International Conference on Emerging Trends in Engineering (ICETE): Emerging Trends in Smart Modelling Systems and Design [1st ed. 2020] 978-3-030-24313-5, 978-3-030-24314-2

Table of contents :
Front Matter ....Pages i-xxxiv
An Empirical Based Porous Flow Approach to Modelling Heterogeneous Traffic (Seelam Srikanth)....Pages 1-8
Effect of Different Aggregates on Alkali Silica Reaction of Geopolymer Concrete (D. Annapurna, Ravande Kishore, K. Anil)....Pages 9-17
Assessment of Aquifer Vulnerability of Nizamabad District, Telangana State, India Using GIS and Drastic Method (B. Ramakrishna, P. Rajasekhar, Shaik Vaheed)....Pages 18-26
Effect of Baffle Wall Parameters on the Modal Responses of Elevated Rectangular Water Tank (Meghana Minnekanti, Mallika Alapati)....Pages 27-38
Bearing Capacity of Strip Footing on Reinforced Foundation Beds with Inclined Reinforcement (P. Rajashekar Reddy, G. V. Narasimha Reddy, E. Saibaba Reddy)....Pages 39-46
Precise Survey Assistance for Civil Structures Using Survey Assist (K. Surya Prakash, Kone Srimannarayana, Pradeep Kumar Kaatnam, Abhinav Dayal)....Pages 47-52
Ground Improvement Using Band Drains at Onshore Gas Terminal Near Kakinada (AP), India (D. Babu Rao, D. Nageswara Rao)....Pages 53-58
New Light Weight Mortar for Structural Application: Assessment of Porosity, Strength and Morphology Properties (Paul Awoyera, Ravindran Gobinath, Sandela Haripriya, Poongodi Kulandaisami)....Pages 59-65
Computing Redistribution Moments of Concrete Members in the Plastic Stage Using Linear Analysis: Short Review (Paul Awoyera, Ravindran Gobinath, Chinyere Nwankwo, Palanisamy Murthi, Annabathina Sivakrishna)....Pages 66-72
Nonlinear Dynamic Analysis (A. A. Waghmare, U. R. Kawade)....Pages 73-79
Evaluation of Coir Geotextile Mats to Enhance the Poor Subgrade Under Repeated Load for Low-Volume Roads (D. Harinder, S. Shankar)....Pages 80-88
Estimation of Maximum Magnitude (mmax) Considering Different Seismic Zones (Mohammad Muzzaffar Khan, Teja Munaga, Gonavaram Kalyan Kumar)....Pages 89-95
Necessity of New Look for Equitable Water Sharing Among Basin States - Krishna Basin (S. Narasimha Rao, K. Venkata Narayana, N. Ravishankar)....Pages 96-104
A Comparative Study on the Performance of ANN, MLR and MNR in the Assessment of Slope Stability for Kalla - Coonoor Hill Road Stretch of Nilgiris (Balendra Mouli Marrapu, Ravi Sankar Jakka)....Pages 105-114
Hydrologic Modeling with Transfer Function Based Approach: A Comparative Study over Godavari River Basin (Chaitanya Lakeshri, Kaustubh Salvi)....Pages 115-122
Study on Interaction of Reinforcing Strips Embedded in Cement Modified Marginal Backfill (Pandu Kurre, Vineeth Reddy Karnati, Heeralal Mudavath)....Pages 123-130
Experimental Study on Interfacial Frictional Properties of Geogrid Reinforced Pond Ash (Mogili Sudhakar, M. Heeralal, G. Kalyan Kumar, Pandu Kurre)....Pages 131-140
Resilient Modulus of Unsaturated Soil – A Comprehensive Review (A. G. Sharanya, M. Heera Lal, Pandu Kurre)....Pages 141-148
Effect of Eccentric Load on Footing Resting on Planar Geosynthetic Reinforcement (Pandu Kurre, M. Heeralal, T. S. D. Venkatesh)....Pages 149-154
Strength Investigation of Fly Ash Based Concrete Waste Steel Fibre and Polypropylene Fibre as Reinforcing Materials (G. Swamy Yadav, N. Prabhanjan, G. Sahithi, G. Sangeetha, A. Srinivas, A. Siva Krishna)....Pages 155-161
Application of Statistics to the Analysis of Corrosion Data for Rebar in Metakaolin Concrete (U. Raghu Babu, B. Kondraivendhan)....Pages 162-169
Earthquake Analysis of High-Rise Building with Floating Column (Mohasinkhan N. Bargir, Ajim G. Mujawar)....Pages 170-178
A Review on the Recent Development of Ambient Cured Geopolymer Composites (Mayank Gupta, N. H. Kulkarni)....Pages 179-188
A Case Study on Structural Assessment of Dudhgaon Grampanchayat Building by Using Non Destructive Testing (Rohit Kukade, Santosh Mohite)....Pages 189-194
Analysis and Design of High-Rise Building Using Diagrid Structural System (Ahmad Muslim Rujhan, Ravande Kishore)....Pages 195-205
Engineered Cementitious Composites (Yogesh Biyani, L. G. Patil, C. N. Kurhe)....Pages 206-214
Influence of Metakaolin on Stone Waste Aggregate Concrete (Sakevalla Vinay Babu, U. Raghu Babu, N. Venkata Ramana, P. Pavithra)....Pages 215-221
Rapid Hardening on the Strength Gain Admixture on Behavior of Concrete with Replacement of Binary Cementitious Materials (R. Kiranmai, A. Rohita Susheela, S. Rajkumar, G. Asheesh, V. M. Sounthararajan)....Pages 222-233
Evaluation of Pullout Resistance of Reinforcing Strips Embedded in Cement Modified Marginal Backfill of Mechanically Stabilized Earth (MSE) Walls (Pandu Kurre, Gannavaram Venkat Praveen)....Pages 234-247
Identification of Artificial Recharge Zones Using GIS (K. A. Patil, Noopur D. Khatik, Shriman N. Jirapure)....Pages 248-257
Seismic Analysis and Design of Mass and Stiffness Irregular R.C. Building Frames with Different Code (Nishant C. Chandanshive, Santosh S. Mohite)....Pages 258-261
Assessment of Spatial Interpolation Techniques on Groundwater Contamination (G. Shyamala, B. Arun Kumar, S. Manvitha, T. Vinay Raj)....Pages 262-269
Sustainability Concepts in the Design of Tall Structures (Prashant Sunagar, Aravind Bhashyam, B. R. Neel, Abhishek Kumar Chaurasiya)....Pages 270-277
Spontaneous Combustion of Coal and Correlation with Its Intrinsic Properties Using Adiabatic Oxidation Method (Ravi Varma Rambha)....Pages 278-284
Extraction of Electricity from Blast Induced Ground Vibration Waves – Case Study (Raghu Chandra Garimella, Rama Sastry Vedala)....Pages 285-292
Evolution of the Probability Distribution Function of Shovel – Dumper Combination in Opencast Coal Mine Using ANN and RWB (N. S. Harish Kumar, R. P. Choudhary, Ch. S. N. Murthy)....Pages 293-302
Temperature Measurement During Rotary Drilling of Rocks - A Statistical Approach (Vijay Kumar Shankar, B. M. Kunar, Ch. S. N. Murthy)....Pages 303-309
Safety in Coal Mines in India-Its Perspective (K. Srihari, A. Sandeep Kumar)....Pages 310-317
Reliability Analysis of LHD Machine - A Case Study (J. BalaRaju, M. Govinda Raj, Ch. S. N. Murthy)....Pages 318-326
Emerging Mining Trends: Preparing Future Mining Professionals (Laxminarayana Chikatamarla, Devulapalli Narasimha Prasad)....Pages 327-336
Prediction of Energy Efficiency of Main Transportation System Used in Underground Coal Mines – A Statistical Approach (N. V. Sarathbabu Goriparti, Ch. S. N. Murthy, M. Aruna)....Pages 337-344
Shortcomings of Vibrating Screen and Corrective Measures: A Review (S. Bharath Kumar, Harsha Vardhan, M. Govinda Raj, Marutiram Kaza, Rameshwar Sah, H. Harish)....Pages 345-351
Quantification of Rock Strength Using the Mechanical Drilling Parameters (C. R. Lakshminarayana, Anup K. Tripathi, Samir K. Pal)....Pages 352-361
Evaluation of Whole Body Vibration of Heavy Earth Moving Machinery Operators (Jeripotula Sandeep Kumar, Mangalpady Aruna, Mandela Govinda Raj)....Pages 362-373
Assessment and Prediction of Specific Energy Using Rock Brittleness in Rock Cutting (Vijaya Raghavan, Ch. S. N. Murthy)....Pages 374-382
Numerical Investigation on Factors Affecting the Performance of Roof Bolts for Continuous Miner Working (K. M. Tejeswaran, Ch. S. N. Murthy, B. M. Kunar)....Pages 383-393
Modelling of Biogas Fueled HCCI Engine for Various Inlet Conditions (Nihal Mishra, Shubham Mitra, Abhishek Thapliyal, Aniket Mahajan, M. Feroskhan)....Pages 394-403
Studies on Pitting Corrosion of Pulsed Electrodeposited Nanocomposite Coating (Chitrada Prasad, K. Srinivasa Rao, K. Ramji)....Pages 404-412
Effect of Condenser Coil Profile and Subcooling on Performance of Vapour Compression Refrigeration System (Sreedhar Vullloju, K. Krishna Reddy, Madhu Kumar Patil)....Pages 413-423
Criteria for Drop-in Replacement of Existing Refrigerant with an Alternative Refrigerant (Srinivas Pendyala, R. Prattipati)....Pages 424-431
Analysis of Characteristics of Launcher Missile System and Its Optimization to Reduce Tip-Off Effect During Launch (P. Ravinder Reddy, A. Dhanalaxmi, M. Rakesh, G. Chandramouli)....Pages 432-439
Performance Analysis of a Horizontal Axis Wind Lens Wind Turbine (P. Usha Sri, Chirla Jeevesh)....Pages 440-448
Experimental Investigation and Optimization of Electrochemical Micro Machining Process Parameters for Al 7075 T6 Alloy (K. Samson Praveen Kumar, G. Jaya Chandra Reddy)....Pages 449-457
Galactic Cosmic Energy - A Novel Mode of Energy Harvesting (Uma Maheshwar Vanamala, Laasya Priya Nidamarty)....Pages 458-465
Infrared Heating - A New Green Technology for Process Intensification in Drying of Purslane Leaves to Reduce the Thermal Losses (D. Kodandaram Reddy, Kavita Waghray, S. V. Sathyanarayana)....Pages 466-475
Grey Relational Analysis of EDM Process Parameters for Incoloy-800 (M. JagadeeswaraRao, Riyaaz Uddien Shaik, K. Buschaiah)....Pages 476-482
Computation of Kinematic Redundancy and Its Workspace in RRRR Planar Kinematic Chain (Shravan Anand Komakula)....Pages 483-491
Physio-Mechanical Properties and Thermal Analysis of Furcreo Foetedo Mediopicta (ffm) Fibers: Its Potential Application as Reinforcement in Making of Composites (Pathan Yasin, M. Venkataramana, Shashidhar K. Kudari)....Pages 492-500
Computational Analysis of Cavitation Structures on a Ship Propeller (C. Syamsundar, P. Usha Sri)....Pages 501-507
Heat Transfer in Food Crop Dryer Using Halogen Lamp (Sowjanya Madireddi, V. Siddharth, Mohd Amaan, M. Adithya)....Pages 508-516
Progressive Damage Analysis of Laminated Composites (Yashasvi Achanta, P. Ramesh Babu, D. Sandeep)....Pages 517-525
Optimization of Pecking Order Layout with Job Shop Scheduling as Constraint: An Approach of Metaheuristics (K. Mallikarjuna, V. Veeranna, K. Hemachandrareddy)....Pages 526-532
Experimental and Simulation Study in Deep Drawing of Circular Cups for Determination of LDR (A. C. Sekhara Reddy, S. Rajesham, T. Mahender)....Pages 533-541
Free Vibration Analysis of Pre-stressed Membrane Using Element Free Galerkin Method (K. R. Unnikrishnan, I. R. Praveen Krishna, C. O. Arun)....Pages 542-550
Xylitol Based Phase Change Material with Graphene Nano Platelets as Fillers for Thermal Energy Management (Praveen Kumar Varadaraj, Sandeep D., Ravi Kiran N., Balaji Padya, P. K. Jain)....Pages 551-558
Orbital Volume Analysis of Midfacial Fractures Using Additive Manufacturing Technologies (L. Siva Rama Krishna, Shiva Dharshan Vanapalli, Sriram Venkatesh, Abhinand Potturi)....Pages 559-567
Parametric Optimization During Wire EDM Taper Cutting on AISI D2 Steel Using Desirability Function (K. L. Uday Kiran, Sanar Zuhair Abbas, K. Saraswathamma, G. Chandra Mohan Reddy, A. M. K. Prasad)....Pages 568-576
Extraction of Coordinate Points for the Numerical Simulation of Single Point Incremental Forming Using Microsoft Excel (Zeradam Yeshiwas, Arkanti Krishnaiah)....Pages 577-586
A Review on Wire Arc Additive Manufacturing (WAAM) Fabricated Components of Ti6AL4V and Steels (P. Satish Kumar, L. Suvarna Raju, L. Siva Rama Krishna)....Pages 587-600
Towards Modeling of Polymer Injection Molding Process – Approaches for Evaluation of the Processing Conditions, Control Factors and Optimization (Vishnuprasad Pattali, P. Govindan, M. P. Vipindas)....Pages 601-609
Design of Sequential Electro-Pneumatic System (Nagashiva Mutyam, T. S. S. Siva Saikumar, Bhanumurthy Soppari)....Pages 610-615
Factors Influencing Hydrodynamic Entry Length in Helical Coils (R. Prattipati, N. Koganti, Srinivas Pendyala)....Pages 616-623
Formability Analysis on the Gas Formed Aluminum Coated Magnesium Alloys in Conical Dies (J. Kandasamy, M. Ranjith Kumar)....Pages 624-634
Investigation of Back Rake Angle on Machining of Al 6061 and Development of Regression Model for Resultant Force (B. Suresh Kumar Reddy, A. Krishnaiah, S. Gajanana)....Pages 635-644
Machining Characteristics of Electro Discharge Machining on NIMONIC 80A by Response Surface Methodology (G. Vishnu Pramod Teja, K. Saraswathamma, T. S. R. V. Padmalatha)....Pages 645-652
Laser Machining of Polymer Materials – Experimental Investigations - Process Challenges and Strategies (R. K. Vishnulal, P. Govindan, M. P. Vipindas)....Pages 653-661
Assessment of Dimensional Accuracy of Reproducibility of Cadaver Skull by FDM Additive Manufacturing (L. Siva Rama Krishna, Uday Kumar Balasany, Sri Ram Venkatesh, Abhinand Potturi)....Pages 662-672
Evaluation of Microstructural and Mechanical Properties of Friction Welded AISI 4140 Grade Steel Pipes (S. K. Abdul Khadeer, P. Ramesh Babu, K. Surender Rao, A. Seshu Kumar)....Pages 673-681
Effect of Aluminum Powder Suspended Dielectric and Silver Coated Copper Electrode on Electrical Discharge Machining Characteristics of Inconel 718 (N. Hima Varsha, K. Saraswathamma)....Pages 682-689
Investigation of the Process Parameters on Hot Rolling of Al 7178-SiC Metal Matrix (M. Nagarjuna, S. Gajanana, A. Krishnaiah)....Pages 690-696
Sensitivity Study of the Parameters Affecting Pressure Recovery in a Two Stage Jet Pipe Electro Hydraulic Servo Valve (Madhusudhan Raju, I. R. Shivakumar, L. Siva Rama Krishna, D. Sandeep)....Pages 697-702
Computational Modeling and Analysis of Intake Taper Manifold of an Internal Combustion Engine (T. Sreedhar, B. Nageswara Rao, K. Mourya Balaji, K. Jagan Mohan Reddy, K. V. Surya Harshith)....Pages 703-711
Evaluation of Wear Rate of Different Liner Materials Used in Bunkers and Silos Using Dry Abrasion Wear Test (D. Venkateshwarlu, Sriram Venkatesh, K. Saraswathamma, Swetha Bhagirathi Sanganabhatla, Shiva Dharshan Vanapalli)....Pages 712-720
Experimental Investigation on Maraging Steel Metal Deposition Using DMLS Process (D. Apparao, M. V. Jagannadha Raju)....Pages 721-730
Intra-ply Damage Modeling of Low-Velocity Impact on Composite Laminates (Y. Sharath Chandra Mouli, C. S. Upadhyay, P. M. Mohite)....Pages 731-738
Static and Fatigue Analysis of Leaf Spring with EPDM Rubber Sandwiched Between the Steel Leaves (R. Naresh, V. B. S. Rajendra Prasad, G. Venkata Rao)....Pages 739-747
Modelling of Surface Roughness and Tool Wear Rate During Turning of AISI 202 Stainless Steel (Nagaveni Thallapalli, Vinay Kumar Goud Balne, T. S. R. V. Padmalatha)....Pages 748-756
Estimation of the Grain Size Number and Microstructure Analysis of AA6061 Alloy Flow Formed Tubes (G. Venkateshwarlu, K. Ramesh Kumar, G. Gopi, T. A. Janardhan Reddy)....Pages 757-766
Production of Biodiesel from Jatropha Oil Using a Heterogeneous Catalyst (Santosh Kumar Dash)....Pages 767-773
Effect of Compression Ratio and Injection Timing on the Performance of a B20 Biodiesel Blend Fueled Diesel Engine (Santosh Kumar Dash, Pradip Lingfa, Dharmeswar Dash)....Pages 774-779
Back Matter ....Pages 781-783

Citation preview

Learning and Analytics in Intelligent Systems 2

Suresh Chandra Satapathy · K. Srujan Raju · Kumar Molugaram · Arkanti Krishnaiah · George A. Tsihrintzis Editors

International Conference on Emerging Trends in Engineering (ICETE) Emerging Trends in Smart Modelling Systems and Design

Learning and Analytics in Intelligent Systems Volume 2

Series Editors George A. Tsihrintzis, University of Piraeus, Piraeus, Greece Maria Virvou, University of Piraeus, Piraeus, Greece Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, NSW, Australia; University of Canberra, Canberra, ACT, Australia; KES International, Shoreham-by-Sea, UK; Liverpool Hope University, Liverpool, UK

The main aim of the series is to make available a publication of books in hard copy form and soft copy form on all aspects of learning, analytics and advanced intelligent systems and related technologies. The mentioned disciplines are strongly related and complement one another significantly. Thus, the series encourages cross-fertilization highlighting research and knowledge of common interest. The series allows a unified/integrated approach to themes and topics in these scientific disciplines which will result in significant cross-fertilization and research dissemination. To maximize dissemination of research results and knowledge in these disciplines, the series publishes edited books, monographs, handbooks, textbooks and conference proceedings.

More information about this series at http://www.springer.com/series/16172

Suresh Chandra Satapathy K. Srujan Raju Kumar Molugaram Arkanti Krishnaiah George A. Tsihrintzis •

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•

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Editors

International Conference on Emerging Trends in Engineering (ICETE) Emerging Trends in Smart Modelling Systems and Design

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Editors Suresh Chandra Satapathy Kalinga Institute of Industrial Technology (KIIT) Bhubaneswar, Odisha, India Kumar Molugaram University College of Engineering Osmania University Hyderabad, India

K. Srujan Raju Department of CSE CMR Technical Campus Hyderabad, Telangana, India Arkanti Krishnaiah Department of Mechanical Engineering Osmania University, University College of Engineering Hyderabad, India

George A. Tsihrintzis Department of Informatics University of Piraeus Piraeus, Greece

ISSN 2662-3447 ISSN 2662-3455 (electronic) Learning and Analytics in Intelligent Systems ISBN 978-3-030-24313-5 ISBN 978-3-030-24314-2 (eBook) https://doi.org/10.1007/978-3-030-24314-2 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Dedicated to Our Alma Mater & Eminent Professors who taught us for their inspiring vision, unwavering conviction and tireless efforts that have resulted in nurturing hundreds of eminent global citizens and effective human beings. “Once an Osmanian Always an Osmanian”

University College of Engineering, Osmania University, Hyderabad, India

University College of Engineering (UCE) has the distinction of being the oldest and the biggest among the engineering colleges of the State of Telangana, India. It was established in the year 1929, eleven years after the formation of Osmania University. The college was the sixth engineering college to be established in the whole of British India. The college moved to its present permanent building in the year 1947. Today, it is the biggest among the campus colleges of Osmania University. The golden jubilee of the college was celebrated in 1979, the diamond jubilee in 1989 and the platinum jubilee in 2004. The college was made autonomous in 1994. University Grants Commission of India conferred autonomy status to the college for a period of 6 years (2016–2017 to 2021–2022). The college offers four-year engineering degree courses leading to the award of Bachelor of Engineering (B.E.) in biomedical engineering, civil engineering, computer science engineering, electrical and electronics engineering, electronics and communications engineering and mechanical engineering. The college also offers graduate programs and Ph.D. in the various branches of engineering. As of today, there is a yearly intake of 320 undergraduate students (full-time) and 290 postgraduate students (full-time and part-time). There are 143 teaching staff members, including 40 professors. The UG programs offered have been accredited by the National Board of Accreditation, New Delhi. Osmania University is accredited by NAAC with “A+” Grade. UCE, OU, is the first engineering college to get ISO 9001 Certification in Telangana State. University College of Engineering was awarded the Best Engineering College by Indian Society for Technical Education (Telangana) in the year 2010. UCE, OU, was adjudged as the Best Engineering College in the country for the academic year 2003–2004 by Indian Society for Technical Education, New Delhi, and by Star News for the years 2010–2011 and 2011–2012. The college has successfully completed the Technical Education Quality Improvement Programme (TEQIP-I) under the World Bank financial assistance of Rs. 15.48 crores during the period 2003–2008. The outcome of the project has resulted in: (i) increase in pass percentage of UG/PG students, (ii) enhancement of research publications of staff by threefolds, (iii) introduction of six PG programs in vii

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University College of Engineering, Osmania University, Hyderabad, India

niche areas, (iv) introduction of credit-based system and (v) substantial increase in internal revenue generation. The college has successfully completed Phase II of TEQIP program with a financial assistance of Rs. 12.5 crores and additional grant of 5 crores under the best-performing institution category. Recently, the college has been approved as a minor center under QIP for full-time Ph.D. programs. The college has been selected for TEQIP Phase III twinning program with a financial assistance Rs. 7 crores. The college has been granted “Visvesvaraya Ph.D. Scheme for Electronics and IT” for full-time Ph.D. program. The GIAN program of MHRD has sanctioned 7 programs in specialized research area to the college. The college has been ranked 80 in NIRF Engineering College Ranking Survey by MHRD Survey, New Delhi, India, for the year 2017–2018.

Alumni Association University College of Engineering, Osmania University, Hyderabad, India

Once University College of Engineering was declared autonomous, the idea of having Alumni Association, whose membership would include all past students of the college and present or past faculty members of the college, gained momentum. Under the dynamic leadership of then Principal, Prof. D. C. Reddy, the first get-together was held on Saturday July 3, 1996. After the revival of the Association in 2015, the two subsequent Executive Committees under the leadership of Er. Rammohan Rao, Er. P. Ram Reddy and Dr. D. Vijay Kumar with the support of patrons, fellow members, learned faculty and students have been set out to fulfill the objectives of Alumni Association. The Association is a not-for-profit organization and works with the staff and students of University College of Engineering, and the objectives are: • Provide a platform for the alumni to connect with each other for the exchange of information and ideas and communicate their accomplishments, interests and concerns. • Foster alumni pride and enhance the reputation of the university and OUCE in particular. • Enrich the emotional bondage among the students, alumni and faculty. • Extend maximum help to the college in the placement and internship of students in reputed organizations. • Recognize alumni for their significant contributions to education. • Propose and execute special projects: buildings, technical projects, seminars, conferences, etc. • Support poor/economically backward students financially by floating scholarships. • Institute awards for meritorious performance for students. • Institute awards for the alumni for their contribution to the college and the society. • Inspire and invoke the spirit of innovation among the students leading to finding technical solutions to the problems of the society leading to patents to students and the college.

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Alumni Association University College of Engineering

In the past four years, the Executive Body set out to execute the above objectives by taking up many initiatives like conducting global alumni meets, alumni talks, funding student innovation, patent and research, facilitating student Internships, industry interactions and career development programs, supporting student clubs and other activities, facilitating in setting up the technology business incubator, etc. To further the objectives of the Association to support the faculty and research scholars, the Association has organized the First International Conference on Emerging Trends in Engineering under its aegis.

Foreword

Alumni Association is constantly setting up new benchmarks with every passing year with a lot of good work done toward furthering the Association goals and giving back to the alma mater. One of the key focus areas has been to bridge the industry academia gap, thereby promoting cutting-edge skill development and research, thereby enabling the university to be a hub of innovation. This publication is an outcome of the First International Conference on Emerging Trends in Engineering (ICETE). As part of the initiatives of the Alumni Association, the conference was organized to enhance the information exchange of theoretical research and practical advancements at national and international levels in key fields of engineering and to promote professional interaction among students, scholars, researchers, educators, professionals from industries and other groups to share latest findings in their respective fields toward sustainable developments. The entire organizing team has worked hard over the last few months in putting together the complete structure for the event and coordinating with all the eminent speakers across the globe to ensure that the 2-day conference brings together the best minds in the industry to come together and share their valuable insights with the entire fraternity. We are honored to have eminent speakers grace the conference this year. We received 619 papers from about more than 100 institutions/organizations in 14 countries. The papers have gone through a rigorous evaluation process, and the best papers have been selected for presenting on the days of the conference. Only, the presented and approved papers have come for publishing. I want to thank the Technical Program Committee for bringing together research scholars from diverse background and working tirelessly in picking the final list and bringing out this publication. With a rich history of over 100 years producing world-class students and alumni who have made a mark all over the world, we aim to continue the tradition by hosting such world-class conferences and live up to the expectations of our alma mater. April 2019

D. Vijay Kumar

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Preface

This book constitutes the thoroughly refereed post-conference proceedings of the First International Conference on Emerging Trends in Engineering (ICETE), held at University College of Engineering (Autonomous) and organized by Alumni Association, University College of Engineering, Osmania University, Hyderabad, India, on March 22–23, 2019. The aim of this conference is to enhance the information exchange of theoretical research/practical advancements at national and international levels in the fields of biomedical engineering, civil engineering, computer science engineering, electrical engineering, electronics and communication engineering, mechanical engineering and mining engineering. This encourages and promotes professional interaction among students, scholars, researchers, educators, professionals from industries and other groups to share latest findings in their respective fields toward sustainable developments. The refereed conference proceedings of the ICETE are published in three volumes covering seven streams, i.e. biomedical engineering, civil engineering, computer science engineering, electrical and electronics engineering, electronics and communication engineering, mechanical engineering and mining engineering. Out of 619 paper submissions from about 14 countries in seven streams of engineering, only 214 papers are being published after reviewing thoroughly; this third volume of “International Conference on Emerging Trends in Engineering (ICETE)” comprises the comprehensive state-of-the-art technical contributions in the areas of civil engineering, mechanical engineering and mining engineering. Major topics of these research papers include latest findings in the respective fields toward sustainable developments include water resource engineering, structural engineering, geotechnical engineering, transportation engineering, mining engineering, production and industrial engineering, thermal engineering, design engineering and production engineering. George A. Tsihrintzis Suresh Chandra Satapathy Kumar Molugaram A. Krishnaiah K. Srujan Raju xiii

Acknowledgements

We thank all the authors for their contributions and timely response. We also thank all the reviewers who read the papers and made valuable suggestions for improvement. We would like to thank Prof. S. Ramachandram, Vice-Chancellor, Osmania University, and Prof. M. Kumar, Principal, University College of Engineering, for having faith in us. Dr. D. Rama Krishna and Prof. K, Shyamala of UCE, OU, for leading from the front; the TPC team, for pulling off a brilliant job; Heads of all departments and all learned faculty, for all the support. Also, last but not the least, we convey our thanks to all the research scholars without whose relentless slogging this conference and publication would not have seen light. We thank our sponsors Power Grid Corporation of India Ltd., Defence Research and Development Organization (DRDO), CCL Products (India) Ltd., The Singareni Collieries Company Ltd., TEQIP-III and all other financial contributors. We extend our thanks to all the Executive Body members of the Alumni Association for their support and Sri. R. V. Rammohan Rao for the support when needed. Finally, we thank the Springer team comprising Prof. Suresh Chandra Satapathy, Prof. K. Srujan Raju and Dr. M. Ramakrishna Murthy for guiding and helping us throughout. April 2019

D. Vijay Kumar

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ICETE Organizing Committee

Chief Patron S. Ramachandram (Vice-chancellor)

Osmania University, Hyderabad, India

Patrons Kumar Molugaram (Principal) P. Laxminarayana (Dean) D. C. Reddy (Former Vice-chancellor) D. N. Reddy (Former Vice-chancellor) R. V. Rammohan Rao (Past President)

University College of Engineering (A), Osmania University, Hyderabad, India Faculty of Engineering, Osmania University, Hyderabad, India Osmania University, Hyderabad, India Jawaharlal Nehru Technological University, Hyderabad, India Alumni Association, University College of Engineering (A), Osmania University, Hyderabad, India

Chairpersons P. Ram Reddy (President) P. V. N. Prasad

Alumni Association, University College of Engineering (A), Osmania University, Hyderabad, India Department of Electrical Engineering, University College of Engineering (A), Osmania University, Hyderabad, India

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ICETE Organizing Committee

Conveners D. Vijay Kumar (General Secretary) D. Rama Krishna

Alumni Association, University College of Engineering (A), Osmania University, Hyderabad, India Department of Electronics and Communication Engineering, University College of Engineering (A), Osmania University, Hyderabad, India

Publication Committee Suresh Chandra Satapathy (Chair)

Kumar Molugaram (Co-chair, Principal) K. Srujan Raju (Co-chair) Sriram Venkatesh

K. Shyamala

D. Vijay Kumar (General Secretary) D. Rama Krishna

School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, Odisha University College of Engineering, Osmania University, Hyderabad, Telangana Department of CSE, CMR Technical Campus, Hyderabad, Telangana Department of Mechanical Engineering, University College of Engineering, Osmania University, Hyderabad Department of Computer Science and Engineering, University College of Engineering, Osmania University, Hyderabad Alumni Association, University College of Engineering (A), Osmania University, Hyderabad, India Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, Hyderabad

International Advisory Committee J. N. Reddy Ramulu Mamidala Chandra Kambhamettu P. Nageswara Rao Suman Das Tariq Muneer Rao S. Govindaraju Nitin K. Tripathi Srinivasulu Ale

Texas A&M University, USA University of Washington, USA University of Delaware, USA University of Northern Iowa, USA Georgia Institute of Technology, USA Edinburgh Napier University, Edinburgh, UK Purdue University, Indiana, USA Asian Institute of Technology, Bangkok The University of Texas, Texas, USA

ICETE Organizing Committee

Prasad Enjeti Akshay K. Rathore Sheldon Williamson Malcolm McCulloch Bimal K. Bose Manos Varvarigos Vijay Vittal Sudhakar M. Reddy Vishnu Pendyala Shantanu Narayen

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Texas A&M University, Texas, USA Concordia University, Canada University of Ontario, Canada University of Oxford, UK University of Tennessee, USA Monash University, Australia University of Arizona, USA University of Iowa, USA CISCO Systems, USA CEO, Adobe Systems, USA

National Advisory Committee A. Venugopal Reddy (Vice-chancellor) V. M. Pandharipande (Former Vice-chancellor) Rameshwar Rao (Former Vice-chancellor) A. K. Tiwari (Director) K. V. S. Hari Sameen Fatima (Former Principal) N. K. Kishore D. Nagesh Kumar B. G. Fernandes Dinesh Bhatia G. Rameshwar R (Chairman) A. K. Singh (OS and Director) J. V. R. Sagar (Director) John D’Souza Arvind Tiwari B. H. V. S. Narayana Murthy (OS and Director) R. Soundara Rajan (Senior Project Manager) Bathini Srinivas

JNTUH, Hyderabad BAMU, Aurangabad JNTUH, Hyderabad CARE Foundation, University of Hyderabad, Hyderabad IISc, Bangalore UCE, OU IIT Kharagpur IISc, Bangalore IIT Bombay NEHU, Shillong, India Institute of Engineering, Hyderabad Chapter DLRL ANURAG NITK Surathkal GE-GRC, JFWTC RCI BDL MathWorks, India

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ICETE Organizing Committee

Finance Committee Sriram Venkatesh A. Krishnaiah P. Ramesh Babu V. Bhikshma G. Mallesham M. Malini B. Rajendra Naik V. Uma Maheshwar P. Naveen Kumar D. N. Prasad (Advisor (Coal)) M. Shyam Prasad Reddy (General Secretary) T. Venkatesam (Superintendent Engineer (Retd.)) M. S. Venkatramayya Satish Naik R. Thomas Syed Basharath Ali P. Narotham Reddy

ME, UCE, OU ME, UCE, OU ME, UCE, OU CE, UCE, OU EE, UCE, OU BME, UCE, OU ECE, UCE, OU ME, UCE, OU ECE, UCE, OU SCCL TREA AA UCE, OU

Mining AA UCE, AA UCE, AA UCE, AA UCE,

OU OU OU OU

Organizing Committee E. Vidya Sagar (Vice-principal) K. Shyamala P. Chandra Sekhar M. Gopal Naik P. Usha Sri M. Venkateswara Rao M. V. Ramana Rao G. Yesuratnam P. Raja Sekhar B. Mangu M. Chandrashekhar Reddy Narsimhulu Sanke M. A. Hameed B. Sujatha L. Nirmala Devi N. Susheela S. Prasanna

UCE, OU CSE, UCE, OU ECE, OU CE, UCE, OU ME, UCE, OU BME, UCE, OU EED, UCE, OU EED, UCE, OU CE, UCE, OU EED, UCE, OU ME, UCE, OU ME, UCE, OU CSE, UCE, OU CSE, UCE, OU ECE, UCE, OU EED, UCE, OU CE, UCE, OU

ICETE Organizing Committee

R. Rajender G. Narender P. Koti Lakshmi M. Srinivas B. Sirisha B. Ramana Naik C. V. Raghava (Chairman) P. Lakshman Rao (President) P. Kishore (Secretary) K. J. Amarnath J. V. Dattatreyulu Raikoti Anand Srinivas K. Praveen Dorna K. Chakradhar Pradeep Kumar Nimma A. Thara Singh Prasanth Kumar Manchikatla

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CE, UCE, OU ME, UCE, OU ECE, UCE, OU BME, UCE, OU EE, UCE, OU Alumnus CVR College of Engineering, Hyderabad OUECE Association OUECE Association Mining B.E (Mining) Alumnus AA, UCE, OU AA, UCE, OU AA, UCE, OU Alumnus Alumnus

Technical Committee K. Shyamala P. V. Sudha M. Manjula B. Mangu P. Satish Kumar J. Upendar M. Malini D. Suman K. L. Radhika K. Shashikanth L. Siva Rama Krishna E. Madhusudan Raju R. Hemalatha M. Shyamsunder

CSE, UCE, OU CSE, UCE, OU EED, UCE, OU EED, UCE, OU EED, UCE, OU EED, UCE, OU BME, UCE, OU BME, UCE, OU CE, UCE, OU CE, UCE, OU ME, UCE, OU ME, UCE, OU ECE, UCE, OU ECE, UCE, OU

Supported and Strengthened By J. Suman (Research Scholar) D. Sai Kumar (Research Scholar) P. Raveendra Babu (Research Scholar) G. Shyam Kishore (Research Scholar) Jaya Prakash (Research Scholar)

CSE CSE ECE ECE EEE

Contents

An Empirical Based Porous Flow Approach to Modelling Heterogeneous Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seelam Srikanth

1

Effect of Different Aggregates on Alkali Silica Reaction of Geopolymer Concrete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Annapurna, Ravande Kishore, and K. Anil

9

Assessment of Aquifer Vulnerability of Nizamabad District, Telangana State, India Using GIS and Drastic Method . . . . . . . . . . . . . B. Ramakrishna, P. Rajasekhar, and Shaik Vaheed

18

Effect of Baffle Wall Parameters on the Modal Responses of Elevated Rectangular Water Tank . . . . . . . . . . . . . . . . . . . . . . . . . . . Meghana Minnekanti and Mallika Alapati

27

Bearing Capacity of Strip Footing on Reinforced Foundation Beds with Inclined Reinforcement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Rajashekar Reddy, G. V. Narasimha Reddy, and E. Saibaba Reddy

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Precise Survey Assistance for Civil Structures Using Survey Assist . . . . K. Surya Prakash, Kone Srimannarayana, Pradeep Kumar Kaatnam, and Abhinav Dayal Ground Improvement Using Band Drains at Onshore Gas Terminal Near Kakinada (AP), India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. Babu Rao and D. Nageswara Rao New Light Weight Mortar for Structural Application: Assessment of Porosity, Strength and Morphology Properties . . . . . . . . Paul Awoyera, Ravindran Gobinath, Sandela Haripriya, and Poongodi Kulandaisami

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53

59

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Computing Redistribution Moments of Concrete Members in the Plastic Stage Using Linear Analysis: Short Review . . . . . . . . . . . Paul Awoyera, Ravindran Gobinath, Chinyere Nwankwo, Palanisamy Murthi, and Annabathina Sivakrishna Nonlinear Dynamic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. A. Waghmare and U. R. Kawade Evaluation of Coir Geotextile Mats to Enhance the Poor Subgrade Under Repeated Load for Low-Volume Roads . . . . . . . . . . . . . . . . . . . . D. Harinder and S. Shankar Estimation of Maximum Magnitude (mmax) Considering Different Seismic Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammad Muzzaffar Khan, Teja Munaga, and Gonavaram Kalyan Kumar Necessity of New Look for Equitable Water Sharing Among Basin States - Krishna Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Narasimha Rao, K. Venkata Narayana, and N. Ravishankar

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A Comparative Study on the Performance of ANN, MLR and MNR in the Assessment of Slope Stability for Kalla - Coonoor Hill Road Stretch of Nilgiris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Balendra Mouli Marrapu and Ravi Sankar Jakka Hydrologic Modeling with Transfer Function Based Approach: A Comparative Study over Godavari River Basin . . . . . . . . . . . . . . . . . 115 Chaitanya Lakeshri and Kaustubh Salvi Study on Interaction of Reinforcing Strips Embedded in Cement Modified Marginal Backfill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Pandu Kurre, Vineeth Reddy Karnati, and Heeralal Mudavath Experimental Study on Interfacial Frictional Properties of Geogrid Reinforced Pond Ash . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Mogili Sudhakar, M. Heeralal, G. Kalyan Kumar, and Pandu Kurre Resilient Modulus of Unsaturated Soil – A Comprehensive Review . . . . 141 A. G. Sharanya, M. Heera Lal, and Pandu Kurre Effect of Eccentric Load on Footing Resting on Planar Geosynthetic Reinforcement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Pandu Kurre, M. Heeralal, and T. S. D. Venkatesh Strength Investigation of Fly Ash Based Concrete Waste Steel Fibre and Polypropylene Fibre as Reinforcing Materials . . . . . . . . . . . . . . . . . 155 G. Swamy Yadav, N. Prabhanjan, G. Sahithi, G. Sangeetha, A. Srinivas, and A. Siva Krishna

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Application of Statistics to the Analysis of Corrosion Data for Rebar in Metakaolin Concrete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 U. Raghu Babu and B. Kondraivendhan Earthquake Analysis of High-Rise Building with Floating Column . . . . 170 Mohasinkhan N. Bargir and Ajim G. Mujawar A Review on the Recent Development of Ambient Cured Geopolymer Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Mayank Gupta and N. H. Kulkarni A Case Study on Structural Assessment of Dudhgaon Grampanchayat Building by Using Non Destructive Testing . . . . . . . . . . . . . . . . . . . . . . 189 Rohit Kukade and Santosh Mohite Analysis and Design of High-Rise Building Using Diagrid Structural System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Ahmad Muslim Rujhan and Ravande Kishore Engineered Cementitious Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 Yogesh Biyani, L. G. Patil, and C. N. Kurhe Influence of Metakaolin on Stone Waste Aggregate Concrete . . . . . . . . 215 Sakevalla Vinay Babu, U. Raghu Babu, N. Venkata Ramana, and P. Pavithra Rapid Hardening on the Strength Gain Admixture on Behavior of Concrete with Replacement of Binary Cementitious Materials . . . . . . 222 R. Kiranmai, A. Rohita Susheela, S. Rajkumar, G. Asheesh, and V. M. Sounthararajan Evaluation of Pullout Resistance of Reinforcing Strips Embedded in Cement Modified Marginal Backfill of Mechanically Stabilized Earth (MSE) Walls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 Pandu Kurre and Gannavaram Venkat Praveen Identification of Artificial Recharge Zones Using GIS . . . . . . . . . . . . . . 248 K. A. Patil, Noopur D. Khatik, and Shriman N. Jirapure Seismic Analysis and Design of Mass and Stiffness Irregular R.C. Building Frames with Different Code . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 Nishant C. Chandanshive and Santosh S. Mohite Assessment of Spatial Interpolation Techniques on Groundwater Contamination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262 G. Shyamala, B. Arun Kumar, S. Manvitha, and T. Vinay Raj Sustainability Concepts in the Design of Tall Structures . . . . . . . . . . . . 270 Prashant Sunagar, Aravind Bhashyam, B. R. Neel, and Abhishek Kumar Chaurasiya

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Spontaneous Combustion of Coal and Correlation with Its Intrinsic Properties Using Adiabatic Oxidation Method . . . . . . . . . . . . . . . . . . . . 278 Ravi Varma Rambha Extraction of Electricity from Blast Induced Ground Vibration Waves – Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Raghu Chandra Garimella and Rama Sastry Vedala Evolution of the Probability Distribution Function of Shovel – Dumper Combination in Opencast Coal Mine Using ANN and RWB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 N. S. Harish Kumar, R. P. Choudhary, and Ch. S. N. Murthy Temperature Measurement During Rotary Drilling of Rocks - A Statistical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Vijay Kumar Shankar, B. M. Kunar, and Ch. S. N. Murthy Safety in Coal Mines in India-Its Perspective . . . . . . . . . . . . . . . . . . . . . 310 K. Srihari and A. Sandeep Kumar Reliability Analysis of LHD Machine - A Case Study . . . . . . . . . . . . . . 318 J. BalaRaju, M. Govinda Raj, and Ch. S. N. Murthy Emerging Mining Trends: Preparing Future Mining Professionals . . . . 327 Laxminarayana Chikatamarla and Devulapalli Narasimha Prasad Prediction of Energy Efficiency of Main Transportation System Used in Underground Coal Mines – A Statistical Approach . . . . . . . . . 337 N. V. Sarathbabu Goriparti, Ch. S. N. Murthy, and M. Aruna Shortcomings of Vibrating Screen and Corrective Measures: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 S. Bharath Kumar, Harsha Vardhan, M. Govinda Raj, Marutiram Kaza, Rameshwar Sah, and H. Harish Quantification of Rock Strength Using the Mechanical Drilling Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 C. R. Lakshminarayana, Anup K. Tripathi, and Samir K. Pal Evaluation of Whole Body Vibration of Heavy Earth Moving Machinery Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 Jeripotula Sandeep Kumar, Mangalpady Aruna, and Mandela Govinda Raj Assessment and Prediction of Specific Energy Using Rock Brittleness in Rock Cutting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 Vijaya Raghavan and Ch. S. N. Murthy Numerical Investigation on Factors Affecting the Performance of Roof Bolts for Continuous Miner Working . . . . . . . . . . . . . . . . . . . . 383 K. M. Tejeswaran, Ch. S. N. Murthy, and B. M. Kunar

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Modelling of Biogas Fueled HCCI Engine for Various Inlet Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 Nihal Mishra, Shubham Mitra, Abhishek Thapliyal, Aniket Mahajan, and M. Feroskhan Studies on Pitting Corrosion of Pulsed Electrodeposited Nanocomposite Coating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Chitrada Prasad, K. Srinivasa Rao, and K. Ramji Effect of Condenser Coil Profile and Subcooling on Performance of Vapour Compression Refrigeration System . . . . . . . . . . . . . . . . . . . . 413 Sreedhar Vullloju, K. Krishna Reddy, and Madhu Kumar Patil Criteria for Drop-in Replacement of Existing Refrigerant with an Alternative Refrigerant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 Srinivas Pendyala and R. Prattipati Analysis of Characteristics of Launcher Missile System and Its Optimization to Reduce Tip-Off Effect During Launch . . . . . . . 432 P. Ravinder Reddy, A. Dhanalaxmi, M. Rakesh, and G. Chandramouli Performance Analysis of a Horizontal Axis Wind Lens Wind Turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440 P. Usha Sri and Chirla Jeevesh Experimental Investigation and Optimization of Electrochemical Micro Machining Process Parameters for Al 7075 T6 Alloy . . . . . . . . . 449 K. Samson Praveen Kumar and G. Jaya Chandra Reddy Galactic Cosmic Energy - A Novel Mode of Energy Harvesting . . . . . . 458 Uma Maheshwar Vanamala and Laasya Priya Nidamarty Infrared Heating - A New Green Technology for Process Intensification in Drying of Purslane Leaves to Reduce the Thermal Losses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 D. Kodandaram Reddy, Kavita Waghray, and S. V. Sathyanarayana Grey Relational Analysis of EDM Process Parameters for Incoloy-800 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476 M. JagadeeswaraRao, Riyaaz Uddien Shaik, and K. Buschaiah Computation of Kinematic Redundancy and Its Workspace in RRRR Planar Kinematic Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 Shravan Anand Komakula Physio-Mechanical Properties and Thermal Analysis of Furcreo Foetedo Mediopicta (ffm) Fibers: Its Potential Application as Reinforcement in Making of Composites . . . . . . . . . . . . . . . . . . . . . . 492 Pathan Yasin, M. Venkataramana, and Shashidhar K. Kudari

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Contents

Computational Analysis of Cavitation Structures on a Ship Propeller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 C. Syamsundar and P. Usha Sri Heat Transfer in Food Crop Dryer Using Halogen Lamp . . . . . . . . . . . 508 Sowjanya Madireddi, V. Siddharth, Mohd Amaan, and M. Adithya Progressive Damage Analysis of Laminated Composites . . . . . . . . . . . . 517 Yashasvi Achanta, P. Ramesh Babu, and D. Sandeep Optimization of Pecking Order Layout with Job Shop Scheduling as Constraint: An Approach of Metaheuristics . . . . . . . . . . . . . . . . . . . 526 K. Mallikarjuna, V. Veeranna, and K. Hemachandrareddy Experimental and Simulation Study in Deep Drawing of Circular Cups for Determination of LDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533 A. C. Sekhara Reddy, S. Rajesham, and T. Mahender Free Vibration Analysis of Pre-stressed Membrane Using Element Free Galerkin Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 542 K. R. Unnikrishnan, I. R. Praveen Krishna, and C. O. Arun Xylitol Based Phase Change Material with Graphene Nano Platelets as Fillers for Thermal Energy Management . . . . . . . . . . . . . . . . . . . . . . 551 Praveen Kumar Varadaraj, Sandeep D., Ravi Kiran N., Balaji Padya, and P. K. Jain Orbital Volume Analysis of Midfacial Fractures Using Additive Manufacturing Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 559 L. Siva Rama Krishna, Shiva Dharshan Vanapalli, Sriram Venkatesh, and Abhinand Potturi Parametric Optimization During Wire EDM Taper Cutting on AISI D2 Steel Using Desirability Function . . . . . . . . . . . . . . . . . . . . . . . . . . . 568 K. L. Uday Kiran, Sanar Zuhair Abbas, K. Saraswathamma, G. Chandra Mohan Reddy, and A. M. K. Prasad Extraction of Coordinate Points for the Numerical Simulation of Single Point Incremental Forming Using Microsoft Excel . . . . . . . . . 577 Zeradam Yeshiwas and Arkanti Krishnaiah A Review on Wire Arc Additive Manufacturing (WAAM) Fabricated Components of Ti6AL4V and Steels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 P. Satish Kumar, L. Suvarna Raju, and L. Siva Rama Krishna Towards Modeling of Polymer Injection Molding Process – Approaches for Evaluation of the Processing Conditions, Control Factors and Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601 Vishnuprasad Pattali, P. Govindan, and M. P. Vipindas

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Design of Sequential Electro-Pneumatic System . . . . . . . . . . . . . . . . . . . 610 Nagashiva Mutyam, T. S. S. Siva Saikumar, and Bhanumurthy Soppari Factors Influencing Hydrodynamic Entry Length in Helical Coils . . . . . 616 R. Prattipati, N. Koganti, and Srinivas Pendyala Formability Analysis on the Gas Formed Aluminum Coated Magnesium Alloys in Conical Dies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624 J. Kandasamy and M. Ranjith Kumar Investigation of Back Rake Angle on Machining of Al 6061 and Development of Regression Model for Resultant Force . . . . . . . . . . 635 B. Suresh Kumar Reddy, A. Krishnaiah, and S. Gajanana Machining Characteristics of Electro Discharge Machining on NIMONIC 80A by Response Surface Methodology . . . . . . . . . . . . . . 645 G. Vishnu Pramod Teja, K. Saraswathamma, and T. S. R. V. Padmalatha Laser Machining of Polymer Materials – Experimental Investigations - Process Challenges and Strategies . . . . . . . . . . . . . . . . . 653 R. K. Vishnulal, P. Govindan, and M. P. Vipindas Assessment of Dimensional Accuracy of Reproducibility of Cadaver Skull by FDM Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . 662 L. Siva Rama Krishna, Uday Kumar Balasany, Sri Ram Venkatesh, and Abhinand Potturi Evaluation of Microstructural and Mechanical Properties of Friction Welded AISI 4140 Grade Steel Pipes . . . . . . . . . . . . . . . . . . . . . . . . . . . 673 S. K. Abdul Khadeer, P. Ramesh Babu, K. Surender Rao, and A. Seshu Kumar Effect of Aluminum Powder Suspended Dielectric and Silver Coated Copper Electrode on Electrical Discharge Machining Characteristics of Inconel 718 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 682 N. Hima Varsha and K. Saraswathamma Investigation of the Process Parameters on Hot Rolling of Al 7178-SiC Metal Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690 M. Nagarjuna, S. Gajanana, and A. Krishnaiah Sensitivity Study of the Parameters Affecting Pressure Recovery in a Two Stage Jet Pipe Electro Hydraulic Servo Valve . . . . . . . . . . . . . 697 Madhusudhan Raju, I. R. Shivakumar, L. Siva Rama Krishna, and D. Sandeep Computational Modeling and Analysis of Intake Taper Manifold of an Internal Combustion Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703 T. Sreedhar, B. Nageswara Rao, K. Mourya Balaji, K. Jagan Mohan Reddy, and K. V. Surya Harshith

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Evaluation of Wear Rate of Different Liner Materials Used in Bunkers and Silos Using Dry Abrasion Wear Test . . . . . . . . . . . . . . . . . . . . . . . . 712 D. Venkateshwarlu, Sriram Venkatesh, K. Saraswathamma, Swetha Bhagirathi Sanganabhatla, and Shiva Dharshan Vanapalli Experimental Investigation on Maraging Steel Metal Deposition Using DMLS Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721 D. Apparao and M. V. Jagannadha Raju Intra-ply Damage Modeling of Low-Velocity Impact on Composite Laminates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 731 Y. Sharath Chandra Mouli, C. S. Upadhyay, and P. M. Mohite Static and Fatigue Analysis of Leaf Spring with EPDM Rubber Sandwiched Between the Steel Leaves . . . . . . . . . . . . . . . . . . . . . . . . . . 739 R. Naresh, V. B. S. Rajendra Prasad, and G. Venkata Rao Modelling of Surface Roughness and Tool Wear Rate During Turning of AISI 202 Stainless Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 748 Nagaveni Thallapalli, Vinay Kumar Goud Balne, and T. S. R. V. Padmalatha Estimation of the Grain Size Number and Microstructure Analysis of AA6061 Alloy Flow Formed Tubes . . . . . . . . . . . . . . . . . . . 757 G. Venkateshwarlu, K. Ramesh Kumar, G. Gopi, and T. A. Janardhan Reddy Production of Biodiesel from Jatropha Oil Using a Heterogeneous Catalyst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 767 Santosh Kumar Dash Effect of Compression Ratio and Injection Timing on the Performance of a B20 Biodiesel Blend Fueled Diesel Engine . . . . . . . . . . . . . . . . . . . . 774 Santosh Kumar Dash, Pradip Lingfa, and Dharmeswar Dash Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 781

About the Editors

George A. Tsihrintzis is Full Professor in the University of Piraeus, Head of its Department of Informatics and Director of Graduate Studies in “Advanced Computing and Informatics Systems.” He received the diploma of Electrical Engineer from the National Technical University of Athens, Greece (with honors), and the M.Sc. and Ph.D. degrees in Electrical Engineering from Northeastern University, Boston, Massachusetts, USA. His current research interests include pattern recognition, machine learning, decision theory, and statistical signal processing and their applications in multimedia interactive services, user modeling, knowledge-based software systems, human–computer interaction and information retrieval. He has authored or co-authored over 300 research publications in these areas, including 6 monographs and 17 edited volumes, which have received over 2500 third-party citations. He is Editor-in-Chief of the International Journal of Computational Intelligence Studies (Inderscience) and the Intelligent Decision Technologies Journal (IOS Press) and Member of the editorial boards of 9 additional journals. He is Founding Co-Editor and Co-Editor-in-Chief of the new book series on Learning and Analytics in Intelligent Systems (Springer). He has chaired over 20 international conferences. He has supervised 10 doctoral students who have received their doctoral degrees and is currently supervising an additional 5 students. He has guest co-edited 8 special issues of international journals. He won the Best Poster Paper Award of the 5th International Conference on Information Technology: New Generations, Las Vegas, USA, April 7–9, 2008, for co-authoring a paper titled: “Evaluation of a Middleware System for Accessing Digital Music Libraries in Mobile Services.” He also won one of the Best Applications Papers Award of the 29th Annual International Conference of the British Computer Society Specialist Group on Artificial Intelligence, Cambridge, UK, December 15–17, 2009, for co-authoring a

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paper titled: “On Assisting a Visual-Facial Affect Recognition System with Keyboard-Stroke Pattern Information.” He also won one of the Best Paper Awards of the 5th IEEE International Conference on Information, Intelligence, Systems, and Applications for co-authoring a paper titled: “Genetic-AIRS: A Hybrid Classification Method based on Genetic Algorithms and Artificial Immune Systems.” He was Keynote Speaker on Software Personalization Using Machine Learning with Imbalanced Samples at the IEEE International Conference on Computing, Power and Communication Technologies (GUCON2018), Greater Noida, Uttar Pradesh, India, September 28–29, 2018. He was Keynote Speaker on Machine Learning Methodologies in Automated Recommendation at the 12th Joint Conference on Knowledge-based Software Engineering (JCKBSE2018), Corfu, Greece, August 27–30, 2018. He was Keynote Speaker on Classification with Significant Class Imbalance and Applications in Software Personalization at the 29th International Conference on Tools with Artificial Intelligence (ICTAI2017), Boston, MA, USA, November 6–8, 2017. He was Keynote Speaker on One Class Classification Problems – Applications in Recommender Systems at the Multitheme Conference on Smart Digital Futures (AMSTA, IDT, IIMSS & STET), Chania, Crete, Greece, June 18–20, 2014. Prof. Suresh Chandra Satapathy is currently working as a Professor, School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India. He obtained his Ph.D. in Computer Science and Engineering from JNTU Hyderabad and M.Tech. in CSE from NIT, Rourkela, Odisha, India. He has 27 years of teaching experience. His research interests are data mining, machine intelligence, and swarm intelligence. He has acted as program chair of many international conferences and edited over 25 volumes of proceedings from Springer series from LNCS, AISC, LNNS, LNEE, SIST, etc. He is also in the Editorial board of few international Journals and has over 130 research publications in International journals and conference proceedings. Prof. Kumar Molugaram obtained his bachelor’s degree in civil engineering from Osmania University, Hyderabad; master’s degree in civil engineering from JNTU, Hyderabad; and doctorate degree from Indian Institute of Technology Bombay. He published over 100 research papers in various international and national journals and conferences. He supervised 78 M.E. dissertations and 5 Ph.D. theses and 8 Ph.D. students. He received “Best Teacher State Award” by the Government of Telangana on September 05, 2018. He also received prestigious award “Engineer of the year 2018” from the Institution of Engineers, India, and Government of Telangana on September 15, 2018 on the occasion of Engineers Day. He received the “National Integration Award-2019”, Health Care International, Hyderabad, Telangana State, India, on January 26, 2019. He completed two research projects

About the Editors

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from UGC and Government of India. He played a key role in start of M.E. transportation engineering course in civil engineering department, OU. He is a member in several committees of Government. of Telangana, AP, and Government of India. He was awarded for best paper in 2010 (national level) and 2014 (international level). He served as Additional Controller of Examinations, Exam Branch, OU; Chairman, Board of Studies in Civil Engineering (local and global), OU; Nodal Officer, TEQIP-I, UCE; Director of Evaluation, Exam Cell, UCE; Director Infrastructure, OU; Head of Civil Engineering Department, OU; and Vice-Principal, UCE, OU. He is a coordinator (STA), PMGSY, NRRDA, Government of India. He is a member of PEC, NRRDA, Government. of India; he is a life member of several research and technical organizations such as FIE, IRC, IUT, CTRG, IIBE, WCTR, TRG and SRS. He visited many international cities such as London (2005), Sydney (2006), Singapore (2006), San Francisco and Chicago (2007), Rome and Capri (2009), Melbourne (2011), Hong Kong (2011), Toronto (2013), Washington D.C. and New York (2013), Bangkok (2010 and 2014), Beijing and Shanghai (2014), Germany, Switzerland, Paris, Netherlands and Belgium (2014), Tokyo-Japan(2015) and Ho Chi Min City—Vietnam (2017) to present his research papers in international conferences/seminars. He delivered 20 keynote lectures at various national and international platforms. He chaired sessions at various international and national conferences. He was Conference Chair for the International Conference on “Innovations in structural Engineering” held in the year 2015 at Katriya hotel by Civil Engineering Department first time in 85 years of history. He organized a national conference on “Civil Engineering Systems—2006” and another national conference on “Recent Research Advances in Civil Engineering—2014”, five workshops and one training program. He delivered more than 300 expert lectures in the areas of transportation engineering, optimization techniques and engineering research methodology on various national-/state-level platforms in India. He is the author of three conference proceedings and two books (one in Elsevier publications international). He has over 23 years of teaching, research, consultancy and industry experience. He was Controller of Examinations (centenary year of OU), Osmania University, for two-year period. He initiated many technology-based reforms in Exam Branch, OU, during his tenure. Presently, He is Principal of University College of Engineering, Osmania University, Hyderabad, and Convener, PGECET —2019, Telangana State. e-mail: [email protected]

Prof. Arkanti Krishnaiah obtained his bachelor’s degree (1994) in mechanical engineering from University College of Engineering, Osmania University, Hyderabad; master’s degree in mechanical engineering (1997) with specilization in production engineering from University College of University Engineering, Osmania University, Hyderabad; doctorate degree (2006) from Indian Institute of Madras, Chennai; and postdoctoral fellowship (2007–2008) from Chungnam National University, South Korea. He received Sudharshan Bhat Memorial Prize for the best Ph.D. thesis in metallurgical and materials engineering for the year 2006 from IIT Madras, Chennai. He published more than 85 research papers in

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various international and national journals and international and national conferences. He is Visitor’s nominee to Pondicherry University, Puducherry. He is also a member in several selection committees of DRDO, Government of Telangana and AP. He served as Additional Controller of Examinations, Exam Branch, OU, during 2013–2015; Chairman, Board of Studies in Mechanical Engineering (local and global), OU; Head, Department of Mechanical Engineering, University College of Engineering (Autonomous), OU, during 2015–2017; and Joint Secretary, Osmania University Teachers’ Association (OUTA). He is Life Member of Indian Society for Technical Education (ISTE), Indian Society of Mechanical Engineers (ISME) and Indian Society of Theoretical and Applied Mechanics (ISTAM). He visited many countries for technical paper presentations such as USA (2005), Canada (2006), South Korea (2007–2008 for PDF), Australia (2010), Thailand (2014), China (2014), France (2014) and Switzerland (2014). He was Chairperson for the International Conference on Advances in Materials and Manufacturing (ICAMM-2016) held during December 8–10, 2016, at Leonia Convention Centre, Hyderabad, jointly organized by Department of Mechanical Engineering, UCE, OU, and DRDL, Hyderabad. Also, he organized national conferences, seminars and workshops in the Department of Mechanical Engineering. He has 22 years of teaching, research and consultancy experience. Presently, he is Director, Entrepreneurship Development Cell, Osmania University. e-mail: [email protected]

Dr. K. Srujan Raju is currently working as Dean Student Welfare and Heading Department of Computer Science & Engineering at CMR Technical Campus. He obtained his Doctorate in Computer Science in the area of Network Security. He has more than 20 years of experience in academics and research. His research interest areas include Computer Networks, Information Security, Data Mining, Cognitive Radio Networks and Image Processing and other Programming Languages. Dr. Raju is presently working on 2 projects funded by Government of India, has filed 7 patents and 1 copyright at Indian Patent Office, edited more than 10 book proceedings published by Springer publications - AISC series, LAIS and other which are indexed by Scopus also authored 4 books, contributed chapters in various books and published more than 30 papers at reputed and peer-reviewed Journals and International Conference. Dr. Raju was invited as Session Chair, Key note Speaker, TPC and reviewer for many National and International conferences. His involvement with students is very conducive for solving their day to day problems. He has guided various student clubs for activities ranging from photography to Hackathon. He mentored more than 100 students for incubating cutting edge solutions. He has organized many conferences, FDPs, Workshops and Symposiums. He has established the Centre of Excellence in IoT, Data Analytics. Professor Raju has acted as reviewer and Technical Member for many conferences and is editorial member for few journals. Raju received Significant Contributor and Active Member awards by Computer Society of India - Hyderabad Chapter.

An Empirical Based Porous Flow Approach to Modelling Heterogeneous Traffic Seelam Srikanth(&) Malla Reddy Engineering College, Main Campus, Hyderabad 500100, Telangana, India [email protected]

Abstract. The data required for the study was collected from YMCA road in Calicut city for frame based approach. Video-graphic technique was used for the data collection by the advantage over other data collection technique. For frame based approach video frames were taken approximately with a time lag of 3 s. From extracted data calculate the speed of vehicles, porous area, areal density, density and space headway of vehicles. From porous area data, pore space distribution function was determined by using easyfit5.5 software. The difficult was arises when extracting the traffic data from video frames. So for solving that problem on screen versus on ground distance graph was prepared. Best parameter to explaining the heterogeneous traffic system obtained as areal density rather than density measurement based on R-square value. The speed, areal-density model obtained was linearly decreasing relationship. Keywords: Frame based approach Pore space distribution


Arial density
Density


1 Introduction Modelling of traffic flow provides the fundamental relationships between the macroscopic traffic stream characteristics and plays an important role in the planning, design and operation of transportation facilities as it predicts the behaviour of the traffic flow. Need for the study of traffic stream characteristics and the relationships between them was recognized during the middle of the last century. The studies resulted in models describing various aspects of traffic flow. Traffic flow may be described as homogeneous or heterogeneous traffic flow. Homogeneous traffic follows strict lane discipline and the vehicles have similar static and dynamic characteristics whereas heterogeneous traffic flow does not follow any lane discipline and the vehicles have varying static and dynamic characteristics. The heterogeneous traffic flow maybe composed of both slow moving and fast moving vehicles like trucks, buses, cars, three wheelers, two wheelers, bicycles and hand drawn or animal drawn carts. In India heterogeneous traffic flow conditions exists. In Heterogeneous traffic, vehicles do not follow the lane discipline. Such traffic will observe in developing countries like India. The traffic models developed in homogeneous traffic conditions are not applicable for heterogeneous traffic conditions. In Heterogeneous traffic, vehicles can occupy any lateral positions based on lateral space © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 1–8, 2020. https://doi.org/10.1007/978-3-030-24314-2_1

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availability and also vehicles move low speed to higher speeds. Traffic stream characteristics such as traffic volume and density is defined as a linear measurement and the characteristics are dependent only headway, are insufficient when traffic is heterogeneous. Measures such as areal density will assessed for suitability. Areal density is defined by “the sum of total area projected by vehicles on the ground per unit area of roadway (vehicles per square meter)”. Research studies on modeling of heterogeneous traffic are limited in developing countries. Previous studies mainly focused on understanding the interaction between vehicles in the case of heterogeneous traffic. A relation needs to be established between the microscopic behavior and the macro performance of the flow. The porous flow approach will conceptualizes such relationship. The aim of the present study is to model the heterogeneous traffic by using an empirical based approach.

2 Literature Review Lighthill and Whitham [1] developed the first dynamic traffic flow model to different vehicle types. For each vehicle type developed the separate fundamental diagram. Authors assumed the traffic flow is related to density and conservation law was used to define LWR model. Hoogendoorn and Bovy [2] developed the mesoscopic gas-kinetic models for heterogeneous traffic. The termed multiple lanes as multi lane multiclass phase space density model. Heterogeneous traffic is explained by help of vehicular headway. The headway data fits the exponential and log normal distribution to explain the mixed traffic [3]. For model the mixed traffic flow, traffic composition, driver behavior, roadway geometry and maneuverability were considered [4]. Marwah and Singh [5] used the simulation analysis to model capacity of mixed traffic. Authors divided the traffic flow into four level of services. Heterogeneous traffic flow was explained with a help of speed and headway distribution of the different vehicle types. The developed model used to evaluate traffic management measures like segregation of vehicles on major urban roads, provision of exclusive lanes for buses/bicycles etc. [6]. This research was continued by to quantifying the vehicular interactions in terms of PCU and considering the effect of road width and traffic volume on PCU values of vehicles [7]. Nair and Mahmassani [8] have developed on heterogeneous traffic model based on non lane based traffic flow. The model uses the concept of pores.

3 Methodology for Frame Based Approach The main criteria considered for this approach was speed of the vehicle is not constant throughout road section i.e., speed of vehicle is changing time to time.

An Empirical Based Porous Flow Approach to Modelling Heterogeneous Traffic

3.1

3

Location for Data Collection

The location selected was YMCA road. The road link was two lane one way traffic road. The 100 m road link was divided in 10 road sections i.e. length of each road section was 5 m. Digital camera used for capturing the traffic data. Digital camera was positioned on a building in such way that the digital cam was covered the entire road link (Table 1). Table 1. Details of study locations for data collection Location no Location name Length of Width of Length section (m) section (m) trap (m) 1 YMCA road 100 7.5 5

3.2

Retrieval of Data

The process of data retrieval includes transferring of the recorded digital video to computer and then time stamping the video graphs. These video graphs are then used to find out the traffic stream characteristics like areal density and speed. The description of the data retrieval process is given below. 3.2.1 Transferring and Time Stamping of Video Graphs Traffic data videos are transferred into computer by using data cable. The videos were then time stamped by overlapping it with another video graph which displayed time to an accuracy of 1/100th of a second. This was done using the software Adobe Premier Pro. The videos were then played in Adobe Premier Pro to record the necessary data. The vehicle classification considered as Auto (A), Bus (B), Car (C), Jeep (J), Truck (T) and Two Wheeler (TW). Traffic video frames were taken with a time lag of approximately 3 s. In the first frame, the frame time was noted. And then type of vehicles and these vehicles crossing at 0 m reference mark i.e. entry time of each vehicles were noted. After that distance at entry time and distance at frame time of vehicles were noted. Next second frame was taken the similar values were note like first frame. But in second frame the entry time of vehicles that were present in first frame were same as frame time of first frame. The similar procedure was followed for next frames. The main difficult was obtained to note down the distance of vehicle from reference mark when vehicle was in between the tape marks i.e. multiple of 5 m length. For solving that problem a graph between on screen distance versus on ground distance were drawn. That was shown in Fig. 1. From the retrieved data, speed of vehicle, pore space area and areal density were calculated. 3.2.2 Stream Speed The speed data was obtained from difference in vehicle distance at frame time and entry time divided by time difference in frame time and entry time of vehicles.

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Fig. 1. Ground distance versus screen distance

3.2.3 Pore Space Area Pore space area is defined as portion of the material (road) that is not occupied by solid material (vehicles). The vehicles length, width and area were shown in Table 2. The composition of vehicles was converted into vehicle areas. Pore space area was obtained by deducting the vehicle area from area of road section. Table 2. Dimensions of different vehicle classes Vehicle TW Auto Car Jeep Bus Truck

Length (m) Width (m) Area (m2) 2.1 0.83 1.74 2.66 1.32 3.51 3.5 1.50 5.25 3.88 1.87 7.25 10 2.58 25.80 6.70 2.30 15.41

3.2.4 Areal Density Areal density was obtained by dividing pore space area with area of road section.

4 Speed-Density-Areal Density Analysis The speed in km/hr was plotted against areal density in vehicles/m2. and is shown in Fig. 2 for frame based approach. The model equation and R2 value is shown in Table 3. It can be observed from the plots that the speed varies linearly with density and there is a considerable scatter in the plot. The average speed range is 50 kmph to 30 kmph and areal density range is 0.01 Veh/m2 to 0.24 Veh/m2.

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Fig. 2. Average speed versus areal density relationship Table 3. Development of speed-areal density models Type of model Model equation R-square value Linear regression model SPEED = (−53.74 * AREAL 0.507 DENSITY) + 45.65

5 Speed-Density Analysis The speed in km/hr was regressed against density in vehicles/km. The model equation and R-square value are given in Table 4. The speed in km/hr was plotted against density in PCU/km. The plot is shown is Fig. 3 for frame based approach. The model equation and R2 value are shown in Table 5. It can be observed from the plots that the speed linearly vary with density and there is a wide scatter in the plot. The average speed range is 50 kmph to 30 kmph and density range is 20 Veh/km to 150 Veh/km.

Table 4. Development of speed-density models with density in Veh/km Type of model Model equation R-square value Linear regression model SPEED = (−0.055 * DENSITY) + 45.01 0.337

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Fig. 3. Average speed versus density relationship Table 5. Development of speed-density models with density in PCU/km for frame based approach Type of model Model equation R-square value Linear regression model SPEED = (−0.853 * DENSITY) + 46.43 0.489

6 Model Development A linear relationship with speed and areal density for frame based approach was best model equation and the R2 value obtained was moderately good value. Exponential relationship between speed and areal density was also fitted and the models are shown in Table 6. These relationships also have nearly same R2 value. Table 6. Regression models of speed and areal density Type of model Model R-square value Linear regression model Speed = (−53.74 * areal density) + 45.65 0.507 Exponential model Speed = 45.78 * (expo(−1.34 * areal density)) 0.500

7 Speed-Porous Area Analysis The speed in km/hr was plotted against porous area in m2 for frame based approach. The plot is shown in Fig. 4 for frame based approach. It can be observed from the plot that the speed values do not vary much with density and there is a wide scatter in the plot. Average speed range is 30 kmph to 50 kmph and porous area range is 655 m2 to

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750 m2 for frame based approach. Average speed range is 30 kmph to 60 kmph and porous area range is 655 m2 to 750 m2 for section based approach.

Fig. 4. Average speed versus porous area relationship

8 Porous Space and Headway Distribution Functions From data regarding porous area are fitted in the distribution. The distribution obtained was GEV distribution. From data regarding space headway are fitted in the distribution. The distribution obtained was log-normal distribution.

9 Conclusions Data analysis was carried out to find out the relationship between speed, areal density and density, speed and porous area and also pore space and headway distribution. After data analysis speed-areal density and speed-density models were developed. The best model obtained was linear regression model of speed-areal density of frame based approach. The R squared value obtain for linear regression model was 0.507. From the data analysis conclude that average speed is linearly decreasing with increasing the areal density and speed of vehicles are increasing with more availability of porous area on the road section. The speed density model obtained as linearly decreasing relationship. Areal density parameter explains the heterogeneous disordered traffic system better than the density parameter. The pore space distribution follows the generalized extreme value distribution and headway distribution follows the log-normal distribution. This work makes the contributions to the limited literature on traffic flow theory.

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References 1. Lighthill MH, Whitham GB (1955) On kinematic waves II: a theory of traffic flow on long, crowded roads. Proc R Soc Lond Ser A 229:317–345 2. Hoogendoorn SP, Bovy P (1999) Gas-kinetic model for multilane hetererogeneous traffic flow. Transp Res Rec 1678:150–159 3. Hossain D, Iqbal G (1999) Vehicular headway distribution and free speed characteristics on two-lane two-way highways of Bangladesh. J Inst Eng India. Civ Eng Div 80(AOU):77–80 4. Khan SI, Maini P (2000) Modeling heterogeneous traffic flow. Transp Res Rec 1678:234–241 5. Marwah BR, Singh B (2000) Level of service classification for urban heterogeneous traffic: a case study of Kanpur metropolis. In: Proceedings of the 4th international symposium on highway capacity, transportation research circular E-C018, transportation 6. Arasan V, Koshy R (2005) Methodology for modelling highly heterogeneous traffic flow. ASCE J Transp Eng 131:544–551 7. Krishnamurthy K, Arasan T (2012) Effect of road width and traffic volume on vehicular interactions in heterogeneous traffic. J Adv Transp 2014(48):1–14 8. Nair R, Mahmassani HS (2011) Elise Miller-Hooks, a porous flow approach to modelling heterogeneous traffic in disordered systems. Procedia Soc Behav Sci 17:611–627

Effect of Different Aggregates on Alkali Silica Reaction of Geopolymer Concrete D. Annapurna1(&), Ravande Kishore2, and K. Anil1 1

Civil Engineering Department, University College of Engineering, Osmania University, Hyderabad, Telangana, India [email protected], [email protected] 2 MIT School of Engineering, MIT-ADT University, Pune, Maharashtra, India [email protected]

Abstract. Geopolymer concrete (GPC) is an emerging environmental friendly construction material in present construction world. Extensive research related to strength and durability studies is being done on this material. But, it is required to check the mechanism of potential reactive aggregates with GPC. Alkali silica reaction (ASR) is one of the major problems with the aggregates. Volumetric expansion of silica gel produce internal stresses in the concrete and leads to strength loss, cracking and failure of the structure. In the present investigation three different types of aggregates were used with Fly ash and GGBS based Geopolymer as binder. Mechanical testing included the potential reactivity of aggregate and length change measurements as per ASTM: C126007 standards. Scanning Electron Microscope (SEM) is used for petrographic analysis. It is observed that the alkali silica reaction in Fly ash and GGBS based geopolymer concrete is comparatively less than the OPC based concrete and the expansions are well below ASTM threshold. Keywords: Alkali silica reaction
Fly ash and GGBS based geopolymer concrete
Scanning Electron Microscope

1 Introduction Ordinary Portland cement is the most abundantly used construction material in the world. Production of OPC consumes large amount of natural resources and also a reason for 5–8% of global CO2 emission [1, 2]. Geopolymer cement can be an alternative to the OPC and environmental friendly construction material [3]. Durability of normal Portland cement is a problem in civil infrastructure industry; in which influence of Alkali Silica Reaction (ASR) on performance of concrete is one of the major issue [4, 5]. ASR is a chemical reaction between hydroxyl ions in the pore pressure within the concrete matrix and certain forms of silica present in the aggregate. This reaction forms silica gel at the interface of the aggregate and the binder matrix. This silica gel expands in volume and exerts stresses on the adjacent binder and aggregates as shown in Fig. 1. It reduces the bond between aggregate and binder matrix. This reaction could lead to strength loss, volume expansion, cracking and potential failure of the structure. © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 9–17, 2020. https://doi.org/10.1007/978-3-030-24314-2_2

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Limited research has done in this area especially in the geopolymer concrete [6]. It is required to check the influence of alkali silica reaction in geopolymer concrete. Alkali silica reaction is a slow reaction which takes lot of time to recognize the problem. In India, there are two major dams which are affected by the alkali silica reaction. One is Hirakud dam located in Odisha and the other is Rihand dam in Uttar Pradesh.

Fig. 1. Mechanism of ASR

It is found that the locally available aggregate was used in the construction of these structures in which some of the aggregates were potentially reactive in nature. These aggregates have undergone alkali silica reaction and silica gel was formed in the concrete. The volumetric expansion of the gel leads to formation of severe cracks in the structures. In the present study, accelerated mortar bar test is done on geopolymer mortar bars as per ASTM standards [7]. Petrographic analysis is carried out by using scanning electron microscopic pictures to check the formation of alkali silica reactive gel and its expansion. Three different types of aggregates i.e. Granite, Quartz and Black trap have been used in the present investigation. Conventional test as per IS 2386- Part-VII, 1963 [8] for alkali silica reaction is time consuming, as it requires more than a year to assess the presence of silica gel in concrete. Keeping this in view, the accelerated mortar bar test is selected for the present study.

2 Experimental Investigation Materials In the present study, the materials used are Fly ash from Kakateeya thermal power plant confirming to ASTM class F of specific gravity 2.08, GGBS from JSW steel plant, Bellary, Karnataka of specific gravity 2.08, alkaline liquids (Sodium Hydroxide and Sodium Silicate), and water. Aggregates used are Granite, Quartz and Black trap of specific gravities 2.61, 2.52 and 2.93 respectively. Preparation of Mortar Bar Specimens Total 36 specimens of size 25  25  250 mm as shown in Fig. 2, having three different types of aggregates each combines with various dosages of Geopolymer binder of three variation dosages of GGBS and OPC were prepared.

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Fig. 2. Mortar bar specimen

As per ASTM C1260-07 standards, the test has to be done on fine aggregate which shall be tested for a grading meeting the requirements of the specifications given by code. As per the guidelines given by code, the quantities of dry materials to be mixed at one time in the batch of mortars for making three specimens shall be 440 g of cement and 990 g of aggregate made up by recombining the portions retained on the various sieves in the grading as prescribed. Fly ash based Geopolymer is used with 10%, 30% and 50% GGBS dosages. The proportion of the Fly ash and GGBS are mentioned in Table 1. Table 1. Proportioning of cementing materials GGBS dosage 10% GGBS 20% GGBS 30% GGBS OPC = 440 g

Fly ash (g) GGBS (g) 396 44 352 88 308 132

Geopolymer mortar is prepared by mixing graded aggregate and corresponding cementing material with 8 M alkaline solution [9]. For OPC mix, the water cement ratio is taken as 0.47 as mentioned in the code. Moulds were cleaned well and wax is applied. The studs were set in the moulds in respective positions and care is taken to keep studs free from wax. The prepared mortar is compacted well by vibration. The specimens were demoulded after 24 h and labeled, as shown in Figs. 3 and 4.

Fig. 3. Moulding of mortar bar specimens

Fig. 4. Prism specimens

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Testing Procedure The specimens were removed from the moulds after 24 h of casting and the initial reading is noted down using length comparator as shown in Fig. 5. After taking the initial reading, OPC specimens were stored in a container with sufficient tap water to totally immerse them, and then the container is sealed and placed in an oven at 80 °C for a period of 24 h. GPC specimens are not immersed in water, but sealed in a container and placed in oven at same temperature as OPC specimens. After the period of 24 h, the specimens length were measured by using length comparator, and is recorded. Then all the specimens are immersed in 1N NaOH at 80 °C in a steel box as shown in Fig. 6 and kept in oven for 14 days continuously. Readings were taken on fifth day and tenth day at nearly same time from the date of placing the container in the oven. The expansion is calculated for fifth day, tenth day and fourteenth day after taking the respective readings of all the specimens. Expansions are calculated using Eq. 1. L ¼ ½ðLx  Li)  100=G

ð1Þ

Where, L = Change in length at an age of ‘x’ days, Lx = Comparator reading of specimen at age ‘x’ days, Li = Initial comparator reading of the specimen, G = Nominal gauge length, i.e. 250 mm. As per ASTM code guidelines, if the final expansion is less than 0.10%, then it is indication of innocuous behavior. If the final expansion is in between 0.10 to 0.20% then it is known to be innocuous and deleterious action and if the final expansion is greater than 0.20%, then it is indicative of potentially deleterious expansion in the field performance.

Fig. 5. Mortar bar specimen with comparator reading

Fig. 6. Steel box in oven

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3 Results and Discussions The expansions of all the specimens are calculated at fifth, tenth and fourteenth day after placing in the oven. The results of quartz, granite and black trap are expressed graphically as shown Figs. 7, 8 and 9.

Fig. 7. Expansions of quartz specimens

Fig. 8. Expansions of granite specimens

Fig. 9. Expansions of black trap specimens

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Quartz with OPC and 30% GGBS samples exhibited 0.13 and 0.027% final expansions, which indicates potentially deleterious expansion and innocuous behavior respectively. Quartz with 10% GGBS sample exhibits 0.017% final expansion, which is indication of innocuous action. 30% GGBS sample with Quartz also shows innocuous behaviour. Granite with OPC have indicated deleterious behavior. Granite with 30% GGBS and 50% GGBS Geopolymer binder shows higher elongations as that of quartz. However, Granite with all Geopolymer binders has shown innocuous behavior. Black trap with OPC also shows deleterious behavior. However Black trap with all Geopolymer binders also proves to be innocuous. In all the type of reactive aggregates, Geopolymer binder combinations shows higher elongations as GGBS dosages are increasing. During geoplymerization, the alkali content is utilized in the geopolymer reaction and alkalis have reacted with the amorphous component in the Fly ash, and then converted to strong cementitious binder. Very less amount of alkali and calcium is present to react with silica present in the aggregate to form silica gel. The residual alkali content after geopolymerization attacks silica present in the reactive aggregates. This alkali silica reaction (ASR) forms silica gel in the mix, which expands with reaction of calcium present in the binder. In the Fly ash based geopolymer binder, there is very less amount of calcium to help the expansion of silica gel. However, in the present study, Fly ash is replaced with GGBS in three different percentages to decrease the setting time of the mix [10]. Calcium which is present in GGBS reacts with the silica gel formation and silica gel expands, which creates micro cracks in the structure. Hence, as the GGBS percentage increases in the mix, the expansion also increases. In most of the cases 10% GGBS showed innocuous action, which means absence of alkali silica reaction (ASR). Scanning Electron Microscope (SEM) Analysis Petrographic analysis is done on granite and quartz specimens using SEM, and the same is shown in Fig. 10, 11 and 12.

ASR expansive gel Aggregateand binder interface

(a)

(b)

Fig. 10. (a) SEM picture of granite with OPC. (b) SEM picture of granite with 10% GGBS

Effect of Different Aggregates on ASR of GPC

15

Formation of crack due to the expansion of the alkali silica gel is found in granite with OPC as shown in the Fig. 10(a). No ASR gel is formed in the case of granite with 10% GGBS at the interface of the aggregate and binder matrix as shown in Fig. 10(b).

A

(A) 20% GGBS

B

(B) 30% GGBS Fig. 11. SEM picture of granite with GGBS

Little ASR gel formation and expansion is observed in granite with 20% and 30% GGBS as shown in Fig. 11. Cracks were developed at the interface of the aggregate and the binder matrix, which reduces the bond strength of the concrete and ultimately leads to the failure of the structure.

ASR Expansive gel

Fig. 12. SEM pictures of quartz with OPC

ASR gel is observed in the case of quartz with OPC as shown in Fig. 12, and with 20% and 30% GGBS, reveals that the aggregate is reactive in nature. No alkali silica reaction was found in the case of quartz with 10% GGBS and the same can be observed through Fig. 10. From the experimental and petrographic analysis, it can be observed that the alkali silica reaction in OPC binder is substantially higher than the Geopolymer binder. ASR gel formation is clearly observed in the case of quartz with OPC binder as shown in

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Fig. 13, which reveals it as a potentially reactive. With increase in the dosage of GGBS in Geopolymer binder, the alkali silica reaction also increases due to the availability of more calcium content in GGBS.

A

B

C

ASR Expansive gel

(A) SEM picture of Quartz with 10% GGBS (B) 20% GGBS

(C) 30% GGBS

Fig. 13. SEM picture of quartz with GGBS

4 Conclusions Based on the experimental work and petrographic analysis, following conclusions are drawn. • Quartz with OPC exhibited final expansion of 0.13%, indicate innocuous and potentially deleterious expansion thus ambiguous related to use as construction material with OPC. • Quartz with Geopolymer binder exhibiting less expansion compared with OPC. Quartz with Geopolymer binder containing 10%, 20%, 30% GGBS has shown innocuous behavior. • No alkali silica gel formation is noticed in quartz with GPC. • Black Trap and Granite with OPC is found to be deleterious in ASR. Granite with GPC exhibiting less expansion compared with OPC. • Black trap aggregates are suitable for construction when used in Geopolymer binder.

