismic load distribution of the equipment can be obtained through the dynamic analysis of the reactor coolant system, and the stress of the equipment can be analyzed. In order to reduce the seismic stress of the equipment, effectively reducing the seismic load of the equipment is an important way. In this paper, aiming at the problem of excessive load at the outlet nozzle of the reactor pressure vessel, combined with the design characteristics of the reactor coolant system under the seismic, the optimal design of the reactor coolant system is carried out by the OPTIMUS [1] which is a kind of software developed by Noesis Solutions Company, by which the multidisciplinary analysis processes can be integrated for collaborative optimization design, and ANSYS [2], MATLAB and other software can be integrated to automate the simulation processes. Firstly, a nonlinear model for the dynamic analysis of the reactor coolant system is established, and then the dynamic response sensitivity analysis of the reactor coolant system is carried out, from which the design variables for subsequent optimization analysis are selected, and then the optimization analysis is processed with the force load at the outlet nozzle of the reactor pressure vessel minimized as the target function, and with the force load at the inlet nozzle of the reactor pressure vessel, the bending moment load at the inlet nozzle of the reactor pressure vessel and the bending moment load at the outlet nozzle of the reactor pressure vessel as the state variables. The results show that after the optimization, the load at the outlet nozzle of the reactor pressure vessel is effectively reduced. Relevant optimization analysis methods and analysis processes can be extended to other systems and equipment in the nuclear power plants. Keywords: Optimization · Reactor coolant system · Seismic response · Sensitivity analysis

1 Introduction In a nuclear power project, the force load at the outlet nozzle of the reactor pressure vessel (RPV) under the earthquake is almost as 6 times as the force load at the inlet nozzle of the reactor pressure vessel and it can be seen that the stress of the outlet nozzle © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 853–859, 2023. https://doi.org/10.1007/978-981-19-8780-9_82

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exceeds the stress limit in the specification, so it is necessary to reduce the seismic load at the outlet nozzle of the reactor pressure vessel. Structural dynamic analysis of reactor coolant systems is an important means of evaluating the safety of nuclear power plants under seismic loads. The seismic load distribution at the nozzle of the equipment can be obtained through the dynamic analysis of the reactor coolant system, and the stress of the nozzle can be analyzed. In order to reduce the seismic stress at the nozzle of the equipment, effectively reducing the seismic load at the nozzle of the equipment is an important way. OPTIMUS is a kind of software developed by Noesis Solutions Company, by which the multidisciplinary analysis process can be integrated for collaborative optimization design. In this paper, the design variables of the optimization analysis are selected by sensitivity analysis first, and then the force load at the outlet nozzle of the reactor pressure vessel is minimized as the target function, with the force load at the inlet nozzle of the reactor pressure vessel, the bending moment load at the inlet nozzle of the reactor pressure vessel and the bending moment load at the outlet nozzle of the reactor pressure vessel as the state variables.

2 Dynamic Analysis of Reactor Coolant System 2.1 Non-linear Finite Element Model The main system consists of three loops which parallel to each other, and the three loops are all connected to the rector pressure vessel. There are a steam generator and a main pump in every loop, and the pressurizer is connected to one of the loops by the surge line. Each steam generator has two upper lateral dampers and each main pump has three horizontal dampers. According to the relevant decoupling criteria [3], the finite element model of the main system is established by ANSYS program. The calculation model is mainly composed of beam unit, tube unit, centralized mass unit and various spring units. The model mainly include several nonlinear factors: there are gaps between the horizontal support and the steam generator, and the gaps can be simulated by the nonlinear spring unit in the ANSYS procedure; the tensile stiffness of the vertical support leg is different from the compressive stiffness, and the vertical support leg can be simulated by the nonlinear spring unit in the ANSYS procedure. The nonlinear seismic model of reactor coolant loop system is shown in Fig. 1. 2.2 Seismic Loads Three safety shutdown earthquake acceleration time-histories (X direction, Y direction and Z direction) are adopted as the seismic input, and the time-histories are not related and can satisfy the envelope requirements of both response spectrum and the power spectral density. The duration of the earthquake was 30s, applied in a total of 2500 load steps, and the load step interval was 0.01 s. The earthquake acceleration time-history curves in the three translation directions are shown in Fig. 2.

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Fig. 1. Nonlinear seismic model of reactor coolant loop system

3 Optimization Analysis 3.1 Sensitivity Analysis In order to make the optimization analysis more effective, the important parameters that have the greatest influence on the optimization goal should be picked out first by the sensitivity analysis before optimization. In order to analyze the sensitivity of the loads at the outlet nozzle of reactor pressure vessel, a sensitivity analysis model is established, taking the damper stiffness of the steam generator and the main pump as input parameters, considering the loads at the outlet nozzle of reactor pressure vessel as output parameters. The reactor pressure vessel has two kinds of nozzles: the inlet nozzles and the outlet nozzles and each kind of nozzle has two kinds of loads: the force and the bending moment, so the reactor pressure vessel has four kinds of nozzle loads. The sensitivity analysis model can be expressed as: y = F(x) x ∈ R5×1 , y ∈ R4×1

(1)

where, x is a vector composing of the stiffness of the damper at the steam generator and the main pump; y is a vector composing of the four nozzle loads of the reactor pressure vessels; F is an abstract definition of the relationship between the input and the output. Based on the above ideas, the OPTIMUS process flow shown in Fig. 3 is established. The process flow consists of two solvers: an ANSYS solver and a MATLAB solver. The program files in FileArray1 are called by the ANSYS solver, and the program files in FileArray2 and FileArray3 are called by the MATLAB solver. The file named stiffness.txt is the input file where the stiffness information of the dampers at the steam generator and the main pump is stored. The file named Result_Nozzle.txt is the output file where the information of the four kinds of nozzle loads is stored. In this paper, F is established by Latin Hypercubic Sampling, and the sample size is 100, and the sample range is around the nominal stiffness, hovering around 10%. The nominal stiffness is obtained through the equipment specification.

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Fig. 2. SSE acceleration time-history

Through the sensitivity analysis, the parameter sensitivity are obtained and shown in Fig. 4, where the input parameters and their meanings are shown in Table 1, and the values in the last column of Table 1 are the nominal value of the input variables. The output parameters and their meanings are shown in Table 2. The closer the absolute value of the data in Fig. 4 is to 1, the greater the correlation [4] is. As can be seen from Fig. 4, the stiffness of the upper transverse damper 2 at the steam generator (Stiffness163), the stiffness of the upper lateral damper 1 at the steam generator (Stiffness162) and the stiffness of the main pump horizontal damper 3 (Stiffness263) are correlated with the force at the outlet nozzle of the reactor pressure vessel (Max_FORCE_outlet_RPV)

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Fig. 3. OPTIMUS integrated workflow

from strong to weak. The remaining two input parameters show a weak correlation with the force loads at the outlet nozzle of the reactor pressure vessel. Therefore, when the force load at the outlet nozzle of the reactor pressure vessel is minimized, the stiffness of the upper damper 2 at the steam generator (Stiffness163), the stiffness of the upper transverse damper 1 at the steam generator (Stiffness162) and the stiffness of the main pump horizontal damper 3 (Stiffness263) are selected as the design variables.

Fig. 4. The sensitivity results

3.2 Optimization Model and Result Combined with the sensitivity analysis results in Sect. 3.1, the three more sensitive parameters were selected as design variables when optimizing the force load at the outlet nozzle of the reactor pressure vessel, and the following optimization analysis model was established: Design variables

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Stiffness263 ∈ [8.0963E + 08, 1.2118E + 09] Stiffness162 ∈ [9.643E + 08, 1.5985E + 09] Stiffness163 ∈ [9.643E + 08, 1.5985E + 09] State variables Max_FORCE_inlet_RPV ≤ 50 Max_MOMENT_inlet_RPV ≤ 64 Max_MOMENT_outlet_RPV ≤ 185 Objective function min Max_FORCE_outlet_RPV In this paper, the sequential quadratic programming method [5] is used to optimize the objective function, and the optimization iterative process diagram of the design variable and the objective function are shown in Fig. 5. The optimized values of the design variables, state variables, and objective functions, as well as the values before, are shown in Table 3.

Fig. 5. Optimizing iterative process diagrams

After optimization, the force load at the outlet nozzle of the reactor pressure vessel is reduced from 2.931E + 06N to 2.781E + 06N,with the force load and moment load at the inlet nozzle of the reactor pressure vessel and the bending moment load at the outlet nozzle of the reactor pressure vessel are basically unchanged.

4 Conclusion The force load at the outlet nozzle of the reactor pressure vessel is optimized with the sequential quadratic programming method, and the force load is reduced by 1.5E + 05N after optimization.The sequence quadratic programming method is a local optimization method, and the objective function will be optimize with the global optimization method and the reliability of the optimization results will be evaluated.

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Table 1. The meaning of the input parameters and the nominal value Input parameter

Symbol

Nominal value /107 N·m-1

The stiffness of the horizontal damper 1 at the main pump

Stiffness261

96.43

The stiffness of the horizontal damper 2 at the main pump

Stiffness262

96.43

The stiffness of the horizontal damper 3 at the main pump

Stiffness263

101.07

The stiffness of the upper horizontal damper 1 at the steam generator

Stiffness162

67.77

The stiffness of the upper horizontal damper 2 at the steam generator

Stiffness163

67.37

Table 2. The meaning of the output parameter Position

Output parameter Symbol

Inlet nozzle of the reactor pressure vessel

Force

Max_FORCE_inlet_RPV

Moment

Max_MOMENT_inlet_RPV

Outlet nozzle of the reactor pressure vessle Force

Max_FORCE_outlet_RPV

Moment

Max_MOMENT_outlet_RPV

Table 3. Optimized parameter values and parameter values without optimization (unit: stiffness/107 N·m-1 , force/104 N, moment/104 N·m)

Design variables

State variables

Objective function

Symbol of the parameters

Initial values

Optimized values

Stiffness263

1.0107E + 09

1.2118E + 09

Stiffness162

1.2714E + 09

1.1634E + 09

Stiffness163

1.2714E + 09

1.5216E + 09

Max_FORCE_inlet_RPV

51.3

49.9

Max_MOMENT_inlet_RPV

61.6

60.4

Max_MOMENT_outlet_RPV

183.0

184.8

Max_FORCE_outlet_RPV

293.1

278.1

References 1. 2. 3. 4.

Noesis Solutions Company, Optimus revision 10 help manual Ansys.Inc. ANSYS User’s manual for Revision.15.0.Ansys.Inc, US (2013) US NRC. SRP3.7.2,Seismic System Analysis[s] (2006) Song, Y., Wang, Y., Liu, J.: Study of diesel performance optimization. In: SAE-China congress proceedings, pp. 54–57 (2008) 5. Chen, B.: Optimization theory and algorithms, Second edn. Tsinghua University Press (2005)

Study on Near Surface Radionuclide Activity Under Vehicle Heat Pipe Small Modular Reactor Accident Condition Zhang Haolei1,2,3 , Zhou Tao1,2,3(B) , Xu Peng2,3,4 , and Tang Jianyu1,2,3 1 Department of Nuclear Science and Technology, School of Energy and Environment,

Southeast University, Nanjing 210096, China [email protected] 2 Institute of Nuclear Thermal-Hydraulic Safety and Standardization, Beijing, China 3 National Engineering Research Center of Power Generation Control and Safety, Nanjing 210096, China 4 School of Nuclear Science and Engineering, North China Electric Power University, Beijing 102206, China

Abstract. The radionuclide leakage of the vehicle heat pipe small reactor will lead to serious consequences. The prediction and evaluation of the consequences are of great value to China in dealing with nuclear accidents. By simulating the release of radioactive gas cloud in vehicle heat pipe small reactor under accident conditions, a model was established and calculated to simulate the atmospheric migration of radionuclides, and then the settlement under different conditions was studied by establishing the model. The results show that the maximum radioactive concentration of radionuclide atmospheric migration in the vehicle heat pipe small reactor is some distance from the accident point. The radioactive concentration distribution of radionuclide atmospheric migration in vehicle-mounted heat pipe small reactor under accident conditions is affected by atmospheric conditions. Different atmospheric conditions will make the shape and value of radioactivity distribution near the ground very different. The effect of wet sedimentation is more obvious than that of dry sedimentation during the radionuclide migration in the vehicle heat pipe small reactor under accident condition. Keywords: Vehicle heat pipe small modular reactor · Radionuclide · Atmospheric migration · Dry fall · Wet deposition

1 Introduction With the development of the times and the consumption of resources, the problem of energy is becoming more and more prominent. Premier Li Keqiang said in the 2022 government work report that we should do a solid job in carbon peaking and carbon neutralization, and formulate an action plan for peaking carbon emissions by 2030. Optimize the industrial structure and energy structure. Promote clean and efficient utilization of coal, vigorously develop new energy, and actively and orderly develop nuclear power © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 860–873, 2023. https://doi.org/10.1007/978-981-19-8780-9_83

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on the premise of ensuring safety. China plans to achieve carbon neutrality in 2060, which makes China’s actual carbon emissions need to be reduced to a certain extent. As a new type of small reactor, heat pipe reactor has good controllability and better thermal transient feedback performance. At the same time, it has high reliability and low maintenance requirements. It is widely used in space exploration, resource development and miniaturization of nuclear power devices. The heat pipe cooled reactor adopts the design concept of solid-state reactor. The heat is directly derived from the reactor core through the high-temperature heat pipe. The system design itself is relatively simplified, which is more suitable for the technical selection of small nuclear power supply. In 2002, Stohl et al. [1] used trajectory calculation to explain the measurement results of atmospheric trace substances; In 2006, Bing [2] and others introduced randomwalk, a self-developed random walk atmospheric diffusion model. In 2008, Ji [3] simulated the diffusion of radionuclides released during the normal operation of Daya Bay nuclear power plant in the atmosphere based on the Gaussian plume model. In 2011, Liu [4] and others summarized various models widely used to simulate nuclide atmospheric dispersion. In 2012, Chen [5] gave a brief introduction to the basic situation of the Fukushima nuclear accident in Japan and made an in-depth analysis of the cause of the accident. In 2014, Jeong and others [6] studied and determined the impact of terrain and buildings on the atmospheric diffusion of radioactive substances in Walson nuclear power plant. In 2017, Sujitha and others [7] conducted reliability analysis on near surface disposal facilities by using pollutant migration model to evaluate continuous failure probability of multi barrier system. In 2022, Toropov et al. [8] used multivariate statistical methods to analyze the geochemical correlation of natural radionuclides (U and Th) migration of different species in Semipalatinsk Test Site. The research directions of researchers at home and abroad are mainly focused on the leakage nuclide migration in the offshore of large nuclear power plants and the nuclide migration in the aeration zone. There is a lack of research on the nuclide diffusion near the ground of small vehicle mounted reactors. Therefore, it is of great theoretical significance to simulate the atmospheric migration of radionuclides by simulating the release of radioactive gas clouds from vehicular heat pipe reactor under accident conditions, and establishing a model and calculating to simulate the atmospheric migration of radionuclides.

2 Research Object 2.1 Regional Model The studied area is within 2000 m of the downwind direction, 1000 m of the crosssectional wind direction and 5 m from the ground at the accident site of the vehicle mounted heat pipe small reactor. Establish the coordinate system as shown in Fig. 1 in this area. In Fig. 1, the origin O represents the orthographic projection of the leakage point of the accident of the on-board heat pipe small reactor on the ground; The x-axis represents the downwind direction; The y-axis represents the cross-sectional wind direction; The z-axis represents the direction of height from the ground; In the model area, it is assumed that the direction is horizontal, the wind direction and wind speed are certain, and the moving speed of the cloud cluster center or the axial spreading speed of the cloud plume

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Fig. 1. Regional model

are the same as the wind speed. It is assumed that the ground within the study area is at the same altitude and there are no obstacles in the study space area. 2.2 Accident Model In the actual nuclear leakage accident, the ways of nuclide release are complex and diverse, and the effects are also different. When the truck carrying the heat pipe reactor was working in the Gobi area, it was impacted by external impactors. After the collision accident, the reactor core broke, and there was a breach in the containment. The truck continued to move forward. It was assumed that the truck would continue to drive against the wind at a constant speed of 5 m/s after the collision accident. The situation after the accident is shown in Fig. 2.

Fig. 2. Radioactive gas cloud

It can be seen from Fig. 2 that some radionuclides are sealed in containers with liquid effluent, which will not cause harm to the environment, so they are not considered; Some radionuclides are rapidly ejected at the breach in the form of radioactive gas clouds, which migrate in the atmosphere and cause harm to the environment. Therefore, taking them as the research object, the atmospheric migration of radionuclides under the condition of vehicle mounted heat pipe small reactor accident is studied. It is assumed that the release mode of nuclide is instantaneous release, that is, the nuclide completes the release at the moment of the accident of the on-board heat pipe reactor and begins the migration and diffusion process of the atmosphere.

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2.3 Representative Nuclide A large amount of radioactive substances will be produced during the operation of the on-board heat pipe small reactor, including the activation products (such as 3 H, 14 C, 34 Mn, 55 Fe, 60 Co, 63 Ni, etc.) obtained by capturing neutrons after the chemical changes of the fuel assembly Actinides (such as 238 Pu, 239 Pu, 241 Pu, 241 Am, etc.) obtained from the decay of a small amount of uranium after continuous capture of neutrons and fission products from fission reactions that play a major role in vehicle mounted heat pipe small reactors (such as 90 Sr, 131 I, 134 Cs, 137 Cs, 160 ru, etc.) among them, the fission products are the main radioactive substances with a large amount. Among the fission products, we are concerned about nuclides with high yield, long half-life and obvious radiation biological effects. First, 131 I, which is regarded as the standard of severity in nuclear accidents, will emit high energy β and γ The half-life of X-ray is 8.04 days, 131 I is a highly toxic nuclide, the vital organ is the thyroid gland, and the effective half-life for human body is 7.6 days. The second is 137 Cs, which has a half-life of 30.17 years and can exist for a long time. It forms compounds CsI and CsOH with iodine and exists as volatile fission products. Under accident conditions, it will quickly leak into the environment and cause serious consequences. Therefore, it has become a representative nuclide in this study. The representative nuclides in the research object this time are 131 I and 137 Cs. Most of them will leak into the environment in the form of aerosols of CsI and CsOH under the conditions of vehicle mounted heat pipe small reactor accident, and then spread to different locations in the atmosphere, resulting in serious radioactive consequences. Therefore, the atmospheric diffusion of 131 I and 137 Cs after leakage under the conditions of vehicle mounted heat pipe small reactor accident is selected as the research object. 2.4 Representative Nuclide (1) Atmospheric conditions Pasquale divides atmospheric stability into six categories: stable, relatively stable, neutral, weak unstable, unstable and strong unstable. The corresponding level of atmospheric instability can be obtained according to different atmospheric conditions, and then the corresponding parameters can be calculated according to the level of atmospheric instability. Select the first atmospheric condition: the downwind ground wind speed is 5.5 m/s, the cross-sectional wind speed is 0, and the solar radiation level is + 1. Then according to Pasquale’s atmospheric instability, the type of diffusion atmosphere is D, and the corresponding diffusion parameter values are shown in Table 1; Select the second atmospheric condition: the downwind ground wind speed is 2.45 m/s, the cross-sectional wind speed is 0, and the solar radiation level is + 2. Then according to Pasquale’s atmospheric instability, the diffusion atmosphere type is B, and the corresponding diffusion parameters are shown in Table 2. (2) Release nuclide parameters Now, the environmental share of radionuclide leakage under accident conditions is more based on the realistic assumption of PWR accident, and the analysis is carried out by using the method of probabilistic safety analysis to obtain the amount of radionuclide leakage after the hypothetical accident. As the on-board heat pipe

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Diffusion parameters

Parameter value

a1

−0.0059

a2

1.1080

b1

−1.3500

b2

0.7930

b3

0.0022

Table 2. Diffusion parameter values corresponding to weather condition B Diffusion parameters

Parameter value

a1

−0.0147

a2

0.2480

b1

−0.8850

b2

0.8200

b3

0.0168

small reactor is a new type of reactor with good prospects, but the corresponding accident sequence has not been reported yet. Therefore, it is assumed that after the accident, the share and leakage amount of radionuclides in the core leaking into the environment are shown in Table 3. Table 3. Represents the leakage of nuclides Nuclide

Core inventory (Bq)

Share of leakage

Leakage (Bq)

131 I

2.7 × 1010

0.09

2.43 × 109

137 Cs

2.1 × 1010

0.04

8.4 × 108

(3) Release height parameter The parameters required in the release height model are shown in Table 4.

3 Computational Modle 3.1 Regional Model In order to quickly calculate the concentration distribution near the ground after the accident, so as to timely and scientifically predict the pollution range, clearly divide the emergency area, and assist in the accident emergency plan and preparation. In this study,

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Table 4. Release height related parameters Symbol

Significance

Parameter value

Unit

u

Wind speed

5.5

m/s

d

Diameter of leakage port

0.1

m

Vs

Outlet velocity of gas cloud

45.83

m/s

Hs

Height of leakage source

3

m

Gaussian plume model is used to simulate the atmospheric diffusion of radionuclides under the condition of on-board heat pipe small reactor accident. It is assumed that the concentration distribution of pollutants in the plume follows Gaussian distribution in both horizontal and vertical directions, which is the characteristic of Gaussian plume model. The specific formula is shown in (1).        −y2 Q (z − He )2 (z + He )2 × exp × exp − + exp − C(x, y, z) = 2π σy σz u 2σy2 2σz2 2σz2 (1) where, C (x, y, z) is the pollutant concentration at (x, y, z), unit: Bq/m3 ; x is the downwind distance of the point, unit: m; y is the distance of the cross-sectional wind direction at this point, unit: m; z is the height from the point to the ground, unit: m; Q is the source intensity of pollutants, unit: bq/m; σ y is the diffusion coefficient in the horizontal direction, unit: m; σ z is the diffusion coefficient in the vertical direction, unit: m; u is the average wind speed, unit: m/s; H e is the effective discharge height, unit: m. The diffusion coefficient in this formula is determined by atmospheric conditions. Different atmospheric conditions have corresponding diffusion parameters. The diffusion coefficient under each type of weather is determined by formula (2) and formula (3). σy = (a1 ln x + a2 ) × x

(2)

 σz = exp b1 + b2 ln x + b3 ln2 x /2.15

(3)

Among them, a1, a2, b1, b2 and b3 can be determined by looking up the weather type table. 3.2 Dry Settlement Model When the radionuclides in the on-board heat pipe small reactor will directly settle to the surface through dry sedimentation under accident conditions, the dry sedimentation model selected assumes that the deposition flux density of radioactive substances is proportional to the airborne radionuclide density near the ground, and the formula is determined by (4). W d = V d × C(x, y, 0)

(4)

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where, W d is the dry deposition flux density, unit: Bq/(s×m2 ); V d is the settlement velocity, unit: m/s; C (x, y, 0) is the nuclide concentration near the ground, unit: Bq/m3 . 3.3 Wet Settlement Model Under the condition of on-board heat pipe small reactor accident, radionuclides leak and diffuse in the atmosphere, which will reduce the concentration of pollutants in the atmosphere due to precipitation. The wet deposition flux is determined by formula (5).

z Ww =  ×

C(x0, y0, z)dz

(5)

0

3.4 Release Height Model In formula (1), he refers to the effective release height, which refers to the height of the gas cloud center from the ground when the gas cloud formed by radioactive substances leaked from the on-board heat pipe small reactor becomes horizontal under accident conditions, which is equivalent to the sum of the leakage source height and the lifting height, that is, H e = H s + H, where H e represents the effective release height, H s represents the leakage source height, and H represents the lifting height. Wilson’s semi empirical formula in the early 1980s is used to calculate the lifting height H, and the formula is shown in (6). H = 2.4 ×

Vs × d u

(6)

where, u is the wind speed, unit: m/s; V s is the outlet velocity of gas cloud, unit: m/s; d is the diameter of the leakage port, unit: m; H is the lifting height, unit: m. 3.5 Decay Model The formula of decay is determined by (7). A = A0 × e−λt

(7)

where, A is the radioactivity after time t, unit: Bq; A0 is the initial radioactivity, unit: Bq; λ is the decay constant, unit: 1/s; t is the time, unit: s. 3.6 Calculation Process The nuclide atmospheric migration under the condition of on-board heat pipe small reactor accident is simulated by MATLAB and Excel, and the near ground radioactivity distribution is calculated by the self-made nuclide atmospheric diffusion simulation Ike program. The calculation process is shown in Fig. 3.

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Fig. 3. Calculation process

4 Calculation Results and Analysis 4.1 Release Height Parameter The results that can be calculated according to the parameters in Sect. 1.4 and the release height model in Sect. 2.4 are shown in Table 5. Table 5. Release height related calculation results Symbol

Significance

Parameter value

Unit

H

Lifting height

1.998

m

He

Effective release height

4.998

m

4.2 Sedimentation Flux (1) Dry settling flux The dry deposition flux is calculated by the dry deposition model in Sect. 2.2 and the nuclide atmospheric diffusion model in Sect. 2.1 through MATLAB programming. The dry deposition flux density of radionuclides 131 I and 137 Cs in the downwind direction of the on-board heat pipe reactor under the accident condition and the wind speed of 5.5 m/s can be calculated, as shown in Fig. 4. It can be seen from Fig. 4 that under accident conditions, the dry deposition flux density of radionuclides 131 I and 137 Cs in the downwind direction decreases with the increase of downwind distance, and the decrease range is very large. Among them, as the downwind distance becomes larger and larger, the action area becomes larger and larger, and it is obvious that the total amount of dry settlement is getting larger and larger; The dry deposition flux density of 131 I at 200m reaches 236 Bq/(s × m2 ), indicating that at the initial stage of atmospheric migration of radionuclide 131 I, its dry deposition effect is obvious, and it is only 79 Bq at 400 m, with a large decline; At 1400 m, the

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Fig. 4. Dry deposition flux density

dry deposition fluxes of 131 I and 137 Cs have been very small; Because the radionuclide core leakage of the on-board heat pipe small reactor assumed in Sect. 1.4 is different in the event of an accident, there are two orders of magnitude differences, so their dry deposition flux is also very different. (2) Wet settling flux From the wet deposition model in Sect. 2.3 and the nuclide atmospheric diffusion model in Sect. 2.1, the wet deposition flux density of radionuclides 131 I and 137 Cs of the on-board heat pipe reactor under the accident condition with the wind speed of 5.5 m/s can be calculated, as shown in Fig. 5.

Fig. 5. Wet deposition flux density

It can be seen from Fig. 5 that under accident conditions, the wet deposition flux density of radionuclides 131 I and 137 Cs in the downwind direction decreases sharply with the increase of downwind distance, and the reduction range is very large. Among them, as the leeward distance becomes larger and larger, the acting area becomes larger and larger, and it is obvious that the total amount of wet settlement is getting larger and larger; The dry deposition flux density of 131 I at 10m reaches 1467 Bq/(s × m2 ), indicating that at the initial stage of atmospheric migration of radionuclide 131 I, its wet

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deposition effect is very obvious, and it is only 68 Bq/(s × m2 ), the decline is very large; At 400 m, the 25 flux of 131 I and 137 Cs is very small. Compared with Fig. 4, it can be seen that under accident conditions, the wet deposition effect of radionuclides 131 I and 137 Cs in the downwind direction is much more obvious than that of dry deposition flux, and the change of wet deposition flux is much more intense than that of dry deposition flux. 4.3 Atmospheric Diffusion of Nuclides at a Wind Speed of 5.5 m/s (1) Diffusion of 131 I in the atmosphere Through MATLAB programming calculation, the near ground radioactivity of radionuclide 131 I can be obtained under the condition that the wind speed is 5.5 m/s under the accident condition of on-board heat pipe small reactor, as shown in Fig. 6.

Fig. 6. 131 Iactivity near the ground

It can be seen from Fig. 6 that at the near ground level, under accident conditions, the radioactivity of radionuclide 131 I at the near ground level of the on-board heat pipe reactor first increases and then decreases rapidly with the increase of the downwind distance. The maximum value is obtained at a distance of about 70 m in the downwind direction and 0 m in the cross-sectional wind direction, reaching nearly 1.2 × 106 Bq/m3 ; At the near ground level, the radioactivity of radionuclide 131 I at the near ground level decreases with the distance in the cross-sectional direction under accident conditions, and the downward trend is more intense than that in the downwind direction. It is symmetrical in the direction of cross-sectional wind direction, which is caused by the assumption that there is no wind in the cross-sectional direction. It can be seen that the distribution of radionuclide 131 I activity near the ground is elliptical in shape.

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(2) Diffusion of 137 Cs in the atmosphere Through MATLAB programming calculation, the near ground radioactivity of radionuclide 137 Cs can be obtained under the condition that the wind speed is 5.5 m/s under the accident condition of on-board heat pipe small reactor, as shown in Fig. 7.

Fig. 7. 137 Cs activity near the ground

It can be seen from Fig. 7 that at the near ground level, under accident conditions, the radioactivity of radionuclide 137 Cs at the near ground level of the on-board heat pipe reactor first increases and then decreases rapidly with the increase of the downwind distance. The maximum value is obtained at a distance of about 55m in the downwind direction and 0 m in the cross-sectional wind direction, reaching nearly 4.5 × 105 Bq/m3 ; According to the radioactive concentration diffusion range in Figs. 6 and 7, the pollution range can be scientifically predicted, the emergency area can be clearly divided, and the accident emergency plan and preparation can be assisted. 4.4 Atmospheric Diffusion of Nuclides at a Wind Speed of 2.45 m/s (1) Diffusion of 131 I in the atmosphere Through MATLAB programming calculation, the near ground radioactivity of radionuclide 131 I can be obtained under the condition that the wind speed is 2.45 m/s under the accident condition of on-board heat pipe small reactor, as shown in Fig. 8. It can be seen from Fig. 8 that at the near ground level, the radioactivity of radionuclide 131I at the near ground level of the on-board heat pipe reactor under accident

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Fig. 8. 131 I activity near the ground

conditions first increases and then decreases rapidly with the increase of the downwind distance. The maximum value is obtained at a distance of about 14 m in the downwind direction and 0 m in the cross-sectional wind direction, reaching nearly 1.8 × 106 Bq/m3 ;At the near ground level, under accident conditions, the radioactivity of radionuclide 131 I at the near ground level decreases in a gradient with the distance in the cross-sectional direction, and the downward trend is more intense than that in the downwind direction, showing symmetry in the cross-sectional wind direction. It can be seen from Fig. 8 that the radioactivity distribution of radionuclide 131I near the ground is shell shaped. (2) Diffusion of 137 Cs in the atmosphere Through MATLAB programming calculation, the near ground radioactivity of radionuclide 137 Cs can be obtained under the condition that the wind speed is 2.45 m/s under the accident condition of on-board heat pipe small reactor, as shown in Fig. 9. It can be seen from Fig. 9 that at the near ground level, under accident conditions, the radioactivity of radionuclide 137 Cs at the near ground level of the on-board heat pipe reactor first increases and then decreases rapidly with the increase of the downwind distance. The maximum value is obtained at a distance of about 10m downwind direction and 0 m cross-sectional wind direction, reaching nearly 6 × 105 Bq/m3 ; At the near ground level, under accident conditions, the radioactivity of radionuclide 131 I at the near ground level decreases in a gradient with the distance in the cross-sectional direction, and the downward trend is more intense than that in the downwind direction, showing symmetry in the cross-sectional wind direction.

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Fig. 9. 137 Cs activity near the ground

4.5 Comparison of Nuclide Diffusion Under Different Atmospheric Conditions Comparing Figs. 6 and 7 with Figs. 8 and 9, it can be found that under different atmospheric conditions, the radioactive activity shape distribution of radionuclides leaked during the accident of on-board heat pipe small reactor near the ground varies greatly, the location and maximum value of the maximum radioactive activity are also different, and the impact is also different, and the scope of the corresponding emergency area is also different. In contrast, when the wind speed is 5.5 m/s, the downwind distance of the maximum radioactivity is larger, and the range of radionuclide migration is larger, while when the wind speed is 2.45 m/s, the maximum radioactivity is larger, but the downwind distance is smaller, and the range of dividing the emergency area is smaller.

5 Conclusion Gaussian plume model is used to simulate the radionuclide atmospheric migration of vehicle mounted heat pipe reactor under accident conditions, and the radioactivity near the ground under different atmospheric conditions is obtained. (1) Under accident conditions, the near ground radioactivity of the on-board heat pipe small reactor has a maximum value in the downwind direction, and there is a distance between the maximum value and the accident place. This maximum value and position are related to atmospheric conditions and the leakage of radionuclides. (2) The radioactive concentration distribution of radionuclide atmospheric migration of vehicular heat pipe small reactor under accident conditions is affected by atmospheric conditions. Different atmospheric conditions will make the shape and value of radioactivity distribution near the ground very different. (3) Wet deposition has a great impact on radionuclide atmospheric migration of onboard heat pipe small reactor under accident conditions, which greatly increases the deposition flux density of radionuclides, so that the pollution range will be greatly reduced.

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Acknowledgements. This work was financially supported by the National Key Research and Development Project (Grant No. 2020YFB1901700) and Jiangsu Province Double Innovation Talent Program (Grant No. JSSCRC2021500).

References 1. Stohl, A., Eckhardt, S., Forster, C., James, P., Spichtinger, N., Seibert, P.: A replacement for simple back trajectory calculations in the interpretation of atmospheric trace substance measurements. Atmos. Environ. 36(29), 4635–4648 (2002) 2. Bing, C., Dong, F., Hong, L.: Development and application of stochastic Walk atmospheric diffusion model in nuclear accident emergency response. Nucl. Sci. Eng. 01, 39–45 (2006) 3. Ji, W.: Calculation of airborne radionuclide concentration and radiation dose in nuclear power plant. Jinan university, Guangzhou (2008) 4. Liu, A., Kuai, L.: A review of radionuclide atmospheric dispersion models. J. Meteorol. Environ. 27(04), 59–65 (2011) 5. Chen, D.: Nuclear energy and nuclear safety: Analysis and reflections on the Fukushima nuclear accident in Japan. J. Nanjing Univ. Aeronaut. Astronaut. 44(05), 597–602 (2012) 6. Jeong, H., Park, M., Jeong, H., Hwang, W., Kim, E., Han, M.: Terrain and building effects on the transport of radioactive material at a nuclear site. Ann. Nucl. Energy 68, 157–162 (2014) 7. Sujitha, S., Dilip, D.M., Sivakumar Babu, G.L.: Time-dependent reliability analysis for radionuclide migration in groundwater in near surface disposal facility using the enhanced Monte Carlo method. Georisk Assess. Manag. Risk Eng. Syst. Geohazards 11(2), 208–214 (2016) 8. Toropov, A.S., Yessilkanov, G.M.: Advanced instruments for identifying geochemical dependences of radionuclide migration in natural waters. Geochem. Int. 60(3), 266–278 (2022)

Multi-physics Numerical Investigation on the Mechanical Stirring in a Cold Crucible Melter Ye Hong1 , Yusong Li2(B) , Dongdong Zhu2 , Dongsheng Qie2 , Runci Wang2 , and Shengdong Zhang2 1 Department of Radiochemistry, China Institute of Atomic Energy, Beijing, China 2 China Institute of Atomic Energy, Beijing, China

[email protected]

Abstract. With an increasing number of nuclear reactors in China, the accumulation of spent nuclear fuel exponentially rises. Unavoidably, high level radioactive waste has been generated from the fuel’s reprocessing. Thus, cold crucible induction melting technology is under active development in several countries. In this study, temperature field and induction heat distribution were numerically investigated for design and operating condition optimization. Then, the calculated power of the coil was validated in the experiment. After that, comparisons among electromagnetic and temperature fields with different power and frequencies were conducted. In addition, the effects of stirring on the flow fields were evaluated for further understanding of the interactions among flow field, glass properties, temperature and electromagnetic fields. The numerical simulation precisely predicts the temperature distribution and electrical power distribution in the coil. Comparisons between calculated power densities of 650 mm and 800 mm cold crucible induction melters indicate that induction power of 800 cold crucible induction melter (CCIM) with 400 mm high molten glass requires at least 247 kW. Additionally, the simulation stirring results indicate that stirring significantly improves the hydrodynamic homogenization. At last, calculation results in this study exhibit their application potential for informing designs of the melter’s temperature distribution and stirring effects. Keywords: High level liquid waste · Glass vitrification · Numerical simulation · Electromagnetic field · Stirrer

1 Introduction Nuclear energy is a form of energy that is clean, scheduled, low-priced and carbonfree, whose utilization reduces the effect of climate change on the environment by avoiding carbon dioxide generation during the combustion process. Releasing plenty of energy in fission reactions, the uranium fuel is gradually depleted. In used nuclear fuel aqueous reprocessing, high level liquid radioactive waste is generated, comprising nitric acid, additives, extractants and nonvolatile fission products. The risk of leakage © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 874–884, 2023. https://doi.org/10.1007/978-981-19-8780-9_84

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constrains its transportation and storage. As a result, HLW vitrification has led to a scientific consensus to immobilize and permanently store HLW. For stabilization of encapsulated glass, improvements in the chemical, structural and thermal stabilities of glass are critical in the technology and device development process [1–3]. A joule heated melter is one international vitrification approach, which has been used in engineering applications in America and Germany for many years. However, corrosion of refractory bricks and electrodes constrains the service life of joule heated melters. This is because high temperature glass flows directly onto refractory bricks and electrodes, and they are attacked by metal ions in the high temperature molten glass flows, resulting in reduction of the melters’ serviceable life. For immobilization of aluminumrich HLW, the melters’ life reduction requires special attention. Correspondingly, dealing with HLW rich in aluminum, zirconium and cadmium, the melter consumes excess borosilicate glass and the concentration of these metal ions must be decreased. Another international approach is a cold crucible melter, which has advantages such as flexible disassembly, large capacity, high operation temperature and wide range of operable wastes. Under protection of a glass skull layer, walls of the cold crucible introduction melter avoid direct contact with the molten glass flow, preventing corrosion to metal walls, resulting in operation temperature up to 1600 °C. Furthermore, due to temperature gradient limitation from the refractory bricks, the startup of joule heated melter takes weeks, while CCIM only needs several hours for initiation, thus increasing production capabilities. Researchers from Russia, Korea, America, France and India have conducted studies on cold crucible introduction melters [4–8]. Electromagnetic, temperature, and flow fields and material properties have complex interactions on each other. The evaporation rate of molten glass, operating parameter selection and unloading rate are all significantly influenced by temperature distribution, which is closely related to heat release. Induction heat from an electric current is dominated by an alternating magnetic field as well as viscosity and electrical conductivity whose temperature-dependent range covers several orders. As a result, accurate predictions of material properties and electromagnetic fields are essential for processing operation optimization and designs [9, 10]. Due to the cold crucible melter’s high operating temperature and corrosion of the glass, experimental investigations typically provide insufficient information for engineers. In addition, numerical simulation informs the designs of the vitrification process with details that are difficult to obtain with thermal couples and Gauss meters because of temperature limitations. Gopalakrishnan et al. have investigated the electromagnetic field and temperature distribution of a cold crucible induction melter with a mechanical stirrer [10]. In their study, an in-house code of Maxwell equations is built and coupled with Ansys Fluent to evaluate the effects of stirring on flow and electromagnetic fields. The temperature results reveal that molten glass near the cooling bottom surface has a lower temperature than the glass in other regions, lower electrical conductivity and higher viscosity. To correct this inhomogeneity, the stirrer was introduced and product quality improvements was achieved. Sauvage et al. [11] have combined FLUX and FLUENT to evaluate the interaction between electromagnetic and flow fields, and figure out the specific threshold of joule power injected for the Marangoni phenomena. Roach et al. adopted ANSYS finite element model to assess the effects of operational conditions and geometry of the

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melter on the temperature distribution and product quality. The numerical simulation accurately predicts the experiment’s results with an error rate below 5% [12, 13]. Numerical simulation of the CCIM includes complex physical and chemical processes. To investigate the effects of induction heating power, operating frequencies and stirring on the temperature distribution in this study, numerical and experimental investigations were achieved. A CCIM with a diameter of 650 mm was built, and its electromagnetic field, temperature field and stirring effects have been numerically evaluated. This study comprises by four parts. First, the experimental facilities are briefly introduced, followed by descriptions of the mathematical models and material properties. The second section is dedicated to comparisons of temperature results between 650 and 800 mm CCIM. In the third part, the effects of induction power on temperature fields are evaluated. In the final fourth part, the stirring influence on hydrodynamic and chemical homogenization is analyzed.

2 Descriptions of Laboratory Facilities and Mathematical Models 2.1 Laboratory Facilities In this study, a CCIM with a diameter of 650 mm was built with 9 induction coils at a distance of 80 mm. Each circle is a copper pipe with a 2 mm wall and a 25 × 25 mm cross-section of a square. The height of the molten glass is 400 mm. The properties of the CCIM are shown in Table 1. The paddle type stirrer is water cooled. The diameter, width, and thickness are 0.26, 0.04, 0.02 m respectively. Table 1. Electromagnetic properties of CCIM component material Component

Material

Relative magnetic permeability

Electric conductivity (S · m−1 )

Coil

Copper

1

5.977 × 107

Melter walls

Stainless steel

1

1.369 × 106

Melter bottom

Stainless steel

1

1.369 × 106

Molten glass

Molten glass

1

16.7

2.2 Mathematical Models Adopting COMSOL software, the mathematical models of the CCIM are built based on the Maxwell equations, including Ampere circular law, Faraday’s law, Gauss’s law, and Gauss’s law for magnetism. Their differential forms are: − → − → − → ∂D ∇×H = J + ∂t

(1)

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− → ∂B − → ∇× E =− ∂t − → ∇· B =0

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(2) (3)

− → ∇· D =ρ

(4)

− → − → D =EE

(5)

− → − → B = μH

(6)

 J = σ E

(7)

E is electric field intensity, D denotes electric displacement vector, H represents magnetic flux density, B is magnetic field induction, J represents conduction current density, ρ is charge density, E is electric permittivity, μ is magnetic permeability, σ is electrical conductivity. To improve solving speed of these equations, magnetic potential and electric potential are introduced as:   =∇ ×A B

(8)

 = −∇ E

(9)

The four Maxwell equations have been transformed to: ∇ 2 A − μE

∂ 2A = −μJ ∂t 2

(10)

∇ 2  − μE

∂ 2 ρ =− 2 ∂t E

(11)

As the CCIM can be approximated by a cylinder, the incompressibility condition, Navier-Stokes, and thermal equations are written in a rotating frame as: →    →− ∂− vr → · −  ×−  ×  × r + ρ0 ∇ vr +  vr + ρ0 2 vr → ∂t   →  +∇  μ∇ − = −∇p vr − ρ0 β(T − T0 ) g

(12)

     ∂ →  · λ∇T  cp T + ρ0 cp T − + Qth vr = ∇ ∂t

(13)

ρ0

ρ0

Qth =

|J |2 2σ

(14)

Here, vr represents the relative velocity in the rotating frame, μ represents the dynamic  denotes the angular velocity vector, p viscosity, g represents the gravity vector, 

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represents the pressure, λ represents the thermal conductivity, and cp denotes the capacity. For the calculation of the flow field, boundary conditions of melter walls and the melter bottom are set as a no slip condition with a constant temperature at 800 K. The fluid is considered incompressible, and Lorentz forces are negligible. As the melter is a tube with symmetry surfaces, only one-twelfth of the melter is calculated.

3 Comparisons of Power Density Distribution Between 650 and 800 mm CCIM 3.1 Power Density Distribution of the 650 mm CCIM As most energy in a CCIM is carried away by the cooling water, the power distribution in a CCIM is an essential consideration for designs. Typically, the total induction power in the glass bath accounts for 30% of the power supplied by the rectifying circuit, and 70% of the total power is lost into the atmosphere and cooling water. In this study, the calculation of the induction power distribution within the glass bath was conducted. The 400 mm high glass bath is heated by a 1500 A alternating current at 400 kHz. After calculation, comparisons of the calculated results between this study and Jacoutot’s research [14] are shown in Table 2. Furthermore, the calculated power supplied by the coil is 165 kW, and the measured experimental power is 170 kW. This indicates that the induction heat in the glass bath accounts for 33% of the power in the coil. Table 2. Comparisons of power distribution between current study and literatures Current study

Jacoutot [14]

Maximum power density

6.21 × 106 W/m3

6.64 × 106 W/m3

Total power within glass bath

56 kW

60 kW

The maximum power in this study was slightly lower than that in Jacoutot’s research[14], suggesting that the CCIM in this study has the ability to transform electromagnetic energy to the induction heat with an acceptable quality. The total power within the glass bath suffers effects from the alternating magnetic fields as well as the electrical inductivity of the molten glass, which is influenced by the temperature. Accurately obtaining power distribution requires precise evaluations of heat flux through the cooling walls, alternating electromagnetic fields and molten glass properties. The temperature distribution within the 650 mm CCIM is shown in Fig. 1. Figure 1 illustrates that the highest temperature in the cold crucible is 1220 °C, which is consistent with Sugilal’s experimental result [15] and Sauvage’s calculated result [11]. Theoretically, the temperature is influenced by the inducted heat and cooling effect. At first, the inducted heat is generated near the walls, and the heat transfers to the center with the motion of flow. As a result, the temperature in the center region is lower than that near the wall. In addition, the temperature of molten glass near the bottom and walls

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Fig. 1. Temperature distribution of 400 mm high molten glass in 650 mm CCIM

is lower than that in the center, because of the cooling effect from the bottom and walls of the melter. The results of calculating the dimensionless induction heat distribution in 650 mm CCIM is shown in Fig. 2.

Fig. 2. Dimensionless induction heat distribution within glass bath in 650 mm CCIM

Figure 2 shows that the highest heat power density is in the outer one-third layer of the molten glass, indicating that most of the heat was released in the outer shell of the CCIM, which accords with the skin effect. The heat generated near the surface passes through the cooling wall into the cooling water, or into the center through natural convection or forced convection by stirring. In addition, the height of the highest generated heat power density declines gradually over time. Starting from ignition, the heat generation region goes down from the top surface to the upper part of the molten glass. This phenomenon may be explained by the influence of electrical conductivity. As the temperature of

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the molten glass increases from top to bottom during the ignition process, electrical conductivity climbs, and the energy generated by electricity in the lower part increases. Finally, after the temperature’s effect on the electrical conductivity declines, the location of heat release is dominated by the height of the induction coils. From collaborative effects of the coils, the heat release location is on the height of 300 mm in the 400 mm molten glass. 3.2 Power Density Distribution of 800 mm CCIM To investigate the effect of an increase of diameter on the power distribution, comparisons of inducted heat distribution between 650 and 800 mm CCIMs were conducted. The power distribution of 800 mm CCIM is shown in Fig. 3. For selection of the 800 mm power, inducted heat per unit volume was selected as a criterion. With the same boundary conditions, glass properties, and frequency of the alternating current, the total inducted heat per volume in 800 mm CCIM was calculated and compared with that of 650 mm. Depending on the difference between the comparisons, the total power is either decreased or increased. Iteratively, the power system of the 800 CCIM was shown to generate the same power per volume as the 650 mm.

Fig. 3. Dimensionless induction heat distribution within glass bath in 800 mm CCIM

The average power density of 650 and 800 mm CCIM is 4 × 106 W/m3 , and the total power in the induction coil of 800 mm is 247 kW. Figure 3 indicates that the area of dimensionless induction power density of 800 mm is smaller than that of 650 mm. One explanation for this phenomenon is that the maximum power density of 800 mm is 8.18 × 106 W/m3 , much higher than that of 650 mm at 6.21 × 106 W/m3 . Correspondingly, one-tenth of 8.18 is larger than one-tenth of 6.21, and the power density area of 800 mm is smaller than that of 650 mm. Though there is differentiation between the areas of 650 and 800 mm, they have similar shapes that represent the generated heat transferred from the walls to the center.

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4 Comparisons of Temperature Distribution Among Different Powers in the 650 mm CCIM Temperature distribution in CCIMs is influenced by induction power and heat accumulation with time. At the beginning of the ignition process, virtually all glass is frit glass, and the induction heat power density is low. As the inducted heat accumulates within the melter, the temperature of the molten glass gradually increases, resulting in the induction power rising, thus increasing the temperature elevation. This temperature increase trend is illustrated by the calculated temperature results in the center at 400 mm of the melter shown in Fig. 4. Figure 4 demonstrates the temperature trend in the center at 400 mm, a representative point in the melter, whose molten glass bath height is 800 mm. The temperature trends heated by different levels of power (257 kW, 285 kW, 317 kW, 348 kW and 380) are shown over time. All five trends show that the temperatures increase with time. When the inducted heat and cooling heat are in balance, the temperature reaches its peak. In addition, the temperature under 380 kW rises faster to higher maximum temperature than under 257 kW, theoretically because of the interactions between temperature and heat generation and how they accelerate each other. Within around 6000 s, the temperature under 380 kW reaches equilibrium. This process takes 3000 s longer than in Hawkes’ research, which simulated a 266 mm CCIM. Theoretically, this is because a larger diameter melter takes longer time to start than a smaller one. Furthermore, the peak temperature under 380 kW is 80 K higher than that under 257 kW. Typically, under higher generation power, the temperature in the center is higher.

Fig. 4. Temperature at 400 mm height in the center under different powers

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5 Stirring Effects on Flow Field in the 650 mm CCIM To improve the homogenization of the molten glass, it is essential to use a mechanical stirring, whose shape, dimension, and rotary velocity have significant effects on the flow field. In designs of stirrers, the reduction of dead zones is a vital consideration. For operating condition optimization, the selection of a stirrer’s rotary velocity is another considerable factor, and a high rotary velocity affects the stability of the facility. In this study, the flow field with a paddle stirrer has been numerically evaluated, and the results are shown in Figs. 5.

Fig. 5. Particle trajectories within 800 mm high molten glass with stirring at 10 rpm

Figure 5 evidences that stirring significantly brings mixing in vertical and horizontal directions. The stirrer accelerates the flow in a horizontal direction at the bottom, and fluid flows into the upper part and vertically generates a recirculation within the whole melter. Definitely, this recirculation causes a more powerful effect than does the natural convection. As a result, hydrodynamic and chemical homogenization must be improved due to these recirculations generated by stirring.

6 Conclusions A mathematical model of a 650 mm CCIM was built, whose temperature distribution, power density and transient startup were calculated. The calculated power supply from the coil was validated by experiments, and the calculated results coincide with the experimental powers released from the coil. This evidences the reliability of the mathematical model. Furthermore, comparisons between calculated density distribution of the 650 mm CCIM and the 800 mm CCIM were conducted. The results indicate that the 800

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mm CCIM has the same shape of power density distribution as the 650 mm, and provide further information for designs of a 800 mm CCIM. Effects from the induction powers on the temperature distribution of the 650 mm CCIM with an 800 mm glass bath were examined. The results illuminate that the induction power significantly influences the temperature and startup process. The stirring effects on the flow fields in the 650 mm CCIM were evaluated, and the calculated recirculations in the CCIM provide explanations for the improvement of homogenization from stirrers. Even though the simulation results precisely predict the induction power from the coil, further investigations are required on the designs of the 800 mm CCIMs. The power density distribution is affected by induction heat, glass properties, and cooling water. Analyzations of these factors are essential for the designs of the 800 mm CCIMs.

References 1. Perez, J.M., Peeler, D.K., Bickford, D.F., Strachan, D.M., Day, D.E., Triplett, M.B., Kim, D.S., Vienna, J.D., et al.: High-level waste melter study report, PNNL-13582, July 2001 2. Ahearn, J., Gentilucci, J.A., Pye, D., Weber, E.T., Woolley, F.E.: High-level waste melter review report, TFA-0108, July 2001 3. Kaushik, C.P.: Indian program for vitrification of high level radioactive liquid waste. Procedia Mater. Sci. 7, 16–22 (2014) 4. Gombert, D., Richardson, J.R.: Cold-crucible induction melter design and development. Nucl. Technol. 141(3), 301–308 (2003) 5. Crum, J., Maio, V., McCloy, J., Scott, C., Riley, B., Benefiel B., et al.: Cold crucible induction melter studies for making glass ceramic waste forms: A feasibility assessment. J. Nucl. Mater. 444(1–3), 481–492 (2014) 6. Sauvage, E., Gagnoud, A., Fautrelle, Y., Brun, P., Lacombe, J.: Thermoconvective flow of molten glass heated by direct induction in a cold crucible. Magnetohydrodynamics 45, 535– 542 (2009) 7. Sauvage, E., Gagnoud, A., Fautrelle, Y., Brun, P., Lacombe, J.: Numerical simulation of glass flow heated by direct induction in a cold crucible. In: Proceedings of the 6th International Conference on Electromagnetic Processing of Materials, Dresden, Germany (2009) 8. Sakai, A., Koikegami, H., Weisenburger, S., Roth, G., Kanehira, N., Komamine, S.: Comparison of advanced melting process for HLW vitrification, Joule-heated ceramic-lined melter (JHCM) and cold-crucible induction melter(CCIM). In: 25th International Conference on Nuclear Engineering, Shanghai, China (2017) 9. Hawkes, G. : Modeling an RF cold crucible induction heated melter with subsidence, ASME 2004 Heat Transfer/Fluids Engineering Summer Conference, Charlotte, USA (2004) 10. Gopalakrishnan, S., Thess, A.: A simplified mathematical model of glass melt convection in a cold crucible induction melter. Int. J. Therm. Sci. 60, 142–152 (2012) 11. Sauvage, E., Brun, P., Bonnetier, A., Lacombe, J., Chauvin, E. : 3-D Thermal, hydrodynamic & magnetic modelling of elaboration of glass by induction in cold crucible, WM2010 Conference, Phoenix, USA (2010) 12. Roach, J.A., Richardson, J.G. : Technical development of new concepts for operation and control of cold crucible induction melters for vitrification for radioactive wastes. In: Proceedings of the Waste Management 2006 Conference, Tucson, AZ, USA (2006) 13. Roach, J.A., Lupukh, D.B., Martynov, A.P., Polevodov, B.S., Chepluk, S.I.: Advanced modeling of cold crucible induction melting for process control and optimization. In: Proceedings of the Waste Management 2008 Conference, Phoenix, USA (2008)

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14. Jacoutot, L., Fautrelle, Y., Gagnoud, A., Brun, P., Lacombe, J.: Numerical modeling of coupled phenomena in a mechanically stirred molten-glass bath heated by induction. Chem. Eng. Sci. 63, 2391–2401 (2008) 15. Sugilal, G.: Experimental study of natural convection in a glass pool inside a cold crucible induction melter. Int. J. Thermal Sci. 47, 918–925 (2008)

Development and Validation of Source Term Model of Corrosion Products in the Primary Circuit Liu Ya-ni(B) , Jin Xin, Liu Xiao-han, Wang Tao, and Chen Wei-lin China Nuclear Power Technology Research Institute, Shenzhen, Guangdong, China [email protected]

Abstract. Corrosion products in primary circuit of nuclear reactor are deposited in the core and irradiated by neutrons to form activated corrosion products, which are shed and migrated, and then deposited outside the reactor to form an ex-core radiation field. The establishment of source term analysis model of corrosion products can effectively calculate the primary circuit radioactivity level, and assess the risk of radiation exposure of power plant personnel. In this paper, the burnup equations are established based on the mass conservation of each nuclide of the activated corrosion products, and the activity and dose rate of radionuclides are calculated by numerically solving for each nuclide through Chebyshev Rational Approximation Method (CRAM). The model is validated by using the primary circuit radioactivity monitoring data and the measured ex-core dose rate of domestic and foreign nuclear power plants, and the results show that the predicted values of ex-core dose rate are in good agreement with the measured values. Keywords: PWR · Fuel crud · Source term model · Program validation

1 Introduction Due to the high temperature and pressure of the reactor water environment, the main pipeline and other metal components will release corrosion product particles and ions, which will circulate with the coolant in the primary circuit. When the corrosion products flow through the core area, they will be irradiated by neutrons. On the other hand, corrosion products may be deposited on the surface of the first-loop system to form fuel crud, and this crud will be irradiated by neutrons to become activated corrosion products, which may be shed, re-released into the coolant, and deposited outside the core, creating an ex-core radiation field that contributes the vast majority of the collective dose to plant personnel. Therefore, it is important for nuclear safety to establish a source term model and accurately calculate the distribution of radioactivity levels in the core, ex-core and coolant [1, 2].

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 885–897, 2023. https://doi.org/10.1007/978-981-19-8780-9_85

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The key to calculating the radioactivity is to solve the burnup equation. The burnup equation is based on the mass conservation of each nuclide, and used to describe the change of nuclide composition. At present, analytical calculations of burnup problems are implemented by computer programs. Table 1 is a brief introduction to common burnup programs, from which it can be seen that the solution of the burnup equation can be divided into two categories: analytical methods and numerical methods. The analytical method, namely the linear sub-chain method (TTA) [3], is to split all the reactions into multiple linear burnup chains, and then calculate them separately to obtain the analytical results for each chain, finally, the results of each chain are superimposed to get the real results [4]. In the calculation, the analytical method needs to pre-linearize the reaction chains in order to ensure the high accuracy, however, the linearization of the nuclide chains is very complicated. On the other hand, Numerical methods allow direct calculation, and usually can obtain a high accuracy by choosing a suitable time step, but the half-life of the nuclides varies widely, causing the problem of inaccurate calculation of short-lived nuclides. The common numerical methods in the burnup calculation include Euler method, Euler exponential method, Longy-Kuta method, Taylor expansion method of matrix exponential function, Pade approximation method, Krylov subspace method, Chebyshev rational approximation method (CRAM), among which CRAM is widely recommended for its high calculation accuracy and efficiency. Table 1. Common domestic and international burnup calculation procedures Program

Nation

Laboratory

Algorithm

ORIGEN

America

ORNL

Taylor unfolding method

CINDER

America

LANL

TTA

SEPRENT

Finland

VTT

TTA/CRAM

BINSON

Japan

The University of Tokyo

TTA

FISPACT

Europe

EURATOM

Euler exponential method/Jill method

DEPTH

China

Tsinghua University

TTA

The CRAM is a numerical algorithm for solving the burnup equation based on the burnup matrix. The essence is to find the best rational approximation of the matrix exponent eAt . The advantage of this method is that it does not require preprocessing to calculate the matrix exponent, and can calculate the short-lived nuclides directly and accurately. The accuracy of the CRAM is very close to that of the analytical method, but the former is relatively faster to compute. Based on the above background, the main work of this study is to develop a source term calculation program based on the CRAM, and having the capability of calculating high-burnup and activation. In this paper, the source term model of the primary circuit includes coolant source term, core deposition source term and ex-core (steam generator and main pipeline) deposition source term, considering the five most important nuclides contributing to the radioactivity level. The source term model is also added to CAMPISIS, and was validated using data of the primary circuit radioactivity monitoring data and

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measured ex-core dose rates, which are from CGNPC’s Ling’ Ao units 1, 2 and 3, as well as the Swedish commercial pressurized water reactor (PWR) Ringhals. In summary, the research flow chart is shown in Fig. 1.

Mechanistic analysis of corrosion products

Source term model building and calculation

Program development

Nuclear power plant data validation

Fig. 1. Research flow chart

2 Theoretical Model 2.1 Burnup Equations The source term of the primary circuit includes the coolant source term, core deposition source term and ex-core deposition source term, and their corresponding burnup equations are described below. Core deposition source term: The burn-up equation for the core deposition source term is shown in Eq. (1) and Eq. (2). The first term to the right of the equal sign is the rate change of amount of substance of the subnucleus due to irradiation of the parent nucleus, the second term is the change rate of amount of substance of the subnucleus due to decay, the third term is the change rate of amount of substance of the subnucleus due to deposition of coolant into the core, and the fourth term is the change rate of amount of substance of the subnucleus due to shedding of the sub nucleus deposited in the core into the coolant. Equation (2) is the change rate of amount of substance of parent nucleus due to irradiation activation Nbulk (t) Nk (t) dNki (t) = Nki,f σi,f ϕ − λi Nki (t) + i bulk Vkdep,elei − i k Vkfall,elei dt Nelei Nelei dNki,f dt

= Nki,f σi,f ϕ

(1)

(2)

Ex-core deposition source term: The burn-up equation for the ex-core deposition source term is shown in Eq. (3), which does not take into account the effect of neutron irradiation on the parent nucleus in the ex-core deposition source term. The first term on the right side of Eq. (3) is the change rate of amount of substance of subnucleus due to the self-decay, the second term is the change rate of amount of substance of subnucleus due to the deposition of coolant outside the core, and the third term is the change rate of amount of substance of subnucleus due to the shedding of subnuclei deposited outside the core into the coolant. Nbulk (t) Nk (t) dNki (t) = λi Nki (t) + i bulk Vkdep,elei − ik Vkfall,elei dt Nelei Nelei

(3)

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Coolant source term: A certain percentage of the time the nuclides in the coolant flow through the core, are irradiated and produce radionuclides. Activation corrosion products deposited in the core and outside the reactor are also shed to the coolant. In addition, the chemical volume system (CVS) removes some of the activation corrosion products by down draining. The burnup equation for the coolant source term is shown in Eq. (4) and Eq. (5). The first term to the right of the equal sign is the change rate of amount of substance of the subnucleus due to irradiation of the parent nucleus, the second term is the change rate of amount of substance of the subnucleus due to the selfdecay, the third term is the difference between the change rate of amount of substance shed into the coolant by all control bodies (core and ex-core) and the change rate of amount of substance deposited on surface of the core and ex-core by the coolant, and the fourth term is the change rate of amount of substance of the subnucleus due to the downward removal of the CVS. ⎛ ⎞  Nk (t) dNbulk (t ) Nbulk (t) k Nbulk bulk (t) + k bulk i i i i ⎝ ⎠ = Nbulk σ ϕη − λ N V − V i,f i i i,f i i − bulk Vdown,elei k bulk fall,ele dep,ele dt N i N i N i k ele ele ele

dNbulk i,f dt

= −Nbulk i,f σi,f ϕη

(4) (5)

In the above equation, Nik is the amount of substance of core control volume k k is the amount of substance of the parent nucleus of core control subnuclei i (mol). Ni,f k is the amount of substance of the element corresponding volume k subnuclei i (mol). Nele i to subnuclei i in core control volume k (mol). Nibulk is the amount of substance of coolant bulk is the amount of substance of the element corresponding to subnuclei i (mol). Nele i bulk is the amount of substance of the parent nucleus of coolant subnuclei i (mol). Ni,f subnuclei i in the coolant (mol). σ i,f is the reaction microscopic cross leg of the parent nucleus (cm2 ). ϕ is the neutron injection rate in the core region (n/cm2 /s). λi is the decay k constant of subnuclei i. η is the time ratio of coolant flow through the core. Vdep,ele i is the deposition rate of the elements corresponding to the core control volume k subnuclei k i (mol/s). Vfall,ele i is the is the deposition rate (mol/s) of the element corresponding to bulk subnuclei i in the core control volume. Vdown,ele i is the rate of element shedding (mol/s) of subnuclei i in the core control volume. The radionuclides considered in the source term model and the way they are produced are shown in Table 2, and there are five main ones: 51 Cr, 54 Mn, 59 Fe, 58 Co, and 60 Co, which come from the activation of metallic elements (Ni, Fe, Cr, Co, Mn) in the corrosion products outside the core (steam generator and main pipeline) and contribute more to the source term.

2.2 Chebyshev’s Rational Approximation Method (CRAM) The burnup equations for all nuclide systems are coupled to form a system of first order constant coefficient differential equations [5]. This system of equations can be expressed in matrix form as: dN(t) = AN(t) dt

(6)

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Table 2. Radionuclides and their sources of production Nuclides

Half-life

Generation method

51 Cr

27.7days

54 Mn

312.2days

50 Cr (n, γ)51 Cr 54 Fe (n, p) 54 Mn

59 Fe

44.51days

58 Fe (n, γ)5 9 Fe

58 Co

70.88days

58 Ni (n, p) 58 Co

60 Co

5.27years

59 Co (n, γ) 60 Co

A is the coefficient matrix of the system of equations, the so-called burn-up matrix. N(t) is denoted as the nucleon the rate of amount of substance vector of each nuclide. The form of the solution is shown below. N(t) = eAt N(0)

(7)

The key to solving the burnup equation is the exact solution of eAt . The burnup equation Eqs. (7) are solved using the CRAM, and because the burnup matrix usually contains elements of large magnitude, direct power expansion will produce very large numbers, which will be infinite in programming, and secondly, there are both positive and negative elements in the burnup matrix, so in the actual calculation we use the Chebyshev rational approximation in the form of partial fractional decomposition (PFD) [6], with order 14. According to previous studies, since the eigenvalues of the burnup matrix fall close to the negative real axis of the complex flat surface, so the best rational approximation is made on (−∞, 0] [7]. The expansion factors are shown in Table 3. Its approximation to the e-exponent is as follows [8–10].

e ≈ a0 + 2Re( x

k/ 2  j=1

aj ) x − θj

(8)

where α0 is the retention value when x is taken to be infinite. αj is the corresponding retention value. And θj is the pole. Applying Eqs. (6, 7, 8) to the matrix calculation of Eq. (7), the actual solution is obtained in the form of

N(t) = e N(0) = a0 N(0) − 2Re( At

k/ 2 

((At + θj I)−1 a0 N(0)))

(9)

j=1

N (0) is the nuclide the rate of amount of substance vector at the initial moment, N (t) is the nucleon the rate of amount of substance vector at moment t, and A is the corresponding fuel matrix. In summary, the calculation flow chart of the source term analysis module is shown in Fig. 2. The whole calculation is divided into five steps: (1) Initialization of elemental source term the rate of amount of substance. (2) Obtaining the total mass of each element

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Coefficient

Real Part

Imaginary Part

θ1

+5.6232 × 100

+1.1941 × 100

θ2

+5.0894 × 100

+3.5888 × 100

θ3

+3.9934 × 100

+6.0048 × 100

θ4

+2.2698 × 100

+8.4618 × 100

θ5

−0.2087 × 100

+ 1.0991 × 100

θ6

−3.7032 × 100

+1.3656 × 100

θ7

−8.8977 × 100

+1.6631 × 100

α1

−2.7876 × 101

−1.0215 × 102

α2

+4.6935 × 101

+4.5646 × 101

α3

−2.3499 × 101

−5.8090 × 100

α4

+4.8074 × 100

−1.3209 × 100

α5

−3.7639 × 10–1

+ 3.3519 × 10–1

α6

+ 9.4403 × 10–3

−1.7185 × 10–2

α7

−7.1554 × 10–5

+ 1.4362 × 10–4

α0

+2.8082 × 10–14

+0.0000 × 100

of the current main fluid from upstream, and the mass of crud back to the main fluid. (3) Passing in the initial elemental source term amount of substance and creating the CRAM. (4) Constructing the stiffness matrix of the burnup Eq. (5) Call the cram solver to solve the burnup equation to obtain the new main fluid and control element source term amount of substance.

3 Model Validation In this study, we use data from Ling’ Ao Nuclear Power Plant Unit 1 & 2 & 3, which are all CPR1000 units from CGNPC, and we also use the operating data of Ringhals Unit C from 2000 to 2010 for model validation comprehensiveness. Ringhals Nuclear Power Plant Unit C is a commercial pressurized water reactor commissioned in 1981 in Sweden. The measured operational data from the plant used for the validation include radioactivity levels of the crud in the SG and the main pipeline. The validation results are as follows. 3.1 Ling’ Ao Unit 1 Data A comparison between the dose rates measured in the main pipeline and the calculated values from Cycle 2 to Cycle 13 for Ling’ Ao Unit 1 is shown in Figs. 3, 4 and 5. The dose rate is obtained by multiplying the activity of five radionuclides (51 Cr, 54 Mn, 59 Fe, 58 Co, and 60 Co,) and the corresponding dose conversion factor. Among all

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Fig. 2. Calculation flow chart of source term analysis module

Fig. 3. Cold leg dose rate of Ling’ Ao Unit 1

radionuclides, 58 Co and 60 Co are the main contributors to the dose rate, as shown in Figs. 3–5. It is evident that the measured dose rates of the pipeline are in the order of 10–100 μSv/h. The predicted dose rates in the cold leg, hot leg and cross leg are in the same order of magnitude as the measured dose rates, and the relative errors of the predicted and measured values are 30.07%, 50.45% and 30.05%, respectively, which are less than 100%, indicating that the predicted values are in good agreement.

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Fig. 4. Hot leg dose rate of Ling’ Ao Unit 1

Fig. 5. Cross leg rate of Ling’ Ao Unit 1

3.2 Ling’ Ao Unit 2 Data Figures 6 through 8 show the measured dose rates for cycles 2 through 10 on the main pipeline of Ling’ Ao Unit 2 compared to the model calculated values. Similarly, the measured dose rate of the pipeline ranges from 10 to 100μSv/h. The predicted dose rates for the cold, hot and cross legs are in the same order of magnitude as the actual measured dose rates, with relative errors of 36.97%, 47.52% and 48.00%, respectively, and the predicted results are in good agreement with the measured values Fig. 7. 3.3 Ling’ Ao Unit 3 Data Figures 9, 10 and 11 show the measured dose rates for the main pipeline supply at Ling’ Ao Unit 3 from cycle 2 to cycle 6 compared to the calculated values from the source term model. The relative errors of the predicted dose rates for the cold, hot, and cross legs with the measured dose rates for Ling’ Ao Unit 3 are 43.50%, 62.30%, and 41.10%,

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Fig. 6. Cold leg dose rate of Ling’ Ao Unit 2

Fig. 7. Hot leg dose rate of Ling’ Ao Unit 2

respectively, both are of the same order of magnitude. The relative errors are within the acceptable range and the predictions are accurate. 3.4 Ringhals Unit C Data Figure 12 show a comparison between the measured 58 Co dose rates and the calculated values for Ringhals Unit C in the main pipeline. These operation data are from ten consecutive years of Ringhals Unit C. Similarly, the 58 Co predicted by the source term model is in the same order of magnitude as the measured values, with a relative error of 47.70%, and in the latter cycles, the predicted values are slightly higher than the measured values in Ringhals C unit, which may be related to the long-term corrosion behavior of Inconel 690.

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Fig. 8. Cross leg dose rate of Ling’ Ao Unit 2

Fig. 9. Cold leg dose rate of Ling’ Ao Unit 3

3.5 Validation Results Discussion The main pipeline mainly compares the dose rates of cold leg, hot leg and cross leg. The dose rate predictions of the source term model for the crud radioactivity were compared with the measured values of operating data for four units with a total of 32 cycles, and it should be noted that the operating data cycles for most of the plant data are incomplete and the measurement errors for some cycles are too large to be used for validation, especially for Ringhals C unit where the operating data are from cycle 18 to cycle 23, which means that the previous core crud quality and radioactivity data are unknown, affecting the validation accuracy of the data for this unit. However, good results can also be seen for compliance validation using existing plant data, and the results showed that the source term model predicts well for the operational data of Ling’ Ao Units 1–3, and for the unit 2000–2010 Ringhals Unit C dose rate measured in the main pipeline and the source term model calculated values are also in the same order of magnitude. In summary, the usability and accuracy of the source term model was initially validated.

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Fig. 10. Hot leg dose rate of Ling’ Ao Unit 3

Fig. 11. Cross leg dose rate of Ling’ Ao Unit 3

4 Conclusions This study developed a source term model for calculating reactor primary circuit activation corrosion products, and validated the model with the operation data of domestic Ling’ Ao Units 1, 2 and 3 and foreign commercial pressurized water reactor Ringhals Unit C. The conclusions are as follows: 1. The corrosion product source term calculation program is based on Chebyshev’s rational approximation method to solve the burnup equation, and then predict the radioactivity level, and the CRAM solver can take into account the solution of different lifetime nuclides, which enables the program to calculate the distribution of radioactivity level in core, ex-core and first-loop coolant with high accuracy and efficiency.

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Fig. 12. Ringhals Unit C hot leg 58 Co activity

2. The model validation results show that, for domestic and foreign pressurized water reactor nuclear power units, the calculated values of this program are in good agreement with the first-circuit radioactivity monitoring data and the measured values of ex-core dose rate. The usability and accuracy of the source term calculation model for the first loop activation corrosion products established in this paper are initially demonstrated. 3. The source term program developed in this paper can predict the ex-core radiation dose caused by the activation of metal corrosion products more accurately and quickly, which can provide a reference for the radiation safety protection of the shutdown and maintenance personnel.

5 Funding Category Shenzhen Science and Technology (JSGG20210629144537005)

Research

and

Development

Fund

References 1. Yang, S.Y., He, M.L., Jiang, D.F., et al.: Optimization design of radiation shielding materials for 235 U fission source in reactor. Chin. J. Comput. Phys. 34(1), 73–81 (2017) 2. Shen, Y.S., Li, K.B., Shi, X.M., et al.: Study on disposing high-level transuranic waste in a fusion fission reactor. Chin. J. Comput. Phys. 34(2), 142–148 (2017) 3. Cetnar, J.: General solution of Bateman equations for nuclear transmutations. Ann. Nucl. Energy 33(07), 640–645 (2006) 4. Wu, M.Y., Wang, S.X., Yang, Y.: Coupling calculation of linear nuclide chain-based burnout algorithm with Monte Carlo procedure. Intense Laser and Particle Beam 01, 248–252 (2013) (in Chinese) 5. Xie, Z.S.: Physical analysis of nuclear reactors, pp. 168–169. Xi’an Jiaotong University Press, Xi’an (in Chinese) (2004)

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6. Yan, Y.H., Qiu, C.H., Zhang, B.: Development and validation of the burnup calculation module of the COSINE package component calculation program LATC. At. Energy Sci. Technol. (SI), 372–375 (in Chinese) (2013) 7. Pusa, M.: Rational approximations to the matrix exponential in burnup calculations. Nucl. Sci. Eng. 169(2), 155–167 (2011) 8. Cody, W.J., Meinardus, G., Varga, R.S.: Chebyshev rational approximations to e-z in [0, + inf) and applications to heat-conduction problems. J. Approx. Theory 2(1), 50–65 (1969) 9. Huang, K.: Research on high-fidelity burnup calculation method for numerical reactors. Xi’an Jiaotong University, Xi’an (in Chinese) (2016) 10. She, D.: Study on burnup and source convergence based on RMC for autonomous stack. Tsinghua University, Beijing (2013) (in Chinese)

A Review on Adsorption Mechanisms and Distribution Coefficient (K d ) of Cesium in Clay/Host Rock Yuling Wu1 , Sheng Fang1 , Jingchi Zhang1 , Xinyuan Mo1 , and Longcheng Liu1,2(B) 1 China Institute of Atomic Energy, Beijing 102413, China

[email protected] 2 KTH Royal Institute of Technology, S-100 44 Stockholm, Sweden

Abstract. For the disposal of high-level radioactive waste (HLW), the deep geological disposal is recognized as an effective method. The distribution coefficient (K d ) of radionuclides on buffer/backfill materials or host rock is one of the key parameters used in the safety assessment of geological repository. 137 Cs is one of the high-yield (t1/2 = 30.1 y, 6%) fission products in spent fuels, its high solubility makes it likely to migrate through groundwater to the biosphere. Multibarrier system prevents leakage of radionuclides to the environment. The present review discusses the general mechanisms of cesium adsorption by minerals, elaborates the parameters which influence adsorption of cesium contain concentration of cesium, pH, humic acid, competitive cations and properties of minerals. Furthermore, we have collected the K d values from cesium adsorption studies concerned with the minerals conducted during the past two decades, and analyzed by the probabilistic modelling to obtain the best-estimated K d values of Cs adsorption on bentonite, granite and clay under different solution conditions. Keywords: Safety assessment · Cesium · Sorption · Probabilistic modelling

1 Introduction A large amount of high-level radioactive waste (HLW) was generated during the nuclear industry production, especially the nuclear fuel cycle. The deep geologic disposal is recognized as the most feasible and safest disposal method for HLW in the world [1– 4]. This requires a multiple barrier isolation system, including both engineered and natural, constructed in bedrock several hundred meters below the ground surface to prevent radioactive elements from entering the biosphere over long time scales [5–7]. Compacted bentonite is often considered as candidate for buffer/backfill materials in such repository concepts, due to its low hydraulic conductivity and high cation exchange capacity [8– 14]. An important function of compacted bentonite is to retard the migration of radionuclides from the waste forms to surrounding host rock. Among many rock types under consideration as potential host formations for repositories, clay [15–19] and granite [20–27] are particularly important because of their abundance in natural systems, and their favourably high sorption properties. Retention of radionuclides from groundwater © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 898–912, 2023. https://doi.org/10.1007/978-981-19-8780-9_86

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flow can be expected to diffuse into the pore water of host rock, additionally, adsorbed by the rock surfaces [28, 29]. It is necessary to understand the mechanism of sorption. The distribution coefficient (K d ) is a useful parameter to predict the sorption behavior and the mobility of radionuclides in the geosphere. The study here especially focused on the sorption behavior of cesium, the only oxidation state of cesium is +1, and in soils and solutions cesium is present as hydrated Cs+ cation. Cesium does not hydrolyze nor precipitates [30–32]. It does not complex with the typical compounds in the hydrosphere or form colloids. 137 Cs produced by nuclear fission of U and Pu, is a major component of HLW. In some cases, large amounts of 137 Cs have been released into the environment accidentally, such as Chernobyl, Hanford [33] and Fukushima accidents [34–36]. Its high solubility and chemical similarity to K, facilitating it readily assimilated by terrestrial and aquatic organisms.

2 Mechanism of Cesium Adsorption on Minerals As it well-know that adsorption of Cs by ion exchange, as a large cation, it has a very small hydration energy which makes it possesses highly sorption selectivity [37]. Cs could interact strongly and selectively with the phyllosilicate of soil, sediment, and clay, through weak electrostatic attraction or the strong bonds formed by the partial sharing of electrons between Cs and the oxygen-donor ligand sites. There are at least four kinds of adsorption sites to Cs in micaceous minerals [38–43]. The high affinity/lowcapacity site type is often referred to as frayed edge sites (FES) locating at the wedge edge and dominate Cs uptake at low concentrations [15, 16, 25, 42, 44]. These sites are predominantly accessible to cations with low hydration energies such as K+ , Rb+ and NH4 + [45, 46], particularly K+ compete most effectively against Cs+ for these sites [15, 31, 47–49]. Bivalent cations such as Ca2+ , Mg2+ and Sr2+ , with relatively large hydration energies show non-competitive for the frayed edge sites [50]. In most cases, Cs concentrations would be low enough that the FES are far from saturation. Once FES saturated, excess Cs are adsorbed by the non-specific Type II sites (TIIS) and Planar sites (PS). Cs are adsorbed on TIIS which at the edges of the phyllosilicates by electronic bonding [40]. The third type of sites are the PS, associated with the fixed negative charge on the surface of illite arising from isomorphous substitutions e.g. Al3+ ↔ Si4+ . Finally, interlayer sites (ITS), existing in the expanding clays, not available for non-expansive mica mineral [51]. The cation to shed its hydration shell upon entering the interlayers in the clay, thus causing interlayer dehydration and collapse [50]. Both PS and TIIS own high capacities but low adsorption affinities to Cs, while FES with limited capacity have the highest selectivity and adsorption affinity. 2-site and 3-site models [20, 39, 43, 47, 52–54] based on the different selectivity coefficients of Cs at the different adsorption sites are successfully developed to model Cs adsorption on illites and argillaceous rocks.

3 Parameters Which Influence Adsorption of Cesium on Minerals For cesium adsorption, the most important parameters are the concentration of the nuclide, the property of the mineral itself and chemical conditions of the aqueous phase; the latter include ionic strength, pH, temperature and the presence of competing ions.

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3.1 Concentration of Cesium At low cesium concentrations (10−6 M), the K d is highly dependent on the initial concentration. It decreased with an increase in the initial concentration of cesium in the solution [9, 34, 45, 47, 54, 55]. At low concentrations, Cs adsorption on illite can be two to three orders of magnitude higher than that at concentrations up to 10−4 M [45, 47, 55]. Owing to cesium mainly adsorbed on FES [16, 44], with an increasing initial concentration, these sorption sites are fully occupied and cesium adsorbed additionally to the TIIS and PS [5]. A maximum sorption is reached with lower concentrations because more sites on the minerals are available [37]. It is important to note that, radionuclides migrate in the environment, the total chemical concentration of radioactive cesium is expected to be extremely low, Cs ions are preferentially distributed onto sorption sites with high affinity [15]. 3.2 pH Cesium always present as Cs+ over a wide pH range, due to it is hard to hydrolise or form complexes. Therefore, the pH of the system only affects the exchange properties of the minerals and does not have any effect on the cesium species [51, 56]. Cesium sorption on illite at trace concentrations varied significantly with pH, particularly under acidic conditions. The effect is explained as competition of protons and the dissolution of the illite. At the acid pH, the hydrogen ions compete with Cs for the sorption sites in the sorbent and may hinder the binding of positively charged metal ions. At the same time, the constituent elements of illite (Si, Al, Mg and K) are released into solution through the partial dissolution of the clay. Thus, the released cations, in particular K, are compete with Cs to sorption sites. As the pH increases, more negatively charged surfaces become available thus leading to greater adsorption of Cs [9]. However, it is found that the sorption capacity of granite does not increase noticeably under alkaline environment [23]. 3.3 HA It is found that K d values of clay minerals decreased with the existence of humic acid (HA), the primary cause of is that HA adsorbed to the mineral surface and block the access of Cs to the FES and TIIS [38, 57, 58]. The degree of inhibition for the Cs sorption by the minerals depends on the types of minerals [58, 59]. The strong inhibition of Cs adsorption in the presence of HA in the illite system appears at low Cs concentration, but the presence of HA inhibits Cs adsorption over the entire Cs concentration range in the vermiculite system [38]. 3.4 Effect of Cations The K d values of Cs increase when initial concentration of competitive solution cations decreases [10, 60]. The most important ions which compete with cesium for adsorption

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sites on minerals are Na+ , K+ , Ca2+ and Mg2+ . The influence of cations on adsorption of Cs is possible to explain by competitive ion exchangeable reactions and by occupying of active adsorption sites of minerals [61]. Among these cations, the presence of K+ constraints the Cs adsorption strongly [31], especially when potassium concentration exceeds 10−3 M [15], which cations, with similar radius (ionic radius of 138 pm in comparison with 167 pm of Cs+ ions) and hydration energy. While Ca2+ as well as Na+ also affects the Cs adsorption to a certain extent, this is due to their high contents in groundwater solutions. These results show that the ability of the cations to depress the sorption of cesium can follow this order [15, 21, 48, 49, 62, 63]: K+ > Ca2+ > Mg2+ > Na+ . 3.5 Properties of the Minerals K d values can vary of several orders of magnitude from one mineral to another. The highest distribution coefficients have been reported for the illite, smectite, kaolinite. Minerals such as fluorite, quartz, apatite, calcite, gibbsite, manganese dioxide and the iron oxides are poor sorbents of cesium [64]. The differences between the minerals are partly explained by their SSA and CEC [64], and the retention can be thought to increase with increasing SSA and CEC. However, the main differences in the retention of cesium are created by the type of minerals’ retention sites. Illite is a non-expanding 2:1 aluminosilicate clay. Cesium sorption to illite has been described as a cation exchange process on either the basal/planar edge or interlayer sites [63]. Potassium ions in interlayer are unexchangeable in contrast to those of expanding smectite and this is why illite has a lower CEC than clays with expandable interlayers [65]. Nevertheless, it is reported that weathering of the edges of micaceous minerals results in expansion of the interlayer spaces and make interlayer cations partially exchangeable [65, 66]. Cs is reversibly adsorbed to kaolinite and montmorillonite [55, 67], but the sorption behavior with illite is apparent irreversible observed in some cases. This irreversibility has been attributed to sorption to high affinity FES, and their subsequent collapse that prevents access to the sites [66]. Montmorillonite is a 2:1 smectite clay with an expanding interlayer, that allows for the charge compensating cations both in the basal planes of the clay particles and in the interlayers, can be readily exchanged by other cations present in solution [19]. As a consequence of this, montmorillonite usually has high CEC capacity and could adsorb large amounts of cesium. Unlike the illite, the montmorillonite does not possess sites of high cesium selectivity [68]. It is found that the adsorption capacity of montmorillonite to Cs is significantly stronger than calcite, kaolinite, chlorite and glauconite under the same experimental conditions [69]. Vermiculite is an expanding 2:1 layer, trioctahedral clays. It has large CEC due to the sorption sites are found both on the planar surface and in the interlayer. Vermiculite is an effective sorbent for Cs [70], and cesium dehydrates in the interlayer sites [40, 71] and replace the location of K+ resulting the partial collapse [2, 38, 67, 72, 73]. Kaolinite is a 1:1 aluminosilicate non-expanding clay. Due to its low CEC and absence of FES sites [74], kaolinite sorbs Cs less effectively than montmorillonite or illite. Micaceous minerals present a favorable environment for Cs adsorption. They are common phyllosilicate minerals with a crystallographic sheet structure that consists of metal ions, principally aluminum and

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silicon. Biotite and muscovite are weathered from mica minerals, under identical weathering conditions, muscovite could be expected to develop fewer Cs-complexing FES than biotite. According to the experiment of the sorption behaviour of Cs on granite, biotite minerals dominate Cs adsorption and SEM/EDS mapping further clearly shows the adsorbed Cs ions are highly concentrated on the fracture surface of biotite [23, 61]. The distribution coefficients of cesium on biotite (0.304 ± 0.005 m3 /kg in 10−8 M) are 1 or 2 orders of magnitude higher than that of potassium feldspar and plagioclase under the similar concentration [5].

4 Recommended K d Value of Cs Adsorbed on Specified Minerals K d is one of the most significant parameters in predicting the fate of a radionuclide in the environment, it is a common input in risk assessment models. Due to the variability in influencing parameters such as pH, CEC, grain sizes, organic matters, oxidation/reduction conditions, major ion chemistry, contact time of the radionuclides and soil, soil: solution ratio, and chemical characteristics of the solution, the measured K d values can range over several orders of magnitude. To make the K d data derived from compilations suitable for risk assessment purposes, it must be ensured that these data are derived from experiments with adsorbate, solution and radionuclide species similar to those present in the actual contamination scenario. Ideally, the K d values of site-specific are used in the risk assessment models. However, for the safety reasons, it is difficult to obtain such data from the actual situation especially the actinide radionuclides. To overcome this limitation, probabilistic model is a useful option [75, 76]. Probabilistic model use K d compilations to derive the best-estimate K d values, statistical functions describing the overall variability of K d values, and to derive confidence intervals at specified significance levels. 4.1 Data Collection and Treatment We have collected 620 pieces of data on Cs adsorption on different minerals, the overall dataset contained values ranging within up to seven orders of magnitude (Min-Max range of 5 × 10–2 —2.3 × 105 mL/g). Reasonable recommended K d valus for clay/host rock are derived from the K d dataset using the probabilistic model, combined with appropriate classification criteria. Based on the introduction of the adsorption mechanism of cesium mentioned above, the mineral/pH/initial concentration strategy is then developed and applied to create partial datasets with less variability. First, according to the selection of geological repository in China, bentonite is considered as candidate for buffer/backfill materials, granite is the host rock of the repository. The datasets are divided into three categories: bentonite, granite and clay. Based on this classification, geometric mean (GM) and geometric standard deviation (GSD) of the three categories are calculated. The calculation results are shown in Table 1. The analysis results show that the GM obtained by processing the data only according to the minerals type has a large GSD in the relevant groups, and cannot effectively represent the K d values of the relevant dataset. There will be a large difference between the recommend K d value and the actual adsorption situation. In order

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to deal with this difference, the dataset should be further grouped. According to the main influencing factors of the adsorption process described above, pH of solution and initial concentration of cesium are selected as the grouping standard. Then the three datasets are classified according to pH (pH < 7, 7 ≤ pH ≤ 10, pH > 10). The range of pH is determined by the groundwater, which is usually neutral or weakly alkaline. In Hanford site’s tank leakage and other nuclear accidents, the adsorption pH environment of Cs is highly alkaline [37]. The last classification is concentration, a high level waste tank leaked approximately 1.51 × 1015 Bq of 137 Cs, but when radionuclides migrate in the environment, the total chemical concentration of radioactive cesium is expected to be extremely low(~10–8 M). As a result, the concentration is divided into two ranges:the low concentration area(Cint < 10–4 M) and the high concentration area(10–4 M ≤ Cint < 5 × 10–3 M). The aim of establish a series of criteria is to reduce the variability of K d values, to create K d partial datasets properly and construct cumulative distribution function (CDF) to derive best-estimate values for each group. Table 1. K d (Cs) for dataset grouped according to mineral/(mL/g) Dataset

N

GM

GSD

Min

Max

Bentonite

270

274

1.640

1.08

6675

Granite

105

9

1.449

0.12

715

Clay

198

304

5.72

25

3700

4.2 Construction of Cumulative Distribution Functions (CDF) to Describe K d Variability K d value is affected by multiple factors, and has a wide distribution range, so it can be regarded as a multi-factor random variable. In statistics, there are many mathematical tools to study the properties of random variable, such as CDF and probability density function. The calculation method of K d shows that it is closely related to the concentration, so scholars believe that it is expected to follow the logarithmic distribution [75, 76]. Due to the particularity of K d value distribution in statistics and the use of “hypothetical data rules - model data calculation - source data verification” method, CDF function is selected as a mathematical tool to study the K d distribution. In the first step “hypothetical data model”, the data is considered to be logarithmic distribution, and the CDF function is selected as the data processing tool. The second step, model data calculation, is to process the data according to the assumed random variable distribution law and obtain the mathematical index GM and GSD (see Eqs. (1, 2)) representing the relevant properties of random variables. The third step is to verify the original data. The K d data within every dataset are sorted by increasing value and an empirical frequency (Fexp,i ) equal to 1/N (where N is the total number of K d entries in the respective dataset) is assigned to each entry. Experimental cumulative frequency distribution are constructed by assigning to each sorted K d value their corresponding

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cumulative frequency (Fexp,i ) (see Eq. (3)) [76]. GM (Kd ) = (

N 

1 N

Kdi )



=e

1 N

N

i=1 ln(Kdi )



= eμ = antiln(μ)

(1)

i=1 

GSD(Kd ) = e

(

N Kd 2 i=1 (ln GM ) ) n

  j F Kd ,j =

i=0

= eσ = antiln(σ )

(2)

f (Kd ,i )

(3)

If the cumulative frequency distribution obtained is consistent with the CDF distribution, the distribution law selected in the first step can be considered to have high reliability, and the recommended K d value is the GM value. 4.3 Obtaining the Recommended K d Value for the Sorption of Cs on Bentonite The relevant information of the original data of bentonite group is shown in Table 2. In this group, K d values cover multiple orders of magnitude (100 –103 ), presenting a large GSD (1.640). Then, the following processing are carried out: the dataset is further grouped in accordance with the mineral/pH/initial concentration strategy, and the data (6675, etc.) with great differences in the dataset are eliminated. Table 2. K d (Cs) for dataset according to bentonite/pH/initial concentration criterion (mL/g) Dataset overall pH < 7

Cint

< 10−4 M

10−4 M < Cint < 5 × 10−3 M 7 < pH < 10

Cint

< 10−4 M

10−4 M < C pH > 10

int

< 5 × 10−3 M

Cint < 10−4 M 10−4 M < C

int

< 5 × 10−3 M

N

GM

GSD

Min

Max

270

274

1.640

1.08

6675

21

464

0.133

186

848

11

148

0.628

42

276

65

1073

0.747

115

5761

74

436

0.769

38

1810

7

420

0.253

313

678

–

–

–

–

–

Figure 1 shows the CDF curve of K d in different pH ranges and initial concentration ranges abide by the assumption of logarithmic distribution, and the points in the figure represent cumulative frequency distribution of each K d value. The data distribution in Fig. 1(a) and 1(e) is relatively concentrated, and the GSD value is small ( 10, Cint < 10–4 M. Thus, the recommended values have good reliability, 464 mL/g and 420 mL/g respectively. In Fig. 1(b), 1(c) and 1(d),

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the data distribution is relatively discrete, and the GSD value is relatively large (>0.6), so the derivative of CDF is not equal to 0 in a large range. The test results of the data points showed that the logarithmic distribution can reflect the distribution of K d in the relevant groups to a certain extent. Thus, the recommended value obtained have certain availability.

Fig. 1. CDFs and descriptors of K d (Cs) distributions for bentonite grouped according to the experimental approach. Data for the overall dataset are included for comparison. Points indicate individual dataset values whereas lines indicate logarithmic distribution

4.4 Obtaining the Recommended K d Value for the Sorption of Cs on Granite The relevant information of the original data of the granite group is shown in the Table 3. In this group, K d values cover multiple orders of magnitude (10–2 –102 ), showing a large GSD (1.449). Through analysing the data with great differences (~10–2 , etc.) in the dataset. These K d values are so small that could be considered that cesium is not adsorbed on these granite samples. We check the mineral composition of these samples, and find that the content of biotite mineral which recognized as the main contribution to adsorption in these samples is very low or even absent, while the proportion of quartz mineral is very high. In general, the biotite content of most granite selected for geological repository bedrock is about 10% [22, 24]. Then, the following processing are carried out:

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the data (~10–2 , etc.) with great differences in the dataset are eliminated and the dataset is further grouped in accordance with the mineral/pH/initial concentration strategy. Table 3. K d (Cs) for dataset according to granite/pH/initial concentration criterion (mL/g) Dataset overall pH < 7 7 < pH < 10

Cint

< 10−4 M

GM

GSD

Min

Max

105

9

1.449

0.12

715

2

–

–

5.5

5.6

10−4 M < Cint < 5 × 10−3 M

4

5

0.131

4.16

6

Cint < 10−4 M

19

63

1.171

12

715

10−4 M < C pH > 10

N

int

< 5 × 10−3 M

Cint < 10−4 M 10−4 M < C

int

< 5 × 10−3 M

47

12

0.729

4

57

0

–

–

–

–

0

–

–

–

–

In Fig. 2(a) and 2(c), the data distribution is relatively discrete, and the GSD value is relatively large (> 0.6), so the derivative of CDF is not equal to 0 in a large range. The test results of the data points show that the logarithmic distribution can reflect the distribution of K d in the relevant groups to a certain extent. Since there are few effective data on Cs adsorption by granite, the adsorption can only be compared within the pH environment of common groundwater (7 ≤ pH ≤ 10). Thus, the recommended value obtained have certain availability, which are 63 mL/g and 12 mL/g respectively. The best-estimated value of K d at trace concentration is 5 times higher than that at high concentration. It is also found that the K d values of Cs on granite are 1 ~ 2 orders of magnitude lower than that on clay and bentonite.

Fig. 2. CDFs and descriptors of K d (Cs) distributions for granite grouped according to the experimental approach. Data for the overall dataset are included for comparison. Points indicate individual dataset values whereas lines indicate logarithmic distribution

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4.5 Obtaining the Recommended K d Value for the Sorption of Cs on Clay The relevant information of the original data of the clay group is shown in the Table 4. In this group, K d values cover multiple orders of magnitude (10–2 –103 ), showing a large GSD (2.537). Then, the following processing are carried out: the dataset is further grouped in accordance with the mineral/pH/initial concentration strategy, and the data with great differences in the dataset are eliminated. Table 4. K d (Cs) for dataset according to clay/pH/initial concentration criterion (mL/g) Dataset overall pH < 7

Cint < 10−4 M 10−4 M < C

7 < pH < 10

Cint

int −4 < 10 M

< 5 × 10−3 M

10−4 M < Cint < 5 × 10−3 M pH > 10

Cint

< 10−4 M

10−4 M < C

int

< 5 × 10−3 M

N

GM

GSD

Min

Max

198

304

5.72

25

3700

11

339

0.932

122

1732

4

372

0.373

220

622

54

406

0.832

131

2581

24

332

0.805

47

1156

4

459

0.462

219

780

1

–

–

–

–

The data distribution in Fig. 3(a), 3(b) and 3(c) are relatively discrete, and the GSD values are large (>0.8), but the GM values are similar with those obtained by bentonite group. This indicates that the recommended value obtained from this method has certain availability, but the data distribution obtained after grouping is not centralized enough. This may be caused by the following reasons: (1) the composition of clay minerals is complex, the composition and content of minerals varies in different regions, and their adsorption capacities for Cs are different [77], which leads to a large range of data changes; (2) The data in the database is not rich enough, or the data information reported from the literature is incomplete, these lead to information insufficient validity. For the first analysis, subsequent studies can create new classification criteria and new mathematical models for further processing. For the latter, we can find the existing authoritative database, which can greatly improve the validity of the data.

5 Conclusions This paper mainly summarizes the adsorption process of cesium, Cs is mainly adsorbed on minerals by ion exchange. Independent of the sort of minerals, the most important parameters affecting Cs adsorption are pH and the initial concentration of cesium. At low concentrations, the K d is independent of the concentration of cesium, cesium preferentially distributed on the FES. At high concentrations, the pH value and competitive cations of the system are very important due to the multipoint nature of Cs+ adsorption on these minerals. Because of these factors, the K d values reported in the literature had a wide range (10–2 – 103 ). According to the adsorption mechanism, we

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Fig. 3. CDFs and descriptors of K d (Cs) distributions for clay grouped according to the experimental approach. Data for the overall dataset are included for comparison. Points indicate individual dataset values whereas lines indicate logarithmic distribution

classify the database according to the mineral/pH/initial concentration strategy, and use the probabilistic modelling model for analysis to reduce the variability of the data. After the experimental cumulative frequency distribution verification, the recommended K d value obtained from probabilistic modelling model processing has a certain representative significance. In the disposal environment, under the conditions of 7 ≤ pH ≤ 10 and at trace concentration, the best estimated K d values of Cs in bentonite, granite and clay are 1073 mL/g, 63 mL/g and 406 mL/g respectively. After grouping, some groups conform to the corresponding mathematical model, but the GSD value is relatively large. It may be solved in the following ways: (1) Study the differences of adsorption influence conditions between groups; (2) Further grouping of relevant groups; (3) Explore more appropriate mathematical models. The distribution of K d value of clay is the most discrete (10–2 – 103 ), which is related to its complex composition. Propose the following solutions: (1) Experiments were carried out on clay with different components; (2) Select more appropriate mathematical models for processing; (3) Obtain more key parameters affecting K d during the experiment, such as Radiocesium Interception Potential (RIP), the sum of K and NH4 + concentrations in soil solution, etc.

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Is Perchlorate Innocent in the Study of Weak Complexations of Nitrate with Metal Ions by Spectrophotometric Titration? Yuning Yang, Zhijin Zhao, Jing Chen, Jianchen Wang, and Taoxiang Sun(B) Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China [email protected]

Abstract. Spectrophotometric titration is an effective and commonly used technique to study the complexation of metal ions with various ligands. This technique has been applied to study the complexation of nitrate in aqueous solutions with metal ions such as UO2 2+ and Nd3+ , and the results have demonstrated the weak interactions of NO3 − with UO2 2+ /Nd3+ . In the spectrophotometric titration of NO3 − to UO2 2+ /Nd3+ , the sodium perchlorate was usually used to control the ionic strength, because the perchlorate anion was considered to be non-complexing for UO2 2+ or Nd3+ . In this work, we re-examined the spectrophotometric titration of NO3 − to UO2 2+ and Nd3+ , in which the concentrations of the metal ions, perchlorate, and nitrate were varied. In both the UO2 2+ and Nd3+ systems, at the same concentration ratio of perchlorate to the metal ions, the stability constant of the complexation varied insignificantly upon the varying of the final concentration ratio of nitrate to the metal ions. This result indicated that the change in the absorption spectra in the titration of NO3 − to UO2 2+ and Nd3+ is due to the complexation but not the ionic medium effect. At the same final concentration ratio of nitrate to UO2 2+ /Nd3+ , the obtained stability constant decreased with the decrease in the concentration ratio of perchlorate to the metal ions, indicating that perchlorate has an obvious influence on the study of weak interactions between NO3 − and UO2 2+ /Nd3+ , which should be attributed to the complexation of perchlorate with the metal ions. Keywords: Perchlorate · Nitrate · Uranyl ion · Neodymium ion · Spectrophotometric titration

1 Introduction The implementation of spent nuclear fuel reprocessing can effectively address the issues such as actinide recovery and radioactive waste minimization in the sustainable development of nuclear energy. In the processes of spent nuclear fuel reprocessing, the spent nuclear fuel is dissolved in nitric acid. The concentration of nitrate is very high in the radioactive solution. In this context, some of the metal ions including lanthanides and actinides in the spent nuclear fuel can complex with nitrate, although nitrate is usually considered as a weak ligand [1–3]. The study of the complexation with nitrate is helpful © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 913–927, 2023. https://doi.org/10.1007/978-981-19-8780-9_87

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to understand the speciation and chemical behavior of lanthanides and actinides in the reprocessing processes. There have been a lot of studies on the complexation of nitrate with actinides and lanthanides, especially for the uranyl ion (UO2 2+ ) and the neodymium ion (Nd3+ ). Tables 1 and 2 summarize the stability constants in the complexation of nitrate with UO2 2+ and Nd3+ , respectively, at different temperature and ionic medium in aqueous solution that have been reported in the literature [4–10, 11–18]. Disregarding the different methods used and the different measurement temperatures, the stability constant is significantly affected by the ionic medium. For example, the stability constant for UO2 2+ changes from 0.5 to 2.94 when the ionic medium of the solution varies from 1.0 mol/L Na(NO3 /ClO4 ) to 8.0 mol/L HClO4 , while the stability constant for Nd3+ changes from 0.6 to 1.77 when the ionic medium of the solution varies from 2.0 mol/L Na(ClO4 /NO3 ) to 2.74 mol/L NH4 NO3 . To our knowledge, the inconsistency in the stability constants at different ionic medium may originate from two aspects. One is the medium effects on the determination of the stability constant. For the weak complexation of NO3 − with metal ions in aqueous solution, it is hard to distinguish between complex formation and medium effects, because the using of high concentration of NO3 − in the study of weak complexation can cause the change of ionic medium significantly. Rao speculates that weak metal complexes with nitrate may in fact exist based on the fact that the ion interaction coefficient for nitrate is actually smaller than that for the “non-complexing” perchlorate [12]. Besides, in the study of lanthanide complexation with nitrate by solvent extraction, assuming that the changes in the activity coefficients are linear with concentration, Choppin et al. found that the variation of distribution ratio was significantly larger than that expected from the medium effects, which confirmed the formation of weak lanthanide nitrate complexes [19]. Even though the formation of complex has been confirmed, however, the medium effects cannot be excluded. The other one is, as mentioned above, the “non-complexing” perchlorate ion may complex with metal ions. In almost all the studies of the complexation of nitrate with metal ions, perchlorate is regarded as that it cannot participate in the coordination with metal ions. In our opinion, the role of perchlorate in the study of weak interactions between nitrate and metal ions is worth discussing. In this work, the complexation of NO3 − with UO2 2+ and Nd3+ were detected by spectrophotometric titration, to examine the medium effects and the role of perchlorate in the complexation. The technique of spectrophotometric titration has been proven to be effective and has been widely used in the study of the complexation of metal ions with nitrate to obtain species distribution and stability constant [12, 18, 20–26]. Both UO2 2+ and Nd3+ have characteristic absorption feature in the UV-Vis region. The titrations were carried out using UO2 (ClO4 )2 /Nd(ClO4 )3 as titrate and NaNO3 as titrant. The medium effect is examined by varying the final ratio of NO3 − to metal ions while setting a constant mole ratio of ClO4 − to the metal ions in the titrations. Whether perchlorate play a role in the complexation of NO3 − with UO2 2+ and Nd3+ was examined by varying the mole ratio of perchlorate to the metal ions while setting a constant final mole ratio of NO3 − to the metal ions in the titrations. The modeling of the data was also corrected by considering the activity coefficient, to exclude the effect of activity on the data of stability constant.

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Table 1. The stability constants in the complex of nitrate with UO2 2+ at different medium and temperature in aqueous solution Medium

T (°C)

β1

0.54 M Na(NO3 /ClO4 )

25

0.37

0.82 M Na(NO3 /ClO4 )

25

0.20

1.06 M Na(NO3 /ClO4 )

25

0.19

1.0 M (H/Na)ClO4

32

0.04

1.0 M Na(NO3 /ClO4 )

20

2.0 M NaClO4

10

2.0 M NaClO4

25

0.24

2.0 M NaClO4

40

0.17

5.38 M Na(NO3 /ClO4 )

25

0.21

7.05 M Na(NO3 /ClO4 )

25

0.27

6.25 M Na(NO3 /ClO4 )

23

0.15

8.0 M HClO4

20

2.94

3.0 M NaClO4

25

0.66

5.0 M NaClO4

25

0.81

7.0 M NaClO4

25

0.98

1.0 M Na(NO3 /ClO4 )

25

0.24

β2

β3

Method

Ref

gl

[4]

ix

[5]

0.5

pot

[6]

0.30

ex

[7]

sp

[8]

sp

[9]

ex

[10]

ex

[11]

sp

[12]

0.04

0.03

0.32

0.19

gl: Glass electrode; ix: Ion exchange; pot: Potentiometry; ex: Extraction; sp: Spectrophotometry

2 Experimental 2.1 Chemicals All reagents used in this work were analytical grade or higher. Milli-Q water was used in preparations of all solutions. The stock solutions of uranyl perchlorate and neodymium perchlorate were obtained by dissolving UO3 and Nd2 O3 , respectively, in concentrated perchloric acid. The amount of perchloric acid was far in excess with respect to the stoichiometric ratio. The concentrations of metal ions and perchloric acid in the stock solutions were determined by ICP-OES and acid-base titration, respectively. The stock solutions of sodium nitrate and sodium perchlorate were prepared by dissolving appropriate amounts of sodium nitrate solid and sodium perchlorate solid, respectively, in water. The ionic strength of all solutions in the spectrophotometric titration was adjusted to 1.0 mol/L using sodium perchlorate. 2.2 Spectrophotometric Titrations UV/Vis absorption spectra of UO2 2+ (365–500 nm, 0.5 nm interval) and Nd3+ (555– 605 nm, 0.1 nm interval) were collected on a Cary 6000i spectrophotometer equipped

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Table 2. The stability constants in the complex of nitrate with Nd3+ at different medium and temperature in aqueous solution Medium

T (°C)

β1

β2

Method

Ref

5.0 M Na(NO3 /ClO4 )

22

1.06

0.12

ex

[13]

2.0 M Na(NO3 /ClO4 )

25

0.6

cal

[14]

4.2 M Na(NO3 /ClO4 )

20

0.77

sp

[15]

2.74 M NH4 NO3

25

1.77

1.28

ex

[16]

3.6 M HClO4

17

0.99

0.05

sp

[17]

3.6 M HClO4

25

1.24

0.04

3.6 M HClO4

35

1.16

0.05

3.6 M HClO4

50

0.95

0.04

3.6 M HClO4

61

0.96

0.05

1.0 M Na(NO3 /ClO4 )

25

0.64

sp

[18]

ex: Extraction; cal: Calorimetry; sp: Spectrophotometry

with sample holders that were maintained at constant temperatures by a 1 × 1 Peltier controller. For each titration, an initial 0.8 mL of UO2 2+ /Nd3+ solution was placed in a 10 mm cuvette. Appropriate amounts of the titrant were added into the cuvette and mixed thoroughly before the absorption spectrum was collected. The solution in cuvette is considered to be thoroughly mixed if the spectrum remains unchanged after repeated measurement. A set of 15–25 spectra were obtained in each titration. All spectrophotometric titrations in this study were carried out at 25 °C. The stability constant of the complex was calculated based on the spectrophotometric titration data by non-linear least-square regression method using the Hyperquad program [27].

3 Results and Discussion 3.1 Effect of the Concentration Ratio of NO3 − to Metal Ions The normalized absorption spectra in the titration of nitrate to UO2 2+ are shown in Fig. 1 (top). The concentration ratio of ClO4 − to UO2 2+ is fixed at 99 during the titration. One can see from Fig. 1 that the intensity of the spectra was increased, especially in the region from 400 nm to 475 nm, as the concentration of nitrate was increased. Analysis by the Hyperquad program indicated a formation of 1:1 complex, UO2 NO3 + , and the deconvoluted spectra of UO2 2+ and UO2 NO3 + are shown in Fig. 1 (bottom). In comparison with UO2 2+ , UO2 NO3 + has absorption features at 402.5 nm, 414 nm and 426 nm that are slightly red-shifted, and its molar absorptivity is greater than that of UO2 2+ . The stability constants are also obtained by Hyperquad program, and have been listed in Table 3. When the concentration ratio of ClO4 − to UO2 2+ is fixed at 99, the stability constants of UO2 NO3 + are approximately − 0.20 when the final concentration

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917

Fig. 1. Top: normalized absorption spectra in the spectrophotometric titration of uranyl-nitrate complexation. Bottom: calculated molar absorptivity of UO2 2+ and UO2 NO3 + . Initial solution in cuvette: 0.8 mL, 0.01 mol/L UO2 (ClO4 )2 /0.1 mol/L HClO4 ; titrant: 0.9 mol/L NaNO3 /0.1 mol/L HNO3 ; final volume: 2.4 mL; I = 1.0 mol/L

ratio of nitrate to UO2 2+ increased from 50 to 200. This value of the stability constant of UO2 NO3 + is greater than that of − 0.62 ± 0.04 at I = 1.0 mol/L and T = 25 °C obtained by Rao and coworkers [12]. Besides, the molar absorptivity of UO2 NO3 + in this work is also a little smaller. The main reason for the different results is very likely due to the different concentrations of the components in the titrations. In the work by Rao et al., the concentration of UO2 2+ is 0.177 mol/L, and the final concentrations ratio of UO2 2+ :ClO4 − :NO3 − is 1:4.65:16.9. Table 3. Stability constants of UO2 NO3 + in different concentration ratio of NO3 − to UO2 2+ UO2 2+ concentration (mol/L)

UO2 2+ /ClO4 − /NO3 −

log β

0.01

1/99/50

− 0.20 ± 0.08

0.01

1/99/100

− 0.20 ± 0.05

0.01

1/99/150

− 0.21 ± 0.06

0.01

1/99/200

− 0.22 ± 0.06

Figure 2 (top) shows the normalized absorption spectra in the titration of nitrate to Nd3+ when the concentration ratio of ClO4 − to Nd3+ is fixed at 97. In the titration, the spectra of Nd3+ were red-shifted and the intensity of the spectra was increased as the concentration of nitrate was increased. Analysis by the Hyperquad program indicated

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that Nd3+ formed a 1:1 complex, NdNO3 2+ , with nitrate. Deconvoluted spectra of Nd3+ and NdNO3 2+ are shown in Fig. 2 (bottom). The absorption peak of Nd3+ at 575 nm is slightly red-shifted, which makes the spectrum of NdNO3 2+ different from that of Nd3+ , and the molar absorptivity of NdNO3 2+ is greater than Nd3+ .

Fig. 2. Top: normalized absorption spectra in the spectrophotometric titration of Nd(III)-nitrate complexation. Bottom: calculated molar absorptivity of Nd3+ and NdNO3 2+ . Initial solution in cuvette: 0.8 mL, 0.01 mol/L Nd(ClO4 )3 /0.1 mol/L HClO4 ; titrant: 0.9 mol/L NaNO3 /0.1 mol/L HNO3 ; final volume: 2.4 mL; I = 1.0 mol/L

The stability constants of NdNO3 2+ are obtained by Hyperquad program, and have been listed in Table 4. When the concentration ratio of ClO4 − to Nd3+ is fixed at 97, the stability constants of NdNO3 2+ remain the same at 0.05 when the final concentration ratio of nitrate to Nd3+ increased from 50 to 200. Similar to the case of the formation of UO2 NO3 + , the value of stability constant of NdNO3 2+ is also greater than that of −0.19 ± 0.02 at I = 1.0 mol/L and T = 25 °C obtained by Rao and coworkers [18]. Besides, the difference of molar absorptivity between Nd3+ and NdNO3 2+ at 577 nm is smaller than that reported in the literature [18]. The concentrations of the components in the titrations are different in the studies. The concentration of Nd3+ in this work is 0.01 mol/L, and the concentration ratio of Nd3+ to ClO4 − is 1:97. The concentration of Nd3+ in the work by Rao et al. is 0.0832 mol/L. Although the volume of NaNO3 added to the cuvette is not clear, it can be determined that the concentration ratio of Nd3+ to ClO4 − is 1:9.02. In both the UO2 2+ and Nd3+ systems, the stability constant of the complexation varied insignificantly with the varying of the final concentration ratio of nitrate to metal ions. When the concentration ratio of nitrate to metal ions reaches a certain value, the stability constant seems to be independent of the concentration of nitrate. In the titration,

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919

Table 4. Stability constants of NdNO3 2+ in different concentration ratio of NO3 − to Nd3+ Nd3+ concentration (mol/L)

Nd3+ /ClO4 − /NO3 −

log β

0.01

1/97/50

0.05 ± 0.01

0.01

1/97/100

0.05 ± 0.01

0.01

1/97/150

0.05 ± 0.01

0.01

1/97/200

0.05 ± 0.01

the ionic medium of the solution varies from 0.67 mol/L NaClO4 /0.33 mol/L NaNO3 to 0.33 mol/L NaClO4 /0.67 mol/L NaNO3 when the concentration ratio of nitrate to metal ions changes from 50 to 200. The change of the ionic medium of the solution has no effect on the stability constant, suggesting that the medium effects are not involved in the weak complexation of NO3 − with metal ions. Therefore, the change in the absorption spectra in the titration of NO3 − to UO2 2+ or Nd3+ is due to the complexation but not the ionic medium effect. As the ionic medium effect has been excluded, the difference between the stability constant as well as the calculated molar absorptivity obtained in this study and that in the literature remain to be discussed. Comparing the experimental conditions in the literature and in the present work, we inferred that the stability constant may be related to the concentration of perchlorate or, more explicitly, the concentration ratio of perchlorate to metal ions. For UO2 2+ , the stability constant changes from −0.20 to −0.62 when the concentration ratio of ClO4 − to UO2 2+ changes from 99 to 4.65. For Nd3+ , the stability constant changes from 0.05 to −0.19 when the concentration ratio of ClO4 − to Nd3+ changes from 97 to 9.02. The stability constant appears to decrease with decreasing the concentration ratio of perchlorate to metal ions; therefore, the role of perchlorate in the titration is examined by varying the mole ratio of perchlorate to metal ions as shown below. 3.2 Effect of the Concentration Ratio of ClO4 − to Metal Ions Figure 3 shows the normalized absorption spectra of spectrophotometric titrations of NO3 − to UO2 2+ when the concentrations of UO2 2+ are 0.01, 0.02, 0.05 and 0.1 mol/L respectively. The concentration ratio of NO3 − to UO2 2+ is 20 at the end of spectrophotometric titrations for all the titrations, whilst that of ClO4 − to UO2 2+ decreases from 279 to 9. The normalized absorption spectra show almost no change in the titration as the concentration ratios of ClO4 − to UO2 2+ are 279 and 129. In the case that the concentration ratios of ClO4 − to UO2 2+ are 39 and 9, the wavelength of maximum absorption is red-shifted and the intensity of spectra are increased, especially in the region from 400 nm to 475 nm. Meanwhile, the red-shift in wavelength and the increase in intensity become larger as the concentration ratio of ClO4 − to UO2 2+ decreases. The formation of 1:1 complex is confirmed by fitting the spectrophotometric titration data and the stability constants of UO2 NO3 + have been listed in Table 5. When the concentration ratios of ClO4 − to UO2 2+ are 279 and 129 respectively, the stability

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Fig. 3. Normalized absorption spectra of UO2 2+ -nitrate complexation at different concentration ratio of ClO4 − to UO2 2+ . I = 1.0 mol/L Na(ClO4 /NO3 ). (a) Initial solution in cuvette: 0.8 mL, 0.01 mol/L UO2 (ClO4 )2 /0.1 mol/L HClO4 ; titrant: 0.1 mol/L NaNO3 /0.1 mol/L HClO4 ; final volume: 2.4 mL. (b) Initial solution in cuvette: 0.8 mL, 0.02 mol/L UO2 (ClO4 )2 /0.1 mol/L HClO4 ; titrant: 0.2 mol/L NaNO3 /0.1 mol/L HClO4 ; final volume: 2.4 mL. (c) Initial solution in cuvette: 0.8 mL, 0.05 mol/L UO2 (ClO4 )2 /0.1 mol/L HClO4 ; titrant: 0.5 mol/L NaNO3 /0.1 mol/L HClO4 ; final volume: 2.4 mL. (d) Initial solution in cuvette: 0.8 mL, 0.1 mol/L UO2 (ClO4 )2 /0.1 mol/L HClO4 ; titrant: 0.9 mol/L NaNO3 /0.1 mol/L HNO3 ; final volume: 2.4 mL

constants cannot be obtained by Hyperquad Program since there is no change in the normalized absorption spectra. The stability constant can be obtained as the concentration ratio of ClO4 − to UO2 2+ decreases. The stability constant of UO2 NO3 + is −0.23 when the concentration ratio of ClO4 − to UO2 2+ is 39, and this value is a little higher than − 0.35 when the concentration ratio of ClO4 − to UO2 2+ is 9. Figure 4 shows the normalized absorption spectra of spectrophotometric titrations of NO3 − to Nd3+ when the concentrations of Nd3+ are 0.01, 0.02, 0.05 and 0.1 mol/L respectively. The concentration ratio of NO3 − to Nd3+ is 20 at the end of spectrophotometric titrations for all the titrations, whilst that of ClO4 − to Nd3+ decreases from 277 to 7. In each titration, the wavelength of maximum absorption is red-shifted and

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921

Table 5. Stability constants of UO2 NO3 + in different concentration ratio of ClO4 − to UO2 2+ UO2 2+ concentration (mol/L)

UO2 2+ /ClO4 − /NO3 −

0.01

1/279/20

0.02

1/129/20

0.05

1/39/20

− 0.23 ± 0.07

0.1

1/9/20

− 0.35 ± 0.02

log β

the intensity of spectra are increased. The red-shift in wavelength and the increase in intensity become larger as the concentration ratio of ClO4 − to Nd3+ decreases. The formation of 1:1 complex, NdNO3 2+ , is also confirmed in the variation of the concentration ratio of ClO4 − to Nd3+ . The stability constants of NdNO3 2+ have been listed in Table 6. Although the spectra change not very significantly when the concentration ratio of ClO4 − to Nd3+ is 277, the stability constant can still be fitted based on the spectrophotometric titration data. The changes of spectra are also reflected in the stability constant, which decreases from 0.83 to − 0.05 with the decrease in the concentration ratio of ClO4 − to Nd3+ from 277 to 7. It is obvious that the change in the concentration ratio of ClO4 − to metal ions causes the change in the normalized absorption spectra and the stability constant. Note that in the fitting of the data by Hyperquad program, the stability constant and the molar absorptivity are both variables, and they change simultaneously. In this context, it is not appropriate to directly compare the value of stability constant in Table 5 or Table 6. Here we have recalculated the stability constant of NdNO3 2+ by fixing the deconvoluted spectrum. The deconvoluted spectrum obtained from the titration in the final concentration ratio of Nd3+ :ClO4 − :NO3 − at 1:277:20 is used in all the fittings, and the results are tabulated as Table 7. One can see that the calculated stability constants increase with decreasing in concentration ratio of ClO4 − to Nd3+ . It is reasonable to speculate that a competitive coordination with Nd3+ between nitrate and perchlorate in fact exists. Since the weak complexation between nitrate and Nd3+ has been demonstrated, there must be a complexation between perchlorate and Nd3+ . 3.3 Effect of the Activity Coefficient In the calculation of the stability constant and the molar absorptivity by Hyperquard program, it is assumed that the activity coefficients remain constant at constant ionic strength during the spectrophotometric titration. In the study on the effect of the concentration ratio of NO3 − to metal ions, the concentration of NO3 − is 1.0 mol/L. In the study on the effect of the concentration ratio of ClO4 − to metal ions, the concentrations of NO3 − are 0.1, 0.2, 0.5 and 1.0 mol/L respectively. In both cases, the concentration ratio of nitrate to perchlorate continually increases along the titration, and the activity of metal ions, NO3 − , and ClO4 − have changed in this process. The effect of activity on the stability constant is worth to be studied. Here we use the titration of NO3 − to Nd3+ as an example, to examine the effect of activity on the stability constant.

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Fig. 4. Normalized absorption spectra of Nd3+ -nitrate complexation at different concentration ratio of ClO4 − to Nd3+ . I = 1.0 mol/L Na(ClO4 /NO3 ). (a) Initial solution in cuvette: 0.8 mL, 0.01 mol/L Nd(ClO4 )3 /0.1 mol/L HClO4 ; titrant: 0.1 mol/L NaNO3 /0.1 mol/L HClO4 , final volume: 2.4 mL. (b) Initial solution in cuvette: 0.8 mL, 0.02 mol/L Nd(ClO4 )3 /0.1 mol/L HClO4 ; titrant: 0.2 mol/L NaNO3 /0.1 mol/L HClO4 , final volume: 2.4 mL. (c) Initial solution in cuvette: 0.8 mL, 0.05 mol/L Nd(ClO4 )3 /0.1 mol/L HClO4 ; titrant: 0.5 mol/L NaNO3 /0.1 mol/L HClO4 , final volume: 2.4 mL. (d) Initial solution in cuvette: 0.8 mL, 0.1 mol/L Nd(ClO4 )3 /0.1 mol/L HClO4 ; titrant: 0.9 mol/L NaNO3 /0.1 mol/L HNO3 , final volume: 2.4 mL

The activity coefficient can be calculated by the extended Debye-Hückel Eq. 2 [28] as shown below. √ −z 2 A I  log γM = ε(M, X)mX (1) √ + 1+aB I X where γ M , z, a, I, ε(M,X), and mX are the activity coefficient of the ion M, the ion charge, the ion size parameter, the ionic strength, the ion interaction coefficient, and the molality of the ion X, respectively. The constants A and B at 298.15 K are 0.5091 (mol kg−1 )−0.5 and 0.3282 × 108 (mol kg−1 )−0.5 m−1 , respectively [29]. The a value was

Is Perchlorate Innocent in the Study

923

Table 6. Stability constants of NdNO3 2+ in different concentration ratio of ClO4 − to Nd3+ Nd3+ concentration (mol/L)

Nd2+ /ClO4 − /NO3 −

0.01

1/277/20

0.83 ± 0.08

0.02

1/127/20

0.19 ± 0.04

0.05

1/37/20

− 0.04 ± 0.02

0.1

1/7/20

− 0.05 ± 0.01

log β

Table 7. The calculated stability constants of NdNO3 2+ using the same deconvoluted spectrum in different concentration ratio of ClO4 − to Nd3+ Nd3+ concentration (mol/L)

Nd3+ /ClO4 − /NO3 −

log β

0.01

1/277/20

0.83 ± 0.08

0.02

1/127/20

0.92 ± 0.01

0.05

1/37/20

1.33 ± 0.03

0.1

1/7/20

1.91 ± 0.15

evaluated using the empirical equation by Brüll [30]. The parameters in Eq. 1 for Nd3+ can be obtained from its analog Cm3+ , referring to the literature [25]. The values for aB, ε(Nd3+ , ClO4 − ), and ε(Nd3+ , NO3 − ) are thus 1.5 (mol kg−1 )−0.5 , 0.49 ± 0.03, and 0.27 ± 0.02, respectively. Although the ionic strength remains as constant during the titration, the γ value changes along with the addition of nitrate because the second term in Eq. 1 changes due to the change of the concentration of ClO4 − and NO3 − . As shown in Table 8, for example, the γ value for Nd3+ changes from 0.0454 to 0.0323 when the mole ratio of NO3 − to Nd3+ increases from 0 to 200. Meanwhile, the γ value for NO3 − changes insignificantly from 0.505 to 0.508, due to the low concentration of Nd3+ and thus the insignificant effect on the second term in Eq. 1. A modeling of the data by Hyperquad program indicates that, for a specific titration, the change of γ values for Nd3+ and NO3 − has no effect on the calculation of the stability constant; therefore, only the stability constant calculated by the γ value at the final of titration are listed in Table 8. In the titration of NO3 − to Nd3+ , the value of log β remains nearly constant (around 0.30) as the final mole ratio of NO3 − to Nd3+ varies from 50 to 200. Although the value of log β calculated by activity is higher than that calculated by concentration, both the data in Table 4 and 8 demonstrate that the change of the ionic medium of the solution has no effect on the stability constant of NdNO3 2+ . Therefore, the ionic medium effects are not involved in the weak complexation of NO3 − with Nd3+ . Table 9 shows the activity coefficients and the stability constants calculated using the data of activity in the titration of Nd3+ by NO3 − at different mole ratio of ClO4 − to Nd3+ . Consistent with the results in Table 6, the stability constants decrease with

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Y. Yang et al.

Table 8. Activity coefficients of Nd3+ and NO3 − , and the calculated stability constant using the activity data in the titrations in different concentration ratio of NO3 − to Nd3+ . The experimental conditions are the same as in Table 4 Nd3+ /ClO4 − /NO3 −

Nd3+

NO3 −

log β

1/97/0

0.0454

0.505

1/97/50

0.0384

0.508

0.29 ± 0.01

1/97/100

0.0353

0.507

0.29 ± 0.01

1/97/150

0.0335

0.507

0.30 ± 0.01

1/97/200

0.0323

0.506

0.30 ± 0.01

decreasing concentration ratio of ClO4 − to Nd3+ , demonstrating a weak complexation between perchlorate and Nd3+ may exist. Table 9. Activity coefficients of Nd3+ and NO3 − , and the calculated stability constant using the activity data in the titrations in different concentration ratio of ClO4 − to Nd3+ . The experimental conditions are the same as in Table 6 Nd3+ /ClO4 − /NO3 −

Nd3+

NO3 −

Before titration

0.0454

0.505

1/277/20

0.0439

0.506

1.06 ± 0.08

1/127/20

0.0424

0.508

0.43 ± 0.04

1/37/20

0.0380

0.512

0.19 ± 0.01

1/7/20

0.0312

0.519

0.16 ± 0.01

log β

3.4 Discussion on the Complexation Ability of Perchlorate For the study on the complexation of metal ions with the ligands of strong coordination ability, it is acceptable to consider perchlorate to be non-complexed with metal ions [31, 32]. However, for the study on the complexation of metal ions with weak ligands such as nitrate, it will be problematic to consider perchlorate as non-complexation. Actually, the complexation ability of ClO4 − with metal ions has been studied in the literature; however, the conclusions obtained by different techniques of characterization are often contradictory. In the study of the interaction between UO2 2+ and ClO4 − by NMR spectroscopy, Bardin et al. found that five water molecules occupy the first coordination sphere of UO2 2+ in perchloric acid solution, suggesting perchlorate is not participate in the coordination with the uranyl ion [32]. This conclusion is also confirmed by EXAFS and quantum mechanical calculations [31]. Even in perchloric acid of 11.5 mol/L, the number of equatorial water molecules bound to UO2 2+ is still around five. However, when

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925

using the Time-Resolved Laser-induced Fluorescence spectroscopy (TRLFS) to study the effect of HClO4 /NaClO4 on the emission spectra and the lifetime of the UO2 2+ [33, 34], the same group found that a complexation of UO2 2+ with ClO4 − may occurs when the concentration of perchlorate is greater than 4.5 mol/L. In addition, the crystal structures of perchlorate with some of transition elements, lanthanides and actinides have been studied [35–37], and the oxygen atoms from ClO4 − can directly coordinate with the metal ions [36]. For example, in the complex [Nd(napyo)4 ClO4 ]2+ , where napyo is 1,8-naphthyridrne-N-monoxide, one oxygen atom from ClO4 − is coordinated to the central Nd3+ , suggesting that perchlorate is able to coordinate with Nd3+ . The above results show that the complexation of ClO4 − with UO2 2+ is as yet inconclusive. Nevertheless, results in the present work indicate the complexation of perchlorate with UO2 2+ and Nd3+ , and this complexation ability has an obvious influence on the determination of the stability constant.

4 Conclusions The stability constants of UO2 2+ /Nd3+ -nitrate complexation were determined by spectrophotometry titration, in which the concentrations of the metal ions, perchlorate, and nitrate were varied. The stability constant remains unchanged upon varying the concentration ratio of NO3 − to metal ions, thus it can be concluded that the change in the absorption spectra during the titration is due to the complexation between NO3 − and UO2 2+ /Nd3+ but not the ionic medium effect. Differently, the stability constant varies with the variation of the concentration ratio of ClO4 − to metal ions, indicating the complexation of ClO4 − with UO2 2+ /Nd3+ . In short, ClO4 − cannot be regarded as non-complexing anion in the study of the complexation of metal ions with weak ligands.

References 1. Vasudeva Rao, P.R., Gudi, N.M., Bagawde, S. V. et al.: The complexing of Np(V) by some inorganic ligands. J. Inorg. Nucl. Chem. 41(2), 235–239 (1979) 2. Spahiu, K., Puigdomenech, I.: On weak complex formation: re-interpretation of literature data on the Np and Pu nitrate complexation. Radiochim. Acta 82(s1), 413–420 (1998) 3. Gaunt, A.J., May, I., Neu, M.P., et al.: Structural and spectroscopic characterization of plutonyl(VI) nitrate under acidic conditions. Inorg. Chem. 50(10), 4244–4246 (2011) 4. Ohashi, H., Morozumi, T.: Electrometric determination of stability constants of uranylchloride and uranyl-nitrate complexes with pCl-stat. J. At. Energy Soc. Jpn 9(2), 65–71 (1967) 5. Banerjea, D., Tripathi, K.K.: Association of uranium(VI) with anions in aqueous perchloric acid medium. J. Inorg. Nucl. Chem. 18, 199–206 (1961) 6. Ahrland, S.: On the complex chemistry of the uranyl ion. VI. The complexity of uranyl chloride, bromide and nitrate. Acta Chemica Scandinavica 5(9–10), 1271–1282 (1951) 7. Day, R.A., Powers, R.M.: Extraction of uranyl ion from some aqueous salt solutions with 2-thenoyltrifluoroacetone. J. Am. Chem. Soc. 76(15), 3895–3897 (1954) 8. Betts, R., Michels, R.K.: Ionic association in aqueous solutions of uranyl sulphate and uranyl nitrate. J. Chem. Soc. S58, S286–S294 (1949) 9. Brooker, M.H., Huang, C.B., Sylwestrowicz, J.: Raman spectroscopic studies of aqueous uranyl nitrate and perchlorate systems. J. Inorg. Nucl. Chem. 42(10), 1431–1440 (1980)

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10. Lahr, H., Knoch, W.: Bestimmung von stabilitätskonstanten einiger aktinidenkomplexe: II. Nitrat- und chloridkomplexe von uran, neptunium, plutonium und americium. Radiochimica Acta 13(1), 1–5 (1970) 11. Choppin, G.R., Du, M.: ƒ-Element complexation in brine solutions. Radiochim. Acta 58– 59(1), 101–104 (1992) 12. Rao, L., Tian, G.: Thermodynamic study of the complexation of uranium(VI) with nitrate at variable temperatures. J. Chem. Thermodyn. 40(6), 1001–1006 (2008) 13. Andersson, S., Eberhardt, K., Ekberg, C., et al.: Determination of stability constants of lanthanide nitrate complex formation using a solvent extraction technique. Radiochim. Acta 94(8), 469–474 (2006) 14. Bonal, C., Morel, J.-P., Morel-Desrosiers, N.: Interactions between lanthanide cations and nitrate anions in water. Part 1. effect of the ionic strength on the Gibbs energy, enthalpy and entropy of complexation of the neodymium cation. J. Chem. Soc. Faraday Trans. 92(24), 4957–4963 (1996) 15. Coward, N.A., Kiser, R.W.: A spectrophotometric study of the Nd3+ -NO3 − association. J. Phys. Chem. 70(1), 213–217 (1966) 16. Majdan, M., Sadowski, P.: Nitrate ion association with Nd3+ . Monatsh. Chem. 117(8), 949– 954 (1986) 17. Sadowski, P., Majdan, M.: Spectroscopic investigation of lanthanide nitrates. Monatshefte für Chemie / Chemical Monthly 126(8), 863–870 (1995) 18. Rao, L., Tian, G.: Complexation of lanthanides with nitrate at variable temperatures: thermodynamics and coordination modes. Inorg. Chem. 48(3), 964–970 (2009) 19. Choppin, G.R., Strazik, W.F.: Complexes of trivalent lanthanide and actinide ions I. outersphere ion pairs. Inorg. Chem. 4(9), 1250–1254 (1965) 20. Rao, L., Tian, G.: The effect of temperature on the complexation of Cm(III) with nitrate in aqueous solutions. Dalton Trans. 40(4), 914–918 (2011) 21. Georg, S., Billard, I., Ouadi, A., et al.: Determination of successive complexation constants in an ionic liquid: complexation of UO2 2+ with NO3 − in C4 -mimTf2 N studied by UV−Vis spectroscopy. J. Phys. Chem. B 114(12), 4276–4282 (2010) 22. Liu, L., Tian, G., Rao, L.: Effect of solvation? complexation of neodymium(III) with nitrate in an ionic liquid (BumimTf2 N) in comparison with water. Solvent Extr. Ion Exch. 31(4), 384–400 (2013) 23. Ikeda, A., Hennig, C., Rossberg, A., et al.: Structural determination of individual chemical species in a mixed system by iterative transformation factor analysis-based X-ray absorption spectroscopy combined with UV−Visible absorption and quantum chemical calculation. Anal. Chem. 80(4), 1102–1110 (2008) 24. Ansari, S.A., Liu, L., Dau, P.D., et al.: Unusual complexation of nitrate with lanthanides in a wet ionic liquid: a new approach for aqueous separation of trivalent f-elements using an ionic liquid as solvent. RSC Adv. 4(72), 37988–37991 (2014) 25. Skerencak, A., Panak, P.J., Hauser, W., et al.: TRLFS study on the complexation of Cm(III) with nitrate in the temperature range from 5 to 200 °C. Radiochim. Acta 97(8), 385–393 (2009) 26. Dong, X., Wang, Z., Yan, Q., et al.: Optically “silent” neptunium(V)-nitrate complex in ionic liquid. Chin. Chem. Lett. 33(7), 3531–3533 (2022) 27. Gans, P., Sabatini, A., Vacca, A.: Investigation of equilibria in solution. Determination of equilibrium constants with the HYPERQUAD suite of programs. Talanta 43(10), 1739–1753 (1996) 28. Grenthe, I., Gaona, X., Rao, L., et al.: Second update on the chemical thermodynamics of uranium, neptunium, plutonium, americium and technetium, vol. 14. Amsterdam, Elsevier B.V. (2020)

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29. Grenthe, I., Fuger, J., Konings, R.J., et al.: Chemical thermodynamics of uranium, vol. 1. Amsterdam, Elsevier B.V. (1992) 30. Kielland, J.: Individual activity coefficients of ions in aqueous solutions. J. Am. Chem. Soc. 59(9), 1675–1678 (1937) 31. Sémon, L., Boehme, C., Billard, I., et al.: Do perchlorate and triflate anions bind to the uranyl cation in an acidic aqueous medium? a combined EXAFS and quantum mechanical investigation. ChemPhysChem 2(10), 591–598 (2001) 32. Bardin, N., Rubini, P., Madie, C.: Hydration of actinyl(VI), MO2+ 2aq (M = U, Np, Pu) An NMR study. Radiochimica Acta 83(4), 189–194 (1998) 33. Bouby, M., Billard, I., Bonnenfant, A., et al.: Are the changes in the lifetime of the excited uranyl ion of chemical or physical nature? Chem. Phys. 240(3), 353–370 (1999) 34. Rustenholz, A., Billard, I., Duplatre, G., et al.: Fluorescence spectroscopy of U(VI) in the presence of perchlorate ions. Radiochim. Acta 89(2), 83–90 (2001) 35. Crawford, M.J., Ellern, A., Karaghiosoff, K., et al.: Nitrate and perchlorate complexes of uranium(IV). Inorg. Chem. 48(23), 10877–10879 (2009) 36. Gan, X., Wang, X., Tang, N., et al.: Synthesis and structure of a 1,8-naphthyridine-N-monoxide complex with neodymium perchlorate. J. Coord. Chem. 22(4), 337–344 (1990) 37. Cotton, F.A., Weaver, D.L.: An authenticated perchlorate complex. J. Am. Chem. Soc. 87(18), 4189–4190 (1965)

Design and Experimental Study of Ultra High Pressure Water Jet Decontamination Device Chengtian Zhang(B) , Lei Teng, Xisan Chen, Yongling Zhang, Zhijun Sun, Zhiyong Su, Wei Xue, and Tao Long Nuclear Power Institute of China, Chengdu, Sichuan, China [email protected]

Abstract. When nuclear facilities are overhauled or decommissioned, a highpressure water jet removes radioactive contamination from the project site. During that process, a large amount of water needs to be used for better decontamination effects, producing secondary radioactive waste liquid, and indirectly increasing the cost of decommissioning nuclear equipment. According to the concept and washing devices of a high-pressure water jet, this paper designed a high-pressure water jet decontamination device controlling pressure and water flow desperately and simultaneously, which was enacted by PID automatic adjustment. The research results showed that higher water pressure achieved better decontamination effects. Meanwhile, under the same condition and influence factors, using a large volume of water achieved fewer decontamination factors, but the tendency changed gently. Therefore, an improved decontamination device with a higher-pressure water jet can control the water jet’s output voltage and low flow. It is valuable for engineering applications that this achieved better effects on decreasing secondary waste and removing radioactive contaminations simultaneously. Keywords: High-pressure water jet · Decontamination · Synchronous control · Pressure and Flow

1 Introduction In nuclear facility decommissioning, the most common decontamination methods are high-pressure water jet, laser, mechanical stripping, chemical/electrochemical, dry ice spray, and ultrasonic Compared with other methods, the high-pressure water jet was widely used due to its favourable effects on decontamination and simple operations [1–4]. Water jet cleaning technology is a cleaning and decontamination technology developed in the 1930s, the technology is popular in the cleaning and decontamination industry for its versatility and harmlessness to the environment, and its application is becoming increasingly widespread. High-pressure water jet technology is a further development of water jet technology. 1972, the United Kingdom held the first international water jet conference, the same period, China successfully manufactured high-pressure water jet cleaning machine, to the mid-1980s, began to introduce a series of high-pressure water © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 928–936, 2023. https://doi.org/10.1007/978-981-19-8780-9_88

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jet cleaning technology and equipment from abroad. 1994, China’s first high-pressure water jet technology applied to the field of reactor decommissioning [1]. High-pressure water jet decontamination is the use of high-pressure pumps to pump high-pressure water, positive or tangential impact decontamination object surface contamination, the use of the jet of blow, erosion, stripping, excision and other effects to remove scale, rust spots and cleaning, remove contaminated radionuclides. Highpressure water jet decontamination is particularly suitable for objects that are difficult to achieve scrubbing or for decontaminating the surface of objects with a large scrubbing workload, and is now widely used for decontaminating plants, tanks, pools and hot chambers. In 1994, Liu Yucheng and others from China Blue Star Chemical Cleaning Corporation applied high-pressure water decontamination technology to decontaminate the 452 process pool of the 801 reactor, and the highest level of contamination on the surface of the pool was 7820 Bq/cm2 . 30 MPa of jet pressure and 1 m of jet distance were tested, and the highest decontamination factor reached 41.38, which was a good decontamination effect [5]. In 2004, Wu Qiang et al. from the China Institute of Radiation Protection applied high-pressure water cleaning technology to the decontamination of a pressurized water reactor nuclear power plant’s feed change pool [1]. Practice shows that the process parameters are: water pressure 20MPa, moving speed 1m/min, jet angle 45°–70°, water flow rate of 16L/min, 90% of the pool cover by high-pressure water cleaning, decontamination factor reached more than 30, the maximum can reach 126, decontamination effect is better. The UK Three Mile Island No. 2 reactor (TMI-2) accident repair, high-pressure water on the reactor plant floor decontamination experiments. The high-pressure water jet water pressure of 20MPa, the flow rate of 30 –45L/min, the reactor plant floor decontamination factor of 1000, decontamination effect is good. At the same time, however, the highpressure water jet produced a large amount of waste fluid during the decontamination process, so the water had to be filtered and reused to reduce the amount of waste fluid. The Dounreay fast reactor reprocessing plant (UK) was designed and built in the late 1950s to process enriched uranium fuel from fast reactors. They also applied high pressure water to decontaminate the hot and shielded chambers with a high pressure water jet pressure of 6.88 MPa, with good decontamination results. As can be seen from the above domestic and international applications, high-pressure water jet decontamination is a traditional technique that is widely used in the decontamination of nuclear facilities, thanks to its high decontamination efficiency and superior decontamination results. However, it is also clear from the above that the current state of application of this technology is based on the actual needs of the project, with adjustments made to pressure and jet distance in order to obtain a high decontamination factor, and a lack of systematic decontamination experimental research. This paper mainly focuses on the key process parameters affecting the decontamination effect of high-pressure water jets, developed a high-pressure water cleaning device and test rig, carried out research on high-pressure water jet decontamination technology, designed the impact of each parameter on the decontamination effect test programme, carried out a single decontamination test, and finally the decontamination test results were analysed and discussed.

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2 Improved Design of High Pressure Water Cleaning Device 2.1 Principles of High-Pressure Water Jets High-pressure water jets, static pressure in the water, that through the booster pump to boost pressure and go through one or series of small holes, water jets as tiny streamers attained very high flow velocity and momentum. The process flow diagram of the standard high-pressure water cleaning device is shown in Fig. 1. After the switch-on of the spray gun puts the piston-type pump in motion, the water in the water tank goes through the booster pump to the pipeline, having pressure and flow. Adjusting the pressure regulating valve’s opening extent by hand to regulate pressure makes a positive correlation between pressure and flow, producing the water jets with pressure assigned to spray from the ejection gun.