References 1. Sanni SH, Khadiranaikar RB (2012) Performance of geopolymer concrete under severe environmental condition. Int J Civ Struct Eng 3(2):396 2. Lloyd NA, Rangan BV (2010) Geopolymer concrete with fly ash: second international conference on sustainable construction materials and technologies. Coventry University and the University of Wisconsin Milwaukee Centre for by-Products Utilization, June 2010, ISBN 978-1-4507-1490-7

Effect of Different Aggregates on ASR of GPC

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3. Rangan BV, Hardjito D, Wallah SE, Sumajouw DMJ (2006) Properties and applications of fly ash-based concrete. Mater Forum 30:170–175 4. Abdul Aleem MI, Arumairaj PD (2012) Geopolymer concrete – a review. Int J Eng Sci Emerg Technol 1(2):118–122 5. Kishore K (1992) Alkali silica reaction in concrete. Irrig Power J 49(4):29–30 6. Kupwade-Patil K, Allouche E (2011) Effect of alkali silica reaction (ASR) in geopolymer concrete. In: World of coal ash (WOCA) conference, 9–12 May 2011, Denver, CO, USA 7. ASTM: C1260-07: Standard Test Method for Potential Alkali Reactivity of Aggregates 8. IS: 2386 (Part VII)-1963: Indian Standard, Methods of Test for Aggregates for Concrete, Alkali Aggregate Reactivity 9. Bachhaav SS (2016) Study of acid resistance properties of geopolymer concrete for different molarities. SSRG Int J Civ Eng (SSRG-IJCE) 3(1):35–39 10. Ganapati Naidu P, Prasad ASSN, Adiseshu S, Satayanarayana PVV (2015) A study on strength properties of geopolymer concrete with addition of G.G.B.S. Int J Eng Res Dev 2 (4):19–28

Assessment of Aquifer Vulnerability of Nizamabad District, Telangana State, India Using GIS and Drastic Method B. Ramakrishna1(&), P. Rajasekhar2, and Shaik Vaheed1 1

Civil Engineering Department, RGUKT-Basar, Hyderabad, Telangana, India [email protected], [email protected] 2 Civil Engineering Department, Osmaina University, Hyderabad, Telangana, India [email protected]

Abstract. The precipitation falling on the surface earth, either it runoff over land to the stream or some part of it infiltrates into the ground. The part which moves into the ground either get transplanted by the plants and goes back to the atmosphere or some part of it percolated deep down and contributes water already inside the earth. The water which is existing below the ground surface is commonly called as ground water or subsurface water. Countries like India, most of the people use groundwater for drinking purpose. Due to increase of waste on earth surface which can effect groundwater quality. It can leads to contamination of groundwater. The present study is conducted to assess the groundwater vulnerability and prepare vulnerability map for Nizamabad district, Telangana state using DRASTIC model in ARC GIS 10.4.1 groundwater vulnerability map can be obtained by overlaying seven layers. Such as Depth of water level, Net Recharge, Aquifer Media, Soil Permeability, Topography, Impact of Vadose Zone and Hydraulic conductivity. Keywords: DRASTIC
GIS
Vulnerability
Groundwater Hydro geological parameters
Weighted overlay analysis




1 Introduction Earth is very special because it has much amount of water, the total earth surface covers 71% water. In this 96.5% is salt water that is found in the ocean only just 3.5% of water on the earth is freshwater. In most of this freshwater 68% is glaciers and icecaps. One third is in groundwater and remaining 2% of water is in River, Lakes, Streams and very small amount in atmosphere. From last few decades population is increasing drastically. Due to human activities such as construction of buildings, Industries, Deforestation, Urbanization ground water level is reducing and it is contaminated. The main aim of the study is to evaluate the groundwater vulnerability and prepare vulnerability map. Determine area where groundwater is contaminated. For that area proper management and protection techniques has to follow. This maps are very informative for educating the public. Groundwater vulnerability maps are prepared using standard method. Research and scientist they did lot of work on this and find out so many © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 18–26, 2020. https://doi.org/10.1007/978-3-030-24314-2_3

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19

variety of method for aquifer vulnerability assessment. Aller et al. (1987) proposed DRASTIC method which is explained in this methodology. According to Al-Zabet et al. (2002) Evaluation of aquifer vulnerability of contamination potential using the DRASTIC method. Muhammad et al. (2014) describes that “Groundwater vulnerability assessment shows an extreme sensitivity to in situ anthropogenic pollutants. A dichotomous assessment of geological and hydrological characteristics makes it possible to determine the vulnerability of groundwater. You-Jailin et al. (2005) describes that “The groundwater pollution in many regions is becoming more and more serious because of over-exploiting and industrial activities”. Lathamani et al. (2015) describes that this investigation was carried out to determine the aquifer vulnerability using DRASTIC method which correlated well with then physicochemical characteristics of groundwater in mysore city. This model was primarily developed by National Water Well Association and U.S. Environmental Protection Agency for different hydrogeologic settings. Henceforth, the model has been modified according to geological or hydro-geological setting such as pesticide DRASTIC, modified DRASTIC, modified pesticide DRASTIC, DRASTIC-LU, DRASTIC-Fm and DRASTIC-AHP by different researchers. DRASTIC model employs hydro-geological data in a Geographical Information System (GIS) environment to compute aquifer vulnerability index. DRASTIC is a shortening of seven physical parameters of hydro-geological which used to define groundwater system and its susceptibility towards pollution. It considers seven parameters, which taken together, provide the acronym such as Depth to groundwater (D), Recharge (R), Aquifer type (A), Soil type (S), Topography (T), Impact of the vadose zone (I), Hydraulic conductivity. 1.1

Study Area

Nizamabad district located in the north - western region in India. The study area is 4288 km2 with an average elevation of 395 m above mean sea level. The average annual rainfall in the study area varying from 660 and 1340 mm. Annual average rainfall is 970 mm, the average temperature varying from 24 °C to 40 °C. Location map of nizamabad district is shown in Fig. 1.

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Fig. 1. Location map of nizamabad district

2 Methodology The present study is to investigate the aquifer vulnerability assessment, DRASTIC model is adopted. DRASTIC means D for Depth to water table, R for Net Recharge, A for Aquifer media, S for Soil media, T for Topography, I for Impact of Vadose zone and finally C for Hydraulic Conductivity, This 7 parameters are overlayed in Geographical Information System and divided into number of units based on rate and weight of each hydrological unit as for Aller et al. (1987). DRASTIC index map is created and this map is reclassified using DRASTIC vulnerability index as shown in Eq. 1. VI = DrDw + RrRw + ArAw + SrSw + TrTw + IrIw + CrCw:

ð1Þ

In the above Eq. 1, w represents weight, r represents rate. The vulnerability index is weighted sum of rate of all the evaluation factors given by Aller et al. 1987 is shown Table 1 and schematic representation of methodology used in DRASTIC model is shown in Fig. 2.

Assessment of Aquifer Vulnerability

21

Table 1. DRASTIC parameters ratings and classes (Aller et al. 1987) Layer

Range

Rating

Depth to water (m)

0–1.5 1.5–4.5 4.5–9 9–15 15–22.5 22.5–30 30< 254< 178–254 102–178 51–102 0–51 Karst Limestone Basalt Sand and Gravel Massive Limestone Massive Sandstone Bedded Standstone, Limestone and Shale Sequences Glacial Till Weathered Metamorphic/Igneus Metamorphic/Igneus Massive Shale Thin or Absent Gravel Sand Peat Shrinking and/or Aggregate Clay Sandy loam Loam Silty Loam Clay Loam Muck Non shirnking and Non aggregated clay 0–2 2–6 6–12 12–18 18 0.1b) there is marked reduction in bearing capacity.

References 1. Patra CR, Das BM, Bhoi M, Shin EC (2006) Eccentrically loaded strip foundation on geogridreinforced sand. Geotext Geomembr 24:254–259 2. Das BM, Puri VK, Omar MT, Evgin E (1996) Bearing capacity of strip foundation on geogrid-reinforced sand-scale effects in model tests. In: The sixth international offshore and polar engineering conference. International Society of Offshore and Polar Engineers 3. El Sawwaf M (2009) Experimental and numerical study of eccentrically loaded strip footings resting on reinforced sand. J Geotech Geoenviron Eng 135(10):1509–1518 4. El Sawwaf M, Nazir A (2011) Behavior of eccentrically loaded small scale ring footings resting on reinforced layered soil. J Geotech Geoenviron Eng 138:376–384 5. Klimar KVSP (2010) Bearing capacity of square footing on geocell sand mattress overlying clay bed, M. Tech Thesis, National Institute of Technology, Warangal 6. Yeo B, Yen SC, Puri VK, Das BM, Wright MA (1993) A laboratory investigation into the settlement of a foundation on geogrid-reinforced sand due to cyclic load. Geotech Geol Eng 11(1):1–14

Strength Investigation of Fly Ash Based Concrete Waste Steel Fibre and Polypropylene Fibre as Reinforcing Materials G. Swamy Yadav1,2(&), N. Prabhanjan1, G. Sahithi1, G. Sangeetha1, A. Srinivas1, and A. Siva Krishna1,2 1

Department of Civil Engineering, S R Engineering College, Warangal, India [email protected] 2 Center for Construction Methods and Materials, Warangal, India

Abstract. The improper management of waste steel fibres causes a huge environmental damage. Proper utilization of these waste steel fibres can be done in civil engineering. Concrete obtained by adding these fibres is considered to show a good mechanical improvement of brittle matrix, moreover it is a promising candidate for both structural and non-structural applications. In the present work, as a continuation of research already performed in this field by the other authors, the post cracking performances of FRC (fibre reinforced concrete) were evaluated by means tests on flexural elements and slabs. All fresh and hardened concrete properties are estimated experimentally. By the means of flexural test the post-cracking behavior of SFRC is obtained. These specimens showed a good energy absorption and good residual strength after cracking. Moreover, cracks have an important role in concrete structures as they are the permeable components and have high risk of corrosion. Cracks make the structure aesthetically unacceptable and make structure weak. Cracks are considered to be neither harmful to structure nor affects its serviceability if they are in limited width. Hence it is important that crack width must be less and this is achieved by adding polypropylene fibres to concrete. This work examines the mechanical properties of concrete by using waste steel fibres from mechanical labs and recronS polypropylene fibres and flyash as replacement of cement. In this work cement was replaced by flyash by 20% of cement by weight in each mix, waste steel fibres with varying percentages like 1, 2, 3, 4, 5, 6 in the mix, and the combinations were tested and in the second case with same combinations 1% of polypropylene fibre is added and tested for workability, compressive strength, tensile strength, flexural strength. In this study we observed that the strength was increased with increase in fibre content but workability decreased and optimum percentage of steel fibre was observed at 5% so super plasticizer has been used to increase workability and the effect of polypropylene was not on the strength but crack width reduced and observed controlled brittle failure. Keywords: Flexural strength
Waste steel fibre
Polypropylene fibre Tensile strength
Flyash
Workability
Compressive strength

© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 155–161, 2020. https://doi.org/10.1007/978-3-030-24314-2_21




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1 Introduction The major problems existing in the present world are: Growing population is a major problem being faced by the present world. To provide water, food and shelter with clean environment to this growing population is a major challenge. The necessity arises for innovation in various technologies. Lack of proper infrastructural and other requirements of the populace: Role of Civil Engineer is vital in meeting the ever increasing infrastructural requirements. Concrete and mortar utilization in development of infrastructure of any country is very high. Due to fast depleting natural resources, it is imperative on us to think of development of materials, which can possibly reduce the cost of structure, clean the environment for us and provide better service. To meet the above requirement suitability of different materials for partial or total replacement in concrete, mortar and soils is investigated. Flyash Concrete Flyash is a pozzolana, or siliceous material which in the presence of water and lime, will react to form a cementitious material. This property makes flyash ideal for a multitude of function in the construction industry. Flyash is an extremely variable by product and will not only differ from one plant to another but will vary within the same plant. Both the physical and chemical characteristics of flyash will affect the hardened concrete material. In the modern world, the most used material for construction buildings and to build infrastructure the most used material is concrete. and concrete is normally manufactured by mixing with ingredients such as cement, cores aggregates, fine aggregates, water, and mineral admixtures for latent concrete nowadays such as, fly ash, GGBS and also usage of super plasterers for improving strength of concrete. The materials used for concrete manufactures such as fine aggregates are found in nature and used by trimming them into required shape & size. Water is also available in nature and clan water is used, but cement is manufactured for raw materials available in nature. So some study is been made by replacing cement completely with fly ash and GGBS [1]. Fibre Reinforced Concrete FRC has a cracking arresting property of which is well proven in many experiments and it is applied extensively. Million tons waste is produced around the world every year and most part of this waste is recycled. Furthermore, recycling waste consumes energy and produces pollution. Accumulation of waste in suburbs and the disposal of waste has dangerous effects to the environment. Using this waste in civil engineering works may be considered as an appropriate method for achieving the goals of eliminating waste produced and adding positive properties in concrete. We introduced a glass fiber reinforced polymer (GFRP)-steel hybrid bar with a core of a deformed steel bar (steel core). Around six types of hybrid cross sections were considered and 48 specimens to calculate tensile test using uniaxial tensile testing machine and the modulus of GFRP hybrid bar. The test results revealed that the GFRB hybrid bar showed higher elasticity modulus and lesser tensile strength than that of usually shown normal GFRB bar [3].

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Need of Polypropylene Fibre Cracks developed in concrete during the curing process propagate rapidly when stresses are applied which results low tensile strength. Addition of PP fibers improves the concrete strength. And the problem of cracks can be reduced by using PP fibers in concrete. Moreover they provide the strength to concrete while protection of fibres is done by matrix. The major role of fibres in a cement composite mix is to control cracks, importantly to increase the tensile strength, Flexural strength and to improve the deformation characteristics. The type of fibres used will definitely effect the performance of FRC. Inclusion of polypropylene fibers will reduce the water permeability of the composite and increases flexural strength because it is high in young’s modulus. In the stage of post cracking as the fibres are pulled out the crack is reduced by absorbing the energy.

2 Materials and Methodology Materials Properties of Cement Used Indian Standards Grade Specific Gravity Fineness of Cement Consistency

IS 12269-1987 53 3.12 7% 31%

Properties of Flyash Used Indian Standards IS3812-1981 Specific Gravity 2.12

Properties of Fine Aggregates Used Sand Availability Zone-II Fineness Modulus 2.51 Specific Gravity 2.65

Properties of Coarse Aggregates Used Type Minimum Size Specific Gravity Water Absorption

Granite 20 mm 2.5 1.2%

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Water Drinking water available near to the field was used in the present work both for casting and curing. Waste Steel Fibre The steel fibre used in this work has taken from mechanical workshop, They are of irregular sizes and shapes. Super Plasticizer Conplast SP 430 FOSROC is used for the experimental study. Methodology The mix design followed according to IS 10262-2009 for M25 concrete. The experimental investigation is carried out by replacing cement with flyash by 20% cement weight and varying percentages of waste steel fibres in steps of 1% and 1% of polypropylene the results were observed. The required materials were weighed and mixed manually and tests were conducted. The cube of 150 mm  150 mm  150 mm. Cylinder of 150 mm diameter 300 mm height. Beam of size 100 mm  100 mm  500 mm was casted. The specimens has been de molded after 24 h from the casting and the casted specimens were cured at a room temperature in water tank.

3 Results See Tables (1, 2, 3, 4, 5, 6 and 7).

Table 1. Workability of concrete using waste steel fibre alone vs Using waste steel fibre and polypropylene (1%). %Fibres

Compaction Factor

%Fibres

Compaction Factor

0

0.927

0

0.927

1

0.921

1

0.923

2

0.918

2

0.920

3

0.915

3

0.917

4

0.909

4

0.911

0.890

5

0.903

0.871

6

0.892

5 6

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Table 2. Compressive strength of concrete using waste steel fibre alone. %Fibres 7 days compressive strength 0 28.64 1 32.24 2 34.18 3 35.42 4 35.82 5 36.12 6 36.96

28 days compressive strength 42.21 46.63 47.46 48.12 48.48 48.63 48.77

Table 3. Compressive strength of concrete using waste steel fibre and polypropylene (1%). %Fibres 7 days compressive strength 0 28.64 1 32.42 2 34.40 3 35.68 4 35.89 5 36.21 6 37.03

28 days compressive strength 42.21 46.71 47.51 48.17 48.51 48.69 48.78

Table 4. Split tensile strength of concrete using waste steel fibre alone. %Fibres 0 1 2 3 4 5 6

Load P1(kN) 112.1 124.2 124.6 127.7 127.4 128.5 128.8

Load P2(kN) 115.7 121.2 124.3 125.3 127.6 127.9 129.1

Avg. load P(kN) F = 2P/pdl (N/mm2) 113.9 3.62 122.7 3.90 124.5 3.96 126.5 4.02 127.5 4.05 128.2 4.08 128.6 4.10

Table 5. Split tensile strength of concrete using waste steel fibre and polypropylene (1%). %Fibres 0 1 2 3 4 5 6

Load P1(kN) 112.1 122.9 125.2 127.3 128.2 128.8 129.2

Load P2(kN) 115.7 123.4 124.9 126.6 127.5 128.6 129.0

Avg. load P(kN) F = 2P/pdl (N/mm2) 113.9 3.62 123.1 3.92 125.0 3.99 126.8 4.03 127.8 4.07 128.7 4.09 129.1 4.11

160

G. Swamy Yadav et al. Table 6. Flexural strength of concrete using waste steel fibre alone. %Fibres 0 1 2 3 4 5 6

a (cm) 15.0 16.3 16.4 16.8 17.1 17.4 17.7

P(kN) 13.0 14.8 15.3 15.7 15.8 16.1 16.4

fb = Pl/bd2 2.60 3.08 3.22 3.38 3.45 3.56 3.67

Table 7. Flexural strength of concrete using waste steel fibre and polypropylene (1%). %Fibres 0 1 2 3 4 5 6

a (cm) 15.0 15.5 16.5 16.7 16.6 17.2 17.5

P(kN) 13.0 15.2 15.3 15.9 16.3 16.6 16.8

fb = Pl/bd2 2.60 3.10 3.25 3.41 3.48 3.64 3.73

4 Conclusions • The workability of fresh concrete is decreased by adding the steel fibres. • Gradual increase in Compressive strength, Tensile strength and flexural strengths are observed by increasing percentage of steel fibres. • Polypropylene fibres reduce cracks and brittle failure. • Polypropylene fibres increased the initial crack load of the structure. • Finally the partial replacement of flyash and waste steel fibres in concrete is economical.

References 1. Ravi Kumar T, Siva Krishna A (2017) Design and testing of fly-ash based geo polymer concrete. Int J Civ Eng Technol 8(5):480–491 2. Tavakoli D, Hashempour M, Heidari A (2018) Use of waste materials in concrete: a review. Pertanika J Sci Technol 26(2):499–522 3. Ju M, Lee S, Park C (2017) Response of glass fiber reinforced polymer (GFRP)-steel hybrid reinforcing bar in uniaxial tension. Int J Concr Struct Mater 11(4):677–686 4. Gobinath R, Awoyera P, Selvaraj Kumar P, Murthi P (2018) Eco friendly high strength concrete production using Silica mineral waste as aggregate - an ecological approach. Ecol Environ Conserv 24(2):904–915

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5. Gobinath R, Ganapathy GP, Akinwumi II, Kovendiran S, Hema S, Thangaraj M (2016) Plasticity, strength, permeability and compressibility characteristics of black cotton soil stabilized with precipitated silica. J Cent South Univ 23(10):2688–2694 6. Sathanandham T, Gobinath R, NaveenPrabhu M, Gnanasundar S, Vajravel K, Sabariraja G, Manoj Kumar R, Jagathishprabu R (2013) Preliminary studies of self curing concrete with the addition of polyethylene glycol. Int J Eng Res Technol 2(11):313–323 7. Murthi P, Awoyera P, Selvaraj P, Dharsana D, Gobinath R (2018) Using silica mineral waste as aggregate in a green high strength concrete: workability, strength, failure mode, and morphology assessment. Aus J Civ Eng. https://doi.org/10.1080/14488353.2018.1472539 8. Venkat Reddy P Experimental Investigation on RCC by using multiple Admixtures 9. Shiva Krishna A (2017) Study on concept of smart city and its structural components. IJCIET 8(8):101–112

Application of Statistics to the Analysis of Corrosion Data for Rebar in Metakaolin Concrete U. Raghu Babu(&) and B. Kondraivendhan Applied Mechanics Department, Sardar Vallabhbhai National Institute of Technology, Surat 395007, Gujarat, India [email protected], [email protected]

Abstract. The present paper reports the effect of chloride, sulphate and the combined chloride-sulphate solutions on the corrosion behaviour of rebar embedded in concretes made with Ordinary Portland Cement (OPC) and Metakaolin (MK). The corrosion test data collected on the reinforced slab specimens made with mix water contaminated with 5% sodium chloride, 2% magnesium sulphate and the combination of both salts. The corrosion performance was monitored for every 30 days throughout 180 days, in terms of corrosion rate values. The analysis of the obtained data was carried out as per the specifications given by ASTM G16-13. The analysis of the corrosion data includes the descriptive statistics of the Normal, Weibull lognormal, and Smallest Extreme Value probability distribution functions and the test of fit significance by the Anderson-Darling (AD) goodness of fit statistics. In addition, the analysis of variance was also carried out to determine the influence of each factor on the corrosion data. This detailed analysis of the test data is useful to carry out the further investigation on corrosivity of reinforcement bar exposed to aggressive environments or marine environment. The statistical analysis from the present dataset is helpful for further research on the MK and the effect of concomitant presence of chlorides and sulphates on the corrosivity of rebar embedded in concrete. In addition to this Analysis of Variance is also carried out on the obtained corrosion data to assess the effect of cementitious material type and salt type on corrosion data. Keywords: Corrosion Sulphate


Metakaolin
Probability distribution
Chloride


1 Introduction Corrosion of the rebar embedded in concrete and deterioration of concrete due to sulphates are the most common causes of the failures of concrete structures. Corrosion due to chlorides and carbonation are not only the deterioration mechanisms of concrete, some other aggressive salts like sulphates, carbonates, high temperature and humidity variations contribute to the early initiation and propagation of reinforced steel corrosion [1]. However, It would be anticipated that the existence of sulphate ions would affect

© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 162–169, 2020. https://doi.org/10.1007/978-3-030-24314-2_22

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the mineralogical composition and the characteristics of corrosion resistance of rebar in concrete [2]. Sulphate salts are also present in ground water and sea water in addition to chloride salts. The environmental conditions in the coastal areas contribute to a reduction in the durability of the structures mainly due to the contaminated soil and ground water with the chloride and sulphate ions. In the marine environments and groundwater prevailing in the Arabian Gulf countries [3], Northwest of China [4, 5] and elsewhere, chloride and sulphate ions existed concomitantly. Khan and Tayyib [6] demonstrated that sulphate ions are corrosive, but not much severe than chloride ions. The reaction of sulphates with Ca(OH)2 reduces the pH of concrete pore solution and results the sulphate induced corrosion of rebar. Holden et al. [7] have shown, the concomitant presence of sulphates with chloride ions, decreases the chloride binding capacity due to reaction with the aluminates, and increases the corrosion of steel in concrete. The concentration of sulphates present in concrete also influences the binding of chlorides. Some other research work also evident that the chloride binding capacity of cement hydrates decrease with the sulphate ions [4, 11]. This was ascribed to the reaction of C3A with sulphates to produce ettringite, which inhibits the formation of Friedel’s salt (FS). Brown and Badger [8], indicated that FS can convert to ettringite in Na2SO4 solution and releases the bound chlorides. Furthermore, due to the competition between the sulphate and chloride ions to adsorb on the C–S–H phase, the physically bound chlorides may release. Thus, the presence of sulphates is considered as a potential factor to release of bound chlorides in concrete. Keeping this in view, the present study is designed to bring the understanding of the impact of the sulphates on the chloride induced corrosion of rebar in concrete. This was done by analysing the corrosion performance of rebar in both normal and blended concrete specimens contaminated with chlorides, sulphates and combination of both the salts. In addition, the experimental results were analysed with Analysis of Variance to investigate the influence of salt type and cement type on corrosion data.

2 Experimental 2.1

Materials Used and Specimen Preparation

The Ordinary Portland cement (OPC) 53 grade conforming to IS:12269-2013 [9] was used in preparing normal cement concrete specimens. The blended concrete specimens were made with the replacement of cement with Metakaolin (MK) by 10% weight of OPC cement. The oxide composition of the cement and MK used in current study were shown in Table 1. The diameter of the steel reinforcing bar was 10 mm. The Coarse aggregate of size 20 mm and 10 mm maximum size of aggregate (MSA) respectively, were used in the ratio of 1.5:1. The specific gravity and water absorption of coarse aggregate were 2.8, and 2.25% respectively, and the corresponding values for fine aggregate were 2.7, and 0.75%. Locally available river sand is confirming to grading zone II as per IS:383:1970 (Reaffirmed 2002) [10] was used as fine aggregate. Tap water from the laboratory was used for the preparation of concrete mixtures with water to cementitious material (w/cm) ratio 0.51. Analytical reagent grade chemicals were

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added to the concretes. Magnesium sulphate as the source of sulphate ions and sodium chloride as the source of chlorides were added in mix water. The composition of admixed solution is as detailed below: Solution 1: 5% NaCl Solution 2: 5% NaCl + 2% MgSO4 Solution 3: 2% MgSO4.

Table 1. Chemical composition of cementitious materials. Chemical compound (%) OPC MK

Al2O3 Fe2O3

SiO2

5.32 40– 42

20.65 1.13 52– 1.0 54

4.23 1.3 (Max)

MgO TiO2 – 0.5 (Max)

CaO

SO3 Na2O LOI

64.12 2.16 – 1.0 – –*

2.39 1.5

* Na2O K2O: 0.5–2.5%

Reinforced concrete slab specimens with size 320 mm  320 mm  52 mm were prepared with a centrally embedded Thermo Mechanically Treated (TMT) steel of diameter 10 mm. The steel specimens were cleaned and prepared as per the recommendations of ASTM G109-99a. The reinforcing steel was prepared such that the central portion of length 250 mm remains bare to expose to corrosive environment as shown in Fig. 1.

Fig. 1. Steel specimen for corrosion monitoring slab specimen

Fig. 2. Analysed results of I admixed concrete

corr

data of NaCl

Application of Statistics to the Analysis of Corrosion Data

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Corrosion Monitoring

The advanced corrosion monitoring (ACM, Gill AC guard serial no. 1824-sequencer) instrument was used to monitor the corrosion of rebar in slab specimens. Linear Polarization Resistance (LPR) sweep technique was adopted to evaluate the corrosion current density (Icorr) of the concrete slab specimens. This LPR sweep test was conducted with IR compensation, using the guard ring arrangement. The slab specimens were polarized to ±20 mV with offset to the rest potential at a sweep rate of 0.1 mV/s.

Fig. 3. Analysed results of Icorr data of MgSO4 admixed concrete

Fig. 4. Analysed results of Icorr data of concrete with NaCl plus MgSO4

3 Results of Statistical Fitting Analyses of Corrosion Data The Normal, Lognormal, Weibull and Smallest Extreme Value probability distributions (pdf’s) were adopted for determining the descriptive statistics for the scatter of measured corrosion data as per ASTM G16-13 [11] and for detailing the performance of metakaolin in mitigation of corrosion of rebar. According to ASTM G16-13 [11], statistical evaluation is necessary in the analysis of the results. Results of statistical analyses of test-data measurements from each OPC and MK blended concrete specimen contaminated with chlorides, sulphates and combination of both were shown in Figs. 2, 3 and 4. In order to choose the best fitting distribution, the results of Anderson-Darling (AD) goodness-of-fit test of the scattering of corrosion test data were also reported in Table 2. This table consists the AD statistic, AD p-value and the validation decision of the corresponding distribution. In Table 2 decision of “A” indicated that the modelled dataset significantly drawn from the fitting distribution, while the decision of “B” suggests that the modelled data not significantly drawn from the corresponding distribution. From the Table 2. It is noted that all the Icorr data scattered like Weibull. It is also observed that, except the corrosion data of MK contaminated with combined solution all the set scattered like Lognormal probability distribution. As the Weibull and

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Lognormal distribution are provide reasonable fit to the data in most of the datasets, the best distribution can choose by comparing the AD statistics. Generally, the Smaller AD values indicate that the distribution fits the data better. The overall performance of OPC and MK concrete specimens was evaluated from the use of these selected distributions as shown in Fig. 5. These identifications of descriptive statistics for detailing the corrosion data highlights the importance of the use of goodness-of-fit test for determining the probabilistic distribution fitting followed by the corrosion data. While observing the influence of chlorides and sulphates on corrosion of rebar from the Fig. 5, it is observed that the corrosion due to sulphate ions alone is very less as compared to chlorides. As well as it is also observed that the attendance of sulphates with chlorides enhances the chloride induced corrosion. The replacement of cement with 10% of MK enhanced the corrosion resistance of rebar in concretes contaminated with chlorides, sulphates and combination of both the salts. Table 2. The results of Anderson-Darling goodness-of-fit data Statistical parameters Normal

Weibull

Lognormal

Smallest extreme value

AD P-Value Decision AD P-Value Decision AD P-Value Decision AD P-Value Decision

OPC NaCl MgSO4 NaCl plus MgSO4 1.066 0.476 0.984 0.005 0.177 0.008 B A B 0.561 0.248 0.409 0.136 0.25 0.25 A A A 0.399 0.446 0.314 0.286 0.213 0.485 A A A 1.275 0.825 1.125 0.01 0.025 0.01 B B B

MK NaCl MgSO4 NaCl plus MgSO4 0.656 0.905 0.608 0.06 0.013 0.078 A B A 0.151 0.467 0.818 0.25 0.229 0.26 A A A 0.193 0.682 0.734 0.856 0.051 0.035 A A B 0.958 1.34 0.593 0.012 0.01 0.104 B B A

Fig. 5. The overall performance of OPC and MK concrete contaminated with different salts

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Analysis of Variance (ANOVA)

Tables 3 and 4 reports the ANOVA results of Icorr data of OPC and MK blended concrete specimens contaminated with NaCl, MgSO4 and NaCl plus MgSO4 respectively. For this three replicate of Icorr values at two levels of cementitious material type i.e., OPC, MK are prepared in tabular form to calculate the sum of the squares (SS) for factor and residual error. After that, their corresponding SS values were divided by associated degree of freedom (DF) to calculate the mean squares (MS). Then the effect of individual factors is assessed by hypothesis test of equality of variances. In analysis of variance the most important statistics is the P-value, which used to determine whether the level of means are significantly different from each other about the hypothesis with 95% confident level (a = 0.05) The hypothesis for the present study is given below [12]: Table 3. Results of ANOVA for Icorr values of NaCl contaminated concrete Source DF SS MS F-Value P-Value Cementitious material 1 3.080 3.0798 7.29 0.015 Error 17 7.181 0.4224 Total 18 10.261

Table 4. Results of ANOVA for Icorr values of MgSO4 contaminated concrete Source DF SS MS F-Value P-Value Cementitious material 1 0.002009 0.002009 1.87 0.190 Error 16 0.017183 0.001074 Total 7 0.019193

Table 5. Results of ANOVA for Icorr values of NaCl plus MgSO4 contaminated concrete Source DF SS MS F-Value P-Value Cementitious material 1 4.287 4.287 6.38 0.043 Error 17 21.536 1.267 Total 18 25.823

Table 6. Results of ANOVA for Icorr values of OPC concrete specimens Source DF SS MS F-Value P-Value Salt type 2 7.213 3.606 3.24 0.05 Error 25 27.836 1.113 Total 27 35.049

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U. Raghu Babu and B. Kondraivendhan Table 7. Results of ANOVA for Icorr values of MK concrete specimens Source DF SS MS F-Value P-Value Salt type 2 0.1718 0.08591 2.51 0.1 Error 27 0.9257 0.03429 Total 29 1.0975

P value  0.05: The level means are significantly different from each other P value > 0.05: The level means are not significantly different. From Tables 3, 4 and 5, it is observed that the cementitious material type is statistically significant with the p value 0.015 for NaCl and 0.043 for NaCl plus MgSO4 contamination with 95% confident level. The cementitious material factor in other admixed salts namely MgSO4, is not significantly related to Icorr value. It means that the type of cementitious material has the strongest effect on Icorr values of rebar in NaCl and NaCl plus MgSO4 admixed concrete. While observing the impact of salt type on corrosion data of rebar embedded in OPC concretes, it is observed that for Icorr values of rebar, the P-value indicated that salt type is significant at the 0.05 a-level, but not significant for MK concrete. It means that there are significant differences in Icorr value of OPC specimens between salt types (Tables 6 and 7).

4 Conclusions From the presented statistical analysis of the test data the following conclusions were drawn: 1. Corrosion current density values in the study followed Weibull probability distribution function for OPC and MK concretes contaminated with different salts. 2. The rebar in MK blended concrete has shown less corrosion current density values as compared to OPC concrete. 3. The corrosion of rebar in concrete due to sulphates alone is very less as compared to chlorides and chlorides plus sulphates. 4. The presence of MgSO4 enhances the chloride-induced corrosion of rebar in both normal, and MK blended concretes. 5. ANOVA results indicated that there is a significant difference in corrosion performance between OPC and MK concretes contaminated with NaCl and NaCl plus MgSO4. 6. ANOVA results also reported that the type of salt affect the corrosion performance of rebar in OPC concrete.

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References 1. Al-Amoudi OSB, Rasheeduzzafar AA, Maslehuddin M, Abduljauwad S (1994) Influence of sulfate ions on chloride-induced reinforcement corrosion in Portland and blended cement concretes. Cem Con Agg 16:3–11 2. Al-Amoudi OSB, Baghabra OS (1998) Sulfate attack and reinforcement corrosion in plain and blended cements exposed to sulfate environments. Build Environ 33:53–61 3. Jarrah NR, Al-Amoudi OSB, Maslehuddin M, Ashiru OA, Al-Mana AI (1995) Electrochemical behaviour of steel in plain and blended cement concretes in sulphate and/or chloride environments. Constr Build Mater 9:97–103 4. Yang L, Yu H, Ma H, Zhou P (2009) Deterioration of high performance hybrid fibers reinforced expansive concrete exposed to magnesium sulfate solution. In: International conference on transportation engineering 2009, pp 2614–2619 5. Liu R, Jiang L, Huang G, Zhu Y, Liu X, Chu H, Xiong C (2016) The effect of carbonate and sulfate ions on chloride threshold level of reinforcement corrosion in mortar with/without fly ash. Constr Build Mater 113:90–95 6. Khan MS, Tayyib C (1992) Rebar corrosion in MgSO4 solution. J Mater Civ Eng 4:292–299 7. Holden WR, Page CL, Short NR (1980) The influence of chlorides and sulfates on durability. In: Corrosion of reinforcement in concrete construction, pp 143–150. Society of Chemical Industry Ellis Horwood Ltd., West Sussex 8. Brown PW, Badger S, Hime WG, Marusin SL (2001) A discussion of the paper The distributions of bound sulfates and chlorides in concrete subjected to mixed NaCl, MgSO4, Na2SO4 attack. Cem Concr Res 31:1115–1116 9. Bureau of Indian Standards (2013) Ordinary Portland Cement, 53 Grade-Specification (First Revision). IS 122692013 10. IS 383 (Reaffirmed 2002) (1970) Indian standard specification for coarse and fine aggregates from natural sources for concrete (second revision) 11. ASTM G16-13 (2013) Standard Guide for Applying Statistics to Analysis of Corrosion Data 12. Molugaram K, Rao GS, Shah A, Davergave N (2017) Statistical Techniques for Transportation Engineering. BSP Books Pvt. Ltd., Hyderabad Published by Elsevier Inc.

Earthquake Analysis of High-Rise Building with Floating Column Mohasinkhan N. Bargir(&) and Ajim G. Mujawar Department of Civil Engineering, Annasaheb Dange College of Engineering and Technology, Ashta 416301, India [email protected], [email protected]

Abstract. In present scenario construction of high rise building with floating column is a distinctive feature in urban India. As per IS: CODE-1893:2016 clause no-7.1, floating column construction is prohibited but there is no limitation and restriction for research work. The purpose of this research is to study seismic response of a building and to analyze and build the structure in which there will be less damages to the structure and its component under the excitation of earthquake. The paper deals with validation of the software has been done in relation to the literature and further matters have been decided and studied based on the validation result. Finite element-based software like StaadPro has been used, Equivalent static method and response spectrum method have been used for analysis. The results have been obtained in terms of base shear, displacement, storey drift, time period etc. Based on results it was concluded that triangular plate in floating column building reduces displacement and base shear of building. Keywords: High rise building Staad-Pro


Floating column
Dynamic analysis


1 Introduction India is developing country where population growth is increasing per year. A few year ago, the population were not so vast so they used to live in horizontal system (because of large area available per person), but present days people preferring vertical system due to shortage of space. Buildings are the symbol of modern society. Due to lack of space, increasing population and also for aesthetic view and functional requirements, Construction of high rise building in urban cities are required to have column free space. For this purpose, the concept of floating column is coming in picture. These columns are highly disadvantageous in building built in seismically prone areas. As per IS:1893-2002 earthquake code the India is classified into different zone for which it specifies the seismic zone factor and it is very important to analyze & design the building for seismic force to prevent damages occur due to earthquake. The code of earthquake engineering has been designed with the aim that people get enough time to escape from the building, the building is less damaged and the building comes in faster use. When earthquakes take place, the building passes at dynamic motion. The reason is that the building subjected to the inertial forces operating in the opposite direction of © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 170–178, 2020. https://doi.org/10.1007/978-3-030-24314-2_23

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the acceleration of earthquake stimulation. These inertia forces, called seismic loads, are usually handled as assuming forces are external, Since the motion of earthquake varies with time and inertia forces vary with time & direction. Seismic loads are not stable in terms of time and space. 1.1

What Is Floating Column?

Floating column is nothing but a vertical member or element that rests on a beam, but doesn’t transfer load directly to the foundation. Generally, the columns rest on the foundation to transfer loads coming from slabs and beams, floating column acts as a point load on the beam and this beam transfers the load to the column below it, that beam is called a transfer beam. The use of floating columns is intended for an architectural view, site conditions and as much as possible area on a plot within, permissible bye laws. Earthquake is a natural phenomenon in which disasters are mainly caused by damage to the building or the loss of other man-made structures. Generally, the failure of the structures begins at the point of weakness. This weakness arises because of discontinuity in mass, discontinuity in load transferring path, Stiffness and irregular geometry (Figs. 1, 2 and 3).

Fig. 1. Floating building

column

Fig. 2. Model- C

Fig. 3. Model-E, F

2 Literature Review Equivalent static analysis on different models for Zone-2 and 5 are carried out to study the performance of the building in Shrikant et al. [6], and it has been found that displacement increases when the floating column is provided on the edge. Apart from this, Kishalay et al. [1] has perform the response spectrum analysis by the BNBC code on various model, by changing its properties and the size of the elements, and compare with the results of the general building and found that torsional irregularity was exist when floating column was placed unsymmetrically. While Sarode et al. [3] analyzed the

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model by static and response spectrum method by considering beam girder on the model and found that the larger base shear comes in the building due to composite beam girder. Similar work has been done by Wahidi et al. [4] with Pushover Analysis G + 5-storey building on Sap2000 in which some new system of bracing has been suggested based on findings. Floating column building along with the Infill wall in the IDARC program, Banerjee et al. [8] estimates damage index, cracks, yield, plastic hinges and found that infill wall, help in reducing the seismic parameters with floating columns building.

3 Objective of Work To the validation of FEM software with literature. To model the building with floating column by using Staad-Pro V8i Series-6 Software. To Compare building with and without floating column in terms of base shear, displacement, storey drift, moments, time period, etc. 3.1

Problem Formulation

The importance of present research work is to study the performance of 10-storey buildings for different zones. Validation of the software along with the literature is carried out, further cases have been decided and studied based on the results of the validation, the following models and have been modeled and analyzed for the lower and higher seismic zones for the position of medium soil. The results are plotted for the base shear, storey displacement and storey drift, frequency and time period etc. 3.2

Methodology

Article for validation: Seismic Response of Complex building With Floating Column for Zone-II, V (International Journal of Engineering Research, vol.2, issue.4, 2014) 3.2.1

Description of Model and Cases

Model - A: Floating column is provided @ particular location on ground floor Model - B: Floating column is provided by rising the height of storey 4 m on ground floor. Model - C: Floating column is provided by applying heavy load on slab on ground floor Model - D: 10 Storey Normal Building. Model - E: 10 Storey building with floating column provided @ corner on ground floor. Model - F: 10 Storey building with floating column provided @ corner on ground floor with Triangular Plate.

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Cases

I. Floating Column in the middle of interior frame (On Ground Floor) II. Floating Column in the edge of exterior frame (On Ground Floor) III. Floating Column is provided @ corner of building (On Ground Floor) Case-I & II are taken as validation cases for Model-A, B, C & Case-III is taken for research in which high rise building with floating column is provided at the corner of building for Model-E, F (Table 1).

Table 1. Model data & dynamic properties Parameters Model-A Model-B Model-C For Case-I & Case-II (Validation Cases) Plan dimension 15 m  15 m 3 Bay in X & Z direction 5 m @ c/c All column size 450  450 mm All beam size 300  450 mm Support condition Fixed Fixed Fixed Soil type Medium (II) Medium (II) Medium (II) Seismic zone (Z) II & V II & V II & V Method Equivalent static analysis Response reduction factor (R) 5 5 5 Importance factor (I) 1 1 1 Floor height (H) 3.0 M 4.0 M 3.0 M Height of building (H) 30 M 31 M 30 M Thickness of slab 150 MM 150 MM 150 MM Heavy dead load 10 kN/m2 Live load 3.5 kN/m2 3.5 kN/m2 3.5 kN/m2 Heavy live load 10 kN/m2 2 2 Floor finish 1.0 kN/m 1.0 kN/m 1.0 kN/m2 Material properties M25 & FE- 415 Steel grade Parameters Model-D Model-E Model-F For Case-III Plan dimension 9m9m 3 Bay in X-Z direction 3 m @ c/c Column size 450  450 mm Floating column 300  300 mm All beam size 230  380 mm Support condition Fixed Fixed Fixed Soil type Medium (II) Medium (II) Medium (II) Seismic zone (Z) II, III, IV II & V II & V Response reduction factor (R) 5 5 5 (continued)

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M. N. Bargir and A. G. Mujawar Table 1. (continued) Parameters Method Importance factor (I) Floor height (H) Height of building (H) Thickness of slab Live load Floor finish UDL on beam Material properties

3.3

Model-D Model-E Model-F Response spectrum method 1.2 1.2 1.2 3.15 M 3.15 M 3.15 M 31.5 M 31.5 M 31.5 M 150 MM 150 MM 150 MM 2.5 kN/m2 2.5 kN/m2 2.5 kN/m2 1.0 kN/m2 1.0 kN/m2 1.0 kN/m2 2 2 11.75 kN/m 11.75 kN/m 11.75 kN/m2 M30 & FE- 500 Steel grade

Results and Discussions

After analyzing the first three models (Model-A, Model-B, Model-C) in Staad-Pro which were validation cases, it was seen that the results obtained in terms of displacement and base shear are almost identical to literature results. The maximum error between Staad-Pro & Literature results are 9.42%. In validation cases, maximum storey displacement of top floor is 20.48 mm (Case-I) & 26.46 mm (Case-II) for Model-C. As well as Base shear is 450.05 kN (Case-I) & 472.50 kN (Case-II) for Model-C. So, it can be seen that as load increases the storey displacement as well as base shear is also increases for any zone. It was also found that Case-II is more vulnerable as compared to Case-I because in case -1 the floating column, which rests on the beam, is supported on two columns, where as in case -2 that beam is cantilever. On the basis of these results, Model-D, Model-E and Model-F for Case-III have been decided. To find out why Case-2 is unsafe. Case-3 is analyze by response spectrum method as per IS: code 18932016 and results are compared for all Model-D, E & F in terms of base shear, storey displacement, storey drift, time period and frequency. It was seen that maximum storey displacement can be reduced with addition of triangular plate at column beam joint in Model-F for any zone (Tables 2, 3, 4 and 5). In Case-III, the Base shear is less when floating column is added in Model-E than that of normal building (Model-D). But if triangular plate is added at the junction of beam column, the base shear is increased in Model-F which is lesser than that of Model-E (Figs. 4, 5, 6 and 7 ). Table 2. Displacement results of Staad-Pro & literature (Case-1) @ Zone-II, V Storey Results 10 Staad.Pro Literature 9 Staad.Pro Literature 8 Staad.Pro Literature

Model-A 17.91 16.5 17.18 16.02 16.07 15.04

Model-B 17.82 17.52 17.15 17.35 16.10 15.45

Model-C 20.48 19.50 19.64 19.10 18.32 17.50

Model-A 60.77 57.55 58.32 56.3 54.39 50

Model-B 64.15 60.05 61.77 57.5 57.98 54.5

Model-C 68.17 65.05 65.40 62.50 61.01 58.50 (continued)

Earthquake Analysis of High-Rise Building with Floating Column Table 2. (continued) Storey Results 7 Staad.Pro Literature 6 Staad.Pro Literature 5 Staad.Pro Literature 4 Staad.Pro Literature 3 Staad.Pro Literature 2 Staad.Pro Literature 1 Staad.Pro Literature

Model-A 14.48 14.1 12.62 12.3 10.53 9.8 8.29 7.75 5.85 5.45 3.58 3.3 1.34 1.3

Model-B 14.70 14.90 13.00 12.60 11.60 10.90 9.03 9.25 6.87 7.20 4.64 5.05 2.37 2.20

Model-C 16.55 16.35 14.43 14.20 12.05 11.75 9.48 9.50 6.80 6.75 4.09 4.30 1.53 1.50

Model-A 49.14 45 42.84 39.98 36.78 34.6 28.15 27.2 20.2 19.5 12.15 12.1 4.58 4.4

Model-B 52.90 49.5 46.81 43.2 39.94 37.5 32.15 30.2 24.72 23.2 16.71 15.75 8.60 8.1

Model-C 55.11 52.50 48.04 46.05 40.11 39.50 31.57 30.00 22.65 22.20 13.63 13.50 5.10 5.05

Table 3. Base shear results of Staad-Pro & literature (Case-1) @ Zone-II, V Storey Results 10 Staad.Pro Literature 9 Staad.Pro Literature 8 Staad.Pro Literature 7 Staad.Pro Literature 6 Staad.Pro Literature 5 Staad.Pro Literature 4 Staad.Pro Literature 3 Staad.Pro Literature 2 Staad.Pro Literature 1 Staad.Pro Literature

Model-A 100.64 105.15 186.93 195.50 255.11 270.20 307.03 330.10 345.65 375.15 372.28 405.05 389.32 425.15 405.05 435.05 425.35 442.50 461.32 465.05

Model-B 87.94 90.20 164.42 170.50 225.39 232.50 272.61 285.00 307.83 315.10 332.80 345.20 349.28 360.10 358.84 367.50 363.62 375.20 365.41 375.80

Model-C 110.31 112.50 204.47 210.20 278.87 285.15 335.83 345.05 375.05 385.65 406.74 420.20 425.34 435.60 435.80 442.50 440.45 445.50 441.61 450.05

Model-A 414.26 390.15 766.96 795.05 1045.64 1050.00 1259.00 1275.50 1415.76 1425.50 1524.62 1545.1 1594.29 1605.05 1633.48 1665.10 1650.90 1672.25 1655.24 1680.65

Model-B 336.77 345.10 628.55 645.05 861.16 885.20 1041.29 1066.10 1175.64 1186.15 1270.91 1305.20 1333.80 1365.35 1371.02 1383.05 1389.26 1402.50 1395.30 1410.10

Model-C 349.10 320.15 726.30 780.25 1087.25 1065.10 1363.60 1290.25 1566.64 1525.10 1707.64 1590.15 1797.88 1650.05 1848.64 1665.15 1860.15 1680.00 1876.82 1695.10

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M. N. Bargir and A. G. Mujawar Table 4. Displacement results of Staad-Pro & literature (Case-2) @ Zone-II to IV Storey Results

Model-A Model-B Model-C Model-A Model-B Model-C

10

19.51 20.00 18.57 19.00 17.25 17.50 15.54 16.00 13.51 14.00 11.25 11.50 8.81 9.50 6.27 6.75 3.74 4.00 1.20 1.30

9 8 7 6 5 4 3 2 1

Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature

18.05 17.50 17.20 16.50 16.07 15.40 14.61 14.20 12.87 12.50 10.92 10.50 8.82 8.10 6.11 6.00 4.39 4.10 2.04 2.00

26.46 19.50 25.33 18.40 23.68 17.05 21.43 15.50 18.73 14.10 15.68 11.50 12.38 9.50 8.92 7.25 5.43 4.55 2.14 1.85

62.35 58.50 59.38 55.50 55.18 51.50 49.72 46.50 43.25 40.00 36.00 33.50 28.22 26.50 20.08 19.05 11.98 11.00 3.85 4.00

64.82 62.50 61.93 60.05 57.86 56.50 52.59 51.05 46.33 45.10 39.31 39.20 31.74 31.10 25.80 24.50 17.20 16.10 9.38 9.05

92.11 65.00 88.94 62.50 83.49 57.50 75.81 52.05 66.34 45.05 55.52 37.50 43.75 30.05 31.39 21.50 18.85 13.05 7.04 4.50

Table 5. Base Shear results of Staad-Pro & literature @ Zone-II to IV Storey Results

Model-A Model-B Model-C Model-A Model-B Model-C

10

84.79 90.10 193.06 195.10 278.60 295.00 344.09 352.50 392.21 405.00 425.63 430.10 447.02 450.05 459.05 465.05 464.38 465.71 465.71 470.50

9 8 7 6 5 4 3 2 1

Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature Staad.Pro Literature

63.75 68.50 164.66 170.10 245.10 255.50 307.39 315.10 353.85 360.40 386.80 390.35 408.55 412.50 421.42 427.50 427.71 434.50 429.80 435.10

75.24 90.05 210.46 195.00 317.30 300.00 399.10 330.15 459.20 375.10 500.93 420.05 527.64 442.50 542.66 450.00 549.34 465.00 551.02 472.50

320.10 350.25 695.10 750.05 1005.10 1035.10 1191.49 1245.35 1356.62 1395.10 1470.53 1500.00 1543.72 1575.25 1584.89 1605.00 1603.19 1620.10 1607.75 1635.25

310.15 330.10 632.24 640.15 920.25 930.10 1059.10 1080.05 1200.55 1235.05 1310.05 1320.50 1350.20 1380.05 1405.15 1425.10 1430.10 1450.05 1467.16 1480.10

370.85 420.10 757.65 755.15 1142.28 1050.20 1436.77 1245.50 1653.12 1410.05 1803.37 1530.10 1899.53 1575.50 1953.62 1635.10 1977.00 1650.00 1983.66 1665.05

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Fig. 4. Comparison of displacement (Case-III) Fig. 5. Comparison of storey drift (Case-III)

Fig. 6. Comparison of base shear (Case-III)

Fig. 7. Comparison of period and frequency (Case-III)

4 Conclusions In static analysis the building with floating column provided at corner side (Case-II), gives the maximum storey displacement and storey shear for zone-II, V compared to Case-1, while loading and other properties are similar in both cases. The location of floating column is made significant impact on building, by analytically it cannot be said that which location is most appropriate for all types of building. Every time we need to be carried out careful analysis.

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The introduction of triangular plate plays good role in floating column building as In Model-F, the average value of displacement is decreased by 7.3 mm compared to Model-E and 5.04 mm decreased compared to Model-D (Normal Building). The value of storey drift was also found to be decreased in Model-F compared to Model-E. In Case-III, storey shear decreases with introduction of floating column for all zones, due to the reduction mass of column structure. Whereas Base shear is increases in Model-F due introduction of triangular plate. The mode frequency was seen using model analysis by eigen. It has been found that frequency is lower for each mode in Model-E. Therefore, as the Model-E building is more flexible. Whereas Frequency was found more in Model-F, because triangular plate is provided and due to this the building become stiffer.

References 1. Kishalay MT, Kamrujjaman NM (2018) Evaluation of seismic performance of floating column building. Am J Civil Eng 06 2. Elakkiyarajan N, Iyappan G (2018) Seismic analysis of multistory building with floating column. IOSR J Eng 42–44 3. Sarode J, Pote AS (2016) Analysis of floating column building of composite and RCC beam girder and comparison with RCC frame structure by using E-tab. Int J Adv Res 8:1464–1469 4. Wahidi A, Ramaseshu D (2016) Seismic analysis of multistory building with floating column. Asian J Civ Eng 17 5. Singla S, Rahman E (2015) Effect of floating columns on seismic response of multistoried RC framed building. Int J Eng Res Technol 2:1–11 6. Srikanth MK, Holebagilu YR (2014) Seismic response of complex building with floating column for Zone-II and Zone-V. Int J Eng Res Online 2(4) 7. Sekhar TR, Prasad PV (2014) Study of behaviour of seismic analysis of multi storied building with and without floating column. Caribb J Sci Technol 2:697–710 8. Banerjee S, Patro SK (2014) Estimation of the park-ang damage index for floating column building with infill wall. World Acad Sci Eng Technol 8:760–763

A Review on the Recent Development of Ambient Cured Geopolymer Composites Mayank Gupta(&) and N. H. Kulkarni Shri Guru Gobind Singhji (SGGS) Institute of Engineering and Technology, Vishnupuri, Nanded 431606, Maharashtra, India [email protected], [email protected]

Abstract. Geopolymer composite is synthesized with aggregates and industrial by-products materials those are thriving in alumina and silica, energized using a powerful alkali solution. Geopolymer composites required high temperature curing for achieving higher strength which restricted the use in cast-in-situ work. This literature provides a condensed explanation on the recent development of the ambient cured Geopolymer composites. It has been observed that geopolymer composites contributed better physical properties viz. workability and mechanical parameters viz. compressive, split tensile and flexural strength along with the superior durability properties like sulfate and acid resistance, resistance to freezing and thawing, shrinkage, corrosion and water absorptions etc. compared to the cement composites in the ambient cured condition and also reduces the greenhouse gas production. In general, production of high strength geopolymer composites in the ambient cured condition requires concentration of sodium hydroxide 10–12 Molarity, alkali to binder ratio 0.35–0.5 and the ratio between sodium silicate and sodium hydroxides 1.5–2.5. Keywords: Geopolymer
Sodium hydroxide Heat curing and steel fiber


Ambient curing


1 Introduction The requirement of concrete buildings has been rising regularly, especially in the last 2 decades due to the increment of the world population. It is estimated that the demand of concrete for the construction works will become 18 billion tons per year by the end of the year 2050 [1]. Ordinary Portland Cement (OPC) being an essential constituent of the concrete other than aggregates. Every year, approximately one ton cement is produces for every human being and this quantity is increased by 100% by the end of year 2020 [2]. Manufacturing of cement is not only energy intensive but also emits carbon dioxide (CO2) in large quantity. It has been calculated that production of one ton cement required 94.76 mega joule energy and annually, 13,500 million tons or approximately 7% of the total CO2 gas emission is contributed by only OPC industries to the environment [3]. Development of green-house gases from the cement industries is the causal of global warming and ozone layer depletion. On the other hand huge amount of agro and industrial by-products like fly ash, palm oil fuel ash (POFA) slag, red mud, metakaolin and rice husk ash (RHA) etc. is being produce, which also affects © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 179–188, 2020. https://doi.org/10.1007/978-3-030-24314-2_24

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the environment. To overcome these aforementioned issue, many researchers are partially utilizing these industrial and agro waste as one of the constituent of concrete, which enhanced the physical, mechanical and durability properties of the normal concrete. As, everyone knows that for enhancing the properties of concrete, complete replacement is always preferable compared to the partial replacement. In the year 1978, Devidovits [4] (U.S. Patent No. 4349386) introduced geopolymer, which is synthesized with aggregates and aluminosilicate materials such as fly ash, RHA, POFA, ground granulated blast furnace slag (GGBFS) and metakaolin etc. and these are energized with powerful alkali solution of potassium/sodium silicate and hydroxide. Turner and Collins [5] documented that the emission of CO2 was approximately 9% from the geopolymer concrete which was comparatively small for the normal concrete and likewise, Islam et al. [6] reported that inclusion oil palm shell as a coarse aggregate in geopolymer reduced the carbon footprint by approximate 50–60% as compared to the OPC based concrete. Ng et al. [7] informed that production of geopolymer reduced the power consumption up to 15% and greenhouse gas emission 44–64%. Alkali-activated geopolymer concrete showed good workability and abrasion resistance, low drying shrinkage, high initial strength and excellent bonding strength between reinforcing steel bar and geopolymer concrete comparatively that of normal concrete [8, 9]. Along with, in the acid exposure, the loss in weight of the geopolymer concrete was much smaller than that of the OPC based normal concrete, which showed that normal concrete was less durable than the geopolymer concrete up to some degree of aggressive environment exposure [10]. The aforementioned advantages create enthusiasm in the researchers to investigate the different properties of geopolymer composites, therefore, geopolymer composites could become preferable construction material. Geopolymer is one among the polymers. It has a three-dimensional amorphous shape and its chemical structure is similar to the natural zeolite material. In the presence of alkaline environment, when silicon dioxide (SiO2) and aluminium oxides (Al2O3) react with solution of various alkalis then it develops a polymeric Si–O–Al chain, this reaction is known as geopolymerization reaction. The developed polymer structures are sialate (-Si-O-Al-O-), siliate–siloxo (Si-O-Al-O-Si-O) and siliate–disiloxo (Si-O-Al-OSi-O-Al-O) [11]. Microstructural studies done by Nath and Sarker [12] informed that geopolymer reaction formed aluminosilicate or geopolymer (N–A–S–H) gel in the absence of calcium oxides but if the calcium oxide is incorporated in the form of GGBFS [13] or OPC [14], into the fly ash based geopolymer then calcium silicate hydrate (C–S–H) gel is formed along with the geopolymer gel. The formation of C–S– H gel, makes possible to construct self – cured geopolymer composites, which reduce the cost of heat curing. Past studies explained that reinforced geopolymer concrete columns and beams showed approximately equal and better structural behavior to that of normal concrete [12]. Geopolymer composites have potential to become a substitute of OPC composites because they can provide 120 MPa compressive strength [15], better durability properties [16] superior sulphate resistance [9, 10], and excellent thermal performance [17]. Most of the researchers used low/high calcium fly ash as the aluminosilicate material in the geopolymer which required heat curing at 60–100 °C for 24 to 48 h for enhancing

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the geopolymerization reaction and achieving the better workability and higher strength [18] which also leads to the high cost of construction and impedes to cast-in-situ work. So, the researchers are now focusing on to the use of other substitute materials like GGBFS [19], OPC [14], bottom ash [20], cement kiln dust (CKD) [21], iron making slag, Silica fume (SF) [19], nano silica [22], rice husk ash (RHA) [23], metakaolin (MK) [24] etc. as the binder materials in the geopolymer composites. This literature provides a condensed review of the inclusion of the aforementioned source material in the geopolymer composites, and determined the effect of different alkali solution, sodium/potassium silicate to sodium/potassium hydroxides ratio, concentration of sodium/potassium hydroxides and water addition on the various properties of ambient temperature cured geopolymer composites.

2 Previous Studies on Ambient Cured Geopolymer Composites This study focused on the researches completed in the recent duration on the ambient cured geopolymer composites and explain the effect of factors those can influence the properties of geopolymer composites like source materials, alkaline activator and water addition etc. 2.1

Effect of Source Materials

Source material play a vital role for developing geopolymer composites in the ambient environment. Past studies confirmed, development of ambient cured geopolymer composites could be possible by adding calcium Oxide (CaO) into the (with or without fly ash) geopolymer composites from the slag, OPC and other materials. The presence of high amount of calcium (Ca2+) in the OPC and slag is capable to develop C-S-H gel along with the calcium-alumino-silicate hydrate (C-A-S-H) and sodium-aluminosilicate hydrate (N-A-S-H) gel, which is primarily responsible to enhancement in the mechanical properties and decrease setting time of the ambient cured geopolymer binders [25]. Only fly ash is not feasible to develop high strength geopolymer in the ambient cured condition. Addition of calcium oxide (CaO) in the fly ash based geopolymer binder increases the strength of ambient cured geopolymer. Studies revealed that calcium present in the alccofine [26], GGBFS [23] and fly ash [27] reacts with the alkaline activator and generate heat in the matrix due to exothermic reaction which could be the cause of enhancing the mechanical properties of the concrete. The mechanical properties of ambient cured geopolymer concrete was continuously enhanced with the inclusion of GGBFS but the rate of increment was slowdown after the 28 days. The highest compressive strength could be achievable with the utilization of 70–100% GGBFS [28]. Development of only GGBFS based self-compacting geopolymer concrete could be possible in the ambient environment. GGBFS has the potential to develop high strength self-compacting geopolymer concrete [27]. Incorporation of slag in the geopolymer attributed dense microstructure which significantly reduced the porosity and pore volume [25].

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The inclusion of RHA into the GGBFS based geopolymer (self-compacting) concrete reduced the workability because RHA consists high specific surface area and micropores which adsorbed high amount of water which is available for lubrication. Whereas GGBFS have less specific surface area compared to the RHA, hence, 100% GGBFS based geopolymer showed high workability [23]. Replacement of GGBFS with the metakaolin, fly ash and silica fume improved the setting time but decrease the compressive strength of GGBFS based and ambient cured geopolymer concrete [19]. The reason for this increment in the setting time can be attributed to the reaction time between GGBFS and activator compared to the silica fume, fly ash and metakaolin, whereas, with the reduction in the amount of GGBFS, the amount of CaO decreases, thereby slow down the polymerization reaction which decreases the compressive strength of geopolymer in the ambient curing [19, 29], however, Sun et al. [24] reported that metakaolin and GGBFS based pervious concrete provide highest compressive strength, porosity and permeability compared to the only metakaolin or only GGBFS based pervious concrete. Alanazi et al. [25] explained that OPC mixture exhibited more heat compared to the alkali activated fly ash geopolymer. Fang et al. [29] reported that equation bestowed by ACI code and Eurocode for determining the split tensile and flexural strength and dynamic modulus of elasticity of the OPC concrete overestimated these values for the fly ash GGBFS contained geopolymer concrete. Addition of OPC in the geopolymer accelerate the hydration process which reduced the workability and the setting time, whereas, hydration process can be retarded with the addition of citric acid [30]. Cao et al. [31] incorporated calcium aluminate cement in the geopolymer as the source of calcium and alumina, greatly promote the mechanical properties of the ambient cured geopolymer concrete and found that inclusion of calcium aluminate cement significantly enhanced the mechanical properties but reduced the workability. The split tensile strength and elastic modulus was accurately predicted by the ACI 318 and AS 3600 codes, respectively, whereas, these American and Australian standard code underestimated the flexural strength of the geopolymer concrete. Unit weight of ambient temperature cured specimen slightly reduced after the 90 days because of the vaporization of water from the geopolymer matrix [32]. 2.2

Influence of Alkaline Activator

The solution of sodium hydroxide and sodium silicate are mostly used as the alkaline activator compared to the solution of potassium silicate and potassium hydroxide [19]. Sodium/Potassium hydroxide is an important activator for binding and dissolving all ingredients, to develop the geopolymer binders. An increment in the molarity of sodium hydroxide along with the viscosity of the solution decreases the fluidity and flowability of self-compacting geopolymer concrete [33]. Solubility of alumino-silicate materials enhanced with the increment in the concentration of sodium hydroxide. High molarity of sodium hydroxide accelerate the bonding property which leads to enhance the mechanical parameters of geopolymer concrete. The compressive strength increases up to 12 M but after that strength of geopolymer concrete start to decrease. This is due to the more hydroxide ions react in the early stage and slow down the next reaction process [27].

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Parveen et al. [26] has been recognized, slump of the geopolymer concrete were increased by approximately 15% when concentration of sodium hydroxide changes from 16 M to 8 M, however, mechanical properties were improved but rate of absorption of water were decreased with the increment of sodium hydroxide concentration. Microstructural studies verified the formation of the denser structure and less crack which could be the reason for reducing the water absorption. It was documented that drying shrinkage of geopolymer increases with sodium silicate concentration increment because of higher degree of hydration [34]. The flowability of fresh geopolymer mixture reduced with the increased ratio between sodium silicate and sodium hydroxide. It was observed from the results, geopolymer mortar become more cohesive and viscous with the inclusion of alkaline solution, therefore the flow value reduced compared to the of OPC concrete. However, the ratio between alkaline activators did not affect the mechanical properties of metakaolin or silica fume incorporated fly ash based geopolymer whereas, this ratio highly affect the strength parameters of the slag contain geopolymer [25]. 2.3

Impact of Alkali to Binder Ratio (Al/Bi)

Alkali to binder ratio is also an influencing factor for the properties of the geopolymer composites. The participation percentage of Al/Bi for enhancing the mechanical properties was approximate 94% compared to the other factors like concentration of sodium hydroxide etc. [31]. Previous studies revealed that increment in the Al/Bi ratio, enhanced the workability of the geopolymer concrete, whereas the compressive strength of the GGBFS based geopolymer concrete decreases because increment in the amount of alkaline activator increases the quantity of water which obstructs the geopolymerization reaction in the ambient condition, therefore compressive strength reduced and workability enhanced [19, 35]. Coincidentally, in the same manner, slump and strength parameters of the ordinary concrete alter with the augmentation in the water to binder ratio. 2.4

Effects of Aggregates

Size, shape and quantity of coarse aggregate is also influences the properties of alkali activated geopolymer composites similar to the OPC based concrete. It was documented in the past studies that increment in size of aggregate, compressive strength and density of geopolymer pervious concrete were reduced, but in contrast, water permeability and porosity increases. It’s indicated that bigger size aggregate is not able to develop compact structure, therefore the contacting area between aggregate and binder decrease which reduced the mechanical properties [27]. Sun et al. [24] exhibited that modulus of elasticity and compressive strength of the geopolymer pervious concrete were reduced with the increment of aggregate quantity. It was observed that inclusion of fine aggregate in to the concrete not only enhance the mechanical properties, but also enhance the freeze-thaw resistance. Results reported that highest mechanical properties could be achievable by using natural aggregate into the fly ash geopolymer pervious concrete, however best permeability of the that material could be achievable by using recycled coarse aggregate.

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Effect of Bioadditives

Now days, researchers is utilizing some bio-additives like molasses, palm jiggery, honey and terminalia chebula into the geopolymer, to enhance the physical, mechanical and durability properties and also facilitate self cured geopolymer concrete. Study done by Karthik et al. [36] reported that inclusion of bio-additive into the ambient cured geopolymer specimen exhibited better durability properties like loss in strength and weight compared to the ordinary geopolymer concrete after the immersion of specimen into the 5% sodium sulfate, 5% sodium chloride and 5% sulfuric acid solution for the period of 90 days. X-ray diffraction studies informed the formation of crystalline and amorphous phases which developed quartz and aluminosilicate compounds those highly influenced the physical and mechanical parameters of the geopolymer [37]. Mercury intrusion porosimetry test results informed that bio-additive inclusion reduced the porosity of the specimen [36]. Compressive strength test results exhibited that inclusion of bio-additive enhanced the strength by 18–35% after the 28 days of ambient temperature curing [37]. Visual inspection of the immersed specimen exhibited that bio-additive added specimens were less deteriorated compared to the without bio-additive added geopolymer concrete specimen. Microstructural studies informed that the presence of a stable cross-linked polymer in the bio-additives exhibited superior resistance to the sulfate, acid and chloride in the geopolymer concrete specimen [36]. 2.6

Influence of the Other Factors

To magnifying the durability properties of concrete, nowadays air entraining admixtures has been added in the concrete in the form of the tiny air bubble. Air entraining admixtures did not reported a significant effect on the workability, because of the reduction in the air void spacing factor, whereas, these properties were enhanced with the addition of the water and fly ash. Mechanical parameters slightly improve with the addition of air entraining admixtures after the 7 days of ambient curing but it continuously decreases by the addition of the water content [34]. Similar findings were also explained by the M. Albitar et al. [38] when they utilized lead smelter slag as the source material and fine aggregate. Microstructural studies informed, with the inclusion of water and fly ash reduces the number of cracks exists in the GGBFS based geopolymer paste along with the autogenous volume changes. These studies revealed that self-desiccation could be the possible reason for the early cracking of the geopolymer paste, which reduce with the increasing water content [34]. Farhan et al. [39] inquire the influence of steel fiber inclusion into the geopolymer concrete column and understand the behavior under eccentric axial, concentric axial and four point loading condition. Results informed that steel fiber significantly affected the post-peak deformation behavior and prevented from sudden failure by retarding the crack propagation. The strength of geopolymer concrete column improvised with the inclusion of steel fibers whereas, it decreased with the increase in the eccentricity of loading, meanwhile, brittle behavior of specimens’ changes to the ductile behavior due to the confinement of pores available.

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3 Conclusion Production of a small amount OPC from the natural substance required high amount of embodied energy and developed CO2 gas along with other environmental affecting substances. For that reason, OPC concrete has facing acute condemnation from last two decades which encourage the researchers for finding the alternatives of the OPC, those have comparable properties of the OPC. After reviewing the previous published research papers on the ambient cured geopolymer composites, it was found that geopolymer is the best alternative of the OPC which enhanced the physical, mechanical parameters and durability properties of the alkali activated geopolymer concrete and reduce greenhouse gas emission as well as utilize industrial by – products. This review paper epitomizes the utilization and effect of the industrial and agro waste such as slag, fly ash, RHA and metakaolin etc. and other factors like alkaline activators, bioadditives, Al/Bi ratio and air entraining admixtures etc. on the different parameters of the geopolymer composites. On the basis of current work, the following findings can be drawn – • Many components viz. physical and chemical properties of the aluminosilicate materials, ratio between alkaline compounds and alkali to binder ratio along with curing temperature and time influence the various properties of geopolymer composites. • Due to unavailability of any standard design procedure for achieving comparable or even better physical, mechanical and durability qualities, geopolymer composites are not feasible for the cast-in-situ work. • For the production of self-cured geopolymer composites, calcium oxides and calcium hydroxides should be incorporate in the form of GGBFS or OPC. • The behavior of fibers with geopolymer composites were found similar to the normal concrete. Inclusion of fibers into the geopolymer composites also enhanced the microstructure of the composites. • Manufacturing of pervious and lightweight geopolymer concrete and geopolymer brick could also be possible by using geopolymer composites. • In general, production of high strength and durable geopolymer composites in the ambient cured condition needs 10–12 M sodium hydroxide, the alkali to binder ratio 0.35–0.5 and the ratio between sodium silicate and sodium hydroxides 1.5– 2.5.

References 1. Shafigh P, Mahmud HB, Jumaat MZ, Zargar M (2014) Agricultural wastes as aggregate in concrete mixtures - a review. Constr Build Mater 53:110–117. https://doi.org/10.1016/j. conbuildmat.2013.11.074 2. Mehta A, Siddique R (2016) An overview of geopolymers derived from industrial byproducts. Constr Build Mater 127:183–198. https://doi.org/10.1016/j.conbuildmat.2016.09. 136

186

M. Gupta and N. H. Kulkarni

3. Hardjito D, Wallah SE, Sumajouw DMJ, Rangan BV (2015) On the development of fly ashbased geopolymer concrete 467–472 4. Davidovits J (1999) Chemistry of geopolymeric systems, terminology. In: 99 International Conference, France, pp 9–40 5. Turner LK, Collins FG (2013) Carbon dioxide equivalent (CO2-e) emissions: a comparison between geopolymer and OPC cement concrete. Constr Build Mater 43:125–130. https://doi. org/10.1016/j.conbuildmat.2013.01.023 6. Islam A, Alengaram UJ, Jumaat MZ, Bashar II, Kabir SMA (2015) Engineering properties and carbon footprint of ground granulated blast-furnace slag-palm oil fuel ash-based structural geopolymer concrete. Constr Build Mater 101:503–521. https://doi.org/10.1016/j. conbuildmat.2015.10.026 7. Ng C, Alengaram UJ, Wong LS, Mo KH, Jumaat MZ, Ramesh S (2018) A review on microstructural study and compressive strength of geopolymer mortar, paste and concrete. Constr Build Mater 186:550–576. https://doi.org/10.1016/j.conbuildmat.2018.07.075 8. Mehta A, Siddique R (2017) Properties of low-calcium fly ash based geopolymer concrete incorporating OPC as partial replacement of fly ash. Constr Build Mater 150:792–807. https://doi.org/10.1016/j.conbuildmat.2017.06.067 9. Olivia M, Nikraz H (2012) Properties of fly ash geopolymer concrete designed by Taguchi method. Mater Des 36:191–198. https://doi.org/10.1016/j.matdes.2011.10.036 10. Albitar M, Ali MSM, Visintin P, Drechsler M (2017) Durability evaluation of geopolymer and conventional concretes. Constr Build Mater 136:374–385. https://doi.org/10.1016/j. conbuildmat.2017.01.056 11. Saha S, Rajasekaran C (2017) Enhancement of the properties of fly ash based geopolymer paste by incorporating ground granulated blast furnace slag. Constr Build Mater 146:615– 620. https://doi.org/10.1016/j.conbuildmat.2017.04.139 12. Nath P, Sarker PK (2015) Use of OPC to improve setting and early strength properties of low calcium fly ash geopolymer concrete cured at room temperature. Cem Concr Compos 55:205–214. https://doi.org/10.1016/j.cemconcomp.2014.08.008 13. Dep PS, Nath P, Sarker K (2014) The effects of ground granulated blast-furnace slag blending with fly ash and activator content on the workability and strength properties of geopolymer concrete cured at ambient temperature. J Mater 62:32–39. https://doi.org/10. 1016/j.matdes.2014.05.001 14. Suwan T, Fan M (2014) Influence of OPC replacement and manufacturing procedures on the properties of self-cured geopolymer. Constr Build Mater 73:551–561. https://doi.org/10. 1016/j.conbuildmat.2014.09.065 15. Atiş CD, Görür EB, Karahan O, Bilim C, Ilkentapar S, Luga E (2015) Very high strength (120 MPa) class F fly ash geopolymer mortar activated at different NaOH amount, heat curing temperature and heat curing duration. Constr Build Mater 96:673–678. https://doi. org/10.1016/j.conbuildmat.2015.08.089 16. Albitar M, Ali MSM, Visintin P (2017) Experimental study on fly ash and lead smelter slagbased geopolymer concrete columns. Constr Build Mater 141:104–112. https://doi.org/10. 1016/j.conbuildmat.2017.03.014 17. Abdulkareem OA, Al Bakri AMM, Kamarudin H, Nizar IK, Ala’eddin AS (2014) Effects of elevated temperatures on the thermal behavior and mechanical performance of fly ash geopolymer paste, mortar and lightweight concrete. Constr Build Mater 50:377–387. https:// doi.org/10.1016/j.conbuildmat.2013.09.047 18. Joseph B, Mathew G (2012) Influence of aggregate content on the behavior of fly ash based geopolymer concrete. Sci Iran 19:1188–1194. https://doi.org/10.1016/j.scient.2012.07.006

A Review on the Recent Development of Ambient Cured Geopolymer Composites

187

19. Hadi MNS, Farhan NA, Sheikh MN (2017) Design of geopolymer concrete with GGBFS at ambient curing condition using Taguchi method. Constr Build Mater 140:424–431. https:// doi.org/10.1016/j.conbuildmat.2017.02.131 20. Boonserm K, Sata V, Pimraksa K, Chindaprasirt P (2012) Improved geopolymerization of bottom ash by incorporating fly ash and using waste gypsum as additive. Cem Concr Compos 34:819–824. https://doi.org/10.1016/j.cemconcomp.2012.04.001 21. Ahmari S, Zhang L (2013) Utilization of cement kiln dust (CKD) to enhance mine tailingsbased geopolymer bricks. Constr Build Mater 40:1002–1011. https://doi.org/10.1016/j. conbuildmat.2012.11.069 22. Adak D, Sarkar M, Mandal S (2014) Effect of nano-silica on strength and durability of fly ash based geopolymer mortar. Constr Build Mater 70:453–459. https://doi.org/10.1016/j. conbuildmat.2014.07.093 23. Ash RH, Patel YJ, Shah N (2018) Enhancement of the properties of ground granulated blast furnace slag based self compacting geopolymer concrete by incorporating. Constr Build Mater 171:654–662. https://doi.org/10.1016/j.conbuildmat.2018.03.166 24. Sun Z, Lin X, Vollpracht A (2018) Pervious concrete made of alkali activated slag and geopolymers. Constr Build Mater 189:797–803. https://doi.org/10.1016/j.conbuildmat.2018. 09.067 25. Alanazi H, Hu J, Kim Y (2019) Effect of slag, silica fume, and metakaolin on properties and performance of alkali-activated fly ash cured at ambient temperature. Constr Build Mater 197:747–756. https://doi.org/10.1016/j.conbuildmat.2018.11.172 26. Parveen, Singhal D, Junaid MT, Jindal BB, Mehta A (2018) Mechanical and microstructural properties of fly ash based geopolymer concrete incorporating alccofine at ambient curing. Constr Build Mater 180:298–307. https://doi.org/10.1016/j.conbuildmat.2018.05.286 27. Patel YJ, Shah N (2018) Study on workability and hardened properties of self compacted GEOPOLYMER concrete cured at ambient temperature. 11:1–12. https://doi.org/10.17485/ ijst/2018/v11i1/117698 28. Venu Madhav T, Ramana Reddy IV, Ghorpade VG, Jyothirmai S (2018) Compressivestrength study of geopolymer mortar using quarry rock dust. Mater Lett 231:105–108. https://doi.org/10.1016/j.matlet.2018.07.133 29. Fang G, Ho WK, Tu W, Zhang M (2018) Workability and mechanical properties of alkaliactivated fly ash-slag concrete cured at ambient temperature. Constr Build Mater 172:476– 487. https://doi.org/10.1016/j.conbuildmat.2018.04.008 30. Askarian M, Tao Z, Adam G, Samali B (2018) Mechanical properties of ambient cured onepart hybrid OPC-geopolymer concrete 186:330–337 31. Cao Y, Tao Z, Pan Z, Wuhrer R (2018) Effect of calcium aluminate cement on geopolymer concrete cured at ambient temperature. Constr Build Mater 191:242–252. https://doi.org/10. 1016/j.conbuildmat.2018.09.204 32. Nath P, Sarker PK (2017) Flexural strength and elastic modulus of ambient-cured blended low-calcium fly ash geopolymer concrete. 130:22–31 33. Babu DV (2018) Assessing the performance of molarity and alkaline activator ratio on engineering properties of self-compacting alkaline activated concrete at ambient temperature. J Build Eng 20:137–155. https://doi.org/10.1016/j.jobe.2018.07.005 34. Rodrigue A, Duchesne J, Fournier B, Bissonnette B (2018) Influence of added water and fly ash content on the characteristics, properties and early-age cracking sensitivity of alkaliactivated slag/fly ash concrete cured at ambient temperature. 171:929–941 35. Reddy MS, Dinakar P, Rao BH (2018) Mix design development of fl y ash and ground granulated blast furnace slag based geopolymer concrete. J. Build. Eng. 20:712–722. https:// doi.org/10.1016/j.jobe.2018.09.010

188

M. Gupta and N. H. Kulkarni

36. Karthik A, Sudalaimani K, Vijayakumar CT (2017) Durability study on coal fly ash-blast furnace slag geopolymer concretes with bio-additives. Ceram Int 43:11935–11943. https:// doi.org/10.1016/j.ceramint.2017.06.042 37. Karthik A, Sudalaimani K, Vijayakumar CT, Saravanakumar SS (2019) Effect of bioadditives on physico-chemical properties of fly ash-ground granulated blast furnace slag based self cured geopolymer mortars. 361:56–63 38. Albitar M, Ali MM, Visintin P (2017) Experimental study on fly ash and lead smelter slagbased geopolymer concrete columns. 141:104–112 39. Farhan NA, Sheikh MN, Hadi MNS (2018) Behaviour of ambient cured steel fibre reinforced geopolymer concrete columns under axial and flexural loads. Structures 15:184– 195. https://doi.org/10.1016/j.istruc.2018.07.001

A Case Study on Structural Assessment of Dudhgaon Grampanchayat Building by Using Non Destructive Testing Rohit Kukade1(&) and Santosh Mohite2 1

2

Civil Structural Engineering, Annasaheb Dange College of Engineering and Technology, Ashta 416301, India [email protected] Department of Civil Engineering, Annasaheb Dange College of Engineering and Technology, Ashta 416301, India [email protected]

Abstract. Structural Assessment is the overall health and performance checkup of the structure. This assessment gives an idea to repair and retrofitting measures required for the structure by which the service life of the structure can extended. The main criteria for assessment of any structure include the state of the building history, surrounding environment conditions, structural capacity, durability, and professional involvement in construction. RCC structure is often exposed to many types of damages and deteriorations due to different cause and exposure conditions during their life cycle. Keywords: Building inspections
Safety
Repairs and control
Non destructive testing (NDT)
Rebound hammer ultrasonic pulse velocity

1 Introduction Any Building Structure can withstand for a very long period without any damage that only depends on general checkup of that structure. After completing 30 years of any structure periodical monitoring should be carried out. As the building grows old special precaution should be carried out to remain the building for long period. Therefore it is suggested to carry out periodical monitoring of the structure by structural consultant to save the loss of life and loss of structure. Structural Assessment of a building is the preliminary stage in which we can clarify exact in which condition is the structure right now. Indian society of structural engineer has presented methodology on structural assessment. Structural assessment is the examination of the building to give the current situation of the building or members of the structure, the extent of material deterioration is greatly depends on quality of material used for construction and quality of work at construction stage. The damaged part of the building should repair as soon as, to overcome future loss of structure. If the deteriorated part remains as it is, it causes slow poisoning which is corrosion of steel, chemical attack etc. There are several techniques by which we can extend our building life span but there should be exact diagnosis of the damage and correctly applied the techniques. © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 189–194, 2020. https://doi.org/10.1007/978-3-030-24314-2_25

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2 Literature Review Sachin Shelke, Darshana Ainchwar (2018) [1] Structures are the assemblies of beam, column, & slab which safely transfer the superimposed load to the foundation. Concrete is an integral material used for construction purposes. Thus to know the strength of this concrete we used the technique Non destructive testing. The rebound hammer test is a hardness test and it is based on principle that the rebound of an elastic mass depends on the hardness of the surface against which the mass impinges. The assessment of any old structure by which we can extend the life of the structure by suggesting remedies for repair. The ultrasonic pulse velocity test is conducted which gives quality of concrete. Peteris Drukis, Liga Gaile and Leonids Pakrastins (2017) [2] Safety of buildings is very important in all aspects. A commercial building at Riga collapse due to damages caused was very serious and 54 people were died. Safety of structure is the practice of designing, constructing, operation, maintaining and removing structure in ways that no any loss of human or injuries. This paper tells us risk based assessment system of commercial building with target to classify this in common way. The outcome of this paper is exact assessment of the structure and the effect of that risk factor to the safety of public buildings. Mahadik and Jaiswal(4) (2014) [3] This paper create awareness amongst the civil engineering structure and health examination of the concrete structure called as Structural assessment. The need of assessment is for maintenance and repair of existing structures whose life has exceed the age of 15 years according to standard guidelines and to save valuable loss of human life and also the collapse of structure.

3 Objectives To study the various types of structural defects. To identify any signs of material deterioration. To identify the signs of structural distress and deformation. To identify any alteration and addition in the structure. Remedies for restoration of the structure.

4 Problem Formulation Structural Assessment of dudhgaon grampanchayat was necessary because the structure has completed 15 years and almost loss it’s strength due to material deterioration and corrosion of steel and hence to avoid loss of human life and loss of structure collapse it’s necessary to take assessment of that structure and suggest remedies to the respected authority.

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5 Methodology Performing preliminary inspection by which we can get clear idea of the structure. Preparation of Architectural plan and steel design of the structure. Should highlight critical areas which are more damaged. Performing NDT test by which we can get clearer about the structure health. Finding actual strength of the building. Suggesting the remedial measures for extending life span of the structure. 5.1

Case Study of R.C.C Building Basic Information Name of Building:- Dudhgaon Grampanchyat office. Type of Structure:- R.C.C. Building G + 1 Address:- A/p Dudhgaon Grampanchayat, Tal- Miraj Dist. Sangli. Age of Building:-18 years completed. Effects of monsoon:- Yes

(1) Visual Observation This building is a commercial type and located in Dudhgaon village. While visual observing this building it has been cleared that material deterioration in every member has gone to an extreme point and steel has been corroded, and some of the reinforcements which has been corroded the small parts of corroded steel has fallen down and only concrete part is there. The column has gone cracks in huge manner which is easily observed by any common man. Dampness problem in wall due to leakage in slab. (2) Tapping Observation While tapping on the column and beam with hammer the hollow sound was recorded. This clearly indicates that voids are present in those members. (3) Non-Destructive Testing Observations Some of the members of the building were subjected to test by Ultrasonic pulse velocity and Rebound Hammer test and reading were recorded which were evaluated further for remedial measures. 5.2

Report of NDT Test

Inspection & Testing The aim of testing was to arrive at the general quality of concrete, rather than evaluating each RCC members in detail. On site few RCC members at random were tested. Ultrasonic pulse velocity (UPV) measurements were taken on Beam, column, and slab. Corrosion Rusting of reinforcement in structure is very common. Any corrosion of reinforcement which may cause serious damage to the structure if this damage to the structure is not repaired within time then loss of structure and loss of life may cause. Usually the corrosion occurs due to the exposed steel to the atmosphere, this exposed steel if remain for long time rusting occurs. Due to poor workmanship at the time of casting RCC members the voids remain in concrete which may further lead to removal of concrete cover and due to wear and tear the steel exposed to atmosphere.

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Observations on Case Study

Structurally the building appears to be unsound and few structural members show major distress sign at external face & internal area of the building. • This building at external face shows large cracks in column and corrosion in column is at extreme point. • The wall joint with column is not proper and this shows the separation of wall with column by cracks. • The cover of beam has fallen down and steel is exposed to atmosphere. • The plumbing lines are damaged and still now in use. • Dampness in wall due to which the condition of building appears to be quite leaky. • Plaster on RCC member and on brickwork acts as skin but this plaster has fallen down in most of the part. • Due to weather & carbonation force in some place, plaster has deteriorated.

6 Results and Discussions While going through all aspects of the building maintenance and as per our detailed survey, we suggested that building needs to be properly repaired and use without this repair building cannot sustain load for long time. Reinforcement of structure acts as bones in body, so if this are damaged then weight of body cannot be sustain similarly reinforcement are more important in RCC structure so special precaution should be taken. To bring original strength of RCC members which are damaged then polymer modified mortar method should be used. The RCC members and walls which are deteriorated due to excess of water leakage or any other action on these components, to stop this leakage must be stopped. In RCC structure plaster it acts like a skin on bone, this skin should be of good quality we recommended good quality resin based coating. Cracks should be filled with adhesive material. Waterproofing at terrace must be there to avoid leakage (Tables 1, 2 and 3). Table 1. Rebound hammer test results (slab) Sr. No Rebound value

1. 2. 3. 4. 5. 6.

Compressive strength Kg/cm^2 +Ve 90 −Ve 90 +Ve 90 −Ve 90 28 24.75 140 205 27.25 23.75 135 203 31.75 24.75 195 203 27 30.25 125 302 31.75 34.25 195 385 33 31.5 220 325

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Table 2. Rebound hammer test results (beam) Sr. No Rebound value

1. 2. 3. 4. 5. 6. 7. 8.

Compressive strength Kg/cm^2 Horizontal +Ve 90 Horizontal +Ve 90 23.25 24.25 140 0 25.75 31 165 190 32.25 27.5 280 125 27.25 27 202 120 34.5 26.5 305 110 34 26.75 310 112 26 25.5 180 105 28.25 29.5 215 160

Table 3. Results of ultrasonic pulse velocity test Sr. No. Ultrasonic pulse velocity by (Km/sec.) Concrete quality grading SLAB 1. 0.424 Doubtful 2. 1.478 Doubtful 3. 4.640 Excellent 4. 3.78 Good

7 Conclusion For a framed structure structural assessment is necessary so that appropriate remedial measures can be recommended for all type of structural defects. This assessment of any structure gives extra life span to the structure by applying appropriate remedies suggested in assessment report. From above observation we conclude that reinforcement provided for the structural member is in very bad condition and lost its strength due to corrosion. And this corrosion leads to reduction in size of reinforcement and deflection of that member due its own weight therefore it’s unsafe to carry future load on that structural member. And structural defects are due to the faulty workmanship, poor quality of material used at the time of construction and no any specific supervision at the time of construction carried out. The reinforcement provided in members are corroded and there is loss in strength of the member, this loss is due to leakage of water from slab. Also there are combined effects of carbonation, corrosion, and effects of continuous drying and wetting. So the strength and serviceability of structure can be extended by taking necessary measures such as waterproofing walls and slab to stop seepage of water in to structural members to stop corrosion of reinforcement. Providing polymer mortar treatment. Recasting of slab.

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References 1. Breysse D (2012) Nondestructive evaluation of concrete strength: an historical review and a new perspective by combining NDT methods. Constr Build Mater 33:140–163 2. Shelke SS, Ainchwar D (2018) Structural health monitoring, Audit Repair and rehabilitation of building in construction industry. Int J Eng Technol Sci Res 05(3). ISSN 2394-3386 3. Mahadik AB, Jaiswal MH (2014) Structural audit of building. IJCER 05:411–416 ISSN 22783652 4. Chafekar BH, Kadam OS, Kale KB, Mohite SR, Shinde PA, Koyle VP (2013–2014) Structural audit. IJCSER. 01(1):42–46 5. McCann DM, Forde MC (2001) Review of NDT methods in the assessment of concrete and masonry structures. NDT E Int 34:71–84 6. Lim MK (2013) Combining multiple NDT methods to improve testing effectiveness. Constr Build Mater 38:1310–1315 7. The Government of Maharashtra vide their notification date 07/02/2009 has communicated that Government has modified the M.M.C. act, 1888 thereby incorporation a new section 353B for making structural audit compulsory in respect of existing building, which have completed 30 years

Analysis and Design of High-Rise Building Using Diagrid Structural System Ahmad Muslim Rujhan(&) and Ravande Kishore Department of Civil Engineering, University College of Engineering, Osmania University, Hyderabad, India [email protected], [email protected], [email protected]

Abstract. Diagrid Structural System is defined as a system that consists of diagonal columns and horizontal members to mitigate and perform against lateral forces by making up a triangular model on the periphery of the building. Diagrid structural system provided by diagonals on the periphery is adopted in tall buildings due to its structural efficiency as result of its triangular configuration against both lateral loads and vertical loads. In this paper, analysis and design of Diagrid module is carried out by three manners innuendo Manual Calculation using stiffness method, ANSYS V12.1 software, and ETABS V9.6.0 software. A floor plan of 36 m by 36 m having six spans of 6 m, and 48 stories with typical storey height of 3.6 m is considered. ETABS software is used for Dynamic Analysis of total Diagrid building, and ANSYS software and Manual calculation are used for Static Analysis of Diagrid’s Diagonal Periphery. Analysis is performed as per IS:800-2007 and IS:1893-2002. Design wind speed is calculated as per IS:875-1987 Part-3, and specification of steel is taken as per IS:2062-2011. Comparison of analysis results in terms of Static and Dynamic control of Diagrid building, therefore Static Analysis is carried out to define Lateral Stiffness/Displacement of Diagrid’s Diagonal Periphery, and Dynamic Analysis is carried out to define Time History, Time Period, Storey Displacement, Storey Drift, Storey Shear, and Load Distribution in Diagrid. Keywords: Analysis of Diagrid in three methods
Static and dynamic control of diagrid structural system

1 Introduction The word “Diagrid” is derived from ‘diagonal-grid’, an efficient triangular structure that can guarantee the stability of a building [1]. Diagrid structural system is used in high-rise steel buildings where the horizontal loads and the accompanying deflection are normative, thus stability of tall buildings against lateral loads is an important point in the analysis and design. There are several lateral resistance systems for tall buildings such as concrete shear wall, infill wall, braced frame, rigid frame, outrigger truss system, braced tube, and framed tube, but Diagrid structural system is found the most reliable and economical structural system for steel tall buildings. The structural efficiency of Diagrid system occurs as a result of its triangular configuration. The triangulation resists both © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 195–205, 2020. https://doi.org/10.1007/978-3-030-24314-2_26

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gravity and lateral load by axial stresses of its members where each member simply acts in tension or compression with no bending. Diagrid structural system is a special form of space truss which acts as a rigid shell and it is considered as a new seismic force-resisting system. For analysis purposes it can be considered as a very thin deep beam [9]. Among the large-span buildings some examples are represented as the Seattle Library, the London City Hall, the Sydney One Shelley Street, and the Astana National library. Among tall buildings, noteworthy examples are the Swiss Re building in London, the Hearst tower in New York, the CCTV Headquarters building in Beijing, the Capital Gate in Abu Dhabi, and Tornado Tower in Doha (Fig. 1).

(a). Poly Int. Plaza Diagrid building

(b). Hearst tower Diagrid building

Fig. 1. Diagrid’s diagonal periphery with Diagrid module.

Diagrid system has a bold appearance and the orientation of Diagrid system causes to reduce the number of structural steel element required on the façade of the buildings. The structural efficiency of Diagrid system also helps in reducing interior and corner columns; therefore diagonal periphery of Diagrid system saves approximately 20% of the structural steel in weight when compared to a conventional moment-frame structure [8]. Diagonal members in Diagrid structural system can carry both gravity loads and lateral forces due to its triangulated configuration [10]. Diagrid structures are more effective in minimizing shear deformation because they carry lateral shear by axial action of diagonal members. Diagrid structures generally do not need high shear rigidity cores because lateral shear can be carried by the diagonal members located on the periphery of the building [8, 9].

2 Derivation of Load Deflection Formulae for Diagrid [1] In this paper, stiffness method is applied to find the deformation of Diagonal Periphery of Diagrid building for Bending Deflection and Shear Deflection [10]. The adopted Diagrid’s Diagonal Periphery is consisting of 16 triangular frames, locating three stories along the height of the building. The 16 nodes are changed to 16 concentrated loads, which are evenly distributed along the height of the building. Bending Deflection

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and Shear Deflection formulae for a cantilevered rod with 16 concentrated loads can be derived with the help of two existing formulas that are deflection and rotation at top of the rod. Discretization of Diagrid module into continuous load pattern is shown in Fig. 2. Deflection ðyÞ and Rotation ðuÞ at top of the discretized module calculated respectively are based on stiffness formulae of cantilever rod [10].

Fig. 2. Discretization of 16 nodes 6 spans Diagrid module into 16 continuous load pattern.

2.1

y¼

F
l3 3EI

ð1Þ

u¼

F
l2 2EI

ð2Þ

Bending Deflection of Diagrid’s Diagonal Periphery

By combining the above formulae, bending deflection formula is found based on a cumulative principle for the rod with 16 concentrated loads. ybending ¼

16 X

ðyi þ ui
li Þ

ð3Þ

i¼1

ybending ¼

2.2

833 F
l3
384 EI

ð4Þ

Shear Deflection of Diagrid’s Diagonal Periphery

Similarly, Shear deflection formula for the cantilever rod with 16 concentrated loads is found.

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yshear ¼

 16  X Fi
li i¼1

yshear ¼

2.3

Gi Ai i

17 F
l
2 GA

ð5Þ ð6Þ

Racking Shear Stiffness of Diagrid’s Diagonal Periphery

The expression for the racking shear stiffness for 16 concentrated loads and 6 pans of 6 m with having span (a), diagonal length (d), and height (h) for the Diagrid structure (trussed tube) is derived as bellow. The expression needs to be multiplied with 2 since shear occurs in the two web planes of the tube. GA ¼ 2 

2.4

3a2
h
E
Ad d3

ð7Þ

Bending Stiffness of Diagrid’s Diagonal Periphery

For 3D Diagrid module with 6 spans and 16 nodes (16 triangles, each triangles segment covers three floors throughout the height) provided in an orthogonal grid, Bending Stiffness formula is found.  EI ¼

2.5

1 5 4
ð aÞ2 þ 4
ð aÞ2 þ 8
ð2aÞ2 2 2


EAequ ¼ 58
a2
E
Ad

ð8Þ

Total Displacement of Diagrid’s Diagonal Periphery

Total displacement of the of Diagrid’s Diagonal Periphery is equal to sum of Bending Deflection and Shear Deflection. Ytotal ¼ ybending þ yshear

ð9Þ

3 Analysis and Design of 48 Stories Diagrid Building 3.1

Analysis of Diagrid Structural System

A floor plan of 36 m by 36 m in size having a rigid slab without openings and no core wall is considered for the 48 stories steel Diagrid building. Typical storey height is taken 3.6 m. The angle of inclination 74.48° is kept uniform throughout the height. The diagonal columns are provided at 6 m spacing along the perimeter. The interior frame of the Diagrid structures is designed only for gravity load. For linear static and dynamic analysis, beams and columns are modeled as beam elements and braces are modeled as

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truss elements. The support conditions are assumed as fixed. Secondary effect like temperature variation is not considered. Typical cross sections are shown in Fig. 3.

(a). Interior Column (Built-Up Section)

(b). Diagonal Circular Column (CHS)

Fig. 3. Cross section detail of columns.

Analysis is carried out in three manners, by Manual Calculation, by ANSYS software, and by ETABS software. Manual Calculation and ANSYS software are used to define lateral stiffness/displacement of Diagrid’s Diagonal Periphery, and ETABS software is used for Dynamic control of total Diagrid building. All structural members are designed as per IS:800:2007 and IS:1893-2002. Diagonal sections are taken as circular hollow section-CHS and horizontal beams are taken as wide flange grade E300-Fe440. Specification of steel is taken as per IS:2062-2011. Dynamic along wind and across wind is considered. Design dead load, live load, and cladding load are 3.75 KN/m2, 2.5 KN/m2, and 3.5 KN/m2 respectively. The dynamic along wind load and Across wind load is calculated based on the basic wind speed of 42 m/sec (Kabul city wind load [1]) for terrain category 3 class B, and Design wind speed is calculated 55.01 m/sec based on IS:8751987 Part-3 (Gust factor method) [4]. Fundamental natural period of the 48 stories Diagrid building is calculated 4.05 s as per IS:1893 Part-1 and time history analysis is performed based on El-Centro 1940 vibration data in ETABS V9.6.0. Material damping factor of the steel building is taken 4% as per IS:1893 Part-4 for MCE-Maximum Considered Earthquake. Total Time Elapse is calculated 52 s as per IS:1893 Part-1. Design earthquake load is applied as per IS:1893 Part-1 based on the Seismic Zone Factor of 0.24 for seismic zone IV. Importance Factor of 1.75, Soil Type of III, and Response Reduction Factor of 5 are taken for seismic analysis as per IS:1893 Part-1. 3.2

Design of Diagrid Structural System

For design criteria, two load combinations are assigned to Diagrid Structure in ETABS software as per IS:1893-Part-1 due to Dead Load (DL), Earthquake Load (EL), and Imposed Wind Load (IL), as ISCOMBO1 of 1.7x(DL+EL) and ISCOMBO2 of 1.3x (DL+IL+EL). All structural members are designed as per IS:800-2007. The yield stress of steel is taken 290 N/mm2 and tensile stress is taken 440 N/mm2 as per IS:2062-2011

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for grade E300-Fe 440. Spans of each Diagrid are 6 m and total height of the building is 172.8 m. Sizes of typical members are shown in Table 1. Floor plan and elevation are shown in Fig. 4. Table 1. Cross sectional sizes of typical members of the 48 stories steel Diagrid structure. Diagonal columns

Interior columns

Diagonal column, Storey 1–30 CHS: 508 mm  38 mm (20″  1.5″) Diagonal column, Storey 31–48 CHS: 406 mm  16 mm (16″  0.63″)

Built-up Column Section 1500 mm  1500 mm 40 mm Thick Plate Web and Flange 50 mm Think Plate Side Wall

(a). Diagrid Peripheral detail

Beams (typical all stories) B1: ISMB550 B2: ISWB600

(b). Diagrid Floor Plan

Fig. 4. Typical floor plan and elevation of the 48 stories Diagrid.

Details of Internal Column which resists mainly gravity loads and Diagonal Columns are shown in Fig. 3. Sizes of the members can be reduced by considering higher yield strength of structural steel. Cross Sections of Interior Column (Built-Up Section) and Diagonal Circular Column (CHS) is shown in Fig. 3.

4 Results and Discussions 4.1

Results

The Static Analysis performed by Manual calculation and ANSYS software results in terms of Members Stresses and Lateral Displacement of Diagrid’s Diagonal Periphery which are shown in Table 2 and Fig. 5 respectively. Lateral Displacement of Diagrid’s

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periphery calculated 582.7 mm and 595.9 mm at top storey by Manual calculation and ANSYS software respectively (Table 3). Table 2. Maximum and minimum axial deformation in elements found by ANSYS software.

Storey Level at Diagrid 's Periphery

Axial Stress Comp. (N/mm2) Min Values (Elm. No. 1115) −290.73 Axial Force Comp. (N) Min Values (Elm. No. 132) −0.35362E+07 Axial Strain Comp. (N/mm2) Min Values (Elm. No. 840) −0.93912E−04

Max Values (Elm. No. 1120) 243.85 Max Values (Elm. No. 134) 0.64740E+06 Max Values (Elm. No. 867) 0.21381E−03

Hand Calculation Storey Displacement (mm) ANSYS, Inc. Calculation (mm) 48 42 36 30 24 18 12 6 0 0

100

200

300

400

500

600

Displacement (mm)

Fig. 5. ANSYS and manual calculation displacement calculation. Table 3. Maximum load distribution comparison on 48 stories steel Diagrid structure. Loading on Diagrid columns Gravity loads Lateral WIND-X loads WIND-Y EQ-X EQ-Y

Loading on internal columns (KN) 84,821.30 1,487.04 1,671.96 638.08 715.86

Loading on diagonal columns (KN) 16,244.60 5,950.48 6,023.44 1,998.95 2,015.59

Total load on Diagrid columns (KN) 101,065.90 7,437.52 7,695.40 2,637.03 2,731.45

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EQ-X

EQ-Y

WIND-X

WIND-Y

ISCOMBO1-UX

48 42 36 30 24 18 12 6 0

Storey

Storey

The Dynamic Analysis performed by ETABS software results in terms of Time History, Time Period, Storey Displacement, Storey Drift, Storey Shear, and Load Distribution in Diagrid. The first mode time period is calculated 3.1 s, while by manual calculation as per IS:1893 Part-1 it is found 4.05 s. It is observed that Displacement, Inter-Storey Drift, and Storey Shear in x-direction and y-direction due to wind load is higher compared to earthquake load, thus wind load is the governing load for the design of structure. Results of Dynamic Analysis performed in ETABS software are shown in Figs. 6, 7, 8, 9, 10 and 11. Results of Time History Analysis which is performed as per IS:1893-Part-I based on EL-Centro vibration data for 4% damping steel structure-MCE with total time elapse of 52 s as calculated, are shown in Figs. 9, 10 and 11.

0

50

ISCOMBO2-UX

48 42 36 30 24 18 12 6 0

100 150 200 250 300

0

50 100 150 200 250 300 350 400

Displacement (mm)

Displacement (mm)

EQ-X

EQ-Y

ISCOMBO1-UX

WIND-X

WIND-Y

ISCOMBO2-UX

48 42 36 30 24 18 12 6 0

Storey

Storey

Fig. 6. Displacement of 48 stories Diagrid structure due to dynamic and combined load.

0

0.001

0.002

Storey Drift (mm)

0.003

48 42 36 30 24 18 12 6 0 0

0.001

0.002

0.003

0.004

Storey Drift (mm)

Fig. 7. Inter-Storey Drift of 48 stories Diagrid structure due to dynamic and combined load

EQ-VX

EQ-VY

ISCOMBO2-VX

WIND-VX

WIND-VY

ISCOMBO3-VX

48 42 36 30 24 18 12 6 0 0.00E+00 1.00E+07 2.00E+07 3.00E+07

Storey

Storey

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48 42 36 30 24 18 12 6 0 0.00E+00 1.00E+07 2.00E+07 3.00E+07

Storey Shear Force (N)

Storey Shear Force (N)

Fig. 8. Storey Shear of 48 stories Diagrid structure due to dynamic and combined load

Acceleration (m/sec²)

Time vs. Acceleration of the 48 stories Diagrid structural system 3.5 2.0 0.5 -1.0 0 -2.5 -4.0

10

20

30

40

50

ACCELERATION UX Storey 48 Min is -2.419E+03mm/sec² Max is 3.049E+03mm/sec²

60

ACCELERATION UX Storey 1 Min is-1.56E+02mm/sec² Max is 1.82E+02mm/sec²

Time (sec)

Fig. 9. Top and bottom storey time vs. acceleration as Per IS:1893 and El-Centro data.

Displacement (mm)

Time vs. Displacement of the 48 stories Diagrid structural system DISPLACEMENT UX Storey 48 Min is -1.272E+02mm Max is 1.17E+02mm

200 100 0 -100 -200

0

10

20

30

40

50

60

DISPLACEMENT UX Storey 1 Min is -1.589E+00mm Max is 1.42E+00mm

Time (sec)

Fig. 10. Top and bottom storey time vs. displacement as Per IS:1893 and El-Centro data.

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Time vs. Base Shear of the 48 stories Diagrid structural system 1.50E+08 Base Shear (N)

1.00E+08

BASE SHEAR-X Min is -1.79E+07N Max is 2.04E+07N

5.00E+07 0.00E+00 -5.00E+07

0

10

20

30

-1.00E+08 -1.50E+08

40

50

60

BASE SHEAR-Y Min is -1.32E+08N Max is 1.29E+08N

Time (sec)

Fig. 11. Time vs. base shear X-direction and Y-direction as Per IS:1893 and El-Centro data.

4.2

Discussions

Comparison of Fundamental Time Period of the building (4.05 s) to First Mode Shape Time Period (3.1 s) shows that, a 23.5% reduction occurs in the time period of the building by using only Diagrid system (Fig. 12). Comparison of Displacement of Diagrid’s Diagonal Periphery, found by stiffness formulae and ANSYS, to Maximum Allowable Displacement (Height of Building/500) [9] (MAD) shows that, Diagrid’s Diagonal Periphery resists (40 to 42) % of the total lateral displacement of the building. Displacements found 272.2 mm and 345.6 mm by ETABS (WIND-X) and Maximum Allowable Displacement (MAD) respectively, shows that Diagrid system resists the total displacement of the building by 21.2%. From the analysis, it is found that the Storey Drift of the Diagrid structure is reduced significantly by 100% for all loads by using Diagrid structural system; it is observed from the Maximum Allowable Storey Drift as per IS:1893 Part-1 which is calculated 14.4 mm, and the Maximum Storey Drift by ETABS which are calculated 0.001249 mm, 0.001228 mm, 0.000611 mm, 0.000599 mm, 0.001039 mm, and 0.001808 mm for WIND-X, WIND-Y, EQ-X, EQ-Y, ISCOMBO1-UX, and ISCOMBO2-UX respectively. It is found that Base Shear of the Diagrid Structure is reduced by 23.8%, −118.2%, 55.2%, and −61.7% for ISCOMBO1-VX, ISCOMBO2-VX, EQ-VX-VY, and WIND-VX-VY respectively, while it can be observed that Diagrid system works best against lateral loads and it can also be concluded that its contribution to resisting gravity load is also at the range of satisfaction. It is observed that 21.7% and 78.3% of the Lateral Load is resisted by Interior Columns and Diagonal Columns respectively, while 83.9% and 16.1% of the Gravity Load is resisted by Interior Columns and Diagonal Columns respectively (Fig. 12).

Analysis and Design of High-Rise Building Using Diagrid Structural System Interior Columns

Load Distribution

100% 80%

83.9%

78.3%

60% 40%

21.7%

20%

16.1%

11 Mode shape No.

Diagonal Columns

205

9

Time Period

7 5 3 1

0% Lateral Load Gravity Load Types of Load

0.0

1.0

2.0

3.0

4.0

Time period (sec)

Fig. 12. Time vs. base load distribution and time period vs. mode shape.

5 Conclusion In this paper static and dynamic analysis of the 48 stories Diagrid structure is performed by means of Manual Calculation, ANSYS software, and ETABS software. From the analysis it is found that diagonal periphery resisted 42% of all lateral loads, while as total building, Diagrid resists about 21% of the total displacement due to all loads. It is observed that Diagrid system contributes most to lateral loads in tall buildings, but its contribution to gravity loads is also sufficient. Storey drift of Diagrid is reduced by 100%.

References 1. Rujhan AM (2014) Analysis and design of high-rise building using diagrid structural system. Master’s thesis, Civil Engineering Department, University College of Engineering, Osmania University, Hyderabad, India 2. ANSYS Inc. V12.1 (2012) Finite element analysis package, Pennsylvania, USA 3. ETABS Nonlinear V9.6 (2008) Extended 3D analysis of building systems. Computers and Structures Inc., Berkeley 4. IS:800-2007 (2007) General construction in steel-code of practice. BIS, New Delhi 5. IS:875-1987 Part-3 (2003) Code of practice for design loads other than earthquake. BIS, New Delhi 6. IS:1893 Part-1, Part-4 (2002) Criteria for earthquake resistance design of structures. BIS, New Delhi 7. IS:2062-2011 (2011) Hot rolled medium and high tensile structural steel. BIS. New Delhi 8. Moon KS (2011) Diagrid structures for complex-shaped tall buildings, vol 14. Elsevier 9. Moon KS (2008) Material-saving design strategies for tall building structures. CTBUH Technical Paper, vol 2008, CTBUH 8th World Congress, Dubai 10. Roelofs R (2008) Trussed façade constructions: a study of the opportunities of a structural system. Master’s thesis, Eindhoven University of Technology, Netherland 11. Baker W, Besjak C, Sarkisian M, Lee P, Doo CS (2010) Proposed methodology to determine seismic performance factors for steel diagrid framed systems. CTBUH Technical Paper, 13th U.S. Japan Workshop

Engineered Cementitious Composites Yogesh Biyani(&), L. G. Patil, and C. N. Kurhe Civil Engineering Department, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India {2017mse014,lgpatil}@sggs.ac.in, [email protected]

Abstract. Cementitious Composite (CC) is the mix of various materials, which in a combination form a composite, which would help in bonding of various components in the structure. It is combination used from ancient times and still used in the construction industry. In this paper, we have tried to modify the mix to get better results for the composite as CC plays a vital role in strength of building. It may be any component, masonry, concrete, etc. presence of CC helps in capacity of building with provision of proper bonding in two elements of the structure in a form of connection. Therefore, it is necessary to have a proper CC for the construction. As usually failure in the structure occurs usually through the CC itself, failure may be in connection, failure in bonding in two elements or structural component, etc. In this paper, we are discussing the results obtained of the prepared Engineered Cementitious Composite (ECC), which we found to have higher strength with a Lower Cost and Lower Carbon-Emission as well as higher ductility. This would help in making the structures more durable for the lateral shocks due to seismic loads. Keywords: CC = Cementitious Composite
ECC = Engineered Cementitious Composite
CBFS = Crushed Blast Furnace Slag
PVA = Polyvinyl Alcohol FRC = Fiber Reinforced Concrete




1 Introduction Cracks are unavoidable during lifetime of a concrete structure. Structures directly exposed to the outer environment are more susceptible to cracking as they influence by shrinkage or expansion weighting and drying as well as other environmental conditions with the factor of excessive loading conditions. These cracks impact the strength of structures by weakening them as the mechanical property gets reduced as well as durability gets lowered as these cracks creates path for penetration of harmful agents in the structures core thus they attack the reinforcing steel and surrounding concrete, as been stated by Li [4, 6]. Due to this, the loading area of the structures component is been reduced causing the weakening if the structure, so the maintenance cost of concrete structures increase and service life is reduced. Therefore, it is necessary to develop a concrete, which would avoid this problems, or development of concrete, which would regain this loss of performance, occur due to cracking in due course of time. Introduction of Fibers in Cementitious Composites can help stabilizing microcracks, as reinforcing the Cementitious Composite by Fibers helps in transfer of the © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 206–214, 2020. https://doi.org/10.1007/978-3-030-24314-2_27

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tensile load in the mix from the Cementitious Composite to the Fibers, as been stated in various research papers [2, 5]. As we know Concrete is a Brittle material, which makes it fail during the application of Tensile Loading, while Fibers are strong in Tension compared to the Cementitious Composite. Capacity Design Phenomenon states that there occurs failure of chain by breaking if there would be all brittle links in the chain, but the same chain would be able to take an excessive load if we replace one brittle link by a ductile link. Similarly, in the designing of the Cementitious Composite if we add few fibers in the mix there may take place presence of a ductile link in the loading chain of the system making it more susceptible to the loading. This suppression in micro-crack development helps in development of pseudostrain hardening behavior of material, making formation of numerous micro-cracks all over the system instead of a single macro-crack. This behavior makes the Composite mix to act more like a Metal body instead of a Concrete Body, as this helps in making the mix ductile instead of its property to be brittle.

2 Materials A. Cement: Cement used during the work was OPC (Ordinary Portland Cement) of 53 Grade. B. Sand: Sand being used during the work was from Godavari River Bed and was being provided by the institution (SGGSIE&T, Nanded) itself. C. Coarse Aggregate: It was been provided from the institution itself, and was been procured from nearby Stone Quarry, best to my knowledge. D. CBFS: It stands for Crushed Blast Furnace Slag, CBFS been used in the work was Iron-based, and was being procured from the crushing plant of Kalika Steels Pvt. Ltd., Jalna, Maharashtra. It was been crushed to until it can be used as a Fine Aggregate. E. Fly Ash: Fly Ash been used for the work was being procured from the Maharashtra State Power Generation Corporation Limited’s Parli Thermal Power Plant, Parli, Beed, Maharashtra. F. PVA Fibers: PVA stands for Poly Vinyl Alcohol, which is plastic based polymer fiber. These polymer fibers were been procured from, The Yarn Guru India Inc., Bhilwara, Rajasthan. G. Brick: Bricks used in the work were mud-based and made with help of dome kiln in the kiln-plants near Vishnupuri. It was been provided by Institute itself. H. Water: Water was been provided by the institution itself and was of slightly basic nature, which made it more favorable for making concrete or mortar.

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3 Inference There has been research in the area but maximum of that research took place in the developed economies, while a negligible research took place in undeveloped or developing economies. This has been reason that usual criteria of Economy was not been considered to a mark in maximum of research. So our work is been concentrated to test the mix for Indian economy and atmosphere, so that we can attain better results for ECC mix to be used in every part of world. My work is concentrated maximum on Indian counterpart. As well as my work is to find the ability of ECC mix to be Eco-friendly.

4 Methodology Initial work was been done to find the optimum content for replacement of Cement and Sand by Fly Ash and CBFS respectively [3]. Content must be such that we get maximum strength with replacement. Then work was been done to find Optimum Content of Reinforcing Fibers, such that we get maximum strength in minimum usage of fibers. The obtained values were been plotted in GeoGebra and maximum value was obtained for strength with variation in replacement and addition of Reinforcing Fibers [8]. In the application of GeoGebra software, we tried to form a parabolic conic with help of values obtained by help of experimentation. By preparation of curve, we found the maximum value of strength with replacement and addition of fibers. All the processes of GeoGebra were been done to find values of Compressive and Tensile Strength with variation of values for preparing optimum mix of ECC. All Procedures were processed with provisions specified in literature by Shetty [9] and Indian Standard Codes [10–12]. Normal mortar of 1:4 proportion was prepared and referred in paper further as Normal Mortar, while Normal Concrete of M20 grade was prepared with proportion 1:1.5:3 and referred in paper further as Normal Concrete. ECC was prepared with optimum replacement of Cement and Sand as well as with optimum content of Fiber Reinforcement with PVA. FRC was prepared with same replacements and same content of PVA as in the case of ECC.

5 Testing and Results Firstly, the mixes were been tested for density, to find the change in loading due to selfweight if any (Table 1), Table 1. Density of mixes Material Normal Mortar ECC Normal Concrete FRC

Density 2187 kg/cum 2159 kg/cum 2450 kg/cum 2401 kg/cum

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From the above values, we can see that ECC has lower density, which would help in making the building-structure lighter than other versions, by reducing the sizes of the support system (Beam and Column) in the framed structure, as well as it would help in reducing weight of masonry block in the case of LBS structures. Then the mixes we tested for Compressive Strength, as it is the usual parameter considered during start of project, it was been calculated for two intervals that is compressive strength of mix after 7 days of curing and compressive strength of mix after 28 days of curing (Tables 2 and 3).

Table 2. Compressive strength of mixes after 7 days of curing Material Normal Mortar ECC Normal Concrete FRC

Compressive strength 10.4 MPa 24.81 MPa 16.22 MPa 20.22 MPa

Table 3. Compressive strength of mixes after 28 days of curing Material Normal Mortar ECC Normal Concrete FRC

Compressive strength 17.4 MPa 41.4 MPa 28 MPa 35.11 MPa

From the above values, we can find that ECC helps in making higher strength mix, which would help in making the structure with smaller sections helping in reducing weight and cost of structure. Further they were tested for Tensile Strength, as usually failure in a concrete structure occur during application of tensile loading, as concrete is a brittle material it is weak in tension, so test was performed to find the ability of ECC to resist Tensile Loads. Specimens for testing were prepared for Split Tensile Testing and testing was performed on CTM (Compressive Testing Machine) Results obtained were as follows (Table 4),

Table 4. Tensile strength of mixes after 28 days of curing Material Normal Mortar ECC Normal Concrete FRC

Tensile strength 1.67 MPa 6.67 MPa 2.78 MPa 5.56 MPa

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From the above values, we can find that ECC helps in making higher tensile strength mix, which would help in providing better strength in members of structure during application of lateral loads. Further they were tested the mixes for Flexural Strength, as concrete is a major component in the flexural components like beams and slab in the structure. Specimens of 100 mm  100 mm  500 mm were prepared, and testing performed on UTM (Universal Testing Machine) on 400 mm span-length. Results obtained were (Tables 5 and 6), Table 5. Flexure strength of mixes after 90 days of curing Material Normal Mortar ECC Normal Concrete FRC

Flexure strength NA 6.97 MPa 2.28 MPa 3.21 MPa

Table 6. Failure slope during testing for flexure Material Normal Mortar ECC Normal Concrete FRC

Slope NA 8º11′39.35″ 1º22′29.41″ 3º07′10.32″

(Flexural strength was calculated with the load at which first crack occurred in the flexure specimen, while the Slope was found by the help of displacement occurred during the Flexure testing. Specimen of Normal Mortar was prepared twice but the specimen failed in Demoulding, which made the results for flexure strength unavailable for it.) From the above values, we can find that ECC helps in making higher flexural strength mix, which would help in providing better strength in flexural members of structure, as well as help in reducing damages in case of progressive collapse. As well larger slope help to get greater ductility, which can help in reducing damages due to lateral shocks, as been stated by Soleimani-Dashtaki [1]. Further the mixes were tested for Water Absorption, during the test the specimens were firstly heated for 24 h in a oven at 105 °C then cooled at room temperature for 24 h in unquenched manner, further placed in potable water for 24 h and the water absorption was calculated (Table 7),

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Table 7. Water absorption of mixes Material Normal Mortar ECC Normal Concrete FRC

Water absorption 7.53% 6.73% 7.21% 6.72%

Water Absorption provides us with an estimate of amount of water present in the voids at the surface or upper sub-surface of the specimen, which is responsible for the initial deterioration of the structure, as expansion as well as evaporation of the water reduces the bond strength of the particles ultimately leading to deterioration of the specimen. From the above figure, we can say that water absorption of ECC is far lower than Normal Mortar and Concrete, while it is nearly similar for ECC and FRC. As ECC does not consist of Coarse Aggregates, we can use it as a binder for Masonry. Therefore, we tested the mixes for Masonry Prism Compressive Strength, as it is a major component for the health of the structure especially in the case of LBS, as the failure in it can lead to total collapse of the whole structure. Results obtained for the test are (Table 8), Table 8. Masonry prism strength for mixes Material Masonry prism compressive strength Normal Mortar 1.36 MPa ECC 1.86 MPa

From above figures we get to know that Compressive Strength of Masonry Prism increases by 36.67%, this could obtain a greater value. In case of Normal Mortar as Binder the failure crack passed through the Binding Material itself causing damage to the whole system, but in case of ECC as a Binder the failure cracks were been placed in the Masonry Units itself while no visible cracks were observed by naked eye in the Binding Material. Usually when a Building Structure goes through an Earthquake (Seismic) Shock, one reason of failure the Structural Component like Columns is due to failure of Masonry in Bond Shear. During failure in Bond Shear, there occurs huge displacement shock to the masonry during Seismic shock, which transfers to the structural component weakening the same, thus causing weakening in structure. Therefore, the mixes were been tested for Bond Shear (Table 9), Table 9. Masonry bond shear of mixes Material Masonry bond shear Normal Mortar 0.295 MPa ECC 0.432 MPa

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From above figures, we can see that about increase of Bond Shear of Masonry was 45–50% more for the ECC-mix compared to Normal Mortar, which would help in making the structure more resistant to Seismic Shocks.

6 Cost Analysis and Carbon Footprint As Economy is an important criterion for the building construction in all type of economies, so needs to be in consideration for the work. The costing of mixes were prepared with market investigation with help of local builders for locally available resources and the costing of the other materials for the mix were obtained from the offices of the site of procurement of the materials (Table 10). Table 10. Cost analysis of different mixes Mix Normal Mortar Normal Concrete FRC ECC

Cost (Rs/cum) 6810 6510 6705 6465

(Costs mentioned are been calculated according to local market cost analysis, they may vary for different areas, amount mentioned are been rounded to nearest multiple of five.) Being a responsible habitant of Earth, is needed by us to develop better and Ecofriendly Methods of Construction as well as Materials, which would help in increasing life of Earth. So during this work we tested all the variation of the mixes that we prepared for the calculation of Carbon Footprint (Amount of Emission of Carbon dioxide for production of one unit of mix), to find their ability to be Eco-friendly. Calculations were been performed by the values of CO2 emissions as been stated in Research paper by Jiménez et al. [7]. Results obtained by the calculations are as stated below (Table 11), Table 11. Carbon footprint of different mixes Mix Normal Mortar Normal Concrete FRC ECC

CO2 released (kg/cum) 521.80 539.34 361.1 321.19

(No CO2 emission is been considered for Fly Ash and CBFS, as they are byproducts of other industries.)

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From above figures we can observe that ECC being prepared, produces the least emission of CO2, thus causing least Carbon Footprint, making ECC to be most Ecofriendly in all tested variations of mix. Therefore, there needs to be proper transmission of the ability of the mix and proper blending by the industry, during provision of the mix with good quality of components in mix. As earlier, these issues have lead to lower consumption of PPC by the Contractors, who still prefer OPC at their respective construction sites, due to provision of inappropriate quality PPC by the cement production plants during that period.

7 Conclusion The above results in the research-work help us in concluding that, 1. ECC is a Lower-Density Mix. 2. ECC has better Compressive Strength in compared to other mixes (For 7 days curing and 28 days curing). 3. ECC being a Fiber-Reinforced Mix helps in providing greater Tensile Strength to the mix. 4. ECC has better flexural strength in combination with a greater deflection, which helps in providing greater evacuation time in case of Failure during Collapse of a component providing better ability of Life-Safety in the structure. 5. ECC has a lower Water Absorption value, which helps in increasing the durability of the structural component from attack of ‘Wet and Dry Cycle’. 6. ECC has better Masonry Prism Compressive Strength to the normal mortar, thus it can be useful for Rehabilitation of Old-Modern Structures (Building Structures constructed in late 19th century and early 20th century). 7. ECC being a Fiber-Reinforced Mix helps in increasing Bond Strength in Masonry and Binder, reducing damage in structure due to Displacement-Shock of Masonry. 8. ECC is cheaper by about 0.65–0.7% than Normal Concrete and cheaper by about 5% than Normal Mortar. 9. ECC has more than 40% lower Carbon Footprint in compared with Normal Concrete making it Eco-friendly. From the points above, it is clear that ECC is a mix having better strength, lower cost and lower Carbon Footprint in compared to the other mixes tested.

8 Future Works Recently during this research work, we came in to know that usage of Fibers are leading to reduction of workability of mix, so this needs to be studied such that we can obtain a good strength mix with a better Workability.

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Acknowledgement. We would also like to acknowledge to Dr. Y. V. Joshi, Director, SGGSIE&T and Dr. N. H. Kulkarni, Head, Civil Engineering Department, SGGSIE&T for their helpfulness to provide Testing Labs to complete this work. As well as other Staff from Civil Engineering Department and other departments at SGGSIE&T, Nanded for their help in this work.

References 1. Soleimani-Dashtaki S, Ventura CE, Banthia N (2017) Seismic strengthening of unreinforced masonry walls using sprayable eco-friendly ductile cementitious composite (EDCC). Procedia Eng 210:154–164 2. Ranade R, Li VC, Stults MD, Rushing TS, Roth J, Heard WF (2013) Micromechanics of high-strength, high-ductility concrete. ACI Mater J 110:375–384 3. Wang S, Li VC (2007) Engineered cementitious composites with high-volume fly ash. ACI Mater J 104:233–241 4. Li VC (2003) Engineered cementitious composites: a review on the material and its application. J Adv Concr Technol 1(3):215–230 5. Redon C, Li VC, Wu C, Hoshiro H, Saito T, Ogawa A (2001) Measuring and modifying interface properties of PVA fibers in ECC matrix. J Mater Civ Eng 13:399–406 6. Li VC (1998) Repair and retrofit with engineered cementitious composites. In: Proceedings FraMCoS-3, Freiburg, Germany 7. Jiménez LF, Domínguez JA, Vega-Azamar RE (2018) Carbon footprint of recycled aggregate concrete. Hindawi Adv Civ Eng 2018, Article ID 7949741 8. Mali JR, Bagul P, Biyani Y, Pandhare P, Bafna H Partial replacement of fine aggregate with GGBS. https://doi.org/10.13140/RG.2.2.32107.18725/1 9. Shetty MS (2014) Concrete Technology-Theory and Practice, Reprint, pp 180–189, 559– 575, 617 10. IS 3812 (Part 1):2003, Indian Standard for, “Pulverized Fuel Ash—Specification Part 1 for Use as Pozzolona in Cement, Cement Mortar and Concrete” (Second Revision) 11. IS 2250:1981 (Reaffirmed 2000), Indian Standard for “Code of Practice for Preparation and Use of Masonry Mortars” (First Revision) 12. IS 2116:1980 (Reaffirmed 2002), Indian Standard for “Specification for Sand for Masonry Mortars” (First Revision)

Influence of Metakaolin on Stone Waste Aggregate Concrete Sakevalla Vinay Babu1(&), U. Raghu Babu2, N. Venkata Ramana3, and P. Pavithra1 1

3

Department of Civil Engineering, G. Pullaiah College of Engineering and Technology, Kurnool 518002, Andhra Pradesh, India [email protected], [email protected] 2 Applied Mechanics Department, Sardar Vallabhbhai National Institute of Technology, Surat 395007, Gujarat, India [email protected] Construction Technology Department, Center of PG Studies, Visveswaraya Technological University, Kalaburagi 585105, Karnataka, India [email protected]

Abstract. The present paper reports the utilization of industrial stone waste in construction works as coarse aggregate. The black stone waste (BSW) aggregates collected from the stone industry was used as coarse aggregate by replacing the natural aggregate with different replacement levels of 0%, 25%, 50% and 75%. In addition, this work also investigates the influence of Metakaolin (MK) on the compressive strength and splitting tensile strength of concretes made with BSW aggregates. BSW aggregate concrete mixtures were prepared the replacement of cement with MK in the replacement levels of 0%, 5%, 10% and 15%. Results have shown that, the addition of MK involve to increase the mechanical properties of black stone aggregate concrete. The optimum strength results were obtained at 10% MK replacement level. Beyond the replacement level of 50% of natural aggregate with BSW aggregate MK blended concretes the strength results were lessened as compared to that of the concrete made with natural coarse aggregate (i.e. 0% BSW aggregate). Keywords: Metakaolin Splitting tensile strength


Stone waste
Concrete
Compressive strength


1 Introduction Now a days, metakaolin (MK) has attracted significant attention of researchers due to its high pozzolanic property. MK is produced by the kaolin clay calcination at high temperature ranges 500 °C to 800 °C. The percentage of MK addition has significant influence of porosity of the cement matrix. Bredy et al. [1] stated that the incorporation of MK below 10% decreases the porosity and the dosage level of more than 30% MK increases the porosity. The addition of MK enhances the compressive strength of concretes [2]. Li and Ding [3] were observed supreme compressive strength at 10% addition of MK to the concrete. © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 215–221, 2020. https://doi.org/10.1007/978-3-030-24314-2_28

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Rajendra et al. [4] investigated the compressive strength measurements of the waste cuddapah stone waste aggregate concrete with Pozzolana Portland cement (Fly ash based). They replaced the natural aggregate (NA) with cuddapah stone aggregate in 20%, 40% and 60% respectively and the specimens were tested for 7 and 28 days. The compressive strength results had shown that it increases up to 40% replacement of coarse aggregate replacement with cuddapah stone. After that there is a gradual decrement in compressive strength measurements of cuddapah stone waste aggregate concrete. Kore and vyas [5] were conducted an experimental investigation to examine the possibility of marble waste utilization in concrete industry as a coarse aggregate with different replacement levels. The results revealed that the workability of marble aggregate concrete mixes was 14% higher than that of made with NA. The replacement of marble aggregate also enhances the compressive strength by 40% and 18% at 7 and 28 days, respectively. The studies conducted by Binici et al. indicated that modified concretes containing made with marble and limestone dust have good workability, abrasion resistance and sulfate resistance. Binici et al. [6] also investigated the durability of the concretes prepared with marble and granite as coarse aggregate, river sand and ground blast furnace slag (GBFS) as fine aggregate. The results indicated that concretes with marble and GBFS was shown superior performance in terms of durability and bonding strength. Venkataramana et al. [7] studied the performance on steel fiber reinforced stone waste aggregate concrete with compressive and bearing strength measurements as the parameters. The different replacement levels of (25, 50, 75 and 100%) NA with stone waste produced from the stone polishing industries was used in concrete with and without steel fibers. The results had shown that 50% replacement of NA by stone waste aggregate is acceptable. The test results of Li et al. [8] revealed that adding marble dust as cement replacement can considerably increase the water resistance and carbonation. In the present experimental investigation, the compressive and split tensile strength of concrete made with various replacement levels of cement and coarse aggregate were investigated. In order to investigate the effect of metakaolin, the concrete specimens were prepared with various replacement levels (0%, 5%, 10%, and 15%) of cement by the metakaolin. Further, to investigate the utilization on Tadipatri black stone in concrete, the concrete was made with Tadipatri black stone waste aggregate (BSWA) with different replacement levels of NA.

2 Materials and Experimentation Concrete mixtures were made with water to binder (w/b) ratio of 0.5, two binders namely, Ordinary Portland cement (OPC) and OPC blended with MK and two types of coarse aggregates namely natural and Black stone waste aggregate. The replacement level of cement with MK are 5%, 10% and 15% and natural aggregate was replaced at different levels of 25%, 50%, 75% and 100% with BSWA. The chemical composition of MK and OPC, is shown in Table 1. River sand confirming to classification Zone II was used as fine aggregate. Black stone waste aggregate of maximum size 20 mm size and 10 mm was made by the crushing of raw stone waste material collected from the

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stone industry, Tadipatri. The performance concretes made with BSW aggregate was evaluated with reference to the concrete made with locally available normal crushed coarse aggregate. The specific gravity of sand, cement and MK were observed as 2.58, 3.05 and 2.50 respectively. The coarse aggregates of 20 mm and 10 mm MSA were mixed in the proportion of 60% and 40% respectively. The specific gravities of natural and BSW aggregate (combination of 20 mm and 10 mm aggregate) were determined as 2.66 and 2.68 respectively. Three replicates of concrete cube of size 150  150  150 mm for compressive strength and three replicates of cylindrical specimen of size 150 mm diameter and 200 mm height for tensile test were cast with each concrete mixture. Concrete specimens were casted by using an electrically operated mechanical mixer, for concrete mixing and vibrating table for good compaction. After 24 h the concrete specimens were demoulded and transferred into the water curing for 27 days. Table 1. Chemical composition of MK and OPC. Substance Al2O3 Fe2O3 SiO2 Cao Na2O TiO2 MgO LOI MK 44 1.2 52 0.5 0.56 0.4 0.4 1.5 OPC 6.02 3.77 21.04 62.93 0.46 – 2.49 1.63

3 Results and Discussion 3.1

Effect of Metakaolin

The compressive strength measurements of concrete cube specimens made with various replacement levels of MK at the of 28 days were shown in Fig. 1. From this figure, it is evident that the incorporation of MK increases the compressive strength of the concrete. It is also observed that the concrete with natural aggregate (i.e. 0% BSWA) has also shown developed compressive strength with increment of MK dosage. However, at 15% replacement level of MK with 75% of BSWA aggregate the strength was lowered as compared to compressive strength at 10% of MK. This can be due to the fact that the higher dosage of BSWA consumes more water, which results the insufficient of water for the pozzolanic reaction of MK. Figure 2 shows the combined effect of BSW aggregate and MK content on the compressive strength measurements of concrete cube specimens at the age of 56 days. As observed from Figs. 1 and 2, the incorporation of metakaolin not only increased the 28 days compressive strength, but also enhanced the strength measurements at 56 days. Concrete made with normal crushed aggregate the compressive strength was increased significantly, up to 10% of cement replacement with MK. The high compressive strength value of 35.5 MPa was observed at 50% BSWA concrete blended with 10% MK. However, the normal crushed aggregate concrete (i.e. 0% BSWA) has shown the highest compressive strength value of 30 MPa at 15% MK replacement level. It was evident from the compressive strength results that the effect of MK was lessened on the increment of compressive strength values of concrete made with BSW aggregates at higher dosage levels.

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Figure 3 shows the effect of BSW aggregates and MK addition on the splitting tensile strength measurements at the age of 56 days. The splitting tensile strength development trend was also similar to that of compressive strength of different replacement levels of BSWA concrete with 0%, 5%, 10% and 15% replacement levels of MK. From the Fig. 3, it was clear, that the splitting tensile strength measurements were increased with the replacement level of cement with MK.

Fig. 1. Compressive strength of BSWA concrete blended with different levels of MK at the age of 28 days.

Fig. 2. Compressive strength of BSWA concrete blended with different levels of MK at the age of 56 days.

Fig. 3. Splitting tensile strength of BSWA concrete blended with different levels of MK.

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Effect of BSW Aggregate

As already reported, the compressive strength measurements were taken on the compressive strength machine with concrete cube specimens. The compressive strength values of concrete made with 0% MK at 28 days were observed to be 29.77 N/mm2, 31 N/mm2, 31.8 N/mm2 and 30.5 N/mm2 at BSW aggregate levels of 0%, 25%, and 50% and 75%. The 28 days compressive strength values of the MK blended concretes with 75% of BSW aggregate have shown lesser strength values than that of the concrete made by OPC. But at 56 days age this strength values of concrete with 75% of BSW aggregate were slightly greater than the OPC concrete up to 5% addition of MK and decreased beyond 5% of MK. With the addition of MK the strength was increasing significantly with natural aggregate concrete, but the less strength development was observed with BSWA concretes. Generally, at lateral ages the strength development was resulted from the pozzolanic reaction of MK, which needs sufficient water to develop CSH gel and refine pore structure. Due to a higher dosage of BSW aggregate, more water was consumed by the BSW aggregates, which leads to insufficient of water for the progress of pozzolanic reaction. Due to this reason, as the MK content increases the strength of BSWA concretes is not increasing significantly. The similar trend was observed in the splitting tensile strength measurements of concretes made with 0%, 25%, 50% and 75% of BSW aggregate and 0%, 5%, 10% and 15% of cement replacement with MK. As shown in Table 2, it is observed that concrete splitting tensile strength is closely related to that of compressive strength. The most commonly 0.5 power relationship between the concrete splitting tensile strength and compressive strength was used. For the purpose of evaluating ACI model for the BSWA concrete blended with MK, the ratio of tensile strength to square root of compressive strength (a) values i.e., pffiffiffiffi a ¼ ft = fc were determined.

Table 2. Proposed modals by several authors S. no. Reference 1. ACI363R-92

Proposed function

2.

ACI318-99

ft ¼ 0:56ðfc Þ0:5

3.

Mohtarzadeg et al. ft ¼ 0:3ðfc Þ0:66 Olukun et al. ft ¼ 0:294ðfc Þ0:69 Neville ft ¼ 0:23ðfc Þ0:67

4. 5.

ft ¼ 0:59ðfc Þ0:5

From Fig. 4 it is observed that the a-values for various concrete mixtures prepared with different dosages of BSW aggregate and MK are in the range of 0.5 to 0.6. It implies that the present experimental data remarkably in agreement with the relation proposed by the authors ACI363R-92 between tensile and compressive strength of concretes.

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Fig. 4. Variation of a-values with replacement levels of BSWA and MK dosages

4 Conclusions Based on the experimental studies conducted on the utilization of stone waste as coarse aggregate in metakaolin blended concrete, the following conclusions were drawn: • The replacement of coarse aggregate with BSW aggregates up to 50% is beneficial regarding the both compressive and tensile strength measurements. • The addition of metakaolin improves the both compressive and tensile strength measurements of BSWA concrete. • The optimum contribution of metakaolin in enhancing the mechanical properties of BSWA aggregate concrete was obtained at 10% replacement of cement. • The strength values of BSWA concretes were lessened at higher replacement levels of metakaolin especially at 15% replacement level.

References 1. Bredy P, Chabannet M, Pera J (1989) Pore structure and permeability of cementitious materials. In: Roberts LR, Skalny JP (eds) Materials research society symposia proceedings, vol 137. Materials Research Society, Pittsburgh, pp 43 l–436 2. Brooks JJ, Johari MAM (2001) Effect of metakaolin on creep and shrinkage of concrete. Cem Concr Compos 23:495–502 3. Li Z, Ding Z (2003) Property improvement of Portland cement by incorporating with metakaolin and slag. Cem Concr Res 33:579–587 4. Rajendra D, Nilgiris T (2017) Effect of strength properties on concrete by partial replacement of coarse aggregate with waste cuddapah stones. Int J Eng Trends Technol 6:433–437 5. Kore SD, Vyas AK (2016) Impact of marble waste as coarse aggregate on properties of lean cement concrete. Case Stud Constr Mater 4:85–92 6. Binici H, Shah T, Aksogan O, Kaplan H (2008) Durability of concrete made with granite and marble as recycle aggregates. J Mater Process Technol 208:299–308

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7. Venkataramana N, Reddy Babu G, Babu UR (2018) Bearing strength of steel fibre reinforced black marble stone waste aggregate concrete. Mater Today: Proc 5:1201–1210 8. Li LG, Huang ZH, Tan YP, Kwan AKH, Liu F (2018) Use of marble dust as paste replacement for recycling waste and improving durability and dimensional stability of mortar. Constr Build Mater 166:423–432

Rapid Hardening on the Strength Gain Admixture on Behavior of Concrete with Replacement of Binary Cementitious Materials R. Kiranmai(&), A. Rohita Susheela, S. Rajkumar, G. Asheesh, and V. M. Sounthararajan Department of Civil Engineering, CMR Technical Campus, Kandlakoya (V), Medchal Road, Hyderabad 501 401, Telangana, India [email protected]

Abstract. The construction industry is being changing rapidly; this change brings in many new technologies with respect to composition, handling, mixing, etc. The laboratory experimental investigation promulgated to achieve the rapid hardening on replacing low calcium fly ash 0–15%, slag (GGBS) 0–25% of replacement of the OPC, stone dust 0–50% is replaced to river sand along with rapid hardening admixture and also inclusion of crimped steel fiber to obtain the maximum strength gain during the first seven days normal curing followed by four hours hot air oven curing for various mixes. Both the destructive test (DT) and the non-destructive test (NDT) were being performed. Keywords: Fly ash
GGBS
Rapid hardening admixture
Flexural strength
Ultrasonic pulse velocity (UPV)
Rebound hammer test

1 Introduction With the increase in the development in the countries around the world, the use of concrete is increasing which in turn results in great impact on the greenhouse effect. According to national Ready Mixed Concrete Association a total of one ton of carbon dioxide is emitted into the atmosphere during the process of cement manufacturing. It contributes about 5% of the greenhouse gases [1, 2]. To reduce the greenhouse effect the use of green building materials as a replacement of the cement are used. The byproduct wastes from various industries such as fly ash, GGBS, rice husk and silica fume are used as a replacement of cement. The paper focus, on achieving strength by replacing fly ash 0–15% and slag (GGBS) 0–25% with cement and stone dust about 0–50% with fine aggregate along with the use of rapid hardening admixture [3–7]. The binary cementitious materials produced from the industrial waste are used in varying proportions to achieve a great extent of strength in the concrete. Fly ash and slag are the major products contributing the waste generated from industries. Copper slag is a big by-product obtained from the smelting and refining of the copper used in the production of electricity [8, 9]. The addition of crimped steel fiber in varying proportions of 0–2% is added to the concrete which increases the durability and strength of © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 222–233, 2020. https://doi.org/10.1007/978-3-030-24314-2_29

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concrete, with the increasing percentage of steel fiber in the concrete there will be a control over the cracks developing in the structure and obtains the maximum strength [10–13]. The manufacturing of cement process is consuming the more energy, which results in emission of carbon dioxide and other greenhouse gases, these gases adversely effect on the environment. The examination is about study of impact of using fly ash with cement and slag with fine aggregate on characteristics of concrete. The characteristics of concrete are tested by both destructive and non-destructive test. The destructive tested conducted on the concrete cubes to determine the compressive strength as concrete is a major source for the compressive strength in the concrete, the strength is improved by the addition of rapid hardening admixture to increase the rate of hydration resulting in achieving more strength gain in concrete. The bending stresses developed in the concrete at tested with three point loading. The flexural strength is improved by the inclusion of crimped steel fibers for various mixes [14–17]. In this different samples were taken with different percentage of slag i.e. it varies from 0–60% and 20% of fly ash replaced with cement for every sample. By various quantities of slag and constant fly ash the compressive strength will be differ and compressive strength is more as compare to control specimen. If, copper slag more than 30% replacement with fine aggregate then the outcomes show the reduction of strengths because the water absorption of copper slag is very less. And this result shows that is 20% as fly ash substitution with cement and up to 30% as copper slag substitution with fine aggregate to acquire good mechanical properties of concrete [18–21]. The use of non-destructive test was conducted for the determining the compressive strength of concrete using rebound hammer and the quality of the concrete is determined with the help of UPV [22–25].

2 Materials 2.1

Cement

Ordinary Portland Cement (OPC) of grade 53 grade was used for preparing the structural concrete mix for all the batches in preparation of the prisms and cubes for destructive testing. The obtained test values of Portland cement is represented in the Table 1.

Table 1. Test values for Portland cement (physical properties) Name of the test Specific gravity of Portland cement Soundness Fineness (IS sieve 90 microns) Standard consistency test Setting time - Initial (minutes) Setting time - Final (minutes)

Obtained values 3.15 3.5 mm 3% 34% 98 250

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Fine Aggregate

The sand available locally is being replaced with stone/quarry dust with 50% of the total weight of fine aggregate required. The sand is sieved through IS 1.18 mm sieve and the stone dust is sieved through IS 2.36 mm sieve. The obtained test values of fine aggregate are given in Table 2. Table 2. Test values for fine aggregate (river sand) Name of the test Specific gravity Water absorption Fineness modulus Density (rodded)

2.3

Obtained values 2.72 1% 2.98 1750 kg/m3

Coarse Aggregate

Locally available crushed blue stone ballast was used. The coarse aggregate passing through 20 mm IS sieve and retaining on 12.5 mm sieve. The obtained test results values are given in Table 3. Table 3. Test values for course aggregate Name of the test Specific gravity Water absorption Fineness modulus Density (rodded)

2.4

Obtained values 2.78 1.0% 6.81 1830 kg/m3

Fly Ash

The low calcium class F fly ash used for the preparation of concrete as per IS ASTM C618 [26]. The color is grey and indicates good characteristics as shown in Fig. 1. The chemical composition of fly ash is rich in SiO2, Fe2O3 with Al2O3 content up to 65%. It is more suitable for binding content in OPC to produced good strength for different days. 2.5

GGBS

The slag produced from steel plant as a byproduct of smelting and refining process, which is more suitable for alternate cementitious materials as a partially replacement of Portland cement and image of slag (GGBS) as shown in Fig. 2.

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Fig. 1. Image of low-calcium fly ash

Fig. 2. Image of GGBS

2.6

Steel Fibers

Crimped steel fibers of 30 mm in length and a diameter of 0.5 mm was used for the preparation of the various mix. The aspect ratio of the steel fiber is 60 as shown in Fig. 3.

Fig. 3. Image of crimped Steel fibers

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Rapid Hardening Admixtures (RHD)

The early gradual strength gain is depending up on the addition of chemical admixtures. The chemical limits range was fixed based on the trial and error methods and exhibited the better improvement range starting from1500 ml–3000 ml for 50 kg quantity of binder content is used for different mixes and image of RHD is shown in Fig. 4.

Fig. 4. Image of rapid hardening admixture

2.8

Curing Methods

The method of curing employed was a regular water bath for 7 days and then dried in hot air oven for 5 h at 105–110 °C. The use of hot air oven increases the temperature up to four hours, thus resulting increases the rate of hydration occurred during the hardening of the concrete.

3 Experimental Program The M25 grade of standard design mix (1:1:2:0.45) consisting of Eight mixes were prepared in accordance with IS 10262-2019 [27]. The detailed mix proportion as represented in Table 4. 3.1

Compressive Strength Test

The axial crushing strength of concrete is determined for various proportions of concrete mix. The size of the cubes 150  150  150 mm is casted and used for conducting the test. The sample mix Id C1–C8 load acting on the cube space rate was constant.

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Table 4. M25 grade of concrete mix proportion details Mix ID

Cement required (kg/m3)

C1 C2 C3 C4 C5 C6 C7 C8

Cement Fly Ash 420 60 360 90 420 60 360 90 420 60 360 90 420 60 360 90

3.2

Slag 120 150 120 150 120 150 120 150

Fine aggregate (kg/m3) River Stone sand dust 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300

Coarse Aggregate (kg/m3) 1200 1200 1200 1200 1200 1200 1200 1200

Steel fiber

w/cm ratio (kg/m3)

%

Rapid hardening (ml for 50 kg of (kg/m3) cement)

1.5 1.5 2.0 2.0 1.5 1.5 2.0 2.0

36 36 48 48 36 36 48 48

270 270 270 270 270 270 270 270

18000 24000 18000 24000 30000 36000 30000 36000

Bending Stress Test

The flexural strength of prism is determined with varying percentage (0–2%) use of steel fiber to attain the maximum strength. The prisms of size 500  100  100 mm are casted and are tested. Three-point loading was used to determine the bending stress and the dial gauge was read to 1 division on the dial gauge as 1.25 kN of load. 3.3

Crack Depth by Using UPV

The UPV test mechanism is used to examine the quality owing to rapid hydration occurred privileged inside the concrete structures, thus indicating the good range of pulse velocity. Therefore, transit surface time taken and pulse velocity measurement values are recorded to determine the crack depth with and without crack. The calculation methodology as mentioned in Eq. (1) and the schematic line diagram as shown in Fig. 5.

Fig. 5. Schematic line diagram for measurement of crack depth in beam

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Without crack  path length ¼ 2x & Surface travel time Ts ¼ 2x V pffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2 Around the crack  path length ¼ 2 x2 þ h2 & Travel time Tc ¼ 2 xVþ h qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi crack depthðhÞ ¼ x ðTc=TsÞ2 1

3.4

ð1Þ

Rebound Hammer Test

The Nondestructive test (NDT) method was employed to calculate the cube strength of concrete as referred from standard chart with the help of a rebound hammer or rebound index. The index number was recorded due to the external force with perfect 90° angle applied on the top surface of concrete and noted all the average values for various mixes.

4 Experimental Test Results and Discussions 4.1

Compressive Strength of Concrete

Compressive strength of concrete (MPa)

The compressive strength test results of the specimen are shown in the Fig. 6. The design mix of M25 was used for the preparation of the samples and achieved the M35 grade of concrete, the best proportions was observed for mix containing of 60% OPC, 25% of slag, 15% of fly ash with rapid hardening chemicals for 3000 ml of 50 kg of cement content along with 2% of crimped steel fibers exhibited the higher ultimate load of 1064 kN (C8 mix ID) for 7days strength of 47.25 MPa. The mix ID C1 also had attained M35 at 7 days.

50 45 40 35 30 25 20 15 10 5 0

C1 (M35) C2 (M30) C3 (M25) C4 (M30) C5 (M30) C6 (M30) C7 (M30) C8 (M35) 43.64

38.65

36.5

38.3

39.7

37.45

Mix ID - Grade- Strength (MPa)

Fig. 6. Compressive strength of concrete at 7 days

40.27

47.25

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4.2

229

Bending Stress of Concrete

The bending stress of concrete test values for different curing days is shown in the Fig. 7. From the various experimental results, the sample C8 specimen obtained more strength compared to the other specimen. The mix proportions for the specimen C8 is 2% of steel fiber, 3000 ml rapid hardening admixture for 50 kg of cement, and the Portland cement is replaced with 15% of low-calcium fly ash, 25% of the slag. The bending stress (flexural strength) of concrete attains the maximum strength 4 N/mm2 and 4.5 N/mm2 for 7 and 14 days curing respectively and remains same for 14 days curing better results are expected for 28 days because all mix proportions of concrete test results values are increased in the case of compressive strength.

C1

C2

C3

C4

C5

C6

C7

C8

7 days

2.8

2.9

3

3

3

3.1

3.3

4

14 days

3.1

3

3.1

3.2

3.1

3.3

3.5

4.5

Bending stress of concrte (MPa)

5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 Mix ID

Fig. 7. Bending stress of concrete at different curing

4.3

Crack Depth by Using UPV

The maximum ultimate load carrying capacity of concrete is depending up on the usage of materials, mixing, handling and curing methods. An UPV technique is to measure the presence of cracks, inside transitional weaknesses zone and quality of concrete is identified in terms of pulse velocity as per IS 13311-1992 part 1 [28]. From Fig. 8 shows the difference in the depth of crack length measured manually and with UPV. The crack developed for the specimen C8 having composition, 60% OPC, 25% slag and 15% fly ash along with 2% of crimped steel fiber contribute the bridging the gap and observed the least crack depth of 73 mm while compared to others obtaining a maximum bending strength.

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Crack depth measured (mm)

120 100 80 60 40 20

Mix ID

0

C1

C2

C3

C4

C5

C6

C7

C8

Crack depth (mm)

96

95

62

86

71

91

86

73

Depth obtained (mm)

98

99

86

95

88

96

91

83

Fig. 8. Crack depth measured using UPV test results at 14 days

4.4

Rebound Hammer Test Results

Compressive strength (MPa)

50 45 40 35 30 25 20 15 10 5 0 C1 Mix ID Strength 42.5 Number

42

C2

C3

C4

C5

C6

C7

C8

40

34

37

31

34

35

47

40

36

38

34

36

37

45

Fig. 9. Compressive strength versus Rebound hammer test results

50 45 40 35 30 25 20 15 10 5 0

Rebound Number

By using the rebound hammer, the compressive strength of the cubes is determined and is shown in the Fig. 9. The rebound hammer gives the strength of the concrete based on

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the number a similar graph is drawn and the strength are determined as per IS 13311 part 2 [29]. The compressive strength of the specimen C1 and C8 have attained maximum strength for 7 days of curing.

5 Conclusion The following laboratory experimental test results are concluded as given below: • M25 grade of concrete casted and tested for 7 days curing and achieved the M35 grade of concrete for 7 days of curing followed by 6 h of hot air oven curing and similarly all mixes was tested. • The bending stress of concrete beams showed a maximum bending stress 4 N/mm2 for a mix proportion of 25% of slag, 15% of fly ash along with addition of 2% of crimped steel fibers for 7 days curing. • The inclusion of crimped steel fibers in varying proportions in the concrete mix decreases the internal cracks and minimizes the crack depth of the concrete beams. • The crack depth in the beams is minimized to a great extent with the use of 2% of crimped steel fiber in the mix proportions. • The concrete prepared is concluded to be green concrete by using supplementary cementitious materials which is collected from different industries like waste by product, and reducing the CO2 emission. • Usage of rapid hardening chemical admixture for various mixes of concrete exhibits the rapid hardening of concrete due to increase the hydration gel formation thus resulting the higher strength at an early age. • It is more suitable for fast track construction and more economical for structural concrete. Acknowledgement. The Authors is thankfully acknowledged to Hon’ble Chairman C. Gopal Reddy, Dr. A. Raji Reddy - Director and Prof. S. Vijaya Bhaskar Reddy- HOD/CED in CMR Technical Campus, Hyderabad – 501 401, Telangana, India for their motivations and providing laboratory facilities to carry out this research work. Conflicts of Interest. The authors declare that they have no conflicts of interest.

References 1. Tiwary A (2018) Effect of copper slag and fly ash on mechanical properties of concrete. Int J Civ Eng Technol (IJCIET) 9(7):354–362. http://www.iaeme.com/ijciet 2. Naga Srinu T, Murali K (2018) Mechanical properties of steel fiber reinforced geopolymer concrete incorporated with fly-ash & GGBS. Int J Civ Eng Technol (IJCIET) 9(3):789–797. http://www.iaeme.com 3. Varun BK, Harish BA (2018) Effect of addition of fly ash and GGBS on cement concrete in fresh and hardened state. Int J Adv Eng Res Dev 5(2). https://www.researchgate.net/ publication/323601780

232

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4. Fang G, Ho WK, Tu W, Zhang M (2018) Workability and mechanical properties of alkaliactivated fly ash-slag concrete cured at ambient temperature. Constr Build Mater 172:476– 487. www.elsevier.com/locate/conbuidmat 5. Ranjbaran F, Rezayfar O, Mirzababai R (2018) Experimental investigation of steel fiberreinforced concrete beams under cyclic loading. Int J Adv Struct Eng 10:49–60. https://doi. org/10.1007/s40091-018-0177-1 6. Gupta H, Saxena AK (2017) Strength properties of steel slag in concrete. Int J Eng Res Technol (IJERT) 6(11). http://www.ijert.org 7. Wang C-C, Wang H-Y, Chang S-C (2017) Prediction models of compressive strength and UPV of recycled material cement mortar. Comput Concr 19:419–427. https://doi.org/10. 12989/cac.2017.19.4.419 8. Sheral RB, Sutar D, Jagdane P (2016) Utilization of waste materials (GGBS + FLY ASH). Int J Latest Technol Eng Manag Appl Sci (IJLTEMAS) 5(7):1–5 9. Singh PR, Goel A, Thakur S, Shah ND (2016) An experimental approach to investigate effect of steel fibers on tensile and flexural strength of fly ash concrete. Int J Sci Eng Appl Sci (IJSEAS) 2(5):384–392 10. Renganathan T, Akiladevi AR, Deby Linsha R, Preethy Dharanya Y (2016) Comparative study on strength properties of concrete with different cementitious materials. Int J Eng Trends Technol (IJETT) 42(12):11–22. http://www.ijettjournal.org 11. Raju S, Dharma B (2016) Mechanical properties of concrete with copper slag and fly ash by DT and NDT. Periodica Polytech Civ Eng 60:313–322 12. Talakokula V, Vaibhav, Bhalla S (2016) Non-destructive strength evaluation of fly ash based geopolymer concrete using piezo sensors. Proc Eng 145:1029–1035. www.elsevier.com/ locate/procedia. International Conference on Sustainable Design, Engineering and Construction 13. Patnaik B, Seshadri Sekhar T, Chandra Sekhar B (2015) An experimental investigation on strength properties of copper slag fiber reinforced concrete. ARPN J Eng Appl Sci 10:1–12 14. Chen C-C, Diaz I, Menozzi K, Murillo A (2015) An experimental study on slag/fly ashbased geopolymer concrete. Int J Mech Prod Eng 3(8):13–17 15. Ashwin Balwaik S (2015) Efficiency of ultrasonic pulse velocity test in life of concrete structure. IOSR J Mech Civ Eng (IOSR-JMCE) 12: 1–6. www.iosrjournals.org 16. Siddharth, Munnur S (2015) Experimental study on strength properties of concrete using steel fibre and GGBS as partial replacement of cement. Int J Eng Res Technol (IJERT) 4 (1):1–5 17. Rathan Raj R, Ganesh Prabhu G, Perumal Pillai EB (2015) Flexural behavior of concrete beam with GGBS and fly ash as supplementary cementitious material. Int J Appl Eng Res 10:47. www.ripublication.comijaer.htm 18. Kim JS, Kim TH (2015) An ultrasonic pulse velocity test on fly-ash based geopolymer concrete in frequency domains. Appl Mech Mater 700:310–313. www.scientific.net 19. Lee H-S, Wang X-Y, Zhang N-A, Koh K-T (2015) Analysis of the optimum usage of slag for the compressive strength of concrete. Materials 8:1213–1229 20. Shah J, Sheth A (2014) Studying the effects of fibers and mineral admixtures on high strength concrete. Int J Innov Res Adv Eng (IJIRAE) 1(4):4. ISSN 2349–2163. http://ijirae. com 21. Anandan S, Vallarasu Manoharan S, Sengottian T (2014) Corrosion effect on the strength properties of steel fibre reinforced concrete containing slag and corrosion inhibitor. Int J Corros 2014:1–7. https://doi.org/10.1155/2014/5950404 22. Parmar A, Patel DM (2013) Experimental study on high performance concrete by using alccofine and fly ash - hard concrete properties. Int J Eng Res Technol (IJERT) 2(12). https:// www.researchgate.net/publication/321421440

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23. Nagalaxmi R (2013) Experimental study on strength characteristics on M25 concrete with partial replacement of cement with fly ash and coarse aggregate with coconut shell. Int J Sci Eng Res 4:1–11. www.ijser.org 24. Agunwambaa JC, Adagba T (2012) A comparative analysis of the rebound hammer and ultrasonic pulse velocity in testing concrete. Niger J Technol (Nijotech) 31:31–39 25. IS ASTM C618 Standard Specification for Coal Fly Ash and Raw or Calcined Natural Pozzolan for Use in Concrete 26. IS 10262-2019 Indian standard concrete mix proportioning – guidelines 27. IS 13311 Part 1 (1992) Non-destructive testing of concrete - methods of test part 1 ultrasonic pulse velocity 28. IS 13311 Part 2 (1992) Non-destructive testing of concrete-methods of test part 2 rebound hammer

Evaluation of Pullout Resistance of Reinforcing Strips Embedded in Cement Modified Marginal Backfill of Mechanically Stabilized Earth (MSE) Walls Pandu Kurre1(&) and Gannavaram Venkat Praveen2 1

Civil Engineering, Sri Indu Institute of Engineering and Technology, Hyderabad, India [email protected] 2 Civil Engineering, S. R. Engineering College, Warangal, India [email protected]

Abstract. Pull–out resistance of reinforcing materials is one of the internal stability criterions in Mechanically Stabilized Earth (MSE) construction. The pull–out resistance is normally calculated taking the friction coefficient equal to tan(d); d being the angle of internal friction in case of rigid reinforcing materials. For flexible/extensible reinforcing strips, the friction coefficient cannot be directly taken and in this direction, several attempts have been made by various researchers to evaluate suitable pull–out parameters. Further, the complexity increases within the use of marginal backfill soils wherever, suitable soils are not available. The present work is an effort to determine the pull–out parameters of different types of reinforcing materials embedded in marginal backfill soil without and with cement modification. The testing was carried out in laboratory using a test box and in the field by constructing model embankments. The pull– out factors F* and a as suggested by FHWA were evaluated using the test data and compared them with those obtained using conventional sand backfill. Keywords: Mechanically Stabilized Earth
Marginal soil
Cement modification
Geotextile reinforcement
Pull–out resistance

1 Introduction Mechanically Stabilized Earth walls gained prominence in place of conventional retaining walls in view of the inherent advantages of such systems. These walls consist of free draining frictional backfills reinforced with suitable materials having connected to facing units. Though the criterion for standard backfill is specified, at several work sites, such materials are not available in abundance and hence, the use of locally available marginal backfills is reported by various investigators (Saran 2006; Long et al. 2007). With this shift from standard backfills, few failures of reinforced soil structures were reported (Farrag 2004; Abu-Farsakh et al. 2007). It is reported that majority of failures were attributed to the use of poor quality backfills with high plasticity and low permeability (Bergado et al. 2001; Hayashi et al. 1999; Glendinning © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 234–247, 2020. https://doi.org/10.1007/978-3-030-24314-2_30

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et al. 2005; Biswas et al. 2003; Saran 2006; Lopez et al. 2005). Few attempts were also made by investigators to overcome the ill-effects of marginal backfills by the use of electrokinetic dewatering (Fourie et al. 2007), prestressing and preloading (Stamatopoulos and Kotzias 1985; Tatsuoka 2002) and the use of cementitious materials (Gill and Bushell 1992; Marchal et al. 1996; Lawson 2003; Tatsuoka 2004; Mohammad and Mohammad 2006). In order to evolve the design parameters for internal stability in case of cement modified backfills, an attempt is made to carryout pull – out tests on various types of reinforcing strips embedded in marginal backfills without and with cement modification. These results were compared with those obtained with sand as backfill.

2 Materials and Methodology The present investigation is undertaken to characterize the reinforced marginal soil without and with cement modification for which the following materials and methodology were adopted. 2.1

Materials Used

2.1 (a) Sand Sand classified under SP (poorly graded sand) was used as reference material to compare the performance of cement modified marginal soil. 2.1 (b) Marginal Soil Locally available soil was used to simulate the marginal backfill soil. The properties of both the sand and marginal soil were determined as per Bureau of Indian Standards (SP 36(Part 1): 1987) and presented in Table 1.

Table 1. Properties of sand and marginal soil Property Specific gravity Grain size distribution (%) Gravel Sand Silt Clay Atterberg limits Liquid limit, wl (%) Plastic limit, wp (%) Unified soil classification

Sand 2.65

Marginal soil 2.67

5 95 -

0 58 24 18

Non-Plastic SP

39 17 SC (continued)

236

P. Kurre and G. V. Praveen Table 1. (continued) Property Sand Marginal soil Compaction properties OMC (%) 12 16.5 1.58 1.78 MDD (Mg/m3) Shear strength parameters (i) UU condition cu (kPa) 52 /u 140 (ii) CD condition c′ (kPa) 0 20 0 /′ 41 290 −3 Coefficient of permeability, k (cm/sec) 1.25  10 2.48  10−6 Note: SC = clayey sand; OMC = optimum moisture content; MDD = maximum dry density; UU = unconsolidated undrained; cu = undrained cohesion; /u = undrained angle of internal friction; CD = consolidated drained; c′ = effective cohesion; /′ = effective angle of internal friction.

2.1 (c) Cement Ordinary Portland cement of 53 grade is used to modify the marginal soil. 2.1 (d) Soil–Cement The marginal soil–cement mixes with different cement contents were tested for their Atterberg limits and UCS values as given in Table 2. From this Table, it can be seen that the soil has become non-plastic (NP) at 2% cement content and in the present pull– out tests 3% cement content by dry weight of soil was used to avoid non uniform mixing of cement to soil. Table 2. Properties of marginal soil–cement mixes Property Atterberg limits (immediately after adding the cement) Liquid limit, wl (%) Plastic limit, wp (%) Atterberg limits (at 3 days curing period) Liquid limit, wl (%) Plastic limit, wp (%) Unconfined compressive strength (UCS) (at 3 days curing period for cement modified samples) kPa

Cement content 0% 2% 3%

5%

10%

39 17

35 17

34 18

30 20

NP

—

NP

NP

NP

NP

74

261

334

618

1216

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2.1 (e) Reinforcement The reinforcing materials used and their thickness and tensile strength are given in Table 3. The properties were determined as per the standard procedures (Mandal and Divshikar 2002). These reinforcements were chosen with variable tensile strength to distinguish the pull–out failure mechanisms of reinforced cement modified marginal soil. Table 3. Properties of reinforcing strips used for pull–out testing Type of reinforcing strip POLYFELT

WOVEN GEOTEXTILE (PD 381) FIBERTEX G–100 (Non-Woven Geotextile) Q 425 D (Non-Woven Geotextile) TENSAR GEOGRID (TYPE 1) TENSAR GEOGRID (TYPE 2) GEOSTRAP CHICKEN MESH

WELDED WIRE MESH

WELDED ROD G I STRIP BAMBOO

Property Material: Polypropylene, (Non-woven web with woven interlaces) Thickness: 3 mm under 2 kPa, Grab strength: 28 kN/m Material: Polypropylene Thickness: 0.62 mm under 2 kPa, Grab strength: 18 kN/m Material: Polypropylene Thickness: 0.6 mm under 2 kPa, Grab strength: 4 kN/m Material: Polypropylene Thickness: 3.80 mm under 2 kPa, Grab strength: 8.3 kN/m Material: Polypropylene Hexagonal openings of 26  28 mm, Grab strength: 7 kN/m Material: Polypropylene Diamond shaped openings of 6  8 mm, Grab strength: 5 kN/m Material: Polyester Thickness: 0. 7 mm, Grab strength: 9 kN/m Material: Galvanized iron Diameter of wire: 0.6 mm, Size of opening: 6  6 mm, Yield strength: 260 MPa Material: Mild steel, Diameter of wire: 1.2 mm, Size of opening: 18  15 mm Yield strength: 250 MPa Material: Mild steel, Diameter of rod: 6 mm, Yield strength: 250 MPa Material: Galvanized iron, Thickness: 0.9 mm, Yield strength: 260 MPa Material: Organic, Thickness: 2 mm, Yield strength: 70 MPa

3 Laboratory Tests Laboratory pull–out tests were performed to determine the soil–reinforcement interaction parameters. The fill materials used were marginal soil and sand for this testing.

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Testing Procedure

The pull–out test apparatus consists of a square box of size 0.30 m  0.30 m  0.30 m, made of 8 mm thick mild steel plates. The schematic illustration of experimental set up is shown in Fig. 1. The size of the box is so chosen that the pull–out tests would be done on smaller length of reinforcing strips such that they tend to slip rather than anchor themselves within their tensile strength. On one side of the box, a horizontal sleeve was made to allow the reinforcing strip to be pulled through. The reinforcing strips of 10 cm wide with different embedment lengths (10, 20 and 30 cm) were placed over the compacted surface of the respective fill and an over hanging of the strip was left for connection (Fig. 2). The upper portion of the box was also filled with same backfill and compacted in 5 cm thick layers at its optimum moisture content to its maximum dry density using a hand hammer. To minimize the friction between soil and side walls of the box, grease was applied on the internal sides of the test box. The friction between the strips and the front wall of pull–out box was assumed to be negligible due to sufficient clearance while no soil escapes through it (Palmeira 2004).

Proving ring & Dial gauge

Normal stress Metal plate

Electric motor Movable ram

Gearbox Pedestal

Clamp Sleeve Reinforcing strip Pull–out Box

Fig. 1. Schematic illustration of the pull–out unit during testing.

Fig. 2. Experimental set up to perform pull–out tests on reinforcing strips.

The protruding part of the respective reinforcing strip was clamped at the end by steel plates with nut and bolt arrangement (Fig. 2) and then connected to a tension proving ring attached to motorized movable ram of specially fabricated pull–out testing unit.

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The test box was mounted on the pedestal of a compression testing machine and the respective normal stress was applied through a proving ring on the top of pull–out box using a steel plate that exactly fits into the box. A series of pull–out tests were carried out under varied normal stresses (7.5, 15 and 22.5 kPa) using different types of reinforcing strips embedded in marginal soil without and with 3% cement modification and also in plain sand. The pull–out load was recorded from tension proving ring while the displacement was obtained from dial gauge readings. In case of sand sample, cotton fabric was used near the sleeve to prevent the fall of sand. The peak pull–out loads were obtained by drawing the pull–out load versus pull– out deformation plots. These peak pull–out loads were divided by the gross area of the respective strip to get the average pull–out resistance (Abu-Farsakh et al. 2007). Then the pull–out resistance versus normal stress plots were drawn to get the pull–out interaction parameters (ca and d, the adhesion value and apparent friction angle respectively). However, these parameters can not be directly used in design calculations especially for flexible reinforcing strips due to their extensibility and hence, pull–out design factors, namely, F*(pull–out resistance factor) and a (scale effect correction factor) were introduced in calculations (FHWA 1998; Mohiuddin 2001; Farrag 2004; Abu-Farsakh et al. 2007). Pull–out resistance (Pr) is calculated using the above factors per unit width using the following equation. Pr ¼ F
a
rn
Le
C Where, F* is the pull–out resistance factor, a is scale effect correction factor, rn is the normal stress, Le is the length of the embedment of the strip and C is generally taken as 2. The values of F* and a depend on the type of reinforcement and its geometry, length and confining pressure. Generally these parameters are determined from the lab and field pull–out tests. F
a is given by Ci tan/ Where, Ci ¼ ðrn tand þ ca Þ = ðrn tan/ þ cÞ The previously obtained ca and d from the pull–out tests are used in the calculation of Ci under different test conditions. The F* and a values were calculated from pull–out test results. These test results were presented in Tables 4 and 5.

4 Field Tests Field pull–out tests were carried out on different reinforcing strips embedded in model embankments constructed using locally available marginal soil without and with cement modification to investigate their pull–out resistance. This work is intended to study the feasibility and practicality of using marginal soils in the field in comparison with conventional backfill and also to ascertain the pull–out resistance of different types of

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reinforcing materials embedded in such fills. These tests were performed at optimum moisture content and in fully wet condition in case of plain marginal soil; and after 7 days of curing period under inundation in case of cement modified marginal soil. The pull–out tests were also conducted on strips embedded in sand at optimum moisture content. The model walls of 1.60 m height, 1.50 m wide and 3.50 m long were constructed with vertical faces. The vertical faces were maintained with the help of steel posts and bamboo mats. The backfill was mixed with water content equal to its optimum moisture content and compacted in 5 cm thick layers to its maximum dry density using a 2 ton stone roller up to a lift thickness of 40 cm. The maximum dry density was controlled by core cutter method. Then the different types of reinforcing strips of 10 cm wide and 35 cm long were placed on the leveled compacted surface on both the sides of wall in replication, in order to conduct the tests both at OMC and after saturation. A metal sleeve was used at the pull–out end within 5 cm to avoid the near face distribution. Similar process was adopted at other lift thicknesses also i.e. at 0.80 m and 1.20 m height. In case of cement modified marginal soil, the pull–out tests were done both at 7 days of moist curing and also after full inundation for 3 days. The pull–out tests were carried out using a specially fabricated pull–out testing apparatus with a strain rate of 1.25 mm/min. Figure 3 shows the test set up on model embankment. The overhanging portion of the reinforcing strips were clamped to the extension plate using nut and bolt system which in turn was connected to the pull–out testing unit through tension proving ring. The dial gauge was attached to the welded flat using magnetic base to measure horizontal displacement of the respective reinforcing strips. The maximum pull–out load obtained from tension proving ring reading was used to determine the soil–reinforcement interface frictional resistance. Further, the pull–out design factors F* and a were calculated using a similar procedure explained under laboratory pull–out testing.

Fig. 3. Embedment of various types of reinforcing strips on compacted backfill at different elevations and pull–out test set up in the field.

5 Discussion on Results 5.1

Laboratory Test Results

These tests were carried out on different reinforcing strips embedded in a pull–out box and pulled at different normal stresses. Typical pull–out load versus pull–out deformation

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241

plot is shown in Fig. 4 for Polyfelt (non-woven) geotextile strip. It can be seen that the pull–out load is steadily increased with pull–out deformation beyond the initial extension of the strip and thereafter, it is decreased. In case of metallic strips, the pull–out load is steadily increased with deformation up to peak value and then decreased. Similar plots were drawn for all the strips under different normal stresses and peak pull–out loads were obtained. As already explained, the peak pull–out load is divided by the original area of the strip (2 sides) to get the average peak pull–out stress (Abu-Farsakh et al. 2007) for a given normal stress.

Fig. 4. A typical plot between pull–out load versus pull–out deformation.

6 Field Pull–Out Test Results These tests were carried out on the same reinforcing strips embedded in model embankments using a specially fabricated pull–out testing unit. The calculation procedures are similar to those for laboratory pull–out tests (Table 4).

7 Interaction Parameters (Ca and d) These parameters were obtained from the plots drawn between peak pull–out stress and normal stress. The typical plot is shown in Fig. 5 for chicken mesh reinforcing strip. The intercept on peak pull–out stress axis is referred to as ca (adhesion intercept) and the slope of the line as d (apparent interface friction angle). These values obtained from laboratory and field pull–out tests for different test conditions are presented in Tables 4 and 5. The rest of the calculation to find F* and a were made by following the procedure already outlined. It can be observed from these tables that the pull–out design factors, F* and a values are ranging from 0.3 to 1.9 and 0.2 to 0.6 respectively. The lower values of these parameters are pertinent to the more extensible materials like geogrid and geonet for both the test moisture contents i.e., optimum moisture content and fully wet condition.

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Fig. 5. A typical plot between peak pull–out stress and normal stress.

For all the reinforcing strips, these parameters were reduced by 50–60% upon wetting for both laboratory and field testing. These parameters were restored back to their presoaked values upon 3% cement modification of the marginal backfill. The laboratory values are about 85 to 95% of those from field tests. These observations are close to those made by others (Farrag 2006). Among the geotextiles, higher values of pull–out design factors (F* and a) were obtained for polyfelt compared to other fabrics. This could be supported by its higher tensile strength and rough surface due to longitudinal laces. The descending order of values of F* and a are observed for polyfelt followed by PD381 and Q425D. The value of F* bears a good relationship with tensile strength of fabric reinforcement, whereas a values are not significantly influenced by tensile strength. For any fabric type, the F* value is slightly decreased with normal stress. Similar observations were also made by others (Mohiuddin 2001; Farrag 2004; Abu-Farsakh 2006). In case of geonet and geogrid, the F* and a values are significantly lower than fabrics due to their higher extensibility as observed during testing. The lower d values also indicate their inadequate pulling out of the fill. In case of low extensible materials like geostrap, bamboo and metallic strips, the F* and a values are observed to be higher than for geosynthetic strips. This could be supported by their ready slippage during pull–out testing. Further, it can be noted that the F* and a values for 3% cement modified marginal soil are almost close to or in certain cases higher than those obtained in sand backfill. It is also interesting to note that the smooth surfaced fabrics have shown relatively lower F* and a values in sand compared to those in cement modified backfill. This could be supported by the fact that the adhesion component in cement modified backfill is higher than that in sand backfill. However, in case of geogrid and geonet, higher values of F* and a were obtained in sand backfill than in cement modified backfill. This could be due to more interlocking and dilatancy effects of sand backfill compared to cement modified backfill. Such observations were also made by other investigators (Johnston and Romstad 1989; Hayashi et al. 1995).