Fig. 1. Process flow chart of conventional high-pressure water cleaning device

2.2 Improvement and Design of the High-Pressure Water Cleaning Device Existing high-pressure cleaning devices were designed to adjust the pressure of the highpressure water jets, not controlled separately by pressure, flow and other parameters. Through experiments to testify a set of flow rate parameters and how much the force of high-pressure water jets can achieve the optimal effects on decontamination, this paper proposed a kind of high-pressure water jets decontamination device that can change pressure and flow rate separately and simultaneously, based on the programmable logic controller (PLC) regulating pipeline outlet cross-sectional area. The flow chart of the improved high-pressure water jet contamination devices is in Fig. 2. After the switch of the spray gun carries the motion of the booster pump, the water in the tank goes through the booster pump into the pipeline, having a certain amount of pressure and flow rate; PLC controls the system to gather the data of the pressure sensor and the flow sensor. Furthermore, it commands the proportional pressure regulating valve and the action of the proportional flow valve to produce set pressure and water flow spurt from the spray gun. According to Fig. 2, the improved high-pressure water jet decontamination device consists of a Water tank, booster pump (including motor), safety overflow valve, proportional pressure regulating valve, proportional flow valve, throttle valve, piezometer, flow meter, spray gun and PLC control system.

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Fig. 2. Process flow chart of improved high-pressure water jet decontamination device

2.3 The PLC Control System In this paper, we have developed a high-pressure water cleaning device based on PLC control, adjusting the outlet cross-sectional area of the pipeline, which can achieve simultaneous pressure and flow rate adjustment. This is shown in Fig. 3. The main parameters of the device are as follows: motor power 22kW; motor speed 1450r/min; pressure adjustment range 0 –50MPa; flow rate adjustment range 0 – 22L/min; total weight about 350kg; pressure adjustment accuracy ± 0.2MPa; flow rate adjustment accuracy ± 0.2L/min. Meanwhile, in order to ensure the accuracy of the decontamination time and spray distance parameters, and to avoid the harm of the radioactive water mist generated during the decontamination process, a glove box test stand for cleaning radioactive workpieces with high pressure water was developed based on the traditional radioactive operation glove box. By arranging stepper motors and clamping devices on the top of the glove box, the travel speed and spray distance of the high-pressure water gun and the angle can be precisely adjusted; by arranging the sample holder inside the glove box to fix the test sample; the glove box is sealed as a whole and the lower part is equipped with a radioactive waste collection device shown in Fig. 4.

3 Experimental Verification 3.1 The Experimental Sample According to the findings of the typical hot chamber source term, the typical contaminating nuclides and radioactive contamination levels were determined to determine the radioactive solution system, mainly simulating Cs-137, Co-60 and other nuclides, and the radioactive waste solution characteristics used for sample preparation are shown in Table 1; the material is consistent with the hot chamber shell, and the stainless steel base material is selected with a thickness of 3mm, and the effective size of the sample is 200mm × 20mm. In the glove box, the radioactive contamination solution was used, and 0.5 mL of radioactive contamination solution was accurately pipetted onto the middle of the two

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Fig. 3. Modified high-pressure water jet cleaning device

Fig. 4. High pressure water decontamination glove box test bench

circles of the stainless steel specimen using a pipette gun, and the contaminated specimen was obtained after drying shown in Fig. 5, and the surface contamination level of the specimen was measured to range from 419.0 Bq/cm2 to 477.9 Bq/cm2 .

Fig. 5. Radioactive contamination sample

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3.2 Experimental Result Based on domestic and international experience in high-pressure water decontamination projects, the range of values for each parameter was determined as follows: jet pressure control range 20 MPa–40 MPa; jet flow control range 14 L/min –18 L/min; decontamination time (residence time on the surface to be decontaminated) control range 3s –9s; jet distance control range 200 mm– 600 mm. Considering the need to effectively reflect the influence of each parameter variable on the decontamination effect and to reduce the amount of secondary radioactive waste generated, three variables were selected for each parameter to carry out the decontamination test, i.e. a 4-factor, 3-variable test model. The 4-factor, 3-variable decontamination test affecting the decontamination effect was carried out on the high-pressure water jet decontamination glove box test rig shown in Fig. 2, and the pre- and post-test comparison results are shown in Table 2. Table 1. Characteristics of radioactive waste liquids used for sample preparation Salt content %

pH

Conductivity (μS/cm)

0.625

5.5

4.256

Major nuclides (Bq)

Specific activity (Bq/mL)

Cs-137

β: 105

Co-60

γ: 104

4.25

Table 2. Test result Number

Test factors

Surface contamination level (Bq/cm2 )

Decontamination factor

Pressure/Mpa

Flow (L/min)

Time/s

Distance/mm

Before decontamination

After decontamination

1

20

14

3

205

477.7

11.48

41.6

2

20

16

6

397

477.9

12.69

37.7

3

20

18

9

586

419.0

13.65

30.7

4

30

14

6

586

420.0

7.60

55.3

5

30

16

9

205

433.0

6.05

71.6

6

30

18

3

397

452.5

7.15

63.3

7

40

14

9

397

467.0

2.12

220.3

8

40

16

3

586

459.2

2.17

211.6

9

40

18

6

205

456.4

1.91

238.9

3.3 Flow Influence Law Test As can be seen from Table 2, for the radioactive stainless steel test samples, as the water pressure increases and the decontamination time increases, the decontamination factor

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gradually becomes larger, in an approximate positive relationship; as the spray distance increases, the decontamination factor decreases, in an approximate inverse relationship; these results and the research data are basically consistent. However, as the water flow rate increased, the decontamination factor did not change significantly, due to the effect of other factors on the decontamination results. In addition, the spray flow rate control range was extended from 8 L/min to 18 L/min, the decontamination pressure was 40 MPa, the decontamination time was 9 s, the spray distance was 200 mm and the spray angle was controlled at 70°. The comparison results before and after the test are shown in Table 3. Table 3. Decontamination test results Number

Flow (L/min)

Surface contamination level (Bq/cm2 ) Before decontamination

After decontamination

Decontamination factor

1

8

412.08

1.51

272.9

2

9

441.45

1.62

272.5

3

10

433.60

1.60

271.0

4

11

446.00

1.65

270.3

5

12

477.22

1.78

268.1

6

13

478.84

1.82

263.1

7

14

465.48

1.80

258.6

8

15

451.44

1.79

252.2

9

16

459.36

1.85

248.3

10

17

461.92

1.88

245.7

11

18

456.19

1.90

240.1

The variation of the sorted decontamination factor with flow rate is shown in Fig. 6, where the decontamination factor gradually increases as the high pressure water flow rate decreases. At constant pressure, as the water flow rate increases, the cross-section of the water jet expands and the energy of the water in the core area is dissipated to a certain extent, so that the decontamination factor is smaller for larger water flow rates, but this trend is levelling off. As can be seen from Table 3, for the radioactive stainless steel test samples with surface contamination levels ranging from 412.08 Bq/cm2 to 478.84 Bq/cm2 , the surface contamination levels were less than 2 Bq/cm2 after decontamination, so it is necessary to investigate the upper limit of the decontamination factor for the optimal combination of decontamination parameters. By concentrating the radioactive waste solution used for sample preparation, test samples with surface contamination levels of 498.3 Bq/cm2 , 1005.2 Bq/cm2 , 1995.1 Bq/cm2 , 4992.8 Bq/cm2 and 10023.6 Bq/cm2 were produced. The test was carried out at room temperature with a decontamination pressure of 40 MPa, a decontamination

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Fig. 6. Variation law of decontamination factor with flow

time of 9 s, a spray distance of 200 mm, a decontamination flow rate of 8 L/min and a spray angle of 70°. The comparison results before and after the test are shown in Table 4. Table 4. Decontamination test results Number

Surface contamination level(Bq/cm2 ) Before decontamination

1

498.3

2

1005.2

3

1995.1

Decontamination factor

After decontamination 1.79

278.4

6.92

288.3

4

4992.8

17.18

290.6

5

10,023.6

34.30

292.2

The pattern of change in the growth rate of the decontamination factor after finishing is shown in Fig. 7.

Fig. 7. Change law of decontamination factor growth rate

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As can be seen from Table 4 and Fig. 7, as the surface contamination level of the test specimen increases, its decontamination level after decontamination becomes larger and the decontamination factor becomes larger, but the growth rate of the decontamination factor decreases significantly and tends to level off.

4 Conclusions Based on the Basic theoretical derivation of the high-pressure water jet, this research designed the scheme of synchronously controlling pressure and flow rate and independently developed the high-pressure water jet decontamination device with a PLC control system. Furthermore, it conducted the high-pressure water jet decontamination experiment. Experiment results showed that. 1. the decontamination factor was greatest for a pressure of 40 MPa, a jet flow rate of 18 L/min, a decontamination time of 6 s and a jet distance of 200 mm, using different combinations of influencing factor parameters. 2. The decontamination factor gradually increases as the high-pressure water flow rate decreases. 3. The single decontamination factor increases as the level of contamination on the surface of the test specimen before decontamination increases, but the growth rate of the decontamination factor decreases significantly. Therefore, the improved high-pressure water jet decontamination device can be adjusted to a higher pressure and small flow rate to achieve a better decontamination effect on radioactive wastes. The high-pressure water jet decontamination device implemented by this research is too large and heavy to be moved conveniently. Therefore, this research advised that the device should be designed modularly to reduce the volume so as to facilitate applicating it in the future.

References 1. Qiang, W.: Application of high pressure water jet technology to reactor pool decontamination. Radiat. Protect. Comm. 24(5), 32–34 (2004) 2. .Junxian, D., Xin, L., Huijuan, H.: Reuse of waste from high pressure water jet decontamination for reactor decommissioning scrap metal. Nuclear Safety (1), 70–72 (2011) 3. Jianping, H., Yongming, H.:. Check experiment of the high pressure water washing technology used to the decommissioning of reactor. At. Energy Sci. Technol. 38(4), 373–378 (2004) 4. Qi, G., Fangyi, L., Shuo, L. et al.: Research on decontamination effect and damage of high pressure water jet cleaning to matrix. Chin. Mech. Eng. 25(6), 817 (2014) [2] Reference2 5. Yucheng, L., Yun, W., Zhong, Z. et al.: Cleaning for processing water poll of 801 reactor by high pressure jet stream. Chem. Clean. 10(4), 7–10 (1994)

Analysis of Shock Wave and Containment Behavior Under Severe Accident Internal Hydrogen Explosion Based on CEL Method Li Rongpeng, Liu Mengsha, Gao Ge, Yao Di, and Jiang Di(B) China Nuclear Power Engineering Company, Beijing, China [email protected]

Abstract. Experience from the Fukushima nuclear accident has shown that the complex reactions caused by the melting of the core in a severe accident can generate large amounts of hydrogen and possibly lead to an explosion. At present, most of the newly built pressurized water reactors with large containment vessels use hydrogen ignition devices or hydrogen recombiners to avoid large-scale hydrogen explosions in reactor building. However, the change of the lower limit of hydrogen explosion caused by the high temperature and high pressure environment in the containment during the accident, and the characteristics that hydrogen is easy to accumulate in the higher dome or local room, make the occurrence of local hydrogen explosion still possible. In this paper, the CEL method is used to simulate the internal explosion test reported in the literature. The numerical value of overpressure and the characteristics of the constrained explosion features are in good agreement with the reported data. Determine the conditions under which the containment dome space is filled with hydrogen at the upper explosive limit. The TNT equivalent mass was calculated using the equivalent method. Then the containment model was established. The internal explosion under the constrained condition of the containment was calculated, and the characteristics of the shock wave and the behavior of the containment were analyzed. Keyword: Severe accident · Hydrogen explosion · Containment · CEL method

1 Introduction The occurrence of various serious accidents should be considered in the design of nuclear power plants, and effective measures should be taken to deal with them. These include possible core melting due to various unforeseen circumstances, The fuel core may melt through the bottom head of the pressure vessel and fall into the reactor pit. The contact of the high temperature melt core with the cooling water remaining in the reactor pit can initiate a steam explosion. The molten material also reacts with the bottom floor concrete and generates a large amount of hydrogen. Besides, the fuel cladding can also react with water to generate hydrogen at a relatively fast rate at the high temperature in the initial stage of the accident. The accumulation of hydrogen in the containment space

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 937–946, 2023. https://doi.org/10.1007/978-981-19-8780-9_89

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may cause combustion or explosion, threatening the integrity of the containment [1]. A hydrogen explosion occurred in the Fukushima nuclear accident. At present, the mainstream advanced nuclear reactor types have a variety of means to deal with the hydrogen explosion hazard. For example, by setting a hydrogen recombiner in the containment to control the hydrogen below the detonation concentration, and setting an igniter to ignite the hydrogen with a higher local concentration to prevent large-scale explosions. Valve is also could be set to enhance the mixing of gases in the containment to prevent local explosion due to high local hydrogen concentration [2]. However, studying the shock wave propagation in the containment after the hydrogen explosion and the response behavior of the containment are still helpful to find the weak parts in the structural design of the containment. Prevent the release of large-scale radioactive substances from the technical means of strengthening the containment. Due to some mitigation measures such as hydrogen recombiners are taken in the containment, this paper mainly studies the shock wave propagation phenomenon in the containment and the behavior of the containment caused by the local explosion because of the accumulation of hydrogen in the dome.

2 Numerical Simulation Method and Parameters 2.1 CEL Method In the field of structural design, the external explosion is generally simplified as a certain load, which is applied to the surface of the structure. Check and calculate key indicators such as its maximum displacement to determine whether it can withstand the impact of a specific explosion. But in contrast, the biggest feature of the internal explosion is that its shock wave is constantly reflected and oscillated in the structure, that makes the damage capability is greatly enhanced. In addition to the short-term shock wave, the gas generated after the explosion cannot be released and a long-lasting quasi-static load will also be generated [3]. Therefore, in the case of an internal explosion, the loads of shock waves and impulses generated are directly related to the volume and shape of the confined space. A simplified equivalent load cannot be formed. The CEL method (Coupled Eulerian and lagrangian) under the abaqus platform uses Eulerian elements to simulate air and explosives, and uses Lagrangian elements to simulate structures. The position of the Eulerian material is continuously updated under each calculation step. The Eulerian element is coupled with the Lagrangian element. In this way, the simulation of the entire explosion process is realized. At present, some studies have applied it to the simulation analysis of external explosion and achieved good results [4]. 2.2 Parameters After a serious accident, the reactions are very complex and the hydrogen production rates vary. But hydrogen density is small and tends to accumulate at the dome. Therefore, in for determining the explosion source item in this paper, it is considered that in the 26,835 cubic meters dome, there is a volume fraction of 4% hydrogen, which reaches

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the lower explosion limit. The hydrogen mass is about 96 kg. This was converted to 325.6 kg of TNT using the equivalent TNT method based on the heat of combustion conversion [5]. It is assumed that the epicenter is located at the center of the height of the dome. The parameters used in the equivalent TNT conversion are as follows. The explosive parameters are shown in Tables 1 and 2. Table 1. Parameters of equivalent TNT method Combustion heat of H2 /(KJ/kg)

Combustion heat of TNT /(KJ/kg)

Equivalent factor

141480

4170

0.1

Table 2. Parameters of TNT ρ/(kg/m3 )

A/Pa

B/Pa



R1

R2

Em0 /(J/kg)

1630

3.71 × 1011

3.23 × 109

0.3

4.15

0.95

4.313 × 106

In the simulation, air is considered as ideal gas, and the parameters are shown in Table 3. Table 3. Parameters of air ρ/(kg/m3 )

R

P/Pa

1.297

287

101.325

The steel liner parameters are shown in Table 4. Table 4. Parameters of steel liner ρ/(kg/m3 )

G/Pa

μ

σyx /MPa

εfracture

7800

2.1 × 1011

0.28

320

0.05

Because this paper is to initially study the shock wave and the overall behavior of the structure in the closed structural space. The reinforcement steel bar is not simulated in the structural material. So the behavior is mainly simulated in the elastic stage. The key parameters of structural are shown in Table 5.

3 Numerical Simulation Method Validation The simulation methods in this paper are evaluated and compared by the internal explosion test of the box structure reported in the literature [6]. The internal space of the

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ρ/(kg/m3 )

E/MPa

μ

2500

3.6 × 1010

0.2

box-type structure is 3.2m × 3.2m × 0.8m (height). The top plate thickness is 0.15m and the thickness of rest wall and plate is 0.1m. In the test, C30 concrete and HPB235 rebar is used. The explosion point is located in the center of the inner space, and the explosion mass is 200g. The overpressure curve at the midpoint of the side wall of the box structure is measured in the experiment. The numerical model of the box-type structure is established and shown in Fig. 1. From the test results, it can be seen that the explosive equivalent does not cause damage to the structure, and the structure is basically in an elastic state. Therefore no reinforcement is included in the model, and the main focus is on the internal shock wave phenomenon. The multiple reflections of the blast shock wave in the structure is shown in Fig. 2.

Fig. 1. Numerical model

It can be seen from Fig. 2 that within the calculation time, three oscillation processes of the explosion shock wave in the structure are observed. The first oscillation starts from the initial explosion, creating a pressure build-up near the sidewall at about 2 ~ 3 ms. During the second oscillation, the pressure wave reflected from each wall and accumulated to the center, and then repeated the process of outward impact, resulting in a secondary impact on the wall. The third oscillation process is similar. However, it can be observed that the pressure distribution of the second and third oscillatory gradually tends to be uniform. The overpressures calculated in the numerical simulations were compared with those reported in the literature. Since the overpressure peak is greatly affected by the position, corresponding to the test position of the test, a number of points were selected in the numerical model for comparison. It can be seen from Fig. 3 that the numerical simulation results are similar to the experiments.

Analysis of Shock Wave and Containment

0.7ms

1.6ms

2.3ms

8.7ms

11.1ms

12.9ms

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Fig. 2. Internal explosion phenomenon

The first peak appears at about 2 –3 ms, which is consistent with the time when the pressure builds up near the sidewall in internal explosion phenomenon. The second peak appears at about 6– 8ms. A third increase in pressure occurred after 10 ms, but did not manifest as a distinct peak. That is, from the macro phenomena, the pressure is uniform in a large area. However, the numerical value of the first peak value of the numerical simulation is smaller than that of the experiment, which may be because the local mesh size is not dense enough considering the computational cost. It can be seen from the above analysis that the numerical simulation method and parameter values in this paper can simulate the shock wave oscillation of the internal explosion of the structure.

4 Calculation 4.1 Model Build a typical containment structure. Its total height is about 82m, the wall thickness is 1.3m, the bottom reinforcement area is 1.9m thick, and the inner diameter is 23.2m. It is mainly composed of a cylindrical wall and a hemispherical dome. The diameter of the hatch opening is about 8m, and the plane size of the thicken area around the opening is about 13 × 14m. The thickened part around the opening and the buttress column are located on the outside of the shell, so the interior of the containment is flat. The inside of the shell is lined with a 6mm thick steel lining to maintain the airtightness of the containment and is not considered as a load-bearing component. The surrounding zone is simulated by an air domain of 58 × 58 × 95m, and a non-reflective boundary is used around the air domain. Explosives and air are both Eulerian elements, and the interface has a common node. The overall model is shown in Fig. 4.

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Numerical simulation

Experiment Fig. 3. Result of numerical simulation and experiment

Fig. 4. Numerical model of containment

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4.2 Result After calculation, the phenomenon of shock wave propagation in the containment is shown in Fig. 5.

3ms

17ms

73ms

91ms

209ms

235ms

56ms

158ms

250ms

Fig. 5. Internal explosion phenomenon of containment

It can be seen from Fig. 5 that the blast shock wave expands in the dome space, and first gathers at the highest point inside the dome. Then the maximum pressure position appears at the height of the horizontal projection of the center of explosion, and moves downward continuously. After the maximum pressure range of the shock wave enters the cylinder area, the phenomenon of flow converging to the center occurs. A horizontal band area of higher pressure forms and continues to push towards the bottom. After reaching the bottom, the phenomenon of pressure accumulation and growth occurs again. The shock wave then converges again towards the center along the perimeter of the bottom, again forming a horizontal annular region of higher pressure and propagating upwards. Under the local explosion conditions in the dome space set in this paper, the maximum pressure in each stage of the shock wave propagation process is analyzed. In the early stage of the explosion, the accumulated pressure at the highest point of the inner space of the dome was the highest, reaching a relative pressure of 0.26MPa, and then the maximum pressure was in the pressure range of 0.1 ~ 0.2MPa during the continuous downward propagation along the inner wall of the dome. When the maximum pressure range enters the cylinder and propagates downward, the pressure first decreases and then

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gradually increases. After entering the cylinder, the pressure drops rapidly to 0.07MPa, reaches a minimum of about 0.055MPa at about 1/3 of the height of the cylinder, and then continues to rise until the pressure reaches the bottom to a maximum of 0.11MPa. During the subsequent rebound of the shock wave from the bottom to the top, the pressure changed in the range of 0.08 ~ 0.09MPa, and finally stabilized to about 0.1MPa and propagated upwards. Therefore, in the explosion scenario studied in this paper, the highest shock wave overpressure appears on the highest point in the dome space. It can be seen from Fig. 6 that the containment displacement response shows repeated oscillations. The response is measured in displacement. In the early stage of the explosion, the maximum response appeared at the highest point of the dome, which is consistent with the phenomenon of the maximum accumulated pressure at the highest point in the previous analysis. The maximum response then appears as a ring around the dome and propagates downward. When it reaches the cylinder wall, its value decreases, but there is a concentrated increase in the horizontal sides of the hatch hole. The maximum displacement then propagates up again and emerges at the dome, repeating the above process. During the calculation time of this paper, three oscillations were captured. For the dome, the maximum displacement occurs at the highest point. During the three oscillation, the absolute value decreases continuously. The maximum displacement of the cylinder wall is concentrated on nearby area along both horizontal sides of the thickening area of the hatch opening. The analysis shows that under the explosion scenario calculated in this paper, the top of the dome and the nearby area of hatch hole thickened area are most dangerous areas.

5 Conclusion Under the conditions of local explosion in the dome set in this paper, 1. The shock wave reflected and oscillated multiple times from the dome to the bottom in the inner space of the containment, and the maximum accumulated pressure appeared at the highest point in the inner space of the dome. 2. The top of the dome and the nearby areas on both sides in the horizontal direction of the thickened area of hatch hole have a large relative displacement, which are more dangerous area under the condition.

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13ms

50ms

64ms

105ms

124ms

149ms

209ms

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Fig. 6. Response of containment during internal explosion/m

Acknowledgement. The study was supported by the National Key R&D Program of China (2020YFB1901403).

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References 1. Deng, J.: Study on hydrogen control for severe accident conditions in the large dry containment. Shanghai Jiao Tong University, Doctor (2008) 2. Huang, W.F.: Studies on the explosion and its preventive measures inside containment during severe accidents. Harbin Engineering University, Master (2016) 3. Liu, S.: Research status and analysis of explosive loads in space. Low Temp. Arch. Technol. 2016(7), 71–73 (2016) 4. Zhu, X.L.: Influence of multi-arch surface on on anti-explosio performance. Sci. Technol. Inf. 2020(1), 35–40 (2020) 5. Hu, S.Q.: Combustion and explosion. Beijing Institute of Technology Press, Beijing (2015) 6. Guo, Z.H., Song, F.L., Liu, F.: Experiment of closed flat box structure subjected to internal detonation. J. PLA Univ. Sci. Technol. 2008(4), 345–350 (2008)

Study on the System Simulation for the Heat Pipe Failure Transient of Heat Pipe Cooled Reactor Chao Tan1(B) , Suizheng Qiu2 , Chenglong Wang2 , Zeqin Zhang2 , Zhengquan Xie1 , and Fei Li1 1 CNNC Key Laboratory on Nuclear Industry Simulation, Wuhan, Hubei, China

[email protected] 2 School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an, Shanxi, China

Abstract. Heat Pipe cooled Reactor (HPR) has the advantages of compact structure, high power capacity, high reliability, and inherent safety. It is often considered to have no loss of flow or loss of coolant accident during the heat pipe cooled reactor operation, because there is no flowing coolant working across the major equipment (the coolant only evaporates, condenses and transfers inside the heat pipe) and the working pressure is low. However, due to the limitations of the thermo-physical properties of the fluids inside the tube and the structure of the wick, the heat pipe has an inherent working temperature range, and the heat flux at the hot end is likely to change sharply which could result in the failure of the heat pipe. While the heat pipes are closely arranged in the reactor, an abnormal operation of a single heat pipe may lead to the continuous failure of multiple heat pipes around it in a short time. Thus, the impact of such accidents on the operation safety and reactor reliability should be considered in the heat pipe nuclear reactor design and safety analysis. This paper provides a system-level simulation scheme to analyze the possible consequences of such kind of accident in a rectangular heat pipe reactor. In addition, the important indexes safety margin of the reactor during the transient of heat pipe failure is evaluated and calculated. Keywords: Heat pipe cooled reactor · System level simulation scheme · Heat pipe failure accident · Safety analysis

1 Introduction Heat Pipe cooled Reactor (HPR), as a type of small modular reactor, has the advantages of compact structure, high power capacity, high reliability, and inherent safety. HPR adopts the solid-state design concept and uses alkali metal high-temperature heat pipes to passively dissipate the fission heat. The entire reactor core eliminates the requirement of coolant loop, which greatly simplifies the structure. Through a certain heat pipe arrangement design, the reactor can avoid single-point failure accidents [1]. In this case, the heat of the failed heat pipe can be dissipated by the adjacent heat pipes, and this passive nature provides the inherent safety of the reactor. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 947–963, 2023. https://doi.org/10.1007/978-981-19-8780-9_90

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Due to these advantages, HPR is considered one of the most promising options for future exploration projects with higher requirements for power, operating time, reliability and concealment [2]. In the last 20 years, many relatively mature HPR designs have been proposed. For example, Los Alamos National Laboratory (LANL) proposed HOMER [3] in 2001, and designed a series of heat pipe cooled reactor power supply systems for planetary surface exploration. Besides, Alkali metal heat pipes and different types of energy conversion systems were selected for the project. MSR [4] is another series of heat pipe cooled reactor for planetary mission, which uses lithium heat pipe and thermionic converter. In 2016, LANL proposed a kilowatt-level heat pipe reactor Kilopower [5], and completed the prototype test. In addition, there are many similar HPR designs, such as SAFE [6], SAIRS [7], HP-STMSs [8], and MegaPower [9]. They all use rotating drum for reactivity control, alkali metal heat pipes for heat conduction, static or dynamic direct thermoelectric conversion for electricity generation and hexagonal core arrangements. According to these studies, the design of HPR has been deeply developed and a consensus has been reached on the framework. However, due to the limitations of the thermo-physical properties of the working fluid inside the heat pipe and the structure of the wick, the heat pipe has an inherent working temperature range, and the heat flux at the hot end is likely to change sharply which could result in the failure of the heat pipe. While the heat pipes are closely arranged in the reactor, an abnormal operation of a single heat pipe may lead to the continuous failure of multiple heat pipes around it in a short time, eventually triggering the cascading heat pipe failure accident. In the current research, the analysis of the heat pipe reactor is mainly focused on the steady-state analysis, while the transient analysis, especially the analysis of the cascading failure accident, is less. Poston et al. [6] developed a self-programming code HPSDIP to analyze the heat pipe failure accident of SAFE-400, and carried out the safety margin of reactor. Apart from that, Kapernick et al. [10] developed a Fluentbased method for thermal and stress analysis of space heat pipe reactors, and evaluated three kinds of design basis accidents. Furthermore, Galloway et al. [11] calculated the steady-state performance of fuel rod, monolith, and heat pipe under heat pipe failure accidents of microreactor based on ABAQUS. Sterbentz et al. [12] also performed a transient analysis of MegaPower based on ABAQUS, and obtained the characteristics of the reactor under the failure conditions of 1–3 heat pipes. In addition, Ma et al. [13] developed a method that coupled RMC and ANSYS to simulate several postulated failure conditions of MegaPower. Although these studies have achieved good results, they lack detailed analysis of the cascading failure process of heat pipe reactors, which is crucial for heat pipe cooled reactor safety analysis. This work provides a system-level simulation scheme to analyze the possible consequences of single heat pipe failure accident and cascading heat pipe failure accident of a rectangular heat pipe reactor NUSTER-100 [2]. Apart from that, the important indexes safety margin of the reactor during the transient of heat pipe failure is evaluated and calculated.

2 Reactor Structure This study takes the NUSTER-100 reactor as the calculation object. The overall structure of NUSTER-100 mainly consists of reactor core and shielding system, energy conversion

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system, waste heat removal system and core emergency cooling system. To be specific, the fission heat in the core is transmitted to the thermoelectric power generation module by the heat pipe, and the remaining waste heat after power generation is transmitted to the cooling water tank by the cold plate and the spiral tube heat exchanger. The entire system does not require rotating parts, and there is no coolant flow in the primary loop of the reactor. The structure of the NUSTER-100 reactor is shown in Fig. 1, and the main design parameters of the system are displayed in Table 1.

Fig. 1. The structure of the NUSTER-100

3 Numerical Model According to the design characteristics of NUSTER-100, this work establishes a heat pipe reactor system analysis model including point reactor kinetics model, core heat transfer model, heat pipe model, thermoelectric generator model and cold plate model, etc. 3.1 Point Reactor Kinetics Model In order to simulate the transient power change of the reactor, point reactor kinetics model is employed to calculate the total fission power of the active zone. In this work, six groups of delayed neutrons have been adopted as follows: ⎧ 6 ⎪ dn ρ−β  ⎪ ⎪ = + λi Ci ⎨ dt  i=1 (1) ⎪ ⎪ dCi β ⎪ i ⎩ = n − λi Ci dt  where t represents time, n represents the neutron density, ρ represents the total reactivity, β represents the total fraction of delayed neutrons,  represents the neutron generation time, βi represents the delayed neutron fraction λi represents the decay constant of

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Component

Parameter

Value

system

Total length

4000 mm

Total diameter

1000 mm

Axial length of core active zone

450 mm

Fuel rod

Heat pipe

TEG

Axial length of safe shell

600 mm

Length of energy conversion system

1140 mm

Design thermal power

1000 kW

Design electric power

120 kW

System thermoelectric conversion efficiency

12%

Fuel pellet outer diameter

11.8 mm

Gas gap thickness

0.1 mm

Fuel clad outer diameter

13 mm

Fuel form

72%/50%/19.75% UO2

Fuel clad material

Mo

Axial reflector inner diameter

12 mm

Axial reflector outer diameter

13 mm

Axial reflector length

225 mm

Vapor space diameter

18 mm

Wick outer diameter

26.4 mm

Wall outer diameter

30 mm

Evaporator length

450 mm

Adiabatic length

600 mm

Condenser length

1200 mm

Working fluid

Na

Wall and wick material

Haynes233

Size

32 × 30 × 20

Semiconductor material layer

3

First layer material

bismuth telluride

Second layer material

skutterudites

Third layer material

half-Heusler

Gasket material

copper

Cold plate material

aluminum

the delayed neutron precursor, Ci represents the concentration of the delayed neutron precursor.

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In the above equations, the core fission power is mainly controlled by the total reactivity, which can be expressed as: ρ(t) = ρCR (t) + ρDOP (t) + ρEXPAN (t) + ρHP (t)

(2)

where ρCR (t) represents reactivity introduced by control rods and shutdown rods, ρDOP (t) represents the reactivity feedback introduced by the Doppler effect of the fuel, ρEXPAN (t) represents the reactivity feedback introduced by reflective layer and matrix expansion, ρHP (t) represents the reactivity feedback introduced by heat pipe material and its internal working fluid properties changes. In this work, since the changes of ρEXPAN and ρHP with temperature are similar, they are combined into one parameter ρEXP . The feedback coefficient of ρDOP (t) and ρEXP can be expressed as: ρDOP (T ) = 5.92140 × 10−10 T 3 − 2.02435 × 10−6 T 2 + 2.02272 × 10−3 T − 0.812965 (3) ρEXP (T ) = −6.94539 × 10−7 T 2 + 2.1474 × 10−3 T − 1.8195

(4)

3.2 Core Heat Transfer Model The core structure of heat pipe cooled reactor is complex, which contains at least 6 layers of solid material regions from the fuel pellet to the heat pipe vapor space. For a single channel, there are some differences in the thermal properties of these solid regions, because the arrangement of fuel rods and heat pipes is not completely symmetrical. For the entire core, this difference can be negligible. In order to simplify the modeling, the irregularly shaped region in the heat pipe unit can be equivalently regarded as a regular ring, and the rectangular core channel can be modeled as an approximately cylindrical channel [2]. The core channel equivalent schematic diagram is shown in Fig. 2. Fuel rod, cladding and matrix regions are calculated according to the equivalent annular ring model, and the boundary conditions are transferred with the heat pipe calculation module.

Fig. 2. Equivalent schematic diagram of single channel [2]

According to the thermal conductivity characteristics of the equivalent cylindrical core unit and the fuel pellets, the internal heat source in the structural materials of each

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layer can be ignored. It is considered that the internal heat source only exists in the fuel pellets. Thus, the heat transfer in the fuel area is heat conduction with an internal source, and the control equation can be expressed as follows:   ∂TU ∂TU 1 ∂ λU r + QV ρU cU (5) = ∂t r ∂r ∂r Since the cladding and the fuel matrix have no internal source, they can be regarded as the same thermally conductive matrix, and their heat transfer is pure heat conduction. The unified control equation can be expressed as follows:   1 ∂ ∂TM ∂TM = λM r (6) ρM cM ∂t r ∂r ∂r where r represents radial position, ρ represents density, c represents heat capacity, λ represents heat conductivity, T represents temperature, and QV represents heat source. Subscript U and M represents fuel and cladding/matrix, respectively. For fuel region, QV is Volume fission power density. If the power in the fuel rod is considered to be uniformly distributed, QV is a constant value. In addition, the axial heat transfer in fuel, gap, cladding and matrix region is ignored due to the high thermal conductivity of heat pipe. For equivalent modeling, the equivalent thickness of each region can be calculated using the formula below: An 2 + rn−1,o − rn−1,o (7) δn,e = π where δn,e refers to the thickness of the nth layer equivalent ring, An refers to the area of the nth layer equivalent ring, and rn−1,o is the radius of the n-1th layer equivalent ring. The metal matrix of the heat pipe reactor core generally features high thermal conductivity. Thus, the radial energy transfer of HPR is much larger than that of the PWR under the same power density. This characteristic provides a certain temperature self-flattening capability for the solid-state reactor core. It will inevitably lead to computational distortion if the radial power transfer is ignored. In order to study the actual temperature distribution of the heat pipe reactor core, the preliminary energy transfer model between the corresponding channels is established. First, the entire core is abstracted into a modular assembly form of different types of heat pipe channels. Its establishment process is presented in Fig. 3. In the actual calculation, it is considered that the axial heat transfer occurs only in the heat pipe area, so that the three-dimensional calculation of the whole stack is simplified into multiple twodimensional calculations. For fuel-cladding-matrix area, axial heat transfer is ignored and only radial heat transfer and circumferential heat transfer are considered. For heat pipe area, ignoring radial heat transfer, only axial heat transfer and circumferential heat transfer are taken into account. Inside the channel, the corresponding fuel-cladding-matrix -heat pipe temperature is still calculated according to the equivalent calculation method as Eqs. (5) and (7). Between channels, the channels are calculated as an averaged ensemble, as shown in Fig. 4.

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(a) Core real geometry

(b) Abstract channel assembly

Fig. 3. Schematic diagram of core modularization (a), Core real geometry (b), Abstract channel assembly

Fig. 4. Schematic diagram of heat transfer between channels

The parameters of the averaging channel are calculated as a weighted average. The density, heat capacity, and temperature are averaged respectively, and then calculated as the weight of the latter to ensure the conservation of enthalpy. The formulas for average density ρ, average heat capacity C p , and average temperature T are respectively:

ρi Vi (8) ρ= V

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Cp =

T=

Cpi ρi Vi ρV

Ti Cpi ρi Vi C p ρV

(9) (10)

where ρi denotes the density of the ith layer equivalent ring, Cpi denotes the heat capacity of the ith layer equivalent ring, Vi denotes the volume of the ith layer equivalent ring, Ti denotes the temperature of the ith layer equivalent ring, and V denotes the total volume. This study calculates the energy transfer between channels according to the above cell temperature, in which the energy transfer is expressed by power. For node P, the power transferred from node W, E, S, and N to node P can be expressed as follows: QWi =

TW − TP λw · y zi x

(11)

QEi =

TE − TP λe · y zi x

(12)

QSi =

TS − TP λs · x zi y

(13)

QNi =

TN − TP λn · x zi y

(14)

For each channel, under the influence of energy transfer between channels, its additional channel power is: QP∗ =

n 

(QWi + QEi + QNi + QSi )

(15)

i=1

where n represents the total number of axial control volume. The channel-to-channel energy transfer calculated by this method is algebraic values, with positive values representing inflows and negative values representing outflows. Its sum is zero and will not affect the total power, which can be shown as: m 

QP∗ = 0

(16)

1

where m denotes the total number of core channel. 3.3 Heat Pipe Model The heat pipe reactor adopts the alkali metal high-temperature heat pipe to transfer the core heat. In this work, the three-stage startup model is employed [14]. The schematic diagram of the heat transfer model is displayed in Fig. 5. In the axial direction, the heat pipe is divided into the evaporation section (evaporator), the adiabatic section (adiabatic)

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Fig. 5. The schematic diagram of heat pipe model [14]

and the condensation section (condenser), which affect the outer boundary conditions of the model respectively. In the radial direction, the heat pipe is divided into the wall, the wick and the vapor space. Meanwhile, each region has different governing equations and boundary conditions. When the high temperature heat pipe is in operation, the flow rate of the alkali metal working fluid in the wick is very low. Thus, the flow process of the liquid working fluid in the wick is generally ignored. On the other hand, the heat transfer along the axial direction cannot be neglected, so that the wall and wick region of the heat pipe are modeled by the two-dimensional pure heat conduction, which can be shown below: ρi ci

∂ ∂ ∂Ti ∂Ti ∂Ti = (λi ) + (λi ) ∂t ∂x ∂x ∂y ∂y

(17)

where x denotes axial position, and y denotes radial position. The wick area of the heat pipe can be regarded as a mixed material of liquid working fluid and solid material, and the volume heat capacity and thermal conductivity of the material are jointly determined by its components. Moreover, the effective volume heat capacity and the thermal conductivity are calculated according to the equation given by Chi [15]: Ce = εCl + (1 − ε)Cs λe = λl ·

(λl + λs ) − (1 − ε)(λl − λs ) (λl + λs ) + (1 − ε)(λl − λs )

(18) (19)

where ε represents the porosity of the wick, C represents the volume heat capacity, subscript e represents effective value, s represents solid part, l represents liquid part. For the vapor space, different control equations are adopted according to whether the continuous flow state of the vapor is completely established [14]. At the liquid-vapor interface, the mass exchange rate can be express as:    Pf Pg M 2aε

− m ˙o = (20) 2−a 2π Ru Tf Tg

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where a represents unit adjustment coefficient, M represents relative atomic weight, Ru represents ideal gas constant, P represents pressure, subscript f represents interface liquid, g represents interface vapor. When the average vapor temperature is greater than the operating temperature, the continuous flow regime is completely established in vapor space, and the onedimensional compressible flow equation is employed to simulate the vapor behavior. The governing equations are: d ˙0 (ρV ) = m dx  D  d dP + ρU 2 dy = −τg D dx dx 0    1 D 1 d 3 ρVDh + ˙ 0 V02 ρU dy = m ˙ 0 h0 + m dx 2 0 2 D

(21) (22) (23)

where D denotes the width of the vapor space, U denotes the average velocity, V denotes the axial velocity, h denotes the enthalpy, P denotes pressure, τg denotes the shear stress of the vapor, and subscript 0 indicates the initial value at the liquid-vapor interface. 3.4 Thermoelectric Generator Model Thermoelectric generator (TEG) is a device that uses physical effects such as the Seebeck effect, Peltier effect, and Thomson effect to realize the mutual conversion of electric and heat energy [16]. When the thermoelectric device forms a closed loop, Peltier heat will be generated on the contact surface between TEG’s leg and electrode under the effect of the current, which will weaken the TEG’s ability to generate electricity. What’s more, Joule heating and Thomson heating combine the low currents and temperature gradients produced in thermoelectric materials. Therefore, the thermoelectric conversion of TEG is a complex thermoelectric coupling process, indicating that solving the computational governing equations requires a lot of iterations. In the design of NUSTER-100, there are as many as 8000 TEGs. In order to avoid the consumption of computing resources, the interpolation method is proposed to decouple the thermoelectric solution process. The thermoelectric conversion part of the thermoelectric generator model is calculated by database real-time interpolation. In addition, the temperature-thermoelectric conversion efficiency database of TEG is established based on the CFD calculation results. The database is indexed by the cold side temperature and the temperature drop between the hot and cold side. The mapping relationship between the thermoelectric conversion efficiency and these two parameters is linearly retrieved, which can be expressed as follows: η = f (Tc , T )

(24)

where η represents the thermoelectric conversion efficiency of TEG, Tc represents the cold side temperature of TEG, T represents the temperature drop between the hot and cold side.

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The heat transfer model is simulated and calculated by the thermal component model of RELAP5 [17]. The thermal component model of the RELAP5 supports onedimensional heat transfer calculations for rectangular, cylindrical and spherical geometries. The code not only uses the user-defined property table to interpolate and calculate the temperature-dependent material thermal conductivity and volumetric specific heat, but also calculates the volumetric heat capacity according to the user-defined heat transfer area coefficient. The model establishes the material property table and thermal structure grid of copper, bismuth telluride, half-Hessler, skutterudite, aluminum, and cold plate in one-dimensional space. The discrete equation in one-dimensional space direction can be expressed as:   n+1 Tm − Tm Gm = t (25)   S S V V −(Tm − Tm−1 )klm δlm + (Tm+1 − Tm )krm δrm + Pf P(t) Qlm δlm + Qrm δrm V V Gm = ρlm clm δlm + ρrm crm δrm

(26)

where Gm denotes the volume heat capacity of grid interval points, ρm denotes the density, cm denotes the heat capacity, δm denotes the grid width, Pf denotes the axial power distribution factor of the thermal component, P(t) denotes the time-varying function of the thermal component power source term, Qm denotes the spatial distribution factor of grid occupied heat component power. As for the boundary, the heat source transferred to the thermoelectric generator model is introduced from the heat pipe model in the form of a real-time interactive input table. The heat loss due to thermoelectric conversion is calculated through the heat flux density and power generation efficiency, and then added to the grid heat conduction model as a heat sink. Then, the final heat sink of system is introduced as a heat flux source term at the surface of cold plate obtained by the convection heat transfer model, which is also calculated using the RELAP5.

4 Heat Pipe Failure Accident Analysis Based on the mathematical and physical model of heat pipe reactor mentioned above, this section carries out heat pipe failure accident calculation for heat pipe reactor. A total of 109 heat pipes have been arranged in the core. Since the heat pipe of the central channel (55#HP) transmits the highest power (11.11kw), the central channel is taken as the object for analysis. Assuming that the central channel heat pipe fails, its power is distributed to the surrounding 8 heat pipes according to the logic described in Chapter 3.3. The number of 8 heat pipes around 55#hp is shown in Fig. 6. Since the cold end is not 1/8 symmetric, the core temperature distribution is no longer centrosymmetric. Therefore, the heat transfer characteristics of 44# and 66# heat pipes are the same, which are as same as the heat transfer characteristics of 54# and 56#. However, the heat transfer characteristics of 44# and 54#, 56# and 66# are different. Assuming that the power transmission capacity of the heat pipe is reduced to zero after the failure of the heat pipe, the transient performance of the above 9 heat pipes is calculated and analyzed.

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Fig. 6. Heat pipe identifier around 55# HP

The transient heat transfer characteristics of 43#, 45#, 65# and 67#HP are shown in Fig. 7. Obviously, since these four heat pipes are at the diagonal position of 55#HP, they receive less power from 55#HP, and the temperature of each structure changes little. The maximum temperature rise is about 32K. 13000

Temperature / K

1400

12500 HP temperature Fuel temperature Cladding temperature

1360 1320

12000 11500

HP surface heat flux 1280

11000

1240

10500

1200

10

20

30

40

HP surface heat flux / W·m-2

1440

10000

50

Time / s

Fig. 7. 43#&45#&65#&67#HP heat transfer characteristics

The transient heat transfer characteristics of 44# and 66#HP are shown in Fig. 8. They accept relatively more power from 55#HP, and the temperature rise is relatively large. The maximum fuel temperature has increased by 45k. The transient heat transfer characteristics of 54# and 56#HP are displayed in Fig. 9. It is worth mentioning that the steady-state temperature of each structure does not increase, but decreases. This is because the power transmitted by 55#HP to the cold plate is reduced to zero, and the cold end temperature is reduced. These heat is indirectly taken away by the two adjacent cold plates. In Fig. 8, the heat flow on the surface of the heat pipe has a large peak, because the temperature difference among 55#HP and them. The transient heat transfer characteristics of 55#HP failure are presented in Fig. 10. Due to the loss of heat transfer capacity of the heat pipe, the fuel no longer conducts

1440

13500

1400

13000

1360

HP temperature Fuel temperature Cladding temperature

12500

HP surface heat flux

11500

12000

1320 1280

11000

1240

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HP surface heat flux / W·m-2

Temperature / K

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10500

1200

10

20

30

40

10000

50

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1440

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12000 HP temperature Fuel temperature 11500 Cladding temperature HP surface heat flux / W·m-2 11000

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Temperature / K

Fig. 8. 44#&66#HP heat transfer characteristics

10500

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Fig. 9. 54#&56# HP heat transfer characteristics

heat inward, and the temperature of each structure is reduced to be consistent with the cold end temperature. During the heat pipe failure accident, the temperature distribution and power distribution of the core are shown in Figs. 11 and 12 respectively. It can be observed that the distortion of power distribution is obviously greater than that of temperature distribution, indicating that the heat pipe reactor core has a certain temperature flattening ability. In addition, the power of the heat pipe adjacent to the failed heat pipe increases significantly. Before the failure of 55#HP, its heat pipe temperature is almost the same as that of 54#HP and 56#HP, indicating that its heat transfer limit is basically the same. If 55\hp has the potential to fail, once the power of 55#HP is distributed to 54# and 56#HP, it will inevitably fail at the same time, resulting in cascading failure. Therefore, this phenomenon needs to be considered in the follow-up work.

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Fig. 10. 55# HP channel

5 Conclusion With many advantages, heat pipe reactor has been widely studied, but there is still a gap in the study of heat pipe failure accidents. This paper presents a mathematical and physical model suitable for heat pipe reactor, and establishes a system-level simulation scheme, which mainly includes point reactor kinetics model, core heat transfer model, heat pipe model, and thermoelectric generator model. Then, based on the simulation of nuster-100, the transient response process of the reactor after the failure of the central channel is analyzed. According to the results, the maximum temperature rise of adjacent channels is 45k after the failure of the central channel. In addition, the power distortion of the reactor is much larger than the temperature distribution distortion, indicating that there is a risk of cascading failure accident in the reactor core. In the future work, it is necessary to combine the channel energy transfer model and the heat pipe heat transfer limit model to further analyze the cascade failure transient.

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(a)

(b)

before 55#HP failure

after 55#HP failure

Fig. 11. Reactor temperature distribution change

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Fig. 12. Reactor power distribution change

Acknowledgements. This work is carried out under the financial support of the National Key Research and Development Program of China (No. 2019YFB1901100).

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References 1. Huang, J., Wang, C., Guo, K., et al.: Heat transfer analysis of heat pipe cooled device with thermoelectric generator for nuclear power application. Nucl. Eng. Des. 390, 111652 (2022) 2. Zhang Z, Wang C, Guo K, et al.: Heart, A specific code for thermal-electrical analysis of heat pipe cooled nuclear reactor. Int. J. Therm. Sci. 179, 107666 (2022) 3. Poston, D.I.: The heatpipe-operated mars exploration reactor (Homer). Office of Scientific & Technical Information Technical Reports (2000) 4. Bushman, A., Carpenter, D.M., Ellis, T.S., Gallagher, S.P., Hershcovitch, M.D., Hine, M.C. et al.: The martian surface reactor: an advanced nuclear power station for manned extraterrestrial exploration (2004) 5. Poston, D.I., Godfroy, Thomas, Mcclure, P.R., Sanchez, R.G.: Kilopower project—Krusty experiment nuclear design. United States (2015) 6. Poston, D.I., Kapernick, R.J., Guffee, R.M.: Design and analysis of the safe-400 space fission reactor//AIP Conference Proceedings. American Institute of Physics 608(1), 578–588 (2002) 7. El-Genk, M.S., Tournier, J.M.: “Sairs”—Scalable Amtec integrated reactor space power system. Progress in Nuclear Energy (2004) 8. El-Genk, M.S., Tournier, J.M.: Conceptual design of Hp-Stmcs space reactor power system for 110 Kwe//AIP Conference Proceedings. American Institute of Physics 699(1), 658–672 (2004) 9. Mcclure, P.R., Poston, D.I., Dasari, V.R. et al.: Design of megawatt power level heat pipe reactors. Report of Los Alamos National Laboratory, USA (2015) 10. Kapernick, R.J., Guffee, R.M.: Thermal stress calculations for heatpipe-cooled reactor power systems. AIP Conf. Proc. 666–672 (2002) 11. Galloway, J.D., Lawdensky, V.J., Poston, D.I. et al.: Effects of heat pipe failures in microreactors. Los Alamos National Lab.(Lanl), Los Alamos, Nm (United States) (2020) 12. Sterbentz, J.W., Werner, J.E., Hummel, A.J. et al.: Preliminary Assessment of two alternative core design concepts for the special purpose reactor. Idaho National Lab.(Inl), Idaho Falls, Id (United States) (2017) 13. Ma, Y., Chen, E., Yu, H., et al.: Heat pipe failure accident analysis in megawatt heat pipe cooled reactor. Ann. Nucl. Energy 149, 107755 (2020) 14. Zhang, Z., Chai, X., Wang, C., et al.: Numerical investigation on startup characteristics of high temperature heat pipe for nuclear reactor. Nucl. Eng. Des. 378, 111180 (2021) 15. Chi, S.W.: Heat pipe theory and practice. Washington, D.C. Hemisphere Publishing Corp.; New York, Mcgraw-Hill Book Co., p. 256 (1976) 16. Champier, D.: Thermoelectric generators: a review of applications. Energy Convers. Manage. 140, 167–181 (2017) 17. Relap5/Mod3 Code Manual Volume I: code structure, system models, and solution methods, Nureg/Cr-55535/Rev 1- Vol I

Prediction of LOCA Break Size Based on 1D Convolutional Neural Network Hao Wang1 and Zheng Liu2(B) 1 Department of Engineering Physics, Tsinghua University, Beijing, China

[email protected] 2 CNNC Key Laboratory on Severe Accident in Nuclear Power Safety, CNPE, Beijing, China

[email protected]

Abstract. Loss of Coolant accident (LOCA) is one of the typical accidents in nuclear power plant (NPP) safety analysis. Different break sizes require different countermeasures, and wrong measures may lead to core meltdown. Therefore, it is necessary to identify the size of the break accurately. In this paper, we use MAAP software to simulate LOCA with different break sizes for pressurized water reactor nuclear power plants, and generate a large amount of time series data. Based on these data, with the help of deep learning techniques, a 1D convolutional neural network (1D-CNN) is trained in this paper, and the trained model is used for break size prediction. The results of the test set illustrate that a well-trained 1D-CNN model can accurately predict the break size of LOCA, and the prediction results can assist the manipulator in making reasonable decisions when LOCA occur. Keywords: LOCA · Break size · Deep learning · 1D-CNN · MAAP

1 Introduction LOCA is a common type of accident in NPPs. It refers to an accident in which the first loop pressure boundary is broken or ruptured, or the valve is opened by mistake, resulting in a reduction of the first loop coolant charge. Due to the high temperature and high pressure characteristics of the first loop, when the pressure boundary is broken, the system pressure, flow, temperature, radioactivity and other state parameters will be rapidly out of balance, while the safety equipment of the nuclear power plant will also be put into operation [1]. Nuclear power plants generally have three safety barriers, i.e., fuel cladding, first-loop pressure boundary, and containment. LOCA disrupts the second safety barrier break and loses the first-loop pressure boundary, allowing the firstloop coolant to enter the containment and radioactive material to be released into the containment environment [2]. Improper disposal of LOCA can lead to a severe accident where the reactor core cannot be cooled sufficiently, resulting in a core meltdown. The break size of LOCA is an important basis for taking countermeasures, so prediction of the break size is important to help operators make sound decisions. With the rise and development of artificial intelligence, the use of machine learning technology to diagnose NPPs accidents has received widespread attention from scholars around the world [3]. It can build a data-driven intelligent analysis method based © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 964–972, 2023. https://doi.org/10.1007/978-981-19-8780-9_91

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on the existing real data of NPPs and industrial-grade simulation data to diagnose and predict accidents of NPPs. In reference [4], a back propagation neural network was used to diagnose four types of accidents in AP1000 NPPs: steam generator relief valve or safety valve misopening, steam system piping rupture, loss of normal water supply and feedwater piping rupture, and the diagnosis results were accurate. The reference [5] used genetic algorithms to simulate fault diagnosis for U-tube rupture accidents of steam generators, and the diagnosis results accurately identified the steam generator where the rupture occurred. The reference [6] combined back propagation neural networks and radial basis functions to diagnose five types of typical NPP design basis accidents, and achieved good results in terms of diagnostic time consumption and accuracy. In reference [7], for the CPR1000 nuclear power system, the LOCA with different break locations and sizes was modeled and simulated using CATHARE software. The four trained neural networks were then used to diagnose the location and size of the break. The results show that among the four neural networks, the support vector machine with optimized parameters has higher accuracy and better stability. The reference [8] used convolutional neural network (CNN) and convolutional long-short term memory (ConvLSTM) techniques to construct an integrated model for early warning and simulation of LOCA, which can effectively identify different break related features of different sizes. In our research, it is found that the existing research on predicting LOCA break size based on deep learning is insufficient, especially the detailed research on the break size corresponding to different responses is lacking. In this paper, based on the safety analysis report of a pressurized water reactor NPP, the MAAP program is used to generate the time series data of LOCA with different break sizes and the corresponding subsequent actions of the NPP system. Based on these data, a 1D-CNN model is trained to diagnostic the break sizes and the diagnostic results are satisfactory.

2 1D Convolutional Neural Network The convolutional neural networks (CNNs) are deep, biologically inspired feed-forward artificial neural networks (ANNs) which constitute a simple model of mammalian visual cortex [9]. CNNs are ANNs with alternating convolutional and subsampling layers. The popularity and the wide range of application domains of deep CNNs can be attributed to the following advantages [10]: 1. CNNs fuse the feature extraction and feature classification processes into a single learning body. They can learn to optimize the features during the training phase directly from the raw input data. 2. Since CNN neurons are sparsely connected with tied weights, CNNs can process large inputs with a great computational efficiency compared to the conventional fully-connected Multi-Layer Perceptron (MLP) networks. 3. CNNs are immune to small transformations in the input data including translation, scaling, skewing and distortion. 4. CNNs can adapt to different input sizes. A common CNN is a 2D-CNN. The configurations of the ancestor 2D-CNN, “LeNet” [11] is shown in Fig. 1. The fundamental two properties of the convolutional layers,

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“weight sharing” and “limited connectivity”, have been used in the architectural design of later CNNs. In fact, these are the main features that distinguish CNNs from traditional MLPs that share common linear neurons.

Fig. 1. The configuration of the ancestor of CNNs, the “LeNet” [11]

The 2D-CNNs are generally used to process two-dimensional data, such as image recognition, target detection. However, 2D-CNNs require a lot of computing time when dealing with large amounts of data, and the characteristics of data in some domains are not the same as 2D data like video, such as time series data recorded by sensors in industrial production. To address this issue, 1D-CNNs have been proposed and successfully applied to different fields such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and electrical motor fault detection [12–15]. Another major advantage is that a realtime and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D-CNNs that perform only 1D convolutions (scalar multiplications and additions) [10]. Figure 2 illustrates a common 1D-CNN, which generally includes two neural network layers: (1) the so-called “CNN-layers” where both 1D convolutions, activation function and subsampling pooling occur, and (2) Fully-connected dense layers that are identical to the layers of a typical MLP and therefore called as “MLP-layers”. As in the conventional 2D-CNNs, the input layer of 1D-CNN is a passive layer that receives the raw 1D signal and the output layer is a MLP layer with the number of neurons equal to the number of classes. The configuration of a 1D-CNN is formed by the following hyperparameters [10]: 1. Number of hidden CNN and MLP layers/neurons (in the sample 1D-CNN shown in Fig. 2, there are 3 and 2 hidden CNN and MLP layers, respectively). 2. Filter (kernel) size in each CNN layer (in the sample 1D-CNN shown in Fig. 5, filter size is 41 in all hidden CNN layers). 3. Subsampling factor in each CNN layer (in the sample 1D-CNN shown in Fig. 5, subsampling factor is 4). 4. The choice of pooling and activation functions.

3 LOCA Simulation This paper uses MAAP for LOCA simulation. MAAP is a computer code that can simulate the response of a light water reactor power plant during severe accident sequences,

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Fig. 2. A sample 1D-CNN configuration with 3 CNN and 2 MLP layers [10]

including actions taken as part of accident management. The code quantitatively predicts the evolution of a severe accident starting from full power conditions given a set of system faults and initiating events through events such as core melt, reactor vessel failure, and containment failure. Furthermore, models are included in the code to represent the actions that could mitigate the accident by in-vessel cooling, external cooling of the reactor pressure vessel, or cooling the debris in containment [16]. We refer to the safety analysis report of a certain pressurized water reactor NPP and set up several system corresponding reactions to simulate the changes of the monitored parameters of the system after the occurrence of LOCA with different break sizes. It is important to note that the parameters of the break size and the system response time should be continuous values, but this would require too much data for the simulation, which is impractical. To enable the study to proceed, we take some discrete values here, which are representative of most of the system. Table 1 shows the parameter settings for our simulation. As shown in Table 1, a total of six parameters are selected for the simulation of LOCA in this paper. As two examples, T equals to 610 means that LOCA occurred at the 610th second after starting the simulation, before which the system operated at steady-state conditions. A1 equals to 120 means that the system took action A1 at the 120th second after LOCA occurred. So, after setting discrete values for each of the six parameters, the number of simulation data is the product of the number of discrete values of these parameters, which is 129,600 data. In each data, we simulated a total of 23 parameters, including temperature, water level, pressure, etc., which means there are 23 columns in each data. In terms of the number of rows of data, we record one row of monitoring parameters every 10 s, from the steady state operation of the reactor until the core meltdown. Since it is not known in advance at what moment the core melts down, the number of rows of data is uncertain and is noted as n, which is roughly between 1000 and 10,000. Table 2 shows the monitoring parameters simulated by MAAP.

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Parameter

Explanation

Value

Unit

D

Break size

0.0508, 0.06191, 0.07302, 0.08413, 0.09524, 0.1064, 0.1175, 0.1286, 0.1397, 0.1508

m

A1

1/2 EAS pump direct spraying

0, 120, 240, 360, 480, 600

s

A2

1/2 high-pressure ampoule pump direct injection via 2/3 injection line

0, 120, 240, 360, 480, 600

s

A3

The operator uses A1.2 in accordance 0, 120, 240, 360, 480, 600 with the 56 °C/h cooling rate protocol for a back to cool down the pressure

s

A4

1/2 combined EAS pump recirculation 0, 120, 240, 360, 480, 600 operation

s

T

Occurrence time of LOCA

s

10, 610, 1210, 1810, 2410, 3010, 3610, 4210, 4810, 5410

4 1D-CNN Model Training 4.1 Data Preprocessing The purpose of data preprocessing is simply to process the n rows and 23 columns of data generated above into a uniform format to facilitate 1D-CNN model training. 1. Parameters Filtering Note that the parameter filtering here is not the same as feature selection in feature engineering. In feature selection, features such as variance, mean, margin factor and other numerical statistics need to be calculated from the raw data first. Then, the features that can characterize the system state are selected from the generated features for modeling. Deep learning can learn features directly from raw data, so there is no need for the feature engineering step. Naturally, this paper uses 1D-CNN modeling and also does not require feature engineering. Parameter filtering is the process of selecting parameters from the 23 parameters simulated in the simulation and deleting those that are unusable. Reasons for unusable parameters include fixed as constants, high correlation with other parameters causing data redundancy, and out of sensor detection range. The parameters deleted in this paper are H, F1 and F2, i.e., the remaining 20 parameters are retained for modeling. 2. Data normalization Data normalization is also known as data scaling. Since the range of feature values of raw data varies widely, the range of all features should be normalized so that every feature’s contribution is comparable. Furthermore, in some machine learning algorithms, objective functions may not work properly without scaling [16]. A variety of linear scaling or nonlinear scaling methods could be employed, such as rescaling, mean normalization, standardization and so on. In this paper, rescaling is

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Table 2. Monitoring parameters simulated by MAAP Parameter

Explanation

W1

collapsed water level in broken S/G downcomer

W2

collapsed water level in unbroken S/G downcomer

P1

pressure in broken S/G

P2

pressure in unbroken S/G

P3

pressure in primary system

P4

pressure in pressurizer

P5

pressure in accumulator

F1

the total (for all pumps) limiting mass flow rate for the HPI system

F2

the total (for all pumps) limiting mass flow rate for the LPI system

T1

thermocouple for core outlet

W3

boiled-up water level measured from bottom of RPV

W4

collapsed water level (from bottom of floor)

H

mole fraction of H2

P6

pressure in containment

T2

temperature of gas

T3

temperature of bottom

P

thermal power

TDC

coolant saturation margin

TBC

temperature of the bottom part of the cold section of the reactor coolant system

TUC

temperature of the upper part of the cold section of the reactor coolant system

TBH

temperature of the bottom part of the hot section of the coolant system

TUH

temperature of the upper part of the hot section of the coolant system

WUL

coolant system flow

used to scale the range of features in [0, 1]. The general formula is given as x∗ =

x − min(X ) max(X ) − min(X )

(1)

where x is an original value, x* is the normalized value, X is the set of all original values. 3. Data length uniformity Theoretically, 1D-CNN can be trained using different sizes of data. However, in practical industrial applications, we must consider the size of the data volume and the actual hardware environment. We now have 129,600 n rows and 20 columns of data, which occupy a large amount of memory, and we have no way to feed them directly to the CPU or GPU for training at the same time. So, the model can only

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read the data in batches. The data length is not uniform, so we unify all the data to 8640 rows, and this approach has been proven to achieve satisfactory results. If the data exceeds 8640 lines, the excess data will be deleted, and if the data is less than 8640 lines, the reactor state will be assumed to be unchanged and the last line of data will be filled to 8640 lines, as shown in the red box in Fig. 3. The horizontal coordinate of Fig. 3 is the time and the vertical coordinate is the value of a parameter over time.

Fig. 3. Data length uniformity

4. Data downsampling and differencing Now, we have 129,600 rows of 8640 and 20 columns of normalized data. For modeling, we still want to reduce the number of rows of data, so that we can speed up the model training. So without affecting the model effect, this paper reduces the data sampling frequency from one row in 10 s to one row in 3 min (180 s). This is done by taking the mean value of every 18 rows of the original data. In this way, the number of data rows is reduced to 480 rows. We can see in Fig. 3 that a significant portion of the data is a straight line after the data length is unified. We differ the data, the straight-line part of the data can become 0, so that the data becomes sparser, which can also speed up the model training. 4.2 Model Training 1. Structure of 1D-CNN The 1D-CNN built in this paper has three convolutional layers, a MLP layer and an output layer, as shown in Fig. 4.

Fig. 4. Structure of 1D-CNN built in this paper

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2. Model Parameters The number of convolution kernels of the three 1D convolution layers are 32, 64, and 128, respectively. The size of the convolution kernels is all [3]. The size of all three pooling kernels is [2]. The value of all three dropout layers is set to 20%. It is worth mentioning that the dropout layer is set to help reduce the possibility of overfitting and enhance the generalizability of the model. 3. Model training The purpose of the 1D-CNN designed in this paper is to perform regression calculations to predict the size of the break in LOCA, so the root mean square error (MSE) is chosen for the loss function. We simulated a total of 129,600 LOCA data, and 90% of the data were randomly selected for the training set and 10% for the test set. The variation of the loss function values of the training set with the number of training epochs is shown in Fig. 5. It can be seen that the 1D-CNN designed in this paper achieves satisfactory results on the training set, and the loss function values have become small after 100 epochs.

Fig. 5. Training loss drops rapidly

4.3 Results In this section, we test the above trained 1D-CNN model using the test set containing 12,960 data. The test set is not involved in the training of the model at all, and for the model, the data in the test set are as if they were newly generated, so the confidence of the test can be guaranteed. We measure the performance of the model in the test set in terms of relative error. The 90% quantile of relative error is 1.75% and the 80% quantile is 0.84%. It can be seen that the model can make an accurate prediction of the break sizes of LOCA.

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5 Conclusions In this paper, we simulated a large amount of LOCA data with different break sizes by MAAP program. Then we divided the data into a training set and a test set. The 1D-CNN model was trained on the training set, and the model was evaluated on the test set. The results show that the model can make an accurate prediction of the break size of LOCA.

References 1. Zhikang, L.I.N., Yuhao, Y.I.N., Guoxing, L.I.A.N.G.: Establishing and application of AP1000 nuclear power plant RELAPS-code SB-LOCA model. Power & Energy 6, 457–461 (2011) 2. Suzuki, M.: Break location effects on PWR small break LOCA phenomena. No. JAERI-M-88–271. Jpn. At. Energ. Res. Inst. (1989) 3. Santosh, T.V., et al.: Application of artificial neural networks to nuclear power plant transient diagnosis. Reliab. Eng. Syst. Saf. 92(10), 1468–1472 (2007) 4. Zhao, Y.-F. et al.: Preliminary study on application of BP neural network in AP1000 nuclear power plant accident diagnosis. At. Energy Sci. Technol. 48, 480 (2014) 5. Cai, M., Zhang, D., Zhang, Y.: Nuclear power plant real-time fault diagnosis system based on genetic algorithm. Nucl. Power Eng. 30(3), 111–114 (2009) 6. Liu, Y.-k., Chun-li, S.J.: Application of BP-RBF neural network to fault diagnosis of nuclear power plant. At. Energy Sci. Technol. 42(3), 193 (2008) 7. Li, S., Liu, J., Shen, Y.: Fault diagnosis of LOCA based on ANN methods. Nuclear Techn. 40(8) (2017) 8. She, J.-K. et al.: Research on intelligent accident warning and simulation for loss of coolant accident in nuclear power plants. In: International Symposium on Software Reliability, Industrial Safety, Cyber Security and Physical Protection for Nuclear Power Plant. Springer, Singapore (2022) 9. Kiranyaz, S. et al.: 1-D convolutional neural networks for signal processing applications. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE (2019) 10. Kiranyaz, S. et al.: 1D convolutional neural networks and applications: a survey. Mechanical systems and signal processing 151, 107398 (2021) 11. Mashor, M.Y.: Hybrid multilayered perceptron networks. Int. J. Syst. Sci. 31(6), 771–785 (2000) 12. Kiranyaz, S. et al.: Convolutional neural networks for patient-specific ECG classification. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE (2015) 13. Avci, O. et al.: Structural damage detection in real time: implementation of 1D convolutional neural networks for SHM applications. Structural Health Monitoring & Damage Detection, vol. 7. Springer, Cham, pp. 49–54 (2017) 14. Abdeljaber et al.: 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing (2018) 15. Ince, T. et al.: Real-time motor fault detection by 1D convolutional neural networks. IEEE Trans. Ind. Electron. 63(11) (2016) 16. Yang, C. et al.: Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model. Renew. Energ. 133, 433–441 (2018)

Quality Control of Small Batch product in Manufacturing Process Limei Peng1,3(B) , Pengbo Ji2,3 , Yueqing Qian1,3 , Yong Yang1,2 , Hongyu Tian1,3 , and Junling Han1,3 1 CNNC Key Laboratory on New Materials Research and Application Development, Baotou,

China [email protected] 2 CNNC Key Laboratory on Fabrication Technology of Reactor Irradiation Special Fuel Assembly, Baotou, China 3 China North Nuclear Fuel Co., Ltd, Baotou, Inner Mongolia, China

Abstract. The production of small batch products generally faces some difficulties, like short production cycle and difficult quality control. Product quality process control is closely related to the technical maturity of test verification. How to effectively improve the production technology maturity before production is the most important part of production preparation. In this paper, a certain product of CNNFC was taken as an example to study how to collect effective data information through a small number of test times to determine the optimal process parameters. The quality control limits are calculated by using the small batch production quality control method based on t distribution dynamic control. The experimental verif1ication was carried out by means of statistical process control theory and small batch condition quality requirements, and the optimum test conditions were established. In this paper, through the precise control of the key parameters established by orthogonal tests, the effective control of the small batch manufacturing process was realized. Keywords: Small batch · Quality control limit · Control chart · Orthogonal test · Quality control

1 Research Background and Current Situation A scientific research and production task undertaken by our unit in 2019 has the characteristics of low production quantity, short production cycle and difficult quality control. It is very important to study how to determine the optimal process parameters in the manufacturing process of small batch products with the least number of tests. Traditional Shewhart control chart is suitable for rigid mass production environment. It can accurately estimate production distribution parameters through a large amount of test data, so as to establish a high-precision control chart to achieve process quality control. The control chart can only be effective when the quality characteristic data of the control chart is large (at least 20 groups of samples with a capacity greater than 5) [1–3]. Under © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 973–983, 2023. https://doi.org/10.1007/978-981-19-8780-9_92

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the flexible production mode of small batch, the number of products produced is limited, and the number of samples that can be used to effectively estimate the process distribution parameters is smaller. If the quality Shewhart chart is established directly according to the traditional method, it will lead to frequent judgment errors, produce unqualified products and affect product quality. Therefore, how to combine the mature theory of statistical process control with the requirements of high quality under the condition of small batch production is an urgent problem to be solved [4–6]. This paper is to determine the best welding process parameters of a product as the research content, mainly through the orthogonal test method with the least number of tests, collect the most favorable data information, so as to determine the best process parameters, and according to the upper and lower limits of the process parameters to determine the process parameters of small batch production median. Reference a cries wolf can maintain the dynamic control of probability limit established by the approximate calculating method of ideal value S which is not affected by the number of samples, even under the condition of little sample can stay in a small fixed on the level of significance, thus can be applied to small batch or sampling difficulties during the production.

2 Research Objectives 2.1 Test Characteristics In view of the short time of product preliminary test and research, it is required to obtain effective experimental data quickly in a short time to determine the best process parameters. Considering the characteristics of small batch production, it is not possible to collect a large amount of experimental data according to the traditional test method to determine the final process parameters. It is necessary to find a new preliminary experimental scheme suitable for the production of small batch products. In the flexible production mode of small batch, the number of products produced is limited, and the number of samples that can be used to effectively estimate the process distribution parameters is less. In this study, the main factors affecting the weld penetration of the product: welding current, focusing current and welding speed were selected as the research content, and the main factors affecting the weld depth were determined and the optimal welding process parameters were determined through the intuitive and variance analysis of orthogonal test results. And by precisely controlling the key parameters established by orthogonal test, draw the relevant control chart, analyze and realize the effective control of small batch product manufacturing process. 2.2 Analysis of Difficulties 2.2.1 Determine the Optimal Process Parameters for Small Batch Products In production and scientific research activities, in order to ensure the quality and reduce the cost, we often encounter the problem of how to choose the best scheme. To conduct tests, it involves the collocation of test times and test factors. This study is to use the orthogonal test method, using the orthorhombic principle of “equilibrium dispersion” and “neat comparability” to select appropriate, representative and typical test points from a large number of test points to solve the multi-factor problem.