Welded rod

Welded Wire mesh

Chicken mesh

Type of reinforcement

Field

Lab

Field

Lab

Field

Lab

7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5

Ca do Ci 19 27 0.95 0.91 0.89 21 30 1.05 1.05 1.03 13 35 0.76 0.75 0.70 15 40 1.08 0.98 1.02 21 31 1.06 1.07 1.02 25 37 1.27 1.29 1.27

Pull–out Normal stress Marginal soil test (kPa) At OMC a F* 0.45 1.12 1.00 1.02 0.50 1.15 1.15 1.13 0.45 1.05 1.00 0.95 0.51 1.14 1.05 1.10 0.45 1.45 1.39 1.20 0.50 1.45 1.42 1.39

Ca do Ci 17 22 0.83 0.79 0.77 18 25 0.89 0.88 0.87 7 24 0.43 0.40 0.35 9 26 0.52 0.58 0.60 10 8 0.46 0.41 0.39 15 9 0.69 0.63 0.57

At saturation A F* 0.37 1.00 1.00 0.98 0.4 1.02 0.96 0.92 0.41 0.78 0.81 0.78 0.41 0.69 0.78 0.81 0.30 1.10 1.06 0.99 0.29 1.20 1.15 1.09

At 7 days of curing period Ca do Ci a 31 18 1.45 0.55 1.39 1.35 32 19 1.51 0.60 1.38 1.28 20 29 0.94 0.50 1.01 1.19 25 41 1.30 0.55 1.34 1.37 25 29 1.21 0.48 1.15 1.01 27 28 1.29 0.51 1.24 1.18

Table 4. Laboratory and field pull–out test results

F* 1.40 1.31 1.28 1.45 1.26 1.17 1.20 1.12 1.05 1.30 1.34 1.37 1.37 1.33 1.29 1.39 1.34 1.27

Ca do Ci 9 18 1.75 1.59 1.45 10 17 1.89 1.25 1.20 10 19 1.93 1.00 0.77 12 16 1.89 1.25 0.92 19 21 1.55 1.45 1.36 19 18 1.75 1.05 0.98

Sand At OMC a F* 0.58 0.69 0.78 0.90 0.61 0.70 1.00 1.75 0.50 1.77 1.05 1.09 0.56 1.85 1.25 0.96 0.54 1.51 1.45 1.56 0.58 1.69 1.50 1.40 (continued)

Evaluation of Pullout Resistance of Reinforcing Strips 243

Bamboo

GI strip

Type of reinforcement

Field

Lab

Field

Lab

7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5

Ca do Ci 27 18 1.22 1.12 1.00 25 25 1.14 1.14 1.07 9 38 0.65 0.61 0.58 10 43 0.72 0.86 0.96

Pull–out Normal stress Marginal soil test (kPa) At OMC a F* 0.41 1.45 1.32 1.00 0.45 1.40 1.39 1.31 0.34 1.28 1.30 1.35 0.29 1.34 1.62 1.82

Ca do Ci 2 13 0.15 0.12 0.10 1 14 0.11 0.16 0.20 3 15 0.25 0.21 0.18 6 16 0.35 0.38 0.39

At saturation A F* 0.18 0.35 0.31 0.36 0.20 0.30 0.29 0.55 0.17 1.34 1.28 1.43 0.15 1.26 1.40 1.40

Table 4. (continued) At 7 days of curing period Ca do Ci a 18 23 0.88 0.48 0.87 0.83 19 29 0.96 0.55 0.97 0.95 16 40 0.92 0.33 1.04 1.13 15 42 0.92 0.28 1.02 1.10 F* 1.01 0.97 0.78 0.96 0.97 0.95 1.56 1.30 1.62 1.78 2.00 2.14

Ca do Ci 10 15 1.84 1.57 0.91 9 18 1.75 1.06 0.81 8 21 1.67 1.05 0.74 8 17 1.08 0.96 0.74

Sand At OMC a F* 0.43 1.26 1.01 0.98 0.50 1.93 1.17 0.89 0.48 1.68 1.43 1.29 0.50 1.70 1.53 1.22

244 P. Kurre and G. V. Praveen

Q425D

PD 381

Polyfelt

Type of reinforcement

Field

Lab

Field

Lab

Field

Lab

7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5

Ca do Ci 16 13 0.73 0.69 0.64 22 15 0.99 0.91 0.86 14 20 0.69 0.67 0.66 15 21 0.74 0.73 0.72 10 10 0.47 0.45 0.42 11 10 0.51 0.48 0.46

Pull–out Normal stress Marginal soil test (kPa) At OMC a F* 0.40 1.00 0.94 0.88 0.38 1.44 1.31 1.23 0.40 0.95 0.92 0.90 0.40 1.02 1.00 1.97 0.31 0.83 0.79 0.72 0.32 0.88 0.81 0.78

Ca do Ci 11 10 0.51 0.48 0.45 21 7 0.90 0.80 0.73 12 10 0.55 0.52 0.44 13 10 0.59 0.55 0.52 5 12 0.29 0.27 0.26 5 12 0.27 0.28 0.30

At saturation A F* 0.25 1.22 1.02 0.99 0.26 1.28 1.09 1.03 0.27 1.12 1.05 0.89 0.25 1.28 1.20 1.12 0.28 0.56 0.53 0.51 0.25 0.56 0.60 0.64

At 7 days of curing period Ca do Ci a 24 21 1.11 0.41 1.06 0.98 32 25 1.49 0.44 1.39 1.32 30 14 1.32 0.42 1.20 1.07 32 25 1.49 0.43 1.44 1.38 21 5 0.90 0.40 0.79 0.69 25 5 1.06 0.38 0.92 0.83

Table 5. Laboratory and field pull–out test results

F* 1.48 1.42 1.31 1.87 1.75 1.66 1.72 1.57 1.40 1.90 1.83 1.76 1.23 1.08 0.95 1.52 1.31 1.21

Ca do Ci 5 15 0.91 0.89 0.75 5 17 1.12 0.73 0.69 5 14 0.97 0.83 0.81 6 15 1.23 0.77 0.60 4 11 0.63 0.53 0.41 4 10 0.81 0.51 0.39

Sand At OMC a F* 0.49 1.02 0.99 0.84 0.50 1.85 1.21 1.14 0.44 1.21 1.03 1.01 0.43 1.37 1.18 1.15 0.39 0.88 0.74 0.60 0.51 1.31 0.83 0.63 (continued)

Evaluation of Pullout Resistance of Reinforcing Strips 245

Geostrap

Geonet

Geogrid

Fibertex G – 100

Type of reinforcement

Field

Lab

Field

Lab

Field

Lab

Field

Lab

7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5 7.5 15 22.5

Ca do Ci 23 12 1.02 0.93 0.89 25 25 1.14 1.14 1.07 7 10 0.34 0.33 0.31 7.5 8 0.35 0.33 0.32 6 10 0.31 0.30 0.28 5 5 0.21 0.20 0.19 13 31 0.78 0.74 0.72 12 30 0.69 0.74 0.79

Pull–out Normal stress Marginal soil test (kPa) At OMC a F* 0.41 1.32 1.27 1.20 0.45 1.40 1.39 1.31 0.32 0.58 0.56 0.53 0.33 0.57 0.54 0.51 0.22 0.77 0.75 0.70 0.22 0.50 0.50 0.45 0.25 1.71 1.56 1.58 0.25 1.52 1.64 1.72

Ca do Ci 3 12 0.19 0.15 0.12 1 14 0.11 0.16 0.20 6 8 0.29 0.28 0.27 5 8 0.27 0.26 0.25 3 5 0.15 0.13 0.10 4 5 0.18 0.17 0.16 4 21 0.38 0.34 0.32 3 31 0.31 0.42 0.50

At saturation A F* 0.20 0.31 0.29 0.32 0.20 0.30 0.29 0.55 0.24 0.56 0.44 0.31 0.24 0.62 0.58 0.54 0.20 0.41 0.35 0.27 0.18 0.50 0.50 0.44 0.16 1.30 1.16 1.10 0.17 1.00 1.35 1.58

Table 5. (continued) At 7 days of curing period Ca do Ci a 20 24 0.97 0.51 0.91 0.89 19 29 0.96 0.55 0.97 0.95 8 10 0.38 0.30 0.36 0.33 8.5 8 0.39 0.31 0.37 0.35 7 11 0.35 0.21 0.31 0.29 5 8 0.21 0.22 0.20 0.19 14 30 0.76 0.28 0.80 0.81 14 31 0.76 0.27 0.81 0.84 F* 0.94 0.91 0.89 0.96 0.97 0.95 0.70 0.66 0.60 0.67 0.64 0.61 0.91 0.81 0.75 0.50 0.50 0.45 1.49 1.57 1.59 1.51 1.57 1.64

Ca do Ci 7 15 1.05 0.98 0.93 9 18 1.25 1.06 0.81 4 14 0.93 0.91 0.89 5 13 1.03 0.65 0.51 4 15 0.91 0.90 0.88 6 15 0.93 0.77 0.60 7 17 1.42 1.31 1.29 8 18 1.56 0.94 0.76

Sand At OMC a F* 0.45 1.56 0.99 0.89 0.50 1.93 1.17 0.89 0.40 1.27 1.25 1.22 0.42 1.95 1.28 1.00 0.30 1.66 1.65 1.61 0.38 1.03 1.67 1.31 0.31 1.51 1.32 1.28 0.61 1.12 1.27 1.03

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8 Summary and Conclusions Pull–out resistance of reinforcing materials embedded in backfill soil is one of the essential requirements of internal stability of reinforced soil structures. Though the soil–reinforcement interaction can be studied by direct shear testing, pull–out testing was considered to be superior as it better represent the in-situ conditions. Several investigators have attempted the pull–out testing on different reinforcing materials with varying test conditions to evaluate the pull–out parameters. The complexity of their assessment increases further with the use of marginal backfills which are susceptible for moisture variations. The present work is attempted to overcome the deficiencies of marginal backfill soils by adopting cement modification and study the pull–out resistance of different types of reinforcing materials embedded in marginal backfill soil without and with cement modification and compared the results with that of conventional sand. The marginal soils could retain its resistance to pull–out of reinforcement even under fully wet condition by 3% cement modification at which the soil became nonplastic while retaining the flexible nature of reinforced soil. The pull–out factors a and F*, as envisaged by FHWA to account for extensibility of flexible reinforcing materials and the geometric variations were determined for different reinforcing materials under different test conditions. The values of these factors are observed to be in the range of those reported in the literature and close to those obtained in sand backfill when marginal soil is modified by 3% cement. In case of virgin marginal soil, the pull–out resistance is reduced to about one-third of its value at optimum moisture content.

References Fourie AB, Johns DG, Jones CJFP (2007) Dewatering of mine tailings using electrokinetic geosynthetics. Can Geotech J 44:160–172 Vidal H (1978) The developments and future of reinforced earth. In: Proceedings of symposium on earth reinforcement. ASCE, Pittsburgh Bergado DT, Teerawattanasuk C, Wongsawanon T, Voottipruex P (2001) Interaction between hexagonal wire mesh reinforcement and silty sand back fill. Geotech Test J 24(1):23–38 Glendinning S, Jones CJ, Pugh RC (2005) Reinforced soil using cohesive fill and electrokinetic geosynthetics. Int J Geomech 5(2):138–146 Saran S (2006) Reinforced soil and its engineering applications. IK International PVT Ltd., New Delhi Tatsuoka F (2002) Geosynthetic-reinforced soil retaining wall as permanent structures. In: Indian geotechnical conference, vol 2, pp 681–699 Mohammad JK, Mohammad A (2006) Durability and mechanistic characteristics of fibre reinforced soil-cement mixes. Int J Pavement Eng 7(1):53–62 Abu-Farsakh M, Coronel J, Tao M (2007) Effect of soil moisture content and dry density on cohesive soil–geosynthetic interactions using large direct shear tests. J Mater Civ Eng 19:540–549

Identification of Artificial Recharge Zones Using GIS K. A. Patil1, Noopur D. Khatik1, and Shriman N. Jirapure2(&) 1

Civil Engineering, College of Engineering, Wellesley Road, Shivajinagar, Pune 411005, Maharashtra, India {kap.civil,khatiknd17.civil}@coep.ac.in 2 College of Engineering, Wellesley Road, Shivajinagar, Pune 411005, Maharashtra, India [email protected]

Abstract. Water is one of the most dynamic and important resources of the world. Hence, it needs special attention towards its conservation. Fresh water is mainly obtained either from surface source or as a ground water. Artificial recharge is the most appropriate practice, which traps the unused surface runoff which leads to increase the groundwater recharge. The present study aims to identify artificial recharge zones. Formation of different thematic layers such as soil, slope, land use, drainage and geomorphology were carried out by using Remote Sensing (RS) and Geographical Information System (GIS). These maps were combined in GIS tool to categorise artificial recharge zones. The knowledge-based weightage was assigned. These thematic maps were used for weighted overlay analysis and the final map was prepared showing artificial recharge zones which are categorised as good, moderate and poor. The information regarding groundwater recharge zones is useful for locating water harvesting structures. Keywords: Artificial recharge structure Thematic maps and GIS




Ground water potential zones




1 Introduction In our day to day life, water is most abundantly used natural resource. Day by day, it is exhausting at very fast rate in both rural as well as in urban areas. This is probably due to increase in per capita demand and irrigation. Ground water is attracting an attention due to scarcity of good quality water. In overcrowded country like India, requirement of ground-water resource is very high. Due to irregularity in monsoon and mismanagement leads to depletion of ground-water levels. This results in increase in the operational costs which leads to increase in investment. To overcome this problem there is a need to increase recharging potential of aquifers by providing artificial recharge structures. So, resourceful planning and management of sub-surface water is getting more importance. For delineating the artificial recharge zones, GIS tool found to be very effective [1].

© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 248–257, 2020. https://doi.org/10.1007/978-3-030-24314-2_31

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GIS is highly accurate and less time-consuming tool. GIS offers skill to analyse the spatial and non-spatial data to fulfil desired objectives. It allows us to develop different thematic layers and merge these thematic layers into a single layer. Modern Remote Sensing tools enable delineation of required areas for groundwater replenishment. It also accounts the diversity of different factors which influences groundwater recharge like geomorphologic parameters including faults, lineaments, land use and soil etc. These factors control ground water potentiality.

2 Study Area A study area is a sub watershed of BM 49. Figure 1 shows the details of study area i.e. Kurukumbh watershed, which lies between latitude 18° 25′ to 18° 15′ N and longitude 74° 25′ to 74° 49′ E in upper Bhima watershed basin in Daund Taluka of Pune district, Maharashtra. This covers an area of approximately 43.15 km2 and perimeter 35.92 km. The study area falls in the semi-arid zone and the average temperature of the study area ranges between 24 °C to 40 °C in summer season and goes down to 10 °C during winter season. The maximum relative humidity ranges 70% to 80% during the rainy season, while in summers it lies near to 30%. Winds flows from light to moderate in dry season. The rainy season starts from second or third week of June and continues till the last week of the September. The region receives its annual precipitation from south west monsoon. The average annual precipitation varies from 400 to 600 mm. The highest elevation ranges from 612 m to lowest elevation of 420 m above the mean sea level.

Fig. 1. Study area

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3 Materials and Method Five hydro-geological parameters that are drainage, slope, soil, geomorphology and land use have been considered to find out the artificial recharge zones. Various maps were obtained from satellite images and existing data sets of respective departments. Thematic maps for this hydro- geological parameter were obtained, categorised, weighted, and combined into a GIS environment. Figure 2 shows the stepwise methodology for preparation of map [2, 5]. Different data such as satellite images, DEM and soil map were obtained from National Bureau of Soil Survey (NBSS) and Land Use Planning (LUP), Nagpur. Land use map was prepared by using IRS-LISS-III data obtain from Bhuvan site which is Indian geo-platform of ISRO for month of January 2016. DEM from Shuttle Radar Topographic Mission (SRTM) having 30 m resolution was used for delineation of watershed and to obtain the slope map. The methodology adopted for the work is as given below: 1. Data compilation. 2. Marking of boundary of study area. 3. Geo-processing of Digital Elevation Model (DEM) and satellite image using Geomantic software. 4. Extraction of geomorphological, land use, drainage, soil and slope etc. in Arc-GIS tool. 5. Spatial and non-spatial data integration GIS environment. 6. Weighted overlay analysis is conducted in GIS environment. 7. Preparation of map showing suitable site for artificial recharge zone.

Fig. 2. Flow chart of methodology

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4 Analysis and Results Soil, geomorphology, drainage, slope and land use plays critical role for identification of artificial recharge zones. Information regarding various thematic maps is given below: 4.1

Soil

There are three types of soils and have been delineated in the study area based on hydrological conditions and details are as shown in Fig. 3. These are very shallow, well drained and loamy calcareous soils found on mildly inclined undulating area having reasonable erosion which are categorised as good, slightly deep, moderately well drained, fine soils which found in very gently sloping plains and moderate erosion categories as moderate. Shallow, well drained, clayey and moderately calcareous soil on gently sloping lands which categories as poor. 4.2

Geomorphology

Geomorphology of the study area is categorised into three different classes i.e. structurally origin and moderately dissected upper plateau shown in Fig. 4. Plateaus are flat topped residual mountains and having high runoff zone. This show green in colour in map and is classified as poor, it accelerates more run off so that it is treated as poor artificial recharge zone. Another one is undulation origin and pediment-pedi plane complex, which have characteristics of highly porous, permeable and having high infiltration rate. This is good for providing artificial recharge structures. Most of the agricultural land of the study area is in pediment-pedi plane complex region. Table 1 shows area under different types of soil and Table 2 shows geomorphology of the study area.

Fig. 3. Soil map

Fig. 4. Geomorphology map

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K. A. Patil et al. Table 1. Type of soil and area

Sr. no 1

2

3

4.3

Type of soil

Area Area (km2) (%) 36 83.45

Very shallow, well drained, loamy calcareous soil 4.27 9.89 Slightly deep, moderately well drained, fine soils 2.87 6.66 Shallow, well drained, clayey moderately calcareous soil Total 43.14 100

Table 2. Geomorphology area Sr. no 1

2

3

Description

Area Area (km2) (%) 32.14 74.50

Undulation origin and pediment pedi plane complex 10.68 24.75 Structurally originmoderately dissected upper plateau Water body 0.32 0.75 Total 43.14 100

Slope

The contour is one of the important terrain parameters to find slope. On the basis of topography and DEM, the study area is classified from undulating terrain having steeply sloping hill to plane surface. Slope represented in degree which varies from 0 to 22. On the basis of degree of slope, slopes are divided into five classes as shown in Fig. 5. Slope in the range of 0 to 2° is very good zone and is classified as very gently slope, which allows more time to percolate the surface runoff is considered good for artificial recharge zone. Slope in the range of 2 to 4° as good zone and is gentle slope. Slope in the range of 4 to7° is a moderate zone and moderate slope. Slope in the range of 7 to 11° as poor zone which is moderately steep slope. Slope in the range of 11 to 20° as very poor zone and is steep slope, which having large surface flow with low residential time and results in fairly less penetration of water causing low ground water potential zone (Tables 3 and 4).

Fig. 5. Slope map

Fig. 6. Land use map

Identification of Artificial Recharge Zones Using GIS Table 3. Slope gradient and area Sr. no Slope (degree) 1 0–2 2 2–4 3 4–7 4 7–11 5 11–20 Total

4.4

Area (km2) 19.32 13.69 5.59 3.15 1.39 43.14

Area (%) 44.78 31.73 12.96 7.31 3.22 100

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Table 4. Land use area Sr. no 1 2 3 4 5

Type of land Agriculture land Built-up Barrel land Fallow land Water body Total

Area (km2) 14.63

Area (%) 33.91

13.16 12.52 1.60 1.23 43.14

30.50 29.03 3.71 2.85 100

Land Use

Remote sensing plays very vital role in land use mapping and to identify groundwater resources. Land use controls infiltration, evapo-transpiration and surface runoff in the water cycle. Study area consists of water body, agriculture area, fallow land, barren land and built up area as shown in Fig. 6. From land use consideration, built-up land and barrel land runoff is high and infiltration rate is low so it is categorised as poor. Fallow land as moderate ground water prospect, and water body and agriculture land have high infiltration rate and less runoff so it is considered as good artificial recharge zone. 4.5

Drainage

In this study area, up to five order streams are observed, it is clear from Fig. 7. Fifth order streams are most suitable for the artificial recharge structures. Other streams are categorised as good, moderate, poor and very poor in decreasing order respectfully.

Fig. 7. Drainage map

Fig. 8. Aritifical recharge zone

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Assign Rank and Weightage

Rank and weight are assigned to different hydrological parameter to obtain the artificial recharge zones. Based on the relative importance of the parameter weight and ranks are as shown in Table 5 [3]. Table 5. Rank and weight for different parameter of artificial recharge zone Sr. no. 1

2

Parameter

Classes

Geomorphology

Undulation origin and pediment pedi plane complex Water body Structurally originmoderately dissected upper plateau Very shallow, well drained, loamy calcareous, soils Slightly deep, moderately well drained, fine soils Shallow, well drained, clayey moderately calcareous soil 0–2 2–4 4–7 7–11 11–20 1 2 3 4 5 Agriculture land Water body Fallow land Barren land Built-up land

Soil

3

Slope (degree)

4

Drainage (order)

5

Land use

4.7

Groundwater prospect Good

Weight (%) 30

Moderate Poor

Good

Rank 3

2 1

20

3

Moderate

2

Poor

1

Very good Good Moderate Poor Very poor Very poor Poor Moderate Good Very good Very good Good Moderate Poor Poor

20

15

15

5 4 3 2 1 1 2 3 4 5 5 4 3 2 1

Artificial Recharge Zone

All hydrological parameter maps were integrated based on the rank and weightage, artificial recharge zone map is obtained which is as shown in Fig. 8. Recharge zones are classified as excellent, good, moderate and poor recharge zones having area 2.94, 27.47, 12.11 and 0.35 km2 respectively (Table 6).

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Table 6. Recharge zones of study area Sr. no Recharge zone 1 Excellent 2 Good 3 Moderate 4 Poor Total

4.8

Area (km2) 2.94 27.58 12.26 0.35 43.14

Area (%) 6.82 63.93 28.43 0.82 100

Artificial Recharge Structures

Analysis of artificial recharge zones is helpful to locate the artificial recharge structures. Based on the above condition map is prepared showing recharge structures the details are as shown in Fig. 9. Check dams, contour bunding, nala bandhs, contour trenching, recharge pits and wells are considered as recharge structures [6, 7]. • For 3rd and 4th order streams check dam can be provided on upstream, as flow is continuing and straight. • On downstream of check dam; percolation tank, recharging pond, injection well and recharge shaft should be provided. • Recharge shaft should be sufficiently enough deep to recharge the ground water level. • Contour bunds may be located where irrigation is required and have possibility of erosion hence provide minimum gradient. • In continues sloping region contour trenching may provide for rainwater runoff.

Fig. 9. Location of artificial recharge structure

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5 Conclusions Artificial recharge is the need of the day. Based on the study carried out the different conclusions arrived at are as given below. 1. Five different thematic layers viz. geomorphology, soil, drainage, slope lineament, and land use are integrated in GIS environment. This study area is divided in to excellent, good, moderate and poor artificial recharge zones and their respective areas are 2.94, 27.58, 12.26 and 0.35 km2. 2. The area having slope 0 to 2°, 5thorder drainage underneath pediment-Pedi plain and agriculture land is identified as excellent artificial recharge zone and incorporating area is 2.94 km2. 3. Area having pediment-Pedi plain, water body and cover with fallow land, having slope 2 to 4°, and 4th order drainage is identified as good artificial recharge zone and its area is 27.58 km2. 4. The area under slope 4 to 7°, 3rd order drainage under pediment-Pedi plain and moderately dissected upper plateau and with fallow and barren land is detected as moderate artificial recharge zone and its area is 12.26 km2. 5. The area under slope 7 to 11°, 2nd and 1st order stream under moderately dissected upper plateau with barren land and built-up land is found as poor artificial zone and its area is 0.35 km2. 6. This artificial recharge zone is very much useful in locating the artificial recharge structure such as check dams, contour bunding, contour trenching, nala bandhs, and recharge pits and wells. 7. The numbers of artificial recharge structures suggested are percolation tank 9 in number, check dam 7 in number with medium runoff and contour bunds are 6 in number.

References 1. Mishra RC, Chandrasekhar B, Naik RD (2012) Remote sensing and GIS for groundwater mapping and identification of artificial recharge sites. Am Soc Civ Eng 3:216–221 2. Bhowmick P, Sivakumar V (2015) GIS based fuzzy modelling approach to identify the suitable sites for artificial recharge of groundwater. Int J Remote Sens Geosci 4:68–79 3. Riad PH, Billib MH, Hassan AA, Omar MA (2011) Overlay weighted model and fuzzy logic to determine the best locations for artificial recharge of groundwater in a semi-arid area in Egypt. Nile Basin Water Sci Eng J 4:24–35 4. Tiwari A, Lavy M, Amanzio G, Maio MD, Singh P, Mahato M (2017) Identification of artificial groundwater recharging zone using a GIS-based fuzzy logic approach: a case study in a coal mine area of The Damodar Valley, Indian. Appl Water Sci 7:13–24 5. Jaiswal RK, Mukherjee S, Krishnamurthy J, Saxena R (2013) Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development an approach. Int J Remote Sens 24:993–1008

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6. Bamne Y, Patil KA, Vikhe SD (2014) Selection of appropriate sites for structures of water harvesting in a watershed using remote sensing and geographical information system. Int J Emerg Technol Adv Eng 4:270–275 7. Haji B, Patil KA, Vikhe SD (2015) Identification of suitable sites for water conservation structures in a watershed using RS and GIS approach. Int J Sci Res Dev 3(9):45–48 ISSN 2321-0613 8. Waikar ML, Nilawar AP (2014) Identification of groundwater potential zone using remote sensing and GIS technique. Int J Innov Res Sci Eng Technol 3:12163–12174

Seismic Analysis and Design of Mass and Stiffness Irregular R.C. Building Frames with Different Code Nishant C. Chandanshive(&) and Santosh S. Mohite Department of Civil Engineering, Annasaheb Dange College of Engineering and Technology, Ashta 416301, India [email protected], [email protected]

Abstract. From past seismic hazardous it is conclude that many structures are fails during seismic activity. So it is important to determine seismic response of such structures during earthquakes. It is necessary to determine how the structure behave during earthquake. The structure having mass and stiffness irregularity and what is the effect of these irregularities on structure by carry out the response spectrum analysis is the objective of the research. After the analysis compare the results of regular and irregular structure.

Keywords: Seismic analysis RSA
Staad Pro


Mass irregularity
Stiffness irregularity


1 Introduction Earthquake forces are the occasional forces that may or may not occur on the structure but when they occur, causes serious impact on structure, which may lead to collapse of structure. Hence in case of multistoried structure, it becomes mandatory to consider response of the structure for earthquakes. As per Indian seismic code IS 18932016, there are five seismic zones having different potential for shaking intensity. During earthquake, failure occurs due to mass and stiffness irregularity present in the structure. The 2016 version of Indian seismic code clearly defines the irregular structure.

© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 258–261, 2020. https://doi.org/10.1007/978-3-030-24314-2_32

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Mass and Stiffness Irregularity

2 Literature Review Carried out numerical analysis due to this results showed that the first store, not be softer than the storey above or storey below that. The mass irregularity is present in the structure which increased the response of the structure during analysis of the structure. It is required the irregularities provided by proper design and analysis. Carried out performance of structure of R.C. building with mass and stiffness irregularities. These irregularities decrease the seismic response of the structure during seismic activity. The study is carried out for what is the effect of these types of irregularity on the building and what is the effect of influence on the various parameters of the structure.

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3 Objective Design the frame models in Staad Pro. To study the effect mass and stiffness irregularity in R.C. Building frames. And compare the results. 3.1

Methodology

In this problem configuration of frames is as given below (Table 1). Table 1. Probleme statement Structure type Residential building No. of stories 14(G + 13) Height of floor 3m Column size (1) G.F and F.F 300 mm  500 mm (2) all above 300 mm  450 mm Beam size 230 mm  450 mm Slab thickness (T.W) 150 mm Masonry wall thickness 230 mm Plan area 26.5 m  29.0 m

3.2

Results

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4 Conclusion 1. Decrease in response of irregular structure as compared to regular structure. 2. If increase the column stiffness base shear increases. 3. Higher the stiffness Displacement, storey drift, Time period reduces.

References 1. IS 13920: Ductile Detailing of Reinforced Concrete Structure Subjects oo Seismic Forces (1993) 2. Soni AG, Agrawal DG, Pande AM (2015) Effect of irregularities in buildings and their consequences. Int J Mod Trends Eng Res 2:14–21 3. Hassaballa AE, Adam MF, Ismaeil MA (2013) Seismic analysis of a reinforced concrete building by response spectrum method. IOSR J Eng (IOSRJEN) 4. Himanshu Bansal G (2012) Seismic analysis and design of vertically irregular RC building frames. Int J Sci Res (IJSR) 2012:2319–7064 5. Poonam AK, Gupta AK (2012) Study of response of structurally irregular building frames to seismic excitations. Int J Civ Struct Environ Infrastruct Eng Res Dev Sci Res (IJSR) (IJCSEIERD) 2:25–31 6. Sarkar P, Prasad AM, Menon D (2010) Vertical geometric irregularity in stepped building frames. Eng Struct 32:2175–2182 7. Athanassiadou CJ (2008) Seismic performance of R/C plane frames irregular in elevation. Eng Struct 30(2008):1250–1261

Assessment of Spatial Interpolation Techniques on Groundwater Contamination G. Shyamala1(&), B. Arun Kumar1, S. Manvitha2, and T. Vinay Raj1 1

2

S R Engineering College, Warangal, India [email protected] Kakatiya Institute of Technology and Sciences, Warangal, India

Abstract. The selected study area, Mettupalayam, India is an important trading hub and transit center for hill products. The hydrochemistry of the groundwater is deteriorated in the past years, but the literature revealed that the groundwater pollution in the study area was not concentrated. Sixty-two discrete locations were selected in the study area. The ground water samples were collected, and the Electrical Conductivity is analyzed, as it is the important irrigation parameter. The spatial interpolation technique such as Spline, Inverse Distance Weightage (IDW) and Kriging is used to predict the value of unknown location, from the known sample location. Cross validation is performed using univariate statistical analysis for the predicted surface to choose the best model. The evaluation of interpolation method by univariate statistical analysis indicated the Root Mean Square Error is least in Kriging method; it indicates Kriging as the best method for interpolating surfaces followed by IDW and Spline. Keywords: Univariate statistical analysis


Kriging
Spatial interpolation

1 Introduction Our planet earth has one unique quality which makes us to survive in this planet, its termed as “water” which supports the life form in bountiful ways. Every species in the world depend on groundwater for their existence. Threat on quality and quantity of groundwater is caused due to the improper management and reckless use of water system [1]. Urban growth, industrialization and agricultural activities has imposed greater stress on soil and groundwater and the risk of contamination have been amplified [2]. Large quantum of groundwater is pumped each day for various usage, the water pollution threats human health, social environment and economic growth of the area [3]. In industrial area there are chances for the pollutants to percolate and migrate through soil and reaches the groundwater. Analyzing the groundwater characteristics in a large space and solving geochemical problem is a challenging task [4]. The constrains in time and cost allows the groundwater data collection and monitoring at a restricted number of sites [5]. The ArcGIS 9.2 is a the spatial analyzing tool for managing, compiling and analyzing data set of sample points representing the changes in pollution is used to visualize variability of observed data across the surface. The interpolation technique is used to estimate the surface values at locations without taking the sample measurement [6]. The selection of algorithm is based on actual data, © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 262–269, 2020. https://doi.org/10.1007/978-3-030-24314-2_33

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accuracy level and software resources available. The spatial interpolation analysis is performed by using the techniques such as Spline Inverse distance Weightage (IDW) and Kriging with the goal of determining the best method to create continuous surface. The area selected for study is Mettupalayam, India, the important trading hub in the foothills of Nilgris. The purpose of this study was to evaluate the spatial distribution of groundwater quality and analyse the limitations and benefits of each method. The data set were obtained from eighty-five stations. The different methods of interpolation result in different surface and arrive at different results. The best method of spatial distribution is analyzed using univariate statistics.

2 Spatial Interpolation The focus of this research is on the spatial interpolation technique to analyze the pollutants in the urban area, which enable to target the pollutants as close as possible. ArcGIS 9.2 spatial analyst is used for managing, compiling and analyzing data that the value of unknown sampled location is predicted based on the known set of readings. The estimated cell values are to estimate the real value of the selected location [7]. The individualities of the spatial surface are controlled by limitation of the input points to calculate the output cell values. Characteristics of different interpolation methods can be generated in different predictions at same locations. The data’s are collected for specific location, interpolation techniques and mathematical function is used to estimate the values at the uniform location. The spatial data is involved in the geographic information science research. The point data of the area is varied and continuous according to space [8]. The data at any location within the boundary is calculated using interpolation formulas and arrived in a spatial pattern. The predicted data are accurate if the data set is closer and is not precise if they are spaced out. Based on these assumptions the interpolation methods are formulated. The data’s collected at discrete locations are used for creating continuous data. The objective of interpolation technique is to create a layer that is intended to represent empirical reality those the method is selected, is assessed for accuracy. The valuable and legitimate method for data creation is interpolation method, which is accepted in Arc GIS.

3 Methodology 3.1

Interpolation Technique

Geo-statistical interpolation techniques is rolling on both statistical and mathematical methods to create surfaces & asses the uncertainly of prediction. The ArcGIS 9.2 was employed as the software for this study. The methods used in this study are stochastic method of Kriging, Inverse Distance Weighting (IDW) and Spline, to retain actual electric Conductivity in the final surface. The exact value of Electric Conductivity is expected in the final output surface. The deciding parameters vary in these spatial interpolation method, it include the options & values used in different modules of

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ArcGIS. Samples were taken from 8 locations in the study area to create the interpolated surface. As the weight increased the more value confirm to the range of sample data. In the study area the range of EC is from 1.7 dS/m to 7.1 dS/m. 10 new Sample locations were selected in the study area and analyzed to validate the interpolation method. Univariate statistical method is used to validate the best fit out of three methods. 3.1.1 Spline Interpolation In ArcGIS Spline Interpolation is a Radial Basis Function (RBF). The perfect smooth curve or close-fitting straight edges can be fitted among measured features. The adequate accuracy between the sampled locations is obtained in the spline method.

Fig. 1. Spatial map of EC-Spline method

The degree of accuracy in the interpolated surface varies based on the data set. The utilities are complex to outliers due to the inclusion of original values at the selected points. Spline works well for Smooth Varying Surfaces. The parameters given as input are Mettupalayam, India boundary, Sampled points interpolation attribute (Electrical Conductivity), the type (tension), weight point to be considered for each know value and the output cell size (Fig. 1). The weight set of 1.0 and the cell size of 100 is taken as the input. The Spline surface is bounded to the observed data range. The cell size of 100 m2 was selected based on the ability to interpolate within the selected area. 3.1.2 Inverse Distance Weightage (IDW) In Inverse Distance Weightage (IDW) method the observation made with greater distance have less impact to the interpolated value than the values taken at the nearer area. The benefit of inverse distance weightage method is, it has the finest fit with uniformly distributed points. If data set is randomly distributed it leads to familiarized errors. Smooth deviation in the interpolated surface is noticed in the central part of Mettupalayam, India, where the data set are closer and sharp drastic variation and errors

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are noted in the north eastern and south western part of the study area (Fig. 2). Smooth diversity is observed in IDW with that of the tension Spline. The parameters Indicated are power option, Search radius number of points a possible maximum distance for impact on the data and output cell size. The input locations (sampling points) contained within a specific area are same for spline, Kriging & IDW methods. The 100 m2 cell size was fixed, power was set to 4 and search radius was represented to variables.

Fig. 2. Spatial map of EC-IDW method

3.1.3 Kriging Interpolation The impact of the adjoining data point is used to incorporate the values of indefinite locations. There are two different functions in Kriging 1: computing the threedimensional structure of the data and creating a prediction. 2: Enumerating the structure, where the spatial-dependence model is fit to the data [10]. Kriging is not formative; it extends the familiarity weighting approach, to comprise unselective modules where precise point location is not known by the function [11]. The Spatial & arithmetical association between the points is used to compute the interpolated surfaces. The major two steps performed in Kriging are semi variance assessment & interpolation. The advantage of this method is the interdependence of the variables and surface errors can be obtained. The disadvantage of Kriging is it required high input and requires more computing and modeling time. Variation in the north eastern part of the study area was observed in kriged surface than that of Spline or IDW Surface (Fig. 3). There are two options in Kriging: ordinary method and universal method [12]. In the study, the ordinary Kriging was selected, as it is the most widely used method. Variable radius of spherical model was selected for analysis due to parse & irregular sampling location.

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Fig. 3. Spatial map of EC-Kriging method

4 Results and Discussions 4.1

Method of Analysis

Evaluation of spatial interpolation method approached by Wilmot is used for analyzing the three interpolation methods. The statistical error between the observed value and predicted electrical conductivity value is evaluated, summated univariate measure (Table 1) include the mean of observed (OEC), mean of predicted (PEC) and their standard deviations (So, Sp). Wilmot used the following measures to evaluate the performance of the interpolation methods.1. Mean Absolute Error (MAE) 2. Root Mean Square Errors (RMSE) 3. Systematic Root Mean Square Errors (RMSES). 4. Unsystematic Root Mean Square Errors (RMSEU) and 5. Index of agreement (d) [13]. The simple regression coefficients a & b are used to compute the systematic & unsystematic root mean square errors. MAE is loss sensitive to extreme values. The better agreement between 0&P is noticed if the d value is closer to 1.0 & complete disagreement if it is 0.0. The analysis of three interpolation method was performed by univariate statistics method. Evaluation Metrics Mean Absolute Error Root Mean Square Errors

MAE ¼ N 1 RMSE ¼ N 1

N  P  i¼1

Systematic Root Mean Square Errors

N P

jPi  Oi j

i¼1

1=2 ðPi  Oi Þ2 

N  2 1=2 P  ^ RMSEs ¼ N 1  P i  Oi  i¼1

Assessment of Spatial Interpolation Techniques on Groundwater Contamination

Unsystematic Root Mean Square Errors

RMSEu ¼ N 1

267

N   1=2 P  ^ i 2   Pi  P i¼1 2

Index of agreement d ¼ 1  NRMSE PE Where P* = a + b Oi; a & b are the coefficients of an ordinary least- squares simple linear regression between O and P.

Table 1. Summary univariate measures-EC Sample no Observed Value (OV) Predicated Value (PV) Spline IDW Kriging 1 3.41 3.12 3.71 3.68 2 2.61 4.85 3.12 2.15 3 5.84 7.65 6.55 5.45 4 1.26 2.65 1.78 1.56 5 4.68 2.98 3.89 4.95 6 6.14 4.67 6.78 5.62 7 2.98 3.98 3.45 3.18 8 4.68 3.48 4.35 4.78 9 5.12 8.01 4.59 4.87 10 3.15 4.12 4.52 3.1

OV-PV Spline IDW 0.58 0.3 1.38 0.51 1.853 0.71 2.73 0.52 0.693 0.79 2.153 0.64 1.01 0.47 0.693 0.33 1.133 0.53 0.84 1.37

Kriging 0.07 0.46 0.17 0.3 0.27 0.52 0.2 0.2 0.25 0.21

Summary statistics the mean of observed (OEC), mean of predicted (PEC) and their standard deviations (So, Sp) is displayed in the Table 2. It shows that the surfaces predict the unknown EC value at the Selected Locations. The Largest average error 0.11 is found in the Spline data and the smallest is found in Kriging Data and it is followed by IDW data. The standard deviation suggests the Kriging method fits good, followed by IDW method and the Spline method. To get the best fit average standard error and the mean standardized prediction error should be as small as possible; further the root-mean-squared standardized prediction error should be close to one [14]. The five difference measures are shown in the Table 2 below. The least value of mean absolute error (MAE) was found in Kriging method. The similar response as MAE is found in RMSES & RMAEu. The Kriging method can be improved by refining the parameters as the majority of the RMSE error is the systematic RMSE.

Table 2. Comparisons between three interpolation methods Method Spline IDW Kriging

So 1.55 1.55 1.55

Sp 1.87 1.50 1.41

MAE 1.50 0.63 0.27

RMSE 0.52 0.22 0.09

RMSEs 0.60 0.29 0.17

RMSEu 1.50 0.50 2.17

d 0.0521 0.0058 0.001

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The final error difference measured indicates Kriging is better than both Spline & IDW method. The examined data is analyzed, followed by empirical semivariogram estimation & interpolation model. The sampling points are closer in the central part of the study area; hence the predicted value is closer to the observed value. To make an informed decision, validation is carried out before producing the final surface. Cross validation (Fig. 4) of the semivariogram model and covariance was performed by selecting ten new sample locations. The predictions are centered on the measurement value. The closest true value is obtained if the root-mean-square prediction errors are minimum. The average standard error and the mean standardized prediction error should be minimum for the ideal prediction. The root- mean-squared standardized prediction errors should be close to one [15]. The evaluation of interpolation method by 5 different measures indicated the Root Mean Square Error is minimum in Kriging method, it indicates Kriging as the best method for interpolating surfaces followed by IDW and Spline. Kriging method has done a responsible job in predicting Electrical Conductivity Value in the unknown location. The 86 Sample locations are worthy over study area of 7.20 Km2. If the number of samples have been increased still an accurate prediction can be made in the study area.

Fig. 4. Cross validation of predicted data

5 Conclusion Surface generation was done to produce the electrical conductivity map that shows the spatial variation in the groundwater contamination, which is high in the Thekampatti and Ramampalayam village. The validation of the model is carried out using cross validation statistics. Best estimate of continuous surface of EC is produced by Kriging Method. Nevertheless the evaluation illustrates that regardless approach taken in these interpolation methods it does not adequately address the electrical conductivity and variability in an urban setting. To get more realistic representation of the area, additional factors of the urban environment may be incorporated.

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References 1. Jaiprakash N, Vijaya K, Puttaiah ET (2007) Fluoride distribution in groundwater of Magadi taluk, Bangalore, rural district of Karnataka. J Curr Sci 10(1):279–282 2. Ackah M, Agyemang O, Anim AK (2011) Assessment of groundwater quality for drinking and irrigation: the case study of Teiman-Oyarifa Community, East Municipality, Ghana. Proc Int Acad Ecol Environ Sci 1(3–4):186–194 3. Milovanovic M (2008) Water quality assessment and determination of pollution sources along the Axios/Vardar River, Southeastern Europe. Desalination 213:159–173 4. Cheboterev II (1955) Metamorphism of natural waters in the crust of weathering-I. Geochimica et Cosmochima Acta 8(1–2):22–48 5. Batisani N (2012) Groundwater hydrochemistry evaluation in rural Botswana: a contribution to integrated water resource management. Ethiop J Environ Stud Manag 5(4):521–528 6. Kholghi M, Hosseini SM (2008) Comparison of groundwater level estimation using neurofuzzy and ordinary Kriging. Environ Model Assess 14:729–737 7. Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 6th edn. Wiley, Hoboken 8. Hartkamp AD, De Beurs K, Stein A, White JW (1999) Interpolation techniques for climate variables. NRG-GIS Series 99-01, pp 1–34 9. ICRA Management Consulting Services Limited (2007) Tamil Nadu Urban infrastructure Financial Services Limited (TNUIFSL) Final Report. Conversion of City Corporate plan to Business Plan for Mettupalayam Municipality 10. Burrough PA, McDonnell R (2000) Principles of geographical information systems. Oxford University Press, New York 11. Kumar D, Ahmed S (2000) Seasonal behavior of spatial variability of groundwater level in a granitic aquifer in monsoon climate. Curr Sci 84(2):188–196 12. Kumar V, Ramadevi (2006) Kriging of groundwater levels-a case study. J Spat Hydrol 6 (1):81–94 13. Willmott CJ (1984) On the evaluation of model performance in physical geography. In: Spatial statistics and models, vol 18, pp 443–460 14. Schut G (1976) Review of interpolation methods for digital terrain models. Can Surveyor 30:389–412 15. Meyers DE (1994) Spatial interpolation: an overview. Geoderma 62:17–28

Sustainability Concepts in the Design of Tall Structures Prashant Sunagar1(&), Aravind Bhashyam2, B. R. Neel1, and Abhishek Kumar Chaurasiya1 1

Department of Civil Engineering, Ramaiah Institute of Technology, Bengaluru, Karnataka, India [email protected] 2 Department of Civil Engineering, Christ University, Bengaluru, Karnataka, India

Abstract. Construction industry is a rapid growing industry with various new technologies coming into practice. Sustainability concept is also a call for the present generation as many natural resources are getting exhausted. Thus the new era of development of Tall Structures with respect to Sustainability concept is being studied by concentrating on the Structural systems that can be adopted for construction of the same. In the present study we have considered two different 3D RC frame structural systems i.e., normal Beam-Column structural system and Outrigger structural system. The following two systems were modelled in ETABS 15.2 software in seismic zone V with three different heights that is 150 m (50 storeys), 240 m (80 storeys) and 300 m (100 storeys). Response spectrum analysis is carried out considering Earthquake forces and the results are tabulated for maximum storey displacement and maximum storey drift. Then finally the structural system which is sustainable in construction of Tall structures is identified. Keywords: Sustainability
Structural systems Storey displacement
Storey drift


Outrigger


1 Introduction Sustainable development means the development that can meet the needs of today without compromising the requirements of future generations to meet their own needs. One of the important aspects is to see that all the resources available are efficiently utilized taking into account other Social, Environmental and Economic factors. Best way to achieve Sustainability in construction field with this rapid growth of population is only through effective usage of all available resources. The Structural system adopted for construction also has a major impact on the Sustainability aspect which is identified and discussed in this work.

© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 270–277, 2020. https://doi.org/10.1007/978-3-030-24314-2_34

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Outrigger Structural System

This system is a very effective lateral load resisting system where in external columns are connected with central core wall with highly stiff outriggers at one or multi levels. The stiffness invoked in outer columns uplifts the resistance against overturning moments. This structural form comprises a central core with braced frame/shear wall with cantilever truss/girder known as Outrigger truss. With core centrally located outrigger extends on both sides or it may be on one side of the building.

2 Modelling and Inputs In the present work 3D models of two different structural systems with 50, 80 and 100 storeys are being modelled in seismic zone V to study and compare the performance of the structures subjected to Earthquake forces with respect to Sustainability of structures. The two different 3D RC frame models considered are Column-Beam structure and Outrigger structure. Outrigger element is modelled at three fourth height of the building as it is found to be the optimum location from previous literature study. All the models have same structural plan dimensions with six bays in both X and Y direction. Column to column spacing in each bay is 5 m. All the columns are considered as Fixed. The Response Spectrum analysis is performed for all the structures using ETABS version 15.2.0 (Tables 1, 2 and Figs. 1, 2). Table 1. Section properties Number of floors Height of each floor Size of the column Size of the beam Thickness of the slab Thickness of shear wall Size of outrigger element

50/80/100 3m (800  800) mm/(1000  1000) mm/(1200  1200) mm 300 mm  450 mm 175 mm 300 mm 300 mm  600 mm

Table 2. Loading Loading Dead load Floor Finish Live load on the slab Seismic loading Seismic zone Soil type Importance factor Response reduction factor

1 kN/m2 2 kN/m2 Zone V Medium soil (Type II) 1 5

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Fig. 1. Plan and elevation of beam-column structure

Fig. 2. Plan and elevation of outrigger structure

3 Results 3.1

Maximum Storey Displacement

The following plots were obtained from E-tabs for Maximum Storey Displacement for 50 storey Beam-Column structural model and Outrigger structural model in both X and Y Directions (Figs. 3, 4, 5 and 6): Similarly the Plots are obtained for 80 and 100 storeys in both X and Y Directions and are compared, because of the symmetry of the plan of the structure the displacement and drift values in both X and Y direction are almost identical, the values obtained in X direction are tabulated (Table 3).

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Fig. 3. Maximum storey displacement for 50 storey beam-column model in X-direction

Fig. 4. Maximum storey displacement for 50 storey beam-column model in Y-direction

Fig. 5. Maximum storey displacement for 50 storey outrigger model in X-direction

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Fig. 6. Maximum storey displacement for 50 storey outrigger model in Y-direction

Table 3. Maximum storey displacement for 50/80/100 storey models in X-direction Maximum storey displacement for 50/80/100 storey models in X-direction Type of system 50 Storey 80 Storey 100 Storey Beam-column 387 1181 2118 Outrigger 135 568 1139 % Reduction 65.116 51.905 46.223

3.2

Maximum Storey Drift

The following plots are obtained from Etabs for Maximum Storey Drift for 50 storey Beam-Column model and Outrigger model in both X and Y Directions (Figs. 7, 8, 9 and 10):

Fig. 7. Maximum storey drift for 50 storey beam-column model in X-direction

Sustainability Concepts in the Design of Tall Structures

Fig. 8. Maximum storey drift for 50 storey beam-column model in Y-direction

Fig. 9. Maximum storey drift for 50 storey outrigger model in X-direction

Fig. 10. Maximum storey drift for 50 storey outrigger model in Y-direction

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Similarly the plots are obtained for 80 and 100 storey Beam-Column and Outrigger models in both X and Y Directions and the values are compared and tabulated below (Table 4): Table 4. Maximum storey displacement for 50/80/100 storey models in X-direction Maximum storey drift for 50/80/100 storey models in X-direction Type of system 50 storey 80 storey 100 storey Beam-column 0.0036 0.0067 0.009 Outrigger 0.0011 0.0032 0.0053 % Reduction 69.444 52.239 41.111

4 Conclusions (a) From the study it can be observed that Sustainability in Tall structures has various aspects to be considered out of which Structural form adopted in construction plays a vital role. (b) It is observed that the maximum storey displacement for a 50 storey 3D RC frame structure varies in accordance with the structural form adopted. For a normal Beam-Column structure the displacement is found to be 387 mm in X-direction. (c) The maximum storey displacement reduces by 65.16% for an Outrigger system in comparison with normal Beam-Column system. Thus it can be concluded that the Outrigger structural form is more Sustainable against Displacement due to seismic forces. (d) The maximum storey drift for a 50 storey Beam-Column structure is found to be 0.0036 and as per IS 1893 the maximum storey drift shall not exceed 0.004 times the storey height which turns out to be 0.012 for all the models considered in this study. (e) The maximum storey drift reduces by 69.44% for an Outrigger system in comparison with normal Beam-Column system. As in case of displacements even when storey drift is considered the Outrigger structural form proves to be more Sustainable against Earthquake forces. (f) Similarly for 80 storey structural models the maximum storey displacement is reduced by 51.90% for an Outrigger system in comparison with normal BeamColumn system and for 100 storey structural models the reduction is 46.22% for an Outrigger system on comparison with normal Beam-Column structural system. (g) For 80 storey structural models the maximum storey drift is reduced by 52.23% for an Outrigger system on comparing with Beam-Column structural system and similarly for 100 storey models the reduction in maximum storey drift is 41.11% for Outrigger system when compared with normal Beam-Column system.

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(h) Even when the height of the building is increased with increase in number of stories the two main parameters maximum storey displacement and maximum storey drift is found to be majorly reduced at least by 40–50% for an Outrigger structural system in comparison with normal Beam-Column system which proves that the Outrigger structural system to be the sustainable system against seismic forces in Tall structures.

References 1. Sharath BN, Claudiajeyapushpa, D (2015) Comparative seismic analysis of an irregular building with a shear wall and frame tube system of various sizes. IJECS. ISSN 2319-7242 2. Itware VA, Kalwane UB (2015) Effects of openings in shear wall on seismic response of structure. IJERA. ISSN 2248-9622 3. Nair RS (1998) Belt trusses & basements as virtual outriggers for tall structures. Eng J 35:140–146 4. Kogilgeri SS, Shanthapriya B (2015) A study on behavior of outrigger system on high rise steel structure by varying outrigger depth. IJRET. eISSN 2319-1163 5. Bayati Z, Mahdikhani M, Rahaei A (2008) Optimized use of multi-outriggers system to stiffen tall buildings, Beijing, China, October

Spontaneous Combustion of Coal and Correlation with Its Intrinsic Properties Using Adiabatic Oxidation Method Ravi Varma Rambha(&) Department of Mechanical Engineering, GITAM (Deemed to be University), Hyderabad, India [email protected]

Abstract. Self-heating of coal promotes spontaneous combustion during mining, transportation, storage, handling and milling processes leading to risk of fires and consequent loss of calorific value of fuel. Adiabatic oxidation method was adopted in this investigation whereby spontaneous heating potential was measured according to total temperature rise (TTR) of coal sample versus time. Aim of this study was to investigate correlation of intrinsic properties of coal and its propensity for spontaneous combustion. Tests were conducted on 14 coal samples at an initial temperature of 40 °C to mimic typical conditions in coal storage and handling plant. A correlation of the TTR values with the proximate and ultimate analyses of the coal samples has been obtained. Keywords: Spontaneous combustion


Coal
Adiabatic oxidation

1 Introduction Spontaneous combustion of coal has been a persistent and widespread problem during underground and surface mining, stockpiling, sea-borne transport and handling in coal milling systems. The loss of coal cargoes, ships and life had become a significant problem through known cases of spontaneous combustion. Although the hazards from such occurrences have been effectively under control, there have been many cases recently where consequences have been costly in terms of lives, equipment and large reserves of coal. Conversely, thousands of underground coal fires around the world could be fuelling toxic pollution, contributing up to 3% of the rate of global warming and threatening wildlife [1, 2]. Such coal fires usually start from spontaneous combustion as the seams react with oxygen and moisture to release heat. Recently concerns have been raised on spontaneous heating problems in Indian coal mines [3, 4]. Recently, power-generating companies in India are importing coals with unknown histories from the world trading market. Spontaneous combustion of coal in milling systems and stockpiles has become a major concern in the selection of coals. Processing of coals including the separation, blending and mixing has increased in recent years. Blending of coals has become popular for improving the performance of coals to meet the specifications of power plants and for reducing cost of coals. © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 278–284, 2020. https://doi.org/10.1007/978-3-030-24314-2_35

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The manipulations in these processes will most likely lead to higher susceptibility of the pulverised fuel deposits to spontaneous heating. With the increase in coal trade, large amounts of coal are being trans-shipped. Stockpiles usually serve the purpose of short-term and long-term storage as a reserve for high tonnage users i.e. energy generating companies. Storage of coals in coal handling plant has presented problems in spontaneous heating for power plants across India. In order to develop a hazard management plan, there is a need to have a better understanding of the self-heating behaviour of coal. Previous experimental investigations in India have been carried out with a heating system used to initiate the selfheating process [5]. The spontaneous combustion index parameters resulting from such tests can produce misleading evaluations of the propensity of a coal to self-heat [6]. On the other hand, adiabatic oxidation test method allows the coal sample to exhibit its self-heating behaviour without any external heating system. This method can be modified to closely replicate in situ conditions in a coal mine and coal handling plant and thus providing a means for benchmarking laboratory determined results [7].

2 Methodology Adiabatic oxidation test apparatus previously developed to study spontaneous combustion liability of pulverized coal samples [8] was used in this study. Samples of coal, pulverized to grind size of −75 lm to +300 lm, and weighing 200 g, were placed in the reaction vessel encased in a calorimeter (Fig. 1) for drying at a preset temperature. The reaction vessel consisted of thermostat cabinet with thermocouples connected to a temperature control unit.

Fig. 1. Schematic diagram of reaction vessel within the calorimeter. Calorimeter with temperature control unit and data acquisition.

Test conditions that demonstrated maximum oxidation potential of the coal sample were established. Detailed description of the test conditions are explained in a previous

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study [9]. Coal samples were nitrogen dried at an initial temperature of 40 °C before air supply was introduced at flow rate of 200 cc/min. Self-heating rate was measured for approximately 8 h. Initially self-heating increased at constant rate. Approximately after 4 h of testing there was no increase in temperature rise. Total temperature rise (TTR) was considered as the difference between the initial temperature and the maximum temperature observed expressed in °C. To check accuracy and sensitivity of the apparatus and verify the self-heating curve is representative of coal sample, repeatability tests were carried out. At least three repeatability tests were conducted within 3 to 4 days for a given coal sample to ensure no pre-oxidation and ageing effect (Fig. 2). Variation in total temperature rise between each test was within ±0.5 °C.

Fig. 2. Self-heating curves of repeatability tests on DM Coal samples.

3 Results and Analyses Tests were carried out on 14 different coal samples and corresponding spontaneous heating propensity was recorded according to the total temperature rise (TTR). Coal samples were selected on basis of commonly used coals from nearby thermal power plant. The results were interpreted by spontaneous combustion liability index based on the TTR values obtained (Table 1). TTR values less than 5 °C were categorized as low risk, coals with values between 5 °C and 10 °C were medium risk and coals which gave values above 10 °C were high risk. Results for proximate and ultimate analyses (percentage, dry ash free basis), and spontaneous combustion test results (TTR values) carried out on each sample are presented in Tables 2 and 3. Moisture content varies from 1.9% to 17.9% for the given

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coal samples. It was observed that there is no clear trend emerging from the results. Previous studies have suggested that low moisture content tends to be more liable to spontaneous combustion [10]. Volatile matter ranges between 24.7% to 38.1%. There is no clear trend observed from the results. Coals with higher volatile matter have exhibited high risk potential for spontaneous combustion by earlier studies [11]. Table 1. Criteria used for spontaneous combustion liability. Category Very high TTR > 15 °C High 10 °C < TTR < 15 °C Medium 5 °C < TTR < 10 °C

Table 2. Proximate analysis and corresponding adiabatic oxidation data for coal samples. Coal HL DM SA54 SA56 RA HU BA CO HA14 BA LI HA16 KU TS

TTR (°C) Risk category Proximate analyses Moisture Ash Volatile 27 V.High 17.9 12 29.4 20.6 V.High 7.4 14.3 31 18.3 V.High 8.7 13.5 24.7 16.6 V.High 8.6 13.7 24.7 13.2 High 17.1 3.9 31.8 11.2 High 3.2 10 30.7 11 High 2.4 8.9 35.2 10.2 High 15.1 8.9 30.7 9.4 Medium 1.9 6.2 33.9 8.8 Medium 4.8 7.6 27.8 8.2 Medium 13.9 19.7 38.1 6 Medium 2.3 17.7 29.6 5.3 Medium 4.7 11.9 32.1 3.2 Low 4.6 13.9 26.2

FCC 40.7 47.3 53.1 53 47.2 56.1 53.5 45.3 58 59.8 28.3 50.4 51.3 55.3

Ash content varies from 3.9% to 19.7% for the given coal samples. Variation in the ash content could be attributed to the changes during pre-oxidation of coal. Lesser the pre-oxidation of coal the lesser the ash content. Coals with low ash content are expected to show higher risk. However, the results did not indicate any such trend. Fixed carbon content varies between 28.3% and 59.8% for the coal samples. Both the coal sample BA with the highest and LI with lowest fixed carbon content indicated medium risk potential. Results do not seem to show any direct relationship. Also, the intrinsic properties obtained from ultimate analysis – Carbon, Hydrogen, Nitrogen, Sulphur and Oxygen percentage did not seem to show any direct relationship with the liability for spontaneous combustion risk.

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Table 3. Ultimate analysis and corresponding adiabatic oxidation data for coal samples. Coal HL DM SA54 SA56 RA HU BA CO HA14 BA LI HA16 KU TS

3.1

TTR (°C) Risk category Ultimate analyses C H N S 27 V.High 75.6 5 1.3 4.1 20.6 V.High 81.3 4.8 1.3 2.2 18.3 V.High 86.3 5.2 2 0.7 16.6 V.High 85 5.2 2 0.8 13.2 High 79.8 6.7 1.5 3.5 11.2 High 83 4.9 1.9 0.5 11 High 83.7 5.3 1.7 1.4 10.2 High 88.3 6.6 1.8 0.7 9.4 Medium 83.1 5.4 1.8 2.9 8.8 Medium 81.8 4.5 1.8 0.4 8.2 Medium 67.9 5.1 1.4 2.7 6 Medium 83.1 5.4 1.8 3 5.3 Medium 82 5.1 2.4 0.4 3.2 Low 83.6 4.7 1.9 0.8

O 14 10 5.8 6.9 8.5 9.7 7.7 2.6 6.6 11.5 22.9 6.3 10.1 9.1

Statistical Analysis of Intrinsic Properties of Coal

Initial evaluation of results was based on TTR values and the intrinsic properties of coal samples. Data was grouped into dependent and independent variables. Statistical analysis was conducted by correlating coal intrinsic properties as independent variables with the values of TTR as dependent variables. R-squared values and correlation coefficients were used to measure the trends and determine any significant relationships between intrinsic properties and TTR values. 3.2

Linear Regression Analysis

Statistical methods were used to analyse the data obtained from spontaneous risk tests and intrinsic factors for the 14 coals. Table 4 gives the results of linear regression analyses for coal samples. The spontaneous risk increases with increasing moisture content and decreases with increasing nitrogen content. The proximate and ultimate analyses indicate that moisture content and nitrogen are the factors influencing the spontaneous risk of coal. The linear regression identifies linear relationships with intrinsic properties and thus indicating the major factors affecting the spontaneous combustion risk of coal. Correlation coefficient/R-squared value between ±0.95 to ±1 was considered a perfect positive or negative linear relationship. Correlation coefficient/R-squared value between ±0.51 to ±0.94 was considered a strong positive or negative linear relationship. Correlation coefficient/R-squared value between ±0.25 to ±0.50 was considered moderate positive or negative linear relationship. Correlation coefficient/Rsquared value between ±0.1 to ±0.24 was considered weak positive or negative linear relationship. Correlation coefficient/R-squared value less than ±0.1 was considered very weak positive or negative linear relationship.

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Moisture content of coal gives R-squared values of 0.26 indicating a moderate positive correlation for coals’ self-heating potential. The R-squared value for nitrogen is 0.26 thus indicating a moderate correlation. The data obtained in this study has shown no strong correlations with individual factors for the prediction of self-heating potential of coal. To improve the correlations between the intrinsic properties and their corresponding self-heating potential, multiple regression analysis was employed. Table 4. Correlation coefficients and R-squared values obtained from linear regression analysis. Independent variables Dependent R-squared Moisture 0.26 Ash 0.00 Volatile 0.05 FCC 0.05 C 0.01 H 0.00 N 0.26 S 0.13 O 0.00

3.3

variable (TTR) Correlation coefficient 0.51 −0.03 −0.23 −0.23 −0.11 −0.02 −0.51 0.37 0.04

Multiple Regression Analysis

Independent factors were grouped as proximate analysis (moisture, ash, volatile matter, carbon content) and ultimate analysis (daf C, H, N, O, S) were taken into account in multiple regression. The deduced model indicates laboratory measurements of inherent properties of coal could be weighed and summed to obtain the best possible prediction of the self-heating of coal. The resultant correlation coefficients shown in Table 5 are much better than those obtained from linear regression analysis. Table 5. Model developed by multiple regression analysis.

The equation gives R-squared value of 0.88 which is a strong positive correlation. This indicates that the adiabatic oxidation method could be used to predict self-heating risk of coal. The model also points out that spontaneous heating occurs due to combined effect of various intrinsic factors. On the other hand, the standard error of 3.97 obtained from the model is very high. The sample base should be enlarged to include coals of varying classification to obtain improved results.

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4 Conclusions Spontaneous combustion risk in coal can be measured using intrinsic properties of coal. Moisture and nitrogen contents indicate linear relationships with spontaneous combustion risk. Multiple regression analysis shows that spontaneous combustion liability has high correlation coefficients with intrinsic properties. However the standard deviations are high. More number of coal samples with varying classification rank should be tested to obtain benchmark values. Spontaneous combustion liability of coal can be predicted by a model consisting of intrinsic factors. Such a model could be incorporated in spontaneous combustion management software by the coal industry to predict the propensity of coal for spontaneous ignition in storage, preparation and handling systems. Acknowledgments. The author acknowledges the European Coal and Steel Community (ECSC) for its financial support of this study. Thanks are due to University of Nottingham, UK Coal, PowerGen Plc, and TXU Europe for their support and generosity in providing useful discussions and information.

References 1. Sloss L (2015) Assessing and managing spontaneous combustion of coal. IEA Report CCC/259 2. Arisoy A, Beamish B (2015) Reaction kinetics of coal oxidation at low temperatures. Fuel 159:412–417 3. Singh RVK (2013) Spontaneous heating and fire in coal mines. In: The 9th Asia-Oceania symposium on fire science and technology, procedia engineering, vol 62, pp 78–90 4. Zutshi A, Ray SK, Bhowmick BC (2001) Indian coal vis-à-vis spontaneous heating problems. J Mines Met Fuels 44:123–128 5. Mohalik NK, Lester E, Lowndes IS (2016) Review of experimental methods to determine spontaneous combustion susceptibility of coal – Indian context. Int J Min Reclam Environ 31:301–332 6. Beamish BB, Theiler J (2017) Recognising the deficiencies of current spontaneous combustion propensity index parameters. In: The Australian mine ventilation conference, Brisbane, Qld., pp 113–117 7. Beamish B, Beamish R (2010) Benchmarking moist coal adiabatic oven testing. In: 10th underground coal operators’ conference. University of Wollongong & The Australasian Institute of Mining and Metallurgy, pp 264–268 8. Ren TX, Edwards JS, Clarke D (1999) Adiabatic oxidation study on the propensity of pulverized coals to spontaneous combustion. Fuel 78:1611–1620 9. Rambha RV, Ren TX (2018) Study of the susceptibility of coal for spontaneous combustion using adiabatic oxidation method. Chem Eng Trans 65:271–276 10. Bhat S, Agarwal PK (1996) The effect of moisture condensation on the spontaneous combustibility of coal. Fuel 75:1523–1532 11. Onifade M, Genc B (2018) Prediction of the Spontaneous Combustion liability of coals and coal shales using statistical analysis. J South African Inst Min Metall 118:800–808

Extraction of Electricity from Blast Induced Ground Vibration Waves – Case Study Raghu Chandra Garimella1(&) and Rama Sastry Vedala2 1

Department of EEE, Methodist College of Engineering and Technology, Hyderabad, India [email protected] 2 Department of Mining Engineering, NIT Karnataka, Surathkal, India

Abstract. Generation of Electrical Energy has become a basic aspect in Power System because of increase in demand from the electrical community. Power can be generated in a different number of ways. Numerous developments were made in power generation technology for the generation of electricity, but those are all dependent on conventional sources. Generation of Electrical Energy using Piezo Sensors will efficiently convert unwanted vibrations into direct electricity. It is also evident that obtained electrical energy will be in the par with the input vibration intensity from the research studies. Keywords: Seismic energy Seismograph


Electricity generation
Piezo electricity


1 Introduction The detonation of explosive charge in a typical blasthole under confinement releases a pressure in the form of chemical energy. The obtained chemical energy will further be converted into heat along with some force at the surroundings with a massive pressure [1]. Detonation of a explosive charge in a rock mass creates three major regions: (1) Explosion cavity, where explosion energy is liberated and the process is hydrodynamic; (2) Transition zone, where plastic flow, crushing and cracking occur; and (3) Seismic zone, where strain waves travel as seismic waves [2–4]. The process of detonation in a typical mine/quarry blast is dependent on the end effects involved. Initially, a part of the ruptured rock is closely associated to the strain wave, makes the ground vibrations to flow near the blasted hole. Later, the rock movement will begin due to fracture of rock mass. The energy transfer in to a rock mass will takes place in steps. Primary, development of fractures, also called as elastic and plastic deformation of the rock mass, will happen. Further, heat transfer in the rock mass will occur. Finally, movement of rock mass will be observed due to gases venting through open fractures and stemming [5]. In the earlier research, various monitoring instrumentation tools were used (viz. Vibration monitors, high-speed camera of 1000 fps capacity, and fragmentation monitoring systems), to analyze the dynamics of the blast and thereby the vibration parameters. © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 285–292, 2020. https://doi.org/10.1007/978-3-030-24314-2_36

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2 Piezo-Gen Technique Piezoelectricity is a generation of electricity in some solid substances due to application of mechanical stress/pressure. The word “piezo” originated from the Greek literature “piezein” indicates to squeeze or press, and “electric” or “electron”, originated from “amber”, which is an ancient source of electricity. French physicists, Curie and Curie, had discovered the concept of piezoelectricity in the year 1880 [6–8]. Piezoelectricity is also the competence of solid materials, viz. crystalline, ceramic substances, to produce an electric potential (EMF) due to the mechanical stress or heat on them [7, 9]. Nevertheless, piezoelectricity is not due to change in the surface charge density, however, by dipole density of the material. From the earlier literature, application of 2kN force over a 1 cm3 volume of quartz material can generate an electrical voltage of about 12500 V [10]. Physical expansion and contraction of a piezoelectric material changes the dipole moment (p = q.d), creating a voltage (Fig. 1). where, p = q.d = dipole moment, C-m q = magnitude of electrical charge, C d = distance between two poles, m.

Fig. 1. Working mechanism of simple piezo transducer [11]

3 Assessment of Seismic Energy 3.1

Ground Vibrations Monitoring

During the research studies, the intensity of blast induced ground vibrations was monitored using three units of Minimate Plus, Instantel, Canada. These ground vibration monitors are of 8-Channels. In the four channel instruments, the first three channels record three mutually orthogonal ground vibration components, namely Transverse, Vertical and Longitudinal. The fourth channel records the noise level-using microphone. Minimates with geophones and microphones connected were placed at different distances covering both short and long distances, from the blast site. The vibration events were later transferred to a computer using advanced blastware software. Generally, the dynamics of blast induced ground vibrations were monitored at

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specified distances from blast site with a geophone/ground vibration monitor in three mutually orthogonal – longitudinal, transverse and vertical directions. Among all these waveforms, whichever is the peak, that particular absolute value was taken as peak particle velocity (PPV). Seismic energy was estimated for all the signals in three directions using DADiSP signal processing software. DADiSP is a signal processing tool/software, using which shock energy dissipated in the form of waves is calculated. Longitudinal, Transverse and Vertical component of blast vibration event were imported from blastware software to digital signal processing software DADiSP in ASCII format. Fast Fourier Transformation (FFT) was performed subsequently to find the frequency component of the time domain of blast wave signal as blast wave recorded by Minimate Plus and processed by Blastware falls in the category of random progressive signal. The estimation of absolute area describes the intrinsic energy of the blast wave signal distributed in various frequency bands. The energy of the signal x(t) R1 is given by 1 jxðtÞj2 dt [12].

4 Field Investigations and Results Blasts were carried out in various mines for the extraction and assessment of seismic energy. Ground Vibration monitors were placed near blast field at various distances to find the impact of blast on nearby structures. Geophones were attached to the ground with the help of Plaster of Paris for proper contact. The developed piezo generator circuits were placed in the similar locations where conventional seismographs are positioned [13]. Altogether, 55 blasts were studied and electrical energy was tapped. In total, 10 blasts were carried out in Choutapalli limestone mine, 11 blasts were carried out in Yepalamadhavaram limestone mine and 34 blasts were carried out in Singareni Collieries Company Ltd. The following are some photographs depicting obtained electricity from undesirable seismic waves, extracted through the developed piezo-gen circuit in various mine locations (Figs. 2, 3 and 4) [13]. The various seismic data collected at various distances in different blasts are compared with the obtained electrical energy data as shown in the below Tables 1 and 2.

Fig. 2. Extraction of electricity using Piezo-Gen circuit from undesirable blast vibrations at limestone mine

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Fig. 3. Observation of obtained voltage from the blast vibrations by multimeter for the assessment of seismic energy

Fig. 4. Piezo-Gen circuit placed beside the geophone underground coal mine

Table 1. Summary of extracted electricity from blasts conducted in limestone mines Sl. no. 1 2 3 4 5

Distance (m) 100.00 125.00 130.00 108.00 120.00

MCD (kg) 30.17 30.17 30.17 36.67 36.67

PPV (mm/s) 30.60 22.40 9.40 6.10 4.95

Seismic energy (MJ) 248254 1299398 80993 347919 49502

Electricity extracted (MJ) 245232 1202928 76343 300540 42653

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Table 2. Summary of extracted electricity from blasts conducted in underground coal mine Sl. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Distance (m) 54.75 58.28 67.80 61.85 63.64 68.74 61.81 66.67 73.62 98.28 110.26 88.71 101.08 40.00 60.00 62.09 35.00 55.00 61.85 70.00 35.11 45.08 55.07 40.00 50.00 60.00 60.00 80.00 100.00

MCD (kg) 2.59 2.59 2.59 3.33 3.33 3.33 2.96 2.96 2.96 2.96 2.96 2.59 2.96 3.33 3.33 3.33 2.96 2.96 2.96 2.96 3.33 3.33 3.33 2.59 2.59 2.59 2.96 2.96 2.96

PPV (mm/s) 2.67 1.02 4.19 7.49 5.08 5.08 3.55 1.52 2.15 1.52 0.64 0.63 0.63 7.37 6.10 5.08 5.59 5.72 4.83 4.45 22.22 17.40 7.24 8.38 1.78 4.70 5.59 6.73 2.54

Seismic energy (MJ) 156122 59990 7133334 486001 61229 4500852 405538 98359408 3215421 4766935 92201932 84232644 75042381 543915 40070581 920512 33162509 28491223 1388977 8638939 37634381 7741455 55507124 17768342 33645 5883851 30312788 150689 5659210

Electricity extracted (MJ) 123185 44402 5250688 439429 33356 3037611 320185 81513971 2113462 3618080 71141568 71975752 71675872 303769 31248263 757222 22980792 21762289 1122289 7328110 17394886 7402328 22103881 11519133 18533 4534639 21312155 125553 3921140

Typical sample event report and FFT reports generated (Figs. 5 and 6). Seismic energy has obtained from the events recorded using signal-processing tool, DADiSP. Sample of signal processing window is shown in Figs. 7 and 8.

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Fig. 5. Typical event report

Fig. 6. Typical FFT Report from Blastware

Fig. 7. Sample of signal processing window

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Fig. 8. Estimation of seismic energy

Also, comparison of seismic energy with the generated electrical energy has made to observe the amount of undesirable vibrations which were converted to electricity (Figs. 9 and 10). From the analysis made (Figs. 9 and 10), it is observed that amount of seismic energy extracted in the form of Electricity is 80–90% of the total seismic energy in limestone mines and that is about 75–80% in the case of underground mine locations.

Fig. 9. Electricity extracted in limestone mine

Fig. 10. Electricity extracted in coal mine

5 Conclusions In the research study, detailed field investigations were carried out to estimate the seismic energy dissipated by ground vibrations caused due to blasting operations, using signal processing approach and to tap electricity from blast induced ground vibrations by piezo generator. Following are the main conclusions drawn from the research study: • It was observed from previous literature, the amount of explosive energy distributed would be more in seismic form. From the results, it is observed that the amount of seismic energy is being increased with the increase in Maximum charge per delay; hence, the optimal usage for MCD will improve the performance of blast by reducing seismic losses further optimizing explosive utilization.

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• From the research studies, it is clear that blast induced ground vibrations may be effectively tapped and converted into useful electrical energy with the developed patented Piezo-Gen circuit (Indian Patent Application No.: 201941002334A, published on Jan. 25, 2019). • The use of DADiSP for the assessment of seismic energy is an excellent advantage to the industry for doing effective signal processing mechanisms in a simple manner. • It is also observed that amount of seismic energy obtained in case of limestone mines is much higher than underground coalmines. Therefore, explosive energy losses in limestone mines are more than in case of underground coal mine. • In addition, it is observed in the case of underground mine that even with the parting more than 70 m between the blast location and monitoring station, the vibrations will travel effectively giving tendency to get more energy loss. In such cases, the application of Piezo-Gen circuit will gives a chance to extract some amount of undesirable blast wave energy. • Hence, the Piezo-Gen circuit has become as a renewable source for generation of electricity from blast vibrations and further will be more useful in assessing the seismic energy in blast field.

References 1. Johansson CH, Persson PA (1970) Detonics of high explosives. Academic Press, Cambridge 2. Atchison TC, Duvall WI, Pugliese JM (1963) Effect of decoupling on explosion-generated strain pulses in rock. Bureau of Mines, College Park, MD (USA) 3. Nicholls HR (1962) Coupling explosive energy to rock. Geophysics 27:305–316 4. Sastry VR (1989) A study into the effect of some parameters on rock fragmentation by blasting. Unpubl. Ph Thesis BHU India (1989) 5. Sanchidrian JA, Segarra P, Lopez LM (2007) Energy components in rock blasting. Int J Rock Mech Min Sci 44:130–147 6. Anon: All About Heaven - Some science behind the scenes. https://allaboutheaven.org/ science/piezoelectricity/121 7. Curie J, Curie P (1880) Development by pressure of polar electricity in hemihedral crystals with inclined faces. Bull Soc Min Fr 3:90 8. Imai K, Tingley D, Yamamoto T (2013) Experimental designs for identifying causal mechanisms. J R Stat Soc Ser A Stat Soc 176:5–51 9. Pramathesh T, Ankur S (2013) Piezoelectric crystals: future source of electricity. Int J Sci Eng Technol 2:260–262 10. Curie J, Curie P (1881) Contractions and expansions produced by voltages in hemihedral crystals with inclined faces. C R 93:1137–1140 11. APC International Ltd. (2002) Piezoelectric ceramics: principles and applications. APC International 12. Sastry VR, Ramchandar K (2014) Assessment of performance of explosives/blast results based on explosive energy utilization. RD Proj. Rep. 13. Garimella RC (2019) Tapping of electricity from ground vibrations caused due to blasting operations in mines and quarries

Evolution of the Probability Distribution Function of Shovel – Dumper Combination in Opencast Coal Mine Using ANN and RWB N. S. Harish Kumar1(&), R. P. Choudhary2, and Ch. S. N. Murthy1

2

1 Department of Mining Engineering, NITK, Surathkal, India [email protected], [email protected] Department of Mining Engineering, Faculty of Engineering and Architecture, Jai Narain Vyas University, Jodhpur, India [email protected]

Abstract. This article presents a new analytic calculation for the shovel – dumper combination in opencast coal mine evolution of the one and two galaxy probability distribution function (PDF). To develop a nonparametric PDF for a combination of shovel and dumper in a opencast coal mine, the historical breakdown data such as time between failure (TBF) of a shovel and dumpers were collected from the mine. Based on the collected TBF, Weibull parameters such as the shape parameter (b), scale parameter (η) and location parameter (c) were calculated under the K-S test (Kolmogorov–Smirnov test). A Weibull distribution model has been developed to obtain the one and two galaxy probability distribution function (PDF) for a collected failure data of shoveldumper system using Reliability Isograph Workbench (RWB). Also, Artificial Neural Network (ANN) model has been developed to predict the PDF for the same shovel-dumper system and compared with the actual obtained value of Reliability Isograph Workbench (RWB). It was found that the values of RMSE and R2 were 0.00068 & 0.9465 for PDF. The statistical results showed that the proposed Reliability Isograph Workbench and ANN model successfully predicts PDF for the shovel-dumper system. Keywords: Nonparametric model
Opencast coal mine
K-S test Weibull distribution
Time between failure
Failure frequency
RMSE and ANN




1 Introduction The function of the PDF to the sum of any random number of independent variables is important for many programs in science and technology [1]. This issue is not entirely clear and there is a theoretical solution in some cases [2–5]. In addition, in some cases, this theoretical solution is applicable for engineering purposes (see, for example, [2–6]) and here, especially in [4, 5], where it is examined and the rate of compression in different metrics as studied well. The purpose of this article is to find a better estimate of PDF files of random variables (RV) 5, the sum of the number of variables Number of independent distribution and the independent and identically distributed (IID) real RVs © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 293–302, 2020. https://doi.org/10.1007/978-3-030-24314-2_37

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Xi (i = 1, 2, …, N). The new model also benefits from providing intuitive support for physical interpretation. In simple terms, the amount of PDF is represented by the sum of ordinary PDF files, according to the N PDF file. Two practical results have been achieved, namely, RWB and ANN. In MATLAB, ANN sample consists of three layers namely information layer, centre as the last layer and a problem-solving layer as production [7]. The statistics for Feedforward System are shown in Fig. 1, each result of the entry into the components (ai) and (wij) is promoted to increase neurons as follows Eq. (1) [8]. In Fig. 1, every result of entering components (ai) and weights (wij) are nourished to accumulation intersection and is added with bias (bj) of neurons as follows Eq. (1) [8]

Fig. 1. Schematic structure of ANN

X¼ð

n X

wij ai Þ þ bj

ð1Þ

i¼1

In hidden layers, the most normally used migration functions are “Tansig and Logsig”. The function which is in activation, is not generally functional line is known as a series function, the output is between zero and one. the summation (X) passes through the transition function (F) that produces equality using the Eq. (2) [8] FðXÞ ¼ uj ¼ F[

n X

ðwij ai Þ þ bj 

ð2Þ

i¼1

The obtained values of RWB and the predicted values of the ANN are interpolated by means of biases and weights of the network to organizing to reduce the error among them.

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2 Failure Data Collection This case study was carried out at Opencast-I (OC-I), The SCCL, Kothagudem area, Telangana. This mine using a number of shovels and dumpers of dissimilar make i.e., Komatsu and BEML having dissimilar capacity. The capacity of the shovel include 12, 11 and 6.5 m3 and dumper includes 100T, 65T and 35T. In this article, one shovel i.e. 11 cubic meters which is made by Komatsu and four dumpers i.e., 100 T each which is made by Komatsu and BEML both were selected based on the match factor (i.e., 1:4). Also, in this case study, the breakdown (TBF) and repair (TTR) details of a shovel and four dumpers were collected for the one-year period during working hours.

3 Estimation of MTBF The basic failure data is analyzed using the trend test and series correlation test [9, 10]. It can provide statistical measurements of mean time between failure (MTBF) for the shovel and dumpers considered. However, the maximum likelihood estimation (MLE) is the method that tests the originally collected failure data of a shovel and dumpers [10]. The calculated MTBF from failure data (TBF and failure frequency) by the Eq. (3) mentioned below. The summary of the failure data of one S1, D1, D2, D3 & D4 from the field such as MTBF and failure frequency are shown in Table 1. n P

MTBF ¼

ðt1 þ t2 þ t3 . . .tn Þ

n¼1

n

¼

Total Operating Time Total No: of Failures

ð3Þ

Table 1. Summary of breakdown characteristics Systems No. of failures MTBF in hr S1 181 43.71 D1 54 117.35 D2 46 138.48 D3 88 78.42 D4 141 54.80

4 Non-parametric Weibull Analysis The K-S test is used to analyze the best-fit distribution functions for TBF data of the S1, D1, D2, D3 & D4. The values of b, η and c are obtained directly by fitting the data collected (TBF) from the mine to cumulative TBF conducted using the RWB software package. The results of the modified K-S test for the three distributions, the best-fitted distribution, and predictable Weibull parameters of the best-fitted distribution function of an S1, D1, D2, D3 & D4 for TBF are listed in Table 2.

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5 Estimation of the Probability Density Function The probability density function (PDF) was obtained by using Maximum Likelihood Estimation under Isograph Reliability Workbench as mentioned in Table 3. Also, the numerical interpretations were carried out using Eq. (4) [11]. f ðtÞ ¼

bðt  cÞb1 ðtcgÞb e ¼ kðtÞ  RðtÞ gb

Table 3. PDF of S1, D1, D2, D3 & D4 S. no Systems Weibull parameters MTBF in hr PDF f(t) η b c 1 S1 29.83 0.7139 0 43.71 0.0057681 2 D1 115.7 1.018 −3.967 117.35 0.0011329 3 D2 83.12 0.5357 −0.2365 138.48 0.000774 4 D3 54.37 1 0 78.42 0.0024717 5 D4 43.61 1 0 54.80 0.0042954

Fig. 2. PDF for a shovel (S1) with different Weibull parameters

ð4Þ

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From Fig. 2, it is observed that the two galaxy PDF (left & right) of the shovel (S1) is 0.0057681 at MTBF is 43.71 h with b = 0.7139 (b < 1). It can be concluded that the PDF of a shovel S1 was found to reject the null hypothesis at 5% (0.05) level of significance for obtained data of a shovel [12].

Fig. 3. PDF for a dumper (D1) with different Weibull parameters

From Fig. 3, it is observed that the one galaxy PDF (Right) of dumper D1 is 0.0011329 with MTBF is 117.35 h and b is 1.018 (b > 1). It can be concluded that the PDF of a dumper D1 were found to reject the null hypothesis at 5% (0.05) level of significance for obtained data of a dumper D1 [12]. From Fig. 4, it is observed that the one galaxy PDF (Right) of dumper D2 is 0.000774 with MTBF is 138.48 h and b is 0.5357 (b < 1). It can be concluded that the PDF of a dumper D2 was found to reject the null hypothesis at 5% (0.05) level of significance for obtained data of a dumper D2 [12].

Fig. 4. PDF for a dumper (D2) with different Weibull parameters

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Fig. 5. PDF for a dumper (D3) with different Weibull parameters

Fig. 6. PDF for a dumper (D4) with different Weibull parameters

From Fig. 5, it is observed that the two galaxy PDF (right & left) of dumper D3 is 0.0024717 with MTBF is 78.42 h and b is 1 (b = 1). It can be concluded that the PDF of a dumper D3 were found to reject the null hypothesis at 5% (0.05) level of significance for obtained data of a dumper D3 [12]. From Fig. 6, it is observed that the two galaxy PDF (right & left) of dumper D4 is 0.0042954 with MTBF is 54.80 h and b is 1 (b = 1). It can be concluded that the PDF of a dumper D4 were found to reject the null hypothesis at 5% (0.05) level of significance for obtained data of a dumper D4 [12].