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2.2.2 How to Effectively Control the Manufacturing Process of Small Batch Products It is difficult to use traditional control chart to analyze the production situation in small batch production process due to the limited amount of production data. The large false alarm probability caused by the number of sample groups, when the number of sample groups m is large (m > 50), the relationship between false alarm probability A and sample group size n is not significant and close to the theoretical value of the traditional control chart (0.27%). When m is small, the smaller the number of sample groups is, the greater the probability of false alarm A is. The smaller group capacity n is, the faster a rises, as shown in Fig. 1. Therefore, it can be seen that the traditional single-value quality control chart is not reliable for the control of small batch production process. Therefore, it is difficult to achieve effective control of the manufacturing process of small batch products to obtain the characteristics of less production data and establish a supporting control chart.

Fig. 1. α -m relationship curves under different groups of capacity n

2.3 Research Objectives This experiment takes the determination of welding process parameters of a product as the research object, and studies how to collect effective data information through a few test times, so as to establish the best process parameters. Under the condition of sample data without prior information, the quality control limit of small batch production based on t-distribution dynamic control limit is calculated. Statistical process control theory and small batch conditional quality requirements were combined to carry out test verification and establish the best test conditions. In this paper, by precisely controlling the key parameters established by orthogonal test, the effective control of small batch product manufacturing process is realized.

3 Research Content 1. Determine welding process parameters: determine the main influencing factors of weld depth and the best welding process parameters;

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2. Realize effective control of small batch product manufacturing process through control chart. 3.1 Determination of Welding Process Parameters In order to select appropriate test parameters and find out the best test conditions, orthogonal design without interaction was selected and data analysis was carried out [7] to verify the main welding parameters. 3.1.1 Design of Orthogonal Test Table The purpose of this test is to find out the best test parameters, to achieve the welding depth ≥ 0.342mm(the larger the better) technical indicators. 1. Setting of factor level table There were 3 factors in this experiment, and each factor included 3 levels, as shown in Table 1. 2. Selection of orthogonal tables Select orthogonal table, row head: q = 3, P = 3, n = 9, optional) orthogonal table, factor arrangement is shown in Table 2. 3. Permutation test conditions According to different factors and levels according to the requirements of the standard to conduct a randomized trial protocol. The test number and scheme are shown in Table 3. 3.1.2 Analysis of Orthogonal Test Results 1. Intuitive analysis of data Weld penetration of all test samples was measured, and the inspection results were shown in Table 4. By observing the influence degree of each factor on the index in the orthogonal table, it can be analyzed from the range of the mean value of the test results at different levels of each factor. If the range value is large, the factor has a great influence on the index. Factors in Table 4: range RA = 0.041, RB = 0.003, RC = 0.013.From the range of the three factors, factor A has the greatest influence, followed by factor C, while factor B has the least influence. Further analysis shows that level 3 of factor A has the largest melting depth, that is, level 3 is the best. Level 3 of factor B has the maximum melting depth, that is, level 3 is the best. Level 1 of factor C has the maximum melting depth, that is, level 1 is the best. According to the above analysis, the optimal test scheme is A3 B3 C1 . 2. Analysis of variance of data Assuming that the test results follow the normal distribution, according to the requirements of square sum decomposition of the test results, the total deviation square sum (ST ) mean square sum (V1 ) and mean square sum of the error (Ve) are performed. Due to the calculation of the ratio with F, the calculation results are shown in Tables 5 and 6.

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The F value of factor A was greater than F0.05.2, 2 and F0.10,2.2, while factor B and factor C is less than F0.05,2.2 and F0.10,2,2, indicating that factor A is at the significant level of 0.05 and 0.10 were significant, while factor B and factor C were insignificant. For significant factors should be the selection of its best level, that is, with a better test scheme for A3 B3 C1 . 3. Experimental verification The best test condition found in the test is A3 B3 C1 , which did not appear in 9 tests, so this test condition was should verified by the test. Under this condition, and the results of six tests were 0.372mm,0.375mm,0.370mm,0.362mm,0.378mm and 0.364mm, with an average of 0.370mm. The fluctuation of test results was small, which were better than the technical requirements of 0.342mm. Table 1. Welding test factor level Level

Factor Welding beam (mA)

Focusing current (mA)

Welding speed (mm/min)

1

3.5

1930

2700

2

3.8

1940

2750

3

4.1

1950

2800

Table 2. Header design L9 (34 ) column number

1

2

3

Factor

A

B

C

4

3.2 Control Chart Drawing 3.2.1 Variable Control Limit Principle and Simple Calculation Method [8]. The existing quality control methods are mainly proposed for mass production with the same distribution of quality characteristics. It is generally believed that when there is no systematic error in the production process, the product quality characteristic X follows the normal distribution N(μ,σ2), that is, X ~ N(μ,σ2). According to the 3σ principle of Shewhart, the center line CLx, upper and lower control boundary UCL and LCL of the design quality control chart are: ⎧ √ ⎨ UCLx = μ + 3σ/ n CLx = μ √ ⎩ LCLx = μ − 3σ/ n n is the sample group capacity.

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L. Peng et al. Table 3. Experimental scheme

Experimental number

Factor Welding beam (mA)

Focusing current (mA)

Welding speed (mm/min)

1

(1) 3.5

(1) 1930

(3) 2800

2

(2) 3.8

(1) 1930

(1) 2700

3

(3) 4.1

(1) 1930

(2) 2750

4

(1) 3.5

(2) 1940

(2) 2750

5

(2) 3.8

(2) 1940

(3) 2800

6

(3) 4.1

(2) 1940

(1) 2700

7

(1) 3.5

(3) 1950

(1) 2700

8

(2) 3.8

(3) 1950

(2) 2750

9

(3) 4.1

(3) 1950

(3) 2800

Experimental result (weld penetration)/(mm)

The probability of product quality characteristic X falling in the interval is 0.9973. Due to the unknown parameter μ and σ, the unbiased estimation of sample value μ and σ is often used to replace the unknown parameter in practical application. Generally, sample standard deviation S is used to replace population standard deviation σ in the process of mass sampling. Sample variance S2 is the unbiased estimator of population variance σ2 , but sample standard deviation S is the biased estimator of population standard deviation σ. When the number of samples is N → ∞, the sample variance is S → σ. Therefore, it is feasible to replace the population standard deviation σ with the sample standard deviation S for the large volume sampling process, but for the small batch production process discussed in this paper, the sample standard deviation must be unbiased. When the sample group m is large (m > 50), the relation between false alarm probability A and sample group size n is not significant and close to the theoretical value of the traditional control chart (0.27%). When m is small, the smaller the sample groups, the greater the false alarm probability A is. The smaller group capacity n is, the faster the false alarm probability A rises, as shown in Fig. 1. Therefore, it can be seen that the traditional single-value quality control chart is not reliable for the control of small batch production process, which is manifested in the increase of false alarm probability and unnecessary shutdown adjustment times, which reduces the production efficiency and increases the production cost.

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Table 4. Experimental result and intuitive analysis Experimental number

Factor Welding beam (mA)

Focusing current (mA)

Welding speed (mm/min)

D

Experimental result (weld penetration)/(mm)

1

(1) 3.5

(1) 1930

(3) 2800

(2)

0.323

2

(2) 3.8

(1) 1930

(1) 2700

(1)

0.363

3

(3) 4.1

(1) 1930

(2) 2750

(3)

0.380

4

(1) 3.5

(2) 1940

(2) 2750

(1)

0.330

5

(2) 3.8

(2) 1940

(3) 2800

(3)

0.357

6

(3) 4.1

(2) 1940

(1) 2700

(2)

0.377

7

(1) 3.5

(3) 1950

(1) 2700

(3)

0.346

8

(2) 3.8

(3) 1950

(2) 2750

(2)

0.360

9

(3) 4.1

(3) 1950

(3) 2800

(1)

0.366

T1

0.999

1.066

1.086

1.059

T = 3.202

T2

1.08

1.064

1.07

1.06

T3

1.123

1.072

1.046

1.083

T1

0.333

0.355

0.362

0.353

T2

0.360

0.355

0.357

0.353

T3

0.374

0.357

0.349

0.361

Range R

0.041

0.003

0.013

0.008

In order to solve the problem that the probability of false alarm is affected by sample size and keep the probability of false alarm at a low and fixed significance level A, this paper draws on the idea of variable control boundary, that is, the dynamic control boundary is established to make the control boundary value change with the change of sample number m and n. The mathematical model is as follows: S UCLx = x + 3A(m, n) √ C n S LCLx = x − 3A(m, n) √ C n When the number of sample group m is greater than 10, the approximate value is in good agreement with the ideal value. When m is small, the relative error between approximate value and ideal value increases. When m is smaller than 5, the error is 40%. Therefore, m = 10 and n = 4 can be selected to calculate and design the center line and upper and lower control boundary of the quality control diagram. √ m(n − 1) + 1 2 ) = 0.27 ( C=√ 2 m(n − 1) (m(n − 1)/2

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L. Peng et al. Table 5. The sum of the squares of deviation

Experimental number

Factor Welding beam (mA)

Focusing current (mA)

Welding speed (mm/min)

D

Experimental result (weld penetration)/(mm)

1

(1) 3.5

(1) 1930

(3) 2800

(2)

0.323

2

(2) 3.8

(1) 1930

(1) 2700

(1)

0.363

3

(3) 4.1

(1) 1930

(2) 2750

(3)

0.380

4

(1) 3.5

(2) 1940

(2) 2750

(1)

0.330

5

(2) 3.8

(2) 1940

(3) 2800

(3)

0.357

6

(3) 4.1

(2) 1940

(1) 2700

(2)

0.377

7

(1) 3.5

(3) 1950

(1) 2700

(3)

0.346

8

(2) 3.8

(3) 1950

(2) 2750

(2)

0.360

9

(3) 4.1

(3) 1950

(3) 2800

(1)

0.366

T1

0.999

1.066

1.086

1.059

T2

1.08

1.064

1.07

1.06

T3

1.123

1.072

1.046

1.083

T = 3.202 Yi2 = 1.142 ST = 0.00305

T1

0.333

0.355

0.362

0.353

T2

0.360

0.355

0.357

0.353

T3

0.374

0.357

0.349

0.361

S

0.00264

0.00001

0.00027

0.00012

Table 6. Anova calculation table Source of variation

The sum of the square of deviance

Degree of freedom f

Sum of square V

F value

Factor A

S1 = 0.00264

2

0.00132

21.51

Factor B

S2 = 0.00001

2

0.00001

0.09

Factor C

S3 = 0.00027

2

0.00014

2.20

Error e

Se = S4 = 0.00012 2

0.00006

Sum total T

ST = 0.00305

F0.05, 2, 2 = 19, F0.10, 2, 2 =9

8

3.2.2 Control Chart Drawing Four samples from each group were selected for ten groups of welding test. After the test, the weld penetration was measure, and the x-R control chart was calculated according to the measurement results. The calculation results are shown in Tables 7 and 8.

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Table 7. An approximation of the coefficient A calculated by the normal approximation n

m

Desired value Approximate value

2

4

6

8

10

20

50

100

∞

4

1.91

1.37

1.23

1.17

1.09

1.07

1.03

1.01

1

5

1.69

1.30

1.20

1.14

1.07

1.05

1.02

1.01

1

4

3.21

1.82

1.42

1.29

1.07

1.04

1.02

1.01

1

5

2.93

1.69

1.37

1.27

1.05

1.03

1.01

1.01

1

Table 8. The data table of x − R control chart Sample no

Measured value (mm)

x

R

x1

X2

X3

X4

1

0.369

0.362

0.356

0.361

0.362

0.013

2

0.349

0.353

0.352

0.365

0.355

0.016

3

0.364

0.346

0.351

0.356

0.354

0.018

4

0.354

0.353

0.367

0.369

0.361

0.016

5

0.345

0.371

0.353

0.370

0.360

0.026

6

0.357

0.367

0.358

0.364

0.362

0.010

7

0.349

0.365

0.362

0.373

0.362

0.024

8

0.351

0.382

0.366

0.362

0.365

0.031

9

0.363

0.378

0.377

0.376

0.374

0.015

10

0.372

0.362

0.377

0.367

0.370

0.015

x control chart CL = 0.362 UCL = 0.412 LCL = 0.312

R control chart CL = 0.018 UCL = 0.068 LCL = 0

Comment

m = 10, n = 4

The x and R of each group were distributed in the corresponding x chart and R chart, and the control chart after drawing was shown in Fig. 2. The computing coefficients of control limit is modified. When m ≥ 10, the calculated value is in good agreement with the ideal value, and the calculated relative error of dynamic control limit is less than 10%. To fully verify the effectiveness of small batch product dynamic control limit control, the upper and lower limits of the control chart in Fig. 2 are modified by 10%, and the revised results are shown in Fig. 3. It can be seen from Fig. 3 that the values (mean and range) on the dynamic control limits are still within the upper and lower limits of the revised control chart, and both the points on the control charts in Figs. 2 and 3 are under statistical control. Accurate control of the welding beam current between 4.05mA and 4.15mA can effectively ensure the

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Fig. 2. The control chart of small batch data (10 x 4 groups)

validation control of girth weld penetration. Thus, the key parameters (welding beam flow) established by the orthogonal test are precisely controlled to realize the effective control of small batch product manufacturing process.

4 Conclusion This experiment verifies the determination of weld process parameters for small batch production. Through a series of tests and production data analysis, the conclusions are as follows: 1. The orthogonal test was used to find out the best test conditions, and the significance and contribution rate of the influencing factors were determined. The main influencing factor of weld penetration was welding beam, and the best test condition was A3 B3 C1 2. The false alarm probability of x-R chart established by approximate method is not affected by the number of samples, and can be applied to the production process of small batch or difficult sampling

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Fig. 3. x-R control chart after modification

3. By accurately controlling the welding beam current between 4.05mA and 4.15mA, the production data is drawn as the control chart, indicating that the weld penetration can be effectively control.

References 1. Yang, Y.: Statistical process control techniques. Aviation Industry Press, Beijing (2003) 2. Duncan, A.J.: Quality control and industrial statistics (5th ed). Homewood, Illinois, Irwin (1986) 3. Shewhart, M.: Interpreting Statistical Process Control (SPC) Charts Using Machine Learning and Expert System Techniques. In: Proceedings of the IEEE 1992 National Aerospace and Electronics Conference, Dayton, OH, USA, 1001–1006 (1992) 4. Yu, Z., Wu, Z.: Research on quality control method for small batch manufacturing process. J. Mech. Eng. 37(8), 60–64 (2001) 5. Del, C.E., Montgomery, D.C.: Short-run statistical process control: Q-chart enhancements and alternative methods. Qual. Reliab. Eng. Int. (12), 159–161 (1996) 6. Yang, X., Pei, X.: Research on Small batch statistical quality control for advanced manufacturing systems. China Mech. Eng. 13(19), 1660–1662 (2002) 7. Li, W., Yang, L.: Statistical calculation of mass, Second edition, Beijing, China Quality Inspection Press/China Standards Press 12, 86–95 (2012) 8. Yang, S., Wu, D., Su, H.: Dynamic quality control limit based on small batch manufacturing process and its simple calculation method, Chinese Construction Machinrey, 1476–1479

Study on the Influence of Reactor System LOCA Modeling Mode on Dynamic Analysis Liu Shuai1,2(B) , Huang Xuan2 , Feng Zhipeng2 , Zeng Zhongxiu2 , Qi Huanhuan2 , and Jiang Xiaozhou2 1 Harbin Engineering University, Harbin, China

[email protected] 2 Science and Technology on Reactor System Design Technology Laboratory, Chengdu,

Sichuan, China

Abstract. The reactor system has a large scale of components. And the system dynamic analysis model for loss of coolant accidents (LOCA) contains many non-linear factors. The transient calculation analysis takes a long time and the convergence is difficult to guarantee. In this paper, two different analysis models (complete three-loop model and single-loop model) of the first loop system of reactor are established for comparative analysis. The analysis shows that the calculation error of the main position load in the two different model is within 10%. The decoupling influence of the wave tube and the reactor inlet and outlet on the system dynamic characteristics is within an acceptable range. When the single loop model is used, it is necessary to decouple the reactor pressure vessel and pressurizer. And the simplified boundary is adapted to simulate its effect on the whole loop. The single-loop dynamic analysis model based on the decoupling criterion can greatly reduce the calculation scale and meet the calculation accuracy, which can be used to calculate the LOCA dynamic characteristics of the reactor system quickly. The model can also be used for seismic analysis of reactor system. And using this model for reactor system dynamic analysis can greatly improve the calculation efficiency. Please copy you summary here. Keywords: LOCA · Loop model · Nonlinear dynamic analysis

1 Introduction In order to ensure the safety of the nuclear power plant and its surrounding environment, the reactor coolant system LOCA (LOSS OF COOLANT ACCIDENT) dynamic analysis [1], which is one of the important hypothetical accidents in the design of the reactor system, is one of the mandatory requirements in the design specification RCC-M. After the loss of coolant accident, the reactor coolant system will be subjected to great external load and may have serious consequences such as equipment damage. The main purpose of LOCA analysis is to provide analysis load for the safety design of equipment structure, which can ensure that the equipment can resist the LOCA accident. So it is necessary to establish the proper LOCA analysis model to obtain the equipment loads under the LCOA accident. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 984–992, 2023. https://doi.org/10.1007/978-981-19-8780-9_93

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The nuclear reactor system consists of multiple loops, including many equipment and structures. In order to ensure the safety of the nuclear reactor, different forms of hypothetical loads need to be considered in the design process, such as loss of coolant accident, earthquake accident and so on. For a comprehensive evaluation of the nuclear reactor system, transient analysis needs to be carried out. However, the reactor system is huge. Even if the simplified model is used for calculation, the model scale is relatively large, and there are many nonlinear factors. The transient calculation takes a long time and has poor convergence. Therefore, this paper studies the dynamic analysis model of reactor system LOCA accident. When performing LOCA dynamic analysis of multiloop reactor coolant system, usually due to the symmetry of multi loop, it was limited by the calculation efficiency and engineering progress in the past. Therefore, only the effect of LOCA accident on each equipment and pipeline on single-loop was considered. With the improvement of calculation resources and calculation accuracy, the LOCA dynamic analysis method of the whole reactor coolant system needs to be improved from the calculation model to make up for the shortage of single-loop LOCA calculation. Taking a three-loop reactor coolant system as an example, different analysis models should be used for LOCA analysis of reactor coolant system in different design stages, so as to carry out more efficient and accurate design and analysis. Scholars have done a lot of research on LOCA dynamic analysis. Mao [2] has described the nonlinear dynamic analysis procedure of re-actor system including the engineering method for nonlinear factors, the establishment of the nonlinear dynamic model and the nonlinear dynamic analysis method. The generation and the application of the LOCA dynamic response for the system are also presented. Qi [3] has studied the process of fuel assembly dynamic analysis for accident condition. Method of axial and lateral dynamic modeling was developed. The axial and lateral dynamic response calculation method was established, and then the grid impact force and guide thimble stress calculation were carried out. Huang [4] has established detail nonlinear finite element model of steam generator (SG) of a domestic 3rd generation nuclear power plant (NPP). This model is then connected with the reactor coolant loop (RCL) to carry out the analysis of dynamic response for SG LOCA shaking. By calculation, the maximum absolute stresses of SG heat transfer tube bundles and its variation with tube diameter and reacting forces of upper supports are obtained.

2 Model Establishmen For LOCA dynamic analysis, it is necessary to determine the location, form and area of the break. The assumed location is shown in Fig. 1. The forms of break mainly include longitudinal and circumferential break (double ended break and limited circumferential break). In this paper, because its main pipes have adopted LBB, which can monitor the occurrence of the break on the main pipes and the surge pipe in advance, it is only necessary to consider the limited circumferential break in Fig. 1. Due to the limitation of pipe support, the relative displacement at both ends of the limited circumferential break is usually less than the pipe thickness. Therefore, it is necessary to calculate the break area to determine the LOCA hydraulic load. The content of this paper is the dynamic response of loading the hydraulic load into the system after the hydraulic load is obtained.

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In Fig. 1, there is no sequence for the different break. It is only an assumption that the LOCA situations many occur. All break is analyzed during calculation.

Fig. 1. Break locations

The research in this paper includes three-loops. The three-loops are evenly distributed around the reactor pressure vessel. Each loop mainly includes the hot pipe, cold pipe, transition pipe, main pump and steam generator. There is a pressure regulator on the hot pipe of the first loop. The three-loops are connected with the reactor pressure vessel through the cold pipe and hot pipe. In this paper, the single-loop analysis model and whole three-loop analysis model of reactor coolant system are established. The three-loop model includes all equipment and supports. The single-loop is established based on the first loop and decoupled based on the decoupling criterion. In the reactor coolant system, different analysis models need to be established according to different assumed break positions. In this paper, the LOCA dynamic analysis models of single-loop and three-loops with five different breaks are established. For the single-loop analysis model under different breaks, according to the requirements of USNRC 3.7.2 [5], decoupling is carried out at the connection between the main pipe of the cold and hot pipes and the connection between the surge pipe of the pressure regulator and the hot pipe. The three-loops analysis model includes all structures of the whole coolant system. The analysis model is shown in Fig. 2.The single-loop analysis model is shown in Fig. 3.

3 Analytical Theory For reactor coolant LOCA dynamic analysis, transient dynamic analysis is generally adopted, and its basic dynamic balance equation is: [M ]{¨u(t)} + [C]{˙u(t)} + [K]{u(t)} = {P(t)}

(1)

where, [M ] is the mass matrix; [C] is the damping matrix; [K] is the stiffness matrix; {P(t)} is the load vector; {¨u(t)} is the acceleration vector; {˙u(t)} is the velocity vector; {u(t)} is the displacement vector.

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Fig. 2. Three-loop analysis model

Fig. 3. Single-loop analysis model

The dynamic equation is usually solved by Newmark direct integration method or modal superposition method, so as to obtain the dynamic response and stress state of the key position under LOCA load. The specific solution process will not be repeated here.

4 Computing Method The LOCA hydraulic load is changing with the time and many nonlinear elements is consisted in the finite analysis model. The transient analysis of ANSYS software is used for time history analysis. The action points of LOCA hydraulic load is shown in Fig. 4. The LOCA hydraulic load time history is shown in Figs. 5, 6 and 7. The nonlinear elements of the analysis model consist of support gap, tension and compression linear spring, damping and so on. The LOCA time history load is loaded at the same break position of the three-loop model and the single-loop model. The time interval is 1 × 10-3s and 300 time steps is concluded. Three directions of LOCA hydraulic loads are applied simultaneously at four points of the SG elbow for transient analysis. In order to accurately simulate the installation state of the coolant system when LOCA occurs, static analysis needs to be carried out to obtain the static displacement of each support. Before applying LOCA hydraulic load, it is necessary

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to apply the static displacement back to the model. Based on the static state, apply the LOCA hydraulic loads to obtain the dynamic response. The load at each position in the different analysis models is obtained. Then the loads of different key parts are extracted and the differences of LOCA response in two different analysis models are compared and analyzed.

Fig. 4. Jet force load points

100

Force/N

50 0 -50

x y z

-100 -150

0

0.1

Time/s

0.2

0.3

Fig. 5. Hydraulic load of point 1

5 Result Analysis In the single-loop analysis, the decoupled single-loop model is calculated and analyzed. The constraint of the position of surge pipe on the main pipe is not considered. Apply the displacement on the main pipe due to the internal pressure of the pressure vessel at the inlet and outlet of the pressure vessel. The simplified calculation method of the applied displacement is as follows: The applied displacement is: R =

v PR2 (1 − ) Et 2

(2)

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100

Force/N

50 0 x y z

-50 -100

0

0.1

Time/s

0.2

0.3

Fig. 6. Hydraulic load of point 2 150 x y z

Force/N

100 50 0 -50 -100

0

0.1

Time/s

0.2

0.3

Fig. 7. Hydraulic load of point 3 150 x y z

Force/N

100 50 0 -50

0

0.1

Time/s

0.2

0.3

Fig. 8. Hydraulic load of point 4

where P is the internal pressure in normal operation; R is the inner radius of reactor pressure vessel; E is the elastic modulus and t is the wall thickness of reactor pressure vessel, ν is Poisson’s ratio. Through analysis, it is found that the load generated on equipment and support at the main steam breach position (Fig. 1, break position 5) accounts for the main factors under all the breaks. Therefore, when the main steam occurs LOCA, the comparison

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between the three-loop and the single-loop is carried out. The load of key supports and main equipment elbows in the reactor coolant system is compared as a representative, the specific comparison is shown in Tables 1 and 2. Table 1. Comparison of extreme load at support positions Location

Single-loop /104 N

Three-loop /104 N

Difference/%

Support1 of SG

178.3

182.2

2.19

Support2 of SG

175.7

176

0.17

Support3 of SG

194.1

194.1

0

Support4 of SG

157.2

159.3

1.34

Support1 of RCP

36.7

35.8

−2.45

Support2 of RCP

36.5

32.2

Support3 of RCP

32.2

33

−11.7 2.48

Table 2. Comparison of extreme load at elbow of main equipment Location

Single-loop /104 N

Three-loop /104 N

Difference/%

Inlet elbow of SG

159.4

151.3

−5.08

Outlet elbow of SG

−6.26

97.5

91.4

Transition elbow at SG side

105.3

105.4

Transition elbow at RCP side

105.3

99.5

−5.51

59.1

49.4

−16.0

Inlet elbow of RPV

0.09

Because the LOCA analysis in this paper is nonlinear dynamic analysis, in addition to comparing the load extreme value at the key position, it is also necessary to verify its dynamic response time history. Select the dynamic time history response of typical position for comparative analysis. Figures 9 and 10 shows the LOCA dynamic response time history of steam generator support 1 and main pump support in the single-loop and three-loop analysis models. It can be seen that under different analysis models at the same position, the dynamic responses are almost the same in the whole LOCA period and there is no significant difference. In addition, by analyzing the other four break positions, the load and displacement at the same position in the single-loop and three-loop analysis models show the same trend. The main load difference is around 10%. From the results, we can infer that the LOCA dynamic response load of different analysis models can match within the acceptable engineering error range. Generally, the extreme load at key equipment and positions is used as the design input to operate the structure security. In the engineer, 20% design allowance is usually considered. The

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Fig. 10. Load time history of RCP support1

extreme load difference of the single-loop and three-loop analysis model is within 10%. Both models can meet the engineering requirements.

6 Conclusion The paper takes a reactor coolant system as an example to compare and analyze the dynamic response differences of LOCA based on different analysis models (single-loop and three-loop). It can be found that there is no obvious difference between different scale analysis models. The difference of extreme load at key equipment and positions in different models is within the acceptable engineering error range. From the results, we can see that the difference of large load base at different position is within 10%. Although some locations differ greatly (more than 10%), due to their small load base, they are not the main load causing damage to the equipment. Therefore, when the LOCA nonlinear dynamic analysis of reactor coolant system was operated, the main analysis results of single-loop model and three-loop model can

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be kept within a certain error range. The model can be reasonably selected and used according to different analysis needs and requirements. The single-loop model is suitable for the initial stage of design, which can quickly acquire the equipment dynamic load when the LCOA occurs. It is beneficial to obtain the input of the equipment structure design. But the single-loop model can only acquire the maximum extreme load in the break loop, which can not acquire the load in the other loops. The three-loop model consists of all loops and can obtain the load at any positon. Due to there are many nonlinear elements in three-loop model, it should be set smaller times steps and load substeps during calculation. So more time may be consumed and non-convergence may occur.

References 1. Hermansky, P., Krajcovic, M.: The numerical simulation of the WWER440/V213 reactor pressure vessel internals response to maximum hypothetical large-break loss of coolant accident. Nucl. Eng. Des. 241(4), 131–137 (2011) 2. Qing, M.: Nonlinear dynamic analysis under LOCA condition for reactor system. Nucl. Power Eng., 8 (1999) 3. Huanhuan, Q., Wanjun, W., Pingchuan, S. et al.: Dynamic analysis of fuel assembly for accident condition based on ANSYS. Nucl. Power Eng., 6 (2018) 4. Qian, H., Xiaofei, Y., Huanhuan, Q. et al.: Numerical analysis of dynamic response for SG LOCA shaking. Nucl. Power Eng., 2 (2019) 5. USNRC, SRP 3.7.2 Rev. 3, Seismic system analysis 3 (2003)

Development and Verification of Solver for Natural Circulation Flow and Heat Transfer Simulation Base on Openfoam Zhengyu Gong, Miao Liang, Wenlan Ou, Qiwen Pan, Ling Zhang, and Zhixing Gu(B) College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, Sichuan, China [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]

Abstract. For the advanced liquid metal cooled pool-type reactors, natural circulation of coolant is essentially important to ensure reactor safety under either normal or accident conditions, thus the thermal-hydraulic analysis of natural circulation behaviors is significant. Compared with the commercial Computational Fluid Dynamics (CFD) software, such ANSYS Fluent, OpenFOAM is one of the most common open-source CFD codes, which has more free programming environment and advantages of convenient function development. In this study, a new solver GTFoam for the natural circulation simulation was developed by adding energy conservative equation model and natural circulation model into the original icoFoam solver of OpenFOAM. For the above GTFoam solver, code verification was carried out by using the square cavity flow driven by regional heat source, and results were compared with the ones calculated by ANSYS Fluent. It was demonstrated that the results simulated by GTFoam solver agreed well with the ones calculated by ANSYS Fluent. This study can be used in the further development of transient safety analysis solver based on OpenFOAM for LFR in our future work. Keywords: GTFoam · Natural circulation · CFD · OpenFOAM · icoFoam · Square cavity flow

1 Introduction Owing to the excellent capabilities in sustainability, miniaturization, and waste transmutation, Lead-based fast reactor (LFR) has attracted much attentions in recent years, and numerous LFR projects were launched. As one of the recommended reactors of the GenIV International Forum (GIF), the Lead-based or LFR has a well performance in term of the thermal-hydraulic and physical characteristics. Due to the large thermal expansion coefficient of Lead or Lead-Bismuth Eutectic (LBE), LFRs have an outstanding natural circulation capability. In 2011, Institutes in Europe proposed ELSY reactor, in which natural circulation was adopted to remove the decay heat [1]. In 2016, a small modular natural circulation lead-cooled fast reactor which was potential for remote power source © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 993–1001, 2023. https://doi.org/10.1007/978-981-19-8780-9_94

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supply was designed by the University of Science and Technology of China (USTC), namely SNCLFR-100 [2]. However, the development in most of LFRs are still in the conceptual or engineering design stage, and the thermal-hydraulic and safety behaviors are still one of the research focuses in the current time. As we known, the CFD-based simulations of nuclear energy system are more accurate and detail. Nowadays, CFD methods have been widely used in the thermal-hydraulic and safety analyses of LBE reactors, the most popular ones were the commercial CFD software, like ANSYS Fluent. Its mature meshing technology, powerful pre- and postprocessing functions are better than the other self-developed codes. These advantages, especially the meshing techniques make ANSYS Fluent available to cope with the thermal-hydraulic and safety behaviors under complex and multi-dimensional circumstances. Furthermore, as a commercial software, its calculation results are trustworthy indeed. Considering the above reasons, the thermal-hydraulic and safety behaviors involved in LBE-cooled reactor have been investigated by using ANSYS Fluent tools in many researches [3–7]. However, since commercial software is closed source, it was difficult for users to develop it in depth. Thus, users can not modify the key algorithms of ANSYS Fluent to satisfy their personal requirements under some special conditions. Under this circumstance, some researchers have paid more attentions to the open-source CFD tools such as OpenFOAM (Open-source Field Operation and Manipulation) due to their superiorities in terms of flexibility and developability. OpenFOAM is able to cope with the deficiencies existing in the above mentioned commercial software. Specific solvers can be freely developed to satisfy individual requirements. At the same time, OpenFOAM had almost the same pre-processing capability, especially the meshing techniques as ANSYS Fluent. In 2016, to investigate the thermal-hydraulic behaviors involved in the liquid metal blankets of fusion reactors, Feng conducted simulations of the magnetoconvection behaviors by using OpenFOAM tools [8]. The comparisons between the numerical results and analytical solutions showed quite good agreements, which indicated the correctness of the magneto-convection solvers based on OpenFOAM. In 2019, to simulate the high pressure compressible multiphase flow, an OpenFOAM solver based on the VOF method was developed by Meng [9]. During the design of Molten Salt Reactor (MSR), J. Groth-Jensen developed the multi-physics coupling code and conducted some applications by the code [10]. Ma established a neutron transportation kinetics solver in OpenFOAM, namely ntkFoam, which can be used for the simulation of the nuclear reactor neutron kinetics behaviors [11]. And the numerical results show that the solver can simulate the multi-group neutron transport kinetics problem accurately and flexibly. Radman [12] proposed a point kinetics model and its associated open-source code solver base on OpenFOAM. The code can easily be integrated in the existing thermal–hydraulic solvers to streamline the use of OpenFOAM in multi-physics coupling investigations. Wang coupled OpenFOAM with OpenMC to perform neutron diffusion analysis on the Very High Temperature gas cooled Reactor (VHTR) [13]. The OpenFOAM triggered a growing interest from the nuclear engineering community, notably for the simulation of nontraditional and advanced nuclear reactor systems [14–16]. In order to apply OpenFOAM to the study of thermal-hydraulic characteristics of LBE-cooled reactor, aiming at the simulation of the natural circulation behaviors in

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LBE-cooled reactor, a new solver GTFoam based on the icoFoam solver of OpenFOAM was developed in this paper. By utilizing ANSYS Fluent program, the code verification for GTFoam solver was conducted by using a typical cavity driven flow case.

2 Models and Algorithms The OpenFOAM is a collection of C++ libraries. This code was based on an unstructured finite volume method (FVM) and developed with the desire of obtaining a more effective numerical platform. The complete free distribution and the flexibility contribute to the development of specific solvers by the users, which can be integrated with already existing tools. It has attracted much interest from the CFD community in recent years because it provides a new approach for developing open-source codes for CFD applications. 2.1 Basic Solver icoFoam IcoFoam was selected as a basic solver, which is designed to be a transient solver for incompressible, laminar flow of Newtonian fluids. The governing equation for incompressible Newtonian fluids is: ∇ ·U =0

(1)

p ∂U + ∇ · (UU ) = −∇ + ∇ · (ν∇U ) ∂t ρ

(2)

In icoFoam solver, the coupling of pressure and velocity is calculated by using the transient PISO algorithm. The PISO algorithm is a classical algorithm in CFD, which can be used to simulate the incompressible fluid. The procedure of PISO algorithm is to calculate the predicted velocity with the pressure at the last moment, and then update the pressure by constructing the pressure Poisson equation, and then modify the predicted velocity. Following this cycle several times, it can be guaranteed that the field group (U,p) also satisfies ▽·U≈0, when it is updated. But the energy conservative equation is not included in the icoFoam solver, therefore, the temperature fields can not be calculated here. In icoFoam, what’s more, the density of the fluid is forced to be one without changing. And the gravity model is not considered in the momentum equations. Hence the natural circulation of incompressible fluids can not be simulated by icoFoam. 2.2 IcoFoam Secondary Development Firstly, owing to the simulation of coupled flow and heat transfer behaviors in the coolant region, the corresponding fluid energy conservative equation including the heat source term, just as shown in Eq. (3), was added to the code of icoFoam solver, which is illustrated in Fig. 1. ∇ · (λ∇T ) q ∂ρT + ρ∇(TU ) = + ∂t CP CP

(3)

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Fig.1. The energy equation

Secondly, it is considered that the density has no effect on the convection term. Thirdly, the gravitational field was added to the momentum equation in Eq. (4). Here, the final term in the right side is just the natural circulation model, in which ρ 0 is the constant reference density used to counteract the static pressure, and ρ is the dynamic density changing with fluid temperature. ∂ρU + ρ∇ · (UU ) = ∇ · (ρν∇U ) + (ρ − ρ0 )g ∂t

(4)

The new momentum equation was formed through the above two steps, the modified code was shown in Fig. 2. And the main solving process of GTFoam was shown in Fig. 3.

Fig.2. The new momentum equation

3 Code Verification In this paper, the cavity driven flow case was selected for the code verification of GTFoam and the result of GTFoam was compared with the ones of ANSYS FLUENT. The case simulated the 2-D flow state of LBE under natural circulation conditions for 100 s. The cavity was a rectangle with a length and width of 0.1 m, and a 2-D rectangular volume

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Fig.3. Chart flow of update algorithm in the GTFoam solver

heat source with 106 W was also located from 0.025 m to 0.075 m in horizontal direction and from 0 m to 0.05 m in vertical direction. The initial temperature of the LBE was set to be 573 K, and the velocity was set to be 0 m/s. For the case simulated by ANSYS Fluent, the UDF tools were used to realize the heat source model and physical properties of LBE fluid, including density, thermal conductivity, and viscosity. Laminar model was adopted to simulate the viscous behavior. And the momentum and energy equation have been discretized by using the First Order Upwind schemes. And the N-S equations was solved by pressure-velocity coupling scheme with SIMPLE algorithm. These solvers both set the acceleration of gravity to be –9.8m/s in the vertical direction. The results and discussion were shown below. As depicted in Fig. 4, the global average temperature calculated with GTFoam solver were in good agreement with the ones simulated by ANSYS Fluent. The temperature of LBE increased with time due to the heat source. In the beginning stage, due to the weak capability of natural circulation, the relative error of temperature was slightly larger. With time goes on, the relative error eventually stabilizes below 10–7 . Just as shown in Fig. 5, the evolutions of global average velocity and the relative error between two codes were illustrated respectively. The temperature gradient inside the LBE pool increased due to the heat source, which gave rise to natural circulation and increasing of fluid velocity. On the flip side, the temperature gradient decreased with the fluid flow and heat mixing, thus the natural circulation was suppressed. From 0 s to 18 s, as the equilibrium state was not established, local temperature gradient still increased, and the velocity raised at the same time. As both processes interacted with each other, oscillation was introduced for velocity variation until about 70 s. In the process, the maximum velocity is 0.1267 m/s at 33s. After 70 s, the velocity became gradually stable and the relative error reached about 7%. Figure 6 showed the variations of temperature and its relative error in the central point. In the early stage, owing to the influence of heat source, the temperature curve raised sharply before 9.55 s. In the next stage, the natural circulating flow continually affects the variations of fluid temperature. After about 55 s, the temperature increased

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Fig. 4. Changes of global average temperature and the profile of relative error

Fig. 5. Changes of global average velocity and the profile of relative error

almost linearly. The oscillations of relative error reduced gradually in the process, and reached about 0.035% at the end of the simulation. As can be seen from figure, the results are almost the same. The evolution of the velocity and its relative error in the central point was demonstrated in Fig. 7. At the beginning of the simulation, the velocity increases dramatically from 0 s to 12 s. Then the curve oscillated, which was caused by the fluid flow and mixing behaviors. In the final stage, the velocity was stable relatively. The maximum velocity arrived at about 0.02557 m/s in 32 s. Due to the poor capability of natural circulation, the velocity in the initial stage was small and the relative error was slightly large, while it decreased immediately. After about 6.4s, apparent oscillations can be observed. At the end of simulation, the relative error was about 4.8%.

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Fig. 6. Temperature changes in Central point and the profile of relative error

Fig. 7. Velocity changes in Central point and the profile of relative error

The comparisons in Figs. 8 and 9 represented the distributions of temperature and velocity fields. It showed that the results simulated by the GTFoam had good agreement with the ones by ANSYS FLUENT in terms of their integral profiles. The tiny difference between the two velocity contours was that the maximum value of the velocity simulated by the GTFoam was higher than the one calculated by ANSYS Fluent. The hotter LBE kept rising until the top boundary, and diffused around while mixing with other cold LBE. The phenomenon can be found in Fig. 8. Therefore, the velocity in the middle, top, left and right of the cavity was higher. Two vortices in left and right side can be observed in the vector distributions. GTFoam doesn’t take into account the difference in density on the boundary of the control volume mesh, which is the reason for the difference in the results calculated by GTFoam and FLUENT.

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Fig. 8. Temperature contours calculated by ANSYS FLUENT and the GTFoam

Fig. 9. Velocity vectors distribution calculated by ANSYS FLUENT and the GTFoam

4 Conclusion For the sake of investigating the thermal-hydraulic and safety characteristics of LBEcooled reactors, an OpenFOAM solver named GTFoam was developed in this paper based on the original icoFoam solver to simulate the natural circulation flow and heat transfer behaviors. The typical cavity driven flow case was used to perform the code verification of GTFoam solver, in which the commercial CFD software was selected for comparison validation. On the whole, the results simulated by GTFoam solver agreed well with the ones simulated by ANSYS Fluent. It was demonstrated that the GTFoam solver proposed in this paper can be able to perform the simulations of natural circulation problems reasonably involved in LBE-cooled reactors.

References 1. Alemberti, A., et al.: European lead fast reactor—ELSY. Nucl. Eng. Des. 241(9), 3470–3480 (2011) 2. Hongli, C., et al.: Conceptual design of a small modular natural circulation lead cooled fast reactor SNCLFR-100. Int. J. Hydrogen Energy 41(17), 7158–7168 (2016) 3. Chen, Z., et al.: Coupling a CFD code with neutron kinetics and pin thermal models for nuclear reactor safety analyses. Ann. Nucl. Energy 83, 41–49 (2015)

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4. Zhao, P., et al.: Natural circulation characteristics analysis of a small modular natural circulation lead–bismuth eutectic cooled fast reactor. Prog. Nucl. Energy 83, 220–228 (2015) 5. Wang, X., et al.: Natural circulation characteristics of lead-based reactor under long-term decay heat removal. Prog. Nucl. Energy 90, 11–18 (2016) 6. Zhao, P., et al.: CFD analysis of the primary cooling system for the small modular natural circulation lead cooled fast reactor SNRLFR-100. Sci. Technol. Nucl. Install. 2016, 1–12 (2016) 7. Luo, X. et al.: Development and application of a multi-physics and multi-scale coupling program for lead-cooled fast reactor. Nucl. Sci.Techn. 33(2) (2022) 8. Feng, J., Chen, H., He, Q., Ye, M.: Numerical investigation of liquid metal magneto-convection in a squared enclosure based on OpenFOAM. J. Fusion Energy 35(3), 498–504 (2016). https:// doi.org/10.1007/s10894-016-0070-5 9. Meng, Z., et al.: Code development for analyzing transient behaviors induced by the in-box LOCA of liquid blanket in hydrogen fusion energy systems. Int. J. Hydrogen Energy 44(5), 3021–3030 (2019) 10. Groth-Jensen, J., et al.: Verification of multiphysics coupling techniques for modeling of molten salt reactors. Ann. Nucl. Energy 164, 108578 (2021) 11. Ma, Y., Wang, Y., Yang, J.: ntkFoam: an OpenFOAM based neutron transport kinetics solver for nuclear reactor simulation. Comput. Math. Appl. 81, 512–531 (2021) 12. Radman, S., et al.: Development of a point-kinetics model in OpenFOAM, integration in GeN-Foam, and validation against FFTF experimental data. Ann. Nucl. Energy 168, 108891 (2022) 13. Wang, J., et al.: Application of a new OpenFOAM-based neutron diffusion kinetics solver to pebble-type VHTRs. Ann. Nucl. Energy 170, 108976 (2022) 14. Jamalipour, M., Cammi, A., Lorenzi, S.: A coupled neutronic and thermal-hydraulic model for ALFRED. Eur. Phys. J. Plus 135(3), 1–23 (2020). https://doi.org/10.1140/epjp/s13360020-00328-5 15. Castagna, C., Introini, C., Cammi, A.: Development and implementation of a multi-physics high fidelity model of the TRIGA mark II reactor. Ann. Nucl. Energy 166, 108704 (2022) 16. Nath, D., Verma, M.K.: Numerical simulation of convection of argon gas in fast breeder reactor. Ann. Nucl. Energy 63, 51–58 (2014)

Economic Indicator Analysis of HPR1000 Process Pipe Engineering in the Integrated Technology Corridor Yang Shi(B) , Wen-An Li, Qian Yu, Li-Juan Chen, and Dan Fa Economic Evaluation Division, China Nuclear Power Engineering CoLtd, Beijing Institute of Industrial Engineering, Beijing, China [email protected]

Abstract. Based on the HPR1000 nuclear island, relying on the first batch of demonstration projects of Fuqing Unit 5 and 6, this paper calculated and analyzed the quantity indicators by summarizing the relevant engineering data of the process pipe engineering in the integrated technology corridor. The cost indicators were calculated according to the “Budget quota of Fujian Provincial municipal engineering (2012 Edition)”, which studyies the distribution of cost and main influencing factors and provides guidance for the design optimization of the integrated technology corridor for the HPR1000; And a rapid estimation method was proposed based on the indicators, which can make quantitative prediction when the capital is not fully raised in the estimation stage. Results showed that the key research directions of the design optimization for integrated technology corridor should be to decrease the specification of flange in the plant fire water distribution system and reduce the quantity of flanges in this system, reduce the engineering quantity of pipeline with large diameter in the drinking water system, and select reasonable materials of the pipeline in different diameter ranges; Besides, taking an actual project as an example, the accuracy of the indicators and quantitative prediction method was verified, which indicates the indicators and quantitative prediction method can be stronger and more definite evidence for future rapid estimation of the process pipe engineering in the integrated technology corridor of the HPR1000. Keywords: Process pipe engineering · Integrated technology corridor · Quantity indicators · Technical and economic indicators · Rapid estimation

1 Introduction The integrated technology corridor of nuclear power plant (hereinafter referred to as GB corridor) is throughout the whole plant area, providing laying channels for the input and output of water, gas (steam), electricity and oil required by most of the construction (structure) in the plant area, and is the link connecting nuclear island, conventional island and the balance of plant (BOP). The GB corridor has a large amount of engineering and high investment, accounting for about 10% of the total BOP cost of nuclear power plant, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1002–1013, 2023. https://doi.org/10.1007/978-981-19-8780-9_95

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whose cost estimation working is very important. In various professional, the project cost of process piping specialty is higher, which is affected by the nuclear power unit type, the plant layout, system settings, and so on multiple factors, and it is very difficult for accurate cost calculation in the early stage of the project by limited design data, and the usual practice is to refer to the engineering data of existing similar nuclear power units, and then reasonably calculate the project cost. As an advanced pressurized water reactor of progressive design, the HPR1000 incorporates advanced design features including an active and passive safety design philosophy [1], and it hass substantial innovation for diversity of safety facilities [2]. Compared with existing nuclear power units, the HPR1000 is quite different in terms of engineering quantity and distribution characteristics, which leads to a difficulty for predicting engineering quantity according to the existing engineering data. Therefore, relying on the demonstration project of HPR1000, it is necessary for analyzing the distribution characteristics of the engineering quantity and the project cost of the process piping specialty in GB corridor, and calculating the quantity indicators and technical and economic indicators of different systems and specifications respectively, and accordingly puting forward a rapid estimation method, which is of great significance for the investment estimation of the batch construction of HPR1000.

2 Study on the Quantity Indicator of Gb Corridor 2.1 Methods Accurate engineering quantity is the premise of reasonable engineering cost. The installation quantity of the process pipeline specialty in GB corridor mainly includes pipeline, pipe fittings, valves, flanges, non-destructive testing, sandblasting anticorrosive, acid pickling/pressure test/flushing and pipeline supports and hangers. Among them, the quantities of non-destructive testing, sandblasting anticorrosive, acid pickling/pressure test/flushing and pipeline supports and hangers change with the pipeline quantities, which are not analyzed in this chapter. Quantity calculation rules refer to the local quota. The amount of production and installation of all kinds of pipeline is calculated according to the length of the designed pipeline center line, which is calculated by “extension meter”, excluding the length of various pipe fittings, valves and components. In case of pipe bending, it is calculated according to the intersection point of the center line where two pipes cross [3]. 2.2 The Quantity Indicators in Every System Different stages of design corresponding to different depth of design data, the changes of engineering volume are reflected in different levels. At the stage of feasibility study and preliminary design of nuclear power projects, designers can usually refine to the system level. At the same time, in the process of design optimization, it usually involves the increase, decrease or simplification of related systems, that is, the changes of corresponding system engineering quantity. Therefore, the quantity indicators are constructed with the system as the unit and the influence of each system on the overall engineering quantity is evaluated, which can provide a reliable benchmark for the rapid estimation and

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design optimization of the process pipeline in the GB corridor of HPR1000 follow-up units. The length and weight units have been selected to calculate the quantity indicators of the pipeline, and “length layout density (the length of pipeline/the length of GB corridor)” and “weight layout density (the weight of pipeline/ the length of GB corridor)” have been defined as indicators to characterize the pipeline layout characteristics of each system, and the calculation results have been shown in Figs. 1 and 2. The number information has been selected to calculate the quantity indicators of pipe fittings, valves and flanges, and “content of pipe fitting (the number of pipe fittings/the length of pipeline in this system)”, “content of valve (the number of valves/the length of pipeline in this system)”, “content of flange (the number of flanges/the length of pipeline in this system)” have been defined to characterize the distribution characteristics of engineering quantity in every system, and the results have been shown in Figs. 3, 4 and 5.

Fig. 1. The pipeline quantity indicators (representation in units of length) in every system of GB corridor for HPR1000

Fig. 2. The pipeline quantity indicators (representation in units of weight) in every system of GB corridor for HPR1000

Calculating the pipeline quantity indicators, the results are obvious from Figs. 1 and 2: the pipeline quantity of the plant fire water distribution system and the drinking water system are all above 2000 m, that is because the two systems undertake the indoor and outdoor water supply guarantee of the main plant, and the distribution range is wide. Besides, the pipeline quantity of the conventional island closed cooling water system is

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Fig. 3. The pipe fittings quantity indicators in every system of Gb corridor for HPR1000

Fig. 4. The valves quantity indicators in every system of GB corridor for HPR1000

Fig. 5. The flanges quantity indicators in every system of GB corridor for HPR1000

much larger than other systems. The” length layout density” of above three systems are all around 0.53, which is about half of the length of GB corridor, and the “weight layout density” of above systems are high which is greater than 85t. Therefore, the pipeline engineering cost of above three types of systems should be paid special attention. According to Figs. 3, 4 and 5, the engineering quantity of pipe fittings and flange of the plant fire water distribution system are significantly higher than other systems, and the engineering quantity of valves is also at a high level. The engineering quantity of pipe fittings of the public compressed air distribution system is large, and the engineering quantity of valves in the power station sewage system is large. Further analysis for the content level shows that: in the above systems, the content of fittings, valves and flanges in the power station sewage system are significantly higher than other systems, but the engineering quantity of each item in the system is small and the engineering cost is low,

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so it is not the focus of attention. Except the power station sewage system, the content of pipes in other systems is between 0.1 /m and 0.3 /m, and the content of valves are all less than 0.1 /m. As for the content of flanges, the plant fire water distribution system is high, so there may be room for optimization in the flange setting of this system. Through on the above analysis, the following conclusions can be predicted. At the system level, the plant fire water distribution system has right great influence on the engineering cost of GB corridor; While at the item level, it should be the key research direction of design optimization for GB corridor that reducing the pipeline quantity and the amount of flange in the plant fire water distribution system. 2.3 The Quantity Indicators in Every Representation Measuring and calculating the engineering quantity indicator of pipeline in terms of specification and material is helpful to select the technical scheme which is more economical in the preliminary design stage. The calculation results have been shown in Figs. 6 and 7.

Fig. 6. The pipeline quantity indicators (representation in units of length) in different specifications of GB corridor for HPR1000

Fig. 7. The pipeline quantity indicators (representation in units of weight) in different specifications of GB corridor for HPR1000

According to the distribution of pipe specifications, the materials and specifications of pipes in GB corridor vary greatly with the design requirements of different systems. The

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nominal diameter of pipes ranges from 1 / 2 “to 14”, and there are 31 pipe specifications in GB corridor regardless of the pipe wall thickness. Further analysis have shown that 10"stainless steel pipeline, 10” to 12 “carbon steel pipeline and 14” galvanized steel pipeline have higher quantities and weights among all pipe specifications, and it is quite possible that greater economic benefits may be obtained if optimizing the design of above four types of pipe specifications; By comparison, the quantities of 2 “1 / 2 stainless steel pipeline, 6” stainless steel pipeline, 1”1/4 carbon steel pipeline, 4” carbon steel pipeline, 1/2 “galvanized steel pipeline and 8” galvanized steel pipeline are less than 30m, and the pipe length and weight account for less than 1%, and it can be predicted that these six types of pipe specifications have little impact on the corridor cost. Besides, considering that too many pipeline specifications are not conducive to the project progress and on-site management, and then affect the cost control, it is suggested to merge and replace the pipeline specifications whose proportion is relatively small within a reasonable range, which can simplify the relevant management work and improve the work efficiency [4], and then reduce the GB corridor cost from another level. 2.4 The Overall Quantity Indicator The above quantities are added and summed to obtain the overall quantity indicator of the process pipeline discipline in GB corridor. It can be seen from Table 1 that the total length of pipeline in the GB corridor is 17km, of which stainless steel pipeline and carbon steel pipeline account for about 42% and 46% of the total length respectively; The quantity of pipe fittings is 2821, with a content of about 0.17/m; The quantity of valves and flanges are relatively low, whose level is lower than 0.1/vice. Table 1. The overall quantity indicator The entry name

The quantity indicators

The quantity of pipeline

17km/540t

Among: The quantity of stainless steel pipeline

7km/148t

The quantity of carbon steel pipeline

8km/257t

The quantity of galvanized steel pipeline

2km/135t

The quantity of pipe fittings

2821

The content of pipe fittings

0.17/m

The quantity of valves

257

The content of valves

0.02/m

The quantity of flanges

1006.5 vice

The content of flanges

0.06 vice/m

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The entry name

The project cost of installation (million)

Technical and economic indicator (CNY/m)

Pipeline

8.98

528

Among: Pipeline with stainless steel

6.33

904

Pipeline with carbon steel

1.68

210

Pipeline with galvanized steel

0.97

2485

Pipe fitting

0.83

294

Valve

0.22

856

Flange The total

0.83

825

10.86

639

3 Study on the Technical and Economic Indicators of GB Corridor Engineering quantity indicators can represent the distribution characteristics of engineering volume in different systems and different pipeline specifications. In order to analyze the composition of the project cost and measure technical and economic indicators, the quota system should be used [5], and then a benchmark for rapid evaluation and design optimization can be provided. The cost of the process pipeline in GB corridor includes division and itemized engineering cost (main material cost plus installation cost plus management cost plus profits), measure project cost, other project cost, stipulated cost and the taxes [6, 7], among them, the measures project cost, other project cost can be calculated according to the method specified in the quote, and the taxes can be calculated with labor cost as the charging base. Therefore, the main material cost, installation cost and labor cost were selected as technical and economic indicators in this paper, and the “Budget quota of Fujian Provincial municipal engineering (2012 Edition)” was taken as the compilation basis, and the main material cost refered to the market price in the same period. 3.1 The Distribution of Technical and Economic Indicators in Different Systems The project cost in different systems of GB corridor have been analyzed, and the technical and economic indicators in different systems are shown in Figs. 8 and 9. According to Fig. 8, the main material cost and its indicators have been analyzed as follows: The engineering cost indicators of drinking water system and conventional island desalinization distribution system are higher, which shows that these systems may be have more space for optimization in terms of material and pipe specification compared with other systems. At the same time, the main material cost of drinking water system is the highest, which can be inferred that reducing the main material cost of drinking water system may greatly reduce the project cost of GB corridor.

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Fig. 8. The installation cost, the main material cost, and the technical and economic indicators in different systems of GB corridor for HPR1000

Fig. 9. The labor cost and economic indicators in different systems of GB corridor for HPR1000

Fig. 10. Composition of the installation cost of GB corridor for HPR1000

The installation cost and its indicator have been analyzed: The installation cost indicators of plant fire water distribution system and drinking water system are higher than other systems, reaching 150 CNY/m and 93 CNY/m respectively. Composition of the installation cost has been further analyzed, and the calculation results were shown in Fig. 10. It is obvious from Fig. 10 that installation cost indicator of the plant fire water distribution system is relatively higher. That is because many large-specification flanges exist in this system, whose installation costs up to 170,000 CNY, and it is about twice of installation cost of pipeline; For the drinking water system, large gauge pipeline account for relatively large proportion is the main reason. In summary, the key research directions of the design optimization for GB corridor should be to decrease the specification of

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flange in the plant fire water distribution system and reduce the quantity of flanges in this system, and reduce the engineering quantity of pipeline with large diameter in the drinking water system. Analysis of the labor cost and its economic indicators showed that: The labor cost indicator is the highest in all systems, that is because the high content of flanges in the plant fire water distribution system, and proportion of the flanges whose diameter is larger than 12” reached 70%. In addition, based on actual engineering calculations, if the content of flanges in the plant fire water distribution system is reduces by 50%, the labor cost and the installation cost of this system can be reduced by about 30%. 3.2 The Distribution of Technical and Economic Indicators in Different Specifications The distribution of technical and economic indicators of GB corridor in different specifications had been showed in Fig. 11.

Fig. 11 The distribution of technical and economic indicators of Gb corridor for HPR1000 in different specifications

Fig. 12. Composition of the main material cost of Gb corridor for HPR1000

As can be seen from Fig. 11, the main material cost of pipeline is the highest when the specification is 10” and the material is stainless steel, indicating that the optimization for such specification-material pipeline can greatly reduce the total cost of GB corridor.

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Fig. 13. The main material cost indicator of pipeline in Gb corridor for HPR1000

Besides, According to the distribution chart of technical and economic indicators, it can be seen that the main material cost of different systems and different specifications is much higher than the installation cost, indicating that the main material cost has greater impact on the total cost of GB corridor. To sum up, the main material cost was had been further analyzed. It can be seen from Fig. 12, the pipeline professional accounted for the highest proportion. The main material cost indicator of pipeline had been measured, and the variation trend of the indicator with different specifications and materials had been shown in Fig. 13, which indicates that the main material cost of pipeline with stainless steel is much higher than the pipeline with carbon steel under the same diameter; For the pipeline with stainless steel, the main material cost increases significantly when the pipe diameter increases from 4” to 6”. Thus it can be seen that: Reasonable selection of pipe materials in different diameter ranges can also reduce the cost of GB corridor. 3.3 Overall Technical and Economic Indicators The overall technical and economic indicators of process pipeline specialty in GB corridor had been calculated by weighting above indicators. It can be seen from Table 8 that the total cost of GB corridor installation project (only including pipeline, pipe fittings, valves and flanges) is 10.86 million, and the technical and economic indicator is 630 CNY/m. Further analysis showed that the cost of pipeline with stainless steel is 6.33 million, accounting for about 58% of the total cost, which should be the key part of GB corridor; The cost of valves is only 0.22 million, accounting for about 2% of the total cost, and the valves have little impact on the total cost of GB corridor, indicating that when the information of this part is insufficient, it can be estimated by proportion.

4 The Application and Accuracy Verification of Indicators The engineering quantity indicator and technical and economic indicator obtained above can be used in the following aspects:

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1. In the stage of design and engineering implementation, the indicators above can provide a basis for the optimization of GB corridor for HPR1000 follow-up models; 2. In the stage of budget, the indicators above can be used for self-proofreading; 3. When the engineering data is incomplete, the project cost can be quantitatively predicted according to the above indicators, and the calculation method had been showed in the formula (1).    C=M+L× (IQi × ITi) + L × (IQi × ILi) × f (1) C: The installation cost (million) M: The main material cost (million) L: The length of GB corridor (meter) IQi: The pipeline quantity indicator of i system/specification ITi: The installation cost indicator of i system/specification ILi: The labor cost indicator of i system/specification f: The cost adjustment factor, which usually varies according to different regions, and the value range is 3.9 ~ 4.1 Another HPR1000 project had benn taken as an example to verify the accuracy of the above indicators and the formula1–1. Due to different regional and time, the main material cost of the verification project is not at the same level as the above indicators, and therefore only the installation cost of the two projects had been compared and verified. According to the formula 1–1, the installation cost of GB corridor of the verification project is 12.0572 million, and there is within 5% from the completed budget, which verifies the accuracy of the above indicators and formula 1–1, indicating that the above indicators can be used for rapid evaluation of GB corridor for the subsequent HPR1000 projects.

5 Conclusions By summarizing the relevant engineering data of the process pipe engineering in GB corridor for HPR1000, this paper calculated the engineering quantity indicators and technical economic indicators, and puted forward a fast estimation method, which can provide a basis for rapid estimation and design optimization of HPR1000 follow-up projects. The specific conclusions had been showed as follows: 1. Through calculating the engineering quantity indicator and the technical and economic indicator, the variation and distribution characteristics of the engineering quantity and the project cost about pipeline, pipe fittings, valves and flanges in different systems and that with different specifications had been revealed. Finally, the results of final calculation had been showed as follows: There are 17km pipeline in GB corridor of HPR1000, while the quantities of pipe fittings, valves, and flanges are 2821, 257 and 1006.5 vice respectively; The technical and economic indicators of pipeline, pipe fittings, valves and flanges are 528 CNY/m, 294 CNY/m, 856 CNY/m and 825 CNY/m respectively.

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2. On the basis of comprehensive consideration of engineering quantity indicator and technical and economic indicator, the results showed that the key research directions of the design optimization for GB corridor should be to decrease the specification of flange in the plant fire water distribution system and reduce the quantity of flanges in this system, reduce the engineering quantity of pipeline with large diameter in the drinking water system, and select reasonable materials of the pipeline in different diameter ranges; Besides, the project cost of GB corridor could be reduced by merging and replacing the pipeline specifications whose proportion is relatively small within a reasonable range. 3. This paper clarified the application scope of the indicator which were calculated above, and a fast estimation method for GB corridor process pipeline had been proposed according to the technical and economic indicators and the cost composition of the project cost, which is beneficial for the economic guidance of HPR1000 subsequent projects.

References 1. Jing Chun-ning, Zhao Ke, Zhang Li-you, et al.: The design philosophy and general technical features of HPR1000. Beijing, China’s nuclear power (2017) 2. Xing, J., Song, D., Yuxiang, W.: HPR1000: advanced pressurized water reactor with active and passive safety. Engineering 2, 79–87 (2016) 3. Fujian Provincial municipal engineering budget quota. Beijing, China Planning Press (2017) 4. Zhenning Zhang: Study on the merging and substitution of piping specifications in the design stage of passive nuclear power plants. Shanghai Jiao Tong University (2018) 5. Nuclear power plant economy. Beijing, Atomic Energy Publishing House (1997) 6. Build the [2013] no. 44. Composition of construction and installation project expenses 7. Compilation and review committee of training materials for national cost engineer qualification examination. construction valuation. Beijing, China Planning Press (2017)

A Kinetic Model for Researching the Rolling Bearing Load Inversion Method Donglin Liu1 , Renqiong Wu2(B) , Xianhe Shang1 , Chun Zeng1 , Zhong Xu1 , Haojun Ma1 , Kun Luo2 , and Lili Zhu3 1 CNNC Nuclear Power Operation Management Co. Ltd., Haiyan, China 2 School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China

[email protected] 3 Suzhou Veizu Equipment Diagnosis Technology Co. Ltd., Suzhou, China

Abstract. Rolling bearing is the key component of rotating machinery, which is known as the joint of industry. Accurately obtaining the dynamic load of rolling bearing under the running state of rotating machinery has important research value and can bring huge economic benefits to relevant enterprises. This paper presents a method to calculate the real-time dynamic load of rolling bearing under a running state of rotating machinery by using the measured vibration acceleration of rolling bearing. Firstly, the method establishes the nonlinear dynamic model of rolling bearing; Secondly, the state space model of rolling element system is established. Combined with the Newmark integral method and the measured vibration acceleration signal of rolling bearing, the motion status and internal dynamic load of rolling bearing under the current conditions are calculated, and the calculated operation status, dynamic load and bearing vibration signal of rolling bearing at the current time are re input into the dynamic model, calculate the nonlinear contact force inside the rolling bearing at the next moment; Finally, the time-domain dynamic load information of rolling bearing is calculated step by step through the inversion method until the full-time dynamic load of rolling bearing is calculated. The effectiveness of this method is verified by experiments. Keywords: Load inversion · Rolling bearing · Dynamics simulation · Signal processing · Fault diagnosis

1 Introduction Rolling bearing is a crucial portion of rotating machinery. The failure of rolling bearing can easily lead to unplanned shutdown of the whole equipment, resulting in economic losses and even casualties. Therefore, monitoring and diagnosis of rolling bearing status are particularly important [1]. In the operation process of rotating machinery, it should not only bear the cyclic load of rotating machinery itself but also bear all kinds of vibration and impact, resulting in the extremely bad working environment [2]. Accurately obtaining the external load data borne by the rolling bearing during the operation of rotating machinery can provide a basis for bearing life prediction research, bearing © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1014–1022, 2023. https://doi.org/10.1007/978-981-19-8780-9_96

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dynamic load simulation analysis, structural design and optimization, provide more technical guarantee for the operation safety of rotating machinery and provide theoretical basis for the operation and maintenance decision of rolling bearing [3]. However, the existing technology is mainly used to directly measure the load of the rolling bearing by pasting strain gauges on the surface of the outer ring or inner ring of the bearing. It is difficult to paste strain gauges on the bearing because of the influence of complex industrial environment. Therefore, a load inversion method based on the dynamic model of rolling bearing is proposed in this paper.

2 Dynamic Model of Rolling Bearing 2.1 System State Model Construction Rolling bearing, Accurately obtaining the external load data borne by the rolling bearing during the operation of rotating machinery can provide a basis for bearing life prediction research, bearing dynamic load simulation analysis, structural design and optimization, provide more technical guarantee for the operation safety of rotating machinery and provide theoretical basis for the operation and maintenance decision of rolling bearing pedestal, and rotating shaft form the intricate system. In order to facilitate the establishment of theoretical model of rolling bearing, the theoretical assumptions are made as follow [4]: 1. The geometry of rolling bearing is ideal; 2. The centrifugal force, friction and sliding of the internal components of rolling bearing are ignored. There is only radial force between the components of rolling bearing; 3. The contact deformation between the rolling element and the raceway is within the elastic deformation range, which satisfies the Hertz contact theory.

Fig. 1. Dynamic model of rolling bearing

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Based on the above assumptions, a dynamic model of 5-DOF (degrees of freedom) rolling bearing [5] is established, as shown in Fig. 1, which include 2-DOF in the horizontal direction of the inner ring and outer ring and 3-DOF in the vertical direction of the inner ring, outer ring and element resonator respectively. According to the kinematics and dynamics of the rolling bearing, the dynamic differential equation can be expressed as: ⎧ ⎪ ms x¨ s + cs x˙ s + ks xs + fx = 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ms y¨ s + cs y˙ s + ks ys + fy = Fr ⎨ mp x¨ p + cp x˙ p + kp xp − fx = 0 (1) ⎪ mp y¨ p + (cp + cr )˙ys + (kp + kr )yp ⎪ ⎪ ⎪ ⎪ −kr yb − cr y˙ b − fy = 0 ⎪ ⎪ ⎩ mr y¨ b + cr (˙yb − y˙ p ) + kr (yb − yp ) = 0 where, m, cs , k s , mp , cp , k p , mr , cr and k r represent the mass, damping, and stiffness of inner ring, outer ring, and unit resonator respectively; f x , f y represent the nonlinear contact force between the inner ring, the outer ring and the rolling element in the horizontal and vertical directions respectively; F r represents the external force acting on the vertical direction of the inner ring; x s , ys , x p , yp represent the horizontal and vertical displacements of the inner ring and outer ring; yb represents vertical displacements of the unit resonator. 2.2 Nonlinear Contact Force of Rolling Bearing The plane structure of the rolling bearing is shown in Fig. 2. The outer ring of rolling bearing is mounted on the bearing pedestal. The inner ring is fixed to the rotating shaft and rotates with it. The rolling elements do pure rolling, which are in contact with the inner ring and outer ring. Combined with the relative displacement of the inner ring and outer ring, the contact deformation of the jth rolling element δj can be expressed as: δj = (xs − xp ) cos φj + (ys − yp ) sin φj − Cr/2

(2)

where, φj is the angular position of the jth rolling element relative to the horizontal direction j = 1, 2, . . . , Nb . N b is the number of rolling elements; C r is the clearance of rolling bearing. The angular position φj can be expressed as: φj = θ0 + ωc t +

2π(j − 1) Nb

(3)

where, θ 0 is the initial angular position of the first rolling element relative to the horizontal direction. ωc is the angular velocity of the cage, which can be expressed as:   Db cos α ωs (4) ωc = 1 − Dp 2 Among them, Dp and Db represent the diameter of pitch diameter of rolling bearing and rolling element respectively. α is the contact angle of rolling bearing, and ωs is the angular velocity of the rotating shaft.

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Fig. 2. Plane structure diagram of rolling bearing

The rolling element has contact deformation only in the bearing area. Therefore, the following switching function is defined by:  1 δj > 0 γj = (5) 0 otherwise Based on Hertz contact theory, the calculation formula of contact force between the raceway and jth rolling element is as follows: fj = kb δjn

(6)

where, k b is the contact stiffness between the raceway and the rolling element; n is the load-displacement index of rolling bearing, where the load-displacement index of the cylindrical roller bearing is n = 9/10. From Eqs. (2)–(6), the nonlinear contact force in the horizontal and vertical directions can be expressed as:

fx = kb δjn cos φj

(7) fy = kb δjn sin φj

3 Load Inversion Method 3.1 Construction of System State Space Model For multi-DOF structural system, the differential equation of motion is: M x˙ + C x˙ + Kx = f

(8)

where, M, C, K are the mass coefficient matrix, damping coefficient matrix, and stiffness coefficient matrix of the system, respectively. x¨ , x˙ , x are the acceleration, velocity, and displacement vectors of the system, and f is the radial external load vector. The dynamic

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model was established based on formula (1), and the corresponding relationship of each coefficient matrix and vector is as follows: ⎡ ⎤ ⎤ ⎡ cs 0 0 ms 0 0 0 0 0 0 ⎢0 c 0 ⎢ 0 m 0 0 0 ⎥ 0 0 ⎥ ⎢ ⎥ ⎥ ⎢ s s ⎢ ⎥ ⎥ ⎢ M = ⎢ 0 0 mp 0 0 ⎥ C = ⎢ 0 0 cp 0 0 ⎥ ⎢ ⎥ ⎥ ⎢ ⎣ 0 0 0 cp + cr −cr ⎦ ⎣ 0 0 0 mp 0 ⎦ 0 0 0 −cr cr 0 0 0 0 mr ⎡ ⎤ ks 0 0 0 0 ⎢0 k 0 0 0 ⎥ ⎢ ⎥ s ⎢ ⎥ K = ⎢ 0 0 kp (9) 0 0 ⎥ ⎢ ⎥ ⎣ 0 0 0 kp + kr −kr ⎦ 0 0 0 −kr kr ⎡ ⎤ ⎡ ⎤ ⎡ ⎡ ⎤ ⎤ x˙ s xs −fx x¨ s ⎢ y˙ ⎥ ⎢y ⎥ ⎢F − f ⎥ ⎢ y¨ ⎥ ⎢ s⎥ ⎢ s⎥ ⎢ r ⎢ s⎥ y⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎥ (10) x¨ = ⎢ x¨ p ⎥ x˙ = ⎢ x˙ p ⎥ x = ⎢ xp ⎥ f = ⎢ fx ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎥ ⎣ y˙ p ⎦ ⎣ yp ⎦ ⎣ fy ⎦ ⎣ y¨ p ⎦ y¨ b y˙ b yb 0 According to the Newmark integral method, the acceleration response of the system is introduced into the state-space model:  xi+1 = xi + x˙ i t + t 2 ((0.5 − β)¨xi + β x¨ i+1 ) (11) x˙ i+1 = x˙ i + ((1 − α)¨xi + α x¨ i+1 ) t where, i is the number of calculation time step; t is the integration time step. α and β are two parameters of Newmark integral method. α ∈ [0, 1], β ∈ [0, 0.5], generally, α is taken as 0.5 and β is taken as 0.25. Combining Eqs. (8) and (11), the state space equation of multi-DOF system is established: Xi+1 = AXi + Bfi ⎡

I A=⎣0 K ⎡ I ⎣ B= 0 K

⎤−1 ⎡ ⎤ 0 −βI t 2 I I t (0.5 − β)I t 2 I −αI t ⎦ ⎣ 0 I (1 − α)I t ⎦ C M 0 0 0 ⎡ ⎤ ⎤−1 ⎡ ⎤ 2 0 −βI t 0 xi ⎣ ⎣ ⎦ ⎦ Xi = x˙ i ⎦ 0 −αI t 0 x¨ i C M 1

(12)

(13)

where, I is the identity matrix, 0 is the zero matrices, A and B represent the system and input influence matrix respectively, and X represents the state variables of the system, including the displacement, velocity, and acceleration responses of the system.