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6 Generation of ANN Model for PDF In the present work, failure date of a shovel and four dumpers were taken out of the field. The anticipated ANN model to calculate the PDF of a shovel and dumpers in open cast coal mine is shown in Fig. 7. The neural structures are formed using three layers namely input, output and hidden layer in among them. In the input layer, four parameters were taken in MTBF, b, η, c and one parameter, i.e. the PDF output layer (f (t)). In this model, 5 data sets and outputs have been taken. In this, three sets of data are used for training, one set of data for a fixed validation and for the test. In this model, Progression Learning Algorithms Feed has been applied even more to learn. Before sampling the neural network, predictions of prediction and output data should be the norm for accuracy. The following Eq. (5) are used to provide the data (−1) and (1) [7]. In this model, 5 data sets and outputs have been taken. In that, three data sets are used for training, one set for testing and one set of data for validation. In this model, before the neural network is modelled, the input and output data must be normal for the precision of the predictions. The subsequent Eq. (5) is used to facilitate data between (−1) and (1) [7] Y ¼ ðHighValue  LowValue Þ

Yi  YMin þ LowValue YMax  YMin

ð5Þ

In the current study, the Levenberg-Marquardt (LM) back propagation was used for the study training process. The number of neurons in hidden layers is evaluated by trial and errors. Using this technique, 8 neurons are selected for PDF with hidden single layers, as shown in Fig. 7. The LEARNGDM was chosen as a function of training coordination after selecting exercise. Transmission features are chosen as hidden layers and linear functions for the resulting layer (output layer). The ANN model is trained by the LM training functions, which include 8 neurons for PDF of shovel and dumper. Each neuronal pattern has been trained more than 100 times. This training algorithm has adjusted the weight and biases to reduce the errors between the values obtained by the RWB and the expected values of the ANN model. It was found that LM, which includes 8 neurons for PDF, is better than the R2 error and the highest value. Several models of ANN are shown in Table 4. Several sampling processes are based on RMSE and R2, which are counted calculated using the Eqs. (6) and (7) [7]. From Table 4, it has been calculated that the RMSE and R2 values are 0.00068 & 0.9465 for PDF in LM-8. vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u1 X RMSE ¼ t ðy  y0 Þ2 N i¼1 N P

R ¼1 2

ð6Þ

ðy  y0 Þ2

i¼1 N P i¼1

ð7Þ ðy0 Þ2

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Fig. 7. The present study of the ANN model

Table 4. The training performance of PDF for different neurons Neurons 5 6 7 8

R2 0.9383 0.9128 0.9444 0.9465

RMSE 0.00071 0.00083 0.00073 0.00068

In comparison with the values obtained by the RWB, with the predicted value of LM-5 offered by the ANN models and its error is described in Table 5. From Table 5, it has been found that the highest error is at 9.04E-5 at sample 5. Least error in the sample −9.33E05 in PDF. The regression calculation for ANN neuron with LM-5 of the training, testing and verification procedure is shown in Fig. 8. It has be concluded that the value of R2 is close to unity and linear, which gives the accurateness of performance of the model. Table 5. Comparison of the obtained and predicted values of PDF Sl. no

Systems Weibull parameters

1 2 3 4 5

S1 D1 D2 D3 D4

MTBF in hr

η

b

c

29.83 115.7 83.12 54.37 43.61

0.7139 1.018 0.5357 1 1

0 43.71 −3.967 117.35 −0.236 138.48 0 78.42 0 54.80

Obtained values by RWB PDF f(t) 0.0057681 0.0011329 0.000774 0.0024717 0.0042954

Predicted values by ANN

Error

0.0056349 0.0009658 0.0008723 0.0039640 0.0043207

0.000133 0.000167 −0.00984 −0.00149 −0.00253

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Fig. 8. R2 plot for predicted and obtained values by ANN and RWB respectively

7 Conclusion In this paper, one and two galaxies of the probability density function, along with the Weibull distribution of S1, D1, D2, D3 and D4 used in open cast coal mine for the period of 1 years. The influencing Weibull parameters b, η, & c were found from the observation and result obtained. It was concluded that the level of significance of 5% (0.05) for the data obtained for S1, D1, D2, D3 and D4. The ANN model has been formed with MTBF, b, η, & c, an output parameter such as one output parameter namely PDF f(t). With the propagation of the forwarded feed backup, the LM algorithm is used to exclude the best model. The hidden layer, LM learning algorithm with 5 neurons for PDF has been found increasingly based on data analysis. The obtained and the predicted value of PDF of a shovel and dumpers with the highest R2 value give satisfactory results. Acknowledgement. The authors are thankful to The General Manager (HRD), HR/Employee Relation Department, The SCCL, Kothagudem, Telangana for giving the authorization to gather the breakdown data of shovels and dumpers and publish this work. Also, wish to convey our gratitude to Dr Ranjan Kumar, Senior Scientist and Dr P K Mandal, Senior Principal Scientist, CIMFR, Dhanbad for supporting us in carrying out research work.

References 1. Gnedenko BV, Korolev VY (1996) Random: summation: limit theorems and applications. CRC Press, Boca Raton 2. Robbins H (1948) The asymptotic distribution of the sum of a random number of random variables. Bull Am Math Soc 54(12):1151–1161

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3. Hung TL, Thanh TT (2010) Some results on asymptotic behaviors of random sums of independent identically distributed random variables. Commun Korean Math Soc 25 (1):119–128 4. Sunklodas JK (2012) Some estimates of normal approximation for the distribution of a sum of a random number of independent random variables. Lith Math J 52(3):326–333 5. Sunklodas JK (2012) On the normal approximation of a sum of a random number of independent random variables. Lith Math J 52(4):435–443 6. Klebanov LB, Kakosyan AV, Rachev ST, Temnov G (2012) On a class of distributions stable under random summation. J Appl Probab 49(2):303–318 7. Benli H (2013) Determination of thermal performance calculation of two different types solar air collectors with the use of artificial neural networks. Int J Heat Mass Transf 60:1–7 8. Ghritlahre HK, Prasad RK (2018) Investigation on heat transfer characteristics of roughened solar air heater using ANN technique. Int J Heat Technol 36(1):102–110 9. Wang X, Yu C, Li Y (2015) A new finite interval lifetime distribution model for fitting bathtub-shaped failure rate curve. Math Prob Eng 1–6 (2015) 10. Xie M, Tang Y, Goh TN (2002) A modified Weibull extension with bathtub-shaped failure rate function. Reliab Eng Syst Saf 76(3):279–285 11. Ghodrati B, Kumar U, Ahmadzadeh F (2012) Remaining useful life estimation of mining equipment: a case study. In: International symposium on mine planning and equipment selection 12. Dubey SP, Uttarwar MD, Tiwari MS (2015) Reliability study of 42 cu. M Shovel and 240 Te Dumper Equipment System with Special Reference to Gevra OCP, SECL, Bilaspur. Procedia Earth Planet Sci 11:189–194

Temperature Measurement During Rotary Drilling of Rocks - A Statistical Approach Vijay Kumar Shankar(&), B. M. Kunar, and Ch. S. N. Murthy National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India [email protected]

Abstract. This paper discusses a statistical analysis to measure the temperature during rotary drilling of fine-grained sandstone (pink) using embedded thermocouple method. The regression models consist of three input variables such as diameter of the bit, rpm and rate of penetration for different depth of thermocouples. Experimental test were conducted in computer numerical control (CNC) vertical machining centre. The measured temperature has been applied to study the influencing parameter using statistical technique. Analysis of variance (ANOVA) shows that the percentage contribution ratio of each operational parameters on temperature (output response). The most influencing parameter for temperature is rate of penetration with a percentage contribution of 71.32%, followed by drill bit diameter and spindle speed which contribute 19.27% and 2.99% respectively. The ANOVA and regression models for temperature give pvalues of less than 0.05. Hence the predicted regression models are statistically significant and good predictive capabilities with acceptable accuracy. Keywords: Drilling
Fine grained sandstone (pink) Embedded thermocouple
Regression analysis


Temperature


1 Introduction Drilling is the process used to drill a hole for placing explosives in blasting, petroleum extraction, mining and so on. The two important drilling methods used in mining industries are percussive drilling and rotary drilling. Rotary drilling is a major process in large open pit mines. During drilling process the temperature in drill bit and rock is around several degrees centigrade, depending on operating conditions and time to drill. Many researchers have conducted experiments to measure transient temperature during drilling operation. Che et al. [1] found that, some of the traditional temperature measurements techniques (e.g., embedded method and infrared radiation pyrometer) in machining processes metal have also been used in the rock cutting/drilling domain with minor adaptations. Szwarc et al. [2] developed a model to predict the drilling temperatures of limestone, by using RTD (Resistance temperature detector). Dreus et al. [3] developed a mathematical model, for the heating process of diamond drill bit. The

© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 303–309, 2020. https://doi.org/10.1007/978-3-030-24314-2_38

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difference between experimental and calculated data temperature of the diamond drill bit was more than 12%. Zacny et al. [4] conducted drilling experiment under Martian condition; the results found that the drilling efficiency is much greater than under terrestrial pressures and temperature. Gunes Yilmaz et al. [5] derived a predicted model for temperature for hard rock and could be applied to rocks which have uniaxial compressive strength in the range of 85–150 MPa. Rittel [6] found that the time response of thermocouple to be generally a limiting factor for the transient temperature to assess in solids. The embedded thermocouple method used as a tool, the response time of the thermocouple of the order of 10 microseconds. Agapiou and Stephenson [7] measured the temperature by using thermocouple method, standard welded thermocouple, and the insulated wires were embedded in the work piece near the drill zone. They found that measured temperatures are reasonable and concluded that the thermal inertia of welded thermocouple is high, with a good time response thin-wire insulated in the workpiece to form a thermal junction measurement to obtain temperature data also considered. Samy and Kumaran [8] used contact measurement techniques such as K-type thermocouple with 1 mm diameter. They also used avoiding contact type measurement of the temperature using FLIR E60 infrared thermal image camera with different operating parameters such as rpm and feed rate conditions. It was concluded that operational parameters increase, the temperature range from 66 °C–135 °C. Cui et al. [9] Developed a prediction model by considering radiation effects, configuration of bit and source of heat. Model shows that the radiation can be calculated for a limited from the drill head. From the above survey it is observed that a limited work has been reported on temperature measurement during rotary drilling using embedded thermocouple technique. Hence this article aims to present the percentage contribution of operational parameters such as bit diameter, rpm, and rate of penetration at different depth of thermocouples and also to identify the temperature for varying all three parameters.

2 Temperature Measurement Techniques In rock drilling, the temperature of the rock sample is very high depending on drilling conditions. To measure these temperature thermocouples are used as it is an extremely versatile device. Measurements of the temperature at the bit-rock interface were made using thermocouple, thermometers, resistance temperature detector (RTD), thermistors, etc. A set of experiments were conducted to develop a thermal model using 4-wire resistance temperature detectors (RTDs) were placed on the interior of a limestone block at a various locations [2]. Most of the rocks drilling process used to measure temperature by thermocouples and are made of two dissimilar metals with good physical contact, to achieve a good agreement of measurement. The K-type

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305

thermocouple is most commonly used in all industrial purpose. This thermocouple is made up of chromal (Ni-cr) - alumel (Ni-al), ranges from 0 °C to 1200 °C with a minimum voltmeter reading of 0 mv to 48.838 mv. The thermocouple sensor is inserted in a 3 mm diameter stainless steel tube, it gives a better sheathing and good contact between the tip of the thermocouple and the covering material for essential to ensure good heat transfer. At the exit of the tube spring contact is used to maintain this contact. In this experiment for measuring transient temperature during the drilling operation, a grounded thermocouple was used and is shown in Fig. 1.

Fig. 1. Fabricated grounded thermocouples

3 Experimental Procedure The rock drilling experiments were carried out on fine-grained sandstone (pink). A commercial masonry drill bit diameters of 6 mm to 16 mm with a U flute of tungsten carbide was used in this experiments. Thermocouples are commonly used versatile device for temperature measurement. These thermocouples are used to measure the actual interface temperature between two dissimilar metals, most of the thermocouples are made of two different pieces of wire, and it is welded into the bead called as a grounded thermocouple with an appropriate size of the wire. K-type thermocouples are measuring range from 0 °C to 1200 °C. In this experiment grounded thermocouples are used, in which the wire can be inserted into the predrilled hole of the rock sample in a Computer Numerical control (CNC) vertical machine centre [10] (Fig. 2).

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Fig. 2. CNC vertical machining center (BMV 45 T20)

4 Results and Discussion 4.1

Analysis of Drilling Temperature

Analysis of variance (ANOVA) analysis is used to identify the influence of parametric level of the temperature produced during rock drilling and observe the percentage contribution of the input variables. Table 1. Analysis of variance (ANOVA) analysis of fine-grained sandstone (pink) 6 mm depth Source DF Seq SS Contribution Adj SS Adj MS F-Value P-Value DD (mm) 1 3327.60 48.62% 269.09 269.09 84.38 0.000 SS (rpm) 1 155.24 2.27% 155.24 155.24 48.68 0.000 PR (mm/min) 1 2669.96 39.01% 637.96 637.96 200.06 0.000 DD2 1 70.07 1.02% 70.07 70.07 21.97 0.000 PR2 1 241.95 3.54% 241.95 241.95 75.87 0.000 Error 119 379.48 5.54% 379.48 3.19 Total 124 6844.29 100.00% (continued)

Temperature Measurement During Rotary Drilling of Rocks Table 1. (continued) 14 mm depth DD (mm) 1 6810.4 SS (rpm) 1 2016.4 PR (mm/min) 1 45778.8 DD2 1 197.0 PR2 1 81.6 Error 119 2299.6 Total 124 57183.6 22 mm depth DD (mm) 1 13518 SS (rpm) 1 4822 PR (mm/min) 1 125888 DD2 1 452 PR2 1 285 Error 119 3316 Total 124 148281 30 mm depth DD (mm) 1 10103 SS (rpm) 1 3968 PR (mm/min) 1 110292 DD2 1 2030 PR2 1 571 Error 119 8666 Total 124 135630 Where DD = Drill bit diameter,

11.91% 3.53% 80.06% 0.34% 0.14% 4.02% 100.00%

650.5 2016.4 1058.4 197.0 81.6 2299.6

650.5 2016.4 1058.4 197.0 81.6 19.3

33.66 104.35 54.77 10.19 4.22

0.000 0.000 0.000 0.002 0.042

9.12% 3.25% 84.90% 0.30% 0.19% 2.24% 100.00%

1399 4822 7262 452 285 3316

1399 4822 7262 452 285 27.9

50.20 173.08 260.65 16.21 10.24

0.000 0.000 0.000 0.000 0.002

928 3968 7691 2030 571 8666

928 3968 7691 2030 571 72.8

12.75 54.49 105.60 27.88 7.84

0.001 0.000 0.000 0.000 0.006

7.45% 2.93% 81.32% 1.50% 0.42% 6.39% 100.00% SS = Spindle

speed, PR = Penetration rate

Fig. 3. Pie-chart of percentage contribution of operational parameters on temperature

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Table 1 shows percentage contribution and significant values of all depth of thermocouples for fine grained sandstone (pink). ANOVA table reveals that the F-value is higher for rate of penetration followed by the diameter of the bit and rpm. Figure 3 Show the pie chart of individual parameter contribution for the response output. From the pie chart it is clear that the penetration rate is the most influencing parameter of 71.32% followed by diameter of the bit of 19.27% and rpm is the least effect of 2.99% respectively. 4.2

Regression Analysis

Regression model was developed for temperature with respect to each depth of thermocouples position. In this method to observe realistic models, a backward elimination method was used as the test procedure. ANOVA was performed for bit-rock interface temperature, corresponding to thermocouple positions (6 mm, 14 mm, 22 mm, and 30 mm) with significant of 95% confidence interval [10]. Influence of the parametric level of the temperature produced were compared using (ANOVA) with Minitab 17, where the P-values equal to or smaller than 0.05 were considered to be statistically significant and corresponding data is shown in Table 1. Multiple regression models to predict temperature for various depths of thermocouple are as follows (Eqs. (1)–(4)). The correlation coefficients (R2) of the obtained models for the rise in temperature are 94.46%, 95.98%, 97.76% and 93.61%. Regression models of bit-rock interface temperature for fine-grained sandstone (pink) Temp: at 6 mm depth ¼ 11:37 þ 3:031D þ 0:01576 SS þ 4:128 PR  0:0691 D2  0:2079 PR2 Temp: at 14 mm depth ¼ 4:90 þ 4:712 D þ 0:05680 SS þ 5:317 PR  0:1158D2 þ 0:12078 PR2 Temp: at 22 mm depth ¼  26:29  6:909 D þ 0:08784 SS þ 13:929 PR 0:1754 D2  0:2257 PR2 Temp: at 30 mm depth ¼ 61:90  5:63 D þ 0:0797 SS þ 14:33 PR þ 0:3719 D2  0:319 PR2

ð1Þ

ð2Þ

ð3Þ

ð4Þ

5 Conclusions In the present study, thermocouple technique has been used to measure the temperature during rotary drilling of rocks. The three controlled parameters has been considered in this study i.e., diameter of the bit, rpm and rate of penetration at different depths of thermocouple to find the most influencing parameters on temperature rise during drilling. The result shows that the penetration rate of 71.32% is the most influencing

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parameter followed by the diameter of the bit of 19.27% and rpm of 2.99%. The ANOVA analysis revels that the overall contribution of the controlled parameters and P-value is less that or equal to 0.05 is considered to be a statistically significant. Coefficient of determination (R2) for all the four models for different depths of thermocouple are 94.46%, 95.98%, 97.76% and 93.61% respectively. Hence the operational parameters are statistically significant and the values are within the expected range.

References 1. Che D, Han P, Guo P, Ehmann K (2012) Issues in polycrystalline diamond compact cutter– rock interaction from a metal machining point of view—part I: temperature, stresses, and forces. J Manuf Sci Eng 134:64001 2. Szwarc T, Aggarwal A, Hubbard S, Cantwell B, Zacny K (2012) A thermal model for analysis and control of drilling in icy formations on Mars. Planet Space Sci 73:214–220 3. Dreus A, Kozhevnikov A, Lysenko K, Sudakov A (2016) Investigation of heating of the drilling bits and definition of the energy efficient drilling modes. East-Eur J Enterp Technol 3:41–44 4. Zacny KA, Quayle MC, Cooper GA (2004) Laboratory drilling under martian conditions yields unexpected results. J Geophys Res 109:E7 5. Gunes Yilmaz N (2007) Prediction of radial bit cutting force in high strength rocks using multiple linear regression analysis. Int J Rock Mech Min Sci 44:964–970 6. Rittle D (1998) Transient temperature measurement using embedded thermocouples. Exp Mech 38:73–78 7. Agapiou JS, Stephenson DA (1994) Analytical and experimental studies of drill temperatures. Trans Am Soc Mech Eng J Eng Ind 116:54 8. Samy GS, Kumaran T (2017) Measurement and analysis of temperature, thrust force and surface roughness in drilling of AA (6351)-B4C composite. Measurement 103:1–9 9. Cui J, Hou X, Deng Z, Pan W, Quan Q (2017) Prediction of the temperature of a drill in drilling lunar rock stimulant in a vacuum. Thermal Sci 21:989–1002 10. Vijay Kumar S, Kunar BM, Murthy ChSN (2018) Experimental investigation and statistical analysis of operational parameters on temperature rise in rock drilling. Int J Heat Technol 36 (4):1176–1180

Safety in Coal Mines in India-Its Perspective K. Srihari(&) and A. Sandeep Kumar Godavari Institute of Engineering and Technology (A)., Rajahmundry, India [email protected], [email protected] http://www.giet.ac.in

Abstract. Mining is a hazardous industry as the working places are always moving ahead, strata conditions and environment are always changing from day to day. Mining industry growth is linked with safety and sustainable minerals development in mines. Always employees should be encouraged for participation at all levels to promote pro-active safety culture awareness up to grass root level. Various initiatives are to be taken on continual basis at all levels to translate the vision of “Zero Harm Potential (ZHP)” into a reality. Mining accounts for only 1% of World Employment but it accounts for 7% of fatal accidents at work place. Companies have to identify work places of hazards and risks in each mining operations, prepare a risk assessment and Safety Management Plan for every mine. Accident rate in mines came down but still it is high as compare to foreign countries. This paper is focused on safety management in coal mines in India for sustainable development. Keywords: Mines safety Recommendations




Accidents statistics in mines




Issues in mines




1 Introduction Mining is a vital contributor for industrial development and to the country’s economy. About 565 Coal Mines, 85 Oil & Gas Projects and Over 8268 Non-Coal Mines are being worked in India employing over one million persons directly on daily average basis. India produces 95 minerals as per the Annual Report 2016–17 of the Ministry of Mines [2]. As per the International Energy Agency World Energy Outlook-2018, coal will continue to be the country’s largest source of energy, maintaining a crucial role in fueling, developing economies and infrastructure. Despite of the significant growth of renewable, coal still accounts for the primary share of 74% of its electricity in India. The government strives to ensure universal electricity access by the early 2020s, India’s power system will need almost quadruple its size by 2040, and investments up to $2 trillion will be required to keep pace with increasing demand [6]. India is the world’s second largest coal producer. In 2017, India produced 716 million metric tons of coal (294 million tons of oil equivalent), accounting for 9.2% of the world coal production (in term of metric tons). Coal plays a vital role in meeting global energy needs and is critical to infrastructure development – 38% of the world’s electricity and 71% of the world’s steel is produced using coal [6].

© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 310–317, 2020. https://doi.org/10.1007/978-3-030-24314-2_39

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Accident rate in coal mines 5 yearly average fatalities in CIL came down from 196 in 2075-79 to 45 numbers in 2015-17 and 5 yearly average of serious injuries came down from 1278 in 2075-79 to 116 numbers in 2015-17. Still it is high in Indian mines due to lack of automation and skilled labor in mine operations [1] (Figs. 1 and 2).

Fig. 1. 5 yearly average fatalities in CIL since Fig. 2. 5 yearly average of serious injuries 1975 [1]. since 1975 [1].

2 Safety Management in Coal Mines Safety and sustainable development must always be top most priority of any company. Company has to frame a well-defined safety policy to ensure safety in all mines and establishments. Company has to establish a multi-disciplinary Internal Safety Organization (ISO) in all subsidiaries for the implementation of the Mine Safety Plan. Safety Management Plan (SMP) has to be prepared and implemented as per 104(3) of Coal Mines Regulations 2017. 2.1 a. b. c. d. e. f. g. h. i.

Causes of Accidents in Mines [6] Ground Movement (fall of roof, fall of side and other ground movement) Transportation machinery (Winding in Shaft) Transportation machinery (other than winding in shaft) Rope haulage, Wheeled Trackless Transportation and other transportation machinery Machinery other than Transportation Machinery Explosives Electricity Gas, dust & other combustible material Fall (other than falls of ground): Fall of persons, fall of objects, other falls

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Accident Analysis in Mines in India

Mining is a hazardous occupation as it fights against natural forces. Coal mines have high rates of accidents and less productivity as compared comparison to other industries. Rajmahal (OC mine) disaster in 2016 considered as the worst disaster ever in Indian mining history. It is caused due to the slope failure and killed 23 miners. a. In coal mines, major concern is the occurrence of disasters at regular intervals. About 55% of fatalities are due to the disasters of fires/explosions and inundations in the past. Strata failures are common causes. This needs a focused effort from all the stake holders. b. For fatal accidents, roof fall continues to be the area of major concern followed by accidents caused by dumpers and trucks in coal mines. c. Death rate per million tonnes coal raised reduced from 0.38–0.08 (2006–2015 years). d. Seriously injured rate per million tonnes coal raised reduced from 2.07–0.47 (2006– 2015 years) as per Table 1 (Figs. 3 and 4). As per the Fig. 5, the no. of fatal accidents in India is more than US and Australia.

Table 1. Trend in number of accidents in coal mines, rates of casualties/seriously injured persons [6] Year Fatal accidents No. of persons Killed Seriously injured 2006 137 2007 78 2008 93 2009 93 2010 118 2011 67 2012 83 2013 82 2014 62 2015 55

15 11 16 14 23 10 6 11 3 9

Serious accidents No. of Accidents Persons seriously injured 861 876 923 940 686 693 636 646 480 488 533 546 536 542 456 457 379 391 302 307

Rates per million Rates per thousand persons tonnes coal raised employed Death Seriously Death Seriously injured injured 0.36 0.21 0.25 0.25 0.32 0.18 0.23 0.23 0.17 0.16

2.31 2.51 1.92 1.76 1.38 1.52 1.53 1.31 1.11 0.93

0.32 0.16 0.18 0.17 0.20 0.11 0.13 0.13 0.10 0.08

2.07 1.98 1.40 1.18 0.85 0.92 0.89 0.74 0.61 0.47

Killed/ seriously injured

Safety in Coal Mines in India-Its Perspective 1000 900 800 700 600 500 400 300 200 100 0

313

Persons killed Seriously injured

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015 Year

Fig. 3. Persons killed and seriously injured for 10 years (2006–2015).

Death / seriously injured rate

6 5 4

Rate of seriously injured/million tonne

3

Rate of death/ million tonne

2

seriously injured rates/ thousand persons employed

1

Death rates/ thousand persons employed

0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

years

Fig. 4. Death/seriously injured rates from 2006–2015.

Fig. 5. Number of fatal accidents UG coal mines in India, the USA and Australia.

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3 Issues Faced by Mining Industry in India a. b. c. d. e. f. g. h. i. j. k. l. m. n.

Slow automation of operations/use of IT in mining sector. Lack of skills/competency of work persons, Occupational health & safety issues in mines, Occurrence of disasters in mines, Illegal mining/theft of minerals, Lack of indigenous machinery for mining sector development, Delay of amendment of statute for the industry need. Ensuring long-term viability of the minerals industry. Control, use, and management of land. Making a positive impact on local communities. Managing the environmental impact of mines. Integrating the approach to using minerals so as to reduce waste and inefficiency. Giving stakeholders access to information to build trust and cooperation. Sector governance: clearly defining the roles, responsibilities, and instruments for change expected of all stakeholders.

High tax rates, poor manufacturing Mining machinery and financing support are major challenges. In spite of adversities, there is a huge scope for technology providers to come forward and meet this challenge of exploration in the mining technology in India. 3.1

India’s Natural Resources Share in World

See (Table 2). Table 2. India’s natural resources share in world. Sl. No. Resources 1 Human population 2 Livestock 3 Geographical area 4 Water resources 5 Forest cover pastures 6 Arable land

Share (%) 17% >11% 2.4% 4.2% 1% 51%

3.1.1 Challenges Faced by India Global environmental Performance index is 177 out of 188 countries. • A population of 1.3 billion likely to stabilize only in 2160 at about 1.6 billion. • Average age of Indians in 2020 will be 26 years. • 70% of Indian population in rural areas depends entirely on agriculture contributing only about 14% of GDP.

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• Population below poverty line is estimated at 20.2% in 2017. • Human Development Index of India dropped to 100 out of 187 countries in 2017.

4 Recommendations for Safety Management in Mines in India (a) R & D works required to be directed towards the expected future scenario in the coal, metalliferous, oil and gas sectors in the country. (b) R & D work should be focused on identified application research projects in association with national and international institutions through mutual cooperation. (c) In order to reduce the accidents, disasters and dangerous occurrences in the mines, automation of mines is required to achieve the expected results. (d) The hazardous mining sector requires effective emergency response and disaster management system installed at mine level, rescue station level as well as at DGMS level. (e) International standards in occupational health needs to be adopted and strategies formulated for its implementation to properly diagnose and detect Occupational Health Hazards and many dust borne diseases notified under the Mines Act 1957. (f) A Center for Standardization, Testing and Certification needs to be developed in DGMS with a National Centre and its branches at identified locations. (g) Mines Safety and Health Academy (MSHA) at DGMS, Dhanbad should impart specialized OSH Training not only to DGMS Officials but also to the Managers, Safety & Ventilation Officers, Supervisors, Workmen’s Inspector etc. (h) Automation in mining can improve the safety, sustainable mining and streamline operations efficiency. (i) Innovations using in mineral mining operations includes, satellite imaging, remote sensing, Geographic Information System (GIS) maps, Global Positions System (GPS) and low-impact seismic methods that minimizes environmental pollution, increases safety and productivity. 4.1

Mine Safety Inspection

• Round-the-clock Supervision of all mining operations by adequate number of competent & statutory supervisors and mine officials. • Periodic mine Inspections by Head Quarter and Area level senior officials. • Surprise back shift mine Inspections by mine and area level officials. • Regular Inspection by Workmen Inspectors appointed in each mine. • Regular mine Inspection by officials of Internal Safety Organization of respective subsidiary and CIL (Fig. 6).

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Exploration Mine planning and design

Mine closure Safe mining operations Environmental Management Plan

Exploitation Project monitoring and control

Fig. 6. Stages of safe mining operations.

Steps for Prevention Accidents in OCPs a. b. c. d. e. f. g.

h. i. j. k.

Formulation and Implementation of Mine-specific Traffic Rules. Code of Practices for HEMM Operators, Maintenance staff & others. Sensitization training of Contractor’s Workmen involved in contractual jobs. Training imparted to dumper operators on Simulators. Lighting arrangement using high mast towers for enhancement of illumination as per stipulated guidelines. Eco-friendly Surface Miners for blast free mining and avoidance of associated risks. Dumpers fitted with Proximity Warning Devices, Rear view mirrors and camera, Audio-Visual Alarm (AVA), Automatic Fire Detection & Suppression System (AFDSS) etc. Ergonomically designed seats & AC Cabins for operators’ comfort. Wet Drilling & water Sprinklers for dust suppression. Use of Shock Tubes & Electronic Detonators for control of ground vibration & fly rocks. GPS based Operator Independent Truck Dispatch System (OITDS) in large OCPs for tracking movement of HEMMs inside OC mine.

5 Conclusion The mineral industry is embracing sustainable mineral development principles, but much has been focused on the level of language and policy, rather than on the operational sphere. If the momentum for change is to be maintained it is critical that these broad principles and commitments are translated into improved performance ‘on the ground’. This will require a concerted effort by the industry, supported by educational

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and research bodies, to develop new skills, new knowledge and new ways of doing business. Company has to establish a multi-disciplinary Internal Safety Organization (ISO) in all subsidiaries for the implementation of stated Safety Policy. All operations, systems and processes are meticulously planned and designed with due regard to SMP, safety, conservation, sustainable development and clean environment. Currently, demand for advanced technology is already present in India. In order to achieve resource efficiency, the Indian mining sector is already looking forward to adopt advanced mine surveying and exploration technologies (using geophysics applications using 2D and 3D seismic surveys) along with the usage of software solutions by utilizing 3D software packages (like Micromine, Gemcom MINEX, SURPAC, Datamin and Vulcan) in the mine planning and design stage.

References 1. 2. 3. 4.

Safety in Coal Mines - Annual Report (2017–18), Ministry of Coal-New Delhi Safety in mines Annual Report 2016–17. Ministry of Mines-New Delhi Coal Mines Regulation (2017) Statistics of mines in India, vol. I. Directorate General of Mines Safety, India. http://dgms. gov.in/writereaddata 5. World Coal 2018–2050: World Energy Annual Report (Part 4) 6. Statistics of mines in India, vol. I (coal), pp 53–57, Directorate General of Mine Safety (2015)

Reliability Analysis of LHD Machine - A Case Study J. BalaRaju(&), M. Govinda Raj, and Ch. S. N. Murthy Department of Mining Engineering, NITK, Mangalore, Karnataka, India [email protected]

Abstract. In the present global scenario, survival of the industry is more critical unless it produces their intended targets. Accomplishment of expected rate of production levels are depends on the performance of equipment. Hence, it is very important to predict the maintenance schedules for replacement or repair actions of the defective parts. Keeping in view, every industry is constantly looking for enhancement equipment life. Reliability analysis is one of the well appropriated techniques used to estimate the life of the equipment. In this paper, performance of Load-Haul-Dumper (LHD) has been analyzed. Renewal process approach has been utilized for reliability investigation. Best fit distribution of data sets were made by the utilization of Kolmogorov-Smirnov (K-S) test. Parametric estimation of theoretical probability distributions was done by utilizing Maximum Likelihood Estimate (MLE) method. Reliability of each individual sub-system has been computed according to the best fit distribution. In addition to that, reliability based preventive maintenance (PM) time schedules were calculated for the expected 90% reliability level. The possible recommendations were suggested for improvement of reliability level. Keywords: Mining


Reliability
Maintenance
Component
Subsystem

1 Introduction Achievement of the projected level of production mainly depends upon the effective utilization of men and machinery. In every production industry, maintaining machinery with a greater level of reliability is also one of the crucial aspects [1]. Underground metal mine’s production for the last few decades in India is not according to the plan. The unavailability of the machine is the major cause for a drop in production levels in the industry [2]. Reliability estimation plays a significant role in the performance evaluation for a complex repairable system. Performance of equipment is mainly depends upon the reliability of the usage, working atmosphere, and effectiveness of maintenance, operational procedures, and technical skill of the operators. These forecasts are helpful to organize the maintenance and operational activities [3]. Unreliable parts lead to fails the system during the operation of the vehicle. Repair and replacement action should be advised to failure components to describe the remaining useful life [4]. The reliability of a complex repairable system can be enhanced by applying proper maintenance strategies. Preventive maintenance (PM) is one of the well appropriate techniques to conduct the maintenance or repair action. The possible © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 318–326, 2020. https://doi.org/10.1007/978-3-030-24314-2_40

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repairs should be performed at the time of repair action. The part which is not possible to repair is called censored failures. These can be replaced with a new modified design. The present investigation has been performed to identify the breakdowns, reasons for occurrence of breakdowns and frequency of its occurrence, classification of subsystems based on the breakdown mode; develop the models to forecast the sub-system reliabilities.

2 Methodology The first and foremost step in the reliability analysis is collection of failure data and it’s classification. The required data should be collected either from real time monitoring of experiments or from the existence of past historical failure data which was stored in the maintenance records. The classification of the data can be done in accordance with type of failure. This data can be useful to estimate the time between failure (TBF) and time to repair (TTR) [5]. The procedure of reliability analysis for a critical subassembly is shown in a flow chart (Fig. 1) as follows [6].

Fig. 1. Reliability analysis procedure of a critical subsystem (Source: Ascher et al. [5])

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3 Case Study The present case study has been carried out in one of the Indian underground Lead and Zinc mines of the northeast region. In this study, 4 number of Sandvick made LHDs (LHD1, LHD2, LHD3 and LHD4) were considered for the analysis. LHD machine is treated as an intermediate level mechanized system used for transporting the ore in underground mining operations. Ore extraction is done by drilling and blasting techniques. Transportation of ore is done by LHD from mined out area to the crushing point. A schematic view of LHD machine at the workshop is shown in Fig. 2.

Fig. 2. A typical LHD machine at workshop

Before performing the intended analysis of data sets, every equipment should be classified into a number of sub-assemblies [6]. These classifications were made on the basis of described reasoning of maintenance records kept by maintenance personnel about the failures. LHD machine has been classified into seven numbers such as Engine (SSE), Brake (SSBr), Tyre (STTy), Hydraulic (SSH), Electrical (SSE), Transmission (SSTr) and Mechanical (SSM). The failure data (Table 1) of LHDs were collected over a period of one financial year (2016-17). This was collected in the form of spreadsheets of maintenance records. This includes the failure frequency (FF), the time between failure (TBF) and time to repair (TTR) (Table 2). Table 1. Metrics of one financial year of various LHDs Equipment LHD1 LHD2 LHD3 LHD4

Scheduled working hours 8680 8680 8680 8680

Scheduled service hours 168 311 314 272

Breakdown hours 3416 2295 1978 2040

Idle hours 1588 2100 2460 1708

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Table 2. Collected data of various LHDs from field visit Equipment Factor LHD1 FF TBF (Hrs) TTR (Hrs) LHD2 FF TBF (Hrs) TTR (Hrs) LHD3 FF TBF (Hrs) TTR (Hrs) LHD4 FF TBF (Hrs) TTR (Hrs)

SS E 38 166 45 34 184 67 36 161 70 25 230 85

SS Br 28 222 60 26 260 89 25 270 100 24 301 83

SS Ty 54 127 49 36 154 80 36 148 88 28 210 77

SS H 37 195 49 27 240 82 28 245 100 22 328 95

SS El 39 180 51 29 252 90 37 169 71 22 274 76

SS Tr 38 197 51 27 207 76 24 236 111 24 297 90

SS M 50 108 58 40 125 80 39 130 99 36 170 64

4 Results and Discussion 4.1

Trend and Serial Correlation Tests

These tests are used to test the presence of trend in the collected data can be determined by plotting the graph between cumulative frequencies of failures (CTFF) versus cumulative time between failures (CTBF). In a trend test, if the dataset points are arranged in a linear manner which indicates that the investigated datasets are free from the trend [7]. Further, the serial correlation test was also carried out to check the relationship between values. This demonstrates the correlation between ith value of TBF and (i − 1)th value of TBF. From the graphical analysis (Figs. 3(a) and (b), 4(a) and (b), 5(a) and (b) and 6(a) and (b)) it was observed that the dataset points are connected in a straight line manner in the trend test. Therefore there is no trend existed in the collected data sets. In the case of serial correlation test, the points are scattered randomly, which exhibited no correlation. The result of these tests shows that, the data sets of all the subsystems were found as trend-free and the points are identically distributed. Hence, IID assumption for the data sets was not denied for each subsystem. 4.2

Goodness of Fit/Best Fit Distribution

After completion of the IID assumption, probability distribution of data sets needs to be performed. These are explained by the goodness of fit distribution analysis. The analysis has performed by utilization of ‘Isograph Reliability Workbench 13.0’ software. In this, a wide variety of parameters, for example, exponential, 1-parameter Weibull, 2-parameter Weibull and 3-parameter Weibull has examined. From the estimated results (Table 3) goodness of fit, allocation has found as 2-parameter Weibull and 3-parameter Weibull distribution for various sub-assemblies. Identification of goodness of fit for the data sets has made by the utilization of Kolmogorov-Smirnov

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ith TBF versus (i-1)th TBF

CTFF versus CTBF CTBF

ith TBF

250 200 ith TBF

CTBF

1400 1200 1000 800 600 400 200 0

0

200 CFF

150 100 50

400

0 0

200 (i-1)th TBF

400

Fig. 3. (a) Trend test plot of LHD1 (b) Serial correlation test plot of LHD1

CTFF versus CTBF CTBF

1500 ith TBF

CTBF

2000

ith TBF versus (i-1)th TBF

1000 500 0

0

200 CFF

400

300 250 200 150 100 50 0

ith TBF

0

200 (i-1)th TBF

400

Fig. 4. (a) Trend test plot of LHD2 (b) Serial correlation test plot of LHD2

ith TBF versus (i-1)th TBF

1000

ith TBF

CTBF

CTFF versus CTBF CTBF 1500

500 0 0

200 CFF

400

ith TBF

300 250 200 150 100 50 0 0

200 (i-1)th TBF

Fig. 5. (a) Trend test plot of LHD3 (b) Serial correlation test plot of LHD3

400

2500 2000 1500 1000 500 0

CTFF versus CTBF CTBF

0

100 CFF

323

ith TBF versus (i-1)th TBF 400 ith TBF

CTBF

Reliability Analysis of LHD Machine - A Case Study

ith TBF

300 200 100

200

0 0

200 (i-1)th TBF

400

Fig. 6. (a) Trend test plot of LHD4 (b) Serial correlation test plot of LHD4

(K-S) test. Parametric estimation for theoretical probability distributions has also made by utilizing the Maximum Likelihood Estimate (MLE) method (Table 4). Table 3. K-S test results of LHDs Machine ID K-S statistics Dmax Best fit model Exponential Weibull 1-P Weibull 2-P Weibull 3-P LHD1 0.2259 0.2031 0.0579 0.2286 Weibull 2-P LHD2 0.2196 0.1970 0.0468 0.0451 Weibull 3-P LHD3 0.2153 0.1924 0.0826 0.0618 Weibull 3-P LHD4 0.2312 0.2080 0.0500 0.0499 Weibull 3-P

Table 4. Results of MLE with the best fitted distribution model Machine ID

Best fit model

LHD1 LHD2 LHD3 LHD4

Weibull Weibull Weibull Weibull

4.3

2-P 3-P 3-P 3-P

ML estimates of the best fit Scale parameter, Shape parameter, ɳ B 188.2 4.078 476.5 9.15 89.62 1.205 284 4.556

Location parameter, c 0 −250.2 116.3 −1.212

Reliability and Maintainability Analysis

Because of Trend-freeness in the data sets, the renewal process has adopted to estimate each individual subsystem reliability. On the basis of the best fit model, the results of reliability percentage (R) (from Figs. 7, 8, 9 and 10) and unreliability percentage (F) was estimated and are illustrated in Table 5. Further, the mean time between failure (MTBF) and the mean time to repair (MTTR) were calculated to evaluate the metrics of

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maintainability and failure rate (Table 6). Maintainability equation for the Weibull distribution is as follows: Maintainability ðMwÞ ¼ 1  eð

MTTR b g

Þ

1 MTBF

Failure rate ðhÞ ¼

Table 5. Reliability and unreliability percentages of each subsystem Equipment Factor LHD1 F (%) R (%) TBF (Hrs) LHD2 F (%) R (%) TBF (Hrs) LHD3 F (%) R (%) TBF (Hrs) LHD4 F (%) R (%) TBF (Hrs)

SS E 36.49 63.51 166 36.49 63.51 184 36.49 63.51 161 36.49 63.51 230

SS Br 80.55 19.45 222 80.45 19.45 260 80.45 19.45 270 70.73 29.97 301

SS Bo 29.97 70.73 127 29.97 70.73 154 29.97 70.73 148 29.97 70.73 210

SS Ty 70.73 29.97 195 63.51 36.49 240 70.73 29.97 245 80.55 19.45 328

SS H 50.00 50.00 180 70.73 29.97 252 50.00 50.00 169 50.00 50.00 274

SS M 36.49 63.51 166 36.49 63.51 184 36.49 63.51 161 36.49 63.51 230

99.9

99

99

2-parameter Weibull Median rank

90

Eta estimator

3-parameter Weibull Median rank

90

η: 188.2

70

η: 476.5

70

β: 4.078

50

γ: 0

Eta estimator

β: 9.15 γ: −250.2

30

30

ρ: 0.9802

20

ε: 0.05796

10

B10: 108.4 5

P0: 0%

3

Unreliability (%)

Unreliability (%)

SS Tr 19.45 80.55 108 19.45 80.55 125 19.45 80.55 130 19.45 80.45 170

LHD2 Cumulative Probability

LHD1 Cumulative Probability 99.9

50

SS El 63.51 36.49 197 50.00 50.00 207 63.51 36.49 236 63.51 36.49 297

ρ: 0.9876

20

ε: 0.04515

10

B10: 122.4 5

P0: 0.2749%

3 2

2 1

1

0.5

0.5

0.3

0.3 0.2

0.2

0.1

0.1 108

137.3

174.6

125

222

Fig. 7. Cumulative LHD1

probability

199.8

319.2

510.2

Time

Time

curve

of

Fig. 8. Cumulative probability curve of LHD2

Reliability Analysis of LHD Machine - A Case Study LHD4 Cumulative Probability

LHD3 Cumulative Probability 99.9

99.9 99

99

3-parameter Weibull Median rank

90

Eta estimator

Median rank η: 284

70

β: 1.205

50

γ: 116.3

Eta estimator

β: 4.556 γ: −1.212

30

30

ρ: 0.982

20

ε: 0.0618

10

B10: 130.1 5

P0: 0%

3

Unreliability (%)

Unreliability (%)

3-parameter Weibull

90

η: 89.62

70 50

325

ρ: 0.9883

20

ε: 0.04998

10

B10: 172.1 5

P0: 1.601E-09%

3 2

2 1

1

0.5

0.5

0.3

0.3 0.2

0.2

0.1

0.1 13.7

37.01

99.96

270

170

21 .9

264.1

329.2

Time

Time

Fig. 9. Cumulative probability curve of LHD3

Fig. 10. Cumulative probability curve of LHD4

Table 6. Results of availability and maintainability Machine ID LHD1 LHD2 LHD3 LHD4

4.4

Total number of failures 288 221 215 178

MTBF (Hrs) 3.99 6.22 6.40 9.98

MTTR (Hrs) 1.86 2.69 2.80 3.22

Failure rate 0.2165 0.1641 0.1959 0.1004

Maintainability 99.01% 100.00% 96.48% 99.64%

Reliability Based Preventive Maintenance (PM) Time Schedules

PM is defined as the actions executed in an effort to hold the components in an indicated condition by giving an efficient assessment, identification of the occurrence of early failure [8]. In this research, PM time schedules, have been calculated (Table 7) against the expected rate of reliability level i.e., 90%. From the computed values it was noticed that if the reliability requirement as 90% for LHD1, then the PM must be conducted in every 107.63 h. Similarly, for LHD2, LHD3 and LHD4 are 123.41 h, 135.28 h and 172.26 h respectively. Table 7. PM time schedules of LHD machines Expected reliability PM time schedules, Hrs LHD1 LHD2 LHD3 LHD4 0.90 105.30 120.25 134.15 170.45

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5 Conclusion Enhancement of continuous operation of equipment can be achieved by organizing the proper maintenance practices. Reliability analysis is a technique exclusively used to estimate the life of the products and to determine the maintenance schedules. This analysis provides a base to mitigate the uncertainties of equipment. The data sets of LHDs were analyzed for determination of reliability of each sub-system. From Table 5, it was noticed that a very least value of the reliability was obtained for SSBr (19.45%), SSTy (29.97%) and SSM (36.49%). It was concluded that the SSBr, SSTy and SSM are the most critical subsystems and assessed that more concentration needs to be kept. Improper or inefficient activities of maintenance and operation are the major cause of the performance drop of these critical sub-systems. Forecasting of reliability based PM time intervals will provide the required information to conduct the scheduled maintenance. From the calculated results of PM time intervals, If the requirement of reliability is 90%, then the PM should conduct for every 105.30 h to LHD1, for LHD2 could be 120.25 h, LHD3 should be 134.15 h and for LHD4 machine should be 170.45 h (Table 7). This study noticed that due to dissimilar operational and environmental conditions different LHD machines should require different maintenance plans. For effective maintenance organization, each equipment’s reliability needs to be evaluated separately.

References 1. Military Handbook (2003) Military Handbook, MIL-HDBK-5H: Metallic Materials and Elements for Aerospace Vehicle Structures, (Knovel Interactive Edition), pp 6–55. U.S. Department of Defense 2. Dhillon BS (2008) Mining equipment reliability maintainability and safety. Springer, London, pp 57–70. https://doi.org/10.1007/978-1-84800-288-3_4 3. Jardine AKS (1998) Maintenance replacement and reliability. Preney Print and Litho Inc., Ontario 4. Vagenas N, Runciman N, Clement SR (1997) A methodology for maintenance analysis of mining equipment. Int J Surf Min Reclam Env 11:33–40 5. Ascher H, Feingold H (1984) Repairable systems reliability modeling inference misconceptions and their causes, vol 45, no 4. Marcel Dekker, New York, pp 222–232 6. Vagenas N, Kazakidis V, Scoble M, Espley S (2003) Applying a maintenance methodology for excavation reliability. J Surf Min Reclam Environ 17:4–19 7. Kumar U, Klefsjo B, Granholm S (1989) Reliability investigation for a fleet of load haul dump machines in a Swedish mine. Reliab Eng Syst Saf 26(4):341–361 8. Nuziale T, Vagenas N (2000) A software architecture for reliability analysis of mining equipment. Int J Surf Min Reclaim Env 114:19–34 9. Esmaeili M, Aghajani A (2011) Reliability analysis of a fleet of loaders in Sangan Iron Mine. Arch Min Sci 56:629–640

Emerging Mining Trends: Preparing Future Mining Professionals Laxminarayana Chikatamarla1(&) and Devulapalli Narasimha Prasad2,3 1

RPMGlobal, Brisbane, Australia [email protected], [email protected] 2 Ministry of Coal, Government of India, New Delhi, India [email protected] 3 Mining, Singareni Collieries, PSU of GoTS and GoI, Kothagudem, India

Abstract. The present-day mining operations are increasingly questioned by various stakeholders i.e. government regulators, non-government organizations, investors, environmentalists, land affected people, consumers etc., on account of unsustainable, inefficient, poor planning & implementation practices. Mining companies cannot succeed in the future unless they adopt business risk management principles, implement change management with the emerging digital technologies for transparency and sustainability of operations to renew the confidence of consumers and regulators. In this changing climate, the new generation of mining and other engineers are expected to embrace the emerging digital technologies i.e. real-time data acquisition, improved data analysis, reporting and enhanced process monitoring covering different aspects right from exploration, planning, project execution, production and marketing. This paper focuses on the need for training mining professionals to face the technological challenges that are pertinent to the mining industry in the rapidly changing global commodity markets. Keywords: Emerging mining trends
Digital mining
Global Reporting Initiative
Business risk management
Mining education and training
Problem Based Learning

1 Introduction With the current climate of global economic uncertainty coupled with adverse impact on commodity prices and access to finance, mining companies are seeking solutions to reduce all costs and increase productivity to weather the cyclical downturn of mining industry tied up to commodity prices. The mining sector has seen the ups and downs in recent years and the trend continues. During this time, many mining companies started adopting innovative and transformative practices. These transformations will continue for the next decade due to declining tier-one assets with poor mineral grades, focus on shareholders returns with environmental obligations. In this environment, mining companies must find ways to remain competitive by revising their traditional mining models. © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 327–336, 2020. https://doi.org/10.1007/978-3-030-24314-2_41

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The miners recognised the power of digital solutions to improve the efficiency of mining operations and embedding digital thinking into the core of their business practices for making corporate decisions [1]. The miners should envision the future digital mine transformation and flow of information to back-office processing for decision making [2–4]. Academic institutions should focus on this direction to train future mining professionals. 1.1

Reforming Talent

Evolution of digital technologies is rapidly changing the innovation every 5 to 10 years. This means today’s innovative technology could be obsolete in the next 5 to 10 years’ time [5]. Hence, the mine plan needs the flexibility to incorporate capital plan and workforce for a life of mine that will run for over 20 years. Thus, there is a mismatch between the lifecycle of technologies and the life cycle of mining projects. Hence, building flexibility in capital investments is critical for mining companies to be competitive in the volatile markets i.e. providing scope for adoption of changing technologies and commodity prices [6]. Considering the rapid changes in technological innovations, the mining companies’ traditional strategy of talent recruitment and training needs a change. They must think out of the box for talent strategy and recruit talent from non-traditional sources to provide a range of skills to enable miners to deal with future technological challenges. To encourage innovation, an entrepreneurial mindset and a startup mentality should be nurtured [6]. Traditional skill set recruitment from a narrow pool of talent will not work in the rapidly growing digital world. To cite examples, Australia’s mining industry use data analytics and information management to achieve automation with the use of robots and remote-controlled equipment [7]. Major mining companies have already implemented driverless trucks, automated drilling, driverless trains to transport ore from pit to port, remote operations in UG mines, application of UAVs to detect greenhouse gas emissions, rehabilitation of land sites, etc. 1.2

21st Century Mining Challenges

21st-century mining poses many challenges as a result of a new cycle of scientific and technological discoveries in mining engineering [8, 9]. Significant challenges are likely to be encountered due to: • Remote mine sites coupled with deeper mineral resources; • Exploring possibilities for subsea mining and asteroid mining; • Evolving new mining technologies, i.e. unconventional gas extraction, i.e. coalbed methane, shale gas, coal gasification, microbial coal conversion, and shale oil; • Ongoing mining automation and robotics; • Increasing environmental restrictions and groundwater management; • Satellite-based technologies for discovery of minerals and environmental monitoring;

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• Data mining, i.e. need for increased real-time monitoring (of machinery movement, temperature, gaseous emissions, wear and tear); and • The need for collaborations with local communities for sustainable mining operations; The biggest challenge the mining industry facing globally is attracting talent to the mining profession. Harsh and remote working conditions in the mining industry coupled with the cyclical nature of ‘booms and busts’ talented students are not getting motivated to opt for a mining engineering discipline. The mining industry should incentivise for driving innovation and encourage contributions from the workforce with a commitment to strengthening government and community relations to attract talent. Hence, the mining engineering education and training must be revised for taking up the challenges of the fast-changing mining technologies of the 21st century.

2 Emerging Trends in Mining According to the McKinsey, PwC and Deloitte’s tracking the mining industry reports, the following trends are fast emerging which will bring a drastic change the way the mining industry is going to be transformed in the coming decades [1, 5, 6]. a. Evolution of Digital mining: The “big data” transformation significantly improves the decision-making process of the corporate world. The information and the ability to sort out, oversee, and process it, is quickly turning into a focused differentiator. Mining organizations must implant innovative and scientific reasoning into the core of the business system and practices to change the manner in which corporate choices are made. The mining companies are not fully utilizing the majority of the information captured from operational frameworks. Some are currently realizing that capturing and dealing with the correct information can unravel huge enhancements in operational profitability, maintenance of assets and safety of workforce [10]. Figure 1 shows an example of an intelligent data platform for integrated operational planning, control and decision support for a typical mining project; the Enterprise Planning Framework’ (EPF) facilitates end-to-end design, planning & scheduling, simulation, financial modelling and execution capabilities across the mining value chain to propel the mining organization’s productivity. b. Overcoming innovation barriers: advancement is fundamental for the mining business to change and it isn’t restricted to technology; it includes the adoption of innovative approaches to appeal with stakeholders, re-imagining the future of work and recognizing the products that will be in prominent demand going ahead. Change the traditional mindset of risk-averse culture hindering the efforts to innovate within the mining industry; c. The future of work: the adoption of innovative technologies, such as robotics, process automation, and artificial intelligence will augment performance in the mining industry. As opposed to dispensing with occupations, it will probably make endeavours to retrain individuals to utilize innovation and upgrade employment at both the mine site and in the back office.

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Fig. 1. Coordinated operational planning, control and decision making process (Source: RPMGlobal intelligent Platform [10])

d. Transforming stakeholder relationships: the mining organizations must decide how to make a solid social impact that adjusts to the advantage of various stakeholders. To restore trust with employees, financial institutions, governments, and the general population, leading mining organizations are embarking on efforts, for example, taking decisive public stances around corporate social responsibility, sticking to voluntary sustainability standards and passing shareholder resolutions regarding increased disclosure on climate change; e. Changing shareholder expectations: Key Performance Indicators should reflect varied objectives to create value for numerous communities i.e. clients, employees, suppliers, communities and not just shareholders. The corporate boards should focus more on long-term strategies and leadership development by linking management compensation to broader corporate goals related to good corporate social responsibility and ethical behaviour; f. Water – finding sustainable solutions to a critical issue: as the UN assess that water shortage impacts about 40% of the worldwide populace, mining organizations must improve their way to deal with water through innovative techniques intended to reduce, reuse and recycle water in water-scarce regions and to contain and treat wastewater to prevent contamination of downstream water streams;

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g. Remediation of mined out areas: Land is a key component in all mining tasks, and systematic reclamation and rehabilitation of mined out zones and dumps are critical for a maintaining an ‘Ecological Balance’ in the mining business. Thus, mining companies are using innovative methods to showcase their competences to remediate the land realistically after mining activities to obtain a social license to operate the mines; h. Reserve replacement woes: as supply limitations plague the business, the challenge for mining organizations is to find replacement reserves by engaging innovative exploration and development techniques that use minimum capital for a short period of time. Hence, mining companies are using digital technology to overcome this challenge; i. Realigning mining boards to drive change: Boards hindered in old mindset will progressively battle to fulfil new directives, such as taking a more active role in challenging the executive team on topics from corporate strategy to digital disruption, talent management, and emerging risk factors. Diverse viewpoints are necessary for the mining boards to challenge organizational assumptions and help determine if the organization is taking on too much risk, or possibly not enough risk [6].

3 Global Reporting Initiative Many mining corporations have a profoundly imbued with conventional thinking of their industry and the factors controlling the production and mining environment. However, this mindset won’t work and miners can’t ignore the role they play in a much wider economic and technological ecosystem, which is fast growing and progressively complex day by day. Henceforth, mining organizations are recognizing that profound transparency is a prerequisite for gaining community trust. Global companies are increasingly definitive in taking corporate social responsibility seriously. Many mining companies have started following voluntary sustainability principles, including those set out by the Taskforce for Climate-related Financial Disclosure, the Global Reporting Initiative (GRI) and the Carbon Disclosure Project [11]. GRI is the world’s most broadly utilised benchmark on sustainability reporting and disclosure. This requires mining organisations coupling their social purpose with financial objectives to articulate the value they bring to society. For instance, mining organisations currently empower neighbourhood communities to test the quality of a field site’s water discharge by setting up CCTV cameras or applications that provide citizens with online access to water quality data. Others give community members the option to physically visit water discharge sites to conduct their own tests. Such type of sweeping transparency urges organisations to keep themselves truthful and creating an environment of shared responsibility. Further, mining organizations have started going past generous gifts that normally end once a mine site closes down. Instead, some companies have begun contributing a portion of their revenues to local establishments that authorize community members to allocate funds based on local needs.

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4 Present Status of Indian Mining Industry The Indian mining industry has grown over a period of time. India is currently producing about 95 major and minor minerals which include 4 fuel, 10 metallic, 23 nonmetallic, 3 atomic and 55 minor minerals. India’s position in 2015 as compared to world production was second in barites, and talc/steatite/pyrophyllite, third in coal & lignite, chromite, and zinc (slab), fourth in kyanite/andalusite/sillimanite, fifth in iron ore, and Steel (Crude), sixth in bauxite ore, seventh in manganese ore and eighth in aluminum [12]. Technology wise major opencast mining projects are on par with any advanced countries and being operated by deploying high capacity shovels and dumpers as well as specialized mining equipment consisting of bucket wheel excavators and stacker reclaimers in conjunction with high capacity conveyor belts both for overburden removal and coal. In-pit crushing and conveying technology, High wall technology has also been successfully adopted. In the case of underground mining, mechanization was adopted as per the needs including continuous miner technology and longwall technology successfully. Extraction and use of Coal Bed Methane (CBM)/Coal Mine Methane (CMM) have been successfully demonstrated and a couple of commercial CBM projects are ongoing. However, automation and application of IoT (internet of things) to mining operations are required to be adopted in a significant manner. The software is in use for geological modelling and mine planning for quite some time but needs to be further enhanced for the actual implementation and monitoring of planned activities as well as equipment utilisation and maintenance for day to day control of mine operations for making quick informed decisions. Majority of the mining industry in India is controlled by Government and Private sector is slowly making its headway into mining operations since opening up the sector in early 2000 for private participation. In this context, there is a need for the Global Reporting Initiative to be taken up seriously for sustainable mining in the country on par with global mining companies. 4.1

Current Indian Mining Education and Training

Current mining training programs in all mining schools across India have practically identical curriculum with minor variations as it is centrally controlled by All India Council for Technical Education (AICTE). Traditionally, basic science fundamentals such as mathematics, physics, chemistry, geology, and introduction to mining are taught in the first two years. The advanced mining subjects which include rock mechanics, mining methods, ventilation, mechanization/mining machinery, mine planning & design, legislation and management are taught in the last two years of the engineering course. Most of them are classroom lectures with laboratory exercises. At the end of each year, students are required to go for practical training in the field of coal and metal mining (total of 8 to 10 weeks) as a part of the curriculum. This type of teaching does not prepare the undergraduates for employment unless they are trained on the job for one year. The mining companies do not have resources or time to train the graduates to meet their job requirements. Hence, it is suggested that the current

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mining curriculum requires total revision in the context of current technology advancements of the mining industry discussed in the previous sections. 4.2

Proposed Mining Education and Training

In view of the emerging innovation and the application of new technologies, the mining companies must focus on dealing with the mineral assets, the money matters and shareholder commitment, public relations and environmental aspects. Hence, financial and stakeholder management will be as crucial as technical and environmental consultants. Therefore, future mining and other engineering teaching and training require critical changes to the curricula and the admissions from other disciplines of engineering is necessary for the mining resources industry. It is possible that many more employment opportunities will emerge within the supporting professional services and these roles will likely be populated with new graduates and retrained staff. The new mining course should offer a variety of learning options including interdisciplinary education in mining engineering combined with geotechnical, civil, mechanical, metallurgy, environmental engineering, Information Technology etc. This augments the position of mining engineering as one of the most sought after professional qualification in the resource-oriented Indian economy. As the young generation grew up with technology during their schooling (i.e. smartphones, basic computing skills, web surfing etc.), the conventional method of attending lectures needs to change. From the beginning, students should be exposed to the mining work environment, nearly full-time with short (i.e. week-long) spans of intensive contact time for lectures and laboratories. The study material could be accessed online using high-quality audio-visual delivery. The present arrangement of 14–16 weeks of contact time per semester should change to, a month of one-week sessions allowing the student to complete work experience in their chosen mining field while obtaining a tertiary education which helps mining employers by creating genuine job-ready graduates. The current system of mining education in India does not provide the required skill set to get on the job in the field. Many a time’s young engineers get frustrated when they first join the mining industry due to lack of clarity of what they studied in the school and its applicability in the field. As a result, bright engineers leave the industry to pursue different fields as they did not get enough challenge in the mining industry. Hence, it is critical to expose students to the challenge of the mining industry in the early stages of their engineering studies to create interest and develop innovation to modernize the current mining industry. Additionally, create courses requiring students to study across borders as part of their qualifications to understand different educational and cultural backgrounds as well as to get exposed to different mining environments overseas. Further, the training framework should simultaneously focus on the critical thinking and administration abilities to establish the foundation for the future CEO’s who will need to be a portfolio chief, expert, strategist and mentor as opposed to the full-time supervisor. Instilling the fundamental characteristics are basic to establish the framework for postgraduate studies. The challenge to the mining organisations is to ensure to have a sufficient number of mining resources i.e. mining engineers, geologists,

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geotechnical engineers, environmental, mineral dressing engineers etc., to meet their needs in either boom or bust cycles of resource industry [8]. 4.3

Problem Based Learning

The present old-style teaching methods in mining and other engineering should be replaced with a ‘Problem Based Learning’ (PBL) method. Issue-based learning (i.e. PBL) is a focused instructional method in which students study about a subject through the experience of tackling an open-ended problem. Students learn both reasoning techniques and subject knowledge. The PBL format originated from the medical school of thought and is currently adopted as a teaching method in other engineering schools. The objectives of PBL are to help students develop flexible knowledge, effective problem-solving skills, self-directed learning, active teamwork skills, and natural inspiration. Problem-based learning is a style of dynamic learning [13, 14]. PBL method encourages students to work in Teams. Working in groups, students recognise what they knew previously, what they need to know, how and where to get new data which may lead to the resolution of the problem. The job of the teacher (PBL Coach) is to encourage learning by supporting, controlling, and checking the learning procedure. The coach or mentor must encourage and build confidence in students to take on the challenges while enhancing their understanding. PBL method of teaching is a paradigm shift and completely different from traditional classroom/lecture teaching and learning method [15]. The PBL teaching method prepares engineers to truly job-ready and fully-trained graduates. However, revising the mining curriculum to the PBL method requires lots of efforts in designing the problems which involve classroom lectures, field visits, and online resources including audiovisuals for training the teachers. 4.4

Finding Innovative Methods for Mining Engineering Education

The search for innovative teaching and training methods for mining engineering education in India need to be given a high priority because of the globalisation of the world economy. This aspect needs to be recognised by the current mining industry leaders, administrative ministries and universities, local professional bodies to control and govern the process. The local professional bodies such as Mining Engineering Association, Institute of Engineers India, mining company CEO’s should get involved in revising the curriculum and teaching methods. Further, Indian mining professional bodies should be associated with international mining associations to assure the required scientific, practical, scholarly and professional skills to ensure sustainable mining of mineral resources on par with the international mining standards [8, 9]. To achieve success in the areas discussed in Sects. 4.2, 4.3 and 4.4, one must focus on strong and relentlessly work on excellent HR (Human Resource) practices that go past planning to implementation and ongoing reviews.

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5 Way Forward Development of mining skillset i.e. teaching and training methods will be playing an extremely crucial role for shaping the Indian mining economy. Hence, searching for innovative methods for mining engineering education is of great significance to make the Indian mining professionals on par with global standards. Benchmarking Indian mining teaching and training methods with international mining educational standards is the need of the hour. To achieve this, the involvement of the mining academics, professional bodies and the participation of mining organisation leaders is critical to developing a curriculum to take up the challenges of 21st-century mining needs. Thus, the mining and other engineers trained by the universities are readily employable with a minimum induction period. The curriculum of mining engineering at graduate and master’s levels to be redrawn keeping industry’s requirements in view. Faculty also need to be trained in these areas such that students find the resource person to repose confidence in learning. PBL method of teaching should be introduced in the mining education to train, challenge, innovate and retain the much-needed talent for the mining industry [14]. The approach suggested is similar to the one being followed by the Minerals Council of Australia to improve the standards of Mining Engineering Education and skill set of young mining professionals [16]. Continuous efforts of all interested parties i.e. educational institutions, professional associations (national and international), research organisations, mining companies, government organisations need to work in a coordinated manner to reach the common goal of preparing tomorrow’s mining and other engineers to embrace the technological challenges i.e. digital thinking, training, certification and continuous professional development of qualified mining professionals for a highly efficient, environmentally sound mining industry for sustainable development [17].

References 1. Behind the mining productivity upswing: technology-enabled transformation, metals, and mining practice. McKinsey & Company Report, August 2018 2. Blockchain explained: what it is and isn’t, and why it matters. McKinsey & Company Report, September 2018 3. Harnessing data to unlock new energy solutions ExxonMobil, 04 February 2019. https:// www.spe.org/en/dsde/dsde-article-detail-page/?art=5065 4. Crompton J (2019) The new world of data science and digital engineering: the hype, the hope, and the reality. SPE DSDE Upstream Oil Gas J. https://www.spe.org/en/print-article/? art=4966 5. We need to talk about the future of mining, PwC’s future insights series. https://www.pwc. com.au/publications/we-need-to-talk-about-future-of-mining-2017.html 6. Tracking the trends 2018 the top 10 issues shaping mining in the year ahead. A report by Deloitte Touche Tohmatsu Limited (2018) 7. Mining industry looks towards a new wave of automation. https://www.abc.net.au/news/ rural/2017-05-23/the-future-of-automation-in-the-mining-industry/8550636

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8. Future mining issues and mining education. https://www.ausimmbulletin.com/opinion/ future-mining-issues-and-mining-education/ 9. A vision for the new challenges to mining education in Australia. https://www.ausimmbulletin. com/opinion/a-vision-for-the-new-challenges-to-miningeducation-in-Australia/ 10. Enabling the Digital Mine with mining’s only Integrated Mining Operational Platform built on industry standards. RPMGlobal mining software products. https://www.rpmglobal.com/ software/ 11. Global reporting initiative standards. https://www.globalreporting.org/standards/ and https:// www.south32.net/sustainability/our-focus/sustainability-reporting 12. Annual Report 2017-18 of the Ministry of Mines, GoI 13. Problem based learning. https://www.tcd.ie/CAPSL/TIC/guidelines/teaching/pbl.php 14. Problem-based learning: an approach to teaching and learning. https://blogs.shu.ac.uk/shutel/ 2014/10/06/problem-based-learning-an-approach-to-teaching-and-learning/?doing_wp_ cron=1550316274.4625930786132812500000 15. Problem based learning. https://en.wikipedia.org/wiki/Problem-based_learning 16. Modernising STEM education for Australia’s future mining workforce. Mineral Council of Australia. https://www.minerals.org.au/news/modernising-stem-education-australia%E2% 80%99s-future-mining-workforce 17. Kazanin OI, Drebenstedt C (2017) Mining education in the 21st century: global challenges and prospects. Zapiski Gornogo institute. 225:369–375. https://doi.org/10.18454/PMI.2017. 3.369

Prediction of Energy Efficiency of Main Transportation System Used in Underground Coal Mines – A Statistical Approach N. V. Sarathbabu Goriparti(&), Ch. S. N. Murthy, and M. Aruna Department of Mining Engineering, National Institute of Technology Karnataka, Surathkal 575025, India [email protected]

Abstract. Transport in underground mines i.e. belt conveyor is used for carrying extracted materials from one station to other. Transportation involves energy as its main consumer. An efficient energy system adapted for transporting extracted materials can minimize energy losses, hence resulting in reduced cost of energy. Energy to transportation is provided by means of an electric motor, the efficiency of the electric motor depend on load carried by the system, the length and height to which the material has to be delivered. The present study was carried on the energy efficiency of three different transportation systems in GDK-1&3 incline underground mine, The Singareni Collieries Company Limited, Ramagundam. The present study was carried out considering two cases with first, load varying from 20% to 100% keeping conveyor speed constant. Secondly, with 20% fixed loading and varying the conveyor speed from 1 m/s to 2 m/s. Estimation of the energy efficiency for a unique electric motor was estimated considering both the cases which involved three different lengths and heights. It was observed that with a constant conveyor speed of 2 m/s and filling rate varying from 27.775 kg/m to 5.555 kg/m, the amount of increase in efficiency was found to be 23.92%, 18.75% and 5.25% for Gantry, 5L and Surface conveyors respectively. Also with a constant filling rate of 5.555 kg/m and conveyor speed varying from 1 m/s to 2 m/s, the amount of decrease in efficiency was found to be 13.63%, 11.52% and 1.64% for Gantry, 5L and Surface conveyors respectively. Further a prediction study was carried on the energy efficiency based on the input parameters load, length and height. The model gives an R2 value 87% which is significant. Keywords: Belt conveyor Energy efficiency


Underground coal mines
Transportation


1 Introduction Energy is one of the basic requirement for modern civilization. Energy efficiency is the reduction of energy consumption subjected to key constraints. The importance of energy efficiency is sustainability and cost reduction. As from ISO 50,001, the energy saving potential in manufacturing sites is 15–20%. The Global energy potential is around 36% and an average saving potential of a plant is 20% [1]. The energy cost for © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 337–344, 2020. https://doi.org/10.1007/978-3-030-24314-2_42

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transportation in underground mines is about 40% of the operational cost, the remaining 60% cost is for operation and maintenance [2]. Therefore, energy efficiency of main transportation system used in underground mines i.e. belt conveyor is a much significant factor to reduce the operational costs [3, 4]. Belt conveyor, a safe and efficient transport used in underground mines. The energy share of belt conveyor, in underground mines is up to 60–70% [5]. DIN 22101 [6] and ISO 5048 [7] are the standards used to model and design the belt conveyor systems for bulk material transfer. According to these standards, there is a scope for getting energy saving from speed control of belt conveyor [3, 8, 9]. The electrical energy given to the conveyor can be converted into useful mechanical energy to run the conveyor, heat energy, noise energy and other power losses. Energy efficiency of belt conveyor is mainly depends on speed of the belt, material filling profile, dynamic behavior, idler and belt dimensions [3, 4, 8–10]. The energy efficiency of belt a conveyor can be improved by design, control and audit methods. The earlier research work was more focused on, equipment design [11] i.e. the design of idler [12] and drive, and speed control [13–15] of drive motor. A very less work has been done on the variation of energy efficiency based on length and height at which material will transfer. In this study an attempt is made to illustrate the influence of load carried by the system, length and height at which material transferred on energy efficiency of the electric motor.

2 Field Study The present study was carried at GDK-1&3 Incline, located at RG-I Area of The Singareni Collieries Company Limited, Ramagundam. The Mine totally consist of 8 belt conveyors to transport the coal from underground to dump area. The specifications of the belt, idler, drive motor and gearbox were collected from the respective equipment catalogs, which are listed in Tables 1 and 2. Table 1. Specifications of all conveyors used in GDK 1&3 incline S.no Name of the conveyor 1 2 3 4 5 6 7 8

Gantry Surface 5L 12L 24L 32L 40L 45L

Details of Capacity (hp) 75 75 75 75 75 75 75 75

the drive Voltage (volt) 440 440 440 440 440 440 550 550

Details of the Type Width (mm) PVC 900 PVC 900 PVC 900 PVC 900 PVC 900 PVC 900 PVC 900 PVC 900

belt Length (m) 60 420 260 260 260 275 150 185

Lift (m)

Tail end Discharge location location

10 40 63 47 45 70 43 41

Surface 5L 12L 24L 32L 40L 45L 49L

Gantry Surface 5L 12L 24L 32L 40L 45L

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Table 2. Specifications of the belt and drive head Details of the belt Parameter Value 200 t/h Maximum capacity, Qm Belt speed, v 2 m/s Belt width, B 900 mm Loading material weight, ml 27.775 kg/m Conveying length, L – Conveying height, H – Inclination angle, b – Belt length, l – Belt thickness, tb 20 mm Belt weight, mb 15 kg/m Idler weight, mr 54 kg/m Radius of drive pulley, r 35.1 cm Friction coefficient, f 0.025 Made PVC

Details of the drive head Parameter Power, P Voltage, V Current, I Frequency, f Power factor, cos u Efficiency, η Moment of inertia, J Torque at full load, Tfl Breakdown torque, Tb Approximate weight, Mm Rated speed, N Pole pairs, p Gear reduction ratio, GR Reduction gear speed, N2

Value 75 hp 415 V 90 amp 50 Hz 0.88 94.3% 2.3 kgm2 354 N-m 2.5 * 354 N-m 540 kg 1482 rpm 2 1:30 50 r.p.m

Table 3. Details of the conveyors which were studied in this work. Name of the conveyor Length, l (m) Height, H (m) Inclination, b (°) Gantry belt conveyor 60 10 9.46 5L belt conveyor 260 63 13.6 Surface belt conveyor 420 40 5.44

3 Belt Conveyor Calculations Along with the field data, presented in Sect. (2) DIN 22101 was also used to do the belt conveyor calculations. The total force to run the conveyor at a given load and inclination can be calculated by [9]. F = CfL[mr + ð2mb + ml Þ cos bg + Hml g + Fs

ð1Þ

where C is represents the Coefficient of secondary resistance, f is the coefficient of friction, L is the conveyor length (distance between two pulleys), mr, mb, ml are the unit weight of idlers, belt, material load respectively, b is the inclination, H is the elevation and g is the acceleration due to gravity.

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The mechanical power required to run the conveyor, Pm is calculated by [9] Pm ¼ F  v

ð2Þ

The electrical power required to run the conveyor, Pe is calculated by Pe ¼ V I cosu

ð3Þ

The efficiency ηd of the drive is calculated by gd ¼

Pm Pe

ð4Þ

4 Simulation Study Among the eight conveyors systems of GDK-1&3 Incline, three conveyors were considered based up on the conveyor length. These three belt conveyors are modelled and simulated using Matlab 2009b. The simulation parameters of these conveyors is same as the parameters of the conveyors used in the field, shown in Tables 1 and 2. The details of the three different conveyors considered in the present study is given in Table 3. Gantry, 5L and Surface belt conveyors were modelled and simulated at two different operating conditions as follows: (1) Variable filling rate and constant speed (2) Constant filling rate and variable speed. In first condition, the load applied on the belt is variable and the speed of the belt is almost constant. Where as in second condition the belt speed was adjusted using a variable frequency drive and the material filling rate has been maintained constant. The simulation parameters of the induction motor (IM) are given in Table 4, which are same as the parameters of the IM used in the field. Table 4. Simulation parameters of IM Nominal parameters Values Nominal parameters Rated Power (P) 55 kW Rotor inductance/ph (lr) Rated Voltage (V) 415 V Magnetizing inductance (lm) Rated Supply Cycles/sec (f) 50 Hz Moment of inertia (J) Stator resistance/ph (rs) 0.086 X Friction factor (FF) Stator inductance/ph (ls) 0.226 mH Pole pairs (p) Rotor resistance/ph (rr) 0.086 X Rotor inductance/ph (lr)

Values 0.226 mH 10.38 mH 2.3 kg m2 0.05421 2 0.226 mH

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5 Results and Discussions Figures 1 and 2 show the variation of motor efficiency of three belt conveyors when they are operated at two different operating conditions such as (1) Variable filling rate and constant speed (2) Constant filling rate and variable speed. Figure 1 represents a graph of efficiency versus load. The data involved motor efficiencies for load varied from efficiency of the motor 5.555 kg/m–27.775 kg/m with a constant rated speed of 2 m/s for Gantry, 5L and Surface conveyors. It was observed that with increase in the load on the conveyor belt the efficiency of the motor increased linearly. The amount of increase in efficiency was found to be 23.92%, 18.75% and 5.25% for Gantry, 5L and Surface conveyors respectively.

Fig. 1. Variation of efficiency of motor, when it is connected to three different belt conveyors when the load is varied from 5.555 kg/m to 27.775 kg/m.

Fig. 2. Influence of conveyor speed on motor efficiency for a constant filling rate of 5.555 kg/m and speed varied from 1 m/s to 2 m/s.

The rated speed of all three belt conveyors is 2 m/s. It is observed that from no load to full load the speed was dropped by 10% that means at full load condition i.e. 27.775 kg/m the corresponding speed of Gantry, 5L and Surface conveyors is same and it is equals to 1.8 m/s, and when the load is varying from full load i.e. 27.775 kg/m to 20% of full load i.e. 5.555 kg/m the speed of all three belt conveyors is 2 m/s, as shown in Fig. 3. It may seems to be the variation of speed is not relaying with load. Figure 2 represents a graph of efficiency versus conveyor speed. The data involved motor efficiencies for speed varied from 1 m/s to 2 m/s with a constant filling rate of 5.555 kg/m for Gantry, 5L and Surface conveyors. It was observed that with increase in the speed of the conveyor belt the efficiency of the motor decreased linearly. The amount of decrease in efficiency was found to be 13.63%, 11.52% and 1.64% for Gantry, 5L and Surface conveyors respectively.

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Fig. 3. Variation of speed of motor, when it is connected to three different belt conveyors.

6 Statistical Analysis Using Minitab 17, an ANOVA (Analysis of variance) based regression analysis was done to predict the most influencing parameters on the drive system efficiency. The obtained Eq. (5) represents efficiency (η, %) in terms of conveyor height (h in m), conveyor length (l in m), conveyor speed (v in m/s) and material load (ml, kg/m) (Tables 6 and 7). Table 5. ANOVA results Source DF Seq SS Adj SS Regression 4 1813.98 1813.98 Height 1 391.64 0.3 Length 1 700.5 683.49 Speed 1 1.29 99.44 Load 1 720.54 720.54 Error 25 264.61 264.61 Total 29 2078.58

Adj MS 453.494 0.297 683.493 99.438 720.543 10.584

F-Value 42.85 0.03 64.58 9.39 68.08

P-Value 0.000 0.868 0.000 0.005 0.000

Table 6. Model summary S R2 Adj R2 R2 (predicted) 3.25335 87.27% 85.23% 81.2%

Contribution 87.27% 18.84% 33.70% 0.06% 34.67% 12.73% 100%

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Table 7. Coefficients of regression model Term Constant Height Length Speed Load

Coef 77.68 −0.0058 0.04117 −6.03 0.6612

SE Coef 3.41 0.0348 0.00512 1.97 0.0801

T-Value 22.75 −0.17 8.04 −3.07 8.25

P-Value 0.000 0.868 0.000 0.005 0.000

Fig. 4. Normal probability and residual plots of output (efficiency)

g ¼ 77:68  0:0058 h þ 0:04117 l  6:03 v þ 0:6612 ml

R2 ¼ 87:27% ð5Þ

From the analysis done with Table 5, it was observed that the most influencing parameters among the input parameters are load, conveyor length and conveyor speed. All the parameters have P-Value less than 0.05 except one parameter i.e. conveyor height (insignificant). Figure 4 shows that normal probability and residues of efficiency.

7 Conclusions 1. With a constant conveyor speed of 2 m/s and filling rate varying from 27.775 kg/m to 5.555 kg/m. The amount of increase in efficiency was found to be 23.92%, 18.75% and 5.25% for Gantry, 5L and Surface conveyors respectively. The highest variation was found to be in Gantry conveyor system. 2. With a constant filling rate of 5.555 kg/m and conveyor speed varying from 1 m/s to 2 m/s. The amount of decrease in efficiency was found to be 13.63%, 11.52% and 1.64% for Gantry, 5L and Surface conveyors respectively. The highest variation was found to be in Gantry conveyor system. 3. It was a common observation found with both the cases that for Gantry conveyor the amount of variation is higher compared to 5L and surface conveyors. With a long distance to travel the efficiency of the conveyor motor the variation in efficiency is lesser which indicates that as the length increases the efficiency becomes high and smooth.

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4. A regression model was developed considering the above two cases the model had an R-square value of 87.27%, and found to be statistically significant.

References 1. Chan Y, Kantamaneni R (2015) Study on energy efficiency and energy saving potential in industry and on possible policy mechanisms. Technical report, ICF Limited 2. Luo J, Huang W, Zhang S (2015) Energy cost optimal operation of belt conveyors using model predictive control methodology. J Clean Prod 105:196–205 3. Pang Y, Lodewijks G (2011) Improving energy efficiency in material transport systems by fuzzy speed control, pp 159–164 4. He D, Pang Y, Lodewijks G (2016) Speed control of belt conveyors during transient operation. Powder Technol 301:622–631 5. Commission of the European Communities (1978) Underground transport in coal mines. Information Symposium, vol 1, Luxembourg 6. Din22101 (2011) Continuous conveyors – belt conveyors for loose bulk materials – basis for calculation and dimensioning 7. ISO-5048 (1998) Continuous mechanical handling equipment - belt conveyors with carrying idlers - calculation of operating power and tensile forces. Standard ISO 5048 8. Hiltermann J, Lodewijks G, Schott DL, Rijsenbrij JC, Dekkers JAJM, Pang Y (2017) A methodology to predict power savings of troughed belt conveyors by speed control. Part Sci Technol 29:14–27 9. He D, Pang Y, Lodewijks G (2017) Green operations of belt conveyors by means of speed control. Appl Energy 188:330–341 10. He D, Pang Y, Lodewijks G (2016) Determination of acceleration for belt conveyor speed control in transient operation. Int J Eng Technol 8:206–211 11. Marx DJL, Calmeyer JE (2004) An integrated conveyor model methodology, vol 3, pp 256– 264 12. Lech G, Witold K, Robert K (2016) Selection of carry idlers spacing of belt conveyor taking into account random stream of transported bulk material. Maintain Reliab 18:32–37 13. Lauhoff H (2005) Speed control on belt conveyors - does it really save energy? Bulk Solids Handling 25:368–377 14. Lodewijks G, Schott DL, Pang Y (2011) Energy saving at belt conveyors by speed control, pp 1–10 15. Marx DJL (2005) Energy audit methodology for belt conveyors. Thesis, University of Pretoria

Shortcomings of Vibrating Screen and Corrective Measures: A Review S. Bharath Kumar1(&), Harsha Vardhan1, M. Govinda Raj1, Marutiram Kaza2, Rameshwar Sah2, and H. Harish1 1

2

Department of Mining Engineering, National Institute of Technology, Karnataka, Surathkal, Mangalore 575025, India [email protected] R & D and SS, JSW Steel Limited, Vijayanagar Works, P.O. Vidyanagar, Ballari 583275, Karnataka, India

Abstract. Screening is a process of separating two or more materials of size ranging from fine to coarse of different shapes, particle sizes and densities. The conventional vibrating screen is widely used in mineral and mining industries for performing sizing operation. This paper will be on the review of the various shortcomings of the conventional vibrating screen. The review was carried out through literature survey and plant visit. The paper also involves the remedial measures to be taken to overcome the shortcomings of the conventional vibrating screen. Some of the corrective measures are reduction in number of components also reduces overall screen load, angular velocities, stress, wear or damages to the screen, screen replacement, downtime and overall cost of production. The circular vibrating motion provided to the screen will give larger amplitude and stroke length of the screen which increases screening efficiency. The overall outcome of remedial action will lead to improved screening efficiency. This paper also provides the idea for the optimization of the vibrating screen design which can reduce the power consumption, friction and also provide high screening output. Keywords: Vibrating screen
Power requirement
Friction
Support system
Screening efficiency

1 Introduction The screening process is usually carried out for separating two or more materials of different shapes, particle sizes and densities [1]. The vibrating screen has wide range of application in minerals industries for the screening of minerals, mineral ores and ore slurries [2]. It also has applications in mining industries especially in coal mining industries [3]. Other applications of screening are in cement industries, food industries and pharmaceutical industries [1]. The two major process of screening are particle stratification and the passage of particles through the screen aperture [4]. During screening process, the feed material is poured on the downwardly inclined vibrating screen deck. Because of the vibration provided to the screen deck, the fine particles will pass near the screen aperture and the process is referred as particle stratification. The © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 345–351, 2020. https://doi.org/10.1007/978-3-030-24314-2_43

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present investigation will be on the review of the various shortcomings of conventional vibrating screen. The review on the shortcomings of the conventional vibrating screen was carried out through literature survey and visit to the plant. The investigation also involves the remedial measures to be considered to overcome the shortcoming of the conventional vibrating screen.

2 Literature Review 2.1

Review on the Basics of Screening and Screening Efficiency

[5] theoretically studied the screening efficiency with respect to the screen slot size, particle size and time length of screening. [4] utilized a flow model to determine the interrelation, the stratification and the passage of particle through the screen aperture. [6] utilized a flow model to study the influence of particle size distribution, feed rate and bed thickness on the stratification and particle passage through the screen aperture. [7] developed a method to control the efficiency of vibrating screens. The authors noticed that the constant feeding rate and the percentage of particle passing during the operation need to maintained for efficient screening process. [8] studied the particle screening efficiency for the various effects of vibration parameters such as frequency and swing declination angle. [9] studied the circularly vibrating screen performance index such as screening efficiency and throughput capacity by considering the effects of the amplitude, frequency and inclination angle of the screen deck. [10] analysed the frequent failure occurrence in the vibrating screen. 2.2

Review on the Different Configuration and Motion Provided to the Vibrating Screen

[11] developed gyratory screen with the increase in the relative movement of the deck and also with improved efficiency. [12] developed a screen deck assembly system for vibrating screen. [13] developed an independent screen deck assembly comprising of at least one positive displacement mechanism and a hinge point about which angle of inclination ‘a’ was provided to the screen deck. The range of angle of inclination ‘a’ provided to the screen are ±5°, ±15° and ±30°. The author described that the material flow rate on the screen deck can be varied by varying the angle of inclination ‘a’ and also the screen deck direction i.e., upward direction (for example −30°) or downward direction (for example +30°). [14] developed a vibrating screen with angle adjustable feeder. The vibrating screen consist of a vibrating motor with vibration force adjustment and a front bracket with a pin and holes at various angle. Several others such as [15, 16] have done significant works in developing different configurations of a screen. 2.3

Review on the Various Parts of the Screen Such as Screen, Motor

[17] developed an improved seal between the hopper and the vibrating box. [3] developed a supporting frame for screening assembly. The authors suggested that the support frame facilitates easy and rapid removal of the screen panel. The authors also suggested that the reduction in downtime of the screen deck and improved efficiency

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could be obtained from the installation of the support frame. [1] developed a method to detect the breakage of a screen system. [2] developed a snap fit fastening arrangement for fixing of the screen panel. [18] developed a screen clamp which can be attached with sealing element such as chemical adhesive, mechanical fasteners or any other method. [19] developed a mechanism for preventing the failure screen modules. [20] developed a screen which is secured with the wedge like screen frame without the use of bolts, clamps or additional parts reducing the number of the parts which reduces the risk of accidental damage of vibrating screen. [21] developed a motor group for providing proper balance on the control of the larger vibrating force and the mass of the vibrator to the vibrating screen.

3 Discussion on the Major Drawback of Conventional Vibrating Screen 3.1

Basic Details of the Conventional Vibrating Screen

The study of the conventional vibrating screen was carried by visiting JSW cement plant, Ballari. The vibrating screen used in cement plant was used to screen the cement residues of particle size under 2 mm. The conventional vibrating screen mainly consists of a base, screen, screen frame, two vibrators and four helical springs. The screen mesh was fixed to the screen frame with fasteners as shown in Fig. 1. The angle of inclination of the screen deck was found to be 7°. The vibration to the screen deck was provided by two vibrating motors which was connected on the two sides of the vibrating screen as shown in Fig. 2. The helical springs was connected between the screen frame and the base structure at the four corners of the vibrating screen as shown in Fig. 3. The fine materials collected from screening was utilized for the concrete manufacturing.

Fig. 1. Screen connected to the screen frame with fasteners

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Fig. 2. Vibrating motor in the conventional vibrating screen

Fig. 3. Helical springs attached between the screen frame and base structure

3.2

Shortcoming of the Conventional Vibrating Screen

The aim of the present study is to determine the shortcomings of conventional vibrating screen. Some of the observation made on the shortcomings of the conventional vibrating screen are as follows.

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1. The vibrating screen has larger force of vibration and friction which causes the wear of the machine parts. The higher friction of the conventional vibrating screen leads to higher power requirement for instance in industries the motor power requirement is around 7.5 kW to 15 kW. 2. The moisture content of the feed materials causes clogging of screen slots which reduces the screening efficiency of the machine. The conventional vibrating screen has reduced effect on the screen clogging because of the type of motion provided on the screen. 3. The vibrating screen will have isolated system for instance in the Fig. 3 which shows the helical springs used to carry out the load of the vibrating screen. If the isolated system is not perfectly maintained, then there will be screen imbalance, loosening of the mechanical fasteners, wear and fatigue of the machine parts. 4. The vibrating screen requires higher structural strength to withstand the load of the machine parts and vibration produced by the machine defects. 5. The drawback such as loosening of the mechanical fasteners will produce the variable critical frequency of one or more machine components which will be vibrating at different stroke compared to the overall machine. The variable critical frequency of one or more machine components will reduce the overall machine life. 6. The vibrating screen has reduced amplitude and stroke length. The amplitude and stroke length can throw the material ahead causing loosening of the particle and also helps stratification of the material on the screen. 7. The vibrating motion and the downward inclination of the screen will provide less residence time to the feed materials on the screen thereby the time required for the particle to meet the screen aperture will be reduced. 8. The overall screening efficiency in the vibrating screen will be reduced because of large number of machine parts, larger force of vibration, friction, wear of the machine parts, overloading, clogging of screen and low residence time of the feed material. 3.3

Corrective Measures Need to Be Considered to Overcome the Drawback of the Conventional Screen

1. The machine should be designed to have reduced number of machine components thereby reducing the friction and power requirement. The reduction in number of machine components will avoid the requirement of the higher structural strength of the machine. 2. The reduction in number of machine components also reduces overall screen load, angular velocities, stress, wear or damages to the screen, screen replacement, downtime and overall cost of production. 3. The linear vibrating motion of the conventional vibrating screen needs to be replaced with a motion such as circular vibrating motion in such a way that the screening operation can be carried out with reduced blinding of the screen. The circular vibrating motion provided to the screen will provide inertial force to the screen

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4. The circular vibrating motion provided to the screen will give larger amplitude and stroke length of the screen which increases stratification of the particles on the screen. 5. The circular vibrating motion provided to the screen will also reduce the velocity of the particles on the screen which increases the residence time of the feed materials on the screen. 6. The angle of screen inclination can be adjusted which can also be used to control the residence time of the feed material. The need of utilizing the circular motion to the screen can be seen from the work of [22]. The work consists of development of a DEM model for the various vibrating mode of the screening such as linear and circular vibration of the screen. It was found that the travel velocity of particles on the screen was found to be higher for the linear vibration and lower for the circular vibration. The results showed that the screening efficiency of the machine was found to be higher for the circular vibrating screen when compared with the linear vibrating screen. From the result, it was cleared that the linear vibrating motion of the vibrating screen can be replaced with circular vibrating motion which increases screening efficiency.

4 Conclusion Some of the observation on the shortcomings of conventional vibrating screen has been presented. The review was carried out through literature survey and plant visit. The remedial measures such as reducing the number of machine components and replacement of linear vibrating motion with circular vibrating motion to the vibrating screen need to be mainly considered to overcome the shortcomings of the conventional vibrating screen. The reduction in number of components also reduces overall screen load, angular velocities, stress, wear or damages to the screen, screen replacement, downtime and overall cost of production. The circular vibrating motion provided to the screen will give larger amplitude and stroke length of the screen which increases screening efficiency. The overall outcome of remedial action will lead to improved screening efficiency.

References 1. DeCenso AJ (2007) System and process for break detection in porous elements for screening or filtering. International publication number-US7182207B2 2. Kriel NP (2009) Screen panel fastener and fastening arrangement. International publication number-US20090301944A1 3. Ronald Johnson RM (2006) Support frame. International publication numberUS20060237348A1 4. Soldinger M (1999) Interrelation of stratification and passage in the screening process. Miner Eng 12(5):497–516

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5. Victor Grozubinsky IJL, Sultanovitch E (1998) Efficiency of solid particle screening as a function of screen slot size, particle size and duration of screening: the theoretical approach. Int J Miner Process 52(4):261–272 6. Soldinger M (2000) Influence of particle size and bed thickness on the screening process. Miner Eng 13(3):297–312 7. Andrzej Niklewski JRC (2007) System for controlling the separation efficiency of vibrating screens. International publication number-WO2007014444A1 8. Xiao J, Tong X (2013) Characteristics and efficiency of a new vibrating screen with a swing trace. Particuology 11(5):601–606 9. Zhao L, Zhao Y, Bao C, Hou Q, Yu A (2017) Optimisation of a circularly vibrating screen based on DEM simulation and taguchi orthogonal experimental design. Powder Technol 3 (10):307–317 10. Ramatsetse B, Mpofu K, Makinde O (2017) Failure and sensitivity analysis of a reconfigurable vibrating screen using finite element analysis. Case Stud Eng Fail Anal 9 (June):40–51 11. William SCM, Lower E (1994) Increasing the relative motion of a screen deck. International publication number-CA2143658C 12. Kriel NP (2005) Screen deck. International publication number-US20050183991A1 13. Carr BS (2004) Independent deck assemble. Patent Publication number-US20110084004A1 14. Right C (2012) Angle-adjustable feeder. International publication number-CN102641849A 15. Schirm P (2013) Platform and ladder interface for variable slope vibrating screens. International publication number-US20130037450A1 16. Payton Schirm GY (2015) Mobile modular screen plant with horizontal and variable operating angles. International publication number-US20150321224A1 17. Lower WE (1981) Screening machine. International publication number-US4251354 18. Brian EC, Carr S, Holton BL (2010) Screen clamp. International publication numberUS20100236995A1 19. Robert J, Waites F (2017) Mechanism for securing screen modules. International publication number-US20100006481A1 20. Cady E (2010) Screen for a vibratory separator. International publication numberUS7819255B2 21. Guozhen Zhao DP, Zhang M (2011) Vibrating screen and motor, motor group. International publication number-US20110133583A1 22. Dong H, Liu C, Zhao Y, Zhao L (2013) Influence of vibration mode on the screening process. Int J Min Sci Technol 23(1):95–98

Quantification of Rock Strength Using the Mechanical Drilling Parameters C. R. Lakshminarayana1(&), Anup K. Tripathi1, and Samir K. Pal2 1

Department of Mining Engineering, N.I.T.K, Surathkal 575025, Karnataka, India [email protected], [email protected] 2 Department of Mining Engineering, IIT, Kharagpur 721302, West Bengal, India [email protected]

Abstract. The estimation of rock strength is most often required for the preliminary stage of rock engineering projects. The determination of rock strength properties in the laboratory is reliable, but the availability of a number of fine quality core samples for lab testing is very difficult. In this study, an attempt is made to investigate the usability of variations of thrust developed at the rock-bit interface and vibration frequency generated in the drilling machine head for estimation of rock strength during the rotary drilling. The variation of thrust and vibration frequency during drilling is measured using sophisticated digital type drilling dynamometer and data acquisition system (DAQ) with accelerometer sensor respectively. The second order regression models were developed to predict the rock strength such as uniaxial compressive strength considering the machine operating parameters and measured variables. The evaluation of the prediction ability of the developed models was checked using the three performance indices known as VAF, RMSE, and MAPE. The results revealed that the approached method is highly efficient for estimation of rock strength during rotary drilling. Keywords: Uniaxial compressive strength
Vibration signal
Vibration frequency
FFT
Frequency domain
Drilling dynamometer

1 Introduction Rock mechanics deals with the study of mechanical and physical properties of rocks. The vital mechanical property of rocks such as a uniaxial compressive strength (UCS) is often used in many engineering projects concerned with a rock engineering background [1]. The determination of UCS in the laboratory is reliable, but it is expensive, time-consuming and often high-quality core specimens are needed for the test [2]. It is not always possible to obtain a sufficient amount of fine quality drilled cores from weak, highly fractured, weathered and thin layers of rock matrix [3]. Therefore, the engineers and geologists have been attracted to quantify the rock strength indirectly through the use of predictive empirical models [4]. Some of the indirect tests such as point load, Schmidt hammer, and p-wave velocity tests are often © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 352–361, 2020. https://doi.org/10.1007/978-3-030-24314-2_44

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used to estimate rock strength [5–7]. However, the standard samples for the indirect test may not always possible for weak or soft rocks such as sedimentary rock type. Recently, the quantification of rock properties during the drilling was investigated by many researchers with acceptable level errors. The mechanical drilling parameters such as bit diameter, bit speed, weight on bit and corresponding penetration rate, sound level and many more physical parameters have been used to correlate with various rock properties. From the earlier investigation on the characterization of rocks, estimation of rock properties during drilling is found easy, less time consuming and also economical as no core samples of rocks are required. The mechanical drilling parameters like bit load or thrust, speed and penetration rate of diamond drill bit have a strong relationship with rock mass strength [8]. Finfinger et al. [9] attempted to identify the properties of overlaying rocks in underground mines during rock bolting operation. It was observed that there is considerable variation of thrust and torque as the drill bit moves to different rock layers. Stuart et al. [10] proposed a method for quantification of formation properties around an oil well by analyzing the acoustic waves generated at the bottom hole assembly of drill unit. An investigation on sound level produced during the drilling of different rocks using a portable pneumatic drilling machine was made by Vardhan et al. [11]. The results revealed that the sound level linearly increases as the UCS of rocks increases. Kumar et al. [12] attempted to predict the uniaxial compressive strength, tensile strength and porosity of sedimentary rocks using sound level produced during rotary drilling. The prediction mathematical model for each property was developed using the drilling parameters and equivalent sound level. It was observed that the sound level produced during drilling was increased as the UCS of rocks increases and the sound level was decreased as the porosity of rocks increases. The evaluation of the prediction model concluded that the equations would be useful for the preliminary stage of mining engineering design projects. Yari and Bagherpour [13, 14] have conducted the experimental investigation to estimate the geomechanical properties of igneous rocks as well as sedimentary rocks using the five dominant frequency of an acoustic signal acquired during rotary drilling. The dominant frequency was extracted from the time domain acoustic signal using Fast Fourier transformation (FFT). The results revealed that the fourth dominant frequency is capable of predicting the UCS and tensile strength with a high coefficient of determination value. The study on identification of rocks based on acoustic signal parameter gathered while drilling was made by the Zborovjan et al. [15]. The results revealed that the analysis of the frequency component of the acoustic signal is useful for identification of rock type and controlling the rock disintegration process. Lakshminarayana et al. [16] attempted to predict some physico-mechanical properties of sedimentary rocks using the drilling parameter and the maximum or sometimes referred as dominant frequency of vibration induced at spindle head of conventional rotary drilling machine. It was observed that the vibration frequency at spindle head was increased as the properties of rocks such as uniaxial compressive strength and tensile strength increases during the drilling. During drilling, an attempt was made by Rostami et al. [17] to detect the voids in rocks using the vibration data acquired at the drill head. It was observed that the amplitude of vibration would be less when the drill bit enters into the porous region of rocks. In this study, an attempt is made to estimate the UCS of sedimentary rocks using the machine operating parameters (penetration rate, bit dia, bit speed) and the

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mechanical data measured during the rotary drilling such as thrust or weight on the bit and vibration frequency of machine head. The different amount of thrusts acting on the bit during drilling was measured by dynamometer and the vibration parameter was measured using the data acquisition system (DAQ).

2 Experimental Investigation The experiment was conducted using the sedimentary rock samples such as shale, sandstone and limestone. The samples were directly collected from the field. The rock specimens were prepared by cutting off the rock samples into a cubic block of size 15 cm  15 cm  15 cm. While collecting the rock samples, a proper inspection was carried out for macroscopic defects such as fractures and joints. 2.1

Experimental Setup

In this experiment, a heavy duty BMV 45 T20 computerized numerical control (CNC) vertical machining center was used for drilling the rock samples as shown in Fig. 1. The thrust and torque variations of the drill bit during the drilling of each type of rock sample were measured using the drill tool dynamometer. Similarly, the vibration frequency of the machine head was measured using the vibration data acquisition system (DAQ). The drilling operation was done using the diamond core drill bits of uniform shank length 30 mm and diameter of 12 and 16 mm. The different drilling operational parameters such as rotational speed of bit and penetration rate were set in the CNC machine by numerical control (NC) programming method.

Accelerometer

Fig. 1. Experimental setup

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Drilling Dynamometer During the drilling of different rocks, the amount of thrust acting and torque developed at the bit-rock interface were measured using the sophisticated drilling dynamometer. The measuring range of thrust in the dynamometer was 0 ± 5000 N and the measuring range for the torque was 0 ± 50 N-m. Basically, the dynamometer consists of a cylinder fitted with steel plates at both ends. The bottom plate can be fixed to t-slots of machine table using bolt and nut. Similarly, the machine vice used for holding the rock sample can be fixed directly to the top plate of dynamometer using suitable bolt and nuts. The analog output from the dynamometer is connected to the digital indicator of dynamometer which shows the numerical value of thrust and torque. Measurement of Vibration Frequency of the Machine Head During Drilling The vibration frequency of the CNC machine head was measured using the sound/vibration data acquisition system (DAQ). The DAQ system basically consists of DAQ hardware, IEPE accelerometer and LabVIEW application software. The NI-9234 model DAQ hardware was used for converting the analog signal into a digital type. The important specification of DAQ hardware is as follows • • • •

Number of the channel - 4 ADC resolution - 24 bits Sampling rate -1.652 Ks/s to 51.2 Ks/s. Frequency - 13.1072 MHz

The mounting of an integrated electronic piezo-eclectic (IEPE) accelerometer on the machine head using the magnetic type mount is shown in Fig. 1. The specification of IEPE accelerometer as follows. • Model: YMC121A10 IEPE • Sensitivity: 9.81 Mv/g Initially, the accelerometer is fixed on the machine head using the magnetic mount. The output from the accelerometer is connected to DAQ hardware using the singleended BNC connector. The DAQ is a signal conditioning device which converts the analog signal captured from the accelerometer into a digital signal. The digital signal coming out from the DAQ hardware is in turn connected to laptop installed with LabVIEW application software. The application software process the signal and representing the vibration data in the time domain as shown in Fig. 2. The maximum vibration frequency (Z) or dominant frequency is usually meant the one that carries more energy with respect to all the other frequencies in the considered spectrum. The example of dominant frequency at which the machine head (Z = 327 Hz) was vibrating for a particular operating parameter during the drilling of shale is shown in Fig. 3. The extraction of frequency data from the time domain vibration signal was achieved using the Fast Fourier transformation (FFT) graphical program in LabVIEW application software.

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Fig. 2. Time domain signal

Fig. 3. Frequency domain signal

3 Methodology 3.1

Determination of Thrust, Torque and Vibration Frequency

During the drilling of different sedimentary rocks using the CNC vertical machining center, the variations of thrust acting and torque developed at the bit-rock interface, and the variations of vibration frequency at the machine head were recorded. The standardized cubical rock sample of size 15 cm  15 cm  15 cm was tightly fixed in the CNC machine vice. The different machine operational parameters such as drill bit diameter, drill bit speed, and penetration rate were used for drilling the rock samples. For each rock type, a total of 32 holes having a constant depth of 50 mm are drilled using the 32 (2 drill bit dia  4 drill bit speed  4 penetration rate) combinations of machine operational parameters (12 and 16 mm drill bit, 400, 500, 600 and 700 r.p.m drill bit speed and a penetration rate of 2, 3, 4 and 5 mm/min). During the drilling for a particular machine operational parameter, the numerical value of thrust was continuously varying as the drill bit was advancing through a 50 mm depth hole. As the drill bit is moving to 10, 20, 30, 40 and 50 mm depth, the corresponding reading of five thrust values were taken down at those depths using the digital indicators of the dynamometer. Later, the arithmetic average of five thrust was calculated for a particular machine operating condition. Similarly, the vibration signal of 1 s having 5 iterations emanated from the machine head was captured and its corresponding vibration frequency was noted down at the same mentioned depths using the frequency domain data in LabVIEW application software. But the vibration frequency captured five times for a particular operating condition was found almost consistent. The direct measurement of UCS is measured in well-established rock mechanics laboratory. The uniaxial compressive strength of different sedimentary rocks was

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measured using the micro-controlled type AIM-317E-MU compression testing machine. An NX size specimen having a diameter of 54 mm and a length of 135 mm were prepared and the UCS of rock specimen was determined as per the guidelines suggested by ISRM. Before testing the specimen for UCS the specimen was completely dried using an electric oven. At least three specimens were used for testing the UCS. The arithmetic mean of all three rock specimen was considered for analysis purpose.

4 Results and Discussion In the current experiment, a total of 192 data (6 rock types  32 test conditions) of each measured parameters i.e., thrust and vibration frequency is acquired during the rotary drilling. For developing the multiple regression models, the thrust and vibration frequency is used along with the machine operational parameters. The maximum and minimum values of measured variables which are selected from the 32 data set of each rock sample are summarized in Table 1. During the drilling process, the variation in thrust and vibration frequency is affected by so many factors in a complex way. Therefore, a detailed process is defined by second-order multiple regression models. The analysis of variance (ANOVA) was carried out in order to know which variable is significantly influencing the response. The considered responses are UCS and BTS. The machine operational parameters are identified as drill bit diameter (D) in mm, drill bit speed(S) in r.p.m and penetration rate (PR) in mm/min. Similarly, the measured variables which are varying due to machine operational parameters and rock properties are thrust (T) in Newton and vibration frequency (Z) in Hertz. Table 1. Mechanical rock properties and range of measured variables during drilling Rock sample

UCS (Mpa)

BTS (Mpa)

Thrust (N) min

max

Vibration frequency (Hz) min max

Shale Sandstone-1 Sandstone-2 Limestone-1 Limestone-2 Limestone-3

19.6 37.5 63.8 93.1 119 142.6

2.3 3.4 4.1 7.5 8.1 10.2

315 397 495 567 617 672

635 802 1009 1130 1207 1315

327 330 337 340 342 345

626 629 642 656 667 669

The variations of selected measured variables (thrust and vibration frequency) are the function of machine operating parameters and mechanical properties of rocks. The mathematical model for establishing the relationship between rock properties and considered variables can be written as y = f (x1, x2, x3, x4, x5) + w where y is the response and x1, x2, x3, x4, x5 are the machine operational parameter and measured variables, and w is fitting error. In general, the second order model can be represented as follows

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f ¼ ao þ

Xn

ax þ i¼1 i i

Xn

a x2 þ i¼1 ij i

Xn i\j

aij xi xj þ w

ð1Þ

where, ai represents the linear effect of xi , aij represents the quadratic effect of xi , and aij in fourth term represents the interaction effect produced due to the linear interaction of xi and xj . In developing the multiple regression models, the backward elimination method was used as a screening technique. In ANOVA table, if absolute t value of an independent variable was not greater than the tabulated t value at 95% confidence level, then that particular independent variable was removed and the multiple regression procedure was continued using the remaining independent variables. The procedure is repeated until the remaining independent variables could not be removed from the model and that corresponding generated regression model was selected. 4.1

Prediction of Uniaxial Compressive Strength

The best prediction model developed for uniaxial compressive strength is: UCS ¼ 77:4  31:60  PR  2:151  S  3:745  D þ 0:1787  T þ 2:861  Z þ 2:698  PR2 0:000804  Z 2 þ 0:01823  PR  S  0:01616  PR  T

ð2Þ The Eq. (2) represents the best second-order multiple regression model developed for prediction of UCS. From Table 2 it was concluded that the developed model explains 93.60% of the total variation in the observed UCS. The significance of regression coefficients are illustrated in Table 3. In this, the value of p for all the terms are statistically significant at p < 0.05 for 95% confidence level and also the calculated absolute t values are much higher than the tabulated t values (for 95% confidence level and 9 degrees of freedom, for k = n − 1, t = 1.860). It is therefore concluded that all the terms generated in the selected regression model are significantly influencing the UCS. The influence of linear, square and interaction terms of the regression model is explained in ANOVA Table 4. The linear terms are significantly influencing the UCS. The calculated F value is sufficiently greater than the tabulated F value of 3.36 to explain the adequacy of the developed model. Figure 4 indicates the comparison of the UCS of rocks measured in the laboratory and the UCS predicted using the prediction model. The predicted values are very close to the measured values. So that it is concluded that the developed models are highly efficient. Table 2. Model summary for UCS 2

R Adjusted R2 Predicted R2 Standard error 93.60 93.28 92.89 11.29

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Table 3. Regression coefficients and their significance Predictors Constant PR S D T Z PR2 Z2 PR  S PR  T

Regression coefficients T-value 77.4 2.66 −31.60 −4.60 −2.151 −14.27 −3.745 −8.97 0.1787 7.96 2.861 9.59 2.698 3.03 −0.000804 −5.14 0.01823 2.72 −0.01616 −3.57

p-value 0.008 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.007 0.000

Table 4. ANOVA (UCS) Source of variations DF Adj SS Adj MS F-value Model 9 339185 37687.3 516.66 Linear 5 58788 11757.4 460.9 Square 2 4553 2276.45 35.69 Interaction 2 2560 1279.75 20.07 Error 182 23214 127.6 – Total 191 362400 – –

p-value 0.000 0.000 0.000 0.000 – –

Fig. 4. Comparison of measured and predicted UCS

4.2

Evaluation of Prediction Performance of the Developed Model

In the present study, the efficiency or prediction capacity of the developed models are investigated using the three indices known as variance account for (VAF), root mean square error (RMSE) and mean absolute percentage error (MAPE).

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 varðm  pÞ VAF ¼ 1   100 varðmÞ vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N u1 X ðm  pÞ2 RMSE ¼ t N i¼1  N  1X ðm  pÞ   100 MAPE ¼ N i¼1  m 

ð3Þ

ð4Þ

ð5Þ

In Eqs. (3)–(5), ‘m’ and ‘p’ are representing the UCS measured in the laboratory and the UCS obtained using the predictive model respectively. Similarly, the N represents the number of data used. The model would predict the response with zero errors if the VAF and RMSE values are 100 and 0 respectively. The MAPE indicates the absolute percentage error or accuracy of the model in terms of percentage. The values of VAF, RMSE, and MAPE for the developed models are tabulated in Table 5. Table 5. Indices of prediction capacity of the derived models Dependent variable Indices of performance VAF (%) RMSE (Dependent variable units) MAPE (%) UCS (Mpa) 93.60 10.99 15.09

5 Conclusions In this experimental investigation, the machine operating parameters along with the measured variables such as thrust developed at bit-rock interface and vibration frequency induced at machine head during the rotary drilling were used to predict some of the mechanical properties of sedimentary rocks. For rock drilling, the CNC vertical milling centre with different penetration rate, speed and drill bit diameter was used as the machine operational parameters. The thrust and vibration frequency was measured for all machine operating conditions and the same was used for developing the prediction models. • It was observed that the thrust developed at the bit-rock interface was significantly increased as the UCS of rocks increases during the drilling. • The vibration frequency was moderately changed as UCS of rocks increased. • The evaluation of the prediction performance of the developed model indicated that the predictive models are well efficient to predict the UCS of sedimentary rocks with an acceptable level error. • Therefore, it was concluded that the suggested approach can be successfully used for preliminary investigation of UCS which is often used as a primary data for the design of mining and civil engineering projects.

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References 1. Kahraman S, Toraman OY, Cayirli S (2018) Predicting the strength and brittleness of rocks from a crushability index. Bull Eng Geol Env 77(4):1639–1645 2. Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol 99(1– 2):51–60 3. Kumar BR, Vardhan H, Govindaraj M (2011) Sound level produced during rock drilling visà-vis rock properties. Eng Geol 123(4):333–337 4. Zhang L (2016) Engineering properties of rocks. Butterworth – Heinemann, Oxford 5. Broch E, Franklin JA (1972) The point-load strength test. Int J Rock Mech Min Sci GeoMech Abs 9(6):669–676 6. Gunsallus KT, Kulhawy FH (1984) A comparative evaluation of rock strength measures. Int J Rock Mech Min Sci Geo-mech Abs 21(5):233–248 7. Gaviglio P (1989) Longitudinal waves propagation in a limestone: the relationship between velocity and density. Rock Mech Rock Eng 22(4):299–306 8. Basarir H, Karpuz C (2016) Preliminary estimation of rock mass strength using diamond bit drilling operational parameters. Int J Min Reclam Environ 30(2):145–164 9. Finfinger G, Peng S, Gu Q, Wilson G, Thomas B (2000) An approach to identifying geological properties from roof bolter drilling parameters. In: Proceedings 19th international conference on ground control in mining. West Virginia University, Morgantown, WV, pp 1–11 10. Stuart RK, Charles FP, Hans T (2007) Method for borehole measurement of formation properties. US patent issued on 30 October. (Application No. 10779885 filed on 17-02-2004) 11. Vardhan H, Adhikari GR, Raj MG (2009) Estimating rock properties using sound levels produced during drilling. Int J Rock Mech Min Sci 46(3):604–612 12. Kumar BR, Vardhan H, Govindaraj M (2011) Prediction of uniaxial compressive strength, tensile strength and porosity of sedimentary rocks using sound level produced during rotary drilling. Rock Mech Rock Eng 44(5):613–620 13. Yari M, Bagherpour R (2018) Implementing acoustic frequency analysis for development the novel model of determining geomechanical features of igneous rocks using rotary drilling device. Geotech Geol Eng 36(3):1805–1816 14. Yari M, Bagherpour R (2018) Investigating an innovative model for dimensional sedimentary rocks characterization using acoustic frequencies analysis during drilling. Min-Geol-Pet Eng Bull 33(2):17–25 15. Zborovjan M, Lesso I, Dorcak L (2003) Acoustic identification of rocks during drilling process. J Acta Montanisti Slovaca 8(4):91–93 16. Lakshminarayana CR, Tripathi AK, Pal SK (2018) Prediction of physico-mechanical properties of rocks using the dominant frequency of vibration during rotary drilling. Int J Eng Technol (UAE) 7(4):3360–3366 17. Rostami J, Kahraman S, Naeimipour A, Collins C (2015) Rock characterization while drilling and application of roof bolter drilling data for evaluation of ground conditions. J Rock Mech Geotech Eng 7(3):273–281

Evaluation of Whole Body Vibration of Heavy Earth Moving Machinery Operators Jeripotula Sandeep Kumar(&), Mangalpady Aruna, and Mandela Govinda Raj Department of Mining Engineering, N.I.T.K., Surathkal 575025, Karnataka, India [email protected], [email protected], [email protected] Abstract. Operators of Heavy Earth Moving Machinery (HEMM) performing routine tasks in surface mines are highly vulnerable to whole body vibration (WBV) due to their continuous exposure to vibration. In the present study seventeen types of machinery were considered for the evaluation of the operator’s exposure to WBV. The measurements were made by placing the triaxial seat pad accelerometer on operator’s seat-surface as well as at the seat-back. Among these machinery one shovel, two front-end loaders, three drills, one grader and one water sprinkler were found to have RMS values in the severe zone as per ISO2631-1:1997 standards for seat-surface measurements. Similarly, for the seat-back measurements, one front-end loader, two drills, one grader and one water sprinkler were experienced the highest RMS value. For both seat-surface and seat-back measurements, Z-axis (i.e. vertical direction) was found to be a prominent axis for most of the machinery. Keywords: Surface mine Mine safety




Whole-body vibration




Occupational hazard




1 Introduction Mechanical oscillation produced by the machinery, when enters the human body through supporting surface either through lower pelvic bones during sitting posture or through the feet when in standing posture is known as Whole Body Vibration (WBV). Professional drivers and industrial vehicle operators are prone to health risk due to exposure to whole body vibration [1]. Occupational exposure to vibration has a wellestablished impact on health [2, 3]. It also has an impact on the operator’s performance and comfort [4]. During the execution of daily tasks in surface mines, a wide range of machineries are used, which imparts WBV risk to their operators [5–7]. Workers exposed to WBV are susceptible to the high prevalence of musculoskeletal disorders (MSDs). The parts of the body most affected by the vibration depend on direction, duration, frequency and magnitude of vibration, and also body posture of the person. Epidemiological studies revealed that long-term exposure to vibration causes health problems, like lumbar spine degeneration, aggravation of lower back pain etc. [8]. The WBV has accounted for sick leave, loss of working hours, disability and chronic pain among workers [9]. © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 362–373, 2020. https://doi.org/10.1007/978-3-030-24314-2_45

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A study performed by the National Institute of Miners Health (NIMH) considering two mechanized mines reported that 18% of the employees were at higher risk due to occupational exposure to vibration [10]. In India, though the Directorate General of Mines Safety (DGMS) recommended suitable measures to ensure the safety and comfort of the workers against WBV, no specific limits of vibration were prescribed for the miners [11]. The DGMS in its tenth conference on mine safety has strongly proposed to conduct vibration studies of heavy machinery before it is put to use for fieldwork [12]. Indian mines legislation has not so far designed a unique plan which deals with measuring and prevention against the vibration exposure. Previous research findings pertaining to WBV of haul trucks in open pit mines demonstrated that the vibration level in most of the cases exceeded the threshold values as prescribed by ISO2631-1:1997 guidelines [13]. Despite drift from non-mechanized to fully mechanized mines, operators spend more time on Heavy Earth Moving Machinery (HEMM) [14]. Mechanization of the mining operation has improved productivity to a large extent; however risk due to occupational exposure to vibration has also increased. Studies conducted so far in Indian surface mines for evaluation of WBV is specifically with respect to the operator’s seat-surface measurements. The main objectives of the present study are to measure and evaluate the WBV levels of HEMM operator’s in Indian surface mines by placing the triaxial accelerometer on operator’s seat-surface as well as at the seat-back. The obtained results were evaluated to perform risk analysis based on ISO-2631-1:1997 [15] and EU directive 2002 guidelines [16].

2 Instrumentation and Methodology 2.1

Field Study and Instrumentation

For carrying out WBV study on HEMM operators two mechanized surface coal mines were selected from the southern part of India, which are named as Mine I and Mine II, here afterward in this paper. Mine I was operated by dragline and shovel-dumper combination, whereas Mine II was operated by shovel-dumper combination along with in-pit crusher conveying (ICC) system. The list of machinery considered for evaluation of WBV from the Mine I and Mine II are encapsulated in Table 1. Table 1. Machinery considered for the study from Mine I and Mine II S. no 1 2A 2B 2C 2D 3A 3B 4A

Mine I I I II II I II I

Machinery* Dragline Shovel Shovel Shovel Shovel Front End Loader1 Front End Loader2 Drill 1

Make BEML TATA-HITACHI KOMATSU TATA-HITACHI KOMATSU L&T 1920 Tata 3036 ATLAS COPCO

Model BHEEM EX-1200 PC-2000-8 EX 1200-V PC-2008 LT-09 T-18 DM-37

Capacity 30.6 m3 5 m3 12 m3 5.5 m3 12.5 m3 4.6 m3 2.1 m3 150 mm (continued)

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S. no Mine Machinery* Make Model Capacity 4B I Drill2 REL DM-22 250 mm 4C II Drill3 REL DM-28 150 mm 5 I Crane Escort ACE FX120 12 ton 6 II Spreader KRUPP FORDER TECHNIK GMBH 813 ton 7A I Grader1 BEML MG-11 16 ft blade 7B II Grader2 BEML MG-15 16 ft blade 7C II Grader3 Volvo MG-19 16 ft blade 8A I Water Sprinkler1 BEML WT-20 28 KL 8B II Water Sprinkler2 BEML WT-24 28 KL *The present study includes seventeen types of machinery with varies make models and capacity.

For measuring the WBV of HEMM operators a triaxial accelerometer named SV106 Human Vibration Meter and Analyzer was used as a data logger. This data logger is compatible with the ISO2631-1:1997 Standards. The accelerometer needs a 4.8 V power supply and it is designed to work in the temperature range of −10 °C to 50 °C. The frequency range of the accelerometer was set at 0.1 Hz 2 kHz. The accelerometer has a data sampling rate of 6 kHz. 2.2

Methodology

As per ISO2631-1:1997 guidelines the readings were taken by placing the accelerometer on the operator’s seat-surface as well as at the seat-back, as shown in Fig. 1. Care was taken to confirm that seat pad triaxial accelerometer is firmly fixed to the operator’s seat-surface and seat-back during the entire measurement process. The readings were recorded at each position (i.e. seat-surface and seat-back) for 15 min for all the machinery under consideration.

(a) At operators seat-surface

(b) At operators seat-back

Fig. 1. Triaxial accelerometer placed for taking readings

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Ethical Clearance

Prior to the collection of data related to the machine vibration a formal consent was obtained from the mine management. The operators were informed in local language about the purpose of the study, and also they were made to understand the risk underlying while performing daily tasks in the mines.

3 Vibration Standards As given in sub-clause 5.1 of ISO 2631-1 (1997) standards, the intensity of vibration is measured considering three orthogonal axes with respect to acceleration values (i.e. in m/s2). In the event of the absence of shock wave, frequency weighted acceleration (The r.m.s vibration magnitude represents the average acceleration over a measurement period) is applied to quantify acceleration intensity, which is given by Eq. (1).  Z aw ¼ 1=T

T

0

1=2 a2w ðtÞdt

ð1Þ

Where, aw(t) = Frequency weighted instantaneous acceleration at time t (m/s2), and T = period of measurement (sec). A parameter is known as Crest Factor (ratio of peak acceleration to RMS) when exceeds a value of 9, an additional parameter known as Vibration Dose Value (VDV) is used for evaluation and prediction of health risk, where peak values are more than 9 times the corresponding RMS values. VDV is based on the fourth power of acceleration and thus more sensitive to shocks compared to RMS magnitude which is given by Eq. (2). Z

T

VDV ¼

½aw ðtÞ

4

14

ð2Þ

0

Where, aw (t) = Frequency weighted instantaneous acceleration at time t (m/s2), and T = Period of measurement (sec). ISO 2631-1:1997 guidelines provide criteria known as “Health Guidance Caution Zone” (HGCZ) based on which health risk prediction is carried out. If the exposure is below HGCZ, it refers to no documentation of any ill health effect. If the vibration exposure is in the range of HGCZ, the operator must be cautioned with regard to potential health risk, and when the exposure exceeds the HGCZ there will be likely health risk for the operator. In addition to ISO 2631-1:1997 guidelines, the measured vibration levels were also analyzed as per European Union Directive 2002. The EU 2002 directive considers two factors, namely the Exposure Action Value (EAV) and Exposure Limit Value (ELV). Exposure action value means the level of daily vibration exposure to whole-body vibration (WBV) for any worker above which steps should be taken to reduce exposure to WBV. Exposure limit value means the level of daily vibration exposure to WBV for any worker which should not be exceeded.

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The threshold values set for risk assessment based on ISO 2631-1:1997 and EU Directive 2002 standards are summarized in Tables 2 and 3, respectively. Table 2. Summarized RMS and VDV threshold values for WBV health risk assessment based on ISO 2631-1:1997 Threshold vibration value (HGCZ)* 0.8 Severe zone (likely risk) VDV (m/s1.75) 17 Severe zone (likely risk) *Health Guidance Caution Zone Parameter RMS (m/s2)

Table 3. Summarized EAV and ELV threshold values for WBV health risk assessment based on the EU 2002 directive Parameter RMS (m/s2) VDV (m/s1.75) 0.5 9.1 EAV* ELV* 1.15 21 *EAV (Exposure Action Value) *ELV (Exposure limit Value)

4 Results and Discussion Three vibration measurement parameters, such as RMS, VDV and CRF for seventeen types of machinery (i.e. dragline-1nos., shovel-4nos., front end loader-2nos., drill3nos., spreader-1nos., crane-1nos., grader-3nos., water sprinkler-2nos.) were recorded by placing the accelerometer on the seat-surface as well as at the seat-back of the operators. Tables 4 and 5 indicate the WBV data w.r.t x-axis (i.e. fore-aft direction), yaxis (i.e. lateral direction) and z-axis (i.e. vertical direction) for seat-surface and seatback measurements, respectively. Table 4. WBV data for seat-surface measurement a

S. no 1 2A 2B 2C

2

RMS (m/s )* Fore-aft Lateral 0.10 0.04 0.26 0.27 0.20 0.39 0.39 0.37

VDV (m/s1.75)** Vertical Fore-aft Lateral 0.24 0.67 0.28 0.55 1.84 2.04 0.49 2.39 3.75 0.80 2.33 2.13

CRF*** Vertical Fore-aft 1.81 6.63 4.45 6.74 8.42 9.12 4.35 5.92

Lateral Vertical 7.70 7.74 6.65 10.58 10.69 39.17 5.77 4.72 (continued)

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Table 4. (continued) a

2

RMS (m/s )* VDV (m/s1.75)** CRF*** Fore-aft Lateral Vertical Fore-aft Lateral Vertical Fore-aft Lateral Vertical 2D 0.27 0.36 0.61 2.40 2.86 4.47 9.79 7.23 7.77 3A 0.82 0.47 0.36 5.30 3.02 2.39 5.41 5.94 6.97 3B 0.35 0.68 0.92 3.99 4.86 7.95 16.22 7.19 14.27 4A 0.43 0.42 0.96 2.32 2.21 5.48 4.31 4.42 6.14 4B 0.61 0.45 1.09 2.88 2.37 5.32 4.60 6.59 4.91 4C 0.48 0.48 0.8 2.98 2.92 5.24 5.30 5.27 6.36 5 0.29 0.33 0.19 1.62 1.88 1.10 3.77 4.04 5.00 6 0.29 0.26 0.56 2.30 2.04 4.88 6.98 7.35 10.17 7A 0.48 0.47 0.92 3.71 4.75 7.61 8.07 10.91 10.32 7B 0.49 0.62 0.64 6.47 6.65 5.92 31.51 24.41 21.93 7C 0.51 0.47 0.76 5.20 4.68 6.58 15.28 9.18 8.38 8A 0.34 0.41 0.76 2.22 3.17 4.81 13.12 14.74 9.77 8B 0.02 0.58 1.00 0.02 3.94 7.49 37.03 5.22 9.32 *RMS (Root Mean Square Measured in m/s2) **VDV (Vibration Dose Value measured in m/s1.75) ***CRF (Crest Factor) a Machineries are designated with different set of Serial numbers for simplified representation.

a

2

S. no

Table 5. WBV data for seat-back measurement RMS (m/s ) VDV (m/s1.75) CRF Fore-aft Lateral Vertical Fore-aft Lateral Vertical Fore-aft 1 0.08 0.08 0.19 0.65 0.68 1.60 8.24 2A 0.19 0.30 0.48 1.48 2.22 4.01 9.0 2B 0.64 0.38 0.77 5.76 4.56 6.20 18.64 2C 0.41 0.28 0.46 2.22 1.62 2.67 4.67 2D 0.26 0.24 0.53 1.92 1.89 4.44 6.90 3A 0.63 0.65 0.40 4.04 4.18 2.83 5.96 3B 0.74 0.73 1.05 4.84 4.77 11.91 6.98 4A 0.46 0.59 0.72 3.45 3.35 5.49 5.64 4B 0.38 0.39 0.86 2.50 2.67 4.99 6.17 4C 0.67 0.51 1.08 2.82 2.25 4.49 4.60 5 0.27 0.43 0.40 1.66 2.46 2.67 12.29 6 0.36 0.42 0.48 2.22 2.00 4.78 6.12 7A 0.70 0.61 1.40 6.06 4.11 12.69 18.79 7B 0.61 0.78 0.79 5.28 5.18 8.86 22.00 7C 0.77 0.62 0.73 7.89 6.18 11.68 12.16 8A 0.86 0.46 0.46 5.11 4.15 5.10 11.16 8B 0.06 0.63 0.50 0.02 4.28 3.75 21.45 a Machinery are designated different set of Serial numbers for simplified S.No

Lateral Vertical 9.71 11.87 5.53 11.79 28.12 12.79 5.82 6.79 13.76 22.54 6.52 9.10 6.98 22.65 6.38 5.74 8.11 4.39 5.16 4.81 4.38 10.21 6.34 10.83 8.46 16.42 11.42 31.92 13.47 34.47 8.98 19.66 5.62 9.61 representation.

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Risk Analysis of Seat-Surface Measurements

As indicated in Table 4, the RMS values of three machineries (i.e., front end loader–3A reported 0.82 m/s2 followed by drill–4B and grader–7C with 0.61 m/s2 and 0.51 m/s2, respectively) in X-direction have exceeded the threshold vibration limit value of 0.5 m/s2 as stipulated by ISO-2631-1:1997 and EU directive 2002 guidelines. Among all the machinery under consideration, though water sprinkler – 8B has shown high crest factor of 37.03 its VDV remains much below the moderate level, as per by ISO2631-1:1997 guidelines. When measurements were evaluated in Y-direction, front end loader-3B has indicated highest RMS value of 0.682 m/s2 followed by grader-7B and water sprinkler-8B with 0.62 m/s2 and 0.58 m/s2, respectively. Hence, the aforementioned machinery was falling in caution zone as per ISO 2631-1:1997 guidelines and also they exceed the EAV as per EU 2002 Directive. Though the crest factor of grader-7B is 24.41, the measured VDV of all the machinery were less than 8.5 m/s1.75 i.e. below moderate zone as per ISO 2631-1:1997 guidelines. It was evident from the obtained results that the WBV in Z-direction is prominent when compared to X-direction and Y-direction, which is corroborates with the literature. Except for four machineries (i.e. dragline, shovel, spreader and front end loader), the RMS value of six machineries I are in caution zone (i.e. shovel–2A with 0.55 m/s2, shovel–2D with 0.61 m/s2, crane–6 with 0.56 m/s2, grader–7B with 0.64 m/s2, grader– 7C with 0.76 m/s2 and water sprinkler–8A with 0.76 m/s2) and seven equipments in severe zone (shovel–2C with 0.80 m/s2, front end loader-3B with 0.92 m/s2, drill–4A with 0.96 m/s2, drill-4B with 1.09 m/s2, drill-4C with 0.8 m/s2, grader–7A with 0.92 m/s2 and water sprinkler–8B with 1.00 m/s2). In Z-direction, the crest factor of the shovel–2B is 39.17, which is quite high compared to all the other machinery. However, it’s RMS and VDV are just nearing the moderate zone values, as stipulated by ISO 2631-1:1997 guidelines. The RMS values in Z-direction for all the machinery was found lower than ELV of 1.15 m/s2, whereas for except four types of machinery (i.e. dragline, shovel–2B, spreader and front end loader–3A), the EAV of all the others were exceeding 0.5 m/s2, as per EU 2002 directive. Among all the machinery under consideration, drill (which is crawler mounted) experiences highest RMS due to frequent marching and drilling operation. Similarly, grader being the earth cutting machine suffers sudden jolting and jarring action when boulders and hard formation hit the cutting blade en route of its movement. Also, front end loader when moving on uneven terrains, its tyres roll over a small boulder, which emanates vibration beyond normal levels (whereas in case of dragline and shovels there will be the only movement of the bucket and its arm during loading and unloading operation). Further, front end loaders are often required to change its direction suddenly, causing lateral and fore-aft vibration. 4.2

Risk Analysis of Seat-Back Measurements

As indicated in Table 5, considering the health risk evaluation in X-direction, water sprinkler-8A was the only machinery found to have RMS value of 0.86 m/s2, which is in the severe zone as per ISO-2631:1997 guidelines. All graders (i.e. 7A, 7B and 7C) showed RMS values in the caution zone with 0.70 m/s2, 0.61 m/s2 and 0.774 m/s2,

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respectively. Likewise, among four shovels, only one shovel (i.e. shovel–2B) of 12 m3 capacity depicted an RMS value of 0.64 m/s2. Despite the high crest factor 22.00 of grader–7B, its VDV is below the moderate zone as per ISO2631-1:1997 guidelines. Though many types of equipment surpass EAV, their ELV value is within the prescribed limit as per EU 2002 Directive. Measurements in the Y-direction revealed the highest RMS value for grader–7B with 0.78 m/s2. As indicated in Table 5, in total eight machineries were crossing moderate zone (i.e. RMS of 0.5 m/s2) and falling in caution zone, as per ISO26311:1997 guidelines. However, there was no indication of a moderate zone based on VDV measured in the lateral direction. Further, the ELV of all the machinery were within the safe limit of 1.15 m/s2, as per EU 2002 Directive. A close look at Tables 4 and 5 reveals that the VDV of four machineries (front end loader-3B, grader-7A, grader-7B, and grader-7C) w.r.t. Z-direction was found to be in caution zone, whereas no machinery has shown any indication of VDV in caution zone as far as seat-surface measurements are concerned. The RMS of four machineries was in the severe zone and that of six machineries in caution zone. The highest RMS value in the vertical direction was evinced by the grader–7A with 1.40 m/s2. Among all the machinery under consideration, ten were found exceeding EAV, out of which grader7A exceeded ELV as per EU 2002 directive. For ready reference, a critical review of Tables 4 and 5 was done to highlight the dominant axis of vibration based on ISO2631-1:1997 guidelines for all the machinery and also its associated health risk as per EU Directive 2002. Table 6 indicates the dominant axis of vibration and health risk based on ISO2631-1:1997 guidelines and Table 7 gives health risk prediction based on the EU 2002 Directive, for both seat-back and seat-surface measurements.

Table 6. Dominant axis of vibration and health risk prediction for different types of machinery based on ISO2631-1:1997 guidelines when measured at operator’s seat-surface and seat-back. S. RMS measurement noa at seat-surface Dominant HGCZ axis 1 Z Moderate 2A Z Caution 2B Z Moderate 2C Z Severe 2D Z Caution 3A X Severe 3B Z Severe 4A Z Severe 4B Z Severe 4C Z Severe

VDV measurement at seat-surface Dominant HGCZ axis Z Moderate Z Moderate Z Moderate Z Moderate Z Moderate X Moderate Z Moderate Z Moderate Z Moderate Z Moderate

RMS measurement at seat-back Dominant HGCZ axis Z Moderate Z Moderate Z Caution Z Moderate Z Caution Y Caution Z Severe Z Caution Z Severe Z Severe

VDV measurement at seat-back Dominant HGCZ axis Z Moderate Z Moderate Z Moderate Z Moderate Z Moderate Y Moderate Z Caution Z Moderate Z Moderate Z Moderate (continued)

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S. RMS measurement VDV measurement at seat-surface noa at seat-surface Dominant HGCZ Dominant HGCZ axis axis 5 Y Moderate Y Moderate 6 Z Caution Z Moderate 7A Z Severe Z Moderate 7B Z Caution Y Moderate 7C Z Caution Z Moderate 8A Z Caution Z Moderate 8B Z Severe Z Moderate a Machineries are designated different set of Serial

RMS measurement VDV measurement at seat-back at seat-back Dominant HGCZ Dominant HGCZ axis axis Y Moderate Y Moderate Z Moderate Z Moderate Z Severe Z Caution Z Caution Z Caution X Caution Z Caution X Severe X Moderate Y Caution Y Moderate numbers for simplified representation.

Table 7. Health risk prediction for different types of machinery based on EU 2002 Directive guidelines w.r.t operator’s seat-surface and seat-back measurements. S. no Type of machinery

For seat-surface measurement Based on Based on RMS VDV EAV ELV EAV ELV 1 Dragline XX* XX XX XX p 3 2A Shovel1 (Tata-Hitachi) 5 m XX XX XX XX XX XX XX 2B Shovel2 (Komatsu) 12 m3 p  2C Shovel3 (Tata-Hitachi) 5.5 m3 XX XX XX p 3 2D Shovel4 (Komatsu) 12.5 m XX XX XX p 3A Front End Loader1 (L&T 1920) XX XX XX p 3B Front End Loader2 (Tata 3036) XX XX XX p 4A Drill 1 (Atlas Copco DM-37) XX XX XX p 4B Drill 2 (REL DM-22) XX XX XX p 4C Drill 3 (REL DM-28) XX XX XX 5 Spreader XX XX XX XX p 6 Crane (12 ton ACE FX120) XX XX XX p 7A Grader1 (BEML) XX XX XX p 7B Grader2 (BEML) XX XX XX p 7C Grader3 (Volvo) XX XX XX p 8A Water Sprinkler1 (BEML) XX XX XX p 8B Water Sprinkler2 (BEML) XX XX XX *XX refers to not exceeded p ** refers to exceeded

For seat-back measurement Based on Based on RMS VDV EAV ELV EAV ELV XX XX XX XX XX XX XX XX p XX XX XX XX XX XX XX p XX XX XX p p XX XX p XX XX XX p XX XX XX p XX XX XX p XX XX XX XX XX XX XX XX XX XX XX p p p XX p XX XX XX p p XX XX p XX XX XX p XX XX XX

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5 Conclusions The whole body vibration of heavy earth moving machinery operators in Indian surface mines were measured with regard to seat-surface and seat-back using a triaxial accelerometer. The obtained results were evaluated based on guidelines as stipulated by ISO2631-1:1997 and EU 2002 Directive. The following conclusions were drawn from the analysis of collected WBV data: 1. The RMS of twelve machineries under study were exceeded EAV with respect to seat-back measurements, whereas for seat-surface measurement it was exceeded for fourteen machineries. From this comparison it is evident that seat-surface vibration is more prominent than that of seat-back vibration. 2. Among all the machinery under consideration, the measured vibration for a grader operator with regard to seat-back was exceeding ELV. Hence, there should be prompt health surveillance especially for grader operators. 3. The WBV of machinery operators demonstrates that nine (as per Table 6) machineries were in the severe zone as per their RMS values; hence these machinery needs suitable mitigation intervention. 4. Crest factors were found to exceed a value of 9 in 44 cases out of 102 measurements, which constitutes 43.13%. This indicates noticeable shock magnitudes during the measurement period. 5. In spite of the high crest factor, VDV of water sprinkler is within the safe limits. This is mainly because this unit is not directly involved in any mining operations, such as loading, excavation, transportation etc. 6. For both seat-surface and seat-back measurements, Z-axis (i.e. vertical direction) was found to be a prominent axis for most of the HEMM. The mine management can adapt the following few recommendations/guidelines to minimize the industrial exposure to vibration, as addressed in this study. 5.1

Recommendations Based on the Present Study

1. Since the dominant axis of the vibration for most of the equipment under consideration is in Z-axis, it is suggested to use pneumatic suspension seats, which can attenuate the vibration in the vertical direction. 2. Since the dragline and spreader operators are exposed to low vibration levels, these operators can be put to work for longer hours. 3. It is recommended to maintain good work conditions, such as smooth terrain especially for loaders in surface mines. 4. Implementation of the participatory ergonomics can boost the safety compliances of the workers which enhances productivity and also the quality of their life. 5. Mechanized mines should strictly comply with the regular vibration monitoring scheme as per DGMS guidelines. 6. By inducting Multi Skilled Operator System the overall exposure to vibration of an individual operator would be minimized.

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Limitations of the Study

1. There should be a spurt in the sample size of the machinery tested to decrease sampling error. 2. The measurements taken in this study were not included in all seasons of the year.

References 1. Langer TH, Ebbesen MK, Kordestani A (2015) Experimental analysis of occupational whole-body vibration exposure of agricultural tractor with the large square baler. Int J Ind Ergon 47:79–83 2. Harris MA, Cripton PA, Teschke K (2012) Retrospective assessment of occupational exposure to whole-body vibration for a case-control study. J Occup Environ Hygiene 9 (6):371–380 3. Paschold HW, Sergeev AV (2009) Whole-body vibration knowledge survey of US occupational safety and health professionals. J Saf Res 40(3):171–176 4. Newell GS, Mansfield NJ (2008) Evaluation of reaction time performance and subjective workload during whole-body vibration exposure while seated in upright and twisted postures with and without armrests. Int J Ind Ergon 38(5–6):499–508 5. Eger T, Salmoni A, Cann A, Jack R (2006) Whole-body vibration exposure experienced by mining equipment operators. Occup Ergon 6(3, 4):121–127 6. Eger T, Stevenson J, Boileau PÉ, Salmoni A (2008) Predictions of health risks associated with the operation of load-haul-dump mining vehicles: part 1—analysis of whole-body vibration exposure using ISO 2631-1 and ISO-2631-5 standards. Int J Ind Ergon 38(9– 10):726–738 7. Smets MP, Eger TR, Grenier SG (2010) Whole-body vibration experienced by haulage truck operators in surface mining operations: a comparison of various analysis methods utilized in the prediction of health risks. Appl Ergon 41(6):763–770 8. Bovenzi M, Hulshof CTJ (1999) An updated review of epidemiologic studies on the relationship between exposure to whole-body vibration and low back pain (1986–1997). Int Arch Occup Environ Health 72(6):351–365 9. Boshuizen HC, Hulshof CT, Bongers PM (1990) Long-term sick leave and disability pensioning due to back disorders of tractor drivers exposed to whole-body vibration. Int Arch Occup Environ Health 62(2):117–122 10. Mandal BB, Srivastava AK (2006) Mechanization, vibration, and the Indian workforce. Asian Pac Newslett Occup Saf Health 13(2):38–40 11. Kaku LC (2004) DGMS classified circulars. Lovely Prakashan, Dhanbad, p 604 12. Directorate General of Mines Safety. Recommendations of 10th National Conference on Safety in Mines (2008). https://www.dgms.net/circulars.html. Accessed 11 Feb 2019 13. Kumar S (2004) Vibration in operating heavy haul trucks in overburden mining. Appl Ergon 35(6):509–520 14. McPhee B (2004) Ergonomics in mining. Occup Med 54(5):297–303 15. International Organization for Standardization, ISO 2631-1:1997 - Mechanical vibration and shock – Evaluation of human exposure to whole-body vibration – Part 1: General requirements. Geneva, Switzerland (1997)

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16. European Union and General Provisions (2002) (Directive 2002/44/EC of the European Parliament and the Council of 25 June 2002 on the minimum health and safety requirements regarding the exposure of workers to the risks arising from physical agents (vibration) (sixteenth individual Directive within the meaning of Article 16 (1) of Directive 89/391/EEC). Official J Eur Commun L, 117(13):6–7

Assessment and Prediction of Specific Energy Using Rock Brittleness in Rock Cutting Vijaya Raghavan(&) and Ch. S. N. Murthy Department of Mining Engineering, NITK, Surathkal, India [email protected], [email protected]

Abstract. In this study, we used picks with point attack angles of 45°, 50°, 55°, and 65° and 45°, 55°, and 65° attack angles in rock cutting experiments. The main objective is to estimate specific energy during the cutting process based on rock brittleness and study the influence of attack angle on specific energy. From the experimental data, we compared the obtained results using multiple linear regressions and ANOVA to predict the specific energy and found that the model developed were statistically significant. R2 of the brittleness B4 is 0.79 in comparision with R2 of density, UCS, BTS and abrasivity as 0.74, 0.83, 0.84 and 0.73. Specific energy not only be predicted from density, UCS, BTS, abrasivity, it can also be predicted using rock brittleness. Keywords: Rock cutting ANOVA
Students t test


Mechanical properties
Brittleness
Regression


1 Introduction Many researchers have studied the brittleness and its effects on the cutting efficiency of picks in rock cutting mechanisms. In fact, there is no globally accepted concept of brittleness to measure cutting efficiency and the brittleness and its effect has not been completely enlightened on rock cutting. Thus, the objective of these findings is to establish relationships between rock properties with specific energy (SE) and brittleness. Also, investigated the different brittleness significance and techniques for rock cutting efficiency. The researcher studied the mechanics of rock cutting whereas the rock and coal brittleness’ effect on cutting pick efficiency has been examined. Brittleness usually measures a materials relative susceptibility to competing two mechanical responses, fracture and deformation, and is characterized by a transition from ductile to brittle. The brittleness concepts we used in this study are given in Eqs. 1, 2 and 3 below. Equation 1 utilise the ratio of Uniaxial Compressive Strength (UCS) rc to the Brazilian Tensile Strength (BTS) rt to evaluate brittleness of the rock (Figs. 1a and b). B1 ¼ rc =rt

© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 374–382, 2020. https://doi.org/10.1007/978-3-030-24314-2_46

ð1Þ

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Equation 2 utilise the UCS and BTS to evaluate rock brittleness. B2 ¼ rc  rt =rc þ rt

ð2Þ

Equation 3 evaluate the rC - rT area under the line of graph (Fig. 1b), B2 ¼ rc xrt =2

ð3Þ

Fig. 1. The graph for the relation between (rc) to (rt) of the rock

In his study, he used information derived from earlier research work and investigated the correlations between SE and rock brittleness. In his study, evaluated two earlier used brittleness concepts, B1 (the ratio of (rc/rt), and B2 (the ratio of (rc − rt/rc + rt), and a new concept brittleness named B3 (rcxrt/2). He established regression analysis to correlate between the concepts of brittleness for rock cutting efficiency. He investigated that brittleness B3 value was correlated strongly with the SE. This demonstrates that the brittleness B3 could be used to indicate rock cutting efficiency analysis [1]. The researchers have performed rock cutting with fully instrumented laboratory drilling tests to determining values of SEcut and SEdrill on five types of rocks. They then performed regression analyses to get relationships between SEdrill and SEcut with rock brittleness values B1 (rc/rt), B2 (rc − rt/rc + rt), and B3 (rc * rt/2). Their results specify strong relationships exists logarithmically, linearly and exponentially between the B1, B2, and B3 brittleness values and the SEcut value of circular diamond saw blades, with R2 of 0.98, 0.93, and 0.85, respectively. They could not get a strong relation between brittleness values of B1 and B3 and picks of diamond impregnated core and picks of non-core and SEdrill of poly diamond crystalline [2]. The researchers have conducted experiments on natural stone cutting based on rock properties and operational parameters of block cutters and predicted SE for large circular saws. They used apparent density, UCS, BTS, bending strength, Shore hardness test, seismic velocity, Schmidt hammer hardness, water absorption at atmospheric pressure, open porosity, point load strength, depth of cut values and saw blade diameter, as input parameters to predict SEcut values in their statistical analysis. SEcut values for Carbonate rock can be predicted using the model developed successfully with large-diameter circular saws in natural stone processing [3]. The researchers have developed a prediction model for SEcut with circular diamond saw blades when sawing granite. They investigated how operating parameters and rock properties influenced SEcut. Statistical analysis were carried out and developed a model to predict which operating parameters and rock properties had most significantly influenced on SEcut [4]. The researchers carried out rock cutting experiments with abrasive water jet cutting (AWJC) and circular sawing (CS) machines on 12 types of rock samples. In their study,

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rock cutting efficiency was compared with SE values. Their results showed that SE values of CS were lower than AWJC. With multiple regression equations, they found the relationship between SE values and rock properties for AWJC system SE (R2 = 0.95) and CS system SE (R2 = 0.98) which were statistically significant [5]. Artificial neural networks were used in evaluate and predict brittleness in hard rock using elastic properties of rocks. A predictive model was developed by Earth Mechanics Institute (EMI), Colorado School of Mines. The model uses density, P and S wave velocities and elastic properties. The results showed that this model is a improved method compared with multiple regression techniques and conventional destructive strength test in predicting rock brittleness. This methodology can be explored to rock mass problems in both tunnelling and underground mining [6].

2 Mechanical Properties of Rock Tested Coal and sandstone blocks were collected from M/S The Singreni Colliery Coal Ltd (SCCL), Ramagundem Area I Telangana, India, and limestone and dolomite blocks were collected from sites operated by Chaitanya Industries, JK cements, in Mudhapur, Bagalkot, Karnataka and Anantapur and Cuddapah districts, Andhra Pradesh. Core samples were prepared and mechanical properties tested were density, UCS, BTS, and Abrasivity of rocks as per ISRM standards and shown in Table 1. 2.1

Density

The density of rock is determined by taking a graduated cylinder filled half full with water. Then find out the exact water volume using cylinder scale. Then dip the rock into the graduated cylinder completely immersed into the water, then note down the level of the water. Again measure the volume of the cylinder. After that subtract the initial volume from the final volume in the cylinder to evaluate the volume of rock and divide mass of the rock by its volume as shown in (Eq. 4) and results of test is shown in Table 1.
Density gm=cm3 ¼ mass of the sample=volume of sample:

2.2

ð4Þ

Uniaxial Compressive Strength (UCS)

Core samples of 54 mm in diameter with a length-to-diameter ratio of 3 were prepared to conduct UCS tests. A loading rate within the range of 0.5–1.0 MPa/second was applied, and the load was applied continuously, at which time the maximum load at failure (in kN) was recorded. The specimen’s UCS was calculated by dividing its maximum load at failure by is original cross-sectional area and the results is shown in Table 1. 2.3

Brazilian Tensile Strength (BTS)

BTS tests were conducted on core samples 54 mm in diameter with a length-todiameter ratio of 1. The tensile load on the specimens was applied at a stress rate of

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200 N/s until the sample failed, and the maximum load at failure (in kN) was recorded. The specimen’s BTS was calculated by dividing its maximum load at failure to its original cross-sectional area and the results is shown in Table 1. 2.4

The Abrasivity of the Rocks

The test sample consists of clean aggregates dried in an oven at 105 °C–110 °C. The abrasive charge and test specimen was placed in the Los Angeles abrasive testing machine. The machine was allowed to rotate for 500 revolutions. The rocks retained after 500 revolutions on the 4.75 and 1.70-mm sieves were combined, weighed and the values were recorded to the nearest 1 g. If the mass of rock retained on the 1.70-mm sieve was determined after 100 revolutions, the entire test specimen, including the rock passing the 1.70-mm sieve, was returned to the testing machine. The opening in the testing machine was closed and operated for the required number of additional revolutions and calculated using the Eq. 5 and values are shown in Table 1. % Wear ¼ ðA  B=AÞ100

ð5Þ

Where, A = Mass of original test specimen, to the nearest 1 g, B = Mass retained on the 1.70-mm sieve after the specified number of revolutions, to the nearest 1 g. 2.5

Brittleness

Brittleness is a concept obtained by the UCS and BTS values of rocks tested in this study. A modified formula was proposed as mentioned in (Eq. 6) to determine brittleness in rock cutting. Brittleness values are shown in Table 1. Brittleness ¼ rc xrt =rc þ rt

ð6Þ

Where rc ¼ UCSðMPaÞ; rt ¼ BTSðMPaÞ

Table 1. Mechanical properties of rocks tested in laboratory. Rock Coal 1 Sandstone 1 Sandstone 2 Sandstone 3 Limestone 1 Limestone 2 Limestone 3 Limestone 4 Dolomite 1 Dolomite 2

Density gm/cm3 1.41 1.92 1.94 1.95 1.99 2.2 2.69 2.7 2.5 2.5

UCS MPa 14.2 14.1 18.3 24.2 46.8 58.6 69.7 70.3 44.4 71.2

BTS MPa 1.4 1.4 1.8 2.5 4.4 5.6 6.8 7.1 4.2 7.2

Abrasivity of rock (%) 17 21 22 25 28 23 26 38 47 54

Brittleness B4 1.27 1.27 1.63 2.26 4.02 5.11 6.19 6.44 3.83 6.53

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3 Description of Rock Cutting Machine (RCM) The RCM, shown in Fig. 2, was fabricated to study the influence of cutting parameters like thrust, torque, and speed on cutting process results. The RCM consists of a firm base with two protruding parts, one of which has a prime mover (motor) mounted on it. The cutter head, which consists of a drum with 12 picks mounted on it, is attached to the shaft by a flange. The sample holder can accommodate a block with dimensions of 0.3  0.3  0.45 m. In laboratory rock cutting, speed and thrust are varied from 225 to 350 rpm and 1.3 to 2.1 kN, respectively. During the cutting process, the cutting force and torque were measured by a cutting tool dynamometer that is calibrated in the rock mechanics laboratory. Ten types of rocks were considered for laboratory experiments: like coal, three types of sandstone, four types of limestone, and two types of dolomite. For each combination of speed and thrust, rock fragments produced during the cutting process were collected and weighed. This experiment considered attack angles of 45°, 55°, and 65°, and four pick angles (45°, 50°, 55°, and 65°) were considered for each attack angle for every pick-rock combination and operational parameter (i.e., speed and thrust) considered during the investigation. The influence of wear on the cutting rate and SE were considered, with a wear rate of 5 mm fabricated and used for all considered pick-rock combinations. Additionally, experiments were carried out for all speed and thrust combinations used. Figures 3, 4, 5 and 6 show the relationship between SE and rock brittleness in this study.

Fig. 2. (a) Rock cutting machine (b) Line diagram of Rock cutting machine Influence of Brittleness on Specific energy 3.6

Specific Energy (KJ/Cu m)

3.4 3.2

Variable 45 degree bit angle R Sq=79.5 50 degree bit angle R Sq=78.8 55 degree bit angle R Sq=78.5 65 degree bit angle R Sq=78.2

3.0 2.8 2.6 2.4

Fig. 3. Influence of Brittleness (B4) on Specific Energy at 45° attack angle.

Fig. 4. Influence of Brittleness (B4) on Specific Energy at 55° attack angle.

Assessment and Prediction of Specific Energy

Fig. 5. Influence of Brittleness (B4) on Specific Energy at 65° attack angle.

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Fig. 6. Influence of Brittleness (B4) on Specific Energy at 45° attack angle with 5 mm wear for all picks.

4 Experimental Result and Discussion 4.1

Linear Regression Analysis

It was found with linear regression analysis that, a strong relationship exists between rock properties and SE which were statistically significant. Through this analysis, independent variable value can be predicted from any dependent variable values. A relationship between SE and rock properties were established with linear regression analysis based on the least squares method as shown in Eq. 7. Regression Equation The obtained Eq. (7) represents specific energy in terms of speed of cutting drum (v in rpm), attack angle (ha in °), pick angle (hp in °), cutting force (Fc in kN), torque (s in N − m), depth of cut (d in mm), density (q in gm/cm3), BTS (rt in MPa), UCS (rc in MPa), brittleness (B), Abrasivity (A in %) Ws ¼2:631 þ 0:000023v þ 0:003456ha þ 0:006703hp þ 0:9585Fc þ 0:1051s  0:47146d 0:0111V  0:0225q þ 0:2635rt þ 0:02758rc þ 0:000003B  0:005373A

ð7Þ

Table 2. Regression model summaries Predictors Density UCS BTS Abrasivity Brittleness

R 0.862 0.902 0.942 0.736 0.748

R2 0.743 0.836 0.846 0.730 0.795

Adjusted R2 0.384 0.764 0.798 0.518 0.442

Std. error of estimation 12.744 14.211 12.899 14.166 13.877

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Regression analysis along with analysis of variance (ANOVA) and the F-test were carried out and found that independent variables could be used in establishing the relationship between SE from the linear equation at a 95% confidence level. Table 3. Analysis of Variance (ANOVA) Results (F tests) Source Regression RPM Attack angle Pick angle Cutting force Torque Depth of cut Volume broken Density UCS BTS Abrasivity Brittleness Error Lack of fit Pure error Total

DF 12 1 1 1 1 1 1 1 1 1 1 1 1 1457 1355 102 1470

Sum of squares Mean squares F-value 749.027 57.617 2708.83 0.002 0.002 0.08 1.117 0.709 34.52 3.310 3.310 155.63 1.979 1.979 93.04 2.312 2.312 108.72 158.578 158.578 7455.36 0.006 0.006 0.28 0.008 0.008 0.38 0.598 0.598 28.10 0.605 0.605 28.45 1.783 1.783 83.81 0.001 0.001 0.06 30.991 0.021 30.976 0.023 0.015 0.000 781.135 DF- Degree of freedom

Significance of P-value 0.000 0.050 0.011 0.000 0.000 0.000 0.000 0.045 0.036 0.000 0.000 0.000 0.003

Table 4. Significance of model components with Student’s t-test Term Constant RPM Attack angle Pick angle Cutting force Torque Depth of cut Volume broken Density UCS BTS Abrasivity Brittleness

Coef 2.631 0.000023 0.003456 0.006703 0.9585 0.1051 0.47146 −0.0111 −0.0225 −0.02758 0.2635 −0.005373 0.000003

SE Coef T-value P-value 0.119 22.04 0.000 0.000081 0.28 0.042 0.000588 5.87 0.000 0.000537 12.48 0.000 0.0994 9.65 0.000 0.0101 10.43 0.000 0.00546 −86.34 0.000 0.0208 −0.53 0.045 0.0363 −0.62 0.036 0.00520 −5.30 0.000 0.0494 5.33 0.000 0.000587 −9.15 0.000 0.000012 0.25 0.023

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ANOVA results for all the attack angle are shown in Table 3. Based on results, models like density, UCS, BTS, Abrasivity, and brittleness were predictors and were statistically significant in terms of linearity. The P-values for all the parameters are less than 0.05; therefore all the parameters are statically significant at 95% confidence intervals. After verifying these regression models through ANOVA to establish whether they could be used to predict SE reliably, we used Student’s t-tests. We tested the model components to find the significances of each at a 95% confidence level. Depending on the (P-values obtained, each of the model components mentioned above could be established statistically significant or not. All the regression models for all attack angle verified through ANOVA were understood to have statistically significant (Table 4). This reveals that, the practicality of these models in predicting SE values [7]. Further, coefficients of determination (R2) to measure the goodness of the proposed regression models. R2 is equal to the square of the correlation coefficient between observed and predicted values. The two statistical measures calculate values of these for the model are given in Table 2. According to these values, most of the SE value changes can be successfully expressed individually by density, UCS, BTS, Abrasivity, and brittleness, in line with the ANOVA shown in Table 3 and Student’s t-test results shown in Table 4.

5 Conclusions 1. Rock properties such as density, UCS, BTS, Abrasivity and brittleness influence SE. It was observed that increases in density, UCS, BTS, Abrasivity, and brittleness correlated with increases in SE. This is because the rock’s resistance to cutting increases with the increase in the rock’s strength. 2. Regression model results showed that attack angle, pick type, and rock mechanical properties are the important operating variables affecting the SE. 3. R2 of the brittleness is 0.79 in comparison with R2 of density, UCS, BTS and abrasivity as 0.74, 0.83, 0.84 and 0.73 respectively. 4. Specific Energy can be efficiently predicted not only with rock properties but also using rock brittleness.

References 1. Altindag R (2003) Correlation of specific energy with rock brittleness concepts on rock cutting. J South Afr Inst Min Metall 103(3):163–171 2. Atici U, Ersoy A (2009) Correlation of specific energy of cutting saws and drilling picks with rock brittleness and destruction energy. J Mater Process Technol 209(5):2602–2612 3. Yurdakul M, Akdas H (2012) Prediction of specific cutting energy for large diameter circular saws during natural stone cutting. Int J Rock Mech Min Sci 53:38–44 4. Aydin G, Karakurt I, Aydiner K (2013) Development of predictive models for the specific energy of circular diamond saw blades in the sawing of granitic rocks. Rock Mech Rock Eng 46(4):767–783

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5. Engin IC, Bayram F, Yasitli NE (2013) Experimental and statistical evaluation of cutting methods in relation to specific energy and rock properties. Rock Mech Rock Eng 46(4):755–766 6. Kaunda RB, Asbury B (2016) Prediction of rock brittleness using nondestructive methods for hard rock tunnelling. J Rock Mech Geotech Eng 8:533–540 7. Tiryaki B, Dikmen AC (2006) Effects of rock properties on specific cutting energy in linear cutting of sandstones by picks. Rock Mech Rock Eng 39(2):89–120

Numerical Investigation on Factors Affecting the Performance of Roof Bolts for Continuous Miner Working K. M. Tejeswaran(&), Ch. S. N. Murthy, and B. M. Kunar Department of Mining Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India [email protected]

Abstract. Optimum support design of roof bolts based on axial load of the bolt plays the major role for effective development of coal seam with continuous miner. Axial load on the roof bolts gives a clear understanding of the behaviour of roof bolts in different working conditions. Therefore, estimation of axial load on the bolts is important for supporting the immediate roof, helps in higher production, productivity and safety. By using the software FLAC 3D, the axial load for different gallery widths and working depths was estimated. From the simulation results, it was observed that for shallow depths of 100 and 200 m, the axial load acting on the bolt is 15% of the bolt capacity at gallery widths of 4 m and 5 m. Whereas for moderate depths 300 m and 400 m, its value is found to be 75% at gallery widths 6 m and 7 m. But, for deeper depths of 500 m and more, its values reaches maximum capacity of roof bolts. Also, the roof convergence in junction, for moderate and deeper depths is 80 mm to 150 mm, whereas for shallow depths its value is 10–25 mm, at 6 m, 7 m and 8 m gallery widths. Keywords: Continuous miner
Roof bolts
Axial load Numerical modeling
FLAC 3D
Convergence




1 Introduction Extraction of coal by mechanisation with continuous miner technology has brought huge hope for the underground coal mining industries in India. Application of continuous miner technology is been proven that, it is protective for the coal extraction in Indian geomining condition [1, 2]. Continuous miner based mechanized is preferring by most the coal mining industry in India mainly due to its adaptability in Indian geomining conditions and a moderate level of investments with comparatively higher production and productivity [3]. Ground control is the major problems faced during the extraction of coal by continuous miner technology. Roof bolts are the primary supports system that are used for supporting the immediate roof. Because roof bolts support system does not affect the movements of continuous miner and shuttle car in the gallery, with carrying a huge load upto 27 tonnes, easy initialization and ensured safety of the working [4–6]. Research has been carried out by various investigators on the design of roof bolts, and have found the serval factors that influence the design of roof © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 383–393, 2020. https://doi.org/10.1007/978-3-030-24314-2_47

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bolts, these includes the roof bolt type, roof bolt length, roof bolt capacity, and pattern of bolting for a particular application. The present research contexts on the attributes of roof bolts including anchorage mechanism, pretension, bolt length, capacity of bolt, installation timing and quality of installation [4, 7, 8]. Roof bolting design is indeed mainly based on the experience and its appears that rock bolting design is simply a business of selecting a rock bolt type. Along with, determination of bolt length and spacing. A study was carried out in 37 U S Mines the parameters affects the roof bolt design where roof geology and stress level by numerical modeling and statistical analysis where carried out to find the significant parameters and some of the preliminary guidelines have been proposed [7]. According to [6], the selection of roof bolt is based on the pressure arch theory resulting in the formation of the natural pressure arc in the rock. If the failure is small, then the roof bolt length should be long enough such that it reaches the nature pressure arc. It is indeed that, the bolt length should be at least 1 m beyond the failure zone [8]. Parameters of resin bolt, such as gloving and back pressure, also effect the roof support performance [9]. Though selection of roof bolts is carried out in various ways the present study focus on behaviour of roof bolts under different gallery widths and different working depths, resulting to the correlation of selecting the roof bolts. Numerical modelling finds wide application in solving geotechnical problems and platform for parametric study, which saves time and money [10]. The objective of this paper is to study the behaviour of roof bolt considering different gallery widths and different working depths in development working with continuous miner.

2 Numerical Simulation 2.1

Selection of Simulation Location

For the parametric study Zero seam of Anjan hill mine, SECL is selected for the study. For the estimation of the roof bolt axial load requires studying the characteristics of the particular mining site, which helps in achieving supports design. Under this study, the elasto-plastic modelling is developed using FLAC3D with the matching Indian geomining conditions of coal mines. Bore hole number CHA-5 of Anjan Hill mine as shown in Fig. 1 is used in the development of a numerical model in FLAC 3D software package. The gallery widths and working depths are varied to assess the roof bolt axial load and convergence in roof. The average thickness of Zero seam of Anjan hill mine, SECL is 5.33 m was developed in a single section along floor. Development is made leaving 0.6 to 1.0 m thick coal along the roof. Average size of the development pillar is 33 m  33 m (centre to centre), working height 4.8 m and average gallery width is 5 m. Total reserve of zero seam (thickness range from 3.0 to 5.33 m) is 1.1 million tons in the lease hold area of the mine. Non extractable overlaying seam is present over the zero seam panel C of the mine as selected for application of the continues miner for development of coal seam.

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Fig. 1. Bore hole of Anjan hill mine.

2.2

The Input Parameters for the Numerical Modelling

The various strengths and elastic constants of the rock mass for the numerical modelling using FLAC3D in the strain-softening mode is shown in Table 1. The friction angle and shear strength is estimated by using Sheorey’s failure criterion for rock mass. As mentioned below, this criterion uses the 1976 version of rock mass rating (RMR) of Bieniawski (1976) for reduce the laboratory tested strength parameters to rock mass values. This criterion defined as: 

 r3 bm MPa r1 ¼ rCm 1 þ rtm   RMR  100 rCm ¼ rC Exp 20   RMR  100 rtm ¼ rt Exp 27

ð2Þ

bm ¼ bRMR=100

ð4Þ

ð1Þ

ð3Þ

Where, r3 is the minor principal stress in MPa, r1 is the major principal stress in MPa, rC is the intact rock Compressive strength, MPa, rt is the intact rock Tensile strength, MPa, b is the Exponent of intact rock which controls the curvature of the triaxial curve. rCm is the Compressive strength of rock mass, MPa, rtm is the Tensile strength of rock mass, MPa, RMR is the Bieniawski (1976) Rock Mass Rating and bm is the Exponent for rock mass corresponding to the intact rock constant lesser than 0.95. The rock mass shear strength ssm the coefficient l0m and the angle of internal friction ;0m are obtained as

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ssm

 ¼ rCm rtm

1=2 bbmm 1 þ bm ð 1 þ bm Þ   s2sm ð1 þ bm Þ2 r2tm

ð5Þ

l0m ¼

ð6Þ

2ssm rtm ð1 þ bm Þ

;0m ¼ tan1 ðl0m Þ

ð7Þ

It is observed that the values of shear strength and friction angle determining from the above equations should be done slightly adjustment. This slight adjustment is required to incorporate the fact that the Mohr-Coulomb strain softening plasticity model in FLAC3D uses the linear Mohr-Coloumb criterion, where as the Sheorey criterion is nonlinear in Table 2. To compensate for the differences, the value of ssm obtained from the Sheorey criterion is increased by 10% and that of ;0m is reduced by 5° to use them as Mohr-Coloumb parameters, Fig. 2 validate this practice.

Table 1. Physico-mechanical properties of rock strata Formation

Floor Coal seams Coal (in roof) Immediate roof Shale

Tensile strength of intact rock (MPa) 2.67 3.25 2.62

RMR

2500 1380 1380

Compressive strength of intact rock (MPa) 40 42.73 42.54

3.61

1910

34.23

2.01

45

5.7

2310

24.37

2.38

40

Young’s modulus (MPa) 7 3.02 3.02

Density (kg/m3)

55 52 50

Table 2. Change in cohesion, friction angle and dilation angle with shear strain Shear strain Cohesion (ssm ) (Mpa) Freiction angle (;0m ) (°) Dilation angle (u) (°) 0 1.1 −5 15 0.005 1.1/5 −7.5 5 0.050 0 −10 0 0.500 0 −10 0

In addition to the peak friction angle and cohesion, the Mohr-Coulomb strain softening model also requires to describe the parameters that the rate of cohesion and friction drop as a function of plastic strain in the post-peak region.

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Fig. 2. Schematic diagram showing the linear Mohr-Coulomb criterion adopted from the nonlinear Sheorey criterion in FLAC3D.

In-situ stresses Both vertical and horizontal in-situ stresses play an major role during the performance evaluation of roof bolts through numerical modelling. For this study, it is necessary to estimate the in-situ stresses with in the coal seam as exists in the field. The vertical stress and horizontal stress can be determined using the following formulas: rv ¼ 0:025H

ð8Þ

rh ¼ 2:4 þ 0:01H

ð9Þ

where, rv is the vertical stress in MPa, rh is the horizontal stress in MPa, and H is the depth of working in m. In this simulation study, the value of in-situ stresses is calculated using Eq. 8 and 9. The values of both minor and major horizontal stresses were taken the same. 2.3

Methodology for the Numerical Modelling

The condition of the site has been simulated in three-dimensional numerical modelling using FLAC3D software, which is based on a finite difference code. The steps involving in modelling gallery and bolts are (i) generating grid as shown in Fig. 3, (ii) Selecting of the appropriate model. (iii) Incorporation of the in-situ stresses, boundary conditions and material properties. (iv) solving the elastic model for equilibrium to generate the in situ stresses. (v) Changing to the strain softening model from elastic model and incorporation of the properties. (vi) Development of different gallery widths. (vii) the roof bolts are modelled using structure elements in the FLAC 3D as shown in Fig. 4, (viii) solving the models and monitoring of the axial load on the bolt [3]. For the galleries with 4 m, 5 m, 6 m, 7 m and 8 m widths and depth 100 m, 200 m, 300 m, 400 m and 500 m are considered in this simulation. The roof bolt properties and grout properties used in the modelling are given in Table 3.

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Fig. 3. FLAC3D showing grid of a quarter pillar model for study.

Fig. 4. Shows the 4 rows roof bolts installed in model Table 3. Grouting properties and Rock bolt properties used in the FLAC3D software. Crosssectional area (sq.m)

Young’s modulus (Pascal)

3.80  10−4

20.60  1010

2.4

Tensile yield strength (Newton) 27.0  104

Bond stiffness, (N/m/m) 2.00  109

Bond cohesive strength, (N/m) 2.5  105

Pre-tension in N

2.94  104

Validation of Numerical Modeling

Validation of the model is done by the field-measured instrumented data from the Anjan hill mine, SECL. The instrumented rock bolt is installed in the development gallery of Anjan hill mine of panel C at the junction and the load on the bolt is measured. Similarly, the load on the bolt is measure in the numerical modeling. The graphs are plotted as shown below.

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Fig. 5. Comparing the model results with field measured data

Figure 5 shows the comparison of model results with field measured data. The maximum load developed in the bolt after final stage of development is plotted along with the maximum axial load obtained from the model after reaching equilibrium. Both field data and model data almost matches each other that the model is validated.

3 Results and Discussion 3.1

Influence of Working Gallery Widths and Working Depths for 4 Row Bolt on Bolt Axial Load

For the parametric study, we have considered 2.4 m bolt length with 22 m bolt diameter and hole diameter is 28 mm. The 0.5 m spacing from pillar edge is kept constant for all the gallery widths. The spacing between each bolt is varied for different gallery width 0.6 m, 0.8 m, 1 m, 1.2 m, and 1.4 m for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths respectively. According for the 4 m, 5 m, 6 m, 7 m and 8 m gallery widths the depth is varied for 100 m, 200 m, 300 m, 400 m and 500 m. The graphs are plotted for maximum axial load developed in the bolt for development stage with continuous miner for the different gallery widths and different depth of working. Bolt length in meters is taken along the y-axis, and axial load in tons is taken along the xaxis. The maximum load-bearing capacity of the roof bolts considered for the parametric study is 27 tons. The Fig. 6 shows the graphs plotted for 100 m depth working for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths. The maximum axial load is observed at the middle of the bolt length for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths the maximum axial load is observed from numerical modelling at junction is 2.9, 3.18, 3.29, 3.56 and 3.9 tons respectively as the gallery width increases the axial load in the bolt increases. The Fig. 7 shows the graphs plotted for 200 m depth working for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths. The maximum axial load is observed at the middle of the bolt length for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths the maximum axial load is observed from numerical modelling at junction is 3.3, 4.3, 4.5, 5.4 and 7.6 tons respectively as the gallery width increases the axial load in the bolt increases. The Fig. 8 shows the graphs plotted for 300 m depth working for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths. The maximum axial load is observed at the middle of the

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Fig. 6. Axial load for 100 m depth vers different gallery widths

Fig. 7. Axial load for 200 m depth vers different gallery widths

Fig. 8. Axial load for 300 m depth vers different gallery widths

Fig. 9. Axial load for 400 m depth vers different gallery widths

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Fig. 10. Axial load for 500 m depth vers different gallery widths

bolt length for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths the maximum axial load is observed from numerical modelling at junction is 4.3, 6.31, 7.81, 10.48 and 18.9 tons respectively as the gallery width increases the axial load in the bolt increases. Figure 9 shows the graphs plotted for 400 m depth working for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths. The maximum axial load is observed at the middle of the bolt length for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths the maximum axial load is observed from numerical modelling at junction is 5.9, 9.29, 13.48, 19.19 and 27 tons respectively as the gallery width increases the axial load in the bolt increases. Figure 10 shows the graphs plotted for 500 m depth working for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths. The maximum axial load is observed at the middle of the bolt length for 4 m, 5 m, 6 m, 7 m and 8 m gallery widths the maximum axial load is observed from numerical modelling at junction is 7.6, 13.7, 14.4, 27 and 27 tons respectively as the gallery width increases the axial load in the bolt increases. 3.2

Influence of Working Gallery Width and Working Depth for 4 Row Bolt on Roof Convergence

Figure 11 shows the convergence plot for different gallery widths observed at junction using FLAC 3D from the graph it is observed that, as depth increases the convergences also increases.

Fig. 11. Convergence at junction for different gallery widths

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For working depth of 100 m, the minimum convergence is 4.3 mm, which is in 4 m gallery width, and the maximum convergence is 12 mm, which is in 8 m gallery width. For working depth of 200 m, the minimum convergence is 8.8 mm, which is in 4 m gallery width, and the maximum convergence is 26.3 mm, which is in 8 m gallery width. For working depth of 300 m, the minimum convergence is 15 mm, which is in 4 m gallery width, and the maximum convergence is 47.6 mm, which is in 8 m gallery width. For working depth of 400 m, the minimum convergence is 23.3 mm, which is in 4 m gallery width, and the maximum convergence is 79.4 mm, which is in 8 m gallery width. For working depth of 500 m, the minimum convergence is 33.81 mm, which is in 4 m gallery width, and the maximum convergence is 150 mm, which is in 8 m gallery width.

4 Conclusions In this paper, the effect of gallery width and working depth on bolt axial load is studied. The observations found are as described below: 1. With increase in gallery width, the axial load on the bolt is increases. For wide gallery widths of 6 m, 7 m and 8 m, the axial load developed in the bolt is more compared to the smaller gallery widths of 4 m and 5 m. 2. With shallow depths of 100 and 200 m, the axial load acting on the bolt is 15% of the bolt capacity at gallery widths of 4 m and 5 m. Whereas for moderate depths 300 m and 400 m, its value is found to be 75% at gallery widths 6 m and 7 m. But, for deeper depths of 500 m and more, its values reaches maximum capacity of roof bolts. 3. The roof convergence in junction, for moderate and deeper depths is 80 mm to 150 mm, whereas for shallow depths its value is 10–25 mm, at 6 m, 7 m and 8 m gallery widths. 4. For the wide gallery widths working (6 m, 7 m and 8 m) and for higher working depth (300 m, 400 m and 500 m), the axial load in the bolt reaches maximum and roof supports fail. 5. In order to ensure the safety of roof supports the amount of convergence must be on lower side from the Fig. 11 it can be observed that minimum convergence is 4.3 mm in 4 m gallery for 100 m working depth for 4 bolts row bolting. 6. If the depth of working is greater than 300 m and gallery width more than 7 m with 4 row bolting, the axial load on the bolt reaches maximum bearing capacity and bolt fails. Acknowledgement. The authors are thankful to the Director, CSIR-CIMFR, Dhanbad for his kind permission to carry out this study at CSIR-CIMFR Dhanbad. Authors also thankful to Dr. P. K. Mandal senior principal scientist and Mr. Arka Jyoti Das, scientist for the valuable suggestions and help.