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The output equation is:



Yi = DXi  D= 00I

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(14)

where, D is the output influence matrix and Y is the system output variable, that is, the collected system acceleration response. 3.2 Dynamic Load Inversion Calculation Process 1) Calculate various coefficient matrices, including mass coefficient matrix M, damping coefficient matrix C and stiffness coefficient matrix K of the system; System state space coefficient matrix A, B, D; 2) Obtain vibration acceleration signal x¨ through signal acquisition; 3) Substitute the collected vibration acceleration signal x¨ into the state recurrence Eq. (11) to calculate the displacement response x i+1 and velocity response x˙ i+1 ; 4) Based on the obtained displacement response x i+1 , substitute into Eq. (7) to calculate f x and f y . The radial load vector f can be calculated by Eq. (10); 5) Repeat steps 3) and 4) to calculate all loads, that is, the calculated dynamic load response.

4 Bearing Dynamic Load Test Verification In this section, the rolling bearing vibration signals collected by the test bench are applied to verify the validity of the algorithm. The acquisition site is shown in Fig. 3. The loading device is located in the centre of the test bench, which can apply a radial load on the shaft. The vibration acceleration sensor is vertically installed on the bearing pedestal. The sampling frequency is 25,600 Hz. The sampling time is 10 s, and the radial load F r is 1000 N.

Fig. 3. Bearing test bench

The structural diagram of the bearing test bench is shown in Fig. 4, in which, l 1 = l 2 = 25 cm, the shaft mass is m = 8.6 kg, and the calculated theoretical load of the tested bearing is 543 N. In order to verify the accuracy of the bearing load inversion method, NU214 rolling bearing is used for the test verification. Bearing parameters and test load are shown in Table 1. Based on the bearing test bench in Fig. 3 and the NU214 rolling bearing, the bearing vibration signal is shown in Fig. 5a, b.

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Fig. 4. Structural diagram of bearing test bench

Table 1. Bearing parameters Bearing model

Number of balls

Outer diameter /mm

Contact angle /°

Internal diameter /mm

Load /N

NU214

16

125

0

70

1000

(a) Vibration signal 1

(a) Vibration signal 2 Fig. 5. Rolling bearing vibration signal

The collected vibration acceleration signal and rolling bearing model parameter matrix are taken as inputs and substituted into the load inversion model. The dynamic load force of rolling bearing is obtained through calculating the load inversion model of the state-space equation, as shown in Fig. 6. Due to the linear relationship between force and energy working, and to evaluate the calculation accuracy of the load inversion method, the average value is used as the evaluation index of the inversion load. Compared with the actual applied load, the

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(a) Inversion load 1

(b) Inversion load 2 Fig. 6. Inverse load time domain diagram

calculation formula of the overall relative error is as follows:     f − F r  Re =   × 100% F r 

(15)

In this paper, the data collected by the test-bed with the acquisition time of 4 s and the rotation frequency of the rotating shaft of 10 are selected for calculation, and good inversion results are obtained. The inversion calculation results are compared with the real applied load, as shown in Table 2: Table 2. Calculation results and errors of inversion load under 10 Hz frequency conversion Load

Maximum/N

Minimum/N

f /N

Error

Inversion load 1

710.25

320.80

489.23

9.90%

Inversion load 2

696.67

331.45

504.11

7.16%

Loading load

543

543

543

/

It can be known from Table 2 that there is a little gap between the average value of the inversion load and the actual loading load. At the same time, the overall relative error is small, and the inversion result is more accurate.

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5 Conclusion 1. A dynamic model of 5-DOF rolling bearing and a nonlinear contact force calculation method are established; 2. The mass coefficient matrix, damping coefficient matrix, and stiffness coefficient matrix are extracted based on the dynamic model of rolling bearing established in Formula (1), and the Newmark integral method is set to establish the load inversion model of multi DOF system based on state space, to realize the load inversion calculation of multi DOF system based on local vibration acceleration; 3. The vibration acceleration information of the rolling bearing obtained by the experimental platform is substituted into the load inversion model, and the vertical load of the rolling bearing is calculated through the state space load inversion model. It can be known from the calculation results that the overall relative error between the average value of the inversion result and the actual load is small and has good inversion accuracy. The effectiveness of this method is verified, and the purpose of calculating the load on the rolling bearing during operation by using the vibration acceleration signal of the rolling bearing is realized.

Acknowledgements. The authors acknowledge gratefully funding by CNNC Nuclear Power Operation Management Co. LTD. (CNNO) (Project no. Y20109).

References 1. Ahmadi, A.M., Howard, C.Q., Petersen, D.: The path of rolling element in defective bearings: observations, analysis and methods to estimate spall size. J. Sound Vib. 366, 277–292 (2016) 2. Tadina, M., Bolte, M.: Improved model of a ball bearing for the simulation of vibration signals due to faults during run-up. J. Sound Vib. 330(17), 4287–4301 (2011) 3. Li, C., Song, D., Zhang, W.: Research on load inversion method for Axle Box bearings of high speed trains. Noise Vibr. Control 40(5), 126–132 (2020) 4. Wu, R., Wang, X., Ni, Z., et al.: Dual-impulse behavior analysis and quantitative diagnosis of the raceway fault of rolling bearing. Mech. Syst. Signal Process. 169, 108734 (2022) 5. Sawalhi, N., Randall, R.B.: Simulating gear and bearing interactions in the presence of faults. Mech. Syst. Signal Process. 22(8), 1924–1951 (2007)

Solar Energetic Particles Hazard Assessment for UK HPR1000 Xujia Chen(B) , Guanghua Yang, and Xunjia Zhuo China Nuclear Power Engineering Co., Shenzhen, Guangdong, China [email protected]

Abstract. Solar Energetic Particles (SEPs) hazard is one of the major impacts at the ground level generated by space weather which may threaten the safety of the Nuclear Power Plants (NPPs). Therefore, it’s necessary to perform the safety assessment of SEPs hazard for the NPPs to eliminate any potential risk. However, the SEPs hazard assessment for the NPPs has not yet been performed at home and abroad and it also does not exist the methodology to guide the assessment. This paper provides a general SEPs hazard assessment way which has been applied to SEPs hazard assessment for UK version of the Hua-long Pressurised Reactor (UK HPR1000). By performing the detailed SEPs hazard assessment, the vulnerabilities and risks of UK HPR1000 regarding SEPs hazard that originates outside of the plant have been identified, and then the suggestions for protection improvement according to the current research have been provided to enhance the ability of the UK HPR 1000 against the SEPs hazard. The outcome of this paper can also provide reference for the SEPs hazard assessment for the other NPPs. Keywords: External hazards · Solar energetic particles · Safety assessment · UK HPR1000 · Nuclear power plant

Abbreviations CPU KDS EDG FPGA IGBT MOSFET SBU DEL SEB SEEs SEFI SEL SEPs SEU SRAM

Central Processor Unit Diverse Actuation System Emergency Diesel Generator Field Programmable Gate Array Insulated Gate Bipolar Translator Metal-Oxide-Semiconductor Field Effect Transistor Single Bit Upset Safety Chilled Water System Single Event Burnout Single Event Effects Single Event Function Interrupt Single Event Latch-up Solar Energetic Particles Single Event Upset Static Random Access Memory

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1023–1030, 2023. https://doi.org/10.1007/978-981-19-8780-9_97

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1 Introduction Space weather describes variations in the sun, solar wind, magnetosphere, ionosphere and thermosphere, which can influence the performance and reliability of space and ground based technological systems. It can disrupt radio transmissions, damage satellites, cause disruption to electricity transmission systems and affect the function of electronic devices. Space weather can therefore affect nuclear safety. Solar storms are linked to sunspot activity and comprise several phenomena which may affect the Nuclear Power Plants (NPPs) [1, 2]. These include: • Solar flares: Flares of X-rays and other electromagnetic radiation; • Coronal mass ejections (CMEs): High-speed bursts of denser solar material; • Solar Energetic Particles (SEPs): Solar radiation storms leading to enhanced fluxes of energetic charged particles; and, • Solar radio bursts (SRBs): Strong bursts of natural radio emissions. Based on the characteristics of space weather mentioned above, the major space weather impacts at ground level are Geomagnetic Induced Current (GIC), SEPs and Electromagnetic Interference (EMI), which may have an impact on NPPs [1]. Space weather has been included on the UK’s national risk register. And according to the UK regulations [3, 4], the protection against space weather should be considered in the design of new NPPs. Therefore, space weather is considered as an external hazard for the design in Generic Design Assessment (GDA) phase of the UK version of the Hua-long Pressurized Reactor (UK HPR1000). Since the safety assessment of GIC and EMI has been performed before, this report focuses on the vulnerability analysis and identification of reasonably practicable enhancements to improve the design’s resilience against the SEPs hazard.

2 Solar Energetic Particles Safety Assessment 2.1 Hazard Effects The major damage caused by the SEPs hazard is the Single Event Effect (SEE). Effects range from soft (correctable) errors to hard (permanent) errors, which include burnout of some digital devices. According to the current research, the single event effect often occurs in the microelectronic devices, such as Central Processor Unit (CPU), Field Programmable Gate Array (FPGA). Therefore, the digital equipment of NPPs which contains microelectronic devices may be vulnerable to the SEPs. 2.2 General Approach The latest research performed by Alex et al. [5] has demonstrated that the SEPs hazard is potentially a significant threat to nuclear safety. In order to identify the potential gaps in GDA phase, the detailed vulnerability analysis for SEPs hazard should be performed. The safety assessment process of SEPs hazard is shown in Fig. 1.

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Fig. 1. Safety assessment process of SEPs hazard

2.3 Design Basis Values The SEPs that are of sufficient intensity at high energies can cause a significant increase to the ground-level neutron flux, which is known as a Ground-Level Enhancement (GLE). A scale of GLE intensity based on the February 1956 event and their recurrence period has been established and the neutron flux has also been calculated in the recent studies [5, 6]. In the design of UK HPR1000, the natural external hazard design basis should be defined as the hazard severity and defined conservatively by the 10–4 /yr point on the hazard curve [3, 4]. The design basis values for SEPs at a frequency of 10–4 /yr is presented in Table 1. These values are used to estimate the severity of impacts caused by the SEPs hazard on the systems and components. Table 1. Neutron flux at ground level of different period Scale factor cf. Feb’56 GLE

Return period (years)

Neutron flux > 10 meV (n cm−2 s−1 )

0.1

12

0.15

0.5

24–36

0.775

1

40–70

1.5

10

500

15

30

1200

45

50

10,000

75

2.4 Safety Assessment Process For the vulnerability analysis, four families of devices: SRAM (Static Random Access Memory), FPGA (SRAM based), microprocessors and power MOSFET (Metal-Oxide

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-Semiconductor Field Effect Transistor) are selected, because these devices are representative of the most common electronic equipment used. In order to test the sensitivity to major SEEs (see Table 2), the probability of failure for the electronic devices during the SEPs hazard have been calculated by using the design basis values of SEPs hazard, the equation given in JESD89A [7] and Poisson probability equation. The results are presented in Table 3 [5]. Table 2. Major single event effects Hard errors

Soft errors

Single event latch-up (SEL)

Single event upset (SEU)

Single event burnout (SEB)

Single event function interrupt (SEFI) Single bit upset (SBU)

Table 3. Failure probability of selected components Component

SEE

Worst case FIT

Probability of occurrence during whole event 10–4 /yr

Frequency SRAM

SEL

5.4E07

3.2E−02

Power MOSFET

SEB

1.9E09

6.8E−01

SiC power MOSFET

SEB

4.8E07

2.8E−02

SiC diode

SEB

7.7E08

4.6E−03

IGBT

SEB

1.2E06

5.2E−01

Based on the analysis results shown in the Table 2, the probability of failure for the silicon power devices, power MOSFETs, and IGBTs are 68% and 52% respectively during a once-in-a-10000-year SEPs hazard event. Hence, the system and components containing the Power MOSFET and IGBT are considered vulnerable to the SEPs hazard. According to this criterion, the I&C system, electrical system and mechanical system susceptible to SEPs hazard are identified. a) Centralized I&C system The digital I&C systems of NPPs contains large number of components vulnerable to SEPs hazard, assuming that all these components are unavailable if SEPs hazard occurs the failure consequence for each digital I&C system is generation of spurious signals and loss of safety function. In this case, the simple hardware technology based KDS system is used to bring the power plant the final state and maintain it. The digital I&C systems vulnerable to SEPs hazard are presented in Table 4.

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b) Electrical system The electrical systems also contain lots of microelectronic devices. The electrical systems vulnerable to SEPs hazard are presented in Table 5. c) Mechanical system Most of the mechanical equipment (such as valves, pumps and fans etc.) do not contain microelectronic devices and the Safety Chilled Water System (DEL) is identified as vulnerable to SEPs hazard because the digital chiller units contain the microelectronic devices susceptible to SEPs hazard as shown in Table 6. Table 4. I&C system potentially vulnerable to SEPs hazard System Protection system Plant standard automation system Safety automation system Severe accident I&C system Plant computer information and control system

Power MOSFET (Si) √ √

IGBT

Failure consequence Generation of spurious signals and loss of I&C safety function

√ √ √

Table 5. Electrical equipment potentially vulnerable to SEPs hazard System

Power MOSFET (Si) IGBT Failure consequence √ Nuclear island 10 kV Failure of excitation control for emergency power supply emergency diesel generator system (EDG) √ Nuclear island 380 V AC Inverter failure and loss of uninterruptible power system uninterruptible power system (2 h) (UPS) nuclear island 220 V AC uninterruptible power system (2 h) Nuclear island 380 V AC uninterruptible power system (24 h)

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System

Power MOSFET (Si) √

Safety chilled water system

IGBT √

Failure consequence Loss of cooling function of safety chilled water system

2.5 Protection Measures In order to mitigate the effects caused by SEPs, some measures have been taken in the design of UK HPR1000. According to the design of UK HPR1000, the proposed protection strategy can be summarised as: a) Diversity of I&C systems Generally, the digital I&C system is implemented by using the microelectronic components involving the CPU, FPGA etc. In the design of UK HPR1000, in order to ensure the diversity of the safety I&C system, simple hardware technology (e.g., relays, analogous electronic circuits or digital logic circuits) is introduced to implement the safety functions of KDS system. The KDS system consists of components that are less-vulnerable to SEPs hazard due to the equipment properties. Therefore, this system is more robust with respect to the SEPs hazard, and the KDS system is considered as a mitigation to cope with the SEPs hazard. b) Attenuation of the SEPs by the civil structures Electrical equipment and I&C systems important to safety are located within buildings of NPP. The neutron fluence at devices within buildings can be attenuated by the concrete structures surrounding the equipment and systems [8]. The thickness of reinforced concrete of these buildings ranges from 800 to 1200 mm. Based on the results of [12], the attenuation of neutron flux by different thickness of concrete is shown in Table 7: Table 7. Reduction rate of neutron flux by different thickness of concrete Concrete thickness (mm)

Neutron energy 10 MeV (%)

50 MeV (%)

100 MeV (%)

1000 MeV (%)

800

99.997

85

63

57

1000

99.999

99.2

85

65

1200

99.999

99.5

98.2

72

As presented in the table above, the neutron flux can be attenuated by the concrete wall and the reduction rate increases as the concrete thickness increases and the neutron energy decreases. Hence, one effective way to reduce the risk caused by SEPs hazard is to increase the wall thickness of the structures.

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2.6 Potential Gaps and Design Improvement Potential gap of Emergency Diesel Generator (EDG) auxiliary systems: the auxiliary systems of EDG contain the component vulnerable to the SEPs hazard, for example, the excitation cubicle/chopper gate interface board is used for excitation control. It may lead to the loss of emergency power supply. Potential gap of inverter: the IGBT technology is applied in the inverter which is vulnerable to SEPs hazard. If the inverter fails, the system automatically switches to the bypass circuit to maintain system operation. The bypass circuit is supplied by EDG. If the EDG is lost at the same time, it may cause the loss of power supply for equipment supplied by UPS system. Potential gap of DEL system: according to the outputs of vulnerability analysis, the chiller units of DEL system contain the microelectronic devices which are vulnerable to SEPs hazard. It may cause the loss of chiller units and lead to the loss of safety functions of the DEL system. Based on the feedback from the equipment supplier, the components and systems mentioned above cannot be manufactured with other technology non-vulnerable to the SEPs hazard. as the technology of the equipment has limited diversity, the potential design improvement is to use different manufacturers to obtain certain level of diversity. According to relevant research, there is a large variability in device response to neutrons. For example, the cross section of SRAM and silicon carbide MOSFET ranges over almost five orders of magnitude (see Fig. 2) [3]. The different data of cross section leads to the different failure probability. It means the components of the same technology may have quite different vulnerability to SEPs hazard. Therefore, the equipment attribute is a key information for the vulnerability and the components manufactured by different suppliers may be used to reduce the risk caused by SEPs hazard.

Fig. 2. Neutron cross sections of SEL exhibited in SRAMs and SEB in silicon power MOSFETs, IGBTs, silicon carbide power MOSFETs, and diodes, showing each of technologies maximum, minimum, and average cross section

In addition, the non-digital devices are less vulnerable to the SEPs hazards, hence, the other measure to reduce the risk caused by SEPs hazard is to replace the digital devices

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to the non-digital devices. The use of non-digital devices would reduce the probability of failure due to SEP to acceptable. But replacing digital with non-digital devices may increase the risk of misuse of safety-critical devices due to site workers use different devices that they are less familiar with. The additional measures, such as monitoring of SEPs hazard, alarms to alert the operators and operator actions to cope with the SEPs hazard, which are considered helpful to mitigate the effects caused by SEPs hazard could also be considered in the design improvement.

3 Conclusion The detailed SEPs hazard vulnerability analysis for UK HPR1000 have been performed and the systems and components vulnerable to SEPs hazard have been identified. According to the detailed analysis above, the following systems and components may be affected by space weather and the failure of these items may cause the unavailability of safety functions: – the excitation control system of EDG; – the inverters; – the chiller units of DEL system. To reduce the risks caused by SEPs hazard, in one way, several appropriate protection measures (e.g., use of capacitor devices, use of non-digital devices, diversity of I&C system etc.) are already considered in the design. In the other way, the design improvements, including replace of the microelectronic devices by the non-digital devices and use of the components manufactured by different suppliers have been proposed to be implemented in the future construction stage. However, since the SEPs hazard is site-specific and the information is quite limited in current stage, further assessment for effects caused by the SEPs hazard should be performed in the future.

References 1. Enabling Resilient UK Energy Infrastructure: Natural Hazard Characterisation Technical Volumes and Case Studies. Space Weather 10 (2018) 2. Royal Academy of Engineering: Extreme Space Weather: Impacts on Engineered Systems and Infrastructure. Royal Academy of Engineering (2013) 3. Office for Nuclear Regulation External Hazards Nuclear Safety Technical Assessment Guide Head Document, Revision 8 (2020) 4. Office for Nuclear Regulation. Safety Assessment Principles for Nuclear Facilities: Edition. Revision 1, 2020 (2014) 5. Alex, D., Alex, H., Keith, R., Clive, D., Ian, F., Alexis, R.: Single-event effects in groundlevel infrastructure during extreme ground-level enhancements. IEEE Trans. Nucl. Sci. 67(6), 1139–1143 (2020) 6. Clive, D., Alex, H., Keith, R., Fan, L.: Extreme atmospheric radiation environments and single event effects. IEEE Trans. Nucl. Sci. 65(1), 432–438 (2018) 7. JEDEC JESD89-3A: Test Method for Beam Accelerated Soft Error Rate (2007) 8. Ziegler, J.F., Lanford, W.A.: Effect of cosmic rays on computer memories. Science 206(4420), 776–788 (1979)

Safety Analysis for Uncontrolled Withdrawal of Rod Cluster Control Assembly Bank After Extend Low Power Operation Kun Xiong(B) and Xin Wang China Nuclear Power Technology Research Institute, Shenzhen, Guangdong, China [email protected], [email protected]

Abstract. In order to adapt to a reduced demand of electricity, delay of core refueling and unexpected maintenance, nuclear power plant needs to operate at extended low power. Extended low power operation changes the power distribution and nuclear data in core, compared with normal operation in same burnup. Then these changes may cause the specific nuclear parameters in uncontrolled withdrawal of rod cluster control assembly bank event to exceed those results in final safety analysis report. It may challenge the safety analysis for the above event. Reactor core safety after reactivity insertion due to uncontrolled withdrawal of rod cluster control assembly bank after extended low power operation was studied. The results show that the reactor core for CPR1000 power plant is safe, even if uncontrolled withdrawal of rod cluster control assembly bank event occurs after extended low power operation. Keywords: Extend low power operation · Bank withdrawal · Safety analysis

1 Introduction Nuclear power plant in China operates basically at base load, which not be involved in peak load regulation of electricity grid. In order to adapt to a reduced demand of electricity, delay of core refueling and unexpected maintenance, nuclear power plant usually uses extended low power operation (ELPO). Extended low power operation in CPR1000 unit is achieved only by boronation, which lasts more than 12 h. In this operation, the power banks (GN banks) and Shutdown banks (S banks) are out of core but temperature control rod assembly banks (R banks). The core power decreases from full power (FP) to a specific power level, such as 75% full power (FP) or 50% FP [1]. In term of reactor physics, ELPO changes the temperature filed in core and, therefore, influences the core power distribution [2], compared with normal operation. The changes of initial core power distribution may make specific neutron parameters worse, which is calculated in the analysis for uncontrolled withdrawal of rod cluster control assembly bank at zero power condition (URWZ). It, therefore, is necessary to study the core safety when URWZ event occurs after ELPO condition.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1031–1039, 2023. https://doi.org/10.1007/978-981-19-8780-9_98

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2 Analysis Methodology 2.1 Calculation Method This paper study the influence of ELPO on core power distribution and then analyze whether the safety requirements could be met. SCIENCE is used to calculate core parameters during ELPO. 3D/1D neutronics model and thermal model are weakly coupled in this accident. Core parameters are calculated by 3D SMART code and 1D ESPADON code, including enthalpy rise hot channel factor FH , radial nuclear hot channel factor Fxy , axial nuclear hot channel factor Fz, hot spot factor FQ , maximal reactivity insertion rate ρmax and so on. The themohydraulic parameters in Primary loop are calculated by thermal-hydraulic transient analysis code (CANTAL). Departure from nucleate boiling ratio (DNBR) is calculated by FLICA code. Pellet temperature is calculated by COMBAT code. 2.2 Basic Assumption The assumptions for the calculation of neutron parameters during ELPO. 1) Two kinds of ELPO: the plant operate for 25 days at 50% FP and 25 days or 90 days at 75% FP. 2) Burnup: beginning of cycle (BOC), middle of cycle (MOC) and end of cycle (EOC). 3) Cycle: first cycle (CY01), transitional cycle (CY02, CY03), balance cycle (S0, L0) and flexible cycle (S1, L1, S3, L3). 4) Bank position: R banks are in the middle of the regulation band. GN banks and S banks are out of core. The assumptions for the calculation of URWZ. 1) Bank position: GN banks and R banks are at the bottom of core, S banks are out of core. 2) Initial power: 10−13 FP and the reactor core is critical. 3) Initial thermal hydraulic parameters: Pressurizer pressure and core inlet temperature are conservative value. 4) Bank withdrawal rate: banks are withdrawn at maximal speed (72step/min).

3 The Influence of ELPO on Core Power Distribution In the end of ELPO, the core power usually returns to full power but sometimes decreases to zero power with banks insertion, considered as the initial condition of URWZ. Therefore, two parts will be discussed in this chapter. In the first part, the influence of ELPO on power distribution will be discussed, compared with normal operation. In the second part, the characteristics of core power distribution after ELPO at zero power. The results of typical cycle and burnup (L0-BLX) are presented in this chapter.

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3.1 The Influence of ELPO on Radial Power Distribution It is shown in Fig. 1, FH increases suddenly when nuclear power plant starts ELPO and has a greater rise with the larger reduction in power. This is because power feedback is reduced with the decrease of power. Moreover, FH is recovered basically after returning to full power. That is because the position of GN banks and R banks are same at ELPO and normal operation. As shown in Fig. 2, ELPO for different days with same power level have the similar phenomenon. 1.480

ELPO-50%FP-25EFPD ELPO-75%FP-25EFPD Normal operation

1.460 1.440

FΔH

1.420 1.400 1.380

1.360 1.340 0

5,000

10,000

15,000

20,000

Burnup, MWd/tU

Fig. 1. The influence of different power level in ELPO on FH 1.480

ELPO-75%FP-90EFPD ELPO-75%FP-25EFPD Normal operation

1.460

1.440

FΔH

1.420 1.400 1.380

1.360

1.340 0

5,000

10,000

15,000

20,000

Bur nup, MWd/tU

Fig. 2. The influence of different ELPO days on FH

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3.2 The Influence of ELPO on Axial Power Distribution As shown in Fig. 3, AO increases suddenly when nuclear power plant starts ELPO. Inlet coolant temperature of core remains basically unchanged during ELPO, but the outlet coolant temperature of core decreases. Due to negative moderator temperature coefficient in reactor, the reactivity of upper half core is greater than that in lower half core, which would, in turn, lead to more positive value of AO. Moreover, after returning to full power, AO is different from normal operation and the difference will diminish over time due to the accumulation effect of burnup during ELPO. As shown in Fig. 4, ELPO for different days with same power level have the similar phenomenon, but the longer the ELPO days, the greater the influence of AO. 8.0

ELPO-50%FP-25EFPD ELPO-75%FP-25EFPD

6.0

Normal operation 4.0

AO, %

2.0 0.0 0

5,000

10,000

15,000

20,000

-2.0 -4.0 -6.0 -8.0 -10.0

Burnup, MWd/tU

Fig. 3. The influence of different power level in ELPO on AO

4.0

2.0

0.0

AO, %

0

5,000

10,000

15,000

20,000

-2.0

-4.0

-6.0 ELPO-75%FP-90EFPD

-8.0

ELPO-75%FP-25EFPD Normal operation

-10.0

Burnup, MWd/tU

Fig. 4. The influence of different ELPO days on AO

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3.3 The Characteristics of Radial Core Power Distribution After ELPO at Zero Power Figure 5 shows that FH of all conditions increase rapidly at beginning of shutdown and then remain basically unchanged and little difference. This is because the power feedback is reduced with shutdown and Xenon oscillation mainly happens in axial direction as a result of high height-diameter ratio in CPR1000 units. 1.650

1.600

FΔH

1.550

1.500

ELPO-50%FP-25EFPD

1.450

ELPO-75%FP-25EFPD Normal operation

1.400 0

2

4

6

8

10

12

14

16

Time h

Fig. 5. The influence of different power on FH after ELPO at zero power

3.4 The Characteristics of Axial Core Power Distribution After ELPO at Zero Power At the beginning of reactor shutdown, AO increases rapidly due to moderator temperature feedback mentioned above. The influence of xenon distribution must be considered over time. If the power distribution before shutdown is inclined to upper half core, the xenon distribution is also be inclined to upper half core at the beginning of shutdown. As time goes by, the xenon concentration of upper half core increases more and axial xenon offset as well as increases which is shown in Fig. 7. Then, AO decreases. When the power distribution is inclined to lower half core, AO after shutdown will increase. Figure 6 shows that the AO of all conditions increase rapidly at beginning of shutdown and then still increase at a slower rate.

4 The Influences of ELPO on the Result of URWZ 4.1 The Influence on Specific Nuclear Parameters in URWZ The detailed analysis of URWZ is described in reference [3]. The analysis process of URWZ is summarized in Fig. 8 and it is shown that FH , ρmax and FQ are the key parameters for the calculation of DNBR and temperature of pellet called specific nuclear

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AO, %

40 30 20 10

ELPO-50%FP-25EFPD

ELPO-75%FP-25EFPD

0

Normal operation -10 0

2

4

6

8 Time, h

10

12

14

16

Fig. 6. The influence of different power on AO after ELPO at zero power 8.0 ELPO-50%FP-25EFPD ELPO-75%FP-25EFPD Normal operation

Axial Xenon offset, %

6.0 4.0 2.0 0.0 -2.0

-4.0 -6.0 0

2

4

6

8

10

12

14

Time, h

Fig. 7. The influence of different power on Axial Xenon offset after ELPO at zero power

parameters. The above key parameters of URWZ in FSAR are usually enveloped, which is used as specific nuclear parameters limit. If the value of these parameters in cycles exceed the limits, URWZ will be re-analyzed. The results of specific nuclear parameters in cycles exceeding the limits are presented in Table 1. It is shown that FH and FQ of CY01 are out of limitation. As mentioned in the previous chapter, xenon concentration offset of zero power after ELPO will be greater, compared with normal operation. It is mean that the distribution of xenon is more inclined to the upper half core. Therefore, the differential worth of withdrawal banks are greater, result in higher value of FH and FQ . Take the BOC of CY01 for example, the influences of ELPO on results of URWZ are presented in the following chapter.

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Fig. 8. The analysis process of URWZ

Table 1. The results of specific neutron parameters in cycles out of limitation CY01

Limit

BOC

2.80*

2.65

MOC

2.50*

2.44

EOC

2.50

2.54

F ΔH

FQ BOC

10.9*

9.86

MOC

10.8

11.08

EOC

12.6

12.81

The asterisk indicates that the values are out of limitation

4.2 The Influence on Nuclear Power in URWZ As shown in Fig. 9, core power of ELPO sharply increases earlier than that in normal operation. Due to greater value of xenon concentration offset at zero power after ELPO, differential worth of withdrawal banks is greater as mentioned above. Then the core power of ELPO increase at shorter reactor period.

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It, meanwhile, is shown in Fig. 8 that the peak power of ELPO is lower than that in normal operation. This is because the negative feedback of core in URWZ is primarily defined by doppler feedback. Although the power increase rate of ELPO is greater, negative feedback of doppler is also greater.

Fig. 9. The core power of URWZ

4.3 The Influence on DNBR in URWZ It is shown in Table 2 that the DNBR and peak pellet temperature of ELPO are worse than those at normal operation. That is mainly because ELPO have greater value of FH and FQ. Meanwhile the DNBR and peak pellet temperature of ELPO are better than the limits. Table 2. The results of DNBR and peak pellet temperature Peak pellet Temperature/°C

DNBR

Pellet limit

DNBR limit

ELPO

1802

1.27

2590

1.19

Normal Operation

1783

1.29

5 Conclusions This paper is targeted to the influences of ELPO on power distribution in core and mainly results of URWZ. The analytical results show that:

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1. ELPO has little effect on radial power distribution, but great effect on axial power distribution which make AO greater. 2. FH of zero power after ELPO has little difference with that after normal operation, but axial xenon offset of zero after ELPO is greater than that after normal operation. 3. The results of URWZ after ELPO agreed with acceptance criteria.

References 1. Zhu, M., et al.: Stretch-out operation and extended low power operation in Daya Bay nuclear power station. Nucl. Power Eng. 23(5), 22–26 (2002) (in Chinese) 2. Xiao, H., et al.: Influence of extended low power operation on neutronics parameters of CNP600. Nucl. Tech. 39(11), 110602-1–110602-7 (2016) (in Chinese) 3. Lu, J.: RCCA bank withdrawal from a subcritical or low power start in condition accident analysis. NPIC, CNIC-01775 (2004)

Discussion on the Application Prospect of Airborne Geophysical Prospecting in Site Selection and Construction of Nuclear Waste Disposal Sites Pei-jian Wang(B) , Xiang Zhang, Bin Wei, Yuanzhi Wang, and Xianhong Wu Airborne Survey and Remote Sensing Center of Nuclear Industry, Shijiazhuang, China [email protected]

Abstract. In recent years, the international climate problem has become increasingly prominent, mankind is facing an unprecedented environmental crisis, and the demand for clean energy such as nuclear energy has become increasingly prominent, especially China’s serious dependence on energy. The utilization of nuclear energy is a complex systematic project, in which the disposal of nuclear waste is related to the development and utilization of nuclear energy, and the site selection and construction of disposal sites is the key to restricting the development of nuclear energy. In order to realize the rapid site selection and construction of nuclear waste disposal sites, this paper relying on the application of airborne geophysical exploration in key projects in china, analyzes the methods of airborne gamma spectroscopy, airborne magnetism, airborne electromagnetism and other methods to solve the geological problems of nuclear waste disposal sites ability: Aerial gamma spectroscopy can provide potassium, uranium and thorium content, which can quickly determine the distribution range of granite bodies; aeromagnetic measurement can provide magnetic field and three-dimensional magnetic spatial distribution, and can analyze regional fractures and deep rock development characteristics; aerial electromagnetic measurement can provide plane time constant and three-dimensional resistivity parameters, which can be used to analyze the deep development characteristics of rock mass and faults. Comprehensive aerial geophysical exploration can analyze the characteristics of deep geological bodies from the aspects of radioactivity, magnetism and electrical properties, and can provide a three-dimensional geophysical basis for the rapid site selection and construction of nuclear waste disposal sites, application prospects. Keywords: Aerophysical prospecting · Nuclear waste disposal site · Site selection and construction · Application prospect

1 Introduction As human beings attach importance to the environment, nuclear energy, as a clean energy, is gradually on the fast track of development and plays an important role in the future Note: This paper is supported by the Southwest Qindanm Basin 1:50,000 aero geophysical survey. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1040–1044, 2023. https://doi.org/10.1007/978-981-19-8780-9_99

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energy demand of human beings [1, 2]. The peaceful and safe utilization of nuclear energy is a complex systematic project, in which the geological disposal of high-level nuclear waste is a key link, which seriously restricts the development and utilization of nuclear energy. The rapid location and construction of high-level nuclear waste disposal sites will facilitate the development and utilization of clean energy and greatly reduce the serious impact of climate change. Airborne geophysical exploration, equipped with instruments and equipment by aircraft, is not affected by the environment such as topography, landform, etc. It can conduct large-scale exploration and provide multiple parameters for analysis from surface to three-dimensional space, greatly promoting the process of site selection for the disposal site of high-level nuclear waste.

2 Analysis of the Ability of Aero Geophysical Prospecting to Solve Geological Problems (1) Analysis of airborne electromagnetic survey to solve geological problems Aerospace electromagnetic measurement available underground 3-d resistivity inversion results, can be analysis from three dimensional space of different electrical distribution of geologic body, which can realize geologic body integrity analysis, especially for fracture, fracture zone, karst and rich water rock mass effect is obvious, in order to solve this provides important basis for high-level radioactive nuclear waste disposal site is stable, is an important means of rapid narrowing the target zone [3]. Figure 1 is the airborne electromagnetic inversion resistivity section of a certain area in Qinghai, which not only clearly reflects the distribution of different electrical geological bodies in the deep, but also reflects the development zone of deep fracture zone (rich in water) and structural development characteristics. It provides effective geophysical data for deep engineering, provides early warning information to avoid floods and other disasters, and guarantees the safety of site selection of high-level nuclear waste disposal site. (2) Analysis of aeromagnetic survey to solve geological problems Aeromagnetic survey can not only obtain aeromagnetic T, but also inversion of magnetic susceptibility, analyze the distribution of different magnetic geological bodies from the three-dimensional space, and roughly distinguish the spatial distribution characteristics of different lithologic geological bodies. Especially large—scale medium—basic rock mass, magnetic acid rock mass. This provides an important basis for understanding the rock mass scale and deep spatial distribution of the high-level nuclear waste disposal site, and is an important means to explore the deep geological conditions of the target area. Figure 2 shows aeromagnetic inversion magnetic susceptibility of a certain area in Gansu province. Aeromagnetic inversion shows obvious weak magnetism, and there are large-scale weak magnetic bodies in the deep. The three-dimensional inversion magnetic susceptibility clearly reflects the spatial distribution characteristics of the deep intact granite body. The data can be used to analyze the development of deep geological

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Fig. 1. Resistivity and geological interpretation section of a survey line in Qinghai. 1—Metamorphic rock; 2—acidic rock; 3—basification; 4—Infer general fracture and numbering; 5—Infer large faults and numbering; 6—Infer low resistance body and number

bodies at the disposal site of high-level nuclear waste and provide important basis for site selection and deep engineering construction. (3) Analysis on solving geological problems by aero-discharge survey The distribution characteristics of potassium, uranium and thorium radionuclides in the region can be roughly identified by aerial emission survey, and the distribution range of different radioactive rock masses can be distinguished, especially the large scale granites show obvious characteristics of high field. As an efficient technical method, aerial discharge survey can be used in the selection of high-level nuclear waste disposal site. Figure 3 is the distribution map of granite in a certain area of Gansu province by aeromagnetic inversion of aeromagnetic overlay. It can be seen that there are largescale weak magnetic bodies (granites) in the deep corresponding to the three high-field intensive distribution areas. Therefore, the dense distribution of aeration elevation field has a good indication significance for rapidly reducing the selection of disposal site.

3 The Application Prospect of Aero Geophysical Prospecting in the Construction of Nuclear Waste Disposal Site With the progress of instruments and the improvement of navigation and positioning accuracy, the overall accuracy of large scale airborne geophysical data is high at present, which can quickly identify regional geological conditions. Aero geophysical prospecting

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Fig. 2. Distribution of aeromagnetic T polarization and inversion weak magnetic objects in a certain area of Gansu Province

mainly includes aero electromagnetic method, aero magnetic method, aero radioactivity method and aero gravity method, which reflect geological body distribution and structural development characteristics from the Angle of resistivity, magnetic susceptibility, radioactivity and density respectively. By integrating aeromagnetic, aeromagnetic, aeromagnetic, aeromagnetic, aeromagnetic and aeromagnetic gravity, the geological situation of disposal site can be analyzed in Angle, multi-parameter and three-dimensional space, which has guiding significance for later drilling and other technical methods, and can greatly save the time and expense of site selection and engineering construction. Therefore, it has broad application prospects and is worth popularizing.

4 Conclusion To sum up, with the constant improvement of the aerogeophysical survey precision, can not only play an important role in the deep mineral exploration in our country, also can play its from the perspective of electrical, magnetic, radioactive, density the ability to solve the problem of complex geological, efficient and other advantages, nuclear materials can be put in the future high location and play a predominant role in the construction of high field, to ensure the safety of nuclear power development.

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Fig. 3. Comprehensive information map of airborne uranium content and deep weak magnetic body in an area

References 1. Cho, Y.J., Yang, H.C., Eun, H.C., et al.: Axial gas phase dispersion in Amolten salt oxidation reactor. Korean J. Chem. Eng. 21(6), 1250–1255 (2016) 2. Yin, C.C., Zhang, B., Liu, Y.H., et al.: Review on airborne EM technology and developments. Chin. J. Geophys. 58(8), 2637–2653 (2015) (in chinese) 3. Liang, S.J., Zhang, L.K., Cao, X.F., et al.: Research progress of the time-do-main airborne electromagnetic method. Geol. Explor. 50(4), 0735–0740 (2014)

Study on China’s Nuclear Power Development and Nuclear Safety Regulation Under the Background of Carbon Peaking and Carbon Neutrality Fangqiang Chen, Zhaotong Li, Qingsong Wang(B) , Chaoying Zheng, and Shuai Chen Nuclear and Radiation Safety Center, MEE, Beijing, China {chenfangqiang,wangqingsong}@chinansc.cn

Abstract. In September 2020, China announced to the world at the United Nations General Assembly: “China will increase its national independent contribution, adopt more effective policies and measures, and strive to reach the peak of carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060.” Achieving the goals of carbon peaking and carbon neutrality is an extensive and profound reform of the economic and social system, which will promote the transformation and upgrading of China’s energy industry and economic structure. In this context, China’s energy system is bound to accelerate the clean and low-carbon transformation. On the one hand, thermal power, especially coal power, will continue to promote clean and low-carbon transformation. On the other hand, clean energy, especially renewable energy, will develop rapidly and release great development potential. As a clean and efficient energy source, nuclear power will become an important option in the context of the dual carbon goal, ushering in a good window period for high-quality development. At the same time, the development of nuclear power is also restricted by many factors, such as public acceptance, economic cost and so on. The paper summarizes and compares the long-term development, changing trends and restrictive factors of various power generation methods such as thermal power, hydropower, wind power, solar power, and nuclear power. After that, the paper analyzes and predicts the constraints, development paths and trends of China’s nuclear power in the process of carbon peaking and carbon neutrality; expounds the current weak links of China’s nuclear and radiation safety supervision, and puts forward countermeasures and suggestions to strengthen nuclear safety supervision and ensure national nuclear and radiation safety. Keywords: Nuclear power · Development · Nuclear safety · Carbon peaking · Carbon neutrality

1 Introduction The Chinese government has made it clear that it will reach the peak of carbon dioxide emissions by 2030 and strive to reach the peak of carbon neutrality by 2060. In this context, nuclear power, as a clean and efficient energy, will become an important choice © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1045–1053, 2023. https://doi.org/10.1007/978-981-19-8780-9_100

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to achieve the dual carbon goal, but the development of nuclear power is also restricted by many factors. Summarize and compare the characteristics and development trends of different power generation methods, analyze the constraints, paths and trends of China’s nuclear power development, and the weak links of nuclear and radiation safety supervision, and put forward suggestions to strengthen the nuclear safety supervision ability and ensure nuclear and radiation safety, which is of practical significance to help the development of China’s nuclear power safety under the dual carbon goal.

2 Current Situation of International Carbon Peaking and Carbon Neutrality According to the statistics of the World Resources Institute, carbon emissions of 54 countries in the world have reached the peak. Among the top 15 carbon emission countries in 2020, the United States, Russia, Japan, Brazil, Indonesia, Germany, Canada, South Korea, the United Kingdom and France have achieved carbon peaking, while Mexico, Singapore and other countries have promised to achieve carbon peaking by 2030. Among them, the EU 27 countries as a whole have achieved carbon peaking. The carbon emissions of Europe and the United States peaked in the early 1990s and 2007 respectively, and then remained stable and showed a slow downward trend. By 2018, the carbon dioxide emissions of the two countries were about 63% and 88% of the peak period respectively [1]. Most developed countries have promised to achieve carbon neutrality by 2050. For example, according to the “green agreement” published in December 2019, the European Commission is striving to achieve the EU wide net zero emissions target in 2050. According to public statistics, as of October 2021, more than 130 countries and regions around the world have put forward carbon neutrality goals or visions. The United States, the European Union, Japan, South Korea and other major economies have committed to achieving carbon neutrality in the middle and later stages of this century [2]. From carbon peaking to carbon neutrality, the United States and Europe have a transition period of about 50 years, while China has only 30 years; At present, China is only eight years away from the carbon peak goal.

3 Carbon Emission Status of Energy and Power Industry For a long time, no matter how the internal use of oil, natural gas, coal and other fossil energy changes, the overall proportion of fossil energy in the world’s total primary energy consumption has been maintained at more than 85%, which is an undisputed social main energy [3]. According to statistics, worldwide, energy production and consumption are the main sources of carbon dioxide emissions, contributing more than 70% of carbon dioxide emissions, of which coal and oil and gas consumption are the main sources [4, 5]. From the perspective of stock structure adjustment, in the 40 years from 1977 to 2017, the global energy consumption structure showed a trend of declining oil, stable coal and rapid development of clean energy. Among them, the proportion of oil consumption in

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primary energy decreased from 49 to 34%, the proportion of coal consumption stabilized at 26–28%, the proportion of natural gas consumption increased from 18 to 23%, and the proportion of other energy sources (such as nuclear energy, hydro energy, wind energy, thermal energy, solar energy, biomass energy, etc.) increased from 7 to 15%. In terms of the proportion of incremental structure, according to the International Renewable Energy Agency, more than 80% of all new power generation capacity in the world in 2020 was renewable energy, more than four times the new installed capacity of fossil energy power generation [4]. Nevertheless, according to statistics, in 2019, China’s carbon emissions from the energy sector accounted for 88% and the emissions from the power industry accounted for about 41%. The low-carbon transformation of the power industry is extremely critical to the realization of the goals of carbon peaking and carbon neutrality. Thermal power still plays an absolutely dominant role in the whole power system, with more than half of the installed capacity and nearly 70% of the power generation. In thermal power, coal-fired power generation accounts for the vast majority, accounting for about 90% in recent years. On the whole, the clean and low-carbon transformation of China’s power system still has a long way to go.

4 Characteristics and Development Trend of Different Power Sources 4.1 Characteristics of Each Generating Power Supply Each power source has its own characteristics, which determine that different power sources will play different roles in the process of carbon peaking and carbon neutrality in China. In China, coal-fired power occupies an absolute position in thermal power, and the analysis of thermal power characteristics focuses on coal-fired power generation. From 2010 to 2021, the average annual equipment utilization hours of hydropower, thermal power, nuclear power, wind power and solar power generation are shown in Fig. 1. The average annual utilization hours of nuclear power equipment are the highest, which are 2.13 times, 1.65 times, 3.84 times and 5.99 times of hydropower, thermal power, wind power and solar power respectively, showing a high stability. On the whole, hydropower, nuclear power, wind power and solar power all have the characteristics of clean and low-carbon; Hydropower, wind power and solar power are renewable energy, while thermal power is fossil energy, in which coal consumption is huge and resources consumption is fast. Although nuclear power fuel is non-renewable, its consumption is small and slow. At present, the global uranium mine can be used for 1000 years, and the fuel resources are not urgent. In addition, hydropower has good peak shaving capacity and medium energy density, but there are fewer and fewer alternative plant sites, which makes it more and more difficult to select plant sites. At the same time, the stability is general, the phenomenon of hydropower in summer and winter is obvious, and the project construction cycle is long; Nuclear power has very good stability and high energy density, but the peak shaving response speed, the site requirements are very high, and the construction period is long; Thermal power has good peak shaving capacity and high energy density, low site requirements, strong stability, moderate construction cycle, but large emissions of

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Fig. 1. Average annual equipment utilization hours of each power supply from 2010 to 2021

carbon dioxide and pollutants; The wind power construction period is short, the site requirements are not high, but the stability is poor, with obvious intermittency and volatility, and the peak shaving capacity is poor; Solar power generation has the shortest construction cycle, low site requirements, and good peak shaving capacity, but it is greatly affected by climate change and has obvious diurnal characteristics. 4.2 Structural Changes of Power System With China’s economic and social development and the increasing demand for electricity, all kinds of power sources have developed to varying degrees, and the absolute installed capacity has increased. By the end of 2021, the total installed capacity of the country had reached 2376.92 million kW, 2.46 times that of 2010, and the total power generation of the country in the same period had reached 8376.8 billion kWh, 1.98 times that of 2010 (shown in Fig. 2).

Fig. 2. Installed capacity of electric power

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For more than a decade, nuclear power, wind power and solar power have been developing continuously in China. The proportion of power generation and installed power generation capacity from 2010 to 2021 are shown in Table 1. Table 1. Proportion of power generation and installed capacity/% Proportion

Year

Hydro

Thermal

Nuclear

Generating capacity

2010

16.24

80.81

2013

16.60

78.58

2016

19.51

2019

17.77

2021 Installed capacity

Wind

Solar

1.77

1.17

0.00

2.08

2.57

0.16

71.85

3.54

4.00

1.10

68.88

4.76

5.53

3.06

16.00

67.40

4.86

7.83

3.90

2010

22.36

73.43

1.12

3.06

0.03

2013

22.30

69.18

1.17

6.08

1.26

2016

20.12

64.28

2.04

8.93

4.62

2019

17.81

59.18

2.42

10.41

10.16

2021

16.45

54.56

2.24

13.82

12.90

It can be seen from Table 1 that from 2010 to 2021, the installed proportion of hydropower decreased slowly, and the proportion of power generation increased first and then decreased and remained roughly stable. At present, both the power generation and the installed proportion are about 16%; The proportion of thermal power decreased year by year, the proportion of power generation decreased by 13.41%, and the proportion of installed capacity decreased by 18.87%; Nuclear power has increased year by year, with an increase of 3.09% in power generation and 1.12% in installed capacity; Wind power has increased year by year, with the proportion of power generation increased by 6.66%, and the proportion of installed capacity increased by 10.76%; Solar power generation has increased year by year, with the proportion of power generation and installed capacity increasing by 3.90% and 12.87% respectively. 4.3 Development Speed of Each Power Supply Based on the annual year-on-year changes in power generation and installed capacity, it can be very intuitive to see the different development speeds of various power generation types. Since China’s solar power generation started in 2009, the early base is very low, so the average value from 2014 to 2021 is calculated. It can be seen that all power sources have developed, but the development speed is obvious (see Table 2). Wind power and solar power have developed rapidly, nuclear power has developed rapidly, while hydropower and thermal power have developed relatively slowly. Although the development speed of thermal power is not high, due to the large base and the reality of more coal and less oil in China, thermal power will still occupy a dominant position in the whole power system for a long time.

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Table 2. Average year-on-year increase in power generation and installed capacity from 2014 to 2021/% Data name

Hydro power

Thermal power

Nuclear power

Wind power

Solar power

Generating capacity

5.36

3.75

17.79

21.81

64.11

Installed capacity

4.26

5.13

18.19

20.34

46.73

5 Prediction of the Development Trend of Nuclear Power in China 5.1 Constraints of Various Power Generation Modes Hydropower is limited by resource endowment, and the total amount of hydropower available for economic development is limited. With the deepening of development, the difficulty of development continues to increase. At the same time, hydropower is restricted by seasonal characteristics, immigration, ecology and other issues. In recent years, wind power and solar power have developed rapidly, the cost has fallen rapidly, and the technology has been progressing continuously. However, wind power is intermittent and volatile. Solar power generation is greatly affected by the climate, and it is diurnal. Both of them are limited by factors such as insufficient adaptability of power system reception and consumption, constraints of land resources, lagging construction of energy storage facilities, etc., and their proportion in the whole power grid cannot be too high. There must be a certain proportion of efficient and stable power supply as the basic component. Natural gas power generation helps to reduce carbon emissions and environmental pollution from thermal power. However, according to the data of the National Bureau of statistics, China imported 542 million tons of oil in 2020, with a year-on-year increase of 7.2%. Its external dependence reached 73.5%, and its external dependence on natural gas reached 40%. Therefore, it is not feasible to replace coal-fired power generation with natural gas power generation on a large scale in China. In this case, the continued development of nuclear power is clearly a priority. The main constraints of nuclear power include the high construction investment cost, the scarcity of plant sites and the strictness of selection, the disposal of high-level radioactive waste, public concerns about safety, etc., of which safety is the premise. In the process of building a new energy system based on low-carbon and clean energy, the higher the proportion of nuclear power with extremely high energy density and stability, the more conducive it is to the safe operation of China’s power grid, and the more conducive it is to the large-scale integration of clean energy such as wind power and Optoelectronics into the power grid, so as to improve the utilization efficiency of wind power and optoelectronics. Therefore, the development of nuclear power is an important measure to promote the low-carbon transformation of China’s energy structure and an inevitable choice for the construction of China’s low-carbon clean new energy system [6, 7].

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5.2 Prediction of Nuclear Power Development Trend On October 24, 2021, the State Council issued the action plan for carbon peaking by 2030. According to the plan, the proportion of non-fossil energy consumption will reach about 20% by 2025; By 2030, the proportion of non-fossil energy consumption will reach about 25%. The plan clearly accelerates the pace of coal reduction, strictly and reasonably controls the growth of coal consumption during the “14th five-year plan” period, and gradually reduces during the “15th five-year plan” period. Vigorously develop wind power and solar power generation, and by 2030, the total installed capacity of wind power and solar power generation will reach more than 1.2 billion kW. By 2030, hydropower will increase by about 80 million kW compared with 2020. Actively, safely and orderly develop nuclear power; Speed up the construction of new power systems. Hydropower, nuclear power, wind power and solar power have become important options in the process of continuous increase in the scale of the power system and accelerating the low-carbon transformation. Nuclear energy production process does not emit greenhouse gases, with small carbon emissions in the whole life cycle. At the same time, it has the advantages of high energy density, no intermittent, small floor area and so on. Under the dual carbon target, nuclear energy will have more development opportunities in the fields of power generation, hydrogen production, district heating, seawater desalination and so on. According to some studies, by 2030, with the continuous economic growth and the expansion of electrification, China’s power demand will reach 10–12 trillion kWh [7]; By 2050, the power demand will reach 16 trillion kWh [1]. At present, the global nuclear power generation accounts for about 10%, and the developed countries with nuclear power account for about 20% on average. Assuming that China’s nuclear power generation will account for 10% or 20% by 2050, the required nuclear power generation will be 1.6 trillion kWh and 3.2 trillion kWh respectively. If the average annual utilization hours of nuclear power equipment are 7571 as shown in Fig. 1, the installed capacity of nuclear power to be operated is 211 million kW and 422 million kW. By the end of 2021, China had 53 nuclear power units in operation, with an installed capacity of less than 55 million kW, a difference of 156 million kW and 367 million kW. In the future, all nuclear power units built in China will be units using third generation nuclear power technology, and the mainstream nuclear power units will be domestic HuaLong-1, GuoHe-1, etc. Assuming that the average unit capacity is 1.2 million kW and the average construction period of the unit is 5 years, 6–12 new units will need to be constructed every year before 2045. Due to the scarcity of nuclear power plant sites, it is necessary to strengthen the investigation and screening of nuclear power plant sites and take effective measures to protect key candidate plant sites.

6 Achievements and Challenges of Nuclear Safety 6.1 Achievements Nuclear safety is an important part of national security and the lifeline of the development of nuclear industry. China has always attached great importance to nuclear safety,

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adhered to the principle of “safety first and quality first”, and adopted the highest international safety standards to build nuclear power. China regards ensuring nuclear safety as an important national responsibility. Based on its national conditions, drawing on the experience of nuclear developed countries and the prevailing practices of the international community, China has established a nuclear safety supervision organization system composed of headquarters organs, regional supervision stations and technical support units, and established a nuclear and radiation safety supervision system in line with national conditions and in line with international standards, ensuring the independence and effectiveness of nuclear safety supervision. Since the 18th CPC National Congress, China’s nuclear safety industry has entered a new period of safe and efficient development. For a long time, China has maintained a good record of nuclear safety, its nuclear power safety operation indicators rank among the top in the world, the safety level of nuclear technology utilization continues to improve, and nuclear material control is strong. 6.2 Challenges While making important achievements, nuclear security also faces some new challenges. It is of great significance for ensuring nuclear safety to face up to problems and promote nuclear safety supervision in a solid and steady manner. First, geopolitical and political conflicts have a certain degree of negative impact on international cooperation in nuclear energy and even nuclear safety. Second, the treatment and disposal capacity of radioactive waste is not strong, which is incompatible with the development of the nuclear industry, laying hidden dangers for the sustainable, safe and stable development of nuclear power. Third, the nuclear safety risks of those first reactors of new types in China need to be highly vigilant. Some construction, commissioning and operation events need to be studied in depth, and operation experience needs to be accumulated continuously. Fourth, with the continuous increase of domestic nuclear power technology routes, the difficulty of nuclear safety supervision is increasing, and nuclear safety review and supervision technology needs to speed up research and development and accumulation. Fifthly, under the background of carbon peaking and carbon neutrality, China’s nuclear power will maintain a sustained and rapid development, and the utilization of nuclear technology will also continue to develop rapidly. The nuclear safety supervision work is becoming increasingly arduous, but the supervision team has not increased correspondingly, there is a shortage of supervisors, and the development of nuclear safety supervision force obviously lags behind the development of the industry, further increasing the difficulty of nuclear safety supervision.

7 Conclusions and Suggestions (1) China will accelerate the development of low-carbon and clean energy. China is only 8 years away from the carbon peaking in 2030. It takes about 50 years for American and European countries to reach the peak and neutralize carbon, while China has only 30 years. China’s energy structure adjustment is bound to accelerate.

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Low carbon and clean hydropower, wind power, solar power and nuclear power will be options, among which wind power and solar power have developed rapidly and will continue to develop rapidly. (2) China must continue to improve and maintain its nuclear safety level. The Chinese government has made it clear that coal consumption will gradually decrease after reaching its peak in recent years, and China’s energy and power demand will further increase during this period. Under this background, nuclear power will maintain rapid development for a long time, and nuclear safety is the bottom line and foundation of development. (3) Do a good job in supporting the safe development of nuclear power. On the one hand, the investigation and screening of nuclear power plant sites and the protection of candidate plant sites should be strengthened to lay the foundation for the subsequent development of nuclear power; On the other hand, we should speed up the construction of radioactive waste treatment and disposal capacity and safety supervision to adapt it to the development of the nuclear industry. (4) Strengthen the construction of nuclear safety supervision team. We should increase investment in the research and development of nuclear safety supervision technology, strengthen the review and supervision ability improvement and talent training of the first and new reactors, and ensure the ability to effectively deal with various nuclear safety supervision problems; A mechanism should be established to dynamically increase the number of nuclear safety supervisors with the development of the nuclear industry. According to the practical experience of nuclear power developed countries, the corresponding proportion of nuclear safety supervision team to the number of nuclear power units should be scientifically determined, so that the nuclear safety supervision force can keep up with the development of the nuclear industry and ensure the effectiveness of nuclear safety supervision.

References 1. Haiyang, W., Jian, R.: Analysis on China’s nuclear energy development path under the goal of peaking carbon emissions and achieving carbon neutrality. Electr. Power China 54(6), 88–94 (2021) 2. Bin, Z.: Analysis on the future development ideas of hydropower under the goal of “double carbon.” China Power Enterp. Manag. 34(12), 67–69 (2021) 3. Chun, L.: Opening a new era of major changes in China’s energy system and innovative development of clean and renewable energy. People’s Forum Acad. Front. 14(7), 28–41 (2021) 4. Ritchie, H., Roser, M.: CO2 and greenhouse gas emissions [EB/OL]. (2020-08-01) [2021-0312]. https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions 5. International Energy Agency: World energy balances [DB/OL]. (2021-03-31)[2021-04-01]. https://www.iea.org/data-and-statistics/datatables 6. Yun, Z.: Analysis on the development trend of nuclear energy in China under the dual carbon target. Nucl. Sci. Eng. 41(6), 1347–1351 (2021) 7. Rocky Mountain Institute: Energy Transitions Commission. Zero carbonization of power growth (2020–2030): the only way for China to achieve carbon neutrality [EB/OL]. (202101-15) [2021-01-22]. https://guangfu.bjx.com.cn/news/20210122/1131729-1.shtml

Effect of Different Improved Designs for ARE Sensors on Core Safety Chen Shijun, Chen Lihui(B) , Luo Wenbo, and Zhang Kuan Suzhou Nuclear Power Research Institute Co., Ltd., Shenzhen, China {shijunchen,chenlihui}@cgnpc.com.cn

Abstract. The main feed water flow control system (ARE) mainly undertakes safety function of the steam generator water level protection. At present, only ARE001MP participates in the main feed water pump speed regulation control. If its fault will cause the risk of automatic shutdown of the unit to increase, several optimizations are designed for this situation. In this paper, we use the probability and deterministic method to evaluate these improved designs. From the perspective of reducing the risk of core damage and potential new risks, the Case 3 is more reasonable. Keywords: Probabilistic safety analysis (PSA) · Main feed water flow control system · Core damage risk

1 Introduction The main feed-water flow control system of nuclear power unit is mainly composed of feed-water pipe, three feed-water regulating stations and orifice plates. The feed water from the main feed water pump is heated by the high-pressure heater and sent to a feed water manifold, from which it is redistributed to three feed water regulating stations, and finally sent to the feed water loops of three steam generators. At present, only ARE001MP is involved in the speed control of the main feed water pump of the unit. If it fails, the unit may automatically shut down due to the low water level of the steam generator and the mismatch of feed water flow and steam flow. In view of this situation, the nuclear power plant has designed three improvement schemes. In this paper, probability theory and certainty theory are used to evaluate the advantages and disadvantages of the three schemes from the perspective of reducing the core damage risk and adding new potential risk points, and the best improvement scheme suggestions are determined.

2 ARE001MP Function and Failure Consequence 2.1 ARE001MP Function ARE001MP participates in the speed regulation control of the main feed water pump. Its function is to measure the difference between the pressure of the feed water main pipe © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1054–1060, 2023. https://doi.org/10.1007/978-981-19-8780-9_101

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and the pressure of the steam main pipe, and compare it with the set value of the pressure difference. The deviation is sent to the PI regulator to generate a signal of 4–20 mA, which is sent to the speed controller of the main feed water pump as the set value of the speed. 2.2 Failure Consequence If the sensor ARE001MP fails and gives a false ARE410KA (measured value of feedwater/steam pressure difference-setting value > 5%) alarm signal, the regulator of the running pump will be put into manual control according to the indication of the alarm card to stabilize the water level of the steam generator (SG). If the operator controls improperly, according to the signal sent by the sensor, the speed of the running feedwater pump will be reduced, and then the outlet pressure of the feed-water pump will be reduced, resulting in the outlet pressure of the feed-water pump being lower than the inlet pressure of the steam generator (the pressure will remain unchanged in the real situation), which will result in the failure of the running feed-water pump to supply water to SG. At the same time, the standby pump will not start because the running pump has not tripped at this time, which will lead to the automatic shutdown of the unit due to the low water level of the steam generator and the mismatch of feed-water flow/steam flow.

3 Improvement Project ARE002/003MP and instrument pressure tap pipeline are newly added, and their measurement signals are sent to KCP519AR cabinet. It is assumed that the improvement schemes of two newly added instrument pipeline pressure taps are shown in Table 1.

4 Qualitative Analysis 4.1 Improvement of Safety Two new sensors ARE002/003MP and instrument pressure pipeline are added, and ARE001/002/003MP takes the average value and outputs it to control APA feed water flow adjustment. After the improvement, the pressure difference between the feed-water header and the steam header is changed from a single sensor to three sensors to measure together, which will help to improve the reliability of the speed regulation control of the main feed-water pump of nuclear power unit and reduce the risk of losing the main feed-water. 4.2 Risks Compared with before the improvement, the improvement scheme brings the following potential risks to the unit: • If the new steam side pressure tap pipeline breaks, it may cause VVP024/025MP to be unusable;

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Project Improve description Case1

1) Water side: the original ARE001MP pressure tap pipeline is used to branch into three branches, namely ARE001/002/003MP low-pressure side pressure tap 2) Steam side: the steam side of ARE001MP is unchanged, and ARE002/003MP uses the original VVP024/025MP pressure pipeline to take pressure

Case2

Water and steam focus on new holes, and take pressure respectively

Case3

The water side and the steam side both use the original ARE001MP pressure tap pipeline, which is divided into three branches, namely ARE001/002/003MP water side and steam side pressure tap respectively

Improved flow chart

• If the main steam pipe is newly opened or the pressure pipeline is broken, it may affect the integrity of the main steam pipeline; • If the new opening of the jellyfish pipe breaks, it may affect the integrity of the main water supply pipeline.

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The following potential risks are analyzed from two aspects: certainty theory and probability theory. 1) Impact analysis of unavailability of VVP024/025MP Combined with the simulation test and PSA analysis results of nuclear power plant, if the VVP024/025MP is unavailable due to the fracture of the new steam side pressure tap pipeline, the bypass water supply function of the main water supply will fail in the transient accident of the secondary circuit with the primary circuit temperature rising. 2) Impact analysis of main steam pipeline integrity Combined with the results of thermal-hydraulic analysis and probabilistic safety analysis, the new opening of the main steam pipe or the fracture of the pressure pipeline will not lead to the small break at the downstream of the isolation valve outside the containment of the main steam pipeline, nor other transient initial events caused by the treatment failure. 3) Impact analysis of main water supply pipeline integrity Under full power condition, the maximum absolute pressure of the main water supply pipeline is 12 MPa; If a new opening of the main feedwater pipe breaks (assuming that the absolute pressure of the breach is 10 MPa at this time), the leakage under this pressure is about 100 kg/s, which is about 10%FP of the corresponding feed-water flow rate, while the designed reactor control system can cope with the maximum step load of 10%FP. As the actual pressure of the breach cannot be determined at present, it is conservative to consider the impact of the initial event of losing the main feed-water. 4.3 Comparative Analysis Combined with the description in Sects. 4.1 and 4.2, the influences of the improvement scheme on the nuclear safety of the unit mainly include: 1) Case 1, Case 2 and Case 3 improve the reliability of the speed regulation control of the main feed water pump and reduce the risk of losing the initial event of the main feed water; 2) In the Case 1, if the adjustment function of VVP024/025MP is unavailable due to the leakage or fracture of the new steam side pressure lead pipeline, it will affect the relief function of the main water supply bypass water supply in the transient accident of the secondary circuit with the temperature rise of the primary circuit; 3) In the Case 2, because two new openings are added on the water side, if the welding fails or the pipeline breaks, the reliability and integrity of the water supply pipeline will be affected, and even the main water supply initiation event will be lost. 4) In the Case 3, as no new openings are added, no new risk points will be brought before the improvement.

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5 PSA Analysis Based on the above qualitative analysis conclusion, the evaluation of this paper is based on the latest version of the internal incident level 1 PSA model of a nuclear power plant, and the model is modified according to the improved scheme and the quantitative calculation is carried out. 5.1 Improvement of Safety Evaluate the impact of each Case from the angle of failure of automatic speed regulation of main feed water pump, initial event of main feed water loss and total CDF of power working condition, as shown in Table 2 for details. Table 2. Quantitative evaluation results Analysis category

Before

Case 1

Case 2

Case 3

Automatic speed regulation of main feed 6.189E−03 1.347E−03 1.303E−03 1.390E−03 water pump failure probability − 78.236% − 78.947% − 77.541%

Relative variation (after-before)/before

–

Main feedwater loss initiation event frequency (1/reactor year)

4.745E−02 3.040E−02 3.024E−02 3.055E−02

Relative variation (after-before)/before

–

− 35.933% − 36.270% − 35.616%

Total At-power CDF (1/reactor year)

6.697E−6

6.672E−06 6.671E−06 6.672E−06

Relative variation (after-before)/before

–

− 0.373%

− 0.388%

− 0.373%

According to the quantitative evaluation results in Table 2, compared with the failure probability of automatic speed regulation of main feed water pump, it can be seen that the improvement can obviously improve the reliability of automatic speed regulation signal of main feed water pump, and at the same time, it can effectively reduce the frequency of initial events of main feed water loss, and the total CDF value has been reduced to some extent under the power condition of the unit, indicating that ARE001MP redundancy improvement can improve the reliability of automatic speed regulation signal of main feed water pump and the safety of the unit core. 5.2 Risks of Case 1 If there is a leak in the new steam-side inter-communication pipeline, VVP024/025MP will fluctuate or become unavailable, which may lead to the failure of the automatic regulation signal of the bypass pneumatic regulating valve ARE242/243/244VL, thus leading to the failure of the bypass water supply of the main water supply, thus affecting the alleviation of the transient initial event of the secondary circuit that causes the temperature rise of the primary circuit. Evaluate the potential risk by modifying the failure parameters of VVP024/025MP in PSA model.

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The operating failure parameters of VVP024/025MP under the power condition of the unit can be increased by a certain multiple, and the CDF value under the power condition of the unit can be obtained, as shown in Table 3. Table 3. VVP024/025MP CDF of different failure parameters CDF(1/RY) Probability

6.10E−03 (benchmark)

6.10E−02 (10 × benchmark)

6.10E−01 (100 × benchmark)

CDF

6.672E−06

6.697E−06

6.924E−06

CDF

–

2.500E−08

2.520E−07

Relative

–

0.370%

3.780%

From the results in Table 3, it can be seen that under the power condition, with the increase of the failure probability of VVP024/025MP, the CDF of the unit increases slowly, but the risk increases slightly in this configuration. If the follow-up daily production strengthens the monitoring of steam side pipeline in the first improvement scheme, and avoids the impact on VVP024/025MP, the impact on the safety of the unit core can be effectively reduced. 5.3 Risks of Case 2 Due to the addition of two openings on the water side, if the welding fails or the pipeline breaks, the reliability and integrity of the water supply pipeline will be affected, and even the main water supply will be lost. Therefore, the risk brought by the improvement to the safety of the unit core is evaluated by adjusting the frequency parameter of the initial event of losing the main feedwater. By increasing the parameters of the initial event of the loss of main feedwater under the power condition of the unit by a certain multiple, the CDF value under the power condition of the unit can be obtained, as shown in Table 4. Table 4. CDF of different parameters CDF(1/RY) 3.024E−02 6.048E−02 1.210E−01 1.814E−01 2.419E−01 3.024E−01 probability (benchmark) (2 × (4 × (6 × (8 × (10 × benchmark) benchmark) benchmark) benchmark) benchmark) CDF

6.671E−06

6.717E−06 6.808E−06 6.899E−06 6.990E−06 7.081E−06

CDF

–

4.500E−08 1.360E−07 2.270E−07 3.180E−07 4.090E−07

Relative

–

0.674%

2.038%

3.402%

4.766%

6.130%

It can be seen from the results in Table 4 that, at-power condition, the CDF of the unit increases slowly with the increase of the frequency of the initial event of losing the

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main feed-water. However, if the initial event leading to the loss of the main feed-water occurs, it has entered the accident regulation at this time, and is not within the scope of technical specification management. In this case, the conditional probability of core damage is 1.463E−06.

6 Conclusions According to the above qualitative and quantitative analysis, the following conclusions can be drawn: 1) All the improved schemes are helpful to improve the reliability of the automatic speed adjustment signal of the main feed-water pump, effectively reduce the frequency of the initial event of the main feed-water loss, and the CDF decreases to a certain extent under the power condition of the unit, which indicates that the improvement can improve the reliability of the automatic speed adjustment signal of the main feed-water pump and the safety of the unit core; 2) When the reliability of VVP024/025MP decreases or fails in the first improvement scheme, the quantitative risk of the unit increases slightly; 3) In the second improvement scheme, if the welding fails or the pipeline breaks, it will affect the reliability and integrity of the water supply pipeline, and may even lead to the loss of the main water supply. 4) In the third improvement scheme, as no new openings are added, no new risk points will be brought before the improvement. Based on the above conclusions, the third scheme will not bring new risk points to the unit, and it will improve the reliability of automatic speed adjustment signal of main feed water pump and the safety of the unit core.

Bibliography 1. ARE001MP Report on Redundancy Improvement Scheme, November 2018 2. First-class probabilistic safety evaluation report of internal events of units 1 and 2 (power condition) in a nuclear power plant, November 2018 3. Final Safety Analysis Report of Unit 1 and Unit 2 of a Nuclear Power Plant, June 2012 4. Feed water flow control system version C0, 2016, 6 5. Nuclear steam supply system control device and safety facilities failure C1 version, 2018

Determination Method and Application of Risk Threshold of Nuclear Power Plant Configuration Shijun Chen, Zichun Wang(B) , Wenbo Luo, and Lihui Chen Suzhou Nuclear Power Research Institute Co., Ltd., Shenzhen, China [email protected]

Abstract. Based on the research of key technologies of risk threshold in nuclear power plant configuration risk management, this paper puts forward a method to determine the risk threshold of domestic nuclear power plants, and gives a brief introduction of the application of this method according to the practice of nuclear power plant configuration risk management. The practice shows that the risk threshold and matching risk management matrix formulated by this method can effectively control the risk of multiple equipment failures, and realize the overall “knowable” and “controllable” risk of nuclear power plants. Keywords: Configuration risk management · Risk threshold · Application

1 Introduction In 1991, the NRC of the United States issued 10CFR50.65 to carry out pilot maintenance rules in nuclear power plants, and it was enforced in nuclear power plants all over the United States in 1996. In 1999, this regulation was amended to increase the requirement of risk assessment before maintenance activities were implemented. In order to meet the above requirements, all nuclear power plants in the United States have established configuration risk management systems. In addition, in 1995, the NRC of the United States issued a policy statement on the application of probabilistic risk assessment technology, and in 1998, it issued a series of technical management guidelines (RG1.174-RG1.178), which required that a configuration risk management system should also be established when applying risk informed technology to optimize the benchmark documents of nuclear power plant licenses. Therefore, configuration risk management technology has been widely used in the United States. In order to make the risk assessment results easy to understand and provide clear instructions for risk management, most nuclear power plants have established risk indicators and a series of risk thresholds to classify the risk levels. The method of establishing risk management threshold should have reasonable technical basis to ensure that the assessed risk level can represent the actual configuration risk of the unit. Too high a risk threshold may defeat the purpose of managing configuration risks through multiple risk areas. On the contrary, an overly conservative risk threshold allows only minor changes in risk level, which may lead to unnecessary restrictions © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1061–1069, 2023. https://doi.org/10.1007/978-981-19-8780-9_102

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or the implementation of risk management measures without realistic conditions, thus resulting in unreasonable allocation of management resources. Based on the above considerations, this paper puts forward a method to determine the risk threshold of nuclear power plants in China by studying the key technologies of risk threshold in nuclear power plant configuration risk management, and gives a brief introduction of the application of this method according to the practice of nuclear power plant configuration risk management, which can provide reference for other nuclear power plant configuration risk management thresholds.

2 Configuration Risk Management Threshold Determination Method In this section, the determination method of allocation risk management threshold is summarized through quantitative risk indicators and the implementation of foreign risk thresholds. 2.1 Quantitative Risk Index NRC’s safety objective for nuclear power is defined by the acceptable level of radiation risk. The safety objective part answers the fundamental question of nuclear safety supervision, that is, how safe is enough? In 1986, the policy statement of security objectives established two qualitative security objectives of NRC, and then established two quantitative security objectives. When determining whether the safety objectives are met, NRC uses two alternative risk indicators, namely Core Damage Frequency(CDF) and large early release frequency (LERF). • CDF is a substitute index for the risk of death from advanced cancer in quantitative safety objectives. • LERF is an alternative indicator of individual early death risk in quantitative safety objectives. Referring to the PSA Application Guide, NUMARC 93-01 “Monitoring the Maintenance Effectiveness of Nuclear Power Plants” gives a quantitative index to evaluate the risk level of maintenance activities based on ICDP or ILERP. In addition, the risk monitoring tool based on CDF/LERF model calculates the instantaneous CDF and LERF; And NUMARC93-01 gives the threshold based on ICDP and ILERP. Therefore, the risk monitoring tool needs to calculate CDF/LERF to meet the requirements of NUMARC93-01. Therefore, it is necessary to use CDF/LERF as a substitute value, assume the maximum duration of each configuration to define the risk area, and track ICDP/ILERP in real time. To sum up, the following two kinds of risk indicators are usually used in nuclear power plant configuration risk management to formulate the risk threshold of configuration risk management: • Transient risk (CDF/LERF) • Accumulated risk (ICDP/ILERP).

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2.2 Implementation of Risk Thresholds Abroad This section will introduce the implementation of risk thresholds for risk management in foreign countries. NUMARC 93-01 “Monitoring the Effectiveness of Nuclear Power Plant Maintenance” states that “if the risk increase factor (RAW) of SSC is greater than 2, then the SSC may be risk-important”, that is, RAW = 2 is equivalent to the instantaneous CDF of SSC failure being at least twice the benchmark CDF. At the same time, the guide points out that the maintenance configuration with instantaneous CDF > 1.0E−03 should be carefully examined before active entry. In addition, the management guideline RG1.160 neither approves nor opposes to take 1.0E−03/reactor year as the upper limit of the risk management zone (the lower limit of the red zone) for the core damage frequency of a specific configuration. The management guideline RG1.174 “Risk Guidance Decision-making Method of Probabilistic Risk Assessment for the Change of Permits Basis of Specific Power Plants” puts forward the risk acceptance guideline for the change of specific permanent permits of power plants. Its risk acceptance criteria are similar to those of NUMARC 93-01. Foreign nuclear power plants mainly adopt the following methods to determine the allocation risk management threshold: 1) For the instantaneous risk index, use the multiple of the benchmark risk (CDF or LERF) (such as 2 times, 10 times and 20 times) to determine the risk threshold; 2) For cumulative risk indicators, ICDP/ILERP is adopted to determine the risk threshold, including: • Use the fixed ICDP/ILERP limit (such as 1.0E−06) to adjust the configuration duration (such as “yellow” in 14 days, “orange” in 7 days, etc.); • Adjust the ICDP/ILERP limit with a fixed configuration duration (such as 7 days) (such as 1.0E−06 for “yellow” and 1.0E−05 for “orange”). The second method of using ICDP/ILERP to determine the risk threshold is adopted by most power plants. ICDP < 1E-6, ILERP < 1E-7 (normal working control) and ICDP > 1E-5, ILERP > 1E-6 thresholds are usually used to define the entry yellow and red respectively; Orange is self-defined, and there is no corresponding guideline. From the perspective of application practice, the CDF/LERF threshold is calculated based on ICDP/ILERP value, and it is used in risk monitoring tools. The CDF/ICDP threshold can be calculated using Formula 1, and the LERF/ILERP threshold is similar. CDFt = ICDPt /t + CDFb Among them: CDF t ICDPt Δt CDF b

CDF threshold; ICDP threshold; the duration of exiting the running configuration, expressed in years; reference CDF, usually zero maintenance reference CDF.

(1)

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1) Use SSC/column with single risk importance to exit the run-time risk value (usually used to determine the “orange zone” threshold); Some nuclear power plants directly use this value or increase the threshold by 5%. 2) Recommended values given by the guidelines (such as CDF = 1.0E−03/year, LERF = 1.0E−04/year). The risk threshold determined by the above method corresponds to the risk of the whole range of initial events. If the PSA range of nuclear power plants is not perfect, additional analysis can be added to expand the range or adjust the risk threshold appropriately. Investigating the application practice of foreign nuclear power plants, most nuclear power plants refer to the multiple of benchmark CDF or LERF to determine the risk threshold: • 2 times the benchmark CDF/LERF as the “yellow zone” threshold; • 10 times the benchmark CDF/LERF as the “orange zone” threshold; • 20 times the benchmark CDF/LERF or CDF = 1.0E−03 and LERF = 1.0E−04 as the “red zone” threshold. Some nuclear power plants determine the risk threshold based on ICDP/ILERP in a specific configuration duration: • ICDP = 1.0E−06 and ILERP = 1.0E−07 are used as “yellow” thresholds, and the configured duration is 4–30 days; • 1.0E−07 < ICDP < 5.0E−05 as the “orange” threshold, and its duration is 4–30 days; • ICDP = 1.0E−05 and ILERP = 1.0E−06 are used as “red” thresholds, and the duration is 4–30 days. 2.3 Risk Threshold Determination Method On the basis of Sect. 2.2, combining several methods with the expectation of power plant configuration risk management, the flow chart of determining the risk threshold of configuration risk management is shown in Fig. 1. When determining the risk management threshold of nuclear power plant configuration, it is necessary to consider not only the risk threshold calculated by two methods, but also the expectation of risk management of nuclear power plant configuration and technical policy requirements, so as to work out a risk threshold that is reasonable and meets the requirements.

3 Application Practice Based on the above determined threshold determination method of configuration risk, the application practice of threshold determination based on CDF in configuration risk management of a nuclear power plant is introduced.

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Fig. 1. Determination process of nuclear power plant configuration risk management threshold

3.1 PSA Model Information of Nuclear Power Plant A nuclear power plant has developed the PSA model of internal events (power condition and shutdown condition), fire, earthquake and flooding, and its baseline risk distribution is shown in Table 1 and Fig. 2. Table 1. PSA model information of NPP PSA range

Baseline CDF(/reactor year)

Proportion (%)

Internal event

7.54E−06

33%

Full range

2.29E−05

/

3.2 Risk Threshold Determination 1) Multiplication method based on benchmark CDF Referring to foreign practical experience, the risk threshold of a nuclear power plant configuration risk management is determined by the multiple method of benchmark CDF, as shown in Table 2.

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Fig. 2. Risk distribution of NPP

Table 2. Risk thresholds based on CDF Risk area

Risk threshold

Normal control area (green area)

CDF < 2CDF0

CDF < 4.57E−05

Management area (yellow area)

2CDF0 ≤ CDF < 10 CDF0

4.57E−05 ≤ CDF < 2.29E−04

Management area (orange area)

10CDF0 ≤ CDF < 20 CDF0

2.29E−04 ≤ CDF < 4.57E−04

Unacceptable area (red CDF ≥ 20CDF0 area)

CDF ≥ 4.57E−04

2) Cumulative risk method based on ICDP Referring to foreign practical experience, the risk threshold of a nuclear power plant configuration risk management is determined by the cumulative risk method, as shown in Table 3. Table 3. Risk thresholds based on ICDP Risk area

Risk threshold (configured duration is 7 days)

Normal control area (green area)

ICDP < 1.0E−06

Risk management area (yellow area)

1.0E−06 ≤ ICDP < 5.0E−06

Risk management area (orange area)

5.0E−06 ≤ ICDP < 1.0E−05

Risk unacceptable area (red area)

ICDP ≥ 1.0E−05

In order to facilitate the application and practice of risk monitoring tools, it is necessary to convert them into instantaneous CDF thresholds according to Formula 1. The corresponding risk thresholds and their ratios to the benchmark CDF are shown in Tables 4 and 5.

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Table 4. Transfer threshold based on ICDP Risk area

Risk threshold (configured duration is 7 days)

Normal control area (green area)

CDF < 7.50E−05

Risk management area (yellow area)

7.50E−05 ≤ CDF < 2.84E−04

Risk management area (orange area)

2.84E−04 ≤ CDF < 5.44E−04

Risk unacceptable area (red area)

CDF ≥ 5.44E−04

Table 5. The ratio based on ICDP Benchmark CDF

Yellow area CDF

Ratio

Red area CDF

Ratio

2.29E−05

7.50E−05

3.28

5.44E−04

23.77

As can be seen from Table 5, the risk threshold determination method based on the cumulative risk (ICDP/ILERP) provides a method to evaluate the cumulative risk based on the benchmark risk value, which can provide greater operational flexibility than the multiple method of benchmark risk. 3) Nuclear power plant configuration risk management threshold Requirements of Technical Policy for Risk Management of Nuclear Power Plant Configuration (for Trial Implementation): • It is recommended to adopt 2 times of benchmark risk (including CDF and LERF) as the lower limit of risk management area (the upper limit of green area); • It is recommended to adopt 1.0E−03/stacking year (CDF) as the upper limit of risk management area (the lower limit of red area); • The National Nuclear Safety Administration encourages nuclear power plants to adopt stricter risk thresholds than the recommended ones, so as to minimize risks and further improve the safety level. The nuclear power plant has put forward the following requirements for setting the allocation risk management threshold: • There is no red zone or orange zone in unit configuration risk, and the yellow zone should be reduced as much as possible. Based on the above requirements, the allocation risk thresholds determined by the two methods are integrated, and the allocation risk management thresholds of nuclear power plants are formulated as shown in Table 6. In addition, the technical policy for risk management of nuclear power plant configuration (for trial implementation) requires that “the risk threshold corresponds to the

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Risk area

Risk threshold

Normal control area (green area)

CDF < 2CDF0

CDF < 4.57E−05

Management area (yellow area)

2CDF0 ≤ CDF < CDF(ICDP = 5.0E−06)

4.57E−05 ≤ CDF < 2.84E−04

Management area (orange area)

CDF(ICDP = 5.0E−06) ≤ CDF < 1.0E−03

2.84E−04 ≤ CDF < 1.0E−03

Unacceptable area (red area)

CDF ≥ 1.0E−03

CDF ≥ 1.0E−03

risk of the whole range of initial events. If the PSA range of nuclear power plants is not perfect, the scope can be expanded or the risk threshold can be adjusted appropriately by supplementing additional analysis”. Referring to foreign practical experience and combining with the application maturity of the current PSA model, it is finally decided that the evaluation scope of risk management of nuclear power plant configuration at the current stage is internal events. According to the distribution in Fig. 2, the results in Table 6 and the requirements of technical policies, the risk thresholds of nuclear power plants are given, as shown in Table 7. Table 7. NPP CRM thresholds (internal events) Risk area

Risk threshold

Normal control area (green area)

CDF < 1.51E−05

Risk management area (yellow area)

1.51E−05 ≤ CDF < 9.37E−05

Risk management area (orange area)

9.37E−05 ≤ CDF < 3.30E−04

Risk unacceptable area (red area)

CDF ≥ 3.30E−04

3.3 Risk Threshold Verification In order to verify whether the established risk threshold can effectively control the risk of the unit and realize the risk management expectation of nuclear power plant configuration. Statistical analysis of the time proportion in the risk area of the daily planned risk assessment (power condition) results of a nuclear power plant after trial operation (4 months) is shown in Table 8. It can be seen from Table 8 that the risk threshold of allocation risk management is reasonable, which can meet the national regulatory requirements and realize the expected requirements of nuclear power plant allocation risk management.

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Table 8. Time proportion of risk area in daily plan of NPP Unit coding

Green

Yellow

Orange

Red

P01

0.961

0.039

0

0

P02

0.942

0.058

0

0

P03

0.930

0.070

0

0

P04

0.932

0.067

0

0

4 Conclusions Based on the research of key technologies of risk threshold in nuclear power plant configuration risk management, this paper puts forward a method to determine the risk threshold of domestic nuclear power plants, and gives a brief introduction of the application of this method according to a nuclear power plant configuration risk management practice. The practice shows that the risk threshold and matching risk management matrix formulated by this method can effectively control the risk of multiple equipment failures, and realize the overall “knowable” and “controllable” risk of nuclear power plants. Acknowledgement. This work is supported by the National Key Research and Development Program of China under Grant 2019YFB1900801.

Bibliography 1. NNSA: Technical Policy for Risk Management of Nuclear Power Plant Configuration, Beijing (2020) 2. USNRC: An Approach for Using Probabilistic Risk Assessment in Risk-Informed Decisions on Plant-Specific Changes to the Licensing Basis, Regulatory Guide 1.174, Washington D.C. (2018) 3. USNRC: Industry Guideline for Monitoring the Effectiveness of Maintenance at Nuclear Power Plants, NUMARC 93-01 Rev.4c, Washington D.C. (2015)

High-Fidelity Drum Controller Design of Thermionic Space Nuclear Reactor Jianghan Fu, Zhao Jin, Chenglong Wang(B) , Zhiwen Dai, Wenxi Tian, Guanghui Su, and Suizheng Qiu Xi’an Jiaotong University, Xi’an, Shaanxi, China [email protected]

Abstract. Space nuclear reactors have the advantages of not relying on solar energy and being able to adapt to complex external conditions. Different from ground reactors, space nuclear reactors work in space and are unattended during the entire operating cycle. Thus, a reliable automatic control system is very important. In this paper, a control drum system is designed based on the high-fidelity thermionic space reactor system code TASTIN. A model predictive controller (MPC) is installed in the control drum system, which can satisfy the optimal control problem under constraints. The model predictive controller adopts point kinetics model with six groups of delayed neutrons, and seven different parts of reactivity feedback models as an internal model, which have relatively high fidelity. By selecting the appropriate prediction horizon, control horizon and input and output weights, the MPC controller with superior performance is finally obtained. Finally, in order to evaluate the control performance of the model predictive controller, the simulations are carried out for the control variable step condition and the nuclear power following condition, and compared with a PID controller with good performance. The results show that, in various transient conditions, the MPC controller has better control performance than the traditional PID controller. In the nuclear power step condition, the MPC controller has better performance, its setting time is 0.12 s, and overshoot is 0.45% (in the PID controller, the setting time is 15.30 s, and the overshoot is 9.08%). In the electric power step condition, the setting time of the MPC controller is 11.95 s, and the overshoot is 0.22% (in the PID controller, the setting time is 71.28 s, and the overshoot is 13.27%). In the outlet temperature step condition, the MPC controller reduces the steady-state error to 0.02 K (0.22 K in the PID controller). In the nuclear power following condition, the MPC controller can better track the target value. Keywords: Thermionic reactor · Model predictive controller · High-fidelity control model · Drum controller · TASTIN

1 Introduction The space nuclear reactor power system is mainly composed of five parts: the nuclear reactor, radiation shielding system, thermoelectric conversion system, heat emission system and automatic control system. The energy conversion methods include static © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1070–1083, 2023. https://doi.org/10.1007/978-981-19-8780-9_103

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conversion (thermocouple conversion, thermionic conversion, alkali metal conversion and magnetic fluid conversion) and dynamic conversion. Among many space nuclear power system designs, thermionic nuclear power supply occupies a very important position and is the most advanced design in the world with successful practical application [1–3]. The former Soviet Union designed and successfully developed the TOPAZ-2 thermionic reactor system in the 1970s. Subsequently, different researchers developed simulation analysis systems such as simulation programs CENTER and TITAM for this system. Transient responses for start-up and accident conditions were analyzed. The control system for TOPAZ-2 is mostly based on the lumped parameter method to simplify the modeling, which is relatively rough compared to the actual system [4]. For the above problems, this paper builds a high-fidelity thermionic system model based on the TASTIN thermionic space reactor system analysis program, designs a control drum system using model predictive control (MPC) based on Simulink, and builds a system simulation platform by coupling the two models to achieve high-fidelity drum control system simulation. Based on this platform, the MPC drum controller was simulated under the nuclear power step condition, the electric power step condition, the coolant outlet temperature step condition and the nuclear power follow condition, and compared with the traditional PID controller.

2 Methodologies The thermionic space reactor power system includes the core, coolant system and auxiliary system. Fig. 1 gives an overall view of the system. The core includes single-section thermionic fuel elements (TFEs), moderator, coolant channels, control drums, reflectors, and support structures; the coolant system includes coolant pipes, EM pumps, volume accumulator, and radiators; The auxiliary system consists of instrument control system, cesium steam supply system, and radiation shielding. The coolant control volume nodes of the TASTIN system program is shown in Fig. 2, including the inlet chamber, the core channel, the outlet chamber, the radiant tube, the coolant pipe, the electromagnetic pump and the volume accumulator. Each circle of fuel in the core channel is divided into an average channel, and the heat pipe radiator is set with two coolant loops, which are modeled symmetrically. The TFE control volume is radially divided into 13 nodes, as shown in Fig. 3, including 5 UO2 fuel pellet nodes, 1 Mo/W emitter node, 1 Mo/Nb collector node, 1 coolant inner casing node,1 coolant node, 1 coolant outer casing node and 3 ZrH moderator nodes. 2.1 Neutron Kinetics Model The point kinetics equation is used to describe the neutron dynamics process of the core, and the influence of delayed neutrons is considered. Six groups of delayed neutrons are introduced to form the following nonlinear ordinary differential equations:  dP 6 ρ−β i=1 λi Ci dt =  P + (1) βi dCi = P − λ C i i dt 

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Fig. 1. Space thermionic reactor power system

Fig. 2. Nodes of coolant system

The parameters in Eq. (1) adopts the data in Kwok [5]. The main reactivity feedbacks considered in the model include: Doppler reactivity feedback of fuel ρfuel (T ), moderator temperature feedback ρmod (T ), temperature feedback of electrodes(emitter and receiver) ρec (T ), coolant temperature feedback ρNaK (T ), reflector temperature feedback ρref (T ), support structure temperature feedback ρbam (T ) and beryllium oxide temperature feedback ρBeO (T ). The overall feedback reactivity is

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Fig. 3. Control volume of thermionic fuel element

the sum of the terms, as in Eq. (2). ρT (T ) = ρfuel (T ) + ρmod (T ) + ρec (T ) + ρNaK (T ) + ρref (T ) + ρbam (T ) + ρBeO (T )

(2)

The core is equipped with a total of 12 control drums, which are evenly arranged in the solid reflector. For the reactivity introduced by the control drums, the polynomial in Kwok [5] is used: ρ(θ ) = 6.89 × 10−10 θ 5 − 2.33 × 10−10 θ 4 − 3.28 × 10−9 θ 3 + 4.57 × 10−6 θ 2 − 5.88 × 10−5 θ + 1.74 × 10−4

(3)

2.2 Thermal Model of TFE The TFE is divided into various hierarchical structures from the inside out, as shown in Fig. 4. In the thermal conductivity model, the materials of each layer are calculated as a whole, and control volume is divided into different nodes according to the properties of the materials. For each node, the following heat balance equation is used, as in Eq. (4). ρi ci Vi

∂Ti = Qgen + Qin − Qout ∂t

(4)

where, ρi is density of control volume (kg m−3 ); ci is specific heat capacity of control volume (J kg−1 K−1 ); V i is volume of control volume (m3 ); T i is temperature of control

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Fig. 4. Cross-section of TFE

volume (K); Qgen is heat generated (W); Qin is heat input (W); Qout is heat output (W). The detailed heat transfer model is given by Dai [7]. 2.3 Model Predictive Control Design Model Predictive Control (MPC) is an advanced control algorithm that can solve realtime optimal control problems under constraints [6]. The structure of the model predictive controller is shown in Fig. 5.

Fig. 5. Internal structure of a model predictive controller

In this study, a discrete state space equation with single input and single output (SISO) is used as the internal model, as shown: Y (k) = Fy x(k) + Gy U (k)

(5)

At time k, the state variable of the system is known, and the state variable of the prediction model at time k+1 has the following relationship with the input sequence:  x(k + 1) = Ax(k) + Bu(k) (6) y(k) = Cx(k)

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Extending the prediction range of the system state to time k + p, and extending the reference range of system input to time k + c (c ≤ p), the future state variable sequence and the input control variable sequence can be obtained through iteration: x(k + 1|k ) = Ax(k|k ) + Bu(k|k )

(7)

Specify a cost function, taking into account error weights Q and input weights R: J = E(k)T QE(k) + U (k)T RU (k)

(8)

The above cost function can be transformed into a univariate quadratic problem with respect to the input sequence. The internal model uses incremental kinetics neutron dynamics equations.  dP 6 ρ0 −β P0 i=1 λi Ci +  ρ dt =  P + (9) βi dCi dt =  P − λi Ci Considering the response of feedback reactivity to the change of nuclear power, the following general transfer function can be obtained: G(s) =

ρ(s) P(s)

(10)

Combining the linearized point kinetics equation with the fitted transfer function, the internal model of the model predictive controller is obtained, and Figs. 6 and 7 show its structure in Simulink. By linearizing the model as a whole, the state space model in the model predictive controller can be obtained.

Fig. 6. Control drum reactivity model

2.4 Coupling Algorithm for System Simulation Control Platform The control system adopts the built-in S function of Simulink to design the data interaction module, which realizes the coupling between the TASTIN thermionic reactor system analysis program and the control drum system, and ensures the high fidelity of the program calculation. The Simulink model of the automatic control system is shown in Fig. 8, which mainly includes two parts: the S-function module and the control drum module.

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Fig. 7. Internal model of MPC

Fig. 8. Automatic control system model

Fig. 9. S-function model

Figure 9 shows the S-function module, which uses 1 input port for receiving the reactivity of the drum, and 5 output ports for sending the parameters of each part of the core in the system program to Simulink. Figure 10 shows the control drum module of the system, which mainly includes an error reference model, a controller, a control drum mathematical model, and a drum angle-reactivity model. The input of this module is the controlled variable and the output is the drum reactivity.

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Fig. 10. Control drum model

The closed-loop control process of the system is shown in Fig. 11. The automatic control system receives the controlled variables sent by the TASTIN system program, then outputs the reactivity of the drum and sends it back to the system model to calculate the system parameters.

Fig. 11. Closed loop system control process

3 Results 3.1 Steady-State Results Analysis The system program carried out the steady-state operation tests of the core at 100% power, 75% power, 50% power and 30% power respectively. Table 1 shows the steadystate parameters of the core under different powers. The nominal electric power is stable at 12.01 kW at this time, and the thermoelectric efficiency can reach 5.72%. 3.2 Control Analysis of Transient Conditions Under the steady-state full power operating condition, without the automatic control system, the step reactivity of ±30 pcm is introduced into the core respectively. The response of nuclear power and the each part of the reactivity are shown in Figs. 12 and 13. The system has the characteristics of positive feedback and does not have the characteristics of self-stabilization and self-adjustment, so the introduction of the control

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Steady state

100% FP

75% FP

50% FP

30% FP

Electric power /kW

12.01

6.92

0.91

0

Thermoelectric efficiency /%

5.72

4.39

0.87

0

Fuel temperature /K

2358.2

2165.0

1918.1

1648.2

Emitter temperature /K

2083.7

1951.0

1774.4

1565.7

Collector temperature /K

870.8

788.3

694.5

600.0

Coolant inlet temperature /K

754.6

768.6

637.5

564.0

Coolant outlet temperature /K

843.8

701.1

682.7

593.0

Inlet pressure /Pa

164,057

138,203

114,489

97,607

Outlet pressure /Pa

160,988

135,092

111,308

94,322

Fig. 12. Response of + 30 pcm step reactivity

system is very important. In this study, an MPC controller was designed to realize automatic control by taking nuclear power, electric power and coolant outlet temperature as controlled variables. Selecting nuclear power as the controlled variable, and the following MPC controller parameters are used: sample time t = 0.001 s, prediction horizon ph = 10, control horizon ch = 2, state variable weight Q = 1, input variable weight R = 0.1. A − 5% nuclear power step was introduced at the steady-state full power, the effect of the MPC controller was compared with that of the PI controller (proportional coefficient P = 0.03, integral coefficient I = 0.005), and the results are shown in Fig. 14.

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Fig. 13. Response of − 30 pcm step reactivity

Fig. 14. Comparison of nuclear power step by − 5%

Table 2 shows the dynamic performance comparison of the two controllers. The MPC controller has smaller overshoot (0.45%) and setting time (0.12 s), which has obvious advantages compared with the PI controller (overshoot 9.08%, setting time 15.30 s). Electric power has a delay of about 50 s relative to nuclear power, which requires a larger prediction interval. In this study, an electric power MPC controller is designed, in which the internal model can predict the change of electric power under control in the next 10 s, aiming to achieve fast dynamic response (t = 0.01 s, ph = 1000, ch = 5, Q = 0.3, R = 3).

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Transient condition

Controller

Reference nuclear power/W

Minimum nuclear power/W

Overshoot/%

Setting time/s  = 50 W

Nuclear power step by − 5%

MPC

199,500

199,452.2

0.45

0.12

PI

199,500

198,546.2

9.08

15.30

The initial state of the system is steady-state full power, and a − 5% electric power step is introduced. Figure 15 shows the comparison of dynamic response of the system under the action of MPC controller and PID controller (P = 0.55, I = 0.017, D = 3.95).

Fig. 15. Comparison of electric power step by − 5%

Table 3 shows the performance of MPC controller and PID controller in electric power control. In the electric power − 5% step condition, the MPC has almost no overshoot, and its setting time is 11.95 s, which is much better than the control performance of the PID controller (overshoot 13.27%, setting time 71.28 s). Compared with the nuclear power, the coolant outlet temperature has a delay of more than 100 s, so it is more difficult to control. Select the parameters of the MPC controller as: dt = 0.05 s, ph = 1200, ch = 3, umax = 0.1, umin = − 0.1. The controller adopts a large prediction range (60 s), which can cover the period when the coolant temperature changes sharply, and can reduce overshoot while responding quickly. When the system is running at full power at steady state, the outlet temperature is 843 K, and a – 20 K outlet temperature step is introduced. The simulation test of the MPC controller was carried out under this working condition and compared with a PID controller (P = 0.025, I = 0.00001, D = 0.8), as shown in Fig. 16.

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Table 3. Performance comparison in electric power control Transient condition

Controller

Reference electric power/W

Minimum electric power/W

Overshoot/%

Setting time/s  = 12 W

Electric power step by − 5%

MPC

11,409.5

11,408.2

0.22

11.95

PID

11,409.5

11,329.8

13.27

71.28

Fig. 16. Comparison of outlet temperature step by − 20K

Table 4 presents the performance of the MPC controller and the PID controller in the outlet temperature step condition. Both MPC and PID controllers have no obvious overshoot. The setting time of the MPC controller is 104.4 s, and the steady-state error is maintained at 0.02 K. The performance is better than that of the PID controller (the setting time is 61.6 s, and the steady-state error is 0.22 K). Table 4. Performance comparison in outlet temperature control Transient condition

Controller

Outlet MPC temperatur step PID by − 5%

Reference outlet temperature (K)

Overshoot (%)

Setting time (s)  = 0.4 K

Steady state error (K)

823

1.65

104.4

0.02

823

1.41

61.6

0.22

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Selecting nuclear power as the controlled variable, a nuclear power following condition is designed, and the step and ramp reactivity disturbances in the process are considered. The changes of power reference value and external reactivity with time are shown in Fig. 17. The dynamic performance of the controller is compared in Fig. 18.

Fig. 17. Reference nuclear power and reactivity perturbations

From Fig. 18, in general, the MPC controller and the PI controller can both track the target value well in the entire core power following condition. The MPC controller has no overshoot at the power reference mutation points B, C, and E, and can quickly overcome the disturbance in the reactivity disturbance regions A and D, so its performance is more superior.

Fig. 18. Dynamic performance comparison in nuclear power follow condition

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4 Conclusion This paper uses the TASTIN system analysis program to establish a high-fidelity mathematical and physical model of the thermionic space nuclear reactor power system. Based on the Simulink simulation platform, a Model Predictive Controller (MPC) is designed and coupled with TASTIN. The nuclear power, electric power and coolant outlet temperature were selected as the controlled variables to carry out the control simulation, and compared with the PID controller. The main conclusions are as follows: (1) In the steady-state operation verification, the thermal-hydraulic parameters of the system under different powers are obtained. In the transient analysis, a ± 30 pcm reactive disturbance is introduced into the system, and the system exhibits positive feedback and does not have the characteristics of self-stabilization and self-adjustment. (2) In the controlled variable step condition, the MPC controller has better performance: in the nuclear power step condition, the overshoot is 0.45%, and the setting time is 0.12 s. In the electric power step condition, the overshoot is 0.22% and the setting time is 11.95 s, which are significantly smaller than the overshoot and setting time of the PID controller. In the step condition of outlet temperature, the steady-state error is greatly reduced to 0.02 K. (3) In the nuclear power following condition, the MPC controller can realize the wholeprocess error-free tracking of the target value, and can quickly overcome the reactive disturbance in the process, and the overall performance is better than that of the PID controller.

References 1. Standley, V.H., Morris, D.B., Schuller, M.J.: CENTAR modelling of the TOPAZ-II: Loss of vacuum chamber cooling during full power ground test. AIP conference proceedings. Am. Inst. Phys. 246(1), 1129–1134 (1992) 2. El-Genk, M.S., Xue, H., Paramonov, D.: Start-up simulation of a thermionic space nuclear reactor system. AIP conference proceedings. Am. Inst. Phys. 271(2), 935–950 (1993) 3. Alvarez-Ramirez, J., Puebla, H., Espinosa, G.: A cascade control strategy for a space nuclear reactor system. Ann. Nucl. Energy 28(2), 93–112 (2001) 4. Zeng, W., Jiang, Q., Liu, Y., et al.: Core power control of a space nuclear reactor based on a nonlinear model and fuzzy-PID controller. Prog. Nucl. Energy 132, 103564 (2021) 5. Kwok, K.S.: Real-time dynamic simulator for the Topaz II reactor power system. Sandia National Labs, Albuquerque, NM (United States) (1994) 6. Camacho, E.F., Alba, C.B.: Model Predictive Control. Springer Science & Business Media (2013) 7. Dai, Z., Wang, C., Liu, X., et al.: Thermoelectric characteristics analysis of TOPAZ-II. Nucl. Sci. Eng. (2021)

A Revision on CEFR Main Vessel Leakage Rate Measurement Based on Argon Chamber Numerical Simulation Xin-Yuan Bian(B) , Guo-Tu Ke, Jian Zhang, and De-Kang Luo China Institute of Atomic Energy, Beijing, China [email protected]

Abstract. China Experimental Fast Reactor (CEFR) is a sodium-cooled pool type fast reactor with primary circuit equipment integrated in the main vessel. The main vessel, which constitutes the primary pressure boundary, is also an essential barrier to contain radioactive gas. In order to further evaluate the radionuclide activity during normal operation and accident conditions, it is necessary to measure the leakage rate accurately, for sake of the operation and the safety requirements. CEFR leakage rate was previously measured based on step-down method. The step-down method requires reactor keeping steady condition for long time which is difficult to maintain. Besides, lack of temperature measuring points in the CEFR main vessel argon chamber add up difficulties to accurately evaluate the influence of the temperature fluctuation on the leakage rate. CFD simulation method is adopted to model argon chamber and essential in-vessel structures to obtain finer temperature field. The main vessel wall temperature and the sodium temperature are taken as boundary conditions to calculate the argon chamber temperature. The maximum deviation between test pressure and simulation pressure is 5.91% in the rapid temperature rise test. The correctness and reliability of the simulation scheme are preliminary verified with uncertainty analysis. Based on the established method, the leakage rate measurement test is inspected and the calculation leakage rate 96.39L is obtained. This method has important application value and practical significance by providing reference for the optimization of main vessel leakage rate measurement. Keywords: CEFR · Main vessel · Leakage rate measurement · Step-down test · Numerical simulation

1 Introduction Safety is the primary goal of nuclear reactor design and operation. Reactor safety affects the operation and maintenance of nuclear reactor, as well as environmental safety and public psychology. The long-term integrity and effectiveness of the primary circuit is a good guarantee for the safe operation of the reactor and protection of the external environment from radioactive contamination. As an important part of the primary circuit, the sealing importance of the main vessel includes two aspects: On the one hand, the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1084–1095, 2023. https://doi.org/10.1007/978-981-19-8780-9_104

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main vessel contains a large amount of gas, which is an important factor in the stable operation of the reactor. If there is a leakage, it will be no good for normal operation of the reactor. On the other hand, the gas in the main vessel contains a large amount of radioactive material. If there is a leakage, it will affect environment and may cause major safety threaten [1]. The China Experimental Fast Reactor (CEFR) has a thermal power of 65 MW and an electric power of 20 MW. It adopts a sodium-sodium-water three-circuit design. The core, the primary circuit main pump, the intermediate heat exchanger, and the independent heat exchanger are all installed in the main vessel. It constitutes an integrated pool structure and undertakes important safety functions such as the pressure boundary of the primary circuit of the reactor and the containment of radioactive materials [2]. The liquid sodium is covered with argon in the main vessel. The radioactivity in argon mainly comes from activated argon, gaseous activation products of impurities in coolant sodium, fission gases or volatile elements leaked from damaged fuel elements, and vapor aerosols of radioactive sodium. There are many penetrations in the cone roof of the main vessel. Part of seals are mechanical, which may cause a leakage of argon [3]. The CEFR specification states that the leakage rate of the main vessel under 100 L/d in normal operating conditions. There are many factors affecting leakage rate measurement of the main vessel, including the volume error and argon chamber temperature. It ensures the accuracy of the temperature correction of the argon chamber by modeling and testing, so as to obtain a more accurate leakage rate.

2 Main Vessel Leakage Rate Measurement Test 2.1 Methods of Leakage Rate Measurement There are many methods that can be measured the leakage rate, including bubble inspection method, helium mass spectrometer, radiometric method and step-down method. The bubble inspection meth method is to fill the inspected part with a certain pressure of leakindicating gas and then put it into the liquid. The gas enters the surrounding liquid through the leak hole to form bubbles. According to the rate of bubble formation and the size of the bubble, the leakage rate of the leak hole can be calculated. However, due to particularity of sodium-cooled fast reactor, the bubble inspection method is not suitable for leakage rate measurement of CEFR. In theory, whether quantitative or semi-quantitative, helium mass spectrometer can be used for CEFR leakage rate measurement test. However, the structural size, site and reactor roof irregularity determine that helium mass spectrometer is not suitable for CEFR overall leakage rate measurement. Radiometric method compares the activity of 41 Ar in the reactor core with the activity from ventilation ducts and calculates the leakage rate. This method is more accurate, but due to the low activity of 41 Ar in CEFR argon chamber and large ventilation volume, it cannot monitor the corresponding data changes. Therefore, CEFR leakage rate measurement can only adopt the step-down method. When the core temperature is stable for a period, record the value of the pressure gauge. After continuous monitoring for a period, the pressure change measured by the pressure gauge can be used as the pressure change caused by leakage, to calculate the reactor body leakage rate [4].

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The objects measured by step-down method include the main vessel and the compensation vessel, which are connected by pipes. The function of the compensation vessel is to provide a compensation space to ensure that the main vessel argon chamber pressure is automatically adjusted to the allowable range during operation of the reactor. Due to the difference in argon chamber temperature between the main vessel and the compensation vessel, the ideal gas equation calculates separately the two parts. For argon in the main vessel, the number of moles in the initial state is nR1 =

P1 VR TR R

After a period, the number of moles is nR2 =

P2 VR TR R

For gas in the compensation vessel, the number of moles in the initial state is ng1 =

P1 Vg Tg R

After a period, the number of moles is ng2 =

P2 Vg Tg R

Then the number of moles of leakage gas is nLeakage = nR1 − nR2 + ng1 − ng2 P1 Vg P2 Vg P2 VR P1 VR − + − . = TR R TR R Tg R Tg R

2.2 Analysis of Influencing Factors of Main Vessel Leakage Rate Measurement There are some errors in measuring main vessel leakage rate by step-down method. In addition to the influence of its own gas leakage on pressure, the volume error and temperature change of main vessel argon chamber also affect pressure, resulting in inaccurate measurement results of leakage rate. Then analyze the influencing factors of the main vessel leakage rate measurement. 2.2.1 Influence of Volume Error of Main Vessel on Leakage Rate Measurement According to the ideal gas equation of state PV = nRT when T is constant, RT is constant, so as a certain mass has a definite PV. The mass of the gas can be expressed by the product PV of the pressure and the volume of the gas P1 V1 = P2 V2

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P1 V1 = P2 V2 The pressure drop due to the leakage of the main vessel is PV = P1 − P2 =

VP1 V1

The leakage rate of the main vessel is V = P1 P2 V1 V2 V

PV V1 P1

main vessel chamber pressure at the beginning of measurement main vessel chamber pressure after time t main vessel chamber volume at the beginning of measurement after time t, gas volume in V 1 is equivalent to the volume under P2 the amount of gas leakage from main vessel after time t = V2 − V1

By using 3D software and model calculation, the volume of the main vessel is 71.5 m3 and that of the compensation vessel is 32.8 m3 , so the total volume V 1 is 104.3 m3 . Due to the errors in theoretical calculation and actual equipment processing and the difficulty of precise calculation of the fine structure volume, the volume calculation is conservative. The argon chamber volume of V 1 is larger than the actual volume. The larger the argon chamber volume V 1 is, the larger the leakage rate V is, so the calculated leakage rate is conservative. 2.2.2 The Influence of the Main Vessel Argon Chamber Temperature on the Leakage Rate Measurement Assuming there is no gas leakage from the reactor, calculating the pressure change caused by argon chamber temperature change according to the ideal gas equation. P1 P2 P2 = = T1 T2 T1 + T PT = P2 − P1 In this way, the pressure drop caused by the temperature change T of argon T × P1 T1 Assuming the pressure drop caused by the main vessel leaking 100 L a day, PT =

145.1 × 10−6 PT × (200 + 273) = 0.453 × T1 = P1 0.151364 When the argon chamber temperature decreases by 0.453 °C, it is equivalent to the pressure drop caused by 100 L/d gas leakage. It can be seen from the above analysis that pressure change caused by argon chamber temperature change is obvious, which has a significant influence on the leakage rate measurement. Moreover, there is no temperature measuring point in the main vessel argon chamber, so it is necessary to calculate the argon chamber temperature. T =

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3 Simulation Analysis of the Main Vessel Argon Chamber Temperature 3.1 Argon Chamber Temperature Simulation 3.1.1 Model Introduction According to the actual size, establish the argon chamber of main vessel and compensation vessel. They are connected by pipe (shown in Fig. 1). The argon chamber of the main vessel is a closed space enclosed by the sodium free liquid surface, the conical top cover of main vessel and parts of various penetrations above the sodium liquid surface. The thickness of the conical top cover is 25 mm. The penetrations include two main pumps, four intermediate heat exchangers, two independent heat exchangers, cocks and central measuring column. The function of the compensation vessel is to provide a compensation space to ensure that the main vessel argon chamber pressure is automatically adjusted to the allowable range during operation of the reactor.

Fig. 1. Main vessel argon chamber and compensation vessel model

Fig. 2. Overall mesh figure

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The grid division of the computational model is completed using ICEM-CFD, which is mainly tetrahedral mesh. It improves the quality by refining thermal boundary layer near the penetration surface. Finally, the total number of grids is 13.98 million (shown in Fig. 2). 3.1.2 Fluent Simulation In general, the Reynolds number is used to judge whether the flow is turbulent in flow calculation. However, in natural convection, Rayleigh number is used instead of Reynolds number. Formula is Ra = β g L T υ α

βgL3 T υα

expansion coefficient acceleration of gravity characteristic length temperature difference kinematic viscosity thermal diffusivity

It is generally considered that the flow model is turbulent when Rayleigh number > 109 . Select standard K-ε model according to various turbulence models in FLUENT software and argon chamber. The material of this calculation includes argon, sodium and stainless steel. The density and thermal conductivity of argon and sodium are compiled into UDF and imported into FLUENT by selecting the French OASIS empirical formula. For radiation heat transfer, DO radiation model is suitable for all optical depth ranges. The emissivity of stainless steel is 0.26, and that of sodium-argon interface is 0.037 [5]. The sodium liquid surface and the wall of the main vessel are set as the constant temperature boundary, which is obtained through the experiments. At the same time, the internal wall structures such as the reactor internals adopt coupled heat transfer to simulate the heat transfer characteristics. 3.2 Rapid Temperature-Rise Test 3.2.1 Theoretical Calculation Method of the Rapid Temperature-Rise Test This paper takes CEFR as the research object, a rapid temperature-rise test was carried out to verify the usability of the model in 11.1–11.3, 2021. The test lasted 51 h. Assume that there is no leakage in the main vessel. According to conservation of mass, the mass of argon in the main vessel and the compensation vessel is equal before and after heating. nR1 + ng1 = nR2 + ng2 From the ideal gas state equation, we can get PV = nRT

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According to the micro-element analysis method, every tiny volume in the system satisfies the ideal gas state equation [6]. n  P1 VR1i i=1

RTR1i

+

n  P1 Vg1j j=1

RTg1j

=

n  P2 VR2i i=1

RTR2i

+

n  P2 Vg2j j=1

RTg2j

After heating the pressure is n P2T = P1 

VRi1 i=1 TR1i

n VR2i i=1 TR2i

n

Vg1j j=1 Tg1j  Vg2j + nj=1 Tg2j

+

The temperature of each micro-element in the argon chamber of main vessel and compensation vessel is obtained by modeling and simulation, and the argon chamber pressure P2T is calculated after heating. Then compare the pressure P2T with the experimental pressure P2 to verify the reliability of the simulation model. 3.2.2 Rapid Temperature-Rise Test Data Assume that there is no leakage in the main vessel. The change of argon chamber pressure is only affected by temperature and has nothing to do with leakage rate. When the core outlet temperature is 243, 270, 280 °C and stabilized for at least 2 h respectively, the main vessel wall temperature, the compensating vessel wall temperature and the argon chamber pressure are recorded. The location of temperature measuring points on the wall of the main vessel and the compensation vessel are shown in Figs. 3 and 4. The wall temperature of the main vessel and the compensation vessel, and the argon chamber pressure are shown in Table 1. The wall temperature, sodium surface temperature and argon chamber pressure are used as the input to verify the reliability of the simulation model.

Fig. 3. Location of temperature measuring points on the wall of the main vessel

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Fig. 4. Location of temperature measuring points on the wall of the compensation vessel

Table 1. Main vessel and compensation vessel wall temperature Measuring point Main vessel

Compensation vessel

Core outlet temperature 243 °C

270 °C

280 °C

Argon chamber pressure (Pa)

134,565.0

139,171.7

140,861.7

T10279 (°C)

107

111

113

T10280 (°C)

148

155

159

T10281 (°C)

168

180

184

T10282 (°C)

185

203

210

T10205 (°C)

197

222

233

T10206 (°C)

201

232

248

T10207 (°C)

209

248

275

T10208 (°C)

239

268

277

TE18001 (°C)

24

24

24

TE18002 (°C)

24

24

24

TE18003 (°C)

23

23

23

3.2.3 Analysis of Rapid Temperature Rise Results According to the rapid temperature rise test, three sets of temperature rise data are obtained, respectively 243–270 °C, 243–280 °C, 270–280 °C. The temperature of each micro-element in the argon chamber of main vessel and compensation vessel is obtained by modeling and simulation, and the argon chamber pressure P2T is calculated after heating. Compare the experimental pressure change with calculated pressure change, as shown in Table 2.

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Temperature rise (°C)

Experimental pressure change P0 /Pa

Calculated pressure change PT /Pa

Relative deviation (%)

243–270

4606.7

4413.9

4.18

243–280

6296.7

6001.8

4.68

270–280

1690.0

1590.1

5.91

According to the above analysis, the maximum deviation between test pressure and simulation pressure is 5.91%, which is basically consistent with each other. The sources of analysis error mainly include: (1) The limitation of temperature and pressure sensor accuracy during the test. (2) Although the temperature rise test time is short, there is also a small leakage in the main vessel, which should be taken into account. (3) In the simulation, the wall temperature fitting curve is used as the input to the wall temperature, and there is a certain error. Considering the experimental measurement error, simulation error and leakage, the simulation scheme is basically correct. In Sect. 3.2.4, a preliminary uncertainty analysis was conducted as confirmation. 3.2.4 Pressure Uncertainty Analysis The measuring pressure of the main vessel is read directly from the pressure gauge. Considering the random error of the pressure gauge, the uncertainty of the pressure of the main vessel is evaluated. The sources of uncertainty mainly include: (1) The standard uncertainty uA brought by the measurement repeatability is generated by the stability and random factors of the instrument used. (2) The standard uncertainty uB brought by pressure gauge range inaccuracy The uncertainty brought by measurement repeatability can obtain a set of data through repeated measurements and calculate the standard uncertainty by statistical analysis method. The formula is  n 2 i=1 (si − s) uA = n(n − 1) According to the description of the pressure gauge, the measurement pressure of the main vessel is gauge √ pressure, the range is 0–200 kPa, the accuracy is ± 0.05% of the range, and k = 3 is taken according to the uniform distribution. uB =

200 × 0.05% a = = 57.74 Pa √ k 3

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Synthetic standard uncertainty uC =



uA2 + uB2

When the confidence probability is 95%, k = 2, the expanded uncertainty U is U = k × uC Finally, the uncertainty of the measurement results is shown in Table 3. Table 3. The uncertainty of the measurement results Core outlet temperature (°C)

243

270

280

Measuring pressure 1 (Pa)

134,565

139,155

140,815

Measuring pressure 2 (Pa)

134,565

139,155

140,865

Measuring pressure 3 (Pa)

134,565

139,205

140,905

Average pressure P0 (Pa)

134,565.0

139,171.7

140,861.7

Standard uncertainty uA

0.0

13.6

26.0

Standard uncertainty uB

57.7

57.7

57.7

Synthetic standard uncertainty

57.7

59.3

63.3

Expanded uncertainty U

115.5

118.6

126.7

The 2 σ uncertainty results indicate that the simulation pressure obtained in Sect. 3.2.3 is relatively confidential. 3.3 Leakage Rate Measurement Test 3.3.1 Theoretical Calculation Method of the Leakage Rate Measurement Test In this paper, the reliability of the model is verified again by using data with stable sodium temperature and little fluctuation for a long time. It is assumed that the mass of initial state gas in the main vessel is m1 , the mass of final state gas is m2 . The lost gas mass is m = m1 − m2 Converting the mass to the volume of gas at atmospheric pressure Va =

m × Vmol M

Then the volume of gas at atmospheric pressure is converted to the volume of gas at P1 Pa Va P1 V1 = Ta T1

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Finally, the daily leakage volume of the main container is V1 =

Pa Va T1 . P1 Ta

3.3.2 Leakage Rate Measurement Test Data This paper selects the leakage rate experimental data of the main vessel during the commissioning period from 2009.10.8–10.30. During this period, the sodium temperature remained at 233 °C. The pressure of the main vessel changes stably and regularly. According to the data collected, the pressure P1 on 10.8 is 151,364 Pa and the pressure P2 on 10.30 is 148,802 Pa in the main vessel. 3.3.3 Analysis of Leakage Rate Measurement Test Results The argon chamber mass on 10.8 and 10.30 is obtained through simulation calculation, as shown in Table 4. Table 4. Argon chamber mass

Argon chamber mass of main vessel (kg) Argon chamber mass of compensation vessel Argon chamber mass of pipe (kg) Total argon chamber mass (kg)

10.8

10.30

111.15

109.03

79.25

77.90

0.12

0.12

190.52

187.05

The argon chamber mass reduced from 10.8 to 10.30 is the mass of the leakage argon. m = m1 − m2 = 190.522 − 187.0572 = 3.4648 kg Converting the lost argon mass to the argon volume in the standard state (0 °C, 101,325 Pa). Vs =

m 3464.8 × Vmol = × 22.4 = 1942.81 L M 39.948

Then converting the argon volume in the standard state to the argon volume in normal temperature and pressure (25 °C, 101,325 Pa). Ps Vs Pn Vn = Ts Tn 101325.0 × 1942.81 101325.0 × Vn = 273.15 25 + 273.15 V1 = 2120.63 L

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The test lasted for 22 days, and the daily leakage of the main vessel is V1 = 96.39 L 22 The leakage rate measurement test result of the main vessel is 89.61L by step-down method. The result of this model is conservative. Vday =

4 Conclusions The main vessel leakage rate of CEFR is measured by the step-down method. Besides of actual leakage, the deviation of the reference volume and temperature also has an influence on argon pressure change, resulting in imprecise leakage rate calculation. This paper mainly studies the influence of argon chamber temperature change on leakage rate and corrects the argon chamber temperature of the main vessel. Due to the main vessel’s sealing property, temperature measuring points cannot be installed in the argon chamber of the main vessel. The temperature field can only be calculated by numerical simulation using the vessel wall temperature as boundary. This research gives the following conclusions: (1) The maximum deviation between test pressure and simulation pressure is 5.91% in the rapid temperature rise test. The correctness and reliability of the simulation scheme are verified in this paper. In the leakage rate measurement test, the leakage rate by this simulation is 96.39 L, which is relatively conservative compared to the previous result. (2) Numerical simulation directly solves the governing equations of mass, momentum and energy. For solving temperature field problems under general model and boundary condition, numerical solution can be obtained. It has obvious advantages to overcome the limitation that the theoretical calculation cannot get the analytical solution and the test cannot be directly measured. This method has important application value and practical significance. It can provide reference for the optimization of main vessel leakage rate measurement in the future.

References 1. Liang, Z.: Sealing Characteristics Analysis of Liquid Metal Cooled Reactor Main Vessel. University of Science and Technology of China (2016) 2. Zhang, Y.: Analysis of thermal characteristics of cold sodium pool and reactor internals in China experimental fast reactor based on integrated 3D numerical simulation. Nucl. Sci. Eng. 40(3), 499–507 (2020) 3. Wu, Q., Zhang, D., Liu, Y. : Experimental principle and analysis of argon space heat transfer in fast reactor main vessel. Nucl. Sci. Eng. 29–34 4. Wang, M.: A brief analysis of measuring the leakage rate of CEFR. Annual Report of China Institute of Atomic Energy, 25–26 (2007) 5. Qi, L., Hong, X., Sun, L. : Numerical study on internal temperature field and pressure field characteristics of high-altitude balloon. Vacuum 54(5), 47–51 6. Zhang, X.: Thermo Hydraulic Behavior of Cover Gas in Complex Space on Top of Fast Reactor Main Vessel. North China Electric Power University (2019)

Emergency Power Supply System Scheme and Safety Analysis of a Project in a Newly-Built Large Nuclear Chemical Plant Shuo Gao(B) , Meng Wang, and Shizhong Tian China Nuclear Power Engineering Co., Ltd., Beijing, China [email protected]

Abstract. With the development of modern society, urban buildings and industrial plants are becoming increasingly large and complex. The informatization and security demands of buildings make a more reliable power supply system is indispensable, especially for those important public buildings, such as nuclear power plants and some large chemical plants, once the interruption of power supply may cause serious consequences. The reliability of power supply directly reflects the power supply capacity of the power supply system to the downstream electrical equipment, it is necessary for electrical designers to come up with the optimal result after comprehensive analysis about all electricity demands and overall power supply scheme. Emergency power supply system is a kind of system that specially set for some crucial electrical equipment to meet the harsh electrical demand of these equipment, which means the power supply is still continuous under accident conditions. In this article, several main schemes of emergency power supply system are mentioned. Next, we focus on introducing in detail the general situation of a newly-built large nuclear chemical plant and the scheme setting of emergency power supply system required by some buildings in the plant. Furthermore, the purpose and significance of the emergency power supply system for downstream buildings are analyzed from the perspective of safety. In the last part of this article, the development prospect of the emergency power supply system for future larger nuclear chemical plants and the key role it plays in the safety on the whole plant are discussed. Keywords: Reliability · Emergency power supply system · Normal condition · Transformer fault · Accident condition · Nuclear chemical plant

1 Introduction In recent years, the scale of many new communities and industrial plants has become increasingly large. The demand for electricity in these projects, both quantity and quality, has been significantly improved, especially some important projects, such as luxury communities, large shopping malls, nuclear power plants and chemical plants, many electricity loads in these projects undertake important safety and production functions, once the power is lost, there will be a significant impact on normal production, personal © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1096–1101, 2023. https://doi.org/10.1007/978-981-19-8780-9_105

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safety and even the whole project. Therefore, these main projects have extremely strict requirements for the reliability and safety of the power supply system. At the beginning of the design, electrical designers need to formulate a set of perfect, reasonable and reliable power supply scheme by means of building function analysis, loads classification and calculation, and safety function analysis of electrical equipment, so as to meet the requirements of users for the reliability of power supply system.

2 Design Principle and Process 2.1 Project Overview A super large-scale nuclear chemical plant is a brand new nuclear chemical project under construction in China. Its scale and complexity are the first time in the field of nuclear chemical industry. The functions undertaken after completion are an indispensable part of the territory of China’s nuclear field. A project is one of the key projects in this nuclear chemical plant. The main function of this project is to provide emergency power supply for those process systems, support systems and control systems which are related to the safety in other key buildings. There is no doubt that this project plays the role of “heart” in the whole super large nuclear chemical plant (Fig. 1).

Fig. 1. Emergency power supply relationship diagram

2.2 Radiochemical Safety Level Loads Analysis According to EJ/T 939-2014, radiochemical safety level equipment refers to the electrical equipment suitable for preventing accidents and protecting workers and the public during and after accidents, which mainly performs or supports the nuclear safety function of reprocessing plant, as well as the function of preventing and mitigating accidents or post-accidents monitoring. Through the analysis of electricity load, this project needs to meet the power demand of about 1300 kW of radiochemical safety level equipment in the four downstream projects. Considering the importance of radiochemical safety level equipment, the power supply mode should be upgraded and some appropriate measures to improve the safety level of the whole power supply system and supporting facilities are utilized in this project.

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2.3 Emergency Power Supply Schemes In the current electrical design field, for the vital load in first grade load and other important loads related to safety, the emergency power supply schemes commonly used are mainly the following. 1) Two normal power supply + Diesel generator set to provide emergency power supply; 2) Two normal power supply + EPS to provide emergency power supply; 3) Two normal power supply + UPS to provide emergency power supply. 2.4 Emergency Power Supply Scheme in This Project The normal power supply system in the project is composed of two transformers and supporting emergency low-voltage switch cabinets. The normal bus sections of the two transformers are connected by the bus bar circuit breaker. The emergency power supply system is mainly composed of two sequences of redundant and independent emergency power sources and emergency power distribution devices. The emergency power sources for each sequence include the emergency diesel generator set and the emergency uninterruptible power supply UPS. Under normal working conditions, the emergency low-voltage switch cabinets are powered by the normal power supply system. When an accident occurs and two normal power supplies are interrupted, the power supply is switched to the emergency diesel generator set to ensure the power demand of the downstream emergency load. The ~ 220 V emergency uninterruptible power supply UPS provides power for the reliable switching between the emergency and the normal power supply system of the project as well as the downstream emergency load with uninterrupted power supply demand. 2.5 Supporting Facilities of the System In this project, in order to ensure the independence of safety grade electrical equipment and circuits, in addition to the independent setting of emergency power supply, emergency low-voltage switch cabinets and other devices in each sequence, the physical separation of equipment and circuits shall be achieved by improving the safety grade of structures and the separation distance or barrier with other irrelevant equipment in the design and actual construction, and some isolation devices shall be used to meet the requirements of electrical isolation. Moreover, all emergency power distribution circuits from this project to other buildings must use K3 type nuclear grade cables and are laid to the corresponding positions in other projects along the safety grade cable tray in the seismic cable trench. All electrical equipment and cables of the emergency power supply system must reach the class 1 seismic level stipulated in EJ/T 939-2014 to ensure normal implementation of nuclear safety function of the emergency system during and after the earthquake.

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Fig. 2. Flow chart of emergency power supply system

3 Operating Mode 3.1 Under Normal Condition As can be seen from Fig. 2, when the whole power supply system is in normal operation, the emergency diesel generators of the two sequences are in standby state, and the power supply of the emergency low-voltage cabinets is provided by transformers. At this time, the outgoing circuit breakers 5QLE and 6QLE of diesel generators and the incoming circuit breakers 1QLE and 2QLE of the emergency bus WBE-I and II are all disconnected. The incoming circuit breakers 1QL and 2QL of the normal bus WB-I and II are closed, and the contact circuit breakers 5QLE and 6QLE are also closed. The bus circuit breaker 3QL between the two normal busbars is disconnected. Meanwhile, the normal bus section WB-I and II are charged by two transformers respectively. As all circuit breakers of the circuit are closed, the emergency bus sections WBE-I and II will also be charged by transformers. 3.2 One Transformer Fault When a normal incoming power supply fails, the incoming circuit breaker of the normal bus in the fault line is disconnected. At this time, the bus bar circuit breaker 3QL automatically closes and manually disconnects the downstream third stage loads. Then one transformer is responsible for the power supply for second and above loads and emergency loads.

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3.3 Under Accident Condition When both transformers fail, the emergency bus section WBE-I and II lose power together. After a delay of 3 s, the system checks and confirms the power failure. Then contact circuit breakers 3QLE and 4QLE are disconnected and a start signal is sent to the emergency diesel generators. When the voltage and frequency of one diesel generator set reach the rated value, close the circuit breaker (5QLE or 6QLE) in this sequence, the circuit breaker 1QLE or 2QLE of this emergency bus will automatically close, and then the emergency bus is charged and supply power for the downstream emergency loads. When the normal power supply is restored, the incoming breaker 1QLE or 2QLE of emergency bus is disconnected firstly, and then the contact breaker 3QLE or 4QLE is closed, the diesel generator set is manually stopped, and the system returns to the normal power supply state.

4 Safety Analysis It can be seen from the design scheme and operation mode that the emergency power supply system in this project can provide safe, reliable and continuous power for the safety stage loads of many downstream projects under both normal and accident conditions, what effectively ensures the smooth operation of important downstream process systems, safety support and control systems, and will not cause secondary disasters such as production interruption, raw material leakage and detection failure because of power loss. The setting of emergency UPS ensures the smooth switching between normal and emergency power supply, and also ensures the continuity of data signal transmission and storage under accident conditions. In addition, as the whole emergency power supply system and related supporting facilities are divided into two redundant and independent sequences, each sequence can independently complete the nuclear safety function, it deeply ensures the reliability and independence of safety level equipment operation. By improving the seismic and electrical isolation level of the safety level power supply path, the emergency power supply system can still reliably supply power to downstream loads under extreme conditions such as floods and earthquakes. Moreover, additional interfaces are reserved at the emergency low-voltage switch cabinet, when the emergency diesel generator fails to start under accident conditions, it can ensure that the emergency power supply system can be externally connected to the mobile diesel generator vehicle for power supplying. Therefore, the emergency power supply system ensures the continuity, safety and economy of nuclear and chemical production and plays a vital role in the safe operation and disaster protection of the whole super large nuclear chemical plant.

5 Conclusions The setting of emergency power supply system based on emergency diesel generator and UPS helps to the emergency support work in large nuclear and chemical plants and even the whole electric power industry. The rational use of modern technology and devices to design the emergency power supply system has significantly improved the rapid response

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ability and flexibility of power supply. It perfectly solves the relationship between longterm storage and emergency use in power supply guarantee. In the construction of phase II and phase III of super large-scale nuclear chemical projects and more large-scale 800 tons nuclear cycle plants in the future, the emergency power supply system will have a broader application space and provide safe and sustainable power for more important safety level loads deeply.

Bibliography 1. Yuanhui, R. (ed.): Industrial and Civil Power Distribution Design Manual. China Electric Power Press (2016) 2. 19DX101-1: Common Data of Architecture Electricity. China Building Standards Design and Research Institute (2019) 3. GB 50054: Code for Design of Low-Voltage Electrical Installations. China National Standard (2011) 4. EJ/T 939: Classification for Structures, Systems and Components of Nuclear Fuel Reprocessing Plant. China Nuclear Standard (2014) 5. Juan, L.: Discussion on typical scheme of emergency standby power supply system for low voltage diesel generator. Electr. Technol. Intell. Build. (15) (2021)

Total Quality Management in the Power Supply Design of a Project in a Newly-Built Large Nuclear Chemical Plant Shuo Gao(B) , Zhi Huang, and Shizhong Tian China Nuclear Power Engineering Co., Ltd, Beijing, China [email protected]

Abstract. In modern engineering projects, owners and users pay more attention to the design quality of the electrical system of the project, because a safe and reliable power supply system is one of the decisive factors to determine the perfect realization of the function of the project, the more important the project, the more the need for high-quality power supply design. And the quality of design work of always depends on how designers control details in the process. Therefore, engineers attach more importance to a full-scale quality management in electrical design process. As for the development of quality management system, it has experienced different stages, from the initial quality inspection stage to the statistical quality control stage. And with the continuous progress of social production and advance of science and technology, now total quality management concept is being widely used. The combination of the total quality management and electrical construction drawing design reasonably has become the key path to complete electrical design. This article mainly introduces the reason why the total quality management theory is applied to the electrical construction drawing design of a project in a newly-built nuclear chemical plant. Meanwhile, how four key links of the total quality management are implemented in the electrical design is presented detailedly. The importance of the quality management in the electrical power supply design of the project is expounded as well. In addition, the application of the total quality management in the power supply design and what it will look like in the future are analyzed in this article. Keywords: Power supply design · Total quality management · Nuclear chemical plant · Electrical construction drawing · Relevant customers

1 Introduction In the age of electricity, all aspects of production and life are inseparable from power supply. In modern engineering projects, whether residential buildings or factories, users have put forward much higher requirements for the stability and reliability of power supply, which means that high-quality electrical system design has become one of the key factors to determine the success of the whole engineering project. Therefore, in the design process of electrical system, electrical designers need to make efforts to integrate © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1102–1108, 2023. https://doi.org/10.1007/978-981-19-8780-9_106

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advanced electrical technology and design concept into the design, and strictly control the design quality and details by adopting appropriate and effective means, so as to produce results that satisfy both designers and customers.

2 Characteristics and Functions of Total Quality Management The application of total quality management is a relatively new idea in the field of engineering design. Many designers are committed to improve the design quality to a greater extent by effectively combining the concept of total quality management with construction design. 2.1 Development of Total Quality Management The development of quality management concept has gone through three stages. The first stage is the quality inspection stage, which means the products quality is inspected by a special person after the production. The second stage is the statistical quality control stage, which uses the principle of mathematical statistics to carry out sampling inspection on product quality. However, a large number of practices have proved that the first two stages cannot fully supervise and control the production process and product quality. Therefore, the third stage, total quality management, has gradually developed. Total quality management means that all individuals of the team and relevant departments make concerted efforts to combine professional technology, operation management and ideological education and establish a quality assurance system for the whole process of product research and design, production and manufacturing, after-sales service and other activities. By using the most efficient means to produce the most satisfactory products for users. 2.2 Combination of Electrical Design and Total Quality Management The large nuclear chemical plant is a large-scale nuclear chemical project currently under construction in China. It is the first project with such a large construction scale and complexity in the field of nuclear chemical industry as well as an indispensable part in the field of nuclear energy utilization and recycling in China. The project is one of the important components of this nuclear chemical plant, and its functions make it obviously irreplaceable. There is a large number of electrical equipment in the project, which means a safe and reliable power supply system is a necessary prerequisite for the project to achieve its functions. In order to design high-quality electrical construction drawings, the design team applied the concept of total quality management to the actual design to ensure the high quality and high efficiency of each link in the whole design process.

3 Application of TQM in the Project In the process of electrical construction drawing design of this project, total quality management is mainly completed in four stages, which are: demand determination,

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construction planning, construction process and construction effect. Figure 1 shows the logic structure of TQM in the design process of electrical construction drawings of the project.

Fig. 1. Logic structure of total quality management in this project

3.1 Demand Determination (1) Clear positioning: the positioning of the electrical design of the project is to ensure that the project can meet the established functional requirements and performance indicators based on accurate electrical knowledge and technology as well as relevant national standards and specifications. (2) Identify relevant customers: through sorting out the design process, as the downstream major, electrical designers need to receive the power consumption data from other majors. Meanwhile, the external interface of the power consumption data is transferred by the procurement department. During the whole design cycle, the quality assurance department is responsible for supervising the design quality. When the design is completed, the electrical construction drawings shall be delivered to

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the proprietor and the construction company for construction. Therefore, relevant customers of the electrical design of this project are identified, as shown in Fig. 2.

Fig. 2. Relevant customers diagram in this project

As the actual user who puts forward the project requirements, the proprietor can be determined as the core customer of the electrical design of this project. (3) Identify requirements: according to the different role positioning and functions of the above customers, their respective key requirements were identified through targeted interviews and research. Table 1 shows the key requirements of those relevant customers.

Table 1. The key requirements of relevant customers No

Customers

Customer requirements

1

Upstream majors

Accurate power supply to equipment

2

Procurement department

Accurate interface data and material list

3

Quality assurance department

Good quality of electrical designs

4

Proprietor

Complete set of design documents and technological guidance

5

Construction company

Clarification and technological guidance

3.2 Construction Planning The core purpose of the electrical design of this project is to ensure that high-quality electrical construction drawings can create value for customers. Therefore, in the process of construction planning, it is necessary to determine the key improvement work and target value of this electrical design firstly.

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A complete set of electrical construction drawings mainly includes electrical system diagrams, lighting diagrams, plan diagrams and material list. Therefore, improving the accuracy of each part can be regarded as the key improvement of this electrical design. Table 2 reflects the target values of each improvement work. Table 2. Improvements and target values Improvements

Target values

Electrical system diagrams

No component parameter error

Lighting diagrams

No lamp and switch error

Plan diagrams

No drawing annotation error

Material list

No device and cable missing

3.3 Construction Process (1) Improve the technical ability of designers: as the starting point of the electrical construction drawings of this project, the technical level of the designers determines the upper limit of the design quality. Therefore, before and during the design process, the team helped the designers improve their knowledge reserve and technical level by organizing training, technical guidance, simulation drawing, manufacturer investigation and other forms. (2) Improve basic management: in addition to improving the technical level of the designers, the team has also established a complete self-inspection management system of design, proofreading, review and approval, and a review process of countersigning by the designers of other majors and spot checking by the quality assurance department. Through self-inspection, experienced senior designers in the team with strong grasp of details can be integrated into the electrical design of this project. Moreover, relying on multi-level verification can ensure that the design progress is completed on time and the design details are accurate. At the same time, the countersignature process ensures that there is no omission in the power supply design for all electric equipment, and the spot check process adds a layer of guarantee to the design quality. (3) Improve process: due to the independence and particularity of the project, the power load, circuit breaker parameters, cable cross-sectional area, etc. need to be calculated separately in the design process. Through accurate calculation and effective improvement, designers attempt to find ways to reduce the excessive consumption of materials under the situation that the component selection meets the load demand, so as to achieve high quality and low energy consumption. 3.4 Construction Effect (1) Achieve goals: through the unity and cooperation of all team members, the electrical construction drawing design of this project was successfully completed within the

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specified time. Through the multi-level participation of proofreading, review and approval, the quality of the final finished document has been significantly improved compared with initial one, and all drawing and principle errors have been eliminated. Figure 3 shows some system diagrams of the initial and final versions. (2) Extract features: in the electrical construction drawing design of this project, through accurate calculation and market research, the design team creatively adopted the current limiting circuit breakers to replace the traditional circuit breakers, relying on the function of limiting the peak energy of the current limiting circuit breakers, the cross-sectional area of some outgoing cables in the low-voltage distribution panels can be reduced without affecting the thermal stability of the cables. It not only improves the economy, but also provides a reference for later engineering designs. (3) User feedback: the electrical construction drawings of this project have been inspected by the quality assurance department, and the qualified rate of the drawings is 100%. The countersignature of each major has been completed before the drawings are published. At present, the project has entered the construction and installation stage. According to the feedback of the proprietor, this set of electrical construction drawings provides a clear and definite construction idea and drawing presentation for the on-site construction unit. The construction personnel carry out the construction according to the drawings very smoothly. The commissioning personnel have not encountered common construction problems such as power supply failure, thermal relay tripping, and the inconsistency between the material list and the drawings during the commissioning process. Therefore, the electrical design team of this project is highly recognized by the proprietor.