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References 1. Raghavan V, Ariff S, Kumar PP (2014) Optimum utilisation of continuous miner for improving production in underground coal mines. Int J Sci Res Publ 4(10):1–10 2. Modi J (2015) Success of continuous miner in bord and pillar method, no. September 2013 3. Mandal PK, Das AJ, Kumar N, Bhattacharjee R, Tewari S, Kushwaha A (2018) Assessment of roof convergence during driving roadways in underground coal mines by continuous miner. Int J Rock Mech Min Sci 108(May):169–178 4. Kushwaha A, Singh SK, Tewari S, Sinha A (2010) Empirical approach for designing of support system in mechanized coal pillar mining. Int J Rock Mech Min Sci 47(7):1063–1078 5. Jena S (2016) Numerical simulation of roof bolt system during depillaring operation in bord and pillar panel. In: Rare, pp 69–73 6. Cheng J Development and calibration of a 3D numerical modeling of roof bolts - a case study, pp 4257–4268 7. Mark C (2000) Design of roof bolt systems. In: Proceedings: new technology for coal mine roof support, no. February, pp 111–131 8. Li CC (2017) Principles of rockbolting design. J Rock Mech Geotech Eng 9(3):396–414 9. Purcell J, Vandermaat D, Callan M, Craig P (2016) Practical investigations into resin anchored roof bolting parameters, no. February, pp 53–63 10. Mandal PK, Singh R, Maiti J, Singh AK, Kumar R, Sinha A (2008) Underpinning-based simultaneous extraction of contiguous sections of a thick coal seam under weak and laminated parting. Int J Rock Mech Min Sci 45:11–28

Modelling of Biogas Fueled HCCI Engine for Various Inlet Conditions Nihal Mishra, Shubham Mitra, Abhishek Thapliyal, Aniket Mahajan, and M. Feroskhan(&) School of Mechanical and Building Sciences (SMBS), VIT Chennai, Chennai 600127, Tamilnadu, India [email protected]

Abstract. A common technique for using biogas in a compression ignition (CI) engine is to blend it with air in the intake manifold, injecting a small quantity of diethyl ether during suction stroke and compress this mixture and ignite it by self-ignition temperature. This is called as homogeneous charge compression ignition (HCCI) mode. This paper evaluates the effects of various intake conditions such as methane fraction, compression ratio, diethyl ether energy fraction, intake temperature and equivalence ratio on output parameters of maximum cylinder pressure, in-cylinder temperature and indicated thermal efficiency using single zone modelling. The modelling result is validated with the experimental data. The increase in compression ratio, diethyl ether energy fraction and intake temperature increase cylinder pressure and in-cylinder temperature. Keywords: Modelling
HCCI engine
Methane
Biogas
Compression ratio

1 Introduction The transportation division has seen an expanding need to create more productive and environment friendly engine. This is to reduce the regulated allowable limits for pollutants emission originating from internal combustion engines (ICE) and instable oil costs. IC Engines have a great significance in recent times and have numerous applications in our society. Much exertion has been put into enhancing and refining engines throughout the years. Starting late, much intrigue has been put into low temperature combustion (LTC) engines, which can possibly decrease emissions related with traditional ICEs while keeping up their high efficiencies [1]. Homogeneous Charge Compression Ignition (HCCI) combustion engines are viewed as LTC system. The fuel and air are premixed to form a homogeneous blend before the compression stroke in HCCI engines [2]. Therefore, the blend ignites throughout the mass without discernible fire engendering because of auto ignition at different areas in the burning chamber (multi-point ignition). This may give rise to a high rates of heat discharge and thus, high rates of pressurization. In HCCI engines, auto-ignition and burning rates are fundamentally controlled by the fuel compound energy or in other words to the charge synthesis and to the pressure and temperature development amid the compression stroke along these lines HCCI ignition is generally thought to be dynamically © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 394–403, 2020. https://doi.org/10.1007/978-3-030-24314-2_48

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controlled [3]. The primary goal of HCCI combustion engines is to lessen the residue and NOx emissions while maintaining high eco-friendliness [4]. In aspects, HCCI combustion combines engines the benefits of both spark ignition (SI) engines as well as compression ignition (CI) engines [5]. The outcomes from analysis and recreation demonstrate that the HCCI ignition has a low temperature heat discharge and a high temperature heat discharge and both heat discharges happen inside certain temperature ranges [3]. The low temperature heat discharge is a standout amongst the most vital elements for HCCI engine activity and its processes depends synthetically on the fuel composition [3]. Such a system faces improvement challenges. Ignition timing isn’t directed by an exact ignition controlling occasion, such as fuel infusion or spark plug. It is rather a very delicate capacity of the fuel’s auto-ignition properties, the gas blend synthesis and thermodynamic conditions in the burning chamber [6]. The beginning of ignition and the resulting heat discharge becomes challenging to anticipate and control.

2 Literature Review The concept of HCCI engines is a recent development in the automobile sector. Basically, HCCI engines combine the benefits of SI and CI engines. The main feature of HCCI engines which distinguishes it from other engines is the fact that fuel and air are premixed and the mixture ignites automatically as the temperature rises during compression stroke. There is a possibility of light load operation without throttling [7], henceforth giving fuel economy like a diesel engine and also allowing full load operation with homogenous charge thereby giving a power density comparable to a gasoline engine. HCCI engines also offer high thermal efficiencies due to the fact that it has low equivalence ratios and rapid energy release [8]. Due to this gaseous and particulate emissions also decrease drastically [9]. HCCI engines like other engines use gasoline or diesel as a fuel. But these resources namely natural gas, petrol etc. are nonrenewable which means they will be eventually exhausted and cease to exist. Therefore, our main objective is to find an alternative fuel which can be used in place of nonrenewable resources and provide us with the same energy output. These fuels are derived from sources other than petroleum. These fuels are extracted from sources apart from petroleum oil. The different energy sources which can be used as an alternate are namely, alcohol, biogas, natural gas and hydrogen. In this paper, biogas is used along with diethyl ether (DEE). As biogas has a high self-ignition temperature, so DEE is been used as ignition source. As biogas contains CO2, so HC emissions are very high. As biogas is a renewable fuel, and also it is easy to produce so biogas can be a great replacement for diesel and petrol. The problems mentioned above with SI and CI engines are solved in HCCI Engines because in HCCI engines homogeneous mixture is used and also NOx emissions are less as in cylinder temperature is less. Addition of DEE (C2H5OC2H5), in HCCI engines reduces NOx emissions; as a result, in-cylinder temperature reduces and overall engine performance was improved [10].

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2.1

Alternative Fuels

The world’s essential energy assets, for example, oil, petroleum gas, coal, and atomic fills are not inexhaustible. Their quick exhaustion, subsequent ascent in costs, expanded worldwide energy request, and worry for ecological insurance have raised the journey for option, inexhaustible wellsprings of energy like sun-oriented energy, hydro energy, wind energy, and biofuels [1]. Besides, oil holds are to a great extent packed in a hardly any districts of the world. Nations in different locales confront extreme emergency in crossing over the hole between energy request and fuel supply [2]. Non-renewable energy source burning likewise results in air contamination, corrosive rain, and develop of carbon dioxide, hence putting people and nature in danger [3–6, 11]. Among the options in contrast to petroleum products, biofuels, for example, biogas, alcohols, and biodiesels have gotten significant consideration due to their inexhaustible nature and their inborn potential to cut down net CO2 discharge [12–15]. Biogas offers a few favourable circumstances over different fills got from biomass. It very well may be transported effectively by means of pipelines or as a packed gas in chambers once the destructive parts, viz. CO2, H2S, and water vapor are expelled [16]. Compared with other fuels like coal, biogas consumes quicker and leaves no deposit and is environment friendly. Biogas can at last be followed back to vegetation. These plants/trees ingest CO2 amid their lifetime, so regardless of the outflow of CO2amid ignition, biogas might be considered as a CO2-unbiased fuel [17]. The creation of biogas additionally requires less efforts and cost compared to other biofuels like alcohols and biodiesel [18–21]. Biogas holds an excellent potential for creating jobs in the society.

3 Model Formulation The closed part of the operating cycle of a four-stroke biogas fuelled HCCI engine is modelled in this work. A single zone model based on the Otto cycle is chosen because of the near homogeneity of the working fluid before and after combustion and rapid energy release rate resulting in almost instantaneous combustion. Details of the engine studied here are given in Table 1. Biogas is modelled as a mixture of methane and CO2. The effect of methane enrichment is described by the parameter methane fraction (x), which indicates the fraction of methane by volume in biogas. The range of methane fraction (x) = 0.5 (raw biogas) to methane fraction (x) = 1 (pure methane) is used in this study. Equivalence ratio varies from very lean (U = 0.4) to stoichiometric (U = 0.6). Instantaneous piston stroke (s) and displacement volume (V) are expressed as functions of crank angle (h) using the slider-crank relationships [22]. V ¼ VC þ

pB2 ðl þ a  sÞ 4

s ¼ a cos h þ l2 þ a2 sin2 h


1=2

Where ‘Vc’ is clearance volume and the crank radius is represented by ‘a’ = L/2.

ð1Þ ð2Þ

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Table 1. Engine specification. Engine parameter Value Bore radius 87.5 mm Stoke length 80 mm Cubic capacity 481 cm3 Compression ratio 17 Number & arrangement of cylinders 1-vertical Working cycle 4-stroke diesel Combustion principle HCCI Peak pressure 7500 kPa Maximum power 5.97 kW Maximum torque 25 Nm Rated speed 2200 rpm

The chemical reaction associated with combustion, incorporating the parameters methane fraction (x) and equivalence ratio (U), is expressed as: /½xyCH4 þ yð1  xÞCO2 þ ð1  yÞC2 H5 OC2 H5  þ ½ð2xÞO2 þ ð7:5xÞN2  ! /½ð4  3yÞCO2 þ 2xy þ 5ð1  yÞH2 O þ ðyð6  2xÞ  6Þ þ ð2xÞO2 

ð3Þ

The compression process is assumed to be adiabatic. The variations in the cylinder pressure (pi) and temperature (Ti) are evaluated at each crank angle interval (i) by solving the ideal gas and energy balance equations simultaneously: ðVi  Vi1 Þ ðVi  Vi1 Þ Pi þ ncv Ti ¼ ncv Ti1  Pi1 2 2

ð4Þ

Vi Pi  nRu Ti ¼ 0

ð5Þ

where ‘n’ is the number of moles of the reactant mixture and ‘Ru’ is the universal gas constant. The terms on the RHS can be calculated from the values at the previous crank angle and the equations can be solved to obtain pi and Ti. The effective specific heat ‘cv’ is estimated as the molar average value considering all species at a particular temperature. The energy release during combustion is expressed as: Qin ¼ nCH4  LCV

ð6Þ

where ‘nCH4’ is the number of moles of methane and LCV is the lower calorific value (800000 J/mol). The resultant product temperature is evaluated from energy balance: Tp ¼ Tr þ

Qin np c v p

ð7Þ

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where subscripts ‘p’ and ‘r’ denote products and reactants respectively. An iterative procedure has to be used, as the value of cv,p depends on Tp. The pressure after combustion is obtained from the ideal gas equation. Pp ¼

np Ru Tp Vtdc

ð8Þ

The pressures and temperatures during the subsequent expansion are solved in a manner identical to that of the compression process, as described by Eqs. (4) and (5) except for the fact that ‘n’ represents the number of product moles. The model calculations are performed using a MATLAB program which gives the indicated thermal efficiency and indicator (p-V) diagram as the outputs. The program can also be used to evaluate the parametric variations of the outputs while varying methane fraction, compression ratio and equivalence ratio. Table 2 shows various intake condition taken in this study. Table 2. Intake condition. Intake condition Methane fraction (x) Equivalence ratio (U) Compression ratio (R) Intake temperature (T) DEE fraction (1 − y)

Level 1 0.5 0.4 17 308 K 0.4

Level 2 0.8 0.5 19 373 K 0.5

Level 3 1.0 0.6 0.6

4 Validation The single zone model is validated with the experimental data and the result is shown in Fig. 1. The model shows good agreement with the experimental data. The single cylinder CI engine (AV1XL, 1900 rpm) is used for the experimental purpose. Biogas is used as primary fuel and DEE is used as secondary fuel. Biogas is induced via manifold and DEE is injected through manifold during suction stroke. Pressure sensor, charge amplifier, DAQ system and angle encoder are used to take pressure readings. Finally, pressure reading of experimental data is compared with the data obtained from the modelling. Inlet conditions of modelling and experiments are same (Intake conditions: methane fraction = 1, equivalence ratio = 0.6, intake temperature = 308 K, DEE fraction = 0.5, combustion ratio = 17). A variation of 4%–8% differences is noticed between modelling and experimental data.

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Fig. 1. Pressure data for validation

5 Results and Discussion The parameters such as Maximum cylinder Pressure, In-cylinder Temperature and Indicated Thermal Efficiency have been studied by varying engine intake conditions such as Intake Temperature, Equivalence ratio, DEE energy fraction, Compression Ratio. 5.1

Analysis of Maximum Cylinder Pressure with Methane Fraction

Figure 2(a) shows the variation of pressure with respect to methane fraction for various intake temperatures. This variation arises due to late combustion. When intake

Fig. 2. Effect of (a) Intake temp, (b) Equivalence ratio), (c) DEE energy, (d) Compression ratio on Max cylinder pressure

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temperature is increased, cylinder pressure decreases due to decrease in the density of intake air. The cylinder pressure is higher at low values of methane fraction due to high intake of DEE. Figure 2(b) depicts the variation of pressure with respect to methane fraction for various values of equivalence ratio. Increase in equivalence ratio increases the cylinder pressure as well as fuel content, which leads to an increase in combustion temperature. Figure 2(c) shows the variation of pressure with respect to methane fraction for various DEE energy fractions. Increase in cylinder pressure is observed due to reduction of biogas consumption. Figure 2(d) depicts the variation of pressure with respect to methane fraction for various values of compression ratio. Increase in compression ratio leads to an increase in maximum cylinder pressure. This trend is observed due to a rise in in-cylinder temperature. Henceforth, the maximum cylinder pressure increases. 5.2

Analysis of In-cylinder Temperature with Methane Fraction

Figure 3(a) depicts the variation of in-cylinder temperature with methane fraction for various intake temperature. When intake temperature increases, in-cylinder temperature increases. This is because In-cylinder temperature is directly proportional to intake temperature. Biogas has high self-ignition temperature, so at high temperature, better combustion takes place and thus an increase in combustion temperature is observed.

Fig. 3. Effect of (a) Intake temp, (b) Equivalence ratio, (c) DEE energy, (d) Compression ratio on In-cylinder temperature

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Figure 3(b) depicts the variation of in-cylinder temperature with methane fraction for various equivalence ratio. Increase in equivalence ratio increases the in-cylinder temperature due to higher fuel content. This leads to an increase in in-cylinder temperature at low values of methane fraction for high equivalence ratio. Figure 3(c) depicts the variation of in-cylinder temperature with respect to methane fraction for various DEE energy fractions. At high DEE fraction, in-cylinder temperature increases due to reduction in biogas intake. Figure 3(d) depicts the variation of temperature with respect to methane fraction for various compression ratio. On increasing the compression ratio, the in- cylinder temperature increases due to increase in In-cylinder pressure. 5.3

Analysis of Indicated Thermal Efficiency with Methane Fraction

Figure 4(a) depicts the variation of indicated thermal efficiency with methane fraction for various intake temperatures. Increase in intake temperature leads to poor air intake which results in lower indicated thermal efficiency. Figure 4(b) depicts the variation of indicated thermal efficiency with respect to methane fraction for various equivalence ratios. On increasing the equivalence ratio, indicated thermal efficiency decreases due to higher fuel content. Figure 4(c) depicts the variation of indicated thermal efficiency with respect to methane fraction for various DEE energy fraction. On increasing DEE energy fraction, indicated thermal efficiency increases due to high ignition energy of DEE. Figure 4(d) depicts the variation of temperature with respect to methane fraction for various compression ratio. On increasing the compression ratio, indicated thermal

Fig. 4. Effect of (a) Intake temp, (b) Equivalence ratio, (c) DEE energy, (d) Compression ratio on Indicated Thermal Efficiency.

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efficiency increases as pressure increases. Due to this, combustion temperature increases, resulting in proper combustion, thereby increasing efficiency.

6 Conclusion This study shows the effect of intake temperature, equivalence ratio, DEE energy fraction and compression ratio with methane fraction on maximum cylinder pressure, in-cylinder temperature and indicated thermal efficiency. A generalised trend is obtained that on increasing intake temperature, maximum cylinder pressure decreases as work done is less, but in-cylinder temperature and indicated thermal efficiency increases. Also, on increasing equivalence ratio, maximum cylinder pressure, incylinder temperature and indicated thermal efficiency increases. As DEE energy fraction increases, maximum cylinder pressure, in-cylinder temperature and indicated thermal efficiency increases. Finally, on increasing Compression ratio, maximum cylinder pressure, in-cylinder temperature and equivalence ration increases.

References 1. Mihic S (2004) Biogas fuel for internal combustion engines. Ann Fac Eng Hunedoara 2:179–190 2. Murugesan A, Umarani C, Subramanian R, Nedunchezhian N (2009) Bio-diesel as an alternative fuel for diesel engines - a review. Renew Sustain Energy Rev 13:653–662 3. Agarwal AK (2007) Biofuels (alcohols and biodiesel) applications as fuels for internal combustion engines. Prog Energy Combust Sci 33:233–271 4. Qian Y, Sun S, Ju D, Shan X, Lu X (2017) Review of the state-of-the-art of biogas combustion mechanisms and applications in internal combustion engines. Renew Sustain Energy Rev 69:50–58 5. Visconti P, Primiceri P, Strafella L, Carlucci AP, Ficarella A (2017) Morphological analysis of injected sprays of different bio-diesel fuels by using a common rail setup controlled by a programmable electronic system. Int J Automot Mech Eng 14:3849–3871 6. Saifuddin N, Refal H, Kumaran P (2017) Performance and emission characteristics of micro gas turbine engine fuelled with bioethanol-diesel-biodiesel blends. Int J Automot Mech Eng 14:4030–4049 7. Singh AP, Agarwal AK (2012) An experimental investigation of combustion, emissions and performance of a diesel fuelled HCCI engine, (No. 2012-28-0005). SAE Technical Paper 8. Feroskhan M, Ismail S (2017) A review on the purification and use of biogas in compression ignition engines. Int J Automot Mech Eng 14:4383–4400 9. Zhao F, Asmus TN, Assanis DN, Dec JE, Eng JA, Najt PM (2003) Homogeneous charge compression ignition (HCCI) engines, (No. PT-94). SAE Publication 10. Hussaini SY, Lahane S, Patil NG (2016) Analysis of performance and emission characteristics of a homogeneous charge compression ignition (HCCI) engine. Procedia Technol 25:854–861 11. Jaichandar S, Annamalai K (2016) Jatropha oil methyl ester as diesel engine fuel - an experimental investigation. Int J Automot Mech Eng 13:3248–3261 12. Komninos N, Rakopoulos C (2012) Modelling HCCI combustion of biofuels: a review. Renew Sustain Energy Rev 16:1588–1610

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13. Nayak S, Mishra P (2017) Emission from a dual fuel operated diesel engine fuelled with CalophyllumInophyllum biodiesel and producer gas. Int J Automot Mech Eng 14(1):3954– 3969 14. Bhaskar K, Sendilvelan S, Muthu V, Aravindraj S (2016) Performance and emission characteristics of compression ignition engine using methyl ester blends of jatropha and fish oil. J Mech Eng Sci 10:1984–1997 15. Bae C, Kim J (2017) Alternative fuels for internal combustion engines. Proc Combust Inst 36:3389–3413 16. Tippayawong N, Thanompongchart P (2010) Biogas quality upgrade by simultaneous removal of CO2 and H2S in a packed column reactor. Energy 35:4531–4535 17. Demirbas A (2004) Combustion characteristics of different biomass fuels. Prog Energy Combust Sci 30:219–230 18. Demirbas MF, Balat M, Balat H (2011) Biowastes-to-biofuels. Energy Convers Manag 52:1815–1828 19. Shukri MR, Rahman MM, Ramasamy D, Kadirgama K (2015) Artificial neural network optimization modelling on engine performance of diesel engine using biodiesel fuel. Int J Automot Mech Eng 11:2332–2347 20. Khalid A, Jaat N, Sapit A, Razali A, Manshoor B, Zaman I (2015) Performance and emissions characteristics of crude jatropha oil biodiesel blends in a diesel engine. Int J Automot Mech Eng 11:2447–2457 21. Azad AK, Rasul MG, Mofijur M, Bhuiya MMK, Mondal SK, Sattar MK (2015) Energy and waste management for petroleum refining effluents: a case study in Bangladesh. Int J Automot Mech Eng 11:2170–2187 22. Heywood JB (1988) Internal combustion engine fundamentals. Tata McGraw Hill

Studies on Pitting Corrosion of Pulsed Electrodeposited Nanocomposite Coating Chitrada Prasad(&), K. Srinivasa Rao, and K. Ramji Andhra University College of Engineering (A), Visakhapatnam 530003, A.P., India [email protected]

Abstract. Present work pertains to studies on the effect of pulsed current waveforms of rectangular and triangular in the formation of nickel metal matrix and Zirconium titanium oxide nanocomposite coating and its pitting corrosion behaviour. Zirconium titanium oxide nanoparticles are synthesized via sol-gel route and characterized by XRD and FESEM with EDAX. Pulsed electrodeposition was carried out using nickel electrolyte watts bath with suspended Zirconium titanium oxide nanoparticles on the mild steel substrate. Surface morphology of the coating was studied using scanning electron microscopy. Phase identification and particle size were determined using X-ray diffraction. Potentiodynamic polarization test was used for studying pitting corrosion behaviour of nanocomposite coating. The hardness of the coating was measured with Vickers hardness testing. The results have shown that the rectangular wave pulse at 50% duty cycle produced higher hardness and it may be due to the finer grain size of the deposited coating obtained with the rectangular waveform. Increased duty cycle variable from 10% to 50% in both the waveform enhanced the peak current density leading to higher hardness of the coating. The highest corrosion resistance was obtained with the triangular waveform with a relaxation time of 10% duty cycle at 10 Hz frequency. Hence present work established that a waveform and duty cycle of pulsed currents strongly influences the corrosion behaviour of nanocomposite coating. Keywords: Zirconium titanium oxide
Rectangular waveform Triangular wave form
Duty cycle
Nanocomposite coating
Corrosion resistance
Micro hardness




1 Introduction Mild steel is inexpensive and widely used in several engineering applications such as naval ship building and automobile industries. It is extremely vulnerable to corrosion due to its high chemical reactivity [1–3]. Pure Nickel and nickel alloy based inert metal matrix nanocomposite coatings have been used as protective and functional coatings in several applications for automotive and aerospace industries due to their beneficial mechanical and chemical properties [4–10]. In order to improve the utilizable properties and uniform particle distribution of nickel layers formed with pulsed current electrodeposition process has its potential applications [11, 12]. Such procedure led to the production of coating layers with ultrafine composite structure and characterized the © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 404–412, 2020. https://doi.org/10.1007/978-3-030-24314-2_49

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properties like corrosion resistance [4, 10, 13], micro hardness [10, 14–17]. Modified wave shapes with considerable wave form variables that increases the peak currents produces the nucleation growth and generates higher hardness values than with DC electrodeposition [17]. Conventional rectangular wave form promotes the good quality of deposition. Sufficient quantities of additives like surfactant CTAB (Cetyltrimethylammonium bromide) added to the electrolyte bath have been strongly used to promote the codeposition process [18]. Very less research work has been reported with pulsed current use of triangular waveform [15, 17, 19]. An electrodeposited nickel coating improves the useful level of hardness and corrosion resistance in industrially utilizing equipment. Investigations are carried out for mechanical and electrochemical properties of nickel metal matrix nanocomposite coatings like TiO2 [4, 10], Al2O3 [6, 14], SiC [15], ZrO2 [20], MoS2 [21]. By mutual influences of ZrO2 and TiO2 films via sol-gel dip coating process produce high protection efficiency and increase hardness and wear resistance properties [3, 22]. By consideration of Potentiodynamic polarization results in Electrodeposited coatings, Ecorr values shifted towards noble values, reduction of Icorr values yields the high thermal stability, high surface area and strong mechanical strength [10, 23]. In this paper, electrodeposition of Ni-50% ZrO2-TiO2 composites coating with rectangular and triangular shaped waveform pulsed current with relaxation time was reported. The resultant deposited nickel metal matrix nanocomposite layers on the mild steel substrate were investigated for phase identification, surface morphology, pitting corrosion and hardness. These studies are aimed at studying the effect of irregular waveform shape and duty cycle of pulsed current on the deposition of ceramic nanocomposites in metal matrix coatings and to improve the corrosion resistance and hardness of nanocomposite coatings.

2 Materials and Methods 2.1

Synthesis of ZrO2-TiO2 Nanocomposites

Nano structured 50% ZrO2-TiO2 powder was synthesized via sol gel route. Powder was prepared using titanium dioxide (TiO2) [avra-98%], ZrOCl2.8H2O [Himedia-99%] as the ZrO2 source, isopropanol [PA-CH3CH (OH) CH3, FINAR] used as reciprocal solvent, 0.1M hydrochloric acid (HCl [  35% Merck. Himedia]) as catalyst and Distilled water. All the chemicals were used without further purification. 50% ZrO2-TiO2 prepared as follows. Firstly (Sol1) 1.34M titanium dioxide solution was prepared by dissolving 1 gm of TiO2 precursors in 9.3 ml of isopropanol then mixed in 100 ml glass beaker through Sonicating for 3 min at room temperature. The solution was stirred for 2 h at 70 °C using magnetic stirrer with 550 rpm. Secondly (Sol 2) 0.8M Zirconium dioxide transparent hydrosol was prepared by adding of 2.6 gm of ZrOCl2.8H2O to 100 ml distilled water, sonicated for 2 min and stirred for 30 min at room temperature. Sol2 was added as droplets using 100 ml of burrete. Therefore 50% ZrO2-TiO2 mixed solution was formed. Subsequently 0.1M HCl, 0.1M NH4OH were added to the resultant solution made pH = 1.3 [24] to control the precipitation. Precipitate solution was magnetic stirred on hot plate magnetic stirrer for 2 h

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at 70 °C. Then the solid mixture was taken out and dried at 100 °C for 3 min. 50% ZrO2-TiO2 powder was obtained later on heat treatment for 3 h at 950 °C [22] in muffle furnace. Afterwards, the resultant Nano sized ZrO2-TiO2 composite powder was characterized by field emission scanning electron microscopy (FESEM - JOEL) with EDAX and X-ray diffraction (XRD). 2.2

Electrodeposition

Mild steel (25 mm  15 mm  3 mm), and a high purity (99.99%) nickel plate (40 mm  40 mm  3 mm) were used as cathode and anode respectively. Before the coating process cathode and anode materials were mechanically dry polished with emery papers series 800, 1/0, 2/0, 3/0, and 4/0. And then wet polished on cloth polishing machine felt with suspensions of alumina until their surfaces became smooth and mirror bright finish. Prior to plating the polished substrates ultrasonically cleaned for 3 min in water and rinsed with acetone, water sequentially for removal of impurities from the surface and dried [2, 3, 13, 25]. Deposition was done in freshly prepared aqueous nickel solution, containing materials and quantity listed in Table 1. Here anode size is greater than that of cathode to minimize the nickel anodic polarization problems particularly at lower duty cycles [14]. Coating conditions and parameters are described in Table 1. Anode is placed vertically 3 cm distance from the cathode in the nickel electrolyte solution. In electrodeposition process the composite coating was obtained at a constant current density 10 mA/cm2 with rectangular and triangular shape pulsed current with 10% and 50% duty cycle in two electrode cell. Coated samples sintered were at 700 °C. Triangular and rectangular wave shape pulsed current used in this process. Direct current was supplied to the pulse width modulator by GW INSTEK GPS-3030D d.c power supply unit. In the deposition boric acid was used as buffering agent. The pH of the electrolyte was 4.21 controlled by addition of 0.1M HCl and 0.1M NaOH. Cetyltrimethyl ammoniumbromide (CTAB) surfactant used as size and shape controllable agent. Coated mild steel with triangular and rectangular shape wave pulses at 10% and 50% duty cycle named as 10%T, 10%R, 50%T, and 50% R respectively. Table 1. Nickel Electrolyte bath composition and Electrodeposition Parameters. Materials NiSO4.H2O NiCl2.6H2O H3BO3 CTAB Polyethylene glycol 50% ZrO2-TiO2 Anode Cathode NaOH HCl

Quantity 260 g/l 60 g/l 40 g/l 0.5 g/l 1 ml/l 3 g/l Nickle Mild steel 0.1M 0.1M

Parameter Temperature Deposition time Frequency Wave form Duty cycle Voltage Current density Stirring speed Separation pH

Value Room temp. (30 °C) 15 min 10 Hz Rectangular and Triangular pulses 10%, 50% 250 mV 10 mA/cm2 250 rpm 3 cm 4.12

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Characterization

The surface morphology of the sol gel synthesized Zirconium titanium oxide powder was examined by using JOEL–FESEM with EDAX. Similarly surface of electrodeposited coatings was observed with SEM. Phase, crystalline size and growth was studied using Cu Ka radiation of X-ray diffraction (XRD) at 40 kV. The electro chemical corrosion studies were carried out in conventional three electrode cell GillAC ACM instrument 1130 electro chemical work station (US make) for 15 min at 3.5% NaCl environment at room temperature. Here AgCl as saturated calomel electrode, platinum as reference and counter electrode, and the deposited specimens are used as working electrode with exposed 1 cm2 area to the 3.5% NaCl corrosive media solution. The micro hardness of the Ni-50% ZrO2-TiO2 deposited coating was measured with Vickers micro hardness indenter by forcing diamond indenter having the Vickers pyramid geometry, using test load of 50 gf on the individual sample. The indentation dwell time of 10 s was used as follows ASTM E92-17 standard.

3 Results and Discussions 3.1

Surface Morphology

The surface morphology of ZrO2-TiO2 nanocomposites is shown in Fig. 1 and it reveals that all the particles are having uniform size and regular shape. Orthorhombic structure exists. No porous structures were appeared due to addition of 0.1M HCl. Hydrochloric acid advances the hydrolysis and condensation reactions in synthesis procedure.

Fig. 1. FESEM image of Zirconium titanium oxide powder particles

Fig. 3. X-ray diffraction of Zirconium titanium oxide powder Fig. 2. EDAX Spectrum of Zirconium titanium oxide powder particles

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Chemical compositions are found to be having EDX of prepared nanocomposites shown in Fig. 2. It shows that no sulfate and chloride ions are present. Surface morphology of uncoated (Fig. 4a) and Electrodeposited coating substrates under rectangular and triangular wave shape of 10% and 50% duty cycles are shown in Fig. 4(b, c, d, and e). Here a typical grain microstructure of embedded Zirconium titanium oxide composites in nickel coating was observed. The coatings shown that surface layer on the samples distributed homogenously without any agglomeration. Few micro pits were observed in Fig. 4c and e on coating made with rectangular pulse with relaxation time of 10% and 50% duty cycle. Electrodeposited nanoparticle size was decreased appreciably with triangular wave form. More uniform and smooth layer was observed in Fig. 4b. It was formed due to the gradual increasing and decreasing of peak current density which develops the formation of uniformly distributed new crystal nuclei in nickel nanocomposite coatings.

Fig. 4. SEM micrographs of uncoated and coated samples annealed at 700 °C

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XRD Measurements

X-ray diffraction patterns of sol gel synthesized Zirconium titanium oxide powder calcined at 950 °C were analyzed with Philips Xpert High score plus software package. Peaks and crystalline growth mechanism were shown in Fig. 3. Zirconium titanium oxide several peaks confirm with ref. 74-1504 and phase is strongly crystalline material with dominant (111) crystal plane at 2Ɵ = 30.21°, exhibiting Orthorhombic structure, space group p b c n, density 3.890 g/cm3. Anatase titanium oxide phase peak (200) was identified with ref. 71-1168 at 2Ɵ = 47.6°. Moreover, Rutile phases titanium oxide peak present at 2Ɵ = 26.8° with the crystalline plane of (111) compared with ref. 77044. It was in tetragonal structure and p42/mnm space group. It was led by sintering at higher temperature of 950 °C. ZrO2 peaks at 2Ɵ = 29.97°, 34.8°, 50.8° with (111), (002), (202) planes respectively having tetragonal structure, space group P42/nmc matched with ref. 71-1282. Average crystalline size was 35 nm calculated using Debye Scherer equation. X ray diffraction peaks of electrodeposited nickel with Zirconium titanium oxide coatings on mild steel with rectangular and triangular wave shape of 10% and 50% duty cycle at 10 Hz frequency and annealed at 700 °C are shown in Fig. 5. The interesting observations of deposits made with 10%T, 10%R, 50%T, 50%R exhibits several peaks revealed the existence of nickel and titanium oxide phase at 2Ɵ = 35.8°, 65.4° and some associated peaks are appeared due to rutile phase TiO2 (JCPDS card No. 21-1276) and ZrO2 phase at 2Ɵ = 33.4° (JCPDS card No: 01-0870712, 00-033-1483). Coatings grain sizes obtained from Debye Scherer equation are listed in Table 2. Diffraction of coated samples have not shown ferrous peaks compared to uncoated sample. Relatively finer size of grains was identified on coated samples made with the triangular wave at 10% and with rectangular wave at 50% duty cycle. This suggests that in electrodeposition, wave form and duty cycle influence strongly on grain size formation. 3.3

Vickers Hardness

Hardness of electrodeposited nickel metal matrix with Zirconium titanium oxide composite coating with two different wave shape and duty cycle, specimens annealed at 700 °C was calculated using Hall – Petch equation [17]. Obtained micro hardness values are given in Table 2. It leads to improvement of hardness values when compared to bare mild steel metal surface from 170.3 HV to 202.89 HV by the formation of nickel- Zirconium titanium oxide composite coating.

Table 2. Grain sizes and hardness values of coated substrates annealed at 700 °C Sample Blank 10%R 50%R 10%T 50%T

Grain size (nm) Hardness (HV) – 170.03 32.41 200.95 25.36 202.89 25.89 199.14 29.23 201.32

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In this rectangular and triangular wave shape pulse current at 50% duty cycle greatly increases the surface hardness of the mild steel sample. It was obtained due to the same average current density with higher surface concentration of nickel and Zirconium titanium oxide nanoparticles were formed by pulsed current with longest transition time.

Fig. 5. XRD patterns of Ni–Zirconium titanium oxide composites coating

3.4

Fig. 6. Potentiodynamic polarization curves of coated and uncoated steel at 3.5% NaCl environment. a. Bare Mild steel, b. 10%R, c. 50%R, d. 10%T, e. 50%T.

Electrochemical Measurements

Potentiodynamic Polarization Curves of the coated samples in 3.5% NaCl acidic solution was employed to test the pitting corrosion of electrodeposited nickel with Zirconium titanium oxide composite coating on mild steel substrate. The Potentiodynamic Polarization curves of coated and uncoated samples were shown in Fig. 6. Electrochemical test parameters are shown in Table 3. In this the corrosion current values are observed in the order of uncoated > 10%R > 50%R > 50%T > 10%T corresponding to −25 mv to +25 mv and Ecorr results also shifted towards noble positive values. The lower corrosion rate was recorded for Ni-50% ZrO2-TiO2 composite coating with triangular shape pulsed current at 10% and 50% duty cycle than rectangular shape pulsed current. In the pulsed electrodeposition at lower duty cycle (i.e. longer Toff) greatly favored the grain refinement of nickel metal matrix composite coating on mild steel substrate [26]. Improvement of pitting corrosion resistance of nanocomposite coating may be due to the presence of ceramic oxide particles embedded in metallic nickel matrix. At lower duty cycle the titanium nanoparticles gets adsorbed with metal ions on the substrate [11]. Oxide particles may act as immolating passives for the electrochemical reactions that cause corrosion. Therefore the protective

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ability of the coating is increased by the use of triangular shaped pulsed current at lower duty cycle in electrodeposition process. Table 3. Electrochemical parameters of the coated and uncoated mild steel from Tafel plots. Sample Uncoated Coated 10%R 50%R 10%T 50%T

ba mV 24.76 17.10 24.55 39.94 17.29

bc mV 26.19 21.11 20.24 30.99 19.08

Ecorr mv −700.53 −320.83 −328.52 57.43 21.99

Icorr mA/cm2 4.92E−02 1.24E−03 3.51E−04 3.25E−05 5.30E−05

Corr. rate mm/yr. 5.703E−01 1.433E−02 4.066E−03 3.750E−04 2.418E−02

LPR ohm/cm2 1.12E+02 3.32E+03 1.37E+04 2.35E+05 7.44E+04

4 Conclusion Zirconium titanium oxide powder was successfully synthesized in sol gel route and was characterized with XRD and SEM. Use of rectangular wave form at 50% duty cycle in electrodeposition of Ni-50% ZrO2-TiO2 nanocomposite coating resulted in nonuniform distribution of grains. However triangular wave form at lower duty cycle strongly influences the surface morphology of nanocomposite coating and resulted in more uniform formation of grains. Hence it can be concluded that use of triangular wave form irrespective of duty cycle has significantly improves the corrosion resistance of mild steel.

References 1. Tiwari SK, Sahu RK, Pramanick AK, Singh R (2011) Development of conversion coating on mild steel prior to sol gel nanostructured Al2O3 coating for enhancement of corrosion resistance. Surf Coat Technol 205(21–22):4960–4967 2. Khalaf MM, El-lateef HMA (2016) Corrosion protection of mild steel by coating with TiO2 thin films co-doped with NiO and ZrO2 in acidic chloride environments. Mater Chem Phys 177:250–265 3. Abd El-Lateef HM, Khalaf MM (2015) Corrosion resistance of ZrO2-TiO2 nanocomposite multilayer thin films coated on carbon steel in hydrochloric acid solution. Mater Charact 108:29–41 4. Birlik I et al (2016) Preparation and characterization of Ni–TiO2 nanocomposite coatings produced by electrodeposition technique. Front Mater 3(October):1–7 5. Tury B, Radnóczi GZ, Radnóczi G, Varsányi ML (2007) Microstructure properties of pulse plated Ni-Co alloy. Surf Coat Technol 202(2):331–335 6. Gül H, Kiliç F, Aslan S, Alp A, Akbulut H (2009) Characteristics of electro-co-deposited NiAl2O3 nano-particle reinforced metal matrix composite (MMC) coatings. Wear 267(5– 8):976–990 7. Zhang HJ, Zhou YB, Sun JF (2013) Preparation and oxidation behaviour of electrodeposited Ni-CeO2 nanocomposite coatings. Trans Nonferrous Met Soc China (English Ed) 23 (7):2011–2020

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8. Benea L, Danaila E (2016) Nucleation and growth mechanism of Ni/TiO2 nanoparticles electro-codeposition. J Electrochem Soc 163(13):D655–D662 9. Guo C, Zuo Y, Zhao X, Zhao J, Xiong J (2008) Effects of surfactants on electrodeposition of nickel-carbon nanotubes composite coatings. Surf Coat Technol 202(14):3385–3390 10. Baghery P, Farzam M, Mousavi AB, Hosseini M (2010) Ni-TiO2 nanocomposite coating with high resistance to corrosion and wear. Surf Coat Technol 204(23):3804–3810 11. Wong KP, Chan KC, Yue TM (2001) Study of hardness and grain size in pulse current electroforming of nickel using different shaped waveforms. J Appl Electrochem 31(1):25–34 12. Hu F, Chan KC (2006) Equivalent circuit modelling of Ni-SiC electrodeposition under ramp-up and ramp-down waveforms. Mater Chem Phys 99(2–3):424–430 13. Cheraghi H, Shahmiri M, Sadeghian Z (2012) Corrosion behavior of TiO2-NiO nanocomposite thin films on AISI 316L stainless steel prepared by sol-gel method. Thin Solid Films 522:289–296 14. Gül H, Akbulut H, Alp A (2012) Effect of reciprocating sliding speed on the tribological performance of nano Al2O3 reinforced Ni MMCs produced by DC and pulse electro codepositions. In: Proceedings of the 15th European conference on composite materials, ECCM 2012, Venice, June, pp 24–28 15. Hu F, Chan KC (2004) Electrocodeposition behavior of Ni-SiC composite under different shaped waveforms. Appl Surf Sci 233(1–4):163–171 16. Wasekar NP, Haridoss P, Seshadri SK, Sundararajan G (2016) Influence of mode of electrodeposition, current density and saccharin on the microstructure and hardness of electrodeposited nanocrystalline nickel coatings. Surf Coat Technol 291:130–140 17. Molina J, Hoyos B (2009) Effect of pulse current waveforms in monocrystalline electrodeposition. J Phys Chem B 76(157):153–161 18. Maharana HS, Basu A (2017) Evolution and structure-property correlation of CTAB assisted high hardness electrodeposited Cu-ZrO2 nano-cone arrays. Surf Coat Technol 310:148–156 19. Yang KS, Jiang Z, Chung JS (2003) Electrophoretically Al-coated wire mesh and its application for catalytic oxidation of 1, 2-dichlorobenzene. Surf Coat Technol 168(2– 3):103–110 20. Zhang W, Ji G, Bu A, Zhang B (2015) Corrosion and tribological behavior of ZrO2 films prepared on stainless steel surface by the sol-gel method. ACS Appl Mater Interfaces 7 (51):28264–28272 21. Huang Z, Xiong D (2008) MoS2 coated with Al2O3 for Ni-MoS2/Al2O3 composite coatings by pulse electrodeposition. Surf Coat Technol 202(14):3208–3214 22. Bu A et al (2017) Corrosion behavior of ZrO2–TiO2 nanocomposite thin films coating on stainless steel through sol-gel method. J Sol-Gel Sci Technol 81(3):633–638 23. Vathsala K, Venkatesha TV (2011) Zn-ZrO2 nanocomposite coatings: elecrodeposition and evaluation of corrosion resistance. Appl Surf Sci 257(21):8929–8936 24. Mohammadi MR, Fray DJ (2011) Synthesis and characterisation of nanosized TiO2-ZrO2 binary system prepared by an aqueous sol-gel process: Physical and sensing properties. Sens Actuators B Chem 155(2):568–576 25. Vlasa A, Varvara S, Pop A, Bulea C, Muresan LM (2010) Electrodeposited Zn-TiO2 nanocomposite coatings and their corrosion behavior. J Appl Electrochem 40(8):1519–1527 26. Chandrasekar MS, Pushpavanam M (2008) Pulse and pulse reverse plating-Conceptual, advantages and applications. Electrochim Acta 53(8):3313–3322

Effect of Condenser Coil Profile and Subcooling on Performance of Vapour Compression Refrigeration System Sreedhar Vullloju1(&), K. Krishna Reddy2, and Madhu Kumar Patil1 1

Department of Mechanical Engineering, Vardhaman College of Engineering, Kacharam, Shamshabad, Hyderabad 501218, India [email protected] 2 Department of Mechanical Engineering, Brindavan Institute of Science & Technology, Kurnool, India

Abstract. The objective of paper is to analyze the performance of the Vapour Compression Refrigeration (VCR) system by change the profile of condenser coil and with sub cooling system. An experiment is conducted on VCR system with different condenser profiles and also with sub cooling system at steady state evaporator temperature and calculated mass flow rate of refrigerant, refrigeration effect, works done and COP. From results of experiment, Refrigeration Effect in case of helical coiled condenser is increased by 3 kJ compared to straight coiled condenser and Refrigeration Effect in case of helical coiled condenser with subcooling is increased by 15.3% compared to helical coiled condenser without sub-cooling. COP in case of helical coiled condenser is increased by 20% compared to straight coiled condenser and COP in case of helical coiled condenser with sub-cooling is increased by 9.7% compared to helical coiled condenser without sub-cooling. Keywords: Vapor compression refrigeration system
Straight coil and helical coil condenser
Sub cooling
COP

Symbol VCR system COP RE QC moref Cpref ΔTref V qref d h1 h2 h3 h4

Description Vapour Compression Refrigeration System Co-efficient of Performance Refrigeration Effect KJ Refrigeration Capacity KJ Mass flow rate of Refrigerant Kg/sec. Specific heat of refrigerant = 1.467 kJ/Kg K Temperature difference in Evaporator °c Flow velocity of the refrigerant in the system is m/s Density of Refrigerant m3/Kg Diameter of coil condenser in mm Enthalpy of refrigerant before compression Enthalpy of refrigerant after compression Enthalpy of refrigerant before throttling Enthalpy of refrigerant after throttling

© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 413–423, 2020. https://doi.org/10.1007/978-3-030-24314-2_50

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1 Introduction Refrigeration can be defined as the process to attain and keep an enclosed space at a temperature below than its surrounding temperature. This is achieved by continuous removal of heat from the enclosed space where as the temperature is lower than that of the surrounding temperature with the help of external work. Refrigerator is working based on Clausius statement of thermodynamics. Refrigerators are used in domestic and in industrial applications to preserve products for long duration without damage by providing low temperature. Refrigerants used and about the types of refrigeration systems are classified and type of refrigerant should be used for what kind of refrigeration processes are given in a detailed manner by Venkatarathnam and Murthy [1]. Ricardo Costa and Garcia [2] applying design of experiments to a compression refrigeration cycle. To know what variables effect on efficiency of a compression refrigeration cycle, designed experiments are conducted and analyze the data. A quadratic polynomial model is fitted to COP and variable arrangements to maximum cycle efficiency observed. ESFuelCell2013-18243 [3] focused on the description of the design and the theory behind the system, design and construction of a solar thermal refrigeration system for Patna, India. Designing of an evaporator using 16 pipes and manufacturing a cold chamber, the outline of the refrigeration system working using solar collectors which can reduce the power consumption. The vapour compression system performs using main components like evaporator, condenser, capillary tube and heat exchanger if needed and compressor Arora [4]. Basic components of the refrigeration system and design of the evaporator coil, condenser, and capillary are to be selected according to the pressure, temperature drops and requirement of the system, capacity of the system. Prasad [5]. Nussbaum [6] studied all relevant type of condensers and investigate the effects of advantages and disadvantages of various condensers on performance of refrigeration systems. Air cooled condensers with small capacity i.e. 5 to 7.5 Hp is commonly used as indoor condensing unit. Condenser is selected based on heat rejection and mass flow rate of refrigerant. Pavkovic [7] studied properties and applications of refrigerants. The refrigerants are given some class numbers which are meant to reduce the emissions of the hydrocarbons and properties like physical, chemical properties are discussed. Saidulu and Guru Dattatreya [8] studied the performance of a domestic refrigerator by placing shell and tube type heat exchanger after the condenser to extract more amount of heat by sub cooling process by using ammonia as an external cooling media, to increase the performance of the system. Vasanthi and Maruthi Prasad Yadav [9] to enhance the performance of the domestic refrigerator by flooding the evaporator with liquid refrigerant. To attain this objective, a low pressure vessel is designed, developed, fabricated and incorporated between evaporator and compressor. The performance of refrigerator is calculated with and without low pressure receiver and analysis is done using R134a and R401c refrigerants. Maruthi Prasad Yadav, Rajendra Prasad, Veeresh [10] studied the performance of refrigerator with liquid line suction line heat exchanger for different lengths of heat exchanger by using R134a and R404a as refrigerants and analyzed with various lengths of liquid line- suction line heat exchanger.

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It is found from the literature review that most of the research carried out is to increase the COP of the system by change refrigerants, the design of condenser, evaporator and sub cooling method. The paper deals with improve the COP of VCR system by change the condenser coil profiles and sub cooling system.

2 Specifications of VCR System 2.1

Condenser

Condenser acts as heat exchangers to transfer heat from one fluid to another fluid. The selection of condenser is very important as it effect the performance of refrigeration system. It is arranged between compressor and expansion valve. 2.1.1 Straight Coiled Condenser Naturally cooled condensers (straight coil condenser) with fins: 1. 2. 3. 4. 5.

Diameter of the coil = 50 mm Length of the coil = 25 feet Material of the coil used is mild steel Number of turn = 16 Fin thickness = 3 mm

2.1.2 1. 2. 3. 4.

Helical Coil Condenser

Diameter of the coil = 50 mm Length of the coil = 25 feet Material used is copper No of helical turns = 9 (Figs. 1 and 2).

Fig. 1. Naturally cooled condenser

Fig. 2. Helical coil condenser

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Evaporator

• Evaporator is chosen with dimensions 35  14  22 cm for 160 L of capacity. • Insulation: polyol, cyclopentane and isocyanate PUF. • Material chosen for the evaporator coil is copper tube and the evaporator compartment is made of cold rolled carbon steel. • Inner cabinet: Aluminum. 2.3 (1) (2) (3) (4) (5) (6)

Compressor full size refrigerator: compressor probably about 1/8–1/6 hp full size refrigerator freezer combo: compressor typically 1/6–1/3 hp chest freezer: compressor typically 1/4–1/2 hp full size freezer: compressor typically 1/5–1/3 hp mini fridge: compressor range 1/20–1/8 hp mini freezer: compressor range 1/10–1/6 hp

We used a 1/10th hp hermetically sealed compressor with model number THK1330YCF with net weight of 7 kg and voltage 230 V – 50 Hz manufactured by TECUMSEH (Figs. 3 and 4).

Fig. 3. Evaporator

2.4

Fig. 4. Compressor

Capillary Tube

• Capillary tube is one of the most commonly used throttling devices in the refrigeration and the air conditioning systems. The capillary tube is a tube which has very small l diameter as to decrease the pressure of refrigerant. • The diameter of the capillary tube is 36 mm. • Pressure gauges used are of max 500 psi and a 250 psi to note down the suction and discharge pressure from compressor. • Filter is used to remove of the moisture and other gases from the condenser coil.

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• Refrigerant used is 134a with brazing torch of isobutene to join the parts and a filler material is used (Fig. 5). 2.5

Refrigerant

Refrigerant used is 134a (Tetra fluro ethane) Boiling point temperature @1 atm (K) = 247 Freezing point (K) = 176.55 Critical temperature (K) = 374.25 Critical bar pressure = 40. Fig. 5. Refrigerant R134a

3 Experimental Setup Experiments are conducted on VCR system to note required readings manually as seen figure given below. Measure the temperatures and pressures using a digital thermometer and pressure gauges in below mentioned locations (Figs. 6, 7 and 8). • • • • •

Before Compressor Before Condenser After Condenser After Capillary tube Inside Freezer/Evaporator The experiment includes three different types of arrangements as follows (Table 1). Table 1. Material used for condenser and dimensions Types of condenser Straight coiled condenser Helical coiled without sub cooling Helical coiled with sub cooling

Length 7.625 m 7.625 m 7.625 m

Tube diameter 0.05 m 0.05 m 0.05 m

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Fig. 6. Straight coiled refrigeration system

Fig. 7. Helical coiled type system

Fig. 8. Helical coiled refrigeration system under sub cooling

4 Analysis of Vapour Compression Refrigeration System The performance of vapour compression refrigeration system is measured by the term Co-efficient of Performance (COP). COP is defined as the ratio between refrigeration effect and work supplied to the system (Fig. 9).

Fig. 9. P-h diagram

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COP ¼ T1 =T2  T 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Net refrigeration effect = h1 − h4 kJ/Kg Mass flow rate to obtain one TR, mr ef = 210/ h1 − h4 = kg/min. Work of compression = h2 − h1 = kJ/kg Heat equivalent of Work of compression per TR = mr ef  (h2 − h1) kJ/min Theoretical power of compressor = mr ef  (h2 − h1)/60 kW Heat rejected in condenser = h2 − h3 kJ/kg Heat rejection per TR = (210/ h1 − h4)  (h2 − h3) = kJ/min Heat rejection ratio = h2 − h3/h1 − h4 Compression pressure ratio = discharge pressure/ suction pressure = P2/P1 Coefficient of performance (COP) = h1 − h4/h2 − h1 QC = moref  cp ref  ΔTref (ΔTref = temperature drop) (Cp ref = specific heat of refrigerant = 1.467 kJ/KG K) QC = Tons of refrigeration capacity 3.5 kJ qref = 1171 kg/m3 from refrigerant properties chart ΔTref = 40 °C QC = moref x cp ref  ΔTref V = flow velocity of the refrigerant in the system is 3 m/s (Taken from the research and ASHRAE data book) moref = qref  P/4  d2  V.

5 Results and Discussions An experiment is conducted on VCR system with straight coiled, helical coiled condenser and helical coiled condenser with sub cooling. Then required parameters are noted and tabulated in the below Table 2.

Table 2. Straight coiled, helical coiled condenser and helical coiled condenser with sub cooling Parameters Compressor discharge temp t2 (°C) Condensing temp t3 (°C) Evaporator temperature t1 (°C) Compressor suction pressure p1 (bar) Compressor discharge pressure p2 (bar) Condenser pressure p3 (bar)

Straight coiled condenser 46 43 4 3.5

Helical coiled condenser 50 43 3.5 1.37

Helical coiled condenser with sub cooling 46 40 1.6 1.6

15.5

18

12.3

15.5

18

12.3 (continued)

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Parameters Evaporator pressure p4 (bar) Result table Enthalpy h1 (kJ/kg) Enthalpy h2(kJ/kg) Enthalpy h3(kJ/kg) Enthalpy h4 (kJ/kg)

Straight coiled condenser 3.5

Helical coiled condenser 1.37

Helical coiled condenser with sub cooling 1.6

395 422 268 268

397 423 270 270

398 422 247.6 247.6

28 Work Done in KJ/Kg

27 27 26 26 25 24 24 23 22 Srtaight Coiled

Helical Coiled (HC)

HC with Sub cooling

Fig. 10. Work done vs different coiled condensers & sub-cooling

NET REFRIGERATION EFFECT kJ

From the above Fig. 10, it is observed that work done on compressor with straight coiled condenser is 27 kJ/kg, helical coil without sub-cooling is 26 kJ/Kg and helical coil with sub-cooling is 24 kJ/Kg as the percentage decrease in compressor work is 12.5% by helical coil with sub-cooling compared with straight coil. Thus it reduces load on compressor and increases the COP of the system.

200 150 150

127

130

100 50 0

Straght Coiled

Helical Coiled(HC)

HC with Sub cooling

Fig. 11. Refrigeration effect vs different coiled condensers and sub-cooling

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From the above Fig. 11, it is observed that Refrigeration Effect in straight coiled condenser is 127 kJ but in case of helical coiled condenser without sub-cooling is 130 kJ and with sub-cooling is 150 kJ. Refrigeration Effect in case of helical coiled condenser is increased by 3 kJ compared to straight coiled condenser and Refrigeration Effect in case of helical coiled condenser with sub-cooling is increased by 15.3% compared to helical coiled condenser without sub-cooling.

COEFFICIENT OF PERFORMANCE

8 6.2

6 4

5.65 4.7

2 0

Straight Coiled

Helical Coiled (HC)

HC with Sub cooling

Fig. 12. COP vs different coiled condensers and sub-cooling

HEAT REJECTION RATIO

From the above Fig. 12, it is observed that COP in straight coiled condenser is 4.7 but in case of helical coiled condenser without sub-cooling is 5.65 and with sub-cooling is 6.2. COP in case of helical coiled condenser is increased by 20% compared to straight coiled condenser and COP in case of helical coiled condenser with sub-cooling is increased by 9.7% compared to helical coiled condenser without sub-cooling.

1.22

1.21

1.2 1.17

1.18

1.16

1.16 1.14 1.12

Straight Coiled

Helical Coiled(HC)

HC with Sub cooling

Fig. 13. Heat rejection ratio vs different coiled condensers and sub-cooling

From the above Fig. 13, it is observed that heat rejection ratio in straight coiled condenser is 1.21 but in case of helical coiled condenser without sub-cooling is 1.17. and with sub-cooling is 1.16. Heat rejection ratio in case of helical coiled condenser is decreased by 3.3% compared to straight coiled condenser. Heat rejection ratio in case of helical coiled condenser is decreased by 4.1% compared to straight coiled condenser.

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6 Conclusions The experiment is conducted on Vapour Compression Refrigeration System with different types of condensers and with sub cooling system and the following statements are concluded. • Work done on compression straight coiled condenser is 27 kJ/Kg and helical coil with sub cooling is 24 kJ/Kg, the percentage decrease is in compressor work as 12.5% thus it reduces load on the system. • The refrigerant effect for straight coiled condenser is 127 kJ and for the helical coil with sub-cooling is increased to 150 kJ and in terms of percentage 18.1% of refrigeration effect increases by sub cooling system compared to without sub cooling system. • COP in straight coiled condenser is 4.7 but in case of helical coiled condenser without sub-cooling is 5.65. and with sub-cooling is 6.2. COP in case of helical coiled condenser is increased by 20% compared to straight coiled condenser and COP in case of helical coiled condenser with sub-cooling is increased by 9.7% compared to helical coiled condenser without sub-cooling. • Heat rejection ratio in case of helical coiled condenser is decreased by 3.3% compared to straight coiled condenser. From the statements is it concluded that the performance of helical coil condenser of vapor compression refrigeration system with sub more cooling is more and it is preferred compared to other two types of condenser without sub-cooling.

References 1. Venkatarathnam G, Murthy SS (2012) Refrigerants for vapour compression refrigeration system. Resonance 17(2):139–162 2. Ricardo Costa M, Garcia J (2015) Applying design of experiments to a compression refrigeration cycle. Cogent Eng 2:992216 3. ES-fuelcell, design and construction of a solar thermal refrigeration system for Patna, India (2013) Minneapolis, Minnesota, USA 4. Arora CP, Refrigeration and air conditioning 5. Prasad M, Refrigeration and air conditioning 6. Nussbaum OJ (2009) Condensers, services application manual SAM chapter 620-32A section 5B 7. Pavkovic B (2013) Properties and air-conditioning applications. REHVA J. Faculty of Engineering in Rijeka, Croatia 8. Saidulu E, Guru Dattatreya GS (2015) Experimental investigation on domestic refrigerator by shell and tube exchanger after the condenser using sub cooling of refrigerating fluid. IOSR-JMCE 12(2):63–67 e-ISSN 2278-1684, p-ISSN 2320-334X 9. Vasanthi R, Maruthi Prasad Yadav G (2015) Experimental analysis of vapour compression refrigeration system for optimum performance with low pressure receiver. IJSRM. ISSN 2231-3418

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10. Maruthi Prasad Yadav G, Rajendra Prasad P, Veeresh G, Experimental analysis of vapour compression refrigeration system with liquid line suction line heat exchanger by using R134a and R404a 11. ASHRAE guide and data book (1965) Fundamentals and equipment 12. ASHRAE guide and data book (1977) Fundamentals and equipment 13. Dasthagiri P, Rangamma H, Maruthi Prasad Yadav G (2015) Fabrication and analysis of refrigerator cum chilled water dispenser. IJSRT. ISSN 2320-3927 14. ASHRAE, guide and data book (1978) Product directory applications 15. Ravindra D (2001) Air cooled condensers; types, rating design, construction, installation and maintenance. ISHRAE 16. Momin GG, Tupe SB, Parate SA, Yewale OG, Thite AP (2016) COP enhancement of domestic refrigerator by sub cooling and superheating using shell & tube type heat exchanger 17. Sunny S, Jayesh S (2015) To improve cop of domestic refrigerator with the help of water cooling condenser. IJIRSET 4(3). ISSN 2319-8753

Criteria for Drop-in Replacement of Existing Refrigerant with an Alternative Refrigerant Srinivas Pendyala(&) and R. Prattipati GITAM (Deemed to be University), Hyderabad, India [email protected]

Abstract. In the present work, criteria for substituting working refrigerant with alternative refrigerant is given through R134a being replaced with ternary mixtures of R134a/hydrocarbons. Thermodynamic properties were compared theoretically using REFPROP software for five refrigerant mixtures. Investigations have been carried out for different operating temperatures in the range of 50 °C to −20 °C. The criterion is established by analyzing the thermophysical properties of refrigerant mixtures and comparing saturation pressures. The procedure to select a drop-in replacement with maximum COP is discussed against comparison of volumetric cooling capacity of the ternary mixtures. Keywords: Ternary mixture
Coefficient of performance (COP) Drop-in replacement
Alternative refrigerants




Nomenclature: ODP Ozone Depletion Potential GWP Global Warming Potential CFCs Chlorofluorocarbons HC Hydrocarbon RE Refrigeration Effect HC mixtue 50%R290/50%R600a Mixture-1 47.5%R290/47.5%R600a/5%R134a Mixture-2 42.5%R290/42.5%R600a/15%R134a Mixture-3 37.5%R290/37.5%R600a/25%R134a Mixture-4 32.5%R290/32.5%R600a/35%R134a Mixture-5 27.5%R290/27.5%R600a/45%R134a

1 Introduction The role of CFCs in the process of ozone depletion is now widely accepted and should be discontinued in spite of its exceptional properties. The search for environmentfriendly refrigerant replacing ozone depleting substances like R12 led to usage of R134a. The performance parameters of R134a are as good as with that of R12. R134a has zero ODP but considerable GWP of 1300 [1, 2]. To replace R134a, HC refrigerants can be considered due to their negligible GWP [3, 4]. They have good thermodynamic properties and locally available at low cost. © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 424–431, 2020. https://doi.org/10.1007/978-3-030-24314-2_51

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Agarwal [5] has proposed a substitute for R12 with R152a, R124/R22 and mixtures of R600a and R290. Pendyala et al. [6] investigated drop in substitute of R134a with HC mixtures and calculated the performance parameters like compressor work, coefficient of performance and refrigeration effect are evaluated at various operating conditions and compared with R134a. Their results show that COP of HC mixture is superior to R134a. Ravikumar and Mohan Lal [7] have conducted experiments on a refrigerator to substitute R12 with R134a. R134a is not compatible with mineral oil. To improve the miscibility, HC mixture was added to the R134a with the mass fraction of 9% and 91% respectively. Sattar et al. [8] examined the substitutes for R134a with a zeotropic mixture of R600/R290/R600a. They concluded that energy input to the compressor using zeotropic mixture decreased by 3% compared with R134a. Colbourne and Ritter [9] presented the guidelines and various safety aspects of flammable HC refrigerants. Saravanakumar and Selladurai [10] studied the performance of a refrigerator to replace R134a using 55% R600a/45%R290. The results showed that COP of the 55% R600a/45%R290 was better than R134a. Yu and Teng [11] evaluated experimentally drop-in replacement for R134a using R600a and R290. The experiments showed that optimum mass was obtained at 40% of that of R134a. From previous studies, it was examined that hydrocarbons mixtures or mixtures of HC/HFC can be used to substitute R134a. HCs are flammable which restrict the use of hydrocarbons for commercial purpose. However, small capacity systems like domestic refrigerator requires charge quantity less than 0.15 kg. A system with refrigerant charge quantity is less than 0.15 kg can be installed any size of the room [5]. In order to use drop-in replacement for the existing system criteria for selecting alternative refrigerant need to be established. In the present work, different masses of R600a/R290/R134a were considered to establish a procedure for replacing R134a.

2 Methodology for the Selection of Ternary Mixture Most of the refrigerators in India are operating with R134a as refrigerant. One of the expensive components of a refrigeration system is the compressor. It would be economical to replace refrigerant instead of changing the compressor or any other hardware of the system. In order to check the drop-in replacement of alternative refrigerant, its saturation properties should match with the base refrigerant. This would not necessitate any modification of compressor. Isobutane (R600a) and Propane (R290) mixture is most commonly available HC refrigerant. However, due to flammability issues, this cannot be used as a refrigerant on its own. The saturation pressure of HC mixture (50%R600a/50%R290) and R134a matches closely [12]. Hence, any combination of R600a/R290/R134a can have saturation pressures close to R134a. For making a ternary mixture, Isobutane, Propane and R134a are selected. For analysis, mass fraction of R134a is varied from 5% to 45% to bring GWP down and the remaining HC mixture is divided equally to reduce flammability issues. Coefficient of performance (COP) is another important parameter to be considered for selecting alternative refrigerants. COP represents energy consumption for a given cooling capacity. To find best alternative refrigerant, calculations were performed at 40 °C and −20 °C of condenser and evaporator temperatures respectively. Properties

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of the refrigerant are taken from REFPROP software [13]. The performance parameters of the proposed ternary mixtures are shown in Table 1. Table 1. Performance of the R134a and ternary mixtures. Refrigerant

RE in kJ/kg

R134a HC mixture Mixture-1 Mixture-2 Mixture-3 Mixture-4 Mixture-5

130.2 266.7 261.7 249.1 233.4 215.4 195.6

Compressor work, kJ/kg 56.4 97 93.4 85.6 78.7 73.2 68.6

COP 2.3 2.74 2.8 2.91 2.96 2.94 2.84

Specific volume, m3/kg 0.1474 0.3343 0.3187 0.2876 0.2565 0.2257 0.1954

3 Results and Discussion Figure 1 shows the variation of vapor pressure with saturation temperature for R134a and selected ternary mixtures with R134a. The graph shows that saturation pressures from mixture-1 to mixture-5 are vary at higher temperatures from R134a. However, as the compressor operates at lower temperature, the saturation pressure is equivalent to that of R134a.

Fig. 1. Effect of saturation temperature with the vapor pressure

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To get maximum performance from a VCR system, quantity of refrigerant charge is one of the influencing factors which affect the COP. Vapor density is an important physical quantity which influences the charge quantity. The variation of Vapor density is plotted in Fig. 2. It shows that, vapor densities of the ternary mixtures are 24.57% to 61.3% lower than R134a. Thus the mass flow rate of the ternary mixtures is lower than R134a due to its lower density when R134a R compressors are used. Any open system component work done is a function of  vdp, where v is the specific volume of the refrigerant entering into the compressor and dp is the pressure ratio across the compressor. Specific volume of the refrigerant influences the work of compression. The Table 1 depicts that variation of specific volume for the selected mixtures and R134a. It shows that specific volume of the ternary mixtures increases from mixture-5 to mixture1 due to increasing mass fraction HCs in the ternary mixture. The specific volume of the ternary mixtures is more than that of R134a.

Fig. 2. Effect of vapor density

Refrigerants with high latent heat (hfg ) can absorb more cooling load for the same flow rate. The Fig. 3 confirms that high latent heat values of the proposed mixtures increases from mixture-5 to mixture-1 by 34% to 76%. This is due to increasing mass quantity of hydrocarbons in the ternary mixture. As a result, there is a chance for the less mass flow of ternary mixtures for the same cooling load compared with R134a. Viscosity of the refrigerant influences the capillary length. Pressure loss decreases with the decrease of viscosity. The Fig. 4 shows that variation of liquid viscosity at different saturation temperatures. It is observed that viscosities of the ternary mixtures are 40% to 47% lower than R134a. Thus, for ternary mixtures, to have the same pressure drop length the capillary length needs to be increased.

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Fig. 3. Effect of latent heat

Fig. 4. Effect of liquid viscosity

In vapor compression system, slight superheating is essential to safeguard the compressor. Specific heat of the vapor controls the degree of superheat. Therefore refrigerant should have more vapor specific heat to have less superheat. This would

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reduce the suction volume and compressor work. The Fig. 5 It is observed that the specific heat of the ternary mixture is 60% higher than R134a. Hence the degree of superheating would be lesser for selected refrigerants as compared to R134a resulting in better performance.

Fig. 5. Effect of vapor specific heat

From the results, it shows that with the increasing HC quantity in the ternary mixture (R290/R600a/R134) specific volume and latent heat values are increases from mixture-5 to mixture-1. Results show that cooling capacity of the ternary mixtures is superior to R134a. However, it consumes more power due to the increase in specific volume. The COP started increasing with the increasing percentage HCs and reaches the maximum value at 37.5%R290/37.5%R600a/25%R134a (mixture-3) and then started decreasing with further increase in HC mixture mass fraction. Volumetric Cooling Capacity (VCC) is another parameter to check for the drop-in replacement. Volumetric Cooling Capacity (VCC) represents amount of heat removed for a given volume of compressor VCC ¼

mr  RE RE ¼ : t V_

ð1Þ

Where mr is the refrigerant flow rate in kg/s, RE is the refrigeration effect in kJ/kg, V_ is the volume flow rate and t is the specific volume at the suction to the compressor. The Fig. 6 shows that variation of VCC of the ternary mixtures and R134a. VCC for mixture-3 is very close than that of R134a (deviating 2.9% only). Therefore, mixture-3 is absorbing same amount of cooling load without changing the compressor of R134a.

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Fig. 6. Comparison of Volumetric Cooling Capacity

4 Conclusions This study presents the procedure for drop-in replacement of new refrigerants in the existing system. Ternary mixtures of R134a and HC mixtures are considered to substitute pure R134a. The following were the conclusions from the obtained results. • Saturation pressures of R134a and HCs match closely. Therefore ternary mixtures can be considered as direct substitutes for R134a. • Among the selected ternary mixtures, mixture-3 has better COP. • Specific work done for the mixture-3 is more than that of R134a. However, energy consumption can be minimized by optimizing the capillary length. • The mass fraction of R134a in the ternary mixture has been reduced to 25% thus reducing the global warming associated with the refrigerant. • VCC of mixture-3 is the closest match for compressor operating with R134a as refrigerant. • Systems with HC refrigerant charge quantity less than 0.15 kg can be installed any size of the room.

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References 1. El-Sayed AR, El Morsi M, Mahmoud NA (2018) Review of the potential replacements of HCFC/HFCs using environment-friendly refrigerants. Int J Air-Conditioning Refrig 26 (3):1830002-1–1830002-24 2. Srinivas P, Narayana Rao K, SitaRama Raju AV (2010) Optimization of visi cooler with the low global warming potential refrigerant mixture as an alternative to R134a and HC blend. Int J Glob Warming 2:316–329 3. Havelsky V (2000) Investigation of refrigerating system with R12 refrigerant replacements. Appl Therm Eng 20:133–140 4. Lee YS, Su CC (2002) Experimental studies of isobutane (R600a) as the refrigerant in domestic refrigeration system. Appl Therm Eng 22:507–519 5. Agarwal RS (1996) Comparative study of hydrocarbon mixtures & MP-39 as refrigerants to retrofit CFC-12 based domestic refrigerator freezers. In: Proceedings of the international refrigeration conference at Purdue, 23–26 July 1996. Purdue University, Wet Lafayette, pp 471–476 6. Pendyala S, Prattipati R, Raju AVSR (2017) Optimization process of a visi-cooler using ternary mixtures of R134a and hydrocarbons. Int J Air-Conditioning Refrig 25(2):17500191–1750019-10 7. Ravikumar TS, Mohan Lal D (2009) On-road performance analysis of R134a/R600a/R290 refrigerant mixture in an automobile air conditioning system with mineral oil as lubricant. Energy Convers Manag 50:1891–1901 8. Sattar MA, Saidur R, Masjuki HH (2007) Performance investigation of domestic refrigerator using pure hydrocarbons and blends of hydrocarbons as refrigerants. Proc World Acad Sci Eng Technol 23:223–228 9. Colbourne D, Ritter TJ (1998) Hydrocarbon refrigerant safety: standards and quantitative risk assessments. In: IIR conference on emerging trends in refrigeration and airconditioning, 18–20 March 1998, New Delhi, India, pp 293–301 10. Saravanakumar R, Selladurai V (2013) Exergy analysis of a domestic refrigerator using ecofriendly R290/R600a refrigerant mixture as an alternative to R134a. J Therm Anal Calorim 114:933–940 11. Yu C, Teng T (2014) Retrofit assessment of refrigerator using hydrocarbon refrigerants. Appl Therm Eng 66:507–518 12. Sekhar SJ, Lal DM (2005) R134a/R600a/R290 a retrofit mixture for CFC12 systems. Int J Refrig 28:735–743 13. McLinden MO, Klein SA, Lemmon EW, Peskin AP (1998) Thermodynamic properties of refrigeration and refrigerant mixtures database, REFPROP V.6.01, NIST

Analysis of Characteristics of Launcher Missile System and Its Optimization to Reduce Tip-Off Effect During Launch P. Ravinder Reddy1, A. Dhanalaxmi1(&), M. Rakesh2, and G. Chandramouli2 1

Mechanical Engineering, Chaithanya Bharathi Institute of Technology, Hyderabad 500075, India [email protected] 2 Defence Research and Development Laboratory, Kanchanbagh, Hyderabad 500058, India

Abstract. This paper focuses on analyzing the motion characteristics of the launcher missile system and locations of interactions between missile shoes and launch rail. The deviation of direction of the thrust force from flight axis of the missile is known as thrust misalignment. And the deviation of direction of the missile is known as tipoff rate. The main objective of this paper is to analyze characteristics of launcher missile system and its optimization to reduce tipoff rate. The analysis is performed by modeling and simulating the launcher missile system using CAD package and ADAMS software. Keywords: Missile Tip-off rate


Launcher missile system
Thrust misalignment


1 Introduction Missile is a self-propelled guided weapon, designed to deliver an explosive warhead at the target with great accuracy at high speed. It moves in the launcher for a certain amount of time during launching phase. Launching device, canister is used for launching of the missile [1]. On application of thrust force, missile attains free flight as it separates from launcher. With front shoe becomes unsupported while the rear shoe still supported by launch rail when missile reaches end of launcher, missile deviates from actual flight path under force of gravity known as known as tipoff rate. Mathematically, the tipoff rate is represented by the angular velocity of the missile with respect to the Z-axis (pitch axis), also known as pitch rate. From the engineering point of view, minimum tipoff rate is desirable [2]. The purpose of this work is to achieve the minimum tipoff rate by analyzing the motion characteristics of the system and optimizing the obtained result by varying the parameters such as clearance between missile shoe and launch rail, location of the shoe with respect to center of mass of the missile and the number of launch lugs. Cochran, [3]: Developed a physical model of a launcher system in order to study the factors © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 432–439, 2020. https://doi.org/10.1007/978-3-030-24314-2_52

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causing mal-launch of missiles. Dynamic mass unbalance is a primary contributor to mal-launch of the system. Thrust misalignment is also an important mal-launch factor. Zeman, [4]: Presented a dynamic simulation of rail launched missile. The shoe reaction forces can be determined and the missile’s velocity, acceleration can be plotted. For structural purposes, the missile’s component stresses can be plotted. Dziopa et al., [5]: Constructed a launcher mathematical model with four DOF. The results show that missile moving along guide rail causes vibrations of the system.

2 Modeling and Its Description Modeling of the system is done by using CAD package, Solid Works. The system analyzed in this work, needed modeling of canister with internal part of launch rail and missile with external part of launch lugs (Fig. 1).

Launch Lugs

Fig. 1. Missile body with launch lugs

2.1

Modeling of Missile

The missile system is modeled with the weight of 270.078 kg and total length of 4225 mm. The material used for missile is Al with properties, young’s modulus of 72.4 KN/mm2 and density of 2800 kg/m3. 2.2

Modeling of Canister

Figure 2 shows that front view of the launch rail, which has the interaction with the missile. The material used for canister is stainless steel with the weight of 281.66 kg and length of 4425 mm. The material properties of canister, young’s modulus is 200 KN/mm2 and density is 7800 kg/m3.

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Launch Rail

Fig. 2. Front view of launch rail

Model of Launcher Missile System Figure 3 shows launcher missile system which is being analyzed for motion characteristics using ADAMS.

Center of mass

Missile

Thrust force

Canister

Fig. 3. Launcher missile system

Missile has interaction with launcher with the help of launch lugs of missile and launch rail of canister. The missile moves along the launch rail with help of launch lugs. The missile is supported by launcher with the help of two launch lugs, also known as missile shoes as shown in Fig. 4. When thrust is provided externally, missile starts moving along the launch rail with the help of these lugs.

Canister Missile position Missile Thrust Misalignment

Thrust

Fig. 4. Line diagram of missile represents launching phase

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3 Simulation and Analysis of the Developed Model Using Adams Software It is recommended to analyze the launcher missile system in order to optimize the tipoff rate. ADAMS motion software has been employed to perform the motion analysis of missile. Thrust misalignment, which causes mal-launch errors, exists in reality [6]. Thrust misalignment should be less than 0.25° [7]. The basic conditions to be considered for simulation and analysis of the launcher missile system are clearance between shoe and launch rail is 0.50 mm, coefficient of friction is 0.16, launch angle is 45° and thrust misalignment is 0.2° (Fig. 5).

Fig. 5. Launcher missile system at launch angle of 45°

From the view of the launcher dynamics, only a small portion of thrust force is needed because there is no effect of the thrust force once the missile leaves the launcher [6]. Ideally, thrust force should be aligned with the flight axis of the missile.

4 Optimization of Launcher Missile System to Achieve Minimum Tipoff Rate Optimization of the launcher missile system is done to achieve the minimum tipoff rate. In this work, optimization is performed by studying the interaction between the missile shoe (launch lug) and the launch rail. Optimization of the launcher missile system is done by varying parameters such as 1. Clearance between the missile shoe and rail. 2. Location of launch lugs with respect to CM position. 3. Number of launch lugs.

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5 Results and Discussion The separation time of missile without considering thrust misalignment is 0.00004 s less than that of considering thrust misalignment (Table 1). Table 1. Tip-off rate and separation time for an ideal system and real system which has thrust misalignment Cases Separation time (sec) Tipoff rate (deg/sec) Without consideration of thrust misalignment 0.09141 0.0117 With consideration of thrust misalignment 0.09145 −3.2250

The tipoff rate of the missile without considering the thrust misalignment is 3.21 deg/sec less than that of considering the thrust misalignment. However, it’s not the big difference between separation times in terms of thrust misalignment but variations in tipoff rates are more. 5.1

Optimization Results

Reference Model: Clearance between the lug and rail is 0.50 mm Front Lug

Center of Mass

Rear Lug

Fig. 6. Front view of missile with two launch lugs

Here, x1 represents the distance between the tip of the missile to the front launch lug and x2 represents the distance between the front launch lug and rear launch lug (Fig. 6). Figure 7 is showing the angular velocity w.r.to time (pitch rate) when the clearance between the missile and the rail is 0.50 mm under the conditions, thrust misalignment is considered and the launch angle is 45°. The missile is separated from launcher at 0.09145 s. The angular velocity at this separating time is −3.2250 deg/sec. Table 2. Tipoff clearance Horizontal clearance (mm) 0.25 0.50 0.75

rates

Vertical clearance (mm) 0.50 0.50 0.50

for

horizontal Tipoff (deg/sec) −3.2362 −3.2250 −3.2252

Table 3. Tipoff rates for vertical clearance Horizontal clearance (mm) 0.50 0.50 0.50 0.50

Vertical clearance (mm) 0.25 0.50 0.75 1.0

Tipoff (deg/sec) −3.155 −3.225 −3.241 −3.263

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From the results obtained for clearance (Tables 2 and 3), it is observed that there is no much difference for varying horizontal clearance. That indicates this parameter doesn’t influence the tipoff rate and among those obtained results for horizontal clearance, minimum tipoff rate, −3.2250 deg/sec, can be achieved for the horizontal clearance of 0.50 mm. It is observed that vertical clearance influences tipoff rate, optimum condition to get minimum tipoff rate, −3.155 deg/sec, is when providing the clearance of 0.25 mm.

Tipoff rate is -3.2250deg/sec at separating time 0.09145sec for clearance of 0.50mm

Fig. 7. Angular velocity vs time showing tipoff rate −3.2250 deg/sec when the clearance is 0.50 mm

Table 4. Tipoff rate for varying the location of launch lugs w.r.to C.M of the missile Cases Case 1 Case 2 Case 3

Ratio of distances from center of mass to lugs (mm) 1:1 1:2 2:1

Tipoff rate (deg/sec) −2.9437 −2.0933 −2.313

From results obtained (Table 4) for varying location of launch lugs w.r.to C.M of missile, minimum tipoff rate can be achieved for placing lugs in ratio of 1:2 w.r.to C.M. And minimum tipoff rate for this condition is −2.0933 deg/sec. Table 5. Tipoff rates for placing the three launch lugs in different locations Dimensions/parameters x1 (mm) x2 (mm) x3 (mm) L1 (mm) L2 (mm) L3 (mm) Tipoff rate (deg/sec)

Case 1 1929 1000 1000 500 500 1500 −2.1808

Case 2 1990 1000 500 500 500 1000 −2.1091

Case 3 1990 1150 350 500 650 1000 −1.0629

Case 4 1990 850 650 500 350 1000 −0.6088

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Here, X1 represents the distance between the tip of the missile to front launch lug, X2 represents the distance between the front launch lug and intermediate launch lug, X3 represents the distance between the intermediate launch lug and rear launch lug, L1 represents the distance from center of mass to the front launch lug, L2 represents the distance from center of mass to intermediate launch lug and L3 represents the distance from center of mass to the rear launch lug. Optimization is done by performing the analysis for four cases. From Table 5, minimum tipoff rate can be achieved for case 4 in which third launch lug is placed between C.M and rear launch lug in such a way that it is near to C.M, far away from the rear launch lug. The dimensions are as follows: X1 ¼ 1990 mm L1 ¼ 500 mm

X2 ¼ 850 mm L2 ¼ 350 mm

X3 ¼ 650 mm L3 ¼ 1000 mm

The minimum tipoff rate for the above condition is −0.6088 deg/sec.

6 Conclusions The following conclusions are drawn from present studies Tipoff rate of the missile without considering thrust misalignment is 0.0117 deg/sec and the pitch rate of the missile with considering thrust misalignment is −3.2250 deg/sec. The tipoff rate of the missile with considering the thrust misalignment is 3.21 deg/sec more than that of without considering the thrust misalignment. After performing the optimization of the launcher missile system, observed the optimum condition to achieve minimum tipoff rate, −0.6088 deg/sec, is placing the front and rear launch lugs in the ratio of 1:2 w.r.to center of mass and placing third (intermediate) lug in such a way that it is near to the center of mass, away from the rear launch lug when the horizontal clearance of 0.50 mm and vertical clearance of 0.25 mm. Acknowledgement. The authors would like to thank Defense Research and Development Laboratory (DRDL), Hyderabad, India, for supporting this project and also likes to thank the Principal, CBIT, Hyderabad, India.

References 1. Acmaz E (2011) Experimental analysis and modeling of wear in rocket rail launchers, 129 p 2. Çiçek BC (2014) Dynamic analysis and modeling of a rocket launcher system, 128 P 3. Cochran EJ (1975) Investigation of factors which contribute to mal-launch of free rockets. Technical report, RL-CR-76-4, Auburn University, AL 4. Zeman P (2001) Rail launch missile simulation using MSC Nastran software 5. Dziopa Z, Krzysztofik I, Koruba Z (2010) An analysis of the dynamics of a launcher-missile system on a moveable base. Bull Polish Acad Sci Tech Sci 58(4):645–650

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6. Işık Ç, Ider SK, Acar B (2013) Modeling and verification of a missile launcher system. Proc Inst Mech Eng Part K J Multi-body Dyn 228(1):100–107 7. Knauber RN (1996) Thrust misalignments of fixed-nozzle solid rocket motors. J Spacecraft Rockets 33(6):794–799

Performance Analysis of a Horizontal Axis Wind Lens Wind Turbine P. Usha Sri(&) and Chirla Jeevesh Department of Mechanical Engineering, University College of Engineering, Osmania University, Hyderabad 500007, India [email protected]

Abstract. In reaction turbines, the main purpose of draft turbine is to increase the available net head and to increase the efficiency. By using the same principle in the case of a wind turbine, we can significantly improve the performance. As we all know that, the power generated by a wind turbine is always directly proportional to the cube of wind velocity at the inlet. If we try to increase inlet velocity by capturing and concentrating locally, there is a significant increase in the output power of a wind turbine. This is to be done by using wind lens technology, which comprises of inlet shroud, diffuser and brim. According to Betz limit, the maximum possible limit of efficiency for any horizontal axis wind turbine is 59.3% [1–3]. But, recent studies had shown that the maximum power coefficient from horizontal axis wind turbine (HAWT) using wind lens technology can be increased beyond the Betz limit. This study compares the maximum efficiency possible from a horizontal axis wind turbine without diffuser and that with diffuser enclosed around a turbine. A numerical simulation is done for the same model using ANSYS CFX with different inlet conditions. The geometry of the diffuser is optimized for the maximum possible efficiency. From this study it is found that a diffuser of divergence angle 8° around wind turbine operated at an optimum wind velocity of 2 m/s (for the chosen wind turbine model) gives the power coefficient of 0.6 which is greater than Betz limit. Keywords: Betz limit Rotor


Diffuser
Wind lens technique
Power coefficient


1 Introduction We all know that, fossil fuels are limited, and we have to utilize the renewable energy resources more effectively [4–6]. Among all these, wind energy has a great advantage for generating electrical energy, as it’s cheap and free from pollution. In general, wind turbine converts the kinetic energy of the wind energy into rotational energy which is further converted into electrical energy when it is coupled to a generator. Wind farms, which contain a group of wind turbines help in harnessing the wind energy are generally located far from the urban places [7, 8]. It is difficult and quite expensive to maintain such lengthy transmission lines that run from wind farms to urban regions. It is best suited if we could harness more energy from the wind, i.e. increasing the power coefficient of the wind turbine. We all know that, the power output of a wind turbine is © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 440–448, 2020. https://doi.org/10.1007/978-3-030-24314-2_53

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always directly proportional to the swept area and cube of wind velocity at the inlet. The rotor diameter is limited by the spatial restrictions in the urban regions. The only option left is to increase the available kinetic energy at the rotor. This is generally done by placing a diffuser around the wind turbine rotor. It increases the overall mass flow and kinetic energy available in the rotor by creating a pressure difference, thereby increasing the energy density of the flow. In 1919, Betz theoretically calculated that the maximum efficiency possible with a wind turbine is 59.3% called as Betz limit [9]. A long diffuser is more preferable than a short one. However a long diffuser makes the setup heavy and out of balance. Hence an optimum length of the diffuser is chosen keeping in view the stability of the wind turbine setup. A tip clearance of 0.2D is chosen (D = Diameter of the rotor). As the wind flows through the diffuser, a low pressure region is created at its entrance; the inlet wind velocity is further accelerated at the inlet of the diffuser. Thus the accelerated wind energy possessing high kinetic energy is harnessed by the wind turbine rotor. Wind with an inlet velocity of Vw approaches the wind turbine and exits with a wind velocity of V2. Let, q be air density (kg/m3), A1 is the cross-sectional area of a wind turbine at inlet, A2 is the cross-sectional area of a wind turbine at the outlet. Power coefficient is defined and calculated as [10, 11] CP ¼


Turbine output power 14 qAT Vw3 ð1  a2 þ a  a3 Þ 1 ¼ ¼ 1  a2 þ a  a3 1 3 Input Wind power 2 qA V T w 2

Where a ¼ VVw2

2 Experimental Setup In this paper, a practical horizontal axis wind turbine is designed and performance studies are performed by having with and without diffuser around the rotor, and the results are also compared with computational results by using ANSYS CFX. The three blade wind turbine rotor of diameter 0.224 m is mounted on a 9 V-2000 RPM DC motor. An LED (0.5 W) is connected to the DC motor in series with a 1 A–1000 V Rectifier diode and an ON-OFF switch as shown in the circuit Fig. 1. Ammeter (A) and Voltmeter (V) are connected in series and parallel to the circuit respectively.