4 Conclusions By integrating total quality management and electrical design, the electrical design team greatly improves the quality of electrical construction drawings and the satisfaction of the proprietor and other relevant personnel. At the same time, during this process, the technical level, management level, team awareness and quality awareness of all members have been effectively improved. The high-quality electrical design ensures the safety and reliability of power supply, which is the cornerstone for the effective realization of its functions after the completion of this project. Through the practice of this project, the effectiveness, rationality and necessity of the application of total quality management in electrical design are strongly demonstrated. As one of the many projects in a newly-built large nuclear chemical plant, the design process and concept of this project also provide a reference for other units with larger scale and more important functions, thus effectively ensuring the high quality of electrical design and the reliability of power supply capacity of the whole plant. In the future, the TQM will play a more important role in the electrical design of 800-ton large-scale nuclear chemical plants and other important plants, the concept of quality management will become more mature in continuous practices.

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Fig. 3. Comparison of initial and final version of system diagrams

Bibliography 1. Wang, H.: Application of total quality management idea in project design schedule management. Electr. Power Surv. Des. 03 (2013) 2. Chen, N., Liu, J., Xing, G.: Total quality management in power grid engineering design. China High Technol. Enterp. 24 (2015) 3. Guo, Y.: Discussion on electrical design and quality control measures. Res. Discuss. 08 (2018)

Surrogate Models Based on Back-Propagation Neural Network for Parameters Prediction of the PWR Core Xinyan Bei1,2 , Maosong Cheng1,2(B) , Xiandi Zuo1 , Kaicheng Yu1,2 , and Yuqing Dai1,2 1 Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China

{beixinyan,mscheng}@sinap.ac.cn 2 University of Chinese Academy of Sciences, Beijing, China

Abstract. Predicting the key neutronics parameters of reactor core accurately and efficiently is necessary during the reactor operation. On account of the nonlinearity and complexity of the reactor core, as well as the current development of computer, it still be difficult to solve the neutron transport equation accurately in real time. The neural network, which has been widely used in the nuclear industry in recent years, has the ability to obtain the accurate numerical solution for complex nonlinear problem in real time. In this paper two non-intrusive surrogate models based on Back-propagation (BP) neural network are proposed to predict the core key parameters accurately and effectively. The proposed surrogate models are applied to the modified 2D C5G7 MOX benchmark problems with a quarter symmetric PWR core. The interested inputs are the Plutonium isotope contents in MOX fuel with different enrichment and the target outputs are the effective multiplication factor (keff ) and neutron pin-by-pin flux distribution. Then the hyper-parameters optimization is performed to improve the prediction performance of the surrogate models. The dataset is generated by using Monte Carlo code OpenMC through changing Plutonium isotope content in MOX fuel. The numerical results show that the predicted values of the surrogate models are in good agreement with the values calculated by the Monte Carlo code. Moreover, the desired outputs can be obtained in seconds through the surrogate models, which is much more efficient than traditional directly numerical methods. The work demonstrates the feasibility and advantage of the proposed surrogate models to predict the key neutronics parameters of the reactor core, which has the important significance for the real-time online prediction of key parameters and the optimization design of the reactor core. Keywords: BP neutral network · Surrogate model · C5G7 · OpenMC · Rapid prediction

1 Introduction To support the nuclear power design and safe operation, it is vital to calculate and predict the core key neutronics parameters quickly and accurately, including keff , flux distribution, etc. Monte Carlo method [1] is being widely used to perform high-fidelity neutronics © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1109–1122, 2023. https://doi.org/10.1007/978-981-19-8780-9_107

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calculations in reactor analysis. However, the Monte Carlo code is time consuming and requires a relatively high amount of computational resources, especially for whole core heterogeneous calculation. The methodology of Artificial Neural Network (ANN) [2], a branch of machine learning, which has been widely used in various industries, may provide a good surrogate model. Over the last few years, ANNs have been applied successfully in different areas of nuclear power plants (NPP). Santos et al. [3] developed a nuclear accident identification model using deep rectifier neural network which can identify a number of operational situation quickly and accurately, especially without the need to separate the event in groups. In 2020, an unsupervised representation clustering methodology based on deep learning was used in automatic nuclear operating transient identification by Li et al. [4], providing a promising tool for the real industrial scenarios. Szames et al. [5] dealt with the modeling of homogenized few-group cross sections by ANN and achieved an acceptable error 0.1%. Guo et al. [6] used deep neural network to detect the defect of the nuclear fuel assembly. Lei [7] proposed a method based on deep convolutional neural network algorithm to evaluate the core refueling loading pattern accurately and effectively, which provided a fast-evaluation method for the refueling loading pattern optimization. In addition to various applications in NPP, deep learning is also widely used on core key parameters predictions. In 2021, Oak Ridge National Laboratory [8] predicted the neutronics parameters within a 2D reflective PWR assembly using deep learning. Zhang [9] from Harbin Engineering University trained a convolutional neural network inspired by the Fully Convolutional Network (FCN) for estimating the flux and power distribution calculated by the diffusion method. Turkmen et al. [10] explored the best performing machine learning (ML) technique for predicting feature parameters of the molten salt reactor core and demonstrated the applicability of the method as well. However, some simplifications including group collapse and fuel-cladding-coolant homogenization are introduced during modeling and simulation mentioned above. In this paper, two surrogate models based on neural network are proposed and designed to predict the key neutronics parameters of the reactor core with heterogeneous geometries, while no spatial homogenization is assumed. The Monte Carlo code OpenMC [11] with continuous-energy cross sections is used to model and generate the dataset through changing Plutonium isotope contents in MOX fuel with different enrichments. Then two BP connected neural networks are constructed to train the data and predict the key parameters of the reactor core. In order to achieve the best performance of the surrogate models, the optimization of the hyper-parameters is carried out based on Random search. Finally, the performance of models is analyzed and the computational efficiency is also compared between physics-based model and data-driven model. The paper is structured as follows: In Sect. 2, the complete workflow for the prediction of core key parameters by ANNs is introduced. In Sect. 3, the numerical results and error analysis are presented. The conclusions and summations are given in Sect. 4.

2 Methodology In this Section, the following contents will be described and introduced in sequence: (1) Specification of the reactor core, (2) Dataset generation, (3) ANNs framework design, (4) Hyper-parameters optimization. The general framework is given in Fig. 1.

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Fig. 1. The general framework of the surrogate models applied to neutronics

2.1 Specification of the Reactor Core In the current paper, the 2D C5G7 MOX benchmark problems [12] is chosen as shown in Fig. 2. The overall dimensions of the two-dimensional core configuration are 64.26 × 64.26 cm. Vacuum boundary conditions are applied to the right and bottom of the region while reflective boundary conditions are applied to the top and left of the region. Fuel region of the problem consists of UO2 assembly and MOX assembly with three different enrichment fuel pins, and the dimensions of each assembly is 21.42 × 21.42 cm. As seen in Fig. 3, each fuel assembly is made up of a 17 × 17 lattice of square pin cells, and the side length of each fuel-pin cell is 1.26 cm as shown in Fig. 4. Tables 1 and 2 give the isotopic contents in fuel-pin cell and control rod cell [13]. The layout of the fuel pin cell and guide tube are shown in Figs. 4 and 5, respectively. 2.2 Dataset Generation In order to obtain the sufficient search and optimization space, some reasonable modifications are introduced based on initial C5G7 problem. In the consideration of the safety criteria for the reactor and the number of sample spaces, the MOX fuel uses a depleted uranium matrix (0.21% enrichment U-235) [14] and the maximum enrichment of the MOX fuel is limited to 20% [15]. The datasets are generated as follows: Firstly, the enrichment of the MOX fuel is assumed to vary in the range of 0–20%. Since there are three regions with different enrichments in MOX fuel assembly, 64,000 samples are generated after permutation and combination. The change of enrichment within MOX fuel assembly is achieved by way of the variation of Pu isotopic contents, including Pu238, Pu-239, Pu-240, Pu-241, Pu-242 and Am-241. And the compositions (wt%) [16] of the Pu isotopic and Am-241 in MOX fuel are shown in Table 3. In addition, adding the remaining nuclide, i.e. the content of U-238 in MOX fuel assembly, there will be 21

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Fig. 2. Core configuration for the 2D C5G7 benchmark problem

Fig. 3. Fuel assembly configuration

input features, which have the different nuclide contents of different enrichment regions in the MOX fuel assembly, including U-238, Pu-238, Pu-239, Pu-240, Pu-241, Pu-242 and Am-241 of three zones. The keff and pin-by-pin flux are the objectives in current work. Of the 64,000 cases, 48,000 cases are used to train the BP neural network, and the remaining 16,000 cases are applied to test the generalization capability of the network.

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Fig. 4. Fuel pin cell geometry

Fig. 5. Guide tube geometry

2.3 ANNs Framework Design Two surrogate models based on BP neural network are designed to predict the keff of the core and pin-by-pin flux respectively. The Mean Square Error (MSE) loss [17], which is the mean square of the errors between the predicted values and the calculated values shown as Eq. (1), is chosen to be as the cost function. The optimal goal of the cost function  2 is to find the appropriate weights to make yi − yi as small as possible, which is also the ultimate purpose of training a neural network. Optimizer will be used to optimize the model parameters according to the gradient calculated by back propagate. The general process of training a neural network is as follows: (1). Making predictions using a randomly initialized model. (2). Calculating the MSE loss between the predicted values and the training sample values. (3). Resetting gradients to zeros. (4). Back propagating through MSE loss, which is used to calculate the direction where the parameters of 

5.0000E−5

2.2100E−2

1.5000E−5

5.8000E−4

2.4000E−4

9.8000E−5

5.4000E−5

1.3000E−5

4.6300E−2

–

–

–

–

U-238

Pu-238

Pu-239

Pu-240

Pu-241

Pu-242

Am-241

O

H2 O

B nat

Zr nat

Al27

MOX1

–

–

–

–

4.6300E−2

2.0000E−5

8.4000E−5

1.5200E−4

3.9000E−4

9.3000E−4

2.4000E−5

2.2100E−2

5.0000E−5

MOX2

Concentrations (1024 at/cm3 )

U-235

Nuclide

–

–

–

–

4.6300E−2

2.5000E−5

1.0500E−4

1.9000E−4

4.9000E−4

1.1600E−3

3.0000E−5

2.2100E−2

5.0000E−5

MOX3

–

–

–

–

4.62200E−2

–

–

–

–

–

–

2.2250E−2

8.6500E−4

UO2

–

–

2.7800E−5

3.3500E−2

–

–

–

–

–

–

–

–

–

Moderator

Table 1. Isotopic contents for each medium (except for control rod cell)

Zr clad

–

4.3000E−2

–

–

–

–

–

–

–

–

–

–

–

Al clad

6.0000E−2

–

–

–

–

–

–

–

–

–

–

–

–

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Table 2. Isotopic contents for control rod cell Concentrations (1024 at/cm3 )

Nuclide

Absorber

Moderator

Al cladding

Ag-107

2.27105E−2

–

–

Ag-109

2.27105E−2

–

–

In-115

8.00080E−3

–

–

Cd-113

2.72410E−3

–

–

H2 O

–

3.3500E−2

–

B nat

–

2.7800E−5

–

Al-27

–

–

6.0000E−2

Table 3. Mass fraction for Pu isotopic in MOX fuel (wt%) Pu-238

Pu-239

Pu-240

Pu-241

Pu-242

Am-241

1

60

25

9

5

1

the model should move. (5). Optimizing the parameters with the optimizer. One epoch representative optimizes the dataset once, and a large number of epochs need to be carried out to optimize the model parameters. MSE =

1 m

m  2  yi − yˆ i

(1)

i=1

2.4 Hyper-parameters Optimization In this section, hyper-parameters of two surrogate models are optimized to improve the performance metrics on predicting the interested core parameters. During optimization, the following parameters are mainly taken into account: number of hidden layers, number of hidden layer neurons, activation function, data scaling, optimizer, learning rate, number of batches, dropout regularization, weight initialization strategy and so on. These hyper-parameters selected are shown in Table 4. Random search is used to optimize the hyper-parameters during optimization. Random search implements a randomized search over parameters, where each setting is sampled from a distribution over possible parameter values, requiring prior knowledge. After optimization, the structures of two models are determined as shown in Figs. 6 and 7, respectively. One hidden layer is enough to fit the relationship in the data because there is only one prediction output with regard to the keff prediction model. As for the pin-by-pin flux prediction model, multiple hidden layers are configured on account of thousands of pins in the reactor core need to be predicted.

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Fig. 6. keff prediction model of the reactor core

Fig. 7. Flux prediction model for reactor core

Table 4. Hyper-parameters of NN Hyper-parameters

Values range

Number of hidden layers

[1, 8]

Number of hidden neurons

[5–1155]

Activation function

ReLU, Sigmoid, Tanh

Optimizer

SGD, Adam, RMSPromp

Learning rate

[0.1, 0.01, 0.001, 0.0001, 0.00001]

Number of batches

[64, 128, 256, 512, 1024]

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The hyper-parameters optimization results of the flux prediction model are shown in Fig. 8. Figure 8a shows the results of all calculations, Fig. 8b shows the top 50 optimization results of each activation function, and Fig. 8c shows the top 3 optimization results of each selected activation function. The optimal number of hidden layers is 7. The optimal number of neurons in each hidden layer is related to the selected activation function.

3 Results and Discussion The constructed models and optimization methods described above are applied to train optimal models. The performance of the surrogate models is evaluated with the absolute error and relative error. The calculation formulas are as follows. absolute error = pred − true

(2)

   −true  relative error =  predtrue 

(3)

where pred is the output of the surrogate model, true represents the values of test cases. The predicted results of the test dataset are shown in Table 5. As listed in the table, the maximum absolute error of the keff prediction surrogate model is 70 pcm, and it greatly meets the accuracy requirements of core calculation compared with the traditional physics-based model method. The absolute error distributions of the predicted keff values are shown in the Fig. 9. It can be seen that most absolute errors are within ± 50 pcm and the maximum absolute error is 70 pcm, which shows the reliability of the keff surrogate model. Figure 10 shows the predicted flux and the true flux calculated by the OpenMC code of one random case in test dataset within the fuel region. It is obvious that there is little difference between the two figures, besides two pins in the bottom right of the region, which demonstrates the feasibility of the pin-by-pin flux prediction surrogate model. The relative error of each pin in test dataset within fuel region is given in Fig. 11, and the numerical results are consistent with Table 5, with a maximum relative error of 8.79%. It can be seen from the figure that the prediction errors are small in the central of the reactor core, however, it gets larger when away from the center with the maximum error appears at the lower right corner of the fuel region. This is because that the flux values calculated by the OpenMC code is relatively small when it is close to the moderator region, which could lead to a nonnegligible impact on the performance of the surrogate model during the training process. Figure 12 shows the statistical result of relative error distributions for the case as shown in Fig. 10. The result shows that the relative errors in most of the pins (96.1%) are within 2%. However, there is still 0.8% of pins with a relative error of more than 8% (all appear in the lower right of the fuel region). Then, the average computation time of the traditional physics-based model and the data-driven surrogate model are presented in Table 6. The traditional physics-based model with 25,000 particles per batch and 250 active batches plus 50 inactive batches

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(a). All hyper-parameters result of each activation function

(b). Top 50 optimization results of each activation function

(c).Top 3 optimization results of each activation function Fig. 8. Optimization results of hyper-parameters

takes about 28 min to simulate a case on personal computer (PC), which is really timeconsuming and resource-consuming. The optimized NN surrogate model only takes about one millisecond to predict the key parameters and is much more efficiency than

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Table 5. The error result in test set Max absolute error

Max relative error

keff

70pcm

–

Pin-flux

–

8.79%

Fig. 9. Absolute error distributions of keff

the traditional physics-based models. The hardware used is a 6GB RAM with a GeForce RTX 2060.

4 Conclusion In this paper, two surrogate models are designed and proposed for the purpose of solving the time-consuming problem in traditional physics-based methods. The input features are the different nuclides contents in MOX fuel with different enrichments, and the output parameters are the keff and the pin-by-pin neutron flux distributions, of which the training data is generated from the Monte Carlo code OpenMC. And then the hyperparameters optimization is carried out separately to further improve the performance of the surrogate models. The predict performance and the computational efficiency of the surrogate models are compared and analyzed. The results indicate that the surrogate models are available and effective to predict the core neutronics parameters accurately in real-time, which has the important significance for an optimization design and safe operation of the reactor in the real industrial scenarios. In the future, a more complex and realistic surrogate model will be designed and developed to predict core key parameters in 3D reactor core scenarios, which will be more challenging.

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(a). The prediction flux in fuel region.

(b). The true flux calculated by OpenMC in the fuel region. Fig. 10. Comparison of flux in one case

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Fig. 11. The relative error of flux in the fuel region

Fig. 12. Statistical result of relative error

Table 6. Comparison of computation time Parameters

Computing time OpenMC

Optimized NN

keff

1678s

0.001s

pin-by-pin flux

1678s

0.002s

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References 1. Bostelmann, F., Skutnik, S.E., Walker, E.D., Ilas, G., Wieselquist, W.A.: Modeling of the Molten Salt Reactor experiment with SCALE. Nucl. Technol. 208(4), 603–624 (2021) 2. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015) 3. Santos, M.C., et al.: Deep rectifier neural network applied to the accident identification problem in a PWR nuclear power plant. Ann. Nucl. Energy 133, 400–408 (2019) 4. Li, X., Fu, X.-M., Xiong, F.-R., Bai, X.-M.: Deep learning-based unsupervised representation clustering methodology for automatic nuclear reactor operating transient identification. Knowledge-Based Syst. 204 (2020) 5. Margulis, M., Szames, E., Ammar, K., Tomatis, D., Martinez, J.M., Blaise, P.: Few-group cross sections modeling by artificial neural networks. EPJ Web Conf. 247 (2021) 6. Guo, Z., et al.: Defect detection of nuclear fuel assembly based on deep neural network. Ann. Nucl. Energy 137 (2020) 7. Lei, K.: Evaluation of core refueling loading pattern with deep convolutional neural network. At. Energy Sci. Technol. 55 (2021) 8. Shriver, F., Gentry, C., Watson, J.: Prediction of neutronics parameters within a twodimensional reflective PWR assembly using deep learning. Nucl. Sci. Eng. 195(6), 626–647 (2021) 9. Margulis, M., et al.: A deep learning based surrogate model for estimating the flux and power distribution solved by diffusion equation. EPJ Web Conf. 247 (2021) 10. Turkmen, M., Chee, G.J.Y., Huff, K.D.: Machine learning application to single channel design of molten salt reactor. Ann. Nucl. Energy 161 (2021) 11. Romano, P.K., Horelik, N.E., Herman, B.R., Nelson, A.G., Forget, B., Smith, K.: OpenMC: a state-of-the-art Monte Carlo code for research and development. Ann. Nucl. Energy 82, 90–97 (2015) 12. Cavarec, C.: Benchmark Calculations of Power Distributions within Assemblies. Electricité de France (1994) 13. DeHart et al.: Preliminary Results for the OECD/NEA Time Dependent Benchmark using Rattlesnake, Rattlesnake-IQS and TDKENO (2016) 14. Liu, C., Bi, G., Yang, B.: Preliminary study on core design with 50% MOX fuel in PWRs. Nucl. Sci. Eng. 35 (2015) 15. Guo, Z., Huo, X.: Application research of 100% MOX fuel in advanced PWRs. At. Energy Sci. Technol. 49 (2015) 16. Massih, A.R.: Models for MOX fuel behaviour (2006) 17. Bauer, R.K.E.: An empirical comparison of voting classification algorithms bagging, boosting, and variants. Mach. Learn. 36 (1999)

Comparison of SCC Results by Different Test Methods for Alloy 600 in High Temperature Water Xiaohui Li, Panpan Wu, Xinhe Xu, Zhanpeng Lu(B) , and Tongming Cui Shanghai University, Shanghai, China [email protected]

Abstract. Nickel-based alloys such as Alloy 600 have been used as structural materials in the nuclear island of pressurized water reactor (PWR) nuclear power plants. Stress corrosion cracking (SCC) of Alloy 600 and its weld metal in PWR primary water has been reported. There are several methods for evaluating SCC resistance or behavior of Nickel-based alloys in high temperature aqueous solutions, such as compact tension (CT) specimens for crack growth rate tests, slow strain rate tests (SSRT), U-bend or reverse U bend tests, C-ring tests. These methods have their own features and application scopes depending on the objective of evaluation. The results obtained by CT specimens or SSRT specimens for SCC behavior of Alloy 600 in simulated PWR primary water are compared and analyzed. The effects of some key parameters such as temperature, cold work and materials parameters on SCC are compared and analyzed, showing that the results from both CT specimens and SSRT specimens indicated the similar trend. Keywords: Pressurized water reactor · Nickel-based alloys · Stress corrosion cracking · Compact tension specimen · Slow strain rate test · Crack growth rate

1 Introduction Nickel-based alloys have been used in PWR primary water systems. SCC has been observed in Alloy 600 and the weld metals Alloy 82/182 (Alloy 132 in Japan) in pressurized water reactor primary water, called PWSCC [1–5]. Alloy 600 has gradually been replaced by materials with better SCC resistance, such as Alloy 690 (weld metal is Alloy 52/152). However, there are still some components of Alloy 600 in PWR primary system. It is necessary to investigate the SCC mechanism and key factors. Stress corrosion of metals refers to a combined process of corrosion environment and stress caused by applied or residual. Occurrence of stress corrosion depends on the exposure conditions, mechanical properties and microscopic characteristics of material. The SCC tests focus on the threshold of stress intensity factor, KISCC (or the stress threshold, σSCC ) and SCC growth rate, da/dt under specific condition. It needs to be comprehensively judged in combination with materials and operating conditions that whether metal (alloy) is sensitive to stress corrosion or not. Therefore, there is no fixed critical stress intensity factor for a material [6]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1123–1130, 2023. https://doi.org/10.1007/978-981-19-8780-9_108

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The most common failure mode of Alloy 600 in primary water system of PWR is intergranular stress corrosion cracking (IGSCC) [7]. The precursors of PWSCC are general corrosion and local corrosion. General corrosion is mostly initial from the appearance of 60 Co and 58 Co caused by Ni and Co in the alloy being activated to become radioactive after entering reactor core. Local corrosion is mostly caused by slip surfaces produced by localized in-situ oxidation or cold work of alloys. Nickel-based alloys will form protective oxide layers in high temperature water. Its thickness (around micron level) is two or three orders of magnitude higher than that of low temperature passivation film. The critical value of high and low temperature is generally considered to be 200 °C. The oxide film of Alloy 600 in high temperature water shows a double-layer structure, the outer one is depleted in chromium and the inner one is rich in chromium. Generally, the PWSCC rate of Alloy 600 is very low [8], and the oxidation rate is less than 50 nm/year after several cycles. There are various methods for investigating the susceptibility of metals to stress corrosion, and each method has its own strengths and weaknesses. Recently, SSRT and CT tests are widely used for investigating SCC performance of Alloy 600 in simulated PWR primary water environment. The main evaluation parameters for SCC from CT crack growth rate (CGR) test results are the information of crack number and the size on fracture surface. Direct evaluation parameters in SSRT results include but not limited to the SCC area and fraction of fracture surface, the number and size of cracks of side surfaces. Strain rate is a vital parameter for SSRT, which determines whether and where SCC occurs. It verifies SCC susceptibility by mechanical properties degradation and secondary cracks at gauge section. The paper focuses on the main trends of effect for temperature, cold work, and thermal treatment on Alloy 600 PWSCC with two test methods, as seen in Fig. 1.

Fig. 1. Three parameters reviewed in this paper by SSRT and CT specimens

2 Effect of Temperature on PWSCC PWSCC of Alloy 600 generally occurs at temperature above 250 °C, many laboratories have begun research on supercooled PWR primary water at 360 °C and hydrogenated superheated steam at 400 °C (usually 200 atmospheres). It is believed that there is a continuous PWSCC mechanism between subcooled primary water and hydrogenated superheated steam. Therefore, most of literatures concentrate on the range of 250–360 °C.

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Literatures of temperature-dependent CGR determination with SSRT specimens can be found from works of Young [9] and Rebak et al. [10]. Young et al. used electron and neutron diffraction pattern techniques to study SCC of mill-annealed Alloy 600 tubes by Hump-SSRT specimen (strain rate was 2.5 × 10–7 s−1 ). It was found that SCC susceptibility at 360 °C is higher than that of 250 °C. Authors suggested that this was related to the (200) plane lattice shrinkage induced by short-range order (SRO), i.e., the SRO rate was lower at 250 °C, resulting in higher ductility and relatively reduced SCC. Rebak and his co-workers used SSRT specimens to investigate the effect of temperature on CGR of mill-annealed Alloy 600 in hydrogenated primary water with initial strain rates of 5x10–7 s−1 and 2x10–7 s−1 . They found that the higher the temperature was, the higher the CGR was for Alloy 600 in high-temperature hydrogenated solution with 7 ppm Li + 108 ppm B. The data of CGR results are marked in black and red in Fig. 2, respectively. The temperature-dependent CGR determination with CT specimens can be summarized from works of Moshier and Brown [11]. They made a detailed study on factors of Alloy 600 SCC, including temperature, cold work, and degree of strain. They used wedge open loading (WOL) specimens and CT specimens to test CGR of active-loaded and bolt-loaded SCC performance in simulated PWR primary water condition. The concrete data of test results are plotted in Fig. 2 by other colors. Tendency of experimental data shows that CGR generally increases with temperature (exception for 31.9% cold-work CT specimen) for both SSRT and WOL/CT specimens. Loading time seems to have little effect on CGR during testing time for CT specimens. The CGR of cold-work specimens are about 5 times that of strain specimens for activeloaded CT specimens. In addition, CGRs of SSRT specimens are higher than WOL/CT specimens. For SSRT specimens, CGR is significantly higher for the one with higher degree of cold work. Based on the analysis of graphical data, it can be concluded that both SSRT and CT specimens reveal that SCC susceptibility of Alloy 600 in simulated PWR primary water increases with temperature in the range of 290–360 °C.

3 Effect of Cold Work on PWSCC Surface stress generated from cold work of nickel-based alloys is also crucial to the initiation of PWSCC, researches revealed that 5% cold work of Alloy 600 is able to increase the CGR by 2–4 times, and 20–30% cold work can increase 30 times or even 3–4 orders of magnitude [12]. Rebak et al. [10] also researched SCC properties of Alloy 600 with SSRT specimens in simulated PWR primary water with virous cold work degrees. According to results from true intergranular CGR, with cold work degree lower than 35%, no determined CGR can be detected with temperature lower than 350 °C. Even at 350 °C, only 16 and 35% cold work specimens exhibited detectable CGR, while 6, 20 and 25% cold work specimens showed no detectable CGR. For specimens at 350 °C with a strain rate of 5 × 10–7 s−1 , CGR increase with cold work degree from 16 to 35%, as seen in Fig. 3. In addition, the temperature-determined relationship is also consistent with the conclusion from last chapter.

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Fig. 2. Temperature dependence of CGR for Alloy 600 in simulated PWR primary water condition with SSRT and CT specimens

Experimental data of CT specimens from the work of Moshier and Brown shows that the CGR of cold-rolled samples are about 4 times to more than one order of magnitude than that of strain samples at 288 °C. While the difference between cold-rolled specimens and strain specimens largely decreases at 316 °C [11]. It seems to consider that heating and cold rolling have an antagonistic effect on CGR of Alloy 600 from the results. The CGR results of CT specimens with virous strain levels and cold-rolled treatment at 288 and 316 °C are displayed in Fig. 3. With the same temperature, the difference of CGR between 10-min loading and 100-min loading specimens is within an order of magnitude. Furthermore, Abhishek [13] investigated the effect of thermomechanical processing (TMP) with iterative cycles of 10% cold work and strain annealing specimens on SCC behavior of Alloy 600. Results of SSRT specimens displayed an increase tendency of elongation to failure and a significant decrease in SCC susceptibility following TMP. Therefore, it can be summarized that the overall CGR is positively correlated with the change trend of cold work degree for both SSRT and CT specimens. The differentiation between SSRT and CT results may reveal a fact that simple cold work is likely to increase SCC susceptibility of Alloy 600 in simulated PWR primary water environment. While the combination of TMP and cold work may be a suitable method to enhance PWSCC resistance.

4 Effect of Thermal Treatment Thermal treatment changes the microstructure of Alloy 600, which is one of the most important variables influencing IGSCC behavior [14]. Previous studies have demonstrated that grain-boundary carbides are of vital to IGSCC resistance. Recent works also revealed that grain-boundary precipitates are beneficial to enhance IGSCC resistance of Alloy 600 in hydrogenated primary water.

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Fig. 3. Cold work dependence of CGR for Alloy 600 in simulated PWR primary water condition at various temperatures of SSRT specimens and various strain degrees and cold-rolled CT specimens

Was [15] compared maximum CGR of thermal treatment at low temperature (LTT), thermal treatment at high temperature (HTT), carbon-doped mill-annealed (CDMA) and carbon-doped thermal treatment (CDTT) for Alloy 600 SSRT specimens in simulated primary water. Results showed that the maximum CGRs of TT specimens are generally slower than that of MA specimens, especially for specimen with the rate of 1 × 10–6 s−1 at 360 °C. The disparity is nearly two orders of magnitude. Furthermore, CGRs of HTT specimens are generally lower than LTT specimens. Konda et al. [16] investigated SCC performance of Alloy 600 CT specimens with two thermal treatment conditions at 325 °C sodium hydroxide solution. Research results demonstrated that, with lower stress intensity factor (K), CGRs of TT samples are significantly slower than that of MA samples. With the K increases to 30 MPa m0.5 , the result is reversed. In other words, K becomes a more dominant factor than TT. Detailed information is shown in Fig. 4.

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Fig. 4. Thermal treatment dependence of CGR for Alloy 600 with virous SSRT and CT specimens

In addition, Yoo et al. [17] investigated the effects of thermal aging and stress triaxiality on PWSCC initiation susceptibility with SSRT specimens. It was found that the longer thermal treatment is, the higher susceptibility to PWSCC is. It can be concluded from results of SSRT and CT specimens that thermal treatment Alloy 600 is generally more resistant to PWSCC than that of mill annealed material. While a long-term thermal treatment will be detrimental to the resistance of PWSCC for Alloy 600 in the interested temperature range.

5 Conclusions The paper compared the PWSCC performance of Alloy 600 with SSRT and CT specimens in simulated PWR primary water environment. The effects of three parameters, temperature, cold work and thermal treatment on PWSCC are discussed. The results from SSRT and CT tests are in general consistent in tendency. While more efforts are

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required to clarify the correlation in details for SCC performance of Alloy 600 under complex conditions. Acknowledgments. This work has been supported by Natural Science Foundation of China (NSFC No. 51771107), National Science and Technology Major Projects Nos. 2018ZX060 02008 and 2019ZX06005002, and Independent Research and Development Project of State Key Laboratory of Advanced Special Steel, Shanghai University (SKLASS 2020-Z00).

References 1. Scott, P.M., Combrade, P.: General corrosion and stress corrosion cracking of Alloy 600 in light water reactor primary coolants. J. Nucl. Mater. 524, 340–375 (2019). https://doi.org/10. 1016/j.jnucmat.2019.04.023 2. Watanabe, Y., Kain, V., Kobayashi, M.: Electrochemical transients observed during slow strain rate test of Alloy 600 in borated and lithiated high temperature water. JSME Int. J. Ser. A-Solid Mech. Mater. Eng. 45, 476–480 (2002). https://doi.org/10.1299/jsmea.45.476 3. Kawamura, H., Hirano, H., Shirai, S., Takamatsu, H., Matsunaga, T., Yamaoka, K., Oshinden, K., Takiguchi, H.: Inhibitory effect of Zinc addition to high-temperature hydrogenated water on mill-annealed and prefilmed Alloy 600. Corrosion 56, 623–637.https://doi.org/10.5006/1. 3280565 4. Le Hong, S.: Influence of surface condition on primary water stress corrosion cracking initiation of Alloy 600. Corrosion 57, 323–333 (2001). https://doi.org/10.5006/1.3290356 5. Henthorne, M.: The slow strain rate stress corrosion cracking test—a 50 year retrospective. Corrosion 72, 1488–1518 (2016). https://doi.org/10.5006/2137 6. GB/T 15970.1—2018/ISO 7539–1:2012, Corrosion of metals and alloys—Stress corrosion testing—Part 1: General guidance on testing procedures 7. Scott, P.M.: 2000 F.N. Speller award lecture: stress corrosion cracking in pressurized water reactors-interpretation, modeling, and remedies. Corrosion 56, 771–782 (2000). https://doi. org/10.5006/1.3280580 8. Scott, P., Combrade, P.: Review of oxidation and surface film formation studies on austenitic materials in light water reactor coolants, October 29, 2015. EPRI report 3002005478 9. Kim, Y.S., Maeng, W.Y., Kim, S.S.: Effect of short-range ordering on stress corrosion cracking susceptibility of Alloy 600 studied by electron and neutron diffraction. Acta Mater. 83, 507– 515 (2015). https://doi.org/10.1016/j.actamat.2014.10.009 10. Rebak, R.B., Xia, Z., Szklarska-Smialowska, Z.: Effect of temperature and cold work on the crack growth rate of Alloy 600 in primary water. Corrosion 51, 689–697 (1995). https://doi. org/10.5006/1.3293632 11. Moshier, W.C., Brown, C.M.: Effect of cold work and processing orientation on stress corrosion cracking behavior of Alloy 600. Corrosion 56, 307–320 (2000). https://doi.org/10.5006/ 1.3287659 12. Yamazaki, S., Lu, Z., Ito, Y., Takeda, Y., Shoji, T.: The effect of prior deformation on stress corrosion cracking growth rates of Alloy 600 materials in a simulated pressurized water reactor primary water. Corros. Sci. 50, 835–846 (2008). https://doi.org/10.1016/j.corsci.2007.07.012 13. Telang, A., Gill, A.S., Kumar, M., Teysseyre, S., Qian, D., Mannava, S.R., Vasudevan, V.K.: Iterative thermomechanical processing of Alloy 600 for improved resistance to corrosion and stress corrosion cracking. Acta Mater. 113, 180–193 (2016).https://doi.org/10.1016/j.act amat.2016.05.009

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14. Domian, H.A., Emanuelson, R.H., Sarver, L.W., Theus, G.J., Katz, L.: Effect of microstructure on stress corrosion cracking of Alloy 600 in high purity water. Corrosion 33, 26 (1977) 15. Was, G.S., Lian, K.: Role of carbides in stress corrosion cracking resistance of Alloy 600 and controlled-purity Ni-16% Cr-9% Fe in primary water at 360 °C. Corrosion 54, 675–688 (1998). https://doi.org/10.5006/1.3284887 16. Konda, N., Toyama, K., Yamanaka, K., Tokimasa, K.: Environmental effects on the crack growth properties of Alloy 600. Int. J. Pres. Ves. & Piping 52, 217–226 (1992). https://doi. org/10.1016/0308-0161(92)90017-A 17. Yoo, S.C., Choi, K.J., Kim, T., Kim, J.H.: Effects of thermal aging and stress triaxiality on PWSCC initiation susceptibility of nickel-based Alloy 600. J. Mech. Sci. Technol. 30(10), 4403–4406 (2016). https://doi.org/10.1007/s12206-016-0901-3

Development of New Spiral Throttling Device Gao Chang1,2(B)

, Tianqi He1 , Xu Kaili1

, and Shuhang Yan1

1 China Nuclear Power Operation Technology Corporation, Ltd, Wuhan, Hubei, China

[email protected] 2 Research Institute of Nuclear Power Operation, Wuhan, Hubei, China

Abstract. At present, most of the throttling devices commonly used in nuclear power plants are single-stage/multi-stage orifice structures. The orifice structure has the advantages of simple structure and stable performance. However, when the size of some pipeline structures is limited and the throttling pressure drop requirements are large, the orifice structure is prone to cavitation, resulting in changes in orifice performance, which may seriously affect the safe operation of pipelines and equipment. Compared with orifice plate, spiral throttling device has the advantages of low maximum flow rate, low risk of local gasification and few structural parameters affecting resistance performance. However, it is rarely used in nuclear power plant system, and the relevant technical data on design and test are rare. This paper analyzes the depressurization principle of spiral throttling device from the mechanism of cavitation, summarizes a set of design method of spiral throttling device based on CFD software, and verifies the design method. The test results show that the error between the simulation results of CFD software and the test results is less than 5%, It shows that the structural design of spiral throttling device using CFD software is feasible and can provide an effective basis for the design of spiral throttling device in the future. At present, the spiral throttling device developed based on this design method has been applied to nuclear power plant. Keywords: Spiral throttling device · Cavitation · Design method

1 Introduction At present, most of the throttling devices commonly used in nuclear power plants are single-stage/multi-stage orifice structures. The orifice structure has the advantages of simple structure and stable performance. However, when the size of some pipeline structures is limited and the throttling pressure drop requirements are large, the orifice structure is very prone to cavitation, resulting in changes in orifice performance, which may seriously affect the safe operation of pipelines and equipments [1–5]. Compared with multi-stage orifice, spiral throttling device has the advantages of low maximum flow rate and low risk of local gasification. Under the condition of limited pipeline structure and large throttling pressure drop, spiral throttling device has obvious advantages. However, it is rarely used in nuclear power plant system at present, so the relevant technical data on design and test are very rare. According to the technical requirements of throttling © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1131–1139, 2023. https://doi.org/10.1007/978-981-19-8780-9_109

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device in a certain system of nuclear power plant, after analysis and demonstration, the spiral throttling device structure is finally determined. In this paper, the resistance characteristics of the spiral throttling device are studied by means of numerical analysis and experiment, which can provide some reference for the design of the device with short distance and large pressure drop in the future.

2 Analysis of Throttling and Pressure Reducing Principle of Throttling Device In this section, the throttling and depressurization principles of orifice structure and spiral structure are analyzed, and the mechanism of cavitation is briefly introduced. When water flows through the throttle orifice, the stream will become thinner or shrink. The flow velocity is the maximum at the contraction section, and part of the static pressure will change into dynamic pressure, resulting in a large pressure drop on both sides, as shown in Fig. 1. It can be seen that the high-speed flow of the fluid in the compact section behind the orifice plate can cause the local pressure P2 to be lower than the corresponding saturation pressure of the liquid. At this time, the water will vaporize and produce bubbles. These gas (steam) bubbles will break at the downstream (pressure recovery section) of orifice plate and other throttling parts to form high-speed micro liquid jet, and locally form impact pressure thousands of times higher than the surrounding pressure, resulting in cavitation, which will cause serious damage to the scouring surface in a very short time, which is manifested in that the scouring surface will produce a rough surface similar to coal cinder.

Fig. 1. Pressure and velocity variation diagram of fluid flowing through throttle orifice

Figure 2 shows the change diagram of fluid pressure and velocity after the fluid flows through the spiral throttling device. It can be seen that when water flows through the spiral throttling device, the fluid in the spiral channel constantly changes the flow direction, but the fluid velocity is basically unchanged. With the increase of fluid flow

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Fig. 2. Pressure and velocity variation diagram of fluid flowing through spiral throttling device

distance, the fluid pressure gradually decreases without step change. When the fluid flows out of the spiral throttling device, the pressure recovery is very little, and there is basically no cavitation. From the above analysis, it can be seen that the spiral throttling device greatly reduces the risk of local vaporization in principle. Therefore, in the case of large throttling pressure drop and limited pipe structure size, compared with multi-stage orifice structure, spiral throttling device has obvious advantages.

3 Research on Design Method of Spiral Throttling Device The resistance performance of spiral throttling device is mainly affected by the flow area and length of flow channel. The flow area of spiral throttling device is determined by the width and depth of flow channel. It is necessary to reasonably determine the pitch, width, depth and length of flow channel. Figure 3 shows the structural diagram of spiral throttling device designed this time. The whole throttling device is composed of spiral core and outer sleeve. Figure 4 shows the structural diagram of spiral core of the designed spiral throttling device, which adopts single spiral flow channel structure.

Fig. 3. Structural diagram of spiral throttling device

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Fig. 4. Schematic diagram of inner core structure of spiral throttling device

When designing the structural parameters of the spiral throttling device, firstly, the structure of the spiral throttling device is preliminarily designed through the resistance calculation formula, and then the designed spiral throttling device is calculated and verified by CFD software. Among them, the empirical formula of resistance of spiral throttling device is as follows: Resistance coefficient of spiral flow channel: f = 1.344Re−0.2 (di /DH )0.1

(1)

Resistance of spiral flow channel: pt = f

l 1 2 ρv di 2

(2)

Among them: di —hydraulic diameter of spiral channel; DH —Pitch diameter of spiral flow channel; L—length along the spiral channel; v—Velocity of fluid in spiral channel; Re—Reynolds number.

4 Turbulence Calculation Model [6] In the CFD numerical calculation of spiral throttling device, three turbulence models are adopted respectively, and the calculation results are compared to select the most suitable turbulence model. The three turbulence models are introduced as follows. 4.1 Standard K-ε Model In the standard K-ε model, the governing equations of turbulent kinetic energy and dissipation rate are: ∂ ∂ μt ∂k ∂ (ρkui ) = [(μ + ) ] + Gk − ρε (ρk) + ∂t ∂xi ∂xi σk ∂xi

(3)

∂ μt ∂ε ∂ C1 ε ε2 ∂ (ρε) + [(μ + ) Gk − C2 ρ (ρεui ) = ]+ ∂t ∂xi ∂xi σε ∂xi k k

(4)

Among them, the turbulent viscosity coefficient μt = ρcμ k 2 /ε and the model constants are cμ = 0.99; c1 = 1.44; c2 = 1.92 σk = 1.3; σε = 1.3.

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4.2 RNG K-ε Model RNG K-ε model is based on the theory of renormalization group. After improvement, its governing equation is the same as the standard K-ε model, but the model constants are slightly different. cμ = 0.085; c2 = 1.68; σk = σε = 0.7179. The main difference is that c1 is no longer a constant, but a function expressed as η (the ratio of turbulence time scale to average strain rate), as shown in Formula 5. c1 = 1.42 − η = Sk/ε; S =



η η0 ] 1 + βη3

η[1 −

(5)

2Sij Sij ; η0 = 4.38; β = 0.015.

where the RNG K-ε model adds the effect of average strain rate. 4.3 Realizable K-ε Model The realizable K-ε model adopts a new dissipation rate equation ∂ ∂ ∂ μt ∂ε ε2 (ρε) + (ρεui ) = [(μ + ) ] + ρC1 Sε − C2 ρ √ ∂t ∂xi ∂xi σε ∂xi k + vε

(6)

The realizable K-ε model constants is c1 = max[0.43, η/η +5]; c2 = 1.9; σk = 1.0; σε = 1.2. The main difference from standard K-ε model and RNG K-ε model is that cμ is no longer a constant, but a function of turbulence time scale and strain tensor and rotation tensor.

5 Comparative Analysis of Calculation and Test Results 5.1 Comparison of Calculation Results of Different Turbulence Models Three turbulence models are used to calculate the designed spiral throttling device, and the results are compared with the test results. Figure 5 shows the variation of the drag coefficient of the spiral throttling device with Reynolds number. It can be seen that the gap between the standard k-ε model and the experimental value is the largest, the RNG k-ε model is the closest to the experimental value, and the realizable k-ε model is between them. This is because the standard k-ε model is a high Reynolds number model, which has poor simulation effect on the medium and low Reynolds number flow close to the wall. However, an analytical formula considering the viscous flow at low Reynolds number is embedded in the RNG k-ε model and the realizable k-ε model, which modifies the turbulent viscosity and considers the rotation and swirl flow in the average flow. Both the realizable k-ε model and RNG k-ε model show better performance than the standard k-ε model in strong streamline bending, vortex and rotation conditions.

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One disadvantage of the realizable k-ε model is that it can not provide natural turbulent viscosity when mainly calculating the rotating and static flow region. This is because the realizable k-ε model considers the influence of the average vorticity when defining the turbulent viscosity. Therefore, RNG k-ε model is used in the subsequent calculation of spiral throttling device.

Fig. 5. Variation of drag coefficient of spiral throttling device with Reynolds number (based on velocity near outer sleeve)

5.2 Comparative Analysis of CFD Calculation Results and Test Results Under the given design parameters, the structure meeting the design parameters is finally designed according to the design process of spiral throttling device in Sect. 3. The inner core of spiral throttling device is shown in Figs. 6 and 7 shows the fluid velocity distribution inside spiral throttling device.

Fig. 6. Inner core of spiral throttling device after processing

It can be seen from Figs. 6 and 7 that when the fluid just flows into the spiral core, the flow area suddenly decreases and the flow state becomes rotary flow, resulting in uneven velocity distribution at the front end of the spiral core, high local velocity, and the maximum velocity can reach 64.8 m/s; Moreover, due to the action of rotating centrifugal force, the speed near the central column of the spiral core is higher than that in other areas. After a certain flow distance, in the middle and rear section of the spiral core, the

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Front end area

Midrange region

Back end area Fig. 7. Velocity distribution in different areas of x = 0 section of spiral throttling device under rated flow rate

Fig. 8. Static pressure distribution along the helix of spiral throttling device

flow velocity is gradually uniform, and the average velocity is about 33 m/s which is acceptable.

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Figure 8 shows the static pressure change of the fluid along the streamline direction. It can be seen that when the fluid just enters the front end of the spiral core, the flow area becomes smaller and the speed increases rapidly, resulting in a sharp decrease in the pressure and then a rise. After a certain distance of flow, the fluid flow velocity is gradually uniform. With the increase of flow distance, the fluid pressure decreases slowly without step change. When the fluid flows out of the spiral core, the pressure recovery is very little, which greatly reduces the risk of local gasification. The calculation results are basically similar to the mechanism analysis above. In order to verify the accuracy of CFD calculation, the designed helical throttling device is tested and verified. Figure 9 shows the comparison between the test values of helical throttling device and CFD calculation results under different flow rates.

Fig. 9. Comparison between CFD numerical calculation results and test results

It can be seen that under different flow rates, the CFD calculation results are consistent with the trend of the test results, and the calculation deviation is within 5%, which shows that the CFD calculation results are reliable, and it is reasonable and feasible to use the CFD method to design the structure of the spiral throttling device.

6 Conclusions In this paper, the depressurization principle of spiral throttling device is analyzed from the mechanism of cavitation. Combined with CFD software, a set of design method of spiral throttling device is summarized, and the design method is verified through experiments. The conclusions are as follows: (1) When the fluid flows through the spiral throttling device, the fluid velocity in the throttling device is relatively uniform, and the velocity is higher only in some parts of the inlet section, and the average velocity is lower; The continuous drop of fluid pressure greatly reduces the risk of local gasification.

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(2) Three turbulence models are used to simulate the spiral throttling device and compared with the experimental data. It is concluded that in the simulation of spiral throttling device, the simulation results of RNG k-ε turbulence model are the closest to the experimental results. Because RNG k-ε turbulence model considers the rotation of fluid, it is more suitable for the calculation of new spiral throttling device. (3) Under the same test conditions, the error between CFD calculation results and test results is less than 5%, which shows that it is feasible to use CFD software to design the structure of spiral throttling device.

References 1. Liu, H., Liang, C., Mo, Z.: Analysis of discharge coefficient of low velocity orifice. J. Hydropower 27(3), 105–109 (2008) 2. Zhang, Y., Mao, Q., Xiang, W.: Design and analysis of multistage orifice in nuclear pipeline. Nucl. Power Eng. 30(4) (2009) 3. Chen, J., Wang, B., Wu, B.: CFD numerical simulation of internal flow field of standard orifice flowmeter. Exp. Fluid Dyn. 22(2), 51–55 (2008) 4. Wang, D., Le, P.: Experimental study on orifice plate energy dissipation in pipe flow. Hydrodyn. Res. Prog. Ser. A 2(3), 41–47 (1987) 5. Yang, G., Li, M.: Simulation study on internal flow field of orifice flowmeter. Gansu J. Sci. 27(6), 79–81 (2015) 6. Wang, F.: Computational Fluid Dynamics Analysis. Tsinghua University Press, Beijing (2004)

Study on Corrosion Behavior of T-22 Alloy in Ultrahigh Temperature Impure Helium and Air Haoxiang Li, Wei Zheng, Bin Du, Huaqiang Yin(B) , Xuedong He, Tao Ma, and Xingtuan Yang Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China [email protected]

Abstract. T-22 alloy is an alternative material for heat transfer tube of steam generator of high temperature gas cooled reactor. The corrosion behavior of T-22 alloy in high temperature impure helium and air at 950 °C was studied. After the experiment, the experimental results were analyzed by electronic balance, scanning electron microscope (SEM), X-ray energy dispersive spectroscopy (EDS) and carbon sulfur analyzer. The results show that the corrosion phenomena and mechanisms of T-22 alloy in the atmosphere of extreme low oxygen partial pressure and extreme high oxygen partial pressure are quite different. There was no obvious oxide layer on the surface of T-22 alloy after corrosion in impure helium, and Fe elements did not participate in the oxidation reaction. After corrosion, the alloy had slight mass loss and complete decarburization. After the corrosion of T-22 alloy in air, a large number of Fe elements participated in the oxidation. After the experiment, the oxide layer of the alloy fell off, and the mass gain was very obvious. The alloy was seriously corroded and damaged. Keywords: HTGR · T-22 alloy · Decarburization · Oxidation · Alloy corrosion

1 Introduction As one of the fourth generation nuclear power systems, high temperature gas cooled reactor (HTGR) has higher inherent safety, higher power generation efficiency and higher economy than pressurized water reactor (PWR). Due to the very high outlet temperature of its primary circuit helium, it has developed rapidly in the field of hydrogen production and chemical process thermal application [1, 2]. All over the world, the dragon high temperature reactor [3] in Britain, AVR [4] and PNP [5] high temperature reactors in Germany and HTTR [6] high temperature reactors in Japan have carried out a series of experiments under ultra-high temperature 950 °C. Previous research showed that there are trace gas impurities in the helium in the primary circuit of HTGR. During the operation of HTGR, the trace impurities (such as CO, CH4 , H2 , CO2 , etc.) contained in the helium gas will react with the alloy materials of the steam generator, causing corrosion of the equipment and reducing the safety and reliability of the equipment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1140–1148, 2023. https://doi.org/10.1007/978-981-19-8780-9_110

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T-22 alloy is a kind of high temperature chromium molybdenum ferritic steel (2.23Cr1Mo) [7], which is mainly used to produce pressure vessels, hydrogenation reactors and other devices, and is widely used in petroleum, petrochemical and other industries [8, 9]. At present, T-22 alloy is selected [10] as the material for the subcooling section of the heat transfer tube of the steam generator of the spherical bed modular high temperature gas cooled reactor (HTR-PM) developed by Tsinghua University. However, the research on T-22 alloy mainly focused on 400 ~ 500 °C [10], most of the research environment is molten salt environment [11], and there was little research on the corrosion of T-22 alloy in specific atmosphere at ultra-high temperature. In order to investigate the corrosion behavior of T-22 alloy under extreme normal working conditions (ppm oxygen partial pressure under HTR-PM limit) and accident working conditions (ultra-high oxygen partial pressure), this work carried out a 50 h corrosion test on T-22 alloy in impure helium and air environment at 950 °C.

2 Experimental Materials and Process 2.1 Experimental Materials The T-22 alloy selected for this experiment was purchased from Minmetals Yingkou Medium Plate Co., Ltd. The main chemical components of the alloy are shown in Table 1. Table 1. Main element content of T-22 alloy% Alloy

C

Cr

Fe

Ni

Mn

Al

Si

Cu

Mo

Ti

T-22

0.14

2.077

Base

–

0.51

–

0.21

0.027

0.967

–

Fig. 1. Schematic diagram of experimental system

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All alloy samples are processed into 20 × 8 × 1 mm sheet pattern by professional manufacturers. Before the experiment, put the sample into an ultrasonic cleaner containing absolute ethanol for ultrasonic cleaning, and then put the sample into a drying oven for drying. 2.2 Experimental Process A group of high-purity helium atmosphere (He-1) with a gas purity of 99.999% was set outside the two groups of impure helium and air as the control experiment without any impurities to compare the mass change and carbon content change of the alloy after corrosion in high-temperature helium. The impurity contents in the experimental atmosphere of high-purity helium (He-1) and impure helium (He-2) are shown in Table 2. This gas configuration is based on the helium impurity limit of HTR-PM [12] to explore the corrosion behavior of T-22 alloy under the most extreme normal working atmosphere. In order to ensure the repeatability of the experimental results, two parallel samples were set for each group. The flow chart of the test bench is shown in Fig. 1. Before the experiment, put the alloy sample into the vacuum tube furnace and check the air tightness of the experimental system, and then use the gas cylinder and vacuum pump unit to replace the gas and vacuum the experimental system. After repeating the above steps for three times, the vacuum tube furnace was continuously filled with experimental gas. In both groups of experiments, the constant gas pressure was 0.1 MPa, and the gas flow was maintained at 300 ml/min. After the above steps were completed, the vacuum tubular furnace started to heat up at the heating rate of 5 °C/min from 20 °C at room temperature, and then it was kept warm for 50 h after reaching 950 °C, and then it was cooled naturally after the completion of heat preservation. Before and after the corrosion test, the specimens were weighed with the High Precision Electronic Balances (accuracy of 0.1 mg, METTLER TOLEDO, Switzerland). Use 600 #, 1000 #, 1200 #, 2000 # sandpaper for mechanical polishing until the surface was free of scratches and stains. Field Emission Scanning Electron Microscopy (FESEM, JEOL JSM-7001F, Japan) with Energy-Dispersive X-ray Spectroscopy (EDS) were used to observe the microstructures and analyze the composition of the specimens. Test and analyze the carbon content of the alloy with a carbon sulfur analyzer (Germany Eltra CS 800). Table 2. Contents of impurities in impure helium (ptot = 0.1 MPa) ppm Impurities

H2

H2 O

CO

CO2

CH4

O2

He-1

–

1

–

–

–

–

He-2

490

1.52

490

70

210

0.50

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3 Results and Discussion 3.1 Alloy Mass Change The mass gain is calculated according to formula (1). ρA =

m2 − m1 A

(1)

where, ρA is the weight gain per unit surface area, mg/cm2 ; m2 is the mass after corrosion; m1 is the mass before corrosion; A is the sample surface area. 19.0

Air He-1 He-2

Mass change(mg/cm2)

18.5

18.0

0.5

0.0

-0.5

Fig. 2. Mass change of T-22 alloy after corrosion

As shown in Fig. 2, it can be seen that the alloy has a very slight mass loss after corrosion in He-1 high purity helium atmosphere for 50 h, and a more obvious mass loss after corrosion in He-2 for 50 h. This indicated that the material in the alloy is transferred to the atmosphere during the corrosion process. The transfer reaction of the substance in the alloy matrix to the atmosphere is more obvious in helium containing impurities. After the air test, it was found that a large number of oxide layers on the alloy surface had fallen off, so the mass gain of T-22 alloy after corrosion in air should be small. However, there was still a very obvious mass gain after corrosion in air. This indicated that the alloy had a very severe oxidation corrosion behavior in air. 3.2 Analysis of Alloy Morphology and Element Distribution The micro morphology and element distribution of the alloy in He-2 and air environment were analyzed. The section morphology and element distribution of T-22 alloy were analyzed by SEM and EDS. As shown in Figs. 3, 4 and 5. Among them, the main elements Fe, O, Cr and C of T-22 alloy were analyzed.

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Fig. 3. Surface morphology of T-22 alloy after corrosion in He-2 atmosphere

Since the corrosion of T-22 alloy in air has been seriously oxidized and the surface corrosion layer has obviously fallen off, it is impossible to give the surface topography and element distribution after corrosion. Only the surface topography and element distribution after corrosion in He-2 atmosphere can be given, as shown in Figs. 3 and 5c. It can be seen that there is only a small amount of carbon in the surface layer of T-22 alloy after corrosion in He-2, which indicated that decarburization may occur in the alloy. There were many oxide particles on the surface layer of the alloy, and it can be seen from the EDS image that the oxide is mainly Cr. It can also be seen in the EDS image that there were a large number of Fe elements at the positions where no oxide is formed, which indicated that Cr elements on the surface layer of the alloy migrate from the inside of the alloy matrix to the alloy surface and form oxides on the alloy surface. However, it can be seen that the oxide in the surface layer is less, not dense and continuous. This should indicate that the oxide layer on the alloy surface has poor protection ability to the alloy matrix. As shown in Figs. 4 and Fig. 5a, b, according to SEM images and EDS results, no continuous and dense oxide layer was formed on the surface of T-22 alloy after corrosion in He-2 atmosphere. Fe was not oxidized, and there was no migration and aggregation. There were only a few uneven and discontinuous Cr oxides on the alloy surface. A large number of carbon elements appeared near the alloy surface, which indicated that during the corrosion process, the carbon elements in the alloy migrated and accumulated to the alloy surface, and the alloy may decarburize. This is consistent with the conclusions obtained in Figs. 3 and Fig. 5c. According to SEM images and EDS results, a large number of Fe elements migrated to the alloy after corrosion of T-22 alloy in air atmosphere, and a layer with a thickness of 200 μm was formed on the surface of the alloy matrix thick oxide layer. It was obvious that the oxide layer was separated from the alloy body. After observation, it can be seen that a large number of oxide layers on the alloy surface have fallen off. This indicated

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Hot mosaic resin Oxide layer

Alloy matrix

a Air

Alloy matrix

b He-2 Fig. 4. Cross section morphology of T-22 alloy after corrosion

that the corrosion layer formed on the surface of the alloy had poor thermal stability and was not dense enough to prevent the penetration of corrosion gas and the corrosion inside the alloy matrix. It can be inferred that if the corrosion time of T-22 alloy in air at 950 °C is long enough, the alloy will be completely corroded and destroyed. It can be seen that the corrosion behavior of T-22 alloy in the air environment under extremely high oxygen partial pressure was completely different from that in the impure helium environment under ppm oxygen partial pressure.

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a Air

b He-2(section)

c He-2 (surface)

Fig. 5. EDS element distribution of section after corrosion of T-22 alloy

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3.3 Alloy Carbon Content Analysis

0.20

Carbon content%

0.16

Original sample He-1 Sample He-2 Sample

0.12

0.08

0.04

0.00



Fig. 6. Carbon content change of T-22 alloy before and after corrosion

Due to the serious damage of T-22 alloy after corrosion in the air, it is impossible to use the carbon sulfur analyzer to accurately test the carbon content of the corroded samples in the air. Therefore, only the carbon content of T-22 samples before and after corrosion in He-1 and He-2 is analyzed here. According to the data in Fig. 6, decarburization of T-22 alloy occurred in He-1 and He-2. Almost complete decarburization occurred after corrosion in He-2. The decarburization of the alloy is consistent with the conclusion that the mass of the alloy is reduced and the elements in the alloy are transferred to the atmosphere. According to the research of Chi et al. [13], the carbon activity of Fe–Cr–C ternary austenitic alloy in carbon tool steel decreased significantly with the increase of chromium content, which indicated that the high carbon activity of the alloy may be due to the low Cr content in T-22 alloy. This indicated that when a continuous dense oxide layer is not formed on the surface layer of the alloy, the carbon activity of the alloy is higher than that of helium due to the high carbon content of T-22 alloy, which caused the carbon element in the alloy to migrate to the surface layer of the alloy, and chemically react with the impurity gas in the helium atmosphere on the surface layer, resulting in decarburization. In He-2, the decarburization phenomenon is more obvious than in He-1 due to the higher concentration of impurity gas in the atmosphere.

4 Conclusion In this project, the corrosion behavior of T-22 alloy in atmospheric pressure, 950 °C helium and air was studied. After the characterization and analysis of the alloy by mass weighing, SEM, EDS and carbon sulfur analyzer, the main conclusions are as follows.

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1. After corrosion of T-22 alloy in impure helium environment under ppm oxygen partial pressure, there was no continuous and dense oxide layer on the surface, Fe did not participate in the oxidation reaction, and the alloy weight was slightly reduced, which should be caused by the complete decarburization of the alloy. 2. After T-22 alloy was corroded in the air under ultra-high oxygen partial pressure, a large number of Fe elements participated in oxidation and generated a large amount of Fe2 O3 , resulting in a very large mass gain of the alloy. After the corrosion test, a large number of oxide layers fell off, and the alloy was severely corroded and damaged. 3. Decarburization of T-22 alloy occurred in both groups of helium environment. In He-2 atmosphere with higher impurity concentration, more obvious complete decarburization will occur. 4. According to conclusions 1, 2 and 3, T-22 alloy can not maintain its performance in the atmosphere of high temperature and high oxygen partial pressure; The decarburization mechanism and its effect on the alloy in high temperature helium environment need to be further studied.

References 1. Kugeler, K., Zhang, Z.: Modular High-temperature Gas Cooled Reactor Power Plant, pp. 1–21 (2019) 2. Zhang, Z.: Development strategy of high temperature gas cooled reactor in China. Strateg. Study Chin. Acad. Eng. 21(1), 12–19 (2019) 3. Simon, R.: The primary circuit of the dragon high temperature reactor experiment. In: Proceedings of the 18th International Conference on Structural Mechanics in Reactor Technology. Beijing (2005) 4. Ziermann, E.: Review of 21 years of power operation at the AVR experimental nuclear power station in Jülich. Nucl. Eng. Des. 121(2), 135–142 (1990) 5. Quadakkers, W.J.: High temperature corrosion in the service environments of a nuclear process heat plant. Mater. Sci. Eng. 87(none), 107–112 (1987) 6. Fujikawa, S., Hayashi, H., Nakazawa, T., et al.: Achievement of reactor-outlet coolant temperature of 950 °C in HTTR. J. Nucl. Sci. Technol. 41(12), 1245–1254 (2004) 7. Pan, F., Yan, Y., Xu, B., Song, B.: Research on heat treatment of T22 steel high-pressure boiler pipe. Steel Pipe 39(1), 60–66 (2010) 8. Manna, G., Castello, P., Harskamp, F., et al.: Testing of welded 2.25CrMo steel in hot high pressure hydrogen under creep conditions. Eng. Fract. Mech. 74, 956–968 (2007) 9. Moro, L., Gonzalez, G., Brizuela, G., et al.: Influence of chromium and vanadium in the mechanical resistance of steels. Mater. Chem. Phys. 109, 212–216 (2008) 10. Li, J., Wu, X., Duan, H., et al.: Study on localization of SA213 T-22 transfer heat tube for nuclear safety class one. Hot Working Technol. 45(08), 82–84 (2016) 11. Cuevas-Arteaga, C., Escalada, V., Trujillo, M.A.: Molten salt corrosion evaluation of alloy T22 applying electrochemical techniques and the conventional weight loss method. 65–82 (2011) 12. Wang, Q., Li, H„ Zheng, W., Yin, H., Li, S., He, X., Ma, T.: Research and model analysis on the effect of coolant impurities on high temperature properties of superalloys in high temperature gas cooled reactor(in Chinese). Nucl. Technol. 43(04), 37–42 (2020) 13. Chi, C.Z., Zhi-Yong, H.E., Gao, Y., et al.: Thermodynamic analysis of carbon migration during T8 steel during plasma surface chromizing. China Surf. Eng. (2004)

Research and Application of Viscoelastic Artificial Boundary for Soil and Nuclear Power Plant Structure Dynamic Interaction Analysis Dongyang Wang1(B) , Xiaoying Sun1,2 , Ziqiao Liu1 , and Yingying Gan1 1 China Nuclear Power Engineering Co., Ltd, Beijing 100840, China

[email protected] 2 Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of

Engineering Mechanics, China Earthquake Administration, Harbin 150080, China

Abstract. Soil-structure interaction (SSI) is the critical issue that needs to be considered in both seismic and isolation analysis of nuclear power plant (NPP) structures constructed on soft-soil or non-rock sites. The substructure method and the direct method are two alternatives to analyze SSI effect. The substructure method has been certified by the US Nuclear Regulatory Commission and widely used in China’s nuclear power engineering, with relatively low calculation cost and accurate energy radiation simulation of the infinite foundation in the frequency domain. However, this approach can only be applied to linear analysis theoretically due to the basis of linear superposition principle, having difficulty considering the nonlinearities of materials and contacts in the near-field. To the research status of seismic analysis of NPP considering SSI effect in China, this paper proposes a three-dimensional time-domain method based on viscoelastic artificial boundary and wave motion theory, which contributes to appropriately simulating the foundation radiation damping and non-uniform seismic input. Comparative SSI analyses are performed for an example of NPP structure using the proposed method and the mature substructure approach respectively. Moreover, influence of oblique incident ground motion on seismic response of NPP is also studied in this paper. Keywords: Nuclear power plant · Viscoelastic artificial boundary · Soil-structure dynamic interaction · Seismic analysis

1 Introduction Soil-structure interaction (SSI) is the critical issue that needs to be considered in both seismic and isolation analysis of nuclear power plant (NPP) structures constructed on soft-soil or non-rock sites. The substructure method and the direct method are two alternatives to analyze SSI effect. In the substructure approach, the dynamic solution is computed for the coupled structure-foundation system, each part of which can be analyzed separately by applicable measure [1]. The substructure method has been certified by the US Nuclear Regulatory Commission and widely used in China’s nuclear power engineering, with relatively low © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1149–1158, 2023. https://doi.org/10.1007/978-981-19-8780-9_111

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calculation cost and accurate energy radiation simulation of the infinite foundation in the frequency domain. However, this approach can only be applied to linear analysis theoretically due to the basis of linear superposition principle, having difficulty considering the nonlinearities of materials and contacts in the near-field. In recent years, some scholars have tried to implement sub-structuring analysis in the time domain with consideration of nonlinear effects. But the time-domain convolution of infinite far field effect makes the solution procedure inconvenient. The direct method takes the foundation soil and structure to compute the seismic response of SSI system at one time, accurately considering the mechanical properties of non-uniform, nonlinear and irregular sites. Decoupled local artificial boundary is usually built to simulate the infinite far field radiation effect, which has certain advantages for solving complex nonlinear wave propagation problems in the near-field. Figure 1 shows the analysis model of direct method.

Near field Artificial boundary

Structure

Free surface Far field Artificial boundary

Far field Elastic half space Incident seismic wave

Fig. 1. Analysis model of direct method

To the research status of seismic analysis of NPP considering SSI effect in China, this paper proposes a three-dimensional time-domain method based on viscoelastic artificial boundary and wave motion theory, which contributes to appropriately simulating the foundation radiation damping and non-uniform seismic input. Comparative SSI analyses are performed for an example of NPP structure using the proposed method and the mature substructure approach respectively. Moreover, influence of oblique incident ground motion on seismic response of NPP is also studied in this paper.

2 Boundary Condition and Ground Motion Input Method To simulate the radiation damping effect of foundation soil, the three-dimensional viscoelastic artificial boundary condition derived from elastic wave theory is adopted in this paper. Compared with the viscous boundary, the viscoelastic artificial boundary has the advantage of simulating the elastic recovery performance of the semi finite medium beyond the artificial boundary, having good high-frequency and low-frequency stability, and solving three-dimensional wave propagation problems more reasonably. Figure 2 is the schematic plot of viscous elastic artificial boundary, specifically, in the threedimensional finite element model, parallel spring-damper elements are arranged in the normal and tangential directions of the artificial boundary nodes respectively [2, 3].

Research and Application of Viscoelastic Artificial Boundary

KBT

CBT KBT

CBN

l

CBT

KBN

y z

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o

x

Fig. 2. Viscous elastic artificial boundary

Spring stiffness and damping coefficient of the viscous elastic artificial boundary are calculated according to the following Eqs. (1) and (2) [4] KBT = αT G/R, CBT = ρcS

(1)

KBN = αN G/R, CBN = ρcP

(2)

where K BT and K BN C BT and C BN α T and α N R G and ρ

αN .

are the tangential and normal spring stiffness, respectively, are the tangential and normal damping coefficient, respectively, are the tangential and normal modified parameter, respectively, is the distance from the wave source to the artificial boundary node, are the shear modulus and the mass density of the soil medium, respectively, cS and cP are the shear wave and the compression wave velocities, respectively.

Table 1 shows the range and recommended value of the modified coefficients α T and

Table 1. Recommended value of modified coefficient Parameter

Value range

Recommended value

αT

0.5 ~ 1.0

2/3

αN

1.0 ~ 2.0

4/3

The input method of ground motion is related to the applied artificial boundary conditions. The wave method transforms seismic wave input into wave source problem. And the input ground motion is transformed into equivalent loads acting on the artificial boundary nodes. The total wave field on the artificial boundary can be decomposed into free wave field and scattered wave field. The equivalent load acting on the viscoelastic artificial boundary should be the sum of the load generated by the free wave field on the boundary and the load required to make the parallel spring and damper reach the

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free-field displacement. The equivalent input seismic load is calculated according to Eq. (3).       F xp , yp , zp , t = f 0 xp , yp , zp , t + f b xp , yp , zp , t (3) where f 0 (x p , yp , zp , t) is determined according to the motion equation of node P, f b (x p , yp , zp , t) is determined by the product of the stiffness coefficient and the free-field displacement, the damping coefficient and the free-field velocity.

3 Soil and NPP Structure Dynamic Interaction Analysis In this section, comparative SSI analyses are performed for an example of NPP structure adopting the viscoelastic artificial boundary and corresponding wave motion method, and the sub-structuring approach respectively. Moreover, influence of oblique incident ground motion on seismic response of NPP is also studied. 3.1 Finite Element Model Figure 3 shows the finite element model of the soil-structure system. The superstructure is simulated by concentrated mass-bar system model. The foundation and soil medium are simulated by three-dimensional soil models. Viscoelastic artificial boundary conditions are set at the bottom and side cut-off of the soil medium. Dimension and material parameter of the finite element model are listed in Table 2. Superstructure

Soil

Foundation slab

Z Y

Viscoelastic artificial boundary

Z X

Y

Fig. 3. Finite element model of the soil-structure system

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Table 2. Dimension and material parameter of the finite element model Density (kg/m3 ) Young’s modulus Poisson’s ratio (Pa)

Computation model Dimension (m) Soil

280 × 280 × 45 1883.5

9.5e8

0.43

Foundation slab

40 × 8

2500.0

4.0e10

0.20

Containment

55

1.0

4.0e10

0.20

3.2 Comparative SSI Analyses The acceleration time-history record with the peak value of 0.3 g shown in Fig. 4 is taken as the seismic input in the comparative SSI analyses. The calculation results of the floor response spectrum at different elevation positions of the superstructure obtained by the two methods are compared, as shown in Figs. 5, 6 and 7, where SSM and DM represent the substructure method and the direct method, respectively. The substructure method is completed by ACS SASSI program. And the direct method is accomplished by ABAQUS software. 0.4

Acceleration /g

0.3 0.2 0.1 0.0 -0.1 -0.2 -0.3 -0.4

0

5

10 15 Time /s

20

25

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The following conclusions can be drawn from Figs. 5, 6 and 7. First, structural response spectrum shape and peak frequency in X-, Y- and Zdirection respectively obtained by the two methods are basically the same, which can accurately describe the dynamic characteristics of the system. Second, in view of this computation model, the envelop of the floor response spectrum obtain by the direct method is stronger, and the calculation results are more conservative and have appropriate margin compared with the substructure method. 3.3 Influence of Oblique Incident Ground Motion on Seismic Response of NPP For the NPP structure with large geometrical size, the oblique incident seismic wave problem caused by the travelling wave effect or the non-uniformity of the supporting

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foundation will have a great impact on its seismic response. In this case, if the assumption of uniform ground motion input is still adopted, the calculation results may be inconsistent with the actual situation. To study the influence of non-uniform ground motion input on the seismic response of NPP structure, SSI analyses under the action of earthquake with different incident angles are completed using the direct method. Calculation conditions are listed in Table 3. And the input SH wave motion is a pulse function with a duration of 0.5 s and a peak displacement of 1 cm. Where, ϕ is the angle between the seismic wave incident direction and the vertical z direction,

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Table 3. Oblique incident angle Calculation condition

Angle ϕ (°)

Angle θ (°)

1

45

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45

30

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45

45

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90

θ is the angle between the horizontal projection of the seismic wave incident direction and the horizontal x direction. The dynamic response analyses of the soil-structure system are completed under the condition of oblique incidence of the seismic wave at different angles. The computation results of the floor response spectrum at different elevation positions of the superstructure are compared, as shown in Figs. 8 and 9. Figure 10 compares the structural peak acceleration response under different incident angles of the input wave motion. From Figs. 9 and 10, the conclusion can be drawn that the variation of incident angle θ will not change the shape of floor response spectrum but will affect the peak value of structural seismic response. The smaller the incident angle is, the larger the peak value of structural acceleration response is. Therefore, it is a safe design method to complete SSI analysis under vertical incidence of seismic wave (θ = 0). However, for large-scale and long-line structures, such as class I and class II underground structures and underground pipelines in nuclear power plants, their seismic response will be affected by the spatial variation of ground motion caused by oblique incidence of seismic waves, and the longitudinal deformation and internal force

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cannot be ignored. It is necessary to carry out seismic response analysis of nuclear power structures under non-uniform ground motion input. Figure 11 shows the displacement nephogram of the soil-structure system at different moments. In the seismic duration, the wave front of the incident wave moves in a planar manner from the lower left corner upward obliquely. There is no reflection on the side due to the effective absorption of the viscoelastic artificial boundary. Since there is no wave type conversion when SH wave is reflected on the ground surface, the free wave field is the superposition of incident SH wave and reflected SH wave. With the effective absorption of the incident and reflected waves at the artificial boundary, the seismic response of the soil-structure system gradually decreases and finally tends to be

60

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stationary. It can be seen from the whole wave field that the traveling wave propagation law is obvious, which verifies the effectiveness of viscoelastic artificial boundary setting and non-uniform seismic input method.

0.30 s

0.50 s

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Fig. 11. Displacement nephogram of the soil-structure system at different moments

4 Conclusion A three-dimensional time-domain method based on viscoelastic artificial boundary and wave motion theory is proposed in this paper. Viscoelastic artificial boundary condition solves the problem of low frequency drift of viscous artificial boundary to some extent, which is commonly used in seismic design of nuclear power engineering. It is a simulation method with higher accuracy and better applicability for the radiation damping effect of infinite foundation. In addition, the non-uniform seismic input method can consider

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the influence of oblique incidence of seismic wave at any angle on the seismic response of NPP structure, to ensure the accuracy of seismic analysis of large NPP structure. Acknowledgements. This paper is supported by China National Nuclear Corporation Scientific Research Project—Research project for Longxing demonstration project.

References 1. Lysmer, J., Tabatabaie-Raissi, M., Tajirian, F., et al.: A System for Analysis of Soil-Structure Interaction. University of California, Berkeley (1981) 2. Liu, J., Wang, Z., Du, X., et.al.: Three-dimensional visco-elastic artificial boundaries in time domain for wave motion problems. Eng. Mech. 22(6), 46–51 (2005) 3. Liu, J., Li, B.: Three-dimensional visco-elastic static-dynamic unified artificial boundary. Sci. Chin. Ser. E Eng. Mater. Sci. 35(9), 966–980 (2005) 4. Liu, J., Lv, Y.: A direct method for analysis of dynamic soil-structure interaction. Chin. Civil Eng. J. 31(3), 55–64 (1998)

Research on Mitigation Measures for Severe Accident Source Terms of Small Modular Reactor Tao Xu1(B) , Dujuan Han1 , Bin Zhang2 , Junlong Wang1 , Jiajia Liu1 , Yirui Wu1 , Chao Tian1 , Mingming Xia1 , and Haifu Ma1 1 Science and Technology On Reactor System Design Technology Laboratory, Nuclear Power

Institute of China, Chengdu, Sichuan, China [email protected] 2 School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China

Abstract. Small modular reactor (ACP100), as a key scientific and technological project of third-generation nuclear power technology of China National Nuclear Corporation, must achieve the purpose of eliminating large-scale radioactive substances release. In order to meet this requirement, it is necessary to study the source term release and mitigation of small modular reactor in severe accident. In this paper, ISAA code is used to model the small modular reactor, and the typical severe accident sequence is selected to simulate the accident and calculate the source term. Containment spray and double containment model are set up respectively. The feasibility and effectiveness of the two measures on the source term mitigation are illustrated by comparing the source term results. The calculation results show that containment spray and double containment can significantly alleviate the release of radionuclides under severe accident conditions, and can play a role in reducing radioactivity. Meanwhile, for double containment, the leak rate from the inner containment to the annular space has a great impact on the mitigation of the source terms. To reduce the release of the source term to the environment, it should be reduced as far as possible. Keywords: Small modular reactor · Severe accident · Source term calculation · Mitigation measures · Sensitivity analysis

1 Introduction In October 2012, the State Council approved the Twelfth Five-Year Plan for Nuclear Safety and Radioactive Pollution Prevention Control and the Vision for 2020, the Nuclear Power Safety Plan and the adjusted Nuclear Power Mid- and Long-Term Development Plan. These above-mentioned plans [1] propose that the newly built nuclear power unit must meet the third-generation safety standards, and have more complete prevention and mitigation measures for severe accidents. The severe core damage frequency (CDF) per reactor year must be designed to be lower than 10–5 , and that of large release must be lower than 10–6 . The nuclear power plants built during the “thirteenth Five-Year Plan” and later will strive to eliminate the possibility of large release from the design. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1159–1168, 2023. https://doi.org/10.1007/978-981-19-8780-9_112

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Small modular reactor (ACP100), as a key scientific and technological project of the third-generation nuclear power technology of CNNC, must meet the above planning requirements. In order to improve the inherent safety of small reactors, minimize radioactive source terms, and achieve the purpose of eliminating large release from design, it is necessary to study the radioactive release and mitigation of small modular reactors in severe accidents. At present, the research on severe accidents of small reactors, especially on severe accident source terms is still insufficient [2], and the research on source term mitigation measures is even less. Regarding source term mitigation measures, containment spray has been initially applied to small reactors, but there are few studies on spray design optimization. Double containment setting has been applied in Hualong series nuclear power plants, but not been used in ACP100. The effect of these two measures on small modular reactor source terms mitigation requires further research and demonstration in both qualitative and quantitative aspects. Based on the above problems, this paper uses the ISAA code to model the small modular reactor, and selects the typical severe accident sequence to carry out the accident simulation and source term calculation. Containment spray and double containment models are set up respectively. The comparison of source term results shows the feasibility and effectiveness of these two mitigation measures for source term mitigation. In addition, recommendations for optimal configuration of mitigation measures are given by sensitivity analysis.

2 Calculation Models 2.1 ISAA Code The ISAA (Integrated Severe Accident Analysis) code [3] is an integrated system-level computer code developed by Xi’an Jiaotong University, which is mainly used to describe the severe accident process in nuclear reactors. A wide range of severe accident phenomena can be modeled by some advanced verified physical models employed in ISAA, including thermal-hydraulic behaviors; heatup, degradation, and relocation of reactor core; combustible gas generation; radionuclide releasing and transporting; etc., as shown in Fig. 1 [4]. ISAA operates on the principle of material classes, which are element groups that have similar chemical properties. The default number of classes is 16. Classes are generally referred to by their class name or representative element, which is shown in Table 1. Simulation and calculation for source terms are performed for each class. 2.2 Calculation Model Settings ACP100 is modeled and calculated using ISAA code for severe accidents under two conditions of containment spray and double containment respectively, and containment spray and double containment mitigation effects on severe accident source terms are obtained. The node diagram of ACP100 model this paper used is shown in Fig. 2.

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Fig.1. Physical phenomena that ISAA code can simulate

Table 1. Class compositions in ISAA Class name

Representative Member elements

Noble gases

Xe

He, Ne, Ar, Kr, Xe, Rn, H, N

Alkali metals

Cs

Li, Na, K, Rb, Cs, Fr, Cu

Alkaline earths

Ba

Be, Mg, Ca, Sr, Ba, Ra, Es, Fm

Halogens

I

F, Cl, Br, I, At

Chalcogens

Te

O, S, Se, Te, Po

Platinoids

Ru

Ru, Rh, Pd, Re, Os, Ir, Pt, Au, Ni

Early transition elements Mo

V, Cr, Fe, Co, Mn, Nb, Mo, Tc, Ta,W

Tetravalent

Ce

Ti, Zr, Hf, Ce, Th, Pa, Np, Pu, C

Trivalents

La

Al, Sc, Y, La, Ac, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb,Lu, Am, Cm, Bk, Cf

Uranium

U

U

More volatile main group Cd

Cd, Hg, Zn, As, Sb, Pb, TI, Bi

Less volatile main group

Sn

Ga, Ge, In, Sn, Ag

Boron

B

B, Si, P

Water

H2 O

H2 O

Concrete

/

/

The model contains the safety system, including the core makeup tank (CMT), the accumulator (ACC), the in-containment refueling water storage tank (IRWST), etc.

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Fig. 2. Node diagram of ACP100

The 16 once-through steam generators are equivalent to 4, which ensures that the heat exchange area of the heat transfer tube remains unchanged. On this basis, the spray and the double containment model are added for subsequent calculations. 2.2.1 Spray Setting When the containment pressure is greater than or equal to 0.24 MPa, the containment spray is turned on, the spray temperature is 298.15 K, and the spray flow rate is set to 850 m3 /h. 2.2.2 Double Containment Setting The atmospheric pressure of inner containment and environment are both one atmosphere, and the annular space maintains a negative pressure of 0.4 kPa. For containment bypass, especially the penetrations bypass, referring to Analysis Criterion of the Design Basis Accident Source Terms for Pressurized Water Reactor Nuclear Power Plant (NB/T 20444-2017RK) [5] and Analysis Criteria for Postulated Siting Accident Source Term for Nuclear Power Plant (NB/T 20470-2017RK) [6], the leak rate of the inner containment

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to the environment through the bypass is set to 0.1%. The leak rate of the containment annular space to the environment is set to 1%. Considering different ventilation and filtering effects, the leak rate of the inner containment to the annular space is analyzed as a sensitivity parameter. 2.3 Typical Severe Accident Sequences Selection According to the requirements of Nuclear Power Plant Design Safety Regulations (HAF102-2016), the ACP100 severe accident sequence is determined by a combination of engineering judgment, determinism and probability theory evaluation [7]. The specific steps are as follows: 1) Identifying different types of severe accident phenomena or containment failure modes, types with the same mitigation means are combined into one. 2) For different types of severe accident phenomena and containment failure modes, combined with the severe accident process analysis and phenomenon and engineering judgment, as well as the design requirements of each severe accident mitigation measure to deal with the working conditions, select an enveloping severe accident sequence. 3) Ensure that the severe accident sequence selected in step 2) can envelop the PSA analysis results, that is, envelope the typical sequence obtained by the PSA analysis. Based on the above principles, through the comparative analysis of different severe accident sequence in ACP100 [8], the double-ended guillotine of direct vessel injection (DVI), which contributes the most to the CDF, is selected. Accidents are calculated using the following assumptions. – – – – – –

A DVI double-ended guillotine occurs in 0 s; Passive residual heat removal system is not available; Core makeup tank is available; Accumulator is available; Rapid depressurization system (RDP) is available; IRWST is not available.

3 Calculation Results 3.1 Analysis of Calculation Results of Containment Spray The containment spray started 145 s after the accident and ended 1700 s after the accident for a total of about 26 min. The calculation results show that the spray has no effect on the noble gases. For other nuclide classes, the spray removal effect is obvious, and the mitigation efficiency (1environmental release share with the spray /environmental release share without spray) is above 98%. Figure 3 shows the comparison curves of the fission products released to environment with and without spray.

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Fig. 3. Comparison of the shares of fission products released to the environment with and without spray

It can be seen from Fig. 3 that the spray removal effects of different classes of nuclides are different, which can be divided into four categories. The noble gases are separate category, for which spray removal is ineffective. The environmental release share after 300,000 s is about 4.0 × 10–3 . Alkali metals, halogens, chalcogens and CsI can be grouped into one group. The environmental release shares without spray are about 10–5 to 10–4 and reduced to about 10–7 with spray. Alkaline earths, early transition elements, more volatile main group and less volatile main group can be divided into one group, whose environmental release shares without spray are about 10–6 to 10–5 and about 10–9 to 10–8 with spray. Platinoids, tetravalent, trivalents, uranium are divided into one group with the smallest environmental release shares, which are about 10–12 to 10–9 without spray, and about 10–16 to 10–13 with spray. Noble gases are extremely inactive, and their behavior and migration process in containment are mainly affected by radioactive decay and containment leak. The leaching removal of the spray system is effective for the removal of molecular iodine, particle iodine and aerosols in the containment atmosphere, but has little effect on the removal of organic iodine, and has no effect on noble gases.

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Fig. 4. Share of single containment and double containment fission products released to the environment

3.2 Analysis of Double Containment Calculation Results 3.2.1 Calculation Results of Leak Rate of 50% The results show that the removal effect of the double containment on the radionuclide is relatively obvious. Among them, the ability to remove noble gases and halogens is relatively poor. The mitigation efficiency (1-environmental release share of double containment/environmental release share of single containment) about 72 h after the accident are 33.84% and 35.46% respectively. For other nuclide classes, the mitigation efficiency is higher, which is about 80% about 72 h after the accident. Figure 4 shows the comparison of the shares of fission products released to the environment from single and double containment.

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The filtration system and deposition of the double containment have no effect on the noble gases. The retention of noble gases by double containment is mainly achieved by the capacity of the containment itself, so the mitigation effect on the noble gases is relatively poor compared to other nuclides. In the inner containment atmosphere, the main factors that play a role in the retention and removal of radioactive aerosols and iodine are radioactive decay, leak of the inner containment, filtration systems, and deposition and resuspension (anti-deposition). Among them, the deposition and resuspension only work on aerosol, molecular iodine and particle iodine, and have little effect on organic iodine. The filter system is effective in removing molecular iodine, particle iodine and aerosols in the inner containment, but has little effect on the removal of organic iodine. In the annular space, the filtration purification, deposition and resuspension of the ventilation filter system works on aerosols, molecular iodine and particle iodine, but not on organic iodine. Therefore, the removal and mitigation efficiency of iodine by double containment is relatively poor. 3.2.2 Calculation Results of Different Leak Rates Taking the leak rate from the inner containment to the annular space as a sensitivity parameter and setting the leak rate as 1%, 5%, 10%, 20%, 25% and 50% respectively, four representative nuclides classes are selected to calculate the environmental release shares, which are noble gases, alkali metals, halogens and CsI. The calculation results are compared in Fig. 5. It can be seen from Fig. 5 that for noble gases and halogens (represented by iodine), the larger the leak rate, the larger the release shares to the environment. So in the double containment setting, the leak rates of the inner containment to the annular space should be minimized as much as possible to reduce environmental releases. For alkali metals (represented by Cs) and CsI, when the leak rate increases from 1% to 25%, the release to the environment is gradually accelerated, and the amount of environmental release is gradually increased too. However, when the leak rate increased to 50%, the release rate to the environment becomes faster, but the total environmental release shares are smaller than that of the 20% leak rate. It can also be seen that the greater the leak rate, the faster the release to the environment reaches equilibrium. The sensitivity analysis of the leak rate from different inner containment to annular space shows that in general, the greater the leak rate, the worse the mitigation effect of the source terms, but when the leak rate increases to a certain extent, the mitigation effect is enhanced. Considering the actual setting of the double containment, the leak of the inner containment to the annular space should be minimized, so as to achieve a better source term mitigation effect.

4 Summary For the small modular reactor, based on the ACP100 model, the ISAA code is used to simulate the severe accident and calculate the source terms. Two mitigation measures, which are containment spray and double containment, are analyzed respectively, and the effectiveness of these two measures for source term mitigation is proved. Through

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Fig. 5. Effects of different leak rates on the environmental release shares of nuclides

sensitivity analysis, suggestion for optimizing the configuration of double containment is given. Analysis and demonstration show that containment spray and double containment can significantly alleviate the release of radionuclides under severe accident conditions, and can play a role in reducing radioactivity. For double containment setting, the leak rate from the inner containment to the annular space has a great influence on the mitigation of the source term. To reduce the release of the source term to the environment, it should be reduced as far as possible. By the comparison of results, the rationality and feasibility of the two source term mitigation measures are proved, and the two mitigation measures can significantly improve the ACP100 security. The calculation results can provide certain data support for severe accident consequences analysis of small modular reactor, emergency planning zone partition, and the cancellation of the off-site emergency planning zone and off-site emergency simplification.

References 1. National Nuclear Safety Administration: The 12th Five-Year Plan for Nuclear Safety and Radioactive Pollution Prevention and the Vision for 2020. Nuclear Safety Administration, Beijing (2012). (in Chinese) 2. Hang, C., Fan, Z., Feng, Y., et al.: Analysis of blackout accident and mitigation measure for small reactors. Nucl. Power Eng. 36(02), 62–65 (2015). (in Chinese)

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3. Zhang, B., Deng, J., Jing, M., et al.: A newly-developed suppression pool model based on ISAA code. Nucl. Sci. Eng. (2020). https://doi.org/10.1080/00295639.2020.1861862 4. Gao, P., Zhang, B., et al.: Development of mechanistic cladding rupture model for integrated severe accident code ISAA. Part I: module verification and application in CAP1400. Ann. Nucl. Energy 3, 25 (2021). https://doi.org/10.1016/j.anucene.2021.108305 5. National Energy Administration: Analysis Criterion of the Design Basis Accident Source Terms for Pressurized Water Reactor Nuclear Power Plant: NB/T 20444-2017RK (2017). (in Chinese) 6. National Energy Administration: Analysis Criteria for Postulated Siting Accident Source Term for Nuclear Power Plant: NB/T 20470-2017RK (2017). (in Chinese) 7. Final Safety Analysis Report (Chapter 19) of Fujian Fuqing Nuclear Power Plant Units 5 and 6 (ACP1000) (Version B). China Nuclear Power Engineering Co., Ltd. (2018). (in Chinese) 8. Wang, J., Liu, J., Liu, C., et al.: Definition of plume emergency planning zone for floating nuclear power plant. At. Energy Sci. Technol. 51(04), 671–675 (2017). (in Chinese)

Study on the Over-Reading of Venturi Flow Measurement of ARE System in M310 Nuclear Power Unit Gongzhan Wang(B) , Xianhe Shang, Pengfei Fan, Yongxiang Zheng, Jiangbo Gao, Qian Min, Xiasheng Lei, Qiang Liu, and Fang Li CNNO Operations Management Co., Ltd, Haiyan, Zhejiang, China [email protected]

Abstract. Over-reading of Venturi-element measurement of main feed water flow is a frequently seen problem in M310 units and has hung over the industry for many years. After test and theory calculation, this study inferred the cause of the over-reading is the extra pressure drop generated by vortex flowing through the Venturi-element, furthermore, T-joint between the pipes of main feed water control valve and auxiliary feed water flow control valve was determined as the source of the vortex. Simulated calculation consists with the actual over-reading curve, and site measurements showed the actual installation bias values of the T-joints conform with the over-reading values of correspondent Venturi elements, and a common problem bothering M310 units was answered. Keywords: ARE · Venturi · Flow · Over-reading · Vortex

1 Problem Description The main feed water flow control system of M310 nuclear power unit (hereinafter referred to as “ARE”) is responsible for controlling the water level of the steam generator. The main feed water flow measured by the Venturi element is directly involved in the control of the water levels of the steam generator and deaerator, and the abnormal water level of the steam generator and deaerator may lead to reactor shutdown. Therefore, this flow is one of the most important measurement signals of the nuclear power unit. In domestic M310 units, the over-reading problem of main feed water Venturi flow elements at medium and low power levels has been common for a long time, which has troubled the industry for nearly 20 years. Taking the ARE feed water flow measurement of steam generation ring 1 of Fangjiashan NPP Unit1 as an example, the typical flow measurement over-reading curve is shown in Fig. 1. It can be seen from the figure that when the main feed water flow is below 600 t/h, the reading of Venturi flow element is significantly higher than that of orifice flowmeter and main steam flow, and the latter two are basically the same. Therefore, it is judged that there is a problem in the reading of Venturi flow element. See Table 1 for incomplete statistics of Venturi flow over-reading in each power plant. The phenomenon of over-reading in different steam generator rings of different power plants is summarized, which has the following characteristics: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1169–1182, 2023. https://doi.org/10.1007/978-981-19-8780-9_113

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Fig. 1. Main feed wate flow of SG#1 of Fangjiashan unit #1

Table 1. Statistics of Venturi flow over-reading 0D[LPXPRYHUUHDGLQJ

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1) The phenomenon of over-reading measurement occurs on the Venturi flow element only, and the downstream orifice flowmeter is basically consistent with the main steam flow. 2) In the same power plant, some units have over-reading measurement, while some units don’t; For example, this problem exists in Fangjiashan unit 1, but does not exist in unit 2. 3) For the same unit, some rings have over-reading measurement, and some rings do not have; For example, in Fangjiashan unit 1, the maximum over-reading of the first ring is 200 t/h, that of the third ring is 100 t/h, and that of the second ring is almost zero. 4) In the same ring, if there is over-reading, the starting point of over-reading and the amplitude of over-reading are basically stable, the performance is the same in the process of startup and shutdown, and can be repeated in all previous startup and shutdown processes. 5) The flow over-reading mainly occurs in the middle and low power stage, and appears in the low power stage when the flow is less than 600 t/h, and the maximum flow over-reading can reach 200 t/h.

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6) When the flow over-reading is significantly higher, the flow fluctuation amplitude also increases significantly. The over-reading and fluctuation of Venturi flow element has a serious impact on the control of steam generator water level and the power stability of the primary circuit. Therefore, different power plants have taken different countermeasures, such as switching the steam generator water level control signal source from Venturi flow element to downstream orifice flowmeter, manually controlling the steam generator water level, or quickly passing through the low power stage. In order to find out the cause of high Venturi flow, various power plants have invested a lot of manpower and material resources. In 2019, power plant A replaced the Venturi flow element remanufactured with new technology, but achieved no improvement after replacement. Power plant B entrusted Dalian Maritime University to carry out simulation calculation. The calculation results show that the over-reading caused by turbulence is only 1.65 t/h when it is 273.65 t/h, the maximum over-reading caused by the change of pipe length is 7.79 t/h, and the maximum over-reading caused by the change of throat diameter is 8.26 t/h, which are far lower than the actual higher value. The study also found that when the steam content in the feed water is 0.01 kg/s, it can lead to an overreading of 60.47% [1]. However, the probability of steam in the pipeline is extremely low: the minimum pressure of the system during operation is 7.6 MPa, the corresponding saturation temperature is 291 °C, and the maximum actual feed water temperature does not exceed 230 °C. Therefore, even if there is steam in the pipeline, it will soon turn into water. Power Plant C entrusted Zhejiang University to carry out calculation. The influence of roughness and pulse flow on feed water flow is not more than 1%, and the influence of branch turbulence on feed water flow is not more than 2% [2]. Therefore, so far, the reason for the Venturi flow over-reading has not been found, and the power plants continue to invest in the research on this problem. In order to solve the problem of feed water flow over-reading, Fangjiashan power plant has systematically carried out long-term analysis and field test, and found the root cause of it. The simulation results are highly consistent with the actual over-reading curve.

2 Background Information The main feed water System of M310 1000 MWe unit has three rings, which supply water to three steam generators respectively. Each ring has a main feed water valve and an auxiliary feed water valve in parallel. During the startup and power increase of the unit, the water level of the steam generator shall be controlled through the auxiliary feed water valve, and the main feed water valve shall be opened after the auxiliary feed water valve is fully opened. When the auxiliary feed water valve is fully open and the main feed water valve starts to open, the flow is about 360 t/h, and the flow of one ring is about 1950 t/h at full power. See Fig. 2 for pipeline layout on site. The outlet pipe of the auxiliary feed water valve from bottom to top is connected with the horizontal outlet pipe of the main feed water valve through a T-joint. The Venturi flow element is located downstream of the T-joint.

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The main feed water supply pipe downstream of the Venturi pipe enters the nuclear auxiliary building from the turbine building after a turning. The orifice flowmeter is installed in the straight pipe section after turning.

Fig. 2. Diagram of pipelines

3 Cause Investigation 3.1 Measuring Principle of Venturi Flow Element Venturi flow element is a flow measuring element designed according to Bernoulli’s principle. When the liquid flows through the necking of the throat, the increase of flow rate will lead to the decrease of pressure, resulting in a static pressure difference between the upstream of the measuring device and the throat. The flow and static differential pressure meet the following formula [3]: Qm = 

C 1 − β4



πd2  Pρ 4

(1)

Wherein Qm Mass flow C Outflow coefficient, the coefficient of the relationship between the actual flow of incompressible fluid through the device and the theoretical flow β Diameter ratio, the ratio of throat inner diameter d to upstream pipe inner diameter D  Coefficient of expansion, for incompressible fluids,  is 1 d Throat diameter ΔP pressure difference, Venturi inlet pressure-throat pressure ρ Fluid density. See Fig. 3 for the structural dimensions of Venturi element in the main feed water supply system of Fangjiashan NPP.

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Fig. 3. Structure and dimentions of main feed water Venturi-element of Fangjiashan NPP

3.2 Possible Causes of Measurement Over-Reading As for the reasons that may lead to measurement over-reading, previous work has ruled out the possibility of Venturi flow element accuracy, turbulence, pipe size and roughness, and the possibility of two-phase flow is also very low. Other possible causes are further analyzed as follows: 1) Flow transmitter drift: one Venturi flow element is connected to four flow transmitters, the readings of the four flow transmitters are completely consistent, so the cause of transmitter drift can be eliminated. 2) The fluid density does not conform to the calibration sheet: the working condition used for calculating the differential pressure in the calibration sheet is 224 °C, 7.4 MPa, and the density ρ 1 is 836.68 kg/m3 ; Taking the main feed water flow of 270 t/h as an example, the actual pressure is 7.82 MPa, the temperature is 148.1 °C, and the corresponding density ρ 2 is 922.85 kg/m3 . According to mass conservation and formula (1), under the same flow rate, the relation between ΔP and ρ is: P2 = P1

ρ1 ρ2

(2)

The higher density will lead to the decrease of measured differential pressure. However, the influence of density is not considered in the conversion formula of power station  control computer. As ρρ21 = 0.95, which will cause the measured flow value to decrease by about 5%, instead of over-reading. Therefore, the density change is not the reason for the flow over-reading. The influence of vortex flow on main feed water flow measurement has not been found in literature. 3.3 Auxiliary Feed Water Valve Closedown Test Although the above calculations have not identified the cause for measurement overreading, it can be seen from the appearance and disappearance of the over-reading phenomenon that the over-reading increases with the increase of flow in the stage of auxiliary feed water valve controlling flow; After the main feed water valve starts to open, the over-reading decreases with the increase of flow. Therefore, it is suspected that the source of over-reading is the feed water from the auxiliary feed water valve. On

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April 2nd, 2022, the auxiliary feed water valve closedown test was conducted on ring 1 steam generator at the power reduction stage of Fangjiashan 106 overhaul. At 210 MWe power level, the auxiliary feed water valve was fully open, the main feed water valve was 8% open, the flow measured by Venturi flow element was 553 t/h, the reading measured by orifice flowmeter was 391 t/h, and the main steam flow reading was 420 t/h. The auxiliary feed water valve was gradually closed down manually, and the control system automatically opened the main feed water valve. When the auxiliary feed water valve was closed down to 50% opening, the flow of Venturi flow element dropped to 414 t/h, the reading of orifice flowmeter was 395 t/h, and the main steam flow reading was 429 t/h, which were basically the same. When the opening of the auxiliary feed water valve was restored to fully open, the readings of the three flows returned to the same values before the valve closedown test. The flow reading change during the test is shown in Fig. 4.

Fig. 4. Closedown test of auxiliary feed water flow control valve

This fully shows that the change of flow field in the pipeline leads to the over-reading of Venturi flow, and the adverse source of flow field change is the water flowing through the auxiliary feed water valve. When the main feed water flow switched from the auxiliary feed water valve to the main feed water valve, the two-phase flow state of the fluid will not change, and the roughness of the pipeline will not change. Therefore, the influence of two-phase flow and roughness on the flow reading can be further excluded, and the possibility of vortex current become much higher.

4 Influence of Vortex on Measurement Result 4.1 Properties of Vortex When there is a linear flow along the pipe direction and a vortex rotating around the central axis in the fluid at the same time, according to the superposition principle, when a

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new composite potential flow is composed of two or more potential flows, it can be treated separately, and then the velocity potential and the stream function can be algebraically added [4]. It is assumed that the vortex in the pipe is pure annular flow, vortex beam at the center and the outside is potential flow rotation zone induced by vortex beam, as shown in Fig. 5. Assuming that the radius of the vortex beam is r 0 , the fluid inside the vortex beam has the same angular velocity ω, the potential flow rotating region outside the vortex beam has a linear velocity u =

r02 ω r

[4].

Fig. 5. Structure and flow speed distribution of pure annular flow

Since the vortex beam is composed of micro vortex tubes, the velocity circulation of the vortex beam is equal to the total strength of all the vortex tubes inside. Helmholtz’s first theorem shows that on the same vortex tube, the rotation angular velocity vector at each point on the section with smaller cross-sectional area of the vortex tube has a larger value, which is similar to the larger value of the velocity vector at the section with smaller cross-sectional area of the flow tube. In the flow field, the head and tail sections of the vortex tube can only terminate at the liquid surface or the solid wall, or the vortex tube becomes a ring [5]. Helmholtz’s second theorem shows that under the effect of potential mass force, the fluid particles that make up the vortex tube will always form the vortex tube. This theory shows that the vortex tube can change its position and shape in the flow, but the fluid particles in the vortex tube will not change [5]. Helmholtz’s third theorem shows that the strength of the vortex tube does not change with time under the effect of the potential mass force of the barotropic ideal fluid [5]. Therefore, the following assumptions are made for the fluid in the main feed water pipeline: 1) The fluid is a compound potential flow of pure annular flow and linear flow;

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2) The pure annular flow revolves around the central axis of the pipe, the inner core is vortex beam, and the outer part is potential flow; 3) Vortex beam can exist in the pipeline for a long time, that is, the vortex beam generated in the upstream still exists after the pipeline turns and changes diameter; 4) The vortex tube strength and velocity circulation of the vortex beam remain unchanged. 4.2 Conservation Law of Angular Momentum The law of conservation of angular momentum is one of the universal laws of physics, which reflects the universal law of motion of particles and particle systems around a point or an axis [6]. When external torque Mz = 0:    i = = ri × pi = mi vi × ri (3) L L i

i

i

When the vortex flows in a pipe without bending, the wall force always points to the axis of the pipe. Therefore, for the fluid rotating around the axis, the external moment of force is always zero, and the angular momentum remains constant without considering the friction. 4.3 Influence of Vortex on Flow Measurement Results According to the superposition principle, first calculate the pressure drop at the pipe wall caused by vortex, and then add it to the pressure drop at the pipe wall caused by linear flow to obtain the total pressure drop at the pipe wall under composite fluid. 4.3.1 Influence of Reducing Diameter on Vortex Angular Velocity Assume that in a horizontal cylindrical pipe, the radius is R1 prior to the Venturi flow element shrinks, the flow is incompressible fluid with a density of ρ. The linear velocity of the fluid is ν L1 , the vortex beam radius in the vortex is r 01 , and the angular velocity is ω1 . Then the linear velocity of rotation of the cylindrical liquid wall with radius r from the center of the circle is [5]: Inside the vortex beam vr = ω1 r, in the potential flow rotation region vr = Calculate the angular momentum of the liquid column flowing in dt time: R1 L1 =

2 ω1 r01 r .

r01 mvr r =

0

ρ(2π rdr)(vL1 dt)ω1 rr 0

R1

2 ω1 r01 r r r01   1 2 2 2 = ρπ ω1 r01 vL1 dt R1 − r01 2

+

ρ(2π rdr)(vL1 dt)

(4)

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The velocity circulation along the vortex beam wall is: 2

1 = 2π r01 ω1

(5)

After a period of time, the liquid column flows to a radius of R2 , where the vortex beam radius is r 02 , the angular velocity of vortex beam is ω2 . Since the liquid is incompressible, according to the law of conservation of mass, the linear flow velocity of the liquid in the pipe section is: vL2 =

R21 R22

vL1

The angular momentum of the liquid column flowing in dt time is:   1 2 2 2 L2 = ρπ ω2 r02 vL2 dt R2 − r02 2

(6)

(7)

The velocity circulation along the vortex beam wall is: 2

2 = 2π r02 ω2

(8)

According to Stokes’ theorem, 2 = 1 , we can obtain: ω2 =

2 r01 2 r02

ω1

(9)

Assuming that there is no friction, according to the law of conservation of angular momentum: L2 = L1 . Substitute (6) and (9) to L2 and L1 , we can obtain: R2 r01 (10) r02 = R1 4.3.2 Pressure Drop at Pipe Wall Caused by Vortex When only pure annular flow is considered, the pressure at different radii can be derived according to the Euler differential equation of motion [5]. The pressure in the potential flow rotation zone is: ρω2 r04 r ≥ r0 2r 2 wherein, P∞ is the pressure at infinity, r 0 is the vortex core radius. The pressure in the vortex core area is: P = P∞ −

(11)

ρω2 r 2 r ≤ r0 (12) 2 Before and after the Venturi diameter changes, the pressure will be redistributed due to the different rotating speeds of the vortex beam. See Fig. 6 for the schematic diagram. Since the pipe wall can only be located on the surface of vortex core or potential flow area, the pressure difference caused by vortex is:    4 4 ω22 r02 ω12 r01 1 1 1 2 4 1 (13) − − dPR = ρ = ρω1 r01 2 R2 R1 2 R2 R1 P = P∞ − ρω2 r02 +

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Fig. 6. Differential pressure introduced by diameter change

4.3.3 Flow Over-Reading Caused by Vortex



2 2 , The pressure difference due to the straight flow of the fluid is dPL = 21 ρ vl1 − vl2

1 1 4 the pressure difference caused by rotation is dPR = 21 ρω12 r01 R2 − R1 . When the Venturi flow element is calibrated, only linear flow exists, so the differential pressure dPL corresponds to the correct flow vL1 , and the additional pressure difference dPR caused by the vortex corresponds to the flow over-reading, and the resulting over-reading value is: √ √ dPL + dPR − dPL vL1 (14) vo = √ dPL

4.4 Source of Vortex It can be seen from the piping diagram of the system that the feed water from the auxiliary feed water valve passes through a 90° elbow and a T-joint before reaching the Venturi flow element. The T-joint is a standard part of reducing outlet tee and is manufactured in accordance with ASME B16.9-2007 Factory-Made Wrought Butt-welding Fittings. Its outline and dimensions are shown in Fig. 7 (d = 363.6 mm, d1 = 142.9 mm) [7].

Fig. 7. Dimensions of reducing outlet tees [7]

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If the axial lines of the vertical and horizontal pipes connected to the T-joint are not on the same plane due to the deviation occurred during manufacturing or field installation, vortex may be introduced.

Fig. 8. T-joint with axes in different planes

As shown in Fig. 8, it is assumed that the deviation between the central axis of the flow flowing out of the vertical pipe section of the T-joint and the horizontal central line is σ, the velocity of vertical water flow is vV . Then the angular momentum of water flow to the center line of horizontal section in dt time is: σ LV =

 2ρ R2V − h2 dh(vV dt)(σ − h)vV

0

RV +

 2ρ R2V − h2 dh(vV dt)(h + σ )vV

0

RV −

 2ρ R2V − h2 dh(vV dt)(h − σ )vV = ρvV2 dtπ R2V σ

(15)

σ

wherein, RV = d21 . Assuming that there is no angular momentum loss during the transition from the vertical section to the horizontal section, the horizontal angular momentum is equal to the angular momentum of the eccentric vertical flow to the centerline of the horizontal pipe. Connect the vertical angular momentum (4) and (15) to obtain the vortex beam angular velocity in the horizontal pipe: ω1 =

vV2 R2V σ

2 1 2 2 v r01 L1 R1 − 2 r01

(16)

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4.5 Simulation Calculation By substituting the relevant dimensions of the pipelines and Venturi and the operating parameters of the system into the formula, the flow over-reading value can be calculated. Considering the potential flow region of pure annular flow, the momentum at different radii remains unchanged, indicating that there is no energy source, but only the rotation driven by vortex beam. The vortex in the horizontal main feed water pipe comes from the eccentricity of the T-joint, and the angular momentum brought by the upstream feed water fills the whole pipe at the same time. Therefore, it can be assumed that the pipe is full of vortex flow, and the pipe wall is the vortex core surface. When σ = 0.033 m is taken, the calculated flow over-reading caused by vortex at different flow levels is shown in Fig. 9. It can be seen that the calculated flow over-reading under different flow levels is highly consistent with the actual flow over-reading in trend and amplitude.

Fig. 9. Comparision between calculated flow over-reading and actual flow over-reading

Taking this over-reading into the flow data in the power reduction stage, the calculated Venturi flow at different power steps is also highly consistent with the actual measurement results, as shown in Fig. 10. Therefore, it can be judged that the direct cause of the main feed water flow over-reading is the vortex generated by the biased T-joint. 4.6 T-joint Measurement Results According to the above analysis, for the ring with over-reading, the T-joint may be installed incorrectly. Therefore, during the 106 overhaul, the wall thickness and bias of the T-joint were measured. The measurement results of the T-joint in the first ring are shown in Fig. 11. The T-joint nozzle installed on site is not vertically downward, and the deviation on the left and right sides reaches 18 mm. Due to the flushing of eccentric fluid and the centrifugal flushing of vortex, the wall thickness on the flushed side is 5 mm thinner than that on the opposite side. The distance deviation between the left and right sides of the T-joint of the third ring road is 5 mm, while that of the second ring is almost

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Fig. 10. Comparison of calculated flow and actual flow during reactor shutdown process

zero, and the wall thickness deviation of the 3rd ring pipe and the 2nd ring pipe is about 1mm. This shows that the size of the field installation deviation is clearly related to the over-reading of each ring, which is enough to prove that the Venturi flow over-reading is caused by the vortex introduced by the inclined installation of the T-joint.

Fig. 11. Measurement results of T-joint in ring#1 of main feed water system

There is a small deviation between the axis deviation calculated from the field measurement data and the calculation results. First, the fluid assumed in the calculation is an ideal fluid, ignoring the friction force of the pipe wall and the viscous force of the liquid; Second, the uneven central axis of T-joint pipe is only one of the sources of vortex, and the irregular shape caused by eccentric fluid also generates additional over-reading. For example, the maximum Venturi flow over-reading in the first ring of Fangjiashan unit 1 in 2015 was 120 t/h (the calculated installation eccentricity was 0.025 m), and the maximum over-reading value reached 200 t/h due to the further eccentric shape caused by flushing six years later. The over-reading of other circuits with over-reading also tends to increase year by year.

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5 Conclusions In view of the problem of ARE Venturi flow over-reading of M310, this paper systematically combs the possible causes, tests, analyzes and calculates the causes that have not been determined in previous studies, deduces that the cause of the over-reading is the vortex in the pipe, and determines that the source of the vortex is the T-joint. The field measurement results confirm the correctness of the analysis. During the manufacturing and installation of field equipment, deviation is inevitable. As a high-precision measuring element, the Venturi flow element has strict requirements on the shape of flow field, and the seemingly insignificant deviation in the flow field will lead to a large distortion of the measurement results. Among the domestic standard [8] and foreign standard [7], only the monomer’s concentricity requirement of the T-joint is available, but no deviation control requirement for site installation, which needs further attention.

References 1. Wang, H., Zhu, Z., Zhang, M., Han, J.: Numerical investigation of the large over-reading of Venturi flow rate in ARE of nuclear power plant. Nucl. Eng. Technol. 53, 69–78 (2021) 2. Chen, L.: Calculation Report on Over-Reading of Venturi Flowmeter in Main Feed Water System. Zhejiang University, Internal Report (2021) 3. Measurement of full pipe fluid flow by differential pressure devices installed in circular crosssection pipelines Part 1: General principles and requirements-1, GB/T 2624.1, 2006, 6 4. He, L.: Engineering Fluid Mechanics, pp. 202–228. Petroleum University Press, Shandong (2001) 5. Zhao, Q., Yang, X., Yan, M.: Engineering Fluid Mechanics, 2nd edn., pp. 118–119. Chongqing University Press, Chongqing (2014) 6. Li, F.: Mechanics Course (I), p. 235. Tsinghua University Press, Beijing (2011) 7. Factory-Made Wrought Buttwelding Fittings, ASME B16.9, 2007, 11, 19 8. Steel butt welded seamless pipe fittings, GB/T 12459, 2005, 593

Exploration and Practice of Industrial Aesthetics in Design of the Guohe One Nuclear Power Plant Yingrong Wang1 , Xiaojie Hu2(B) , Ming Wang2 , Chunguang Liu1 , and Hui Song1 1 State Nuclear Power Demonstration Plant Co.Ltd, Weihai, Shandong, China

[email protected] 2 State Nuclear Power Technology Corporation Ltd, Shanghai, China

[email protected]

Abstract. With the rapid development of nuclear power technology in China, nuclear power plant operators have begun to explore the aesthetic meaning in design of the nuclear power plant. Therefore industrial aesthetics has been characterized by internal facilities and external space. The Guohe One Demonstration Project is taken as an example in this paper and the industrial aesthetics design is embodied in its general layout, architectural modeling and facade design, color application, interior decoration and landscape design. It is reflected that industrial aesthetics is being paid more attention in the engineering design and the cold appearance of the traditional nuclear power plant is being changed. Thus a beautiful and comfortable environment is to be provided for company employees and visitors, and exploration and practices is to be provided for the design of the future nuclear power plant. Keywords: Industrial aesthetics · Nuclear power plant · General layout · Architectural design

1 Introduction In recent years, the advancement and safety of China’s nuclear power technology have been improved with the development of the third-generation nuclear power technology. Just like the high-level demand in Maslow’s hierarchy of needs theory, nuclear power plant operators have gradually begun to pay attention to the image and environment of the plant. Thus the design needs related to “beauty” such as “image design of the whole plant”, “de-industrialization design” and “unified style of decoration” have been put forward, which is the pursuit of industrial aesthetics by nuclear power plant operators. For nuclear power plant operators, industrial aesthetics is an invisible but powerful expression language for corporate culture. For nuclear power plant designers, it is a new round of challenges, which means that the nuclear power building with a large size and a single material shall be designed more beautiful by using various methods of planning and architectural design, and aesthetic meaning shall be given to the nuclear power building.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1183–1191, 2023. https://doi.org/10.1007/978-981-19-8780-9_114

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2 Principles and Applications of Industrial Aesthetics 2.1 The Intertwined Development of Industrial Architecture and Industrial Aesthetics At the end of the nineteenth century, a group of architects proposed opposition to the Academic’s design idea of “only pursuing form without considering function”, and they pursued the functions of architecture and the practical meaning of aesthetics. The design concept of “form follows function” was put forward by functionalism architects represented by Chicago architect Sullivan, which required that the architectural form must be consistent with its use function. At the beginning of the twentieth century, industrial architecture was promoted from pragmatism to industrial aesthetics by the Deutscher Werkbund and the Bauhaus. It was believed that the architecture shall be combined with the industry, and the structural technology shall be expressed from the architecture. Peter Behrens, the representative architect of the Deutscher Werkbund, was the pioneer of the modern industrial building design. His design of turbine workshop for the German General Electric Company used new materials and new forms, so that its steel structure can be seen clearly from the outside of the building, and the large-area glass exterior walls and uniform proportions did not make people feel oppressive. Its simple and clean shape had the characteristics of the new structure of modern buildings, and therefore it was called the first real modern building. Inspired by the architectural ideas of the Deutscher Werkbund, the Fagus Factory designed by the Gropius, the early modern architect, was characterized by simple, lightness and transparency, which determined its status in modern industrial architecture. Mies’ minimalism, on the other hand, was the extension of industrial aesthetics that focused on technology exquisiteness. He proposed the idea that “technology is sublimated into art when it fulfills its true mission”. In a sense, the development history of the modern architecture since the 19th century is also the development history of the industrial architecture. It is a history of industrial architecture sublimating from the technology to the art.

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2.2 The Meaning of Industrial Aesthetics in Design of the Nuclear Power Building

The design style of the traditional nuclear power building was similar to that designed by functionalism school in the history of architectural design. Many early nuclear power plants in China used the most straightforward way to shape the image of the nuclear power buildings. “Ensuring the realization of the functions” was the primary goal, or even the only goal, of the traditional nuclear power building design. Designers mastered and accumulated the basic knowledge of various disciplines through projects. Given that they were better at logic, they could skillfully make overall judgments according to the needs of each discipline in the stage of model development. Generally, the process, electrical, instrumental control, ventilation and other functional spaces were arranged with the containment building as the center and eventually formed a complete “functional group”, which naturally formed the plan layout and facade shape of the Nuclear Island building. They rarely explored new materials and technologies, let alone discussed the culture and meaning behind the design. Satisfying process and service production is the most important design goal of the traditional nuclear power building, which resulted in the monotonous appearance of the early nuclear power plants that could not reflect its own characteristics, let alone industrial aesthetics.

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But just like the development of the school of industrial architectural design, nuclear power architectural design has gradually embarked on exploration of the industrial aesthetics in recent years. Ultimately, the essence of the industrial aesthetics is technology exquisiteness, and the aesthetics produced by technology can reflect the aesthetic principles of harmony, completeness and self-consistency. Designers begin to explore the beauty of structure, precision, lightness and rhythm in the nuclear power buildings. The understanding and knowledge of industrial aesthetics has awakened nuclear power architects’ understanding of form, and also resurrected nuclear power people’s emotions towards architecture.

3 Industrial Aesthetic Design of the Guohe One Nuclear Power Plant 3.1 Aesthetic Design for General Layout Space 3.1.1 General Layout of Main Nuclear Power Plant (NPP) Areas Two CAP1400 units are being built for Guohe One Demonstration Project. Unit 1 arranged on the southwest side and Unit 2 arranged on the northeast side are located on the same floor elevation from the southwest to the northeast. The Site Radwaste Treatment Facility (SRTF) that is on the northeast side of Unit 2 is parallel to Unit 1&2. The main NPP protection zone is formed by setting up the protection zone wall (see the picture). According to the principles of smooth process flow, short pipelines and intensive land use, the Nuclear Island (NI) building of each unit is arranged on the northwest side while the turbine building is arranged on the southeast side. By organizing the building space in a reasonable and orderly manner, the spatial sequence of the NPP area is formed. The 7m-wide main road in the NPP area is set up in the annulus manner with the NI and turbine buildings as the center, and is connected to the buildings through a 6m-wide secondary road and a 4 m-wide branch road. With the paving of large pieces of gravel, an environmental extension in the horizontal space is formed. The comparison and echo of the vertical space and the horizontal space form a rich space level, making the main NPP area have an industrial artistic aesthetic. 3.1.2 General Layout and Landscape Design of the NPP Front Area The NPP front area of the Guohe One Demonstration Project is arranged on the west side, including the gate and the comprehensive office building. Considering that the land used for the NPP is limited, the designer integrates the production technology support center, training center, archives, exhibition center and the canteen into the same building through combination of the internal space of the building to form the comprehensive office building shared by the whole NPP. The office building and the gate are laid out in an asymmetry and echo each other, forming the skyline of the NPP area.

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When it comes to the landscape design, the designer uses the original roads and bridges to make landscaping works and adopts the shape of the reactor fuel control rod as the basic components to form the company LOGO, which impress itself on the minds of the public. At the same time, by making full use of the original topography and landform, a landscape area is set up at the entrance of the NPP as a rest area for visitors and social personnel, which has become the first platform for the NPP to be displayed to the public (see the picture).

3.2 Plan Layout and Facade Composition 3.2.1 Plan Layout of the Main Building Fleet Nuclear power buildings have obvious physical characteristics. Constrained by the reactor technology and the layout of auxiliary facilities, the room for architectural design is relatively limited. Therefore the main buildings of the NPP with the same type are similar in size and easily recognizable. The main buildings of Guohe One Project is taken as an example. This is a group of building groups built next to each other, which is often referred to as the “five main buildings” in the industry. The containment building with a cylindrical shape is located in the center of the group, and its diameter at the widest

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point is nearly 50 m and the height at the highest point is more than 70 m. The auxiliary building with a surface width of about 90 m is arranged in a semi-annulus shape adjacent to the containment building, and its plan is on the second layer radiating outward from the containment building. The annex building, radwaste building and turbine building are arranged on three sides of the auxiliary building, which is the third layer radiating outward from the center of the auxiliary building plan. Among them, the turbine building is the largest, whose height is more than 40 m and width is nearly 140 m (see the picture). The main building fleet naturally forms an exterior shape with different height, and its block changes and hierarchical layout constitute a complete spatial system and form a stable and dignified atmosphere, which is in line with the image of the nuclear power building that is strong and safe. 3.2.2 Facade Composition of the Main Building Fleet The main building fleet for Guohe One Project is interpreted based on the principle of architectural composition. Under the premise of satisfying the process flow, designers find and strengthen the laws of form beauty such as contrast and rhythm from the existing space combination, so that the aesthetic concept in nuclear power architecture is presented to the viewer. The towering containment building and the surrounding lower auxiliary building, annex building, and radwaste building form a contrast laid out in an asymmetry and compose a skyline on a vertical spatial scale, reflecting the iconic feature of the nuclear power plant. The vertically “circular” containment building and the horizontally “square” turbine building form a contrast of geometric shapes. The vertical bar windows and the corrugated steel plate window wall of the turbine building not only make the natural lighting more uniform, but also make the over 100-m facade full of rhythm sense. The nonsegregated bus and related supports installed on the outer walls of the annex building are arranged in parallel in the front facade of the main building fleet, forming a rhythmic beauty. 3.3 Selection and Scrutiny of Facade Colors 3.3.1 Logo Color of Corporate Culture Which color can represent the Guohe One Demonstration Project? The designer selects “Innovation Blue” as its logo color. Blue brings a heavy sense of reliability. The designer uses the collision between the blue and the base colors of gray and white to let the viewer experience the feeling of flow and calm, as if it were the perfect fit of the waves and the blue sky and white cloud in Shidao Bay. Which parts can the logo color of corporate culture be used? In the architectural design of some nuclear power plants, the designer will design the outdoor exposed components of the main building fleet as the logo color, such as awnings, doors and windows, platforms, maintenance ladders, downpipes, etc. But the function-oriented design may lead to the disorder of the final facade components, especially the positions of many shutters, whose sizes and heights on the facade are different. In view of the scenario above, designing the outdoor exposed components as the logo color will easily lead to the uncontrolled color of the facade, making it look messy. Therefore, in the facade color design for Guohe One main building fleet and its balance of plant (BOP),

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the designer has weakened these components and designed them as light gray and sea gray that are the existing color of the facade; What is different is the grayscale of the door frame, window frame and other parts are deepened. Thus the overall feeling is maintained.

The logo color is finally used for external annular corridor of the air path at the height of the shield building. The well-proportioned ring belt and the three-dimensional effect slightly protruding from the adjacent wall not only make this blue a characteristic mark of the Guohe One Project, but also well highlight the “passive” characteristic parts of the advanced model (see the picture).

3.3.2 Color Design for Tanks There are large tanks in the nuclear power plant that are arranged around the main building fleet. The outer surfaces of these tanks are usually provided with stainless steel or color steel plates as protection plates. When the process requirements are met, the process systems are identified by different color design of the outer protection plates, for

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example, red stands for fire protection system, yellow stands for oil system and white stands for water, etc. During the subsequent operation of the nuclear power plant, the tank can also be used as a natural image base for corporate publicity, such as the surfaces of the demineralized water storage tank and the boric acid storage tank in front of the annex building, which can be used to display the corporate logo, historical evolution, cultural publicity and even brand mascots etc., making the cold and serious equipment friendly. These tanks can be displayed as iconic areas during visit reception and brand promotion activities. 3.4 Rational Interior Decoration and Design 3.4.1 Decoration and Design of the Main Control Room (MCR) The interior design of the Main Control Room for the Guohe One Demonstration Project not only achieves a practical and beautiful effect from the perspective of the traditional decoration and furnishing, but also conducts a detailed study of various scales and colors from the perspectives of ergonomics, human factors engineering and even psychology, so that the safety of operators, staff and the public are ensured. This is what the industrial aesthetics distinguishes from the traditional design art. The designer determines the layout of the display screen in the MCR through interviews with several operators. Through the block design of the floor color of the MCR, a psychological suggestion is given to the operator that he/she is entering the control area. Each work station is well equipped with the deep-illuminated lamps to form a light-dark contrast between the console and other areas, thus reducing the probability that operators are easily fatigued in high-illumination areas. The decorative materials with vibration and noise reduction properties are selected to ensure a good sound environment for the operators (see the picture). This is a design aesthetic based on rationality, and it is the unique beauty embodied in the combination of technology and art by NPP designers. 3.4.2 Interior Color Design for the Conventional Island (CI) Building The operation floors of the CI building are important areas of the nuclear power plant, and the main places for inspection and maintenance. Its interior color design is very important to the user’s feeling, and is also the main feature of a nuclear power plant that distinguishes it from other nuclear power plants. Each operating floor of the CI for Guohe One Demonstration Project is peacock blue, the floor of the desalination room is milky white, and the floor of the circulating water pump room is sea gray. Different buildings selecting different floor colors can make each building have its own style characteristics. On the other hand, it can also be used as a tool to prevent human errors, reminding the staff the building he/she is in and avoiding wrong workplace (see the picture).

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4 Conclusions In the process of general layout and architectural design for the Guohe One Demonstration Project, the designer applies the theory of industrial aesthetics and carries out aesthetic-related design for the entire NPP space from the perspectives of the general layout, space modeling, color selection, cultural connotation, etc., which have changed the cold feeling of the concrete appearance of the traditional nuclear power plant and provided a beautiful and comfortable environment for company employees and visitors. This is precisely the design charm based on industrial aesthetics, which can promote employees’ work efficiency and team cohesion, establish a good corporate image, publicize corporate culture and clean energy concepts, and enhance corporate charm. To a certain extent, it reflects the rational beauty of high technology. With the vigorous development of the nuclear power technology in China, the mysterious veil of the nuclear power plant is uncovered and it is gradually presented to the public. It is believed that more and more NPP designers will recognize and practice industrial aesthetic design in the future. The industrial aesthetics applied in the design of the Guohe One Demonstration Project is interpreted and analyzed in this paper in order to provide exploration and practice for the future design of the nuclear power plant.

Bibliography 1. Liu, J., Zhang, M.: The expression of architecture as a series: color of architecture. Ind. Constr. 43–47+431 (2020) 2. Zhang, L.: Buildings for industry: an aesthetics of productivity. World Architect. 09, 8–9 (2020) 3. Huang, X.: The strangeness of industrial architecture. World Architect. 09, 10–15+132 (2020) 4. Wen, X.: Geometric shape and construction design of nuclear power plant. World Architect. 6–11 (2018) 5. Chen, B.: Discussion on aesthetics of modern industrial building. Shanxi Architect. 13, 29–30 (2006) 6. Zheng, Q.: Color formation of modern industrial architecture. Ind. Constr. 01, 24–26 (2002) 7. Wang, B., Ding, Z., Li, Z.: Design for Factory and Mine Architecture and External Spatial Environment. China Coal Industry Publishing House 8. Lu, J.: Daya Bay nuclear power plant under construction. Cent. China Electr. Power 03, 72–76 (1990)

Author Index

A An, Feng 366 Anbo, Yang 347 B Bai, Xu-Tao 166 Bei, Xinyan 1109 Bian, Xin-Yuan 1084 Bingqian, Li 67 Bin, Wang 16 Bin, Xie 78 C Cao, Peigen 366 Cao, Rong 46 Cao, Xuewu 515 Cao, Yonggang 200 Chang, Gao 1131 Chang, Meng 724 Chen, Chao 712 Chen, Chen 712 Chen, Fangqiang 1045 Cheng, Kunlin 384 Cheng, Maosong 1109 Chen, Huafa 144 Chen, Huandong 827 Chen, Jian 809 Chen, Jianwen 444 Chen, Jiayue 827 Chen, Jing 584, 913 Chen, Lihui 1061 Chen, Li-Juan 1002 Chen, Mingya 111 Chen, Ping 259 Chen, Rongchang 574 Chen, Shijun 1061 Chen, Shuai 1045 Chen, Xisan 928 Chen, Xu 375, 481 Chen, Xujia 1023 Chenxu, Hu 67

Chen, Yinqiang 329 Chuanxiang, Jing 607 Chunxin, Fan 753 Cui, Tongming 1123 Cuicai, Dong 565 D Dai, Yuqing 1109 Dai, Zhiwen 1070 Dan, Tichun 46 Dawan, Yu 845 Deng, Chengcheng 490 Deng, Zhixin 246 Di, Jiang 937 Ding, Hongchun 384 Di, Yao 937 Dongxing, Li 354 Dong, Yiman 724 Duan, Lele 444 Du, Bin 1140 F Fa, Dan 1002 Fang, Jun 155 Fang, Sheng 898 Fan, Jiheng 654 Fan, Pengfei 1169 Feng, Bingcheng 428 Feng, Han 144 Feng, Jianxin 783 Feng, Luo 753 Feng, Yu 470, 540 Feng, Zhipeng 323 Fu, Jianghan 1070 G Gan, Yingying 809, 1149 Gao, Chang 316 Gao, Chao 742 Gao, Hongbo 111 Gao, Jiangbo 1169

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 C. Liu (Ed.): Proceedings of the 23rd Pacific Basin Nuclear Conference, Volume 2, SPPHY 284, pp. 1193–1198, 2023. https://doi.org/10.1007/978-981-19-8780-9

1194

Gao, Ruixi 654 Gao, Shuo 1096, 1102 Gao, Zhichao 515 Ge, Gao 937 Gong, Quan 700 Gong, Zhengyu 993 Guanghui, Wu 753 Guanzhong, Zhang 181 Guogang, Bao 191 Gui, Chun 329 Guoqiang, Ma 58 Guo, Yufei 530 Guo, Zihao 144 Gu, Wenlu 654 Gu, Zhixing 993 H Han, Dujuan 1159 Hangzhou, Zhang 753 Hanjie, Xu 565 Han, Junling 973 Han, Xu 470, 540 Haolei, Zhang 860 He, Jiandong 8 He, Jiang 845 He, Tianqi 316, 1131 He, Xuedong 1140 Hong, Ye 874 Hou, Qinmai 218 Huang, Bingchen 724 Huang, Li 724 Huang, Li-Jun 129 Huang, Qian 773 Huang, Xuan 323 Huang, Zhi 1102 Huangfu, Yuzhao 729 Huan, Huang 211 Huanhuan, Qi 984 Hui, Li Hong 689 Huan, Lihuang 428 Hui, Liu 67 Hui, Wang 354 Hui, Yongbo 793 Hu, Jian 712 Hu, Ming-lei 37 Hu, Wenjun 200 Hu, Xiaojie 1183 J

Author Index

Jiajie, Deng 230 Jiang, Di 679 Jiang, Pingting 266 Jiang, Shujie 724 Jiangwei, Ji 211 Jian, Lu 459 Jianqin, Liu 436 Jian, Wang 347 Jianyu, Tang 860 Jiao, Song-kun 589 Jiheng, Fan 753 Jing, Huang 459 Jinlong, Tang 839 Jinna, Mei 78 Jin, Tao 596 Jin, Wen 845 Jin, Xin 616 Jin, Zhao 1070 Ji, Pengbo 973 Jundong, Lu 230 Jun, Fang 230 Junjie, Cao 845 Juntao, Hu 91 K Kai, Gao 436 Kaili, Xu 1131 Ke, Guo-Tu 1084 Ke, L. I.-Shi 129 Kuan, Zhang 1054 Kunfeng, Li 629 L Lai, Hongyu 46 Lang, Xiye 252 Lei, Lei 58 Lei, Xiasheng 1169 Liang, Miao 993 Liang, Ren 144 Liang, Yi 654 Lianshun, Li 347 Li, Chengyuan 549 Li, Fang 1169 Li, Fei 947 Li, Fuhai 155 Li, Guipeng 277 Li, Haoxiang 1140 Lihui, Chen 1054 Li, Kejun 337

Author Index

Li, Li 629 Li, Meifu 549 Lin, Genxian 155 Ling, Shuang-Han 129 Lin, Lei 111 Lin, Li 654 Liu, Chen 574 Liu, Chunguang 1183 Liu, Donglin 1014 Liu, Hang 643 Liu, Hongzhen 240 Liu, Jiajia 1159 Liu, Jiecong 773 Liu, Jingquan 506 Liu, Lianghui 762 Liu, Longcheng 898 Liu, Peng 643 Liu, Qiang 1169 Liu, Shuai 323 Liu, Shuiqing 530 Liu, Ting 724 Liu, Xiao- han 616 Liu, Xiaolin 773 Liu, Yanfang 530 Liu, Ya-Ni 616 Liu, Yutong 419 Liu, Zheng 964 Liu, Zhishuang 337 Liu, Zhiyuan 724 Liu, Zhongguo 246 Liu, Zhuo 470, 540 Liu, Ziqiao 1149 Li, Wen-An 1002 Li, Wenfei 366 Li, Xiaohui 1123 Li, Xin 173, 589 Li, Yang 742 Liye, Zang 181 Li, Youyi 252 Li, Yusong 874 Li, Zhang 436 Li, Zhaotong 1045 Li, Zhenchen 654 Li, Zichao 574 Li, Ziyi 419 Long, Tao 928 Long, Xin 354 Luan, Zhenhua 643 Lu, Hai-Rong 166 Luo, De-Kang 1084

1195

Luo, Kai 277 Luo, Kun 1014 Luo, Wenbo 1061 Luo, Yaoqi 306 Lu, Yong 616 Lu, Zhanpeng 1123 Lv, Weifeng 700 Lv, Xi 323 Lv, Yanxin 724 M Ma, Chao 412 Ma, Fuchen 783 Ma, Gu-Jian 259 Ma, Haifu 1159 Ma, Haojun 1014 Ma, Liang 419 Ma, Na 712 Mao, Jiangjiang 375, 481 Maoquan, Liu 845 Ma, Tao 1140 Ma, Tingwei 240 Mengsha, Liu 937 Meng, Wei 773 Min, Qian 1169 Mo, Xinyuan 898 O Ou, Wenlan

993

P Pan, Qiwen 993 Pei, Min 596 Peng, Cuiting 490 Peng, Haibo 375, 481 Peng, Limei 973 Peng, Lin 629 Peng, Xu 860 Q Qian, Gensheng 506 Qiang, Li 181 Qian, Sheng 729 Qian, Xiaoming 218 Qian, Yueqing 973 Qiao, Hang 155 Qiao, Jianfeng 643 Qiao, Pengrui 200 Qiaozi, Zhao 347

1196

Author Index

Qie, Dongsheng 874 Qi, Huanhuan 323 Qin, Kemian 375, 481 Qiu, Binbin 173 Qiu, Suizheng 947, 1070 Qiu, Yongping 8 Qiu, Zhifang 549 Qunjia, Peng 78 R Ran, Wenwang 700 Ren, Liang 191 Rongpeng, Li 937 Ruixi, Gao 753 S Seidl, Marcus 306 Shang, Xianhe 1014, 1169 Shan, Jianqiang 793 Shen, Ting 596 Sheng, Jiang-Fei 129 Shi, Lei 712 Shi, Xingjin 246 Shijun, Chen 1054 Shi, Yang 1002 Shuai, Liu 984 Shuai, Wang 845 Shuangrong, Muo 753 Shu, Jialong 412 Shuying, Nong 347 Song, Dahu 724 Song, Fei 643 Song, Guangdong 173 Song, Hui 1183 Song, Xiao-lei 589 Su, Guanghui 1070 Su, Jie 337 Su, Yang 712 Su, Zhiyong 928 Sun, Taoxiang 913 Sun, Xiaoyan 742 Sun, Xiaoying 809, 1149 Sun, Yang 252 Sun, Yu 729 Sun, Yun 155 Sun, Zhijun 928 T Taikeng, Jiang

400

Tan, Chao 947 Tan, Xiao 8 Tao, Naigui 762 Tao, Qihao 793 Tao, Wang 885 Tao, Zhou 860 Teng, Lei 928 Tian, Chao 1159 Tianfu, Li 347 Tian, Hongyu 973 Tian, Shizhong 1096, 1102 Tian, Wenxi 1070 Tong, Lili 515 Tong, Mengyao 103 W Wang, Chenglong 947, 1070 Wang, Chonmei 246 Wang, Dongmei 679 Wang, Dongyang 809, 1149 Wang, Gongzhan 1169 Wang, Hangding 428 Wang, Hao 964 Wang, Hongliang 470, 540 Wang, Jianchen 913 Wang, Jianqiang 596 Wang, Jun 46 Wang, Junlong 1159 Wang, Kai-yuan 616 Wang, Li 530, 596 Wang, Meng 1096 Wang, Ming 1183 Wang, Pei-jian 1040 Wang, Qingsong 1045 Wang, Runci 874 Wang, Shaojie 679 Wang, Shuai 773 Wang, Shuangyin 412 Wang, Tieshan 375, 481 Wang, Xi 444 Wang, Xiang 306 Wang, Xiaoyu 827 Wang, Xin 1031 Wang, Yaqi 218 Wang, Yingrong 1183 Wang, Yu 46 Wang, Yuanzhi 1040 Wang, Zefeng 827 Wang, Zhaoming 277 Wang, Zheng 574

Author Index

Wang, Zichun 1061 Wan, Xiaoyuan 419 Wan, Zhao 845 Wei, Bin 1040 Wei, Han 565 Wei, Qiaojuan 783 Wei-lin, Chen 885 Weiwei, Wang 191 Wenbo, Luo 1054 Wen, Jie 37 Wenjing, Lei 91 Wenzhang, Xie 629 Wu, Jianjian 337 Wu, Jianrong 412 Wu, Panpan 1123 Wu, Renqiong 1014 Wu, Shenao 46 Wu, Xianhong 1040 Wu, Yao 596 Wu, Yirui 1159 Wu, Yuling 898 X Xiajie, Liu 629 Xia, Mingming 1159 Xiang, Qin 436 Xiang, Zou 58 Xianhui, Ye 853 Xiaobin, Jiang 230 Xiaobo, Li 211 Xiao, Tan 91 Xiao, Tiaobing 329 Xiao-han, Liu 885 Xiaohan, Zhao 181 Xiaojiang, Wu 845 Xiaojiao, Ding 211 Xiaojun, Wen 845 Xiaoming, Jin 67 Xia, Xiaojun 366 Xiaozhou, Jiang 984 Xie, Zhe 329 Xie, Zhengquan 947 Xie, Zhiguo 240 Xiliang, Guo 436 Xin, Jin 885 Xinmin, Li 230 Xiong, Jun 700 Xiong, Kun 1031 Xiong, Wang 181, 191 Xuan, Huang 984

1197

Xu, Chao 584 Xu, Dan 783 Xu, Decheng 111 Xue, Wei 928 Xu, Kaili 316 Xu, Ke 37 Xu, Tao 1159 Xu, Xinhe 1123 Xu, Yueping 762 Xu, Zhe 240 Xu, Zhong 1014 Xu, Zhu 37, 329 Y Yan, Xu 839 Yang, Fan 375, 481 Yang, Gangqiang 783 Yang, Guanghua 1023 Yang, Jianfeng 428 Yang, Jiang 144 Yang, Jingjie 654 Yang, Jun 490 Yang, Longquan 218 Yang, Qianfei 412 Yang, Shunlong 46 Yang, Xingtuan 1140 Yang, Ye 490 Yang, Yong 973 Yang, Yuning 913 Yang, Zongqiang 240 Yanli, Yuan 853 Ya-ni, Liu 885 Yan, Jiabing 654 Yan, Shuhang 316, 1131 Yao, Bo 166 Yaolei, Han 78 Yao, Sun 27 Yao, Yao 490 Yaqing, Ren 629 Ye, Chunsong 46 Yibo, Guo 400 Yingzhe, Du 629 Yin, Huaqiang 1140 Yiqian, Liu 459 Yong, Hu 354 Yong, Ouyang 181, 191 Yongping, Qiu 91 Youqi, Chai 347 Yuan, Chenliang 419 Yuan, Huang 459

1198

Yuan, Yidan 470, 540 Yuan, Yu 400 Yucheng, Zhuo 91 Yu, Huajin 173 Yu, Kaicheng 1109 Yu, Min 111 Yu, Mingrui 470, 540 Yu, Qian 1002 Yuntao, Zhao 607 Yu, Wenge 252 Yuxiu, Chen 211 Yuyong, Wu 839 Z Zeng, Chenming 412 Zeng, Chun 1014 Zhang, Bin 1159 Zhang, Bo 668, 793 Zhang, Chengtian 928 Zhang, Duoyi 419 Zhang, Furong 337 Zhang, Guangchun 384 Zhang, Hangzhou 530 Zhang, Haochun 384 Zhang, Heng 689 Zhang, Honglin 712 Zhang, Jian 1084 Zhang, Jin-Fei 166 Zhang, Jingchi 898 Zhang, Ke 729 Zhang, Ling 993 Zhang, Pengfei 412 Zhang, Pin 419 Zhang, Shengdong 874 Zhang, Shuang 742 Zhang, Wei 37 Zhang, Xiang 1040 Zhang, Xiao-Chen 166 Zhang, Xiaocong 419 Zhang, Xiaofeng 762

Author Index

Zhang, Xiaoyang 375, 481 Zhang, Yongling 928 Zhang, Zeqin 947 Zhang, Zhenhua 668 Zhan, Haiyan 366 Zhanju, Jia 753 Zhan, Yongjie 246 Zhao, Lei 200 Zhao, Pengfei 252 Zhao, Wenwen 742 Zhao, Xinli 679 Zhao, Yulan 384 Zhao, Zhijin 913 Zhenchen, Li 753 Zheng, Chaoying 668, 1045 Zheng, Jilong 277 Zheng, Wei 1140 Zheng, Yongxiang 1169 Zhengke, Chang 459 Zhihua, Li 845 Zhijian, Wang 27 Zhikang, Lin 191 Zhipeng, Feng 984 Zhongxiu, Zeng 984 Zhou, Jing 700 Zhou, Lijun 173 Zhou, Mengjie 596 Zhou, Runfa 111 Zhou, Shuai 111 Zhou, Tao 574 Zhu, Dongdong 874 Zhu, Guixue 8 Zhu, Lili 1014 Zhuo, Xunjia 1023 Zhuo, Yucheng 8 Zhu, Shikun 375, 481 Zhu, Wei 218 Zilong, Wang 1 Zuo, Xiandi 1109