Fig. 1. Circuit diagram of experimental setup

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An aluminium sheet of 0.3 mm thickness is utilized to fabricate the diffuser around the turbine rotor. For the given turbine rotor diameter (D = 0.224 m), three Diffusers namely Diffuser 1 (D1), Diffuser 2 (D2) and Diffuser 3 (D3) of their respective dimensions are fabricated to study the performance under different wind velocities. The sizes of the diffusers are shown in the Fig. 2(a)–(c). Figure 3(a) and (b) shows the completely assembled horizontal axis wind turbine model without a diffuser and with diffuser respectively. These two models are exposed to various wind velocities. The anemometer is used to record the inlet and exit wind velocities at the rotor. A digital tachometer is used to measure the rotational speed of the wind turbine.

Fig. 2. The dimensions of three diffusers (a) D1, (b) D2 and (c) D3.

3 Experimental Facility and Results Analysis The performance of horizontal axis wind turbine is studied at three different inlet wind velocities i.e. at 1.7 m/s, 2.7 m/s and 3.7 m/s by using with and without diffuser around the rotor and power output and efficiency are calculated. Table 1 shows the performance results for all the cases. It is clearly seen that, as the wind inlet velocity increases, the rotational speed increase results in an increase in power output. The power output and efficiency of D1, D2, and D3 wind turbines are more compared with bare wind turbine. By comparing wind turbines D1 and D2, D2 is having improved performance compared to D1 due to lengthier diffuser. Comparing diffusers D2 and D3, the D3 performance is not that much improved due to less diverging angle. It clearly indicates that, better performance of a wind turbine mainly depending on the optimum length of the diffuser and diverging angle.

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Fig. 3. Experimental test facility (a) horizontal axis wind turbine without diffuser and (b) with diffuser D1 Table 1. Performance results of different wind turbines S. No Wind 1 2 3 Wind 4 5 6 Wind 7 8 9 Wind 10 11 12

Inlet wind Outlet wind velocity V1 (m/s) velocity V2 (m/s) turbine without diffuser 1.7 1.42 2.7 2.05 3.7 3.17 turbine with diffuser D1 1.7 1.38 2.7 1.75 3.7 2.41 turbine with diffuser D2 1.7 0.95 2.7 1.04 3.7 1.85 turbine with diffuser D3 1.7 0.82 2.7 1.29 3.7 2.11

Speed of the turbine N (rpm)

Power output

Efficiency

673 1098 1370

0.04 0.15 0.32

31.89 32.47 29.09

954 1390 1450

0.04 0.22 0.49

39.32 51.24 44.87

878 1282 1490

0.06 0.25 0.63

57.35 59.44 56.40

767 1256 1345

0.06 0.24 0.61

54.41 57.35 54.72

4 Computational Fluid Dynamic Simulations A 3D model of the Wind turbine rotor is modeled using Ansys 19.0 Design Modeler (Fig. 4(a)). A rotating medium of air is created around the rotor in the form of a cylinder (Fig. 4(b)). All the diffusers are modeled in Ansys workbench as shown in Fig. 5(b)–(d). Here we are considering cylindrical domain with 30 cm radius and having a length of 50 cm on both sides of rotor blades. With three different speeds,

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three different diffusers (D1, D2 and D3), total twelve numbers of simulations are conducted as shown in Table 2. Here we are generated, unstructured mess with an element size of 1.4 cm and having a growth rate of 1.5 is realistic. The total number of nodes is 92500 with 1350 mesh elements having inflation size of 5 as shown in the Fig. 6(a) and (b). The total number of nodes for diffuser D1 is 75005, diffuser D2 is 70259, and for diffuser D3 is 74356.

Fig. 4. (a) Three bladed horizontal axis wind turbine, and (b) Cylindrical rotating medium

After mesh generation, in the ANSYS CFX Preprocessor, the boundary conditions are applied as shown in Table 2. Here we consider the working fluid is air at 25 °C at 1 atmospheric pressure. In Ansys CFX solver, we are considering k-e turbulence model with a turbulence intensity of 5%. The selected convergence criteria were set to the residuals smaller than 10−4, having physical time scale of 0.0002 s with maximum number of iterations of 10000. Figure 7(a) and (b) shows the velocity streamlines and pressure contour of the fluid moving at 1.7 m/s from the inlet to the exit for wind turbine without diffuser. It clearly shows, the inlet wind velocity reduces from 1.7 m/s to 1.2 m/s at the exit. There is a loss in kinetic energy of the wind. The changes in pressure and velocity from the inlet to the outlet is plotted as a graph as shown in the Figs. 8(a)–(d) and 9(a)–(d) for a wind turbine without a diffuser, diffuser D1, diffuser D2, and diffuser D3. The velocity ratio, ratio of exit velocity of wind (V2) at the outlet to inlet velocity of wind (V1) at inlet is also calculated as shown in Table 3. The Table 3 also gives the output power and efficiency of wind turbine without a diffuser, diffuser D1, diffuser D2, and diffuser D3 at a different inlet velocity of wind. It clearly shows that by comparing with diffuser type wind turbine, wind turbines without diffuser produces less output power. From Table 3, for wind turbine with diffuser cases, the output power and efficiency is increased by nearly 2 times as compared with wind turbine without diffuser. From this, we can conclude that, wind turbine with a diffuser is very efficient as compared to without diffuser at the same inlet test conditions. It is also important that, diffuser with the angle of diverging of approx. 8° gives high power output and better efficiency. The power output and efficiency at different inlet wind velocity is shown in Fig. 10(a) and (b).

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Fig. 5. Computational fluid model of (a) Horizontal axis wind turbine without diffuser, (b) with diffuser D1, (c) with diffuser D2, and (d) with diffuser D3 Table 2. Test conditions for numerical simulations S. no Inlet wind Diffuser velocity V1 (m/s) 1 1.7 Wind turbine without diffuser 2 2.7 3 3.7 4 1.7 Wind turbine with diffuser D1 5 2.7 6 3.7 7 1.7 Wind turbine with diffuser D2 8 2.7 9 3.7 10 1.7 Wind turbine with diffuser D3 11 2.7 12 3.7

From Fig. 10(a), blue line shows the power output for wind turbine without diffuser, which have least power out as compared with wind turbines with diffusers. Wind turbines with diffuser D2 (Green line) and diffuser D3 (Purple line) produces high power out and efficiency as compared with other wind turbines. The same thing is also supported by Fig. 10(b). From these results, we can conclude that, the wind turbine with diffuser D2 produces high power output and high efficiency as compared with other diffusers. It also have power coefficient of approximately 0.6, which is greater than the Betz’s limit.

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Fig. 6. (a) The generated mesh around the rotor (b) The generated mesh in complete fluid domain and around the rotor

Fig. 7. (a) Velocity streamlines of fluid flow, and (b) Pressure contour at middle cross section

Fig. 8. Variation of pressure along stream line at velocity V1 = 1.7 m/s for (a) wind turbine without diffuser, (b) with diffuser D1, (c) with diffuser D2, and (d) with diffuser D3

Fig. 9. Variation of velocity along stream line at velocity V1 = 1.7 m/s for (a) wind turbine without diffuser, (b) with diffuser D1, (c) with diffuser D2, and (d) with diffuser D3

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Table 3. Summary of results having horizontal axis wind turbine with and without diffusers Inlet wind velocity

Diffuser length

1.7 1.7 1.7 2.7 2.7 2.7 3.7 3.7 3.7

D1 D2 D3 D1 D2 D3 D1 D2 D3

Output power Wind turbine without diffuser 0.03

0.13

0.31

(p0) Wind turbine with diffuser 0.04 0.06 0.06 0.22 0.25 0.24 0.49 0.62 0.60

Efficiency η (%) Wind Wind turbine turbine with without diffuser diffuser 30.8 38.30 56.25 53.40 31.4 50.20 58.40 56.25 28.1 43.86 55.10 53.77

Velocity ratio (VV21 )

0.75 0.50 0.56 0.62 0.43 0.51 0.69 0.53 0.56

Fig. 10. (a) Output power vs inlet wind velocity, and (b) Efficiency vs inlet wind velocity

5 Conclusions Based on experimental and computational analysis, the following conclusions can be drawn: 1. From experimental results, it is clearly evident that, wind turbine with diffuser will have the efficiency two times more than that of wind turbine without diffuser. 2. Wind turbine with diffuser D2, generates high power out and efficiency as compared with other wind turbines. 3. It is also concluded that, diffuser with angle of diverging of approx. 8° gives high power output and good efficiency. 4. Wind turbine with diffuser D2 produces high power output and high efficiency as compared with other diffusers. It also have power coefficient of approximately 0.6, which is greater than the Betz’s limit.

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References 1. Bryans L, Jenkins N, Milborrow D, O’Malley M, Watson R, Fox B, Flynn D, Anaya Lara O (2007) Wind power integration: connection and system operational aspects. The Institute of Engineering and Technology 2. Harries A, Stankovic S, Campbell N (2009) Urban wind energy. Earthscan, Sterling 3. Gilbert BL, Oman RA, Foreman KM (1978) Fluid dynamics of diffuser-augmented wind turbines. J Energy 2:368–374 4. Gilbert BL, Foreman KM (1983) Experiments with a diffuser-augmented model wind turbine. Trans ASME J Energy Resour Technol 105:46–53 5. Igra O (1981) Research and development for shrouded wind turbines. Energy Conv Manag 21(1):13–48 6. Bet F, Grassmann H (2003) Upgrading conventional wind turbines. Renew. Energy 28 (1):71–78 7. Ohya Y, Karasudani T (2010) Shrouded wind turbine generating high output power with wind-lens technology. Energies 3(4):634–649 8. Hjort S, Larsen H (2014) A multi-element diffuser augmented wind turbine Energies 7 (5):3256–3281 9. Foote T (2011) Numerical modeling and optimization of power generation from shrouded wind turbines. All theses and dissertations (ETDs). 545 10. Aranake AC, Lakshminarayan VK, Duraisamy K (2015) Computational analysis of shrouded wind turbine configurations using a 3-dimensional RANS solver. Renew Energy 75:818–832 11. Hansen MOL, Sørensen NN, Flay RGJ (2000) Effect of placing a diffuser around a wind turbine

Experimental Investigation and Optimization of Electrochemical Micro Machining Process Parameters for Al 7075 T6 Alloy K. Samson Praveen Kumar(&) and G. Jaya Chandra Reddy Department of Mechanical Engineering, YSR Engineering College of YVU, Proddatur, Kadapa 516360, AP, India [email protected], [email protected]

Abstract. Electro Chemical Micro Machining (ECMM) is a non-conventional machining technique. The machine is an advanced description of ECM where machining is limited to much smaller area on the workpiece to create high aspect ratio holes, shapes and to machine metals of high hardness. In this paper the optimal values and influence of process parameters on ECMM while machining Al 7075 T6 Alloy are presented by using Grey relation analysis and ANNOVA. The optimum combination levels are presented based on higher MMR and lower value of OC and confirmation tests were carried out to confirm the prediction. To know the effect of NaNO3 on anode EDAX APEX™ analysis has been carried out. Experimental results are in close conformity with the developed model. The optimal process parameters for maximum MRR and minimum OC were determined as machining voltage at 6 V, electrolyte concentration at 30 g/l and frequency at 40 Hz. Keywords: Al 7075 alloy
Electro chemical micro machine
Material removal rate
Over cut
Grey relation analysis
ANNOVA

1 Introduction In the current development the micro products has been rapidly increasing demand in the field of automotive, bio-medical, aerospace, biotechnology, optics, avionics industries and electronics [1] and the quantity materials which are difficult-to-machine like super alloys has considerably increasing because of their improved properties [2]. While machining with conventional machining process on difficult-to-machine materials a lot of issues like heat affected zone, tool wear, high surface roughness, thermal stress and mechanical forces are being occurring [3]. Hence, to attain this requirement, various advance methods have been developed [4]. ECMM is a non-conventional machining technique emerging to be assuring method because of its additional advantages [5]. While machining through ECMM process the complexity of mechanism builds between required performances indicators and process parameters are very difficult. Hence, proper assortment of process parameters should be done. To work out this issue researchers have considered different experimental possibilities [6] and attempted various types of analysis methods [7, 8]. © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 449–457, 2020. https://doi.org/10.1007/978-3-030-24314-2_54

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From the available literature up to now, it is noticed that even though much work was done on the ECMM experimental process investigations and also developed numerical models unfolding the performance on process parameters, very few authors have investigated the optimal process parameters in ECMM and also little attention towards the machining of super alloys and analyzing the optimal values and influence of process parameters. Taking the above issues into consideration, the present investigation focuses on the influence of process parameters (voltage, electrolyte concentration and frequency) on MRR and OC while machining super alloy using ECMM through measuring the optimum combination for multiple performance using Grey relation analysis and to know the influence of process parameters ANOVA has been carried out.

2 Experimental Setup The ECMM works on the principle of Faraday’s laws of electrolysis. The ECMM setup was developed and is shown in the Fig. 1 (a). The ECMM basically consists of Pulse generator, tool feeding arrangement, stepper motor, tool holder, tank, machine chamber, filter and electrolyte pumping system. The Pulse generator or rectifier consists of input power as 110–120 V AC single phase 50–60 Hz, output rating as 0–20 V, 30 A avg. 100 A peak, output pulse wave shape as Bi-polar square wave–50 µsec rise max., 50 µsec fall max., and the output resolution as 20.0 V/99.9 A, meter resolution 20.0 V/30.0 A.

Fig. 1. (a) Electrochemical micro machine setup (b) Workpiece holder

A manual tool feeding system with resolution of 4 lm in the direction of z-axis are provided to stepper motor with motor resolution 1.8°/step and lead screw 30 teeth per inch for 75 mm. The tool holder which was attached to the tool feeding arrangement contains the tool moment per revolution of motor is 0.8467 mm and maximum tool moment is 75 mm. The tank is made with electrically non-conductive material, corrosion resistance, visible and attains capacity of 1.6 L. The electrolyte is filtered up to 5 microns in the filter and the electrolyte is pumped with pump having capacity of 16– 18 L/min. The work holding fixtures are fabricated with non-conductive Perspex material which is used to hold the workpiece as shown in the Fig. 1 (b) is mounted in the machining chamber.

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3 Experimental Planning The procedure for optimization of process parameters are listed below. 1. 2. 3. 4. 5.

First make out process parameters and performance characteristics to be analyzed. Find out the process parameters number of levels for analysis. Choose the suitable orthogonal array and allocate the parameters. Carry out the experimental work as per allocated process parameters to OA. The Grey Relational Analysis and ANOVA are used to analyze the experimental results. 6. Choose the optimal values and conduct confirmation test for verification of the process parameters. In ECMM process the parameters used to carry out machining are voltage, electrolyte concentration and frequency which are considered as variables. The other parameters like current 0.8 A, feed rate 28.2 µm/s and duty cycle 60% are considered as constants. The corresponding values and levels of process parameters are chosen based on past information and preliminary experiments conducted are voltage (6, 8, 10) V, Electrolyte concentration (25, 30, 35) g/l and frequency (40, 50, 60) Hz respectively. The experimental work was designed based of L27 Orthogonal Array method to perform micro hole on Al 7075 T6 of thickness 0.3 mm and size 20 mm 20 mm using ECMM process. The outputs of MRR and OC are calculated by noting down the machining time, initial and final weights of workpiece, and diameter of hole after machining for each trail. In this study the target is to have higher MRR and lower OC. To achieve this multi-response optimization the GRA and ANOVA technique has to perform. 3.1

Grey Relational Approach

In this study, the grey relation approach has been used to investigate the multiple performance characteristics. Here, a single grey relation grade has been calculated from the multiple performance characteristics [9]. The Grey relation procedure for analyzing the process parameters is given below. There are 5 steps involved in GRA for analyzing the process parameters. Step 1: Calculating the normalized for output response of MRR and OC Normalize can be calculated for MRR using the following formula, where MRR is the main response in ECMM which decided the machinability of the material under concern. Normalize is done to ignore the effect of assuming unlike units and to decrease variability. For the higher MRR is the better the original sequence has been normalized as follows and for the smaller OC is the better the original sequence has been normalized as follows Zim ¼

jyi  min yi j max yi  min yi

Zio ¼

max yi  yi max yi  min yi

ð1Þ

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Where Zi is the normalized sequence, yi is the influence factor of i = 1 to 27 for experimental numbers. Step 2: Calculating the deviation sequence of the normalized values For deviation sequence calculate the maximum value in the normalized sequence. It will be 1 only, because the values are normalized from in between 0 to 1 Dio ¼ jmax Zio  Zio j

Dim ¼ jmax Zim  Zim j

ð2Þ

Where Di is deviation sequence, Max Zim value is 1 Step 3: Calculating grey relation coefficient from deviation sequence values In this analysis, grey relational co-efficient has been calculated using the following Eq. 3 and for MRR and OC nim ¼

Dmin þ nDmax Dim þ nDma

nio ¼

Dmin þ nDmax Dio þ nDma

ð3Þ

Where ni is the grey relational co-efficient, D min = 0 and D max = 1 these are the values which can get from deviation sequence.n is distinguishing or identification coefficient. If equal preference is given to all parameters then n is taken as 0.5. Step 4: Calculate the grey relational grade by averaging the grey relational coefficients The grey relational grade has been calculated with the following Eq. 4 ci ¼

nim þ nio n

ð4Þ

Where ci is the grey relation grade and n is number of response variables. In the grey relation grade the higher value reveals stronger relationship between present sequence and ideal sequence. The higher value of the grey relation grade shows nearer to the optimal response in the process. Step 5: Finally rank is given to the grey relation grade and the optimal result has been chosen Give the rank according to the grey relation grade obtained. Thus the multiple output response optimization problems are converted into single response problem through grey relation analysis with Taguchi method.

4 Results and Discussions 4.1

Analysis of Grey Relation Grade

The grey relation grades and ranks which are shown in Table 1 was clearly indicating that the higher value of grey relation grade response and top rank gives the optimal result. It is clear from the below Table 1 that the Voltage of Level 1, the Electrolyte concentration of Level 2 and Frequency of Level 1 got the higher values. Therefore, for higher MRR and lesser OC, the optimal process parameters are 6 V, 30 g/l and 40 Hz. Figure 2 (a) shows the main effect of plot for means.

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Table 1. Response table of grey relation analysis Process parameters Level 1 Level 2 Level 3 Main effect Rank Voltage (V) 0.6096 0.5718 0.5892 0.0378 1 Electrolyte concentration (g/l) 0.584 0.6321 0.5545 0.0776 2 Frequency (Hz) 0.594 0.5889 0.5878 0.0063 3 Total mean value of the grey relational grade (cm) = 0.5922. The optimal combination levels of machining parameters are V1, E2, F1.

Fig. 2. (a) The effect of process parameters on performance characteristics (b) Percentage contribution of process parameters on Grey relational grade

4.2

Analysis of Variance

The analysis of variance has been carried out with ANOVA using statistical software, MINITAB 18 on grey relational grade values to evaluate the influence of each process parameters on combination levels of characteristics. The ANOVA for the grey relation grade is shown in the Table 2. The parameters which significantly influence the output characteristics are with p-value  0.05 under 95% confidence levels. Table 2. Analysis of Variance for Grey relation Grade Parameters DF SS MS Voltage 2 0.245494 0.122747 Electrolyte concentration 2 0.037222 0.018611 Frequency 2 0.005580 0.012790 Error 20 0.403369 0.015168 Total 26 0.691665 MS = Mean of Square, SS = Sum of Square and DF

F 4.88 0.74 0.11

P-values % of contribution 0.019 72.50 0.490 10.99 0.796 7.55 8.96

= Degree of Freedom

Table 3. Analysis of variance results OC Source DF Adj SS Adj MS F-value P-value Voltage 2 4.0570 2.0285 10.48 0.001 Electrolyte 2 1.7149 0.8574 4.43 0.026 Frequency 2 0.2129 0.1065 0.55 0.585 Error 20 3.8699 0.1935 Total 26 9.8547

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It is clear from the above Table 2 that the Voltage is the most influencing factor for multiple performance characteristics because the ions increases in the electrochemical cell which increases the material removal and also increases the current flow through IEG. For the higher values of current there will be the more current density which leads to higher removal of material and higher over cut. From the Table 3 it is clear that the electrolyte concentration is significantly influence factor because when concentration increases the ions association in the machining zone also increases, a higher ion concentration improves the current density in the IEG resulting in increasing overcut. Figure 2 (b) shows the Percentage involvement of process parameters on grey relational grade. Residual plots was drawn to the grey relation grade to check the Residual, fitted values and observation order in Fig. 3 (a) and (b) shows interval Plots of Grey relation grade 95% CI for the mean verses voltage, electrolyte concentration and frequency was drawn which indicates that the lower values of voltage and moderate values of electrolyte concentration and lower values of frequency gives optimal combination levels of machining parameters.

Fig. 3. (a) Residual Plots for grey relation grade and (b) Interval plots of gray relation grade verses voltage, electrolyte concentration and frequency

4.3

Confirmation Test

After identifying the influence and optimal combination levels of process parameters, the results are predicted and verify the progress of optimal combination levels of machining parameters through the confirmation test. The intention of conducting the confirmation test is to confirm conclusions drawn throughout the investigation part. The optimal combination levels for the confirmation test as shown in the Table 4. cp ¼ cm þ

q X

ðci  cm Þ

ð5Þ

i¼1

Where cp is the expected grey relation grade, ci is the mean of grey relation grade, cm is the total mean of the grey relation grade at optimal combination levels and q is the number of performance characteristics. The predicted grey relation grade of optimal levels can be calculated using the above Eq. 5 and with the optimal combinations levels of process parameters new experiment has been conducted to verify the improvement of optimal combination levels of machining parameters. Table 4 shows the confirmation test where the predicted grey relation grade and investigational grey relation grade using optimal process parameters are compared which clearly indicates that the optimal mixture levels of

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process parameters experimental is improved by 10.02% that of the predicted grey relation grade and 32.55% that of initial grey relation grade. Table 4. Confirmation test Initial levels of process parameters Level V1E1F1 Observed machining rate value 0.193 Observed overcut value 81 Grey relation grade 0.5406 Improved grey relation grade is 0.0653

4.4

Optimal combination levels of process parameters Prediction Experiment V1E2F1 V1E2F1 0.268 42 0.6513 0.7166

Analysis Based on SEM Images and EDXAPEX

The SEM diagram of machined micro hole with combination levels of optimal process parameters (6 V, 30 g/l and 40 Hz) is shown in Fig. 4 (a) at entry and (b) at exit. It is clear observed that the size of hole at entry was slightly larger than at exit and also at entry, excess material was removed around the hole, this is because of using of bare electrode during machining using ECMM. To overcome this issue insulated electrode has to be used.

Fig. 4. SEM diagram of machined micro holes at 6 V, 30 g/l and 40 Hz (a) at entry and (b) at exit (c) at machined surface of micro-hole

In ECMM the machining is done by anodic dissolution where the workpiece (Al 7075 T6 alloy) which is having very good corrosion resistance because of the protection formed on the surface by aluminium oxide is completely immersed in the electrolyte solution (NaNO3). This oxide layer is formed when it comes in contact with oxygen. The oxide layer formed is extremely unprotected in nature and delay the anodic dissolution of aluminium alloy. To know the effect of NaNO3 on aluminium alloy EDAX APEX™ software has been used. Energy Dispersive X-Ray Analysis (EADX) is a microanalysis software package which is used to identify elemental composites of materials. Figure 4 (c) shows the SEM image of Al 7075 T6 alloy at machined surface micro-hole which is used to analyses through EADX. The EADX for Al 7075 T6 alloy has been carried out and presented in the Fig. 5 (a) before machining and Fig. 5 (b) EDAX APEX image of Al 7075 T6 alloy after machining. It was clear from the Fig. 5 (a) and (b) that there is inclusion of NaNO3 after machining which supports the development of reactive layer on the workpiece and reduces the dissolution process and therefore decreases the overcut.

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Fig. 5. EDAX APEX image of Al7075 T6 alloy (a) before machining (b) after machining

5 Conclusion In this paper, an experimental investigation has been carried out to know optimal combination levels of process parameters using grey relation analysis method and also to find the influence of process parameters using ANNOVA method during machining micro-hole on Al 7075 T6 alloy using ECMM. It has been made an attempt to know the influence of NaNO3 on aluminium alloy using EDAX APEX™ analysis. Based on the experimental outcomes and analysis, the following conclusions can be made. 1. The optimal combination levels of process parameters were voltage of 6 (V), electrolyte concentration of 30 (g/l) and frequency of 40 (Hz). 2. From ANNOVA analysis the most significant process parameter that influences higher MRR and lower OC is voltage and the contribution of machining parameters are found to be voltage as 72.50%, electrolyte concentration as 10.99% and frequency as 7.55%. 3. Based on confirmation test the improvement of optimal combination levels of machining parameters to initial parameters is about 32.55%. 4. Based on EDAX APEX analysis it seems that the NaNO3 was present after machining which supports the formation of reactive layer on the workpiece and reduces the dissolution process and therefore decreases the overcut. Acknowledgement. The authors thank the organization of Sona College of Technology (Autonomous Institution), Salem. Tamil Nadu, for the encouragement and support. The authors furthermore thank Dr. P. Suresh in-charge for CNM/CMM lab, Dep. Of Mechanical Engineering, Sona College of Technology, Salem, Tamil Nadu, for his supervision and allowing me to utilize the Micro ECM setup with pulse rectifier and optical microscope.

References 1. Rajurkar KP, Levy G, Malshe A, Sundaram MM, McGeough J, Hu X, Resnick R, DeSilva A (2006) Micro and nano machining by electro-physical and chemical processes. CIRP AnnManuf Technol 55(2):643–646 2. Ott EA, Groh EA, Banik A, Dempster I, Gabb TP, Helmink R, Liu X, Mitchell A, Sjoberg GP, Wusatowska-Sarnek A (2010) Superalloy 718 and Derivatives. The most successful alloy system of modern times - past, present and future. TMS (The Minerals, Metals & Materials Society)

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3. Muthuramalingam T, Mohan B (2015) A review on influence of electrical process parameters in EDM process. J Arch Civ Mech Eng 15(1):87–94 4. Ryu SH (2009) Micro fabrication by electrochemical process in citric acid electrolyte. J Mater Process Technol 209(6):2831–2837 5. Spieser A, Ivanov A (2013) Recent developments and research challenges in electrochemical micromachining (lECM). Int J Adv Manuf Technol 69:563–581 6. Lohrengel MM, Rataj KP, Münninghoff T (2016) Electrochemical machining-mechanisms of anodic dissolution. Electrochim Acta 201:348–353 7. Krishnan R, Duraisamy S, Palanisamy P, Veeramani A (2018) Optimization of the machining parameters in the Electrochemical micro-machining of nickel. MTAEC 9 52(3):253. https:// doi.org/10.17222/mit.2017.045 ISSN 1580-2949 8. Lu Y, Rajora M, Zou P, Liang SY (2017) Physics-embedded machine learning: case study with electrochemical micro-machining. Machines 5:4. https://doi.org/10.3390/ machines5010004 9. Muthuramalingama T, Mohan B (2013) Taguchi-grey relational based multi response optimization of electrical process parameters in electrical discharge machining. Indian J Eng Mater Sci 20(6):471–475

Galactic Cosmic Energy - A Novel Mode of Energy Harvesting Uma Maheshwar Vanamala1(&) and Laasya Priya Nidamarty2 1

Faculty, Department of Mechanical Engineering, University College of Engineering, Osmania University, Hyderabad 500 007, Telangana State, India [email protected] 2 Department of Mechanical Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana State 500 007, India [email protected]

Abstract. Due to the exhaustion of conventional energy resources, cosmic energy stands as one of the possibilities that could provide energy for future generations on the planet earth. The cosmic rays are loaded with high energy particles like protons, alpha particles, and other ionized elements. The paper sheds light on the availability of cosmic energy surrounding the earth and intends to theoretically establish a possible way to harvest cosmic energy to meet the energy requirements. Keywords: Cosmic rays
Galactic cosmic rays
Electric field
High energy particle
Proton beam
Solar sail
Compton effect
Inverse compton effect
Energy sources

1 Introduction Man, over centuries, has ventured deep into the secrets that earth has to offer. The energy requirement in terms of electricity is gargantuan and the current mode of such energy extraction is through the non-renewable sources. Since these resources are not everlasting, the quest for finding a better alternative has begun. Many such corners have been identified that could serve as energy sources. The drawback of such energy resources is their availability, which is greatly affected by the seasons and the gravity between the sun, moon and the earth. The outer space is a home for different and probably unexplainable celestial bodies and events. Robert Millikan coined the term ‘cosmic rays’, whose exact origin is unknown. They are predominantly found in the outer space, coming from interstellar regions - thus named as Galactic Cosmic Rays (GCR). It was identified that GCR are found to be in large quantities surrounding the Earth. These rays are detected experimentally to be a stream of high energy particles possessing the energies ranging from 1.5–10 GeV on an average. If such energy-rich rays could be harvested, a part of the energy requirement by mankind could be conveniently met. Radiation is a mode of energy that is emitted in the form of rays, electromagnetic waves and/or particles. The radiation is classified based on its power into ionizing and non-ionizing radiation. The non-ionizing radiation is the low energy radiation, © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 458–465, 2020. https://doi.org/10.1007/978-3-030-24314-2_55

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comprising of microwaves, visible region, infra-red and radio waves, while ionizing radiation is the high energy radiation that causes ionisation of an atom and is caused by a particles, b particles, c rays, protons, electrons and neutrons. Space radiation is caused due to the particles being trapped in the Earth’s magnetic field. The particles shoot up into space during Solar Flares and the movement of GCR, which are composed of high energy particles. Space radiation falls into the category of ionizing radiation [1].

2 Composition of the Cosmic Rays Due to the supposed origin of the cosmic rays, their composition is still obscure to a certain extent. Going by Planck’s radiation law, if the radiation is to have high energy then the wavelength of the rays must be very less, i.e. lesser than the wavelength of c rays of the electromagnetic radiation spectrum. The scientists then narrowed down the scope of the cosmic rays’ composition to high energy protons (90%), a particles (9%), and atomic nuclei that are found in the interstellar spaces (1%). The Transition Radiation Array for Cosmic Energetic Radiation (TRACER) cosmic-ray detector, in 2006 [2] was able to measure the presence of primary cosmic ray nuclei ranging from atomic numbers 5 to 26 i.e. Boron to Iron. It was stated that the relative abundances of the nuclei of C, O and Fe, that constitute the primary cosmic rays are over 10 GeV/amu. The mass composition of the Ultra High Energy Cosmic Rays (UHECR) has been obtained with the help of Telescope Array Surface Detector which employed Boosted Decision Tree (BDT) multivariate analysis that is trained with the Monte-Carlo sets of events induced by Primary protons and Iron thereby presenting the average atomic mass of UHECR for energies ranging between 1018.0 to 1020.0 eV. The experiment detailed that the atomic mass of the primary articles did not show significant energy dependence (Figs. 1 and 2).

Fig. 1. Compilation of different energy spectra measured by TRACER. The dashed line represents simple power law fit over 20 GeV/amu [2].

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Fig. 2. Average atomic mass in comparison with the Pierre Auger Observatory and rise time asymmetry results. The statistical error is shown with error bars, systematic error is shown with brackets [3]

3 Availability and Incidence of GCR Within the Earth’s magnetosphere, two high energy belts are formed by the entrapped particles due to the magnetic field of the Earth. The inner belt ranges from 800 km to 2,000 km from the earth’s surface, while the outer belt occurs at 18,000 km and extends upto 25,000 km. These Van Allen belts also called radiation belts, are not made up of radiation but are made of energy-rich charged particles (Figs. 3 and 4).

Fig. 3. Temporal profile of the daily values of the geomagnetic Ap index [4]

Fig. 4. Temporal profile of the daily values of the cosmic ray intensity (1965–2018) [4]

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It can be deduced from the above figures that the geomagnetic effects on the cosmic rays are not predominant but is observed to vary inversely especially during 1965, 1985–1990, 2005–2015. For such instances, it is safe to draw that the daily incidence of Cosmic Ray Intensity is high when the geomagnetic field is less [4] (Figs. 5 and 6).

Fig. 5. Wavelet power spectrum (WPS) computed of Ap index from 1965–2018 [4]

Fig. 6. Wavelet power spectrum (WPS) computed of cosmic rays from 1965–2018 [4]

The maximum power of the cosmic rays, indicated by the red region, is witnessed to last for a span of a decade. Although the peak geomagnetic effects were found during the span of 1990–1995, the Wavelet power spectrum of the cosmic rays remained less affected by it [4]. A short-term average periodicity of 27 days was observed with the geomagnetic Ap index due to the solar rotation and also with the varying cosmic ray intensities, but the noticeable power of the Cosmic Ray intensity was abundantly more for 11-year periodicity as observed through the given time span of 1965–2018, recorded by neutron monitors [4]. The Cosmic ray intensity during the synodic month did not show any striking variation in the analysis for any range of time periods i.e. long, medium or short ranges, thereby remaining unaffected (Figs. 7 and 8).

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Fig. 7. Lomb–Scargle power spectrum of CRI time series during 1965–2018 [4]

Fig. 8. Hysteresis plot for Solar Cycle 24, and the linear regression fit to the data taken at MCMD (McMurdo), NEWK (Newark), SOPO (South Pole), THUL (Thule) stations [5]

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The count rate of Cosmic rays in the mentioned stations was duly noted and was computed and compared over a large time interval. The result pointed to the fact that the Cosmic Ray count doesn’t peak over 2016 as the model seems to compress beyond that timeline. Also, the Rigidity cut off (Rc) was observed to be the highest at the NEWK station located at 75.8oW 39.7oN, while the least was found at SOPO station with the coordinates of 0.0oE 90.0oS [5] (Fig. 9).

Fig. 9. Cosmic Ray intensity measured by neutron monitors with vertical lines depicting the approximate epochs of solar magnetic field polarity reversals [5].

4 Cosmic Ray Harvesting 4.1

Solar Sail and Electric Field

The Solar Sail made up of the ultra-thin mirror, of different shapes which when incident upon by the solar radiation pressure, results in propelling itself. A part of the irradiation that is absorbed heats up the sail and continues to reradiate thereby causing sufficient momentum to promote propulsion that overcomes earth’s gravity. It is generally made up of aluminized 2 lm Kapton film with reflecting film pointing towards the Sun side. For the uniform intake of charged particles, the Solar sail is made stationary by adjusting the mirrors to achieve hovering effect and thereby producing continuous output in the form of energy to be harnessed. A provision for two electrically conducting copper sheets, is provided to establish an electric field at the entry of the solar sail intake. Due to the electrical field, the charged particles present in the GCR get deflected accordingly towards the respective electrodes [6] (Fig. 10). 4.2

The Possible Conversion of the Charges into Electricity

Solar Sail converts the high energy electrons produced by Solar winds into electricity. The GCR are chiefly composed of high energy protons. The high-powered protons that are deflected by the electric field at the entrance of the sail are to be drawn through a

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Fig. 10. Deflection of the charged particles in the presence of electric field [6]

narrow aperture to produce high-intensity beams. Due to high mass compared to the electron, the scattering of the proton beam is less and thus it helps in promoting better penetration. The focused ionising proton beam, could be then incident on a photoelectric crystal. The possibility of inverse Compton scattering effect could be expected as very high energy proton loses its energy to the metal that could produce relatively lower energy radiation, compared to the incident energy. The inverse Compton effect is to be achieved under supervised conditions in the presence of magnetic field, as the scattering is random motion of particles with high velocities. The scattering of high energy particles from the photoelectric crystal are to be focused on a lens such that the emissions from the lens fall under the IR region of the electromagnetic spectrum. This set up based on available technology requires high powered lens arranged in series. The energy developed by the congregation of the powerful lenses will be made to emit the desired radiation which could be transferred to the ground station on the earth with minimum losses as it cuts through the atmosphere, in the form of highly focused LASER and hence could be recovered using Compton’s scattering effect on the earth’s surface. Once the energy of the CR has been recovered, the electricity could be generated by using the regular system of energy extraction of using steam cycles where the steam is generated by the recovered CR energy at the ground station. 4.3

Practical Limitations

The Solar Sail technology is under experimentation and has not been implemented into practical application to date. Although the launch of the sail could be done by a rocket, the validity of its energy production peaks once in almost every 11 years. During the idle time or the low-intensity phase, the rate of energy production will be less compared to set up cost, there by worsening the impact on the running costs. The orientation of the sail includes its location inside the atmosphere and in the space orienting itself to the fluctuating CR intensities. The frequent shifting of altitude is difficult to achieve. Even if the energy is harvested, the transmission of it from such great altitudes stands as the biggest challenge considering atmospheric conditions like aeroelasticity, earth’s varying magnetic field especially during the change of seasons, interruption by meteorites and meteoroids, solar flares. During the extraction of the energy from the GCR, there is a possibility of backscatter that may or may not be an interruption, depending on the interaction of the processed rays with the incident rays.

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5 Conclusions As a part of the efforts to find an alternative renewable mode of energy which has a scope in the future in regards to the energy generation, the paper presented a hypothetical analysis of the possible conditions that could lead us to harvest the galactic cosmic radiation. The availability of the cosmic radiation around the earth when predicted accurately could help us derive the energy from the incoming galactic radiation. The energy generated in the space could also help the other crafts in the orbits by providing them with energy thereby standing as an energy bank for the satellites and spacecraft. The scope for generating such energy is always on the table and research efforts must be focused to harvest such promising energy resource.

References 1. Jon Rask MA (2008) Space faring-the radiation challenge. Retrieved from NASA. www.nasa. gov/pdf/284273main_Radiation_HS_Mod1.pdf 2. Obermeier A, Ave M (2011) Energy spectra of primary and secondary cosmic-ray nuclei measured with TRACER. Astrophys J 742:14 (11p). https://doi.org/10.1088/0004-637x/742/ 1/14 3. Abbasi RU (2019) Mass composition of ultrahigh-energy cosmic rays with the telescope array. Phys Rev D 99:022002-1–022002-11 4. Tsichla M, Gerontidou M (2019) Spectral analysis of solar and geomagnetic parameters in relation to cosmic ray intensity for the time period 1965–2018. Solar Phys 294:15. https://doi. org/10.1007/s11207-019-1403-0 5. Ross E, Chaplin WJ (2019) The behaviour of galactic cosmic-ray intensity during solar activity cycle 24. Solar Phys 294:8. https://doi.org/10.1007/s11207-019-1397-7 6. Abhiyan P (2014) Energy harvesting from solar wind. J Energy Res Environ Technol 1:33–36

Infrared Heating - A New Green Technology for Process Intensification in Drying of Purslane Leaves to Reduce the Thermal Losses D. Kodandaram Reddy1,2(&), Kavita Waghray2, and S. V. Sathyanarayana1 1

2

Jawaharlal Nehru Technological University, JNTU, Ananthapur, AP, India [email protected] University College of Technology, Osmania University, Hyderabad, TS, India

Abstract. The drying or dehydration of foods is highly important method for the food industry and offers many possibilities for ingredient development with lesser water activity and products with longer shelf life to consumers. The principle of this process is reducing the water content in order to avoid or slow down food spoilage by microorganisms. But foods being biological in composition the contents are more sensitive to heat, as conventional drying methods (conduction/convection) takes longer duration for drying, probability of losing some nutrients is very high. Using an alternative thermal source like infrared heating (radiation) we can reduce the losses during drying by decreasing the process time. Heating by infrared radiation has advantages over conventional heating methods, including time of heating, uniformity in heating, less thermal losses, no migration of solute in food matrix, convenience in handling and operation, and less energy consumption. This present study emphasizes on aspects of infrared heating and its higher drying rates, in turn lesser drying times of purslane leaves and possibilities of reducing the nutrient losses (iron, calcium) and retention of colour using a graphical representation in comparison with conventional tray drying method at 50 °C, 60 °C and 70 °C. In tray drying the duration of drying period decreased from 990 to 270 min, where as in infrared drying the duration of drying decreased from 100 to 35 min when the temperature of drying was altered from 50 °C to 70 °C. With the change in the drying temperature from 50 °C to 70 °C, iron content decreased from 1.599 mg to 1.338 mg per 100 gm and calcium content decreased from 61.23 to 52.56 mg per 100 gm during tray drying where as in infrared drying iron content decreased from 1.78 mg to 1.49 mg and calcium content decreased from 64.17 to 58.44 mg per 100 gm of sample. With the increase in the temperature, brightness decreased and the samples became lighter. The greenness of the samples decreased and yellowness of the samples increased with the increase in the temperature. Infrared radiation could able retain more color than the conventional method of tray drying. Keywords: Infrared heating
Tray drying Purslane leaves
Calcium
Iron


Drying time
Drying rate


© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 466–475, 2020. https://doi.org/10.1007/978-3-030-24314-2_56

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1 Introduction 1.1

Purslane Leaves

Purslane is a common weed which cultivated/grown on all over the globe. It has high resistance against infestation because of its high seed production per plant. Purslane is also called as “power food of the future” as it has many nutritional and medicinal benefits. It can also cultivate in soil with poor availability of water during germination period and can with stand the salt content in the soil moderately. 1.2

Health Benefits of Purslane

Consumption of purslane is done in a variety of dishes which includes leaves and stems, mostly in the form of soups or mixed salads. Because of its high content of Vitamin C, it can be used like anti-scorbutic as well as antiseptic. It can also reduce the skin inflammations and mouth ulcers due to its medicinal properties. The purslane extracts have the anti-fungal effects against the activity of Trichophyton and due to the high potassium concentration it also has the effect of skeletal muscle relaxant [1]. Along with potassium purslane leaves and stem has good amounts of useful minerals and micronutrients like calcium and magnesium which are essential for body metabolism. The purslane extract also has polysaccharides which can control the glucose levels in the blood in turn can act as anti-diabetic when consumed in regular basis. Due to the presence of ample amounts of catecholamine and dopamine, the purslane consumption also helps in reduction of cancer and heart related diseases, because which the purslane is called as “vegetable for long life” [12]. It has better protein content as compared to the traditional vegetables and can also used as alternative protein source for both human consumption as well as animal consumption. It also added in food ingredients as an emulsifier due the presence of food grade gums in the purslane. Purslane is also good sources of omega-3 fatty acids, a very good alternative to some category of consumers who do not eat animal based food which are rich in omega-3 fatty acids. The consumption of purslane leaves based foods is giving longevity due to the possible reduced rate of cardiovascular diseases. Purslane is also filled with anti-oxidant vitamins, including pro-vitamin A, vitamin E and vitamin C, and helps in scavenge free radicals, in turn retards the diseases related to aging. Though the purslane is rich in many nutritional and medicinal components, due to its high water activity or moisture content, the bio-chemical and enzymatic activities takes place after harvesting leads to the loss/reduction of the nutrients. For which they can be stored for long periods with fewer storage losses by storing them in the dried powdered form using drying technology. We can also reduce the thermal losses by using alternative drying methods such as infrared heating techniques which can give rapid drying as compared with conventional tray drying methods which have longer periods of operation.

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Process of Drying/Dehydration

The most common unit operation used in the food industry to reduce the water activity or water content (dehydration) in foods so as to reduce the rates of biochemical and enzymatic reactions takes place, in turn, reduces the nutrient losses during the phase of post-harvest [3]. Drying is a simultaneous process both heat and mass transfer, under specified and control conditions. The main objectives of dehydration are: 1. To prevent (or inhibit) microorganisms. 2. To increase shelf life. 3. To increase the convenience in handling and transport of products by reducing the weight and volume and 4. To make the product ready for the further unit operations. 1.4

Methods of Drying

1.4.1 Tray Drying The usual method of operation in tray drying is by batch, which uses multiple trays or racks to hold the product to be dried. Hot air of pre-decided temperature is circulated over each tray so as to dry the food material and gets humidified, and then the fresh hot air is added to the subsequent tray, to maintain the uniform temperature and humidity. The heat transfer mechanism of forced convection is done with the help of fans inside the drying chamber. Humidified/moist air is vented out through the outlet duct continuously. Usually, the time of drying process is more and leads to some thermal losses with respect to the nutrition in some of the heat-sensitive food products [5]. The temperature range is always below the boiling point of water, most preferable temperatures for drying of vegetables are 50 °C to 70 °C which may take a few hours to dry the products. 1.4.2 Infrared Drying Principle and phenomena of infrared drying: “Flameless Catalytic Infrared Energy (FCIR) is generated by catalyzing natural gas or propane with a proprietary enhanced platinum catalyst. Natural gas, when combined with air across the platinum catalyst, reacts by oxidation-reduction to yield a controlled bandwidth of infrared energy and small amounts of CO2 and water vapor”. (Fig. 1) the significance of this catalytic reaction is that the most of the radiant energy is produced in food friendly wavelength of three to seven microns of far infrared range. The advantages of this heating technique are the uniformity in heat energy distribution and no emission of COx, NOx which makes the process to be called as green technology. Due to the wide range of generating temperature, it has multiple applications in thermal processing of foods as an alternative and quicker process.

Fig. 1. Catalytic reaction

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2 Materials and Methods 2.1

Tray Drying of Purslane

The moisture content on a wet basis of purslane leaves and stems before drying was determined using standard laboratory hot air oven method [2]. The average of the triplicates was taken as the actual moisture content of the sample The percentage moisture values of purslane stems and leaves was found as 94.23%. The samples were dried in the cabinet tray dryer. The loss in the weight of samples was recorded. The samples of the purslane (stems + leaves) weighing about 500 ± 0.5 g were spread on the tray. The triplicates were dried at 50, 60 and 70 °C. The initial weight of the tray plus sample was recorded. During the drying process, the tray was weighed at the intervals of 30 min each. Then, the dried products were collected into aluminum covers and then stored at ambient temperature followed by heat sealing. Then the collected data from the triplicates was used to plot the drying curves [13]. 2.2

Infrared Drying of Purslane

Infrared drying was conducted using an infrared heater. The purslane (stems + leaves) about 10 grams were spread evenly on the pan of moisture meter. The infrared drying was operated at 50 °C, 60 °C and 70 °C. During infrared-drying, the moisture contents were noted at every 5 min interval. Then the collected data from the triplicates was used to plot the drying curves. Determination of moisture content: Moisture content ð%Þ ¼ ½ðInitial weight  Final weight)  ðInitial weight)]  100:

ð1Þ

Determination of drying rate: ðInitial weight  Final weightÞ  ðTime intervalÞ

2.3

ð2Þ

Analysis of Micro Nutrients like Iron and Calcium Minerals

Sample Preparation Principle: “Organic matter in the sample is destroyed by wet digestion. The trace elements in the sample are quantitatively measured by atomic absorption spectrophotometer (AAS) at a specific wavelength”. Reagents: Triacid mixture: Triacid mixture was prepared by mixing concentrated nitric acid, concentrated sulphuric acid and perchloric acid (70%) in the ratio of 10:0.5:2 by volume.

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Preparation of Mineral Solution: One gram of the finely powdered sample was exactly weighed into 100 or 150 ml conical flask. 10 ml of the Triacid mixture was added to the sample and funnel was kept over the flask. The contents were left overnight for cold digestion. The next day the contents were digested at low temperature for about 2–3 h on a hot plate. The temperature of the hot plate was increased to 200 °C, till the contents became white and the major portion of the Perchloric acid ceases to appear on heating. Then the flask was removed from the hot plate, cooled and diluted to 20 ml, passed through filter paper (Whatman No. 1), this filtrate was used for measurement of micro nutrients Atomic Absorption Spectroscopy: The samples were analyzed using atomic absorption spectrophotometer (AAS) model for determination of Iron at a wavelength of 499 nm. The method used was by direct aspiration of sample digest, using an air acetylene flame. “Atomic Absorption Spectrometry (AAS) is a technique for measuring quantities of chemical elements present in environmental samples by measuring the absorbed radiation by the chemical element of interest. This is done by reading the spectra produced when the sample is excited by radiation”. 2.4

Colour Measurement for Purslane Powder

Purslane leaves were exposed to infrared radiation by placing the samples in the pan and then drying is done in the temperature range of 50 °C to 60 °C and 60 °C to 70 °C. Dried leaves were powdered and used for color measurement. The colour of dried purslane leaves was measure using Ultra Scan VIS Spectro-colorimeter (Hunter Lab) and compared with its natural values. The ground samples of both tray drier and infrared drier about 20 g were used for colour determinations. For each sample, the average of the triplicates was calculated.

3 Results and Discussion 3.1

Drying Curve

From Figs. 2 and 4 It is understood that the percent moisture on wet basis is falling regularly as the time of drying is increasing. Among the two methods of drying the time taken in the infrared red is much lower as compared with the tray drying method. From the Figs. 3 and 5, It is evident that the rate of drying at the initial stage of the drying is highest and eventually decreases as the drying proceeds, whereas among the two methods employed, the infrared drying has better drying rates as compared with the conventional tray drying method at all the temperatures (Tables 1, 2 and 3).

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MC (kg/kg)

From the Tables 1, 2 and 3, the yield of the dried products also observed more in the case of infrared drying than tray drying method

80 70 60 50 40 30 20 10 0

50˚C 60˚C 70˚C 0

200

400

600

800

1000

1200

Drying time (min)

DR (kg/kg.min)

Fig. 2. Moisture content vs Drying time of purslane using Tray drier

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

50˚C 60˚C 70˚C

0

20

40

60

80

MC (kg/kg) Fig. 3. Drying rate vs Moisture content of purslane using Tray drier

D. Kodandaram Reddy et al.

MC (kg/kg)

472

90 80 70 60 50 40 30 20 10 0

50˚C 60˚C 70˚C

0

20

40

60

80

100

120

Drying time (min)

DR (kg/kg.min)

Fig. 4. Moisture content vs Drying time of purslane using Infrared drying method

8 7 6 5 4 3 2 1 0

50˚C 60˚C 70˚C

0

20

40

60

80

100

MC (kg/kg) Fig. 5. Drying rate vs Moisture content of purslane using Infrared Drying method

Comparison between Tray Drier and Infrared Drier:

Table 1. Comparison between Tray drier and infrared drier at 50 °C Type of drying at 50 °C Time (min) Moisture removed (%) Yield (%) Tray drying 990 92.9 7 Infrared drying 100 90.33 11.6

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Table 2. Comparison between Tray drier and infrared drier at 60 °C Type of drying at 60 °C Time (min) Moisture removed (%) Yield (%) Tray drying 510 93.5 6.7 Infrared drying 60 91.46 7.6

Table 3. Comparison between Tray drier and infrared drier at 70 °C Type of drying at 70 °C Time (min) Moisture removed (%) Yield (%) Tray drying 270 93.9 6.4 Infrared drying 35 92.26 7

3.2

Micro Nutrients

3.2.1

Iron

Table 4. Comparison between Tray drier and infrared drier at 50 °C, 60 °C and 70 °C Temperature ( °C) Iron (mg/100 gm) Tray drying Infrared Drying 50 1.599 1.78 60 1.437 1.61 70 1.338 1.49

From Table 4 it is evident that during tray drying as the temperature is changing from 50 °C to 60 °C, iron content decreased from 1.599 mg to 1.437 mg and from 60 °C to 70 °C, iron content reduced from 1.437 mg to 1.338 mg. Whereas in the infrared drying, changed from 1.78 to 1.49 mg per 100 gm. The Iron content in fresh purslane leaves was found to be 1.99 mg. Iron content at 50 °C and 60 °C are closer to the iron content in the fresh leaves Table 5. Comparison between Tray drier and infrared drier at 50 °C, 60 °C and 70 °C Temperature ( °C) Calcium (mg/100 gm) Tray drying Infrared drying 50 61.23 64.17 60 57.14 61.32 70 52.56 58.44

3.2.2 Calcium From Table 5 it is observed that in tray drying as the temperature from changed from 50 °C to 60 °C, calcium content reduced from 61.23 mg to 57.14 mg and from 60 °C to 70 °C, calcium content reduced from 57.14 mg to 52.56 mg. where as in infrared

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drying, with the change in the temperature from 50 °C to 60 °C, calcium content reduced from 64.17 mg to 61.32 mg and with the change in the temperature from 60 ° C to 70 °C, calcium reduced from 61.32.14 mg to 58.44 mg. Calcium content in fresh purslane leaves was found to be 65 mg. Table 6. Color comparison values for Tray dried purslane powder. Temperature ( °C) Tray dryer b* L* 50 52.43 −0.32 60 57.76 −1.07 70 62.44 1.03

3.3

Infrared dryer b L* a* 15.31 53.63 −1.92 17.68 55.61 −1.47 20.82 59.45 0.23 *

b* 15.51 16.82 19.27

Colour

For fresh purslane leaves the values are: L* = 54.37, a* = −2.46, b* = 15.60 With the increase in the temperature, brightness decreased and the samples became lighter. The greenness of the samples decreased and yellowness of the samples increased with the increase in the temperature. Infrared radiation could retain more color than the conventional method of tray drying (Table 6).

4 Conclusions Infrared heating took lesser time for drying when compared to tray drying. Yield in Infrared heater is higher when compared to tray dryer. Color measurement indicated that greenness decreased and samples became lighter with an increase in drying air temperature. Analysis of iron has shown that with the increase in the temperature iron content decreased. Iron content at temperature of 50 °C and 60 °C is closer to the iron content in fresh leaves. Analysis of calcium has shown that with the increase in the temperature calcium content decreased. Calcium content at temperature of 50 °C and 60 °C is closer to the calcium content in fresh leaves. Infrared radiation could retain more iron, calcium and color than the conventional method of tray drying. Acknowledgments. The authors wish to thank for the supports received from Head, Department of Food Technology Srinivas Maloo, Principal Prof. R. Shyam Sundar, Dean Prof. Ravindranath and entire teaching and non-teaching staff of Food Technology and College of Technology, Osmania University. We would also like to extend our sincere thanks to the entire teaching and non-teaching staff of JNTU Ananthpuram for their unconditional support

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References 1. Chan K, Islam MW, Kamil M, Radhakrishnan R, Zakaria MNM, Habibullah M, Attas A (2000) The analgesic and anti-inflammatory effects of Portulaca oleracea L. subsp. sativa (Haw) Celak. J Ethnopharmacol 73:445–451 2. Arslan D (2008) Evaluation of drying methods with respect to drying kinetics, mineral content and color characteristics of rosemary leaves. Energy Convers Manag 49(5):1258– 1264 3. Kodandaram Reddy D, Waghray K, Satyanarayana SV, Harika P (2017) The effect of infrared radiation on native enzymes-a study on potato. Int J Eng Sci Inven 6(7):42–45 ISSN (Online): 2319-6734, ISSN (Print): 2319-6726 4. Ranganna S (1986) Handbook of analysis and quality control for fruit and vegetable products, 2nd edn 5. Doymaz I (2013) Hot air drying of purslane (Portulaca oleracea L.). Heat Mass Trans 49:835–841 6. Doymaz I (2007) Thin layer drying of spinach leaves in a convective dryer 7. Ahmed J, Shiv Hare US, Singh G (2001) Drying characteristics and product quality of coriander leaves. Department of Food Science and Technology, Guru Nanak Dev University, India 8. Li Y, Morey V (1987) Thin-layer drying rates and quality of cultivated American ginseng. Trans ASAE 30:842–847 9. Levey GA (1993) The new power food. Parade Magazine, The Washington Post, p 5 10. Lewicki PP, Witrowa-Rajchert D, Nowak D (1998) Effect of drying mode on drying kinetics of onion. Dry Technol 16(1&2):59–81 11. Uddin MK, Juraimi AS, Hossain MS, Un A, Nahar M, Ali ME, Rahman MM (2014) Purslane weed (Portulaca oleracea): a prospective plant source of nutrition, omega-3 fatty acid, and antioxidant attributes. Sci World J 2014, Article ID 951019 12. Gonnella M, Charfeddine M, Conversa M, Santamaria P (2010) Purslane: a review of its potential for health and agricultural aspects. Eur J Plant Sci Biotechnol 4:131–136 13. Kashaninejad M, Tabil LG (2007) Drying characteristics of purslane (Portulaca oleraceae L.) 14. Rocha T, Lebert A, Audouin CM (1992) Effect of drying conditions and of blanching on drying kinetics of mint (MenthaspicataHuds.) and basil (Ocimumbasilicum). In: Mujumdar AS (ed) Drying 1992. Elsevier Science, pp 1360–1368 15. Sandu C (1986) Infrared radiation drying in food engineering: a process analysis. Biotechnol Prog 2(3):109–119 16. Karaaslan S, Erdem T, Oztekin S (2013) Mathematical modelling and color characteristics of purslane (Portulaca oleraceae L.) leaves using different drying methods 17. Zbicinski I, Jakobsen A, Driscoll JL (1992) Application of infrared radiation for drying of particular materials. In: Mujumdar AS (ed) Proceedings of the international drying symposium IDS 1992. Elsevier, part A, New York, pp 704–711

Grey Relational Analysis of EDM Process Parameters for Incoloy-800 M. JagadeeswaraRao1(&), Riyaaz Uddien Shaik2, and K. Buschaiah1 1

Osmania University, Hyderabad, Telangana, India [email protected] 2 Sapienza University of Rome, Rome, Italy

Abstract. This project focuses on electrical discharge machining (EDM) which is a non-conventional machining process used in almost all manufacturing industries but finding the optimum process parameters is really a complex task mainly for alloys. It is known that the demand for alloy materials having unique properties is increasing but machining of these alloys using traditional method is becoming tough. So EDM is used in this project for machining alloy material and few statistical techniques were used to find out the parameters which has high impact on output. The techniques were implemented to improve Material Removal Rate (MRR) and to decrease Electrode Wear Rate (EWR) and Surface Roughness (SR). In this study, the experiments were conducted on INCOLOY800 using copper electrode according to L9 orthogonal array to analyse the effect of input machining parameters viz. current (Ip), pulse on-time (Ton), pulse offtime (Toff) and flushing pressure (Fp) over the responses of MRR, EWR and SR. In this project, effect of machining process parameters viz. current, pulse-on time, pulse-off time and servo-voltage for machining Incoloy-800 using copper electrode in die sinking EDM was investigated. Experiments were performed in three levels by varying the machining process parameters. This statistical technique helps in conducting experiments economically by limiting the number of experiments. Optimization of input parameters for achieving better output responses was performed using Grey Relational Analysis and found that experiment 9’s parameters are highly influencing the output responses. Keywords: Grey relational analysis


Incoloy-800
Copper electrode

1 Introduction EDM (Electrical Discharge Machine) is one of the oldest non-conventional machining processes and it uses thermal energy for machining metals that are impossible to machine with conventional methods. The only constraint is it can machine only electrically conductive materials. It can cut small angles, intricate shapes and contours in super alloys and exotic metals such as Kovar, Inconel, carbide, Hastelloy, Incoloy and Titanium. This method removes material by electric arc formed between workpiece and electrode by electric field. The electrodes moves to near the workpiece but doesn’t touch. The spark produced creates micro-craters on workpiece and removes material by melting and vaporization along cutting path. This process is commonly used for producing parts and making mold in aerospace and electronics industries. © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 476–482, 2020. https://doi.org/10.1007/978-3-030-24314-2_57

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2 Material and Equipment Used The machine, workpiece and electrode material used to carry out the experiments are explained below. The methodology implemented including design factors and response variable for the experimentation is also outlined. 2.1

Electrode Materials

The most required property of EDM electrode material is that it should resist the erosion and easily allow charges stimulated while machining the metals. Copper have better EDM wear resistance and also it is common base material as it has high conductivity but comparatively costlier than graphite. Mostly used for EDM machining of tungsten carbide or in applications where fine finishing is required. 2.2

Workpiece Material

Incoloy-800, a super alloy which has capability to maintain a stable structure even during prolonged high temperature exposures was considered for investigation. It became effective material for use in carbonizing equipment and as heating element because of its high levels of resistance to oxidation and carbonization. This was introduced into the industries to replace the Inconel-600 alloy. It withstands erosion and other decay that is often associated with aqueous settings. Notable property of Incoloy-800 alloy is its higher mechanical strength up to service temperature of 816 °C. Tensile property is most important at that temperature particularly for applications that require high creep or rupture strength. The mechanical properties of Incoloy-800 are as tabulated in Table 1 [1–5]. Table 1. Mechanical properties of Incoloy-800 Properties Tensile strength Metric 500 MPa

Yield strength 210 MPa

Elongation % 45

Thermal Electrical conductivity resistivity 0.989 W/m °C 11.5 lX ∙ m

Poisson’s ratio 0.339

3 Scheme of Experimental Procedure The Machine specifications, workpiece dimension and conduct of experiments were described below in this chapter. 3.1

Specifications of EDM Machine

This experimental work was carried out on CNC EDM, model: CREATOR CR – 6C, die-sinking type having positive polarity for electrode and with servo-head (constant gap) was used to conduct the experiments. Dielectric fluid used in this experiment is EDM oil (commercial grade) having specific gravity = 0.763 and freezing

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point = 94 °C to flush internally with a pressure of 0.70307 kg/cm2. The pulsed discharge current in positive mode was applied in various steps. 3.2

Selection of Work Piece and Tool Material

Incoloy-800 super alloy steel of size 20  20  6 mm3 plate is prepared as workpiece for conducting experiments. Before machining, the workpiece material’s chemical composition was checked using SPECTRO analysis (Table 2). Table 2. Chemical composition of Incoloy-800 Chemical analysis (%) Observed values Specified values Min. Max.

C

Si

Mn

S

Cr

Ni

Al

Cu

Ti

Fe

0.029 0.340 00.74 0.001 19.546 32.391 0.185 0.029 0.296 46.145 – – – – 19 30 0.15 – 0.15 39.5 0.1

1

1.5

0.015 23

35

0.6

0.75

0.6

–

In this experiment, using three different electrode materials viz. copper, graphite and composite electrode CW75 (25% Copper, 75% Tungsten) of size 10  100 mm2 were used to find the optimum electrode with respect to better surface finish. 3.3

Selection of Process Parameters

EDM machine has many process parameters that can be considered for study. But the major four input parameters are considered in this study (i.e.) discharge current (Ip), spark-on time (Ton), spark off time (Toff) and flushing pressure (Fp). 3.4

Design of Experiments

The experimental parameters are spark-on time (Ton), spark-off time (Toff), discharge current (Ip) and flushing pressure (Fp) are mentioned in Table 3. Four process parameters and the three levels were considered. The DoF of all three parameters were two (i.e. number of levels minus 1) and the total degrees of freedom of all the factor are (i.e. 4  2 = 8). The degrees of freedom (DOF) for selected Orthogonal Arrays (OA) (i.e. number of experiments) should be greater than the total number of degrees of freedom of all factors (8). Hence, L9 orthogonal array is considered for present study as tabulated in Table 3. It was found that that there is no interaction between the selected process parameters on the preliminary experimentation. The main aim is to create robustness against (insensitivity to) noise factors by optimizing combination of control factor settings under robust experimentation [6].

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Table 3. The levels of experimental parameters. Machining parameter Symbol Unit Discharge current (A) Pulse-on time (B) Pulse-off time (C) Flushing pressure (D)

3.5

I Ton Toff Fp

Amps µs µs kg/cm2

Levels Level 1 8 50 10 0.23

Level 2 Level 3 12 16 100 150 20 30 0.46 0.70

Conduct of Experiment

In this experiment, duty cycle and voltage are kept constant at 50 v and the experiments conducted are nine. The calculation of surface roughness after machining measured using roughness tester are tabulated in Tables 5, 6 and 7. This machine capacity and accuracy are 300 g and 0.001 g respectively (Table 4). Table 4. Experimental data MRR, EWR and SR. Exp. number Current 1 8 2 8 3 8 4 12 5 12 6 12 7 16 8 16 9 16

TON 50 100 150 50 100 150 50 100 150

TOFF 10 20 30 20 30 10 30 10 20

Fp 3.3 6.6 10 10 3.3 6.6 6.6 10 3.3

MRR 0.010848 0.011798 0.011699 0.015574 0.022039 0.021517 0.021398 0.025524 0.027423

EWR 0.00196 0.0005267 0.0001000 0.01082 0.0016133 0.0007267 0.02384 0.0046533 0.00208

SR 4.2966667 6.9433333 5.5866667 2.8266667 3.6966667 6.1773333 4.76 4.6033333 3.84

Table 5. Normalized data of responses. Exp. number MRR (mm3/min) 1 0.00000 2 0.05732 3 0.05134 4 0.28513 5 0.67517 6 0.64368 7 0.63650 8 0.88543 9 1.00000

EWR (mm3/min) 0.92165 0.98203 1.00000 0.54844 0.93626 0.97360 0.00000 0.80820 0.91660

SR (lm) 0.64291 0.00000 0.32955 1.00000 0.78866 0.18607 0.53036 0.56842 0.75385

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EWR (mm3/min) 0.07835 0.01797 0.00000 0.45156 0.06374 0.02640 1.00000 0.19180 0.08340

SR (lm) 0.35709 1.00000 0.67045 0.00000 0.21134 0.81393 0.46964 0.43158 0.24615

Table 7. Grey relational coefficient. Exp. number MRR (mm3/min) 1 0.33333 2 0.34658 3 0.34515 4 0.41157 5 0.60619 6 0.58389 7 0.57904 8 0.81358 9 1.00000

EWR (mm3/min) 0.86453 0.96530 1.00000 0.52545 0.88693 0.94985 0.33333 0.72275 0.85704

SR (lm) 0.58337 0.33333 0.42719 1.00000 0.70290 0.38054 0.51566 0.53672 0.67010

4 Results and Discussions Experimental data was analyzed using Grey Relational Analysis (GRA). GRA has three major steps involved starting with pre-processing of data. This analysis is required when one data unit is differing from others or if it has large scattering of sequence range and this is the method for studying date sequence by transferring the original to a comparable sequence. So, the data needs to follow normalization, scaling and polarization first into comparable sequence beforehand which is called as generation of grey relation or standard processing. The other processes involve in these steps are normalization and representative of data. 4.1

Grey Relational Analysis

Grey Relational Analysis has four steps as shown below stepwise. Considering ‘lower the better’ and ‘higher the better’ criterion, the normalized data for the responses should be generated. (1) MRR needs to be higher so it follows the higher the better criterion, which can be expressed as

Grey Relational Analysis of EDM Process Parameters for Incoloy-800

XiðkÞ ¼ ½yi ðkÞmin yiðkÞ=½max yi ðkÞmin yi ðkÞ

481

ð1Þ

EWR and Surface Roughness has to be follow the lower the better criterion, which can be expressed as Xi ðkÞ ¼ ½max yi ðkÞyi ðkÞ=½max yi ðkÞmin yi ðkÞ

ð2Þ

Normalized data of responses after step 1 is shown in Table 5. (2) Let the normalized data of MRR, EWR and SR may be represented with k = 1, 2 & 3 respectively. D0j ¼ kx0ðkÞ  xiðkÞk ¼ difference between absolute value of x0ðkÞ and xiðkÞ

ð3Þ

Here x0 (k) = 1, let delta = absolute value difference. The values obtained with step 2 are deviation sequence shown in Table 3. (3) Grey Relational Coefficient ni (k) can be calculated as ni ðkÞ ¼ ½Dmin þ w Dmax=½D0i ðkÞ þ w Dmax

ð4Þ

w = [0 − 1], in this analysis, it is considered as 0.5. Let GRC = Grey Relational Coefficient and GRG = Grey Relational Grade. The grey relational grade ci can be computed by averaging the grey relational coefficients as: ci ¼ 1=n Rnk ¼ 1 ni ðkÞ

ð5Þ

From the Table 8, it is observed that experiment 9 has obtained rank 1, which represents that particular experiment’s parameters helps in achieving better output responses altogether. Table 8. GRC and GRG table. Exp. number MRR (mm3/min) 1 0.33333 2 0.34658 3 0.34515 4 0.41157 5 0.60619 6 0.58389 7 0.57904 8 0.81358 9 1.00000

EWR (mm3/min) 0.86453 0.96530 1.00000 0.52545 0.88693 0.94985 0.33333 0.72275 0.85704

SR (µm) 0.58337 0.33333 0.42719 1.00000 0.70290 0.38054 0.51566 0.53672 0.67010

GRG 1.7812 1.6452 1.7723 1.9370 2.1960 1.9143 1.4280 2.0731 2.5271

RANK 6 8 7 4 2 5 9 3 1

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5 Conclusion Taguchi method was implemented to conduct the experiments economically by following L9 orthogonal array. The obtained experimental data was analyzed with Grey Relational Analysis (GRA) to find out the optimum input parameters for achieving higher MRR and lower EWR and SR. It was found that I = 16, TON = 150, TOFF = 20 and FP = 3.3 are the input parameters used to conduct experiment 9 has given optimum output.

References 1. Muthukumar V, Rajesh N, Venkatasamy R, Sureshbabu A, Senthilkumar N (2014) Mathematical modeling for radial overcut on electrical discharge machining of Incoloy 800 by response surface methodology. Procedia Mater Sci 6(Icmpc):1674–1682 2. Kumar M (2013) Wear studies on Incoloy-800 and prediction of wear by ANN model, pp 1– 12 3. Chen WS, Kai W, Tsay LW, Kai JJ (2014) The oxidation behavior of three different zones of welded Incoloy 800H alloy. Nucl Eng Des 272:92–98 4. Sherif EM, Seikh AH (2015) Effect of exposure period and temperature on the corrosion of Incoloy® alloy 800TM in hydrochloric acid pickling solutions. Int J Electrochem Sci 10:1843– 1854 5. P. V. Committee and S. Metals (1969) INCOLOY alloy 800 6. Dongre G, Zaware S, Dabade U, Joshi SS (2015) Multi-objective optimization for silicon wafer slicing using wire-EDM process. Mater Sci Semicond Process 39:793–806

Computation of Kinematic Redundancy and Its Workspace in RRRR Planar Kinematic Chain Shravan Anand Komakula(&) Department of Mechanical Engineering, Kakatiya Institute of Technology and Science, Warangal 506015, India [email protected]

Abstract. A kinematic chain is an assembly of rigid bodies connected by joints to provide desired motion which gives the mathematical model for mechanical systems such as robotic arms and manipulators. In these systems, links are constrained by their connections to other links. The main issue in industrial robots is to determine its ergonomics and have a better workspace maneuverability, on the same concern in this paper a model of robotic manipulator with RRRR planar kinematic chain is used to determine the workspace of its redundant links and the relationship between its various joint parameters with different end-effector positions is observed using Matlab. This kind of study can be used to efficiently manage workspace by motion planning and avoid redundant paths for robots to successfully perform any desired task with versatility. Keywords: Kinematic chain Workspace
Maneuverability


Robotic arms
Manipulator
Ergonomics

Redundancy
Cartesian
End-effector

1 Introduction A resistant body is one which does not undergo deformation while transmitting the force, each resistant body in a machine that moves relative to another resistant body is called a Kinematic link. A kinematic chain is an assembly of rigid bodies connected by joints to provide constrained motion which provides a mathematical relationship for any mechanical system. When one of the links of a chain is fixed then it is called as a mechanism, these have always been the subject of extensive research interest. In forward kinematics, we use kinematic equations and relations of a robot to compute the position of the end-effector from specific joint and link parameters. Inverse kinematics makes use of the kinematics equations and relations to determine the joint parameters that provide the desired position for each of the robot’s links and end-effector. Kinematic redundancy is having more degrees of freedom than strictly required to perform a specific task. Specifying the movement of a robot such that its end-effectors achieve the desired task is known as motion planning.

© Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 483–491, 2020. https://doi.org/10.1007/978-3-030-24314-2_58

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Research in kinematics synthesis has shown that it is feasible to incorporate complex kinematic requirements in the rational design of such mechanisms. However prior to wide adaptation of spatial mechanisms and chains in high speed and precision operating mechanical systems we must have better knowledge of their approach, the method which based on the matrix method mainly leads to numerical solutions, here the computation for RRRR planar kinematic chain is done using iteration, it should be mentioned here that this kind of approach is useful to study the kinematics of rigid members in any complex mechanical system. If spatial mechanisms and chains can be synthesized quickly there could be many suitable applications like aerospace industry, exercise equipment, and rehabilitation in the medical field. Engineers in aerospace are constantly trying to find ways to make things compact, lightweight and which are able to perform any specific spatial functions like satellite design and deployment. When a manipulator is redundant the inverse kinematics admits infinite solutions, this implies that for a given constant location of the manipulator’s end-effector, it is possible to induce self-motion of the structure, i.e. without changing the location of the end-effector [1]. For a particular end-effector position, the area of locus of redundant joints in a chain helps us to plan the task efficiently by selecting the optimized points in workspace for motion planning, further the range of angles of these links helps us in having better control which can further contribute to decreasing the efforts for computing matrix method inverse kinematics in Cartesian workspace.

2 Methodology The methodology includes three main criterions, firstly preparation of a virtual model as per our specifications, secondly defining the origin and link lengths, finally computing the results by following the experimentation for end-effector criterions. ‘Q°’ is angle of link in (Degrees), ‘A’ is area of Locus in (cm2), ‘L’ is length of link in (cm), (x1, y1) is a point of origin of chain, (x2, y2) is Locus of joint-2, (x3, y3) is locus of joint-3, (x4, y4) is locus of joint-4, (x5, y5) is the position of end-effector. The mentioned methodology is described in detail below. 2.1

Model

Considering a model of RRRR (R- Revolute) planar kinematic chain of four links with lengths L1, L2, L3, L4, angles Q1°, Q2°, Q3°, Q4° as shown in Fig. 1a. An angle of a link is measured relative to the x-axis or horizontal axis where an anti-clockwise direction is positive (+Q°) and the clockwise direction is considered as negative (−Q°) as shown in Fig. 1b.

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Fig. 1. (a) Model representation, (b) Angle representation method

2.2

Computation Method

The program has been developed in Matlab to divide the circumference of circle paths traced by link-1 (circle-1) and link-4 (circle-2) with centers (x1, y1) and (x5, y5), radius L1 and L4 respectively into 2000 points each. Then for link-3, the program computes a circle path with L3 as a radius and a point on circle-2 as the center and further divided into 2000 points. For example, to reach the same end-effector point two different possible paths are shown in Fig. 2, the links that have this kind of characteristics are called redundant links and contribute to kinematic redundancy.

Fig. 2. Computation process

2.3

Experimentation

The main constraints of this kind of workspace are limiting lengths of links and their interference. The parameters are as follows: L1 = 30 cm, L2 = 15 cm, L3 = 15 cm, L4 = 20 cm. After computing arrays of points (x2, y2), (x4, y4) and (x3, y3) distance from each point on (Circle-1) to (x3, y3) is calculated, if the distance equals to L2 with precision of 1e−04 cm then the case is considered as a feasible position, later the locus of (x3, y3) is computed again to next point on circle-2 this process continues till all the points on circles are completed. This process is done for different end-effector (x5, y5)

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points (50, 50), (20, 35), (4, 10) with (x1, y1) as origin. The points and links that satisfy these conditions are plotted between its joint parameters. ðx  x1 Þ2 þ ðy  y1 Þ2 ¼ ðL1 Þ2 :

ð1Þ

ðx  x1 Þ2 þ ðy  y1 Þ2 ¼ ðL2 Þ2 :

ð2Þ

ðx  x1 Þ2 þ ðy  y1 Þ2 ¼ ðL1 þ L2 Þ2 :

ð3Þ

ðx  x5 Þ2 þ ðy  y5 Þ2 ¼ ðL4 þ L3 Þ2 :

ð4Þ

ðx  x1 Þ2 þ ðy  y1 Þ2 ¼ ðL1  L2 Þ2 :

ð5Þ

ðx  x5 Þ2 þ ðy  y5 Þ2 ¼ ðL4  L3 Þ2 :

ð6Þ

(1) is the equation of circle-1 i.e. end path traced with (x1, y1) as centre and radius L1, (2) is the equation of circle-2 i.e. end path traced with (x5, y5) as centre and radius L4, (3) is the equation of circle-3 i.e. end path traced with (x1, y1) as centre and radius L1 + L2, (4) is the equation of circle-4 i.e. end path traced with (x5, y5) as centre and radius L4 + L3, (5) is the equation of circle-5 i.e. end path traced with (x1, y1) as center and radius L1 − L2, (6) is the equation of circle-6 i.e. end path traced with (x5, y5) as center and radius L4 − L3.

3 Results and Discussions After the experimentation acquired results are presented here with respect to graphs in detail according to the case study’s I–III. 3.1

Case Study - I

Conditions: End-effector position (x5, y5) = (50, 50) and (x1, y1) = (0, 0). The number of paths obtained = 1,835. Figure 3a shows the locus of joints-2, 3, 4 and circles-3, 4, 5, 6. We can observe that the locus of joint-2 and joint-4 form arcs but whereas the locus of joint-3 lies inside the overlapping area of circle-3 and circle-4 which is 232.415 cm2. This means any point inside the overlapping area can be used as a coordinate of joint-3 to plan a motion with a precision of 1e−04 cm. Figure 3b shows the paths of links in a possible number of positions obtained in this case in which can be used to efficiently pick a motion plan, this is the actual redundant workspace of the chain. Figure 3c shows the influence of Q3 on Q1, Q2, Q4 similarly Fig. 3d shows the influence of Q4 on Q1, Q2, Q3, these figures can be used to determine the effect of range on other angles with respect to a specific angle, maximum and minimum possible angles at four joints are shown in the table below, which means a link cannot make an angle out of its range to perform this case. Here we can observe that Q1 has no negative angles, the redundancy ranges in positive angles as shown below (Table 1).

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Table 1. Joint parameters of case study-I. Range

Maximum angle Q2° Q3° Q4° Q1 ° Negative NILL −16.85 −16.74 −0.18 Positive (+8.28 to +81.97) +106.85 +106.74 +89.82

Fig. 3. (a) Locus of joints, (b) Possible paths of links, (c) Angle Q3° vs Q1°, Q2°, Q4°, (d) Angle Q4° vs Q1°, Q2°, Q3°

3.2

Case Study - II

Conditions: End-effector position (x5, y5) = (20, 35) and (x1, y1) = (0, 0). The number of paths obtained = 12,277. Figure 4a shows the locus of joints-2, 3, 4 and circles-3, 4, 5, 6. We can observe that the locus of joint-2 and joint-4 form arcs but whereas the locus of joint-3 lies inside the overlapping area of circle-3 and circle-4, and to the exterior of overlapping area of circle-5 and circle-4 also exterior of circle-6 which is 1658.4001 cm2, we can observe

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circle-5 and circle-6 interacting with the locus of joint-3, when distance from origin to end-effector is less than L1 − L2 + L3 + L4 circle-5 influences locus of joint-3, and when distance from origin to end-effector is less than L1 + L2 + L3 − L4 circle-6 influences locus of joint-3. Figure 4b shows the paths of links in a possible number of positions obtained in this case. Figure 4c shows the influence of Q3 on Q1, Q2, Q4 similarly Fig. 4d shows the influence of Q4 on Q1, Q2, Q3, maximum and minimum possible angles at four joints are shown in the table below. From the above figures we can observe a complete discontinuity in angle Q4 from −132.1° to −106.9° this is due to the interaction of circle-6 with circle-3 on the exterior of circle-1 which means for any possible position of the chain for this case Q4 cannot have these angles (Table 2). Table 2. Joint parameters of case study-II Range

Maximum angle Q2° Q3° Q4 ° Q1 ° Negative −28.98 −179.99 −179.82 −179.82 Positive +149.58 +179.98 +180 +180

Fig. 4. (a) Locus of joints, (b) Possible paths of links, (c) Angle Q3° vs Q1°, Q2°, Q4°, (d) Angle Q4° vs Q1°, Q2°, Q3°

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489

Case Study - III

Conditions: End-effector position (x5, y5) = (4, 10) and (x1, y1) = (0, 0). The number of paths obtained = 22,562. Figure 5a shows the locus of joints-2, 3, 4 and circles-3, 4, 5, 6. We can observe that the locus of joint-2 and joint-4 form circles-1, 2 but whereas the locus of joint-3 lies inside the overlapping area of circle-3 and circle-4 and to the exterior of circle-5 and circle-6 which is 3122.0808 cm2. In this case, circle-5 also interacts with circle-6. Figure 5b shows the paths of links in the possible number of positions obtained in this case. Figure 5c shows the influence of Q3 on Q1, Q2, Q4 similarly Fig. 5d shows the influence of Q4 on Q1, Q2, Q3, maximum and minimum possible angles at four joints are shown in the table below. We can observe the discontinuities in angles similar to the previous case (Table 3). Table 3. Joint parameters of case study-III. Range

Maximum angle Q1 ° Q2° Q3° Q4° Negative −179.82 −179.99 −179.82 −179.82 Positive +180 +179.99 +180 +180

Fig. 5. (a) Locus of joints, (b) Possible paths of links, (c) Angle Q3° vs Q1°, Q2°, Q4°, (d) Angle Q4° vs Q1°, Q2°, Q3°

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After the computation has been carried out to determine workspace and kinematic redundancy of different cases, the summarized results of above all case studies are presented below in Table 4 with condition (x1, y1) as the origin (0, 0). Table 4. Summarized results. Case study

Link lengths (cm) L1 L2 L3 L4

I

30

15

15

20

II

30

15

15

20

III

30

15

15

20

Point (x5, y5) (50, 50) (20, 35) (4, 10)

Distance from (x1, y1) to (x5, y5) (cm)

Area of locus of joint-3 (x3, y3) (cm2)

70.71067812

232.4157

40.31128874

1658.4001

10.77032961

3122.0808

From the above case study’s (I-III) we can observe that the coordinates of joint-3 (x3, y3) in Cartesian workspace must satisfy all the below four conditions. ðx3  x1 Þ2 þ ðy3  y1 Þ2  ðL1 þ L2 Þ2  0: ðx3  x1 Þ2 þ ðy3  y1 Þ2  ðL1  L2 Þ2  0: ðx3  x5 Þ2 þ ðy3  y5 Þ2  ðL4 þ L3 Þ2  0: ðx3  x5 Þ2 þ ðy3  y5 Þ2  ðL4  L3 Þ2  0:

The The The The

point point point point

must must must must

lie lie lie lie

on on on on

or or or or

interior circle-3 exterior circle-5 interior circle-4 exterior circle-6

4 Conclusions The results obtained showed that by changing the end-effector position there is a significant change in the redundancy of the robot. As the distance from the origin to the end-effector decreases, there is an increase in the area of locus of joints, redundant workspace and range of joint angles. This type of analysis helps us in effectively positioning the end-effector link with respect to other links and joints in a Cartesian workspace by establishing their angles and locations in order to perform the given task. The number of redundant paths obtained depends on the precision of computation but the area and range of angles remain similar. Though computation has been done for a few points, this kind of study will help us quench the thirst for the hunt of highperformance robots.

References 1. Campos A, Martins D, Guenther R. Differential kinematics of robot manipulators using virtual chains. Robotics Laboratory - Universidade Federal de Santa Catarina Campus Trindade, 88040-900 Florianopolis, SC, Brazil 2. Tsai L-W (1999) Robot analysis: the mechanics of serial and parallel manipulators. Wiley, New York

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3. Siciliano B, Sciavicco L, Villani L, Oriolo G (2008) Robotics: modelling, planning and control, 1st edn. Springer, London 4. Angeles J (1997) Fundamentals of robotic mechanical systems. Springer, New York 5. Galletti C (1986) A note on modular approaches to planar linkage kinematic analysis. Mech Mach Theory 21:385–391 6. Chiaverini S, Oriolo G, Walker ID (2008) Kinematically redundant manipulators. In: Siciliano B, Khatib O (eds) Springer handbook of robotics, 1st edn. Springer, pp 245–268 7. Merlet J-P, Gosselin C (2008) Parallel mechanisms and robots. In: Siciliano B, Khatib O (eds) Springer handbook of robotics, 1st edn. Springer, pp 269–285

Physio-Mechanical Properties and Thermal Analysis of Furcreo Foetedo Mediopicta (ffm) Fibers: Its Potential Application as Reinforcement in Making of Composites Pathan Yasin(&), M. Venkataramana, and Shashidhar K. Kudari Department of Mechanical Engineering, CVR College of Engineering, Hyderabad, Telangana, India [email protected]

Abstract. In the past few decades, vegetable fibers became the viable alternative to petroleum-based fibers in composite industry, due to their renewability, biodegradability and eco-friendly properties. In the present work, a new leaf fiber extracted from Furcraea Foetida Mediopicto (ffm) plant, has been characterized and reported. Morphological, physical, mechanical and thermal properties of ffm fiber were examined by performing comprehensive characterization. Findings revealed that ffm fibers have an average low density and better mechanical properties compared to other fibers. Micro structural examination revealed the cross-section of the ffm fiber is the honeycomb structure. XRD analysis indicated the 49.7% crystalline content of ffm fiber. TG and DTA analysis revealed that ffm fibers are thermally stable up to 360 °C. Present investigation, indicates that ffm fibers are highly suitable as reinforcement agents in polymeric matrices for various light weight-medium load-thermal insulation applications. Keywords: Furcraea Foetida Mediopicto
Tensile properties
FTIR analysis
XRD analysis
Thermal analysis

1 Introduction The mechanical use of natural fibers as composite material inclusions began toward the start of the twentieth century, the synthetic fibers were being the traditional reinforcements for various applications, the drawbacks of having properties which include high density, high cost, non-renewability, non-recyclability, high energy consumption, non-biodegradability and health hazard contents, led the researchers to shift their focus to natural fibers. Yet, the natural fibers are not free from few disadvantages which include moisture absorption and low degree of resilience, people keep investigating to improve the properties for appropriate applications [1, 2]. For instance, research method which demonstrated RTM flax embedded polyester-specifically for wind turbine edges application, acquired the Asia 2013 Innovation Award due to potential supplanting for those embedded with glass fiber [3]. Moreover, due to natural fiber’s many superlative-unique properties, researchers have started to overcome the © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 492–500, 2020. https://doi.org/10.1007/978-3-030-24314-2_59

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drawbacks. Recently, Popzyk and Klein [4] have won 2017’s DNFI Award for their submission on “Reduction of the moisture absorption of natural fibers and production of no-twist yarns for use in structural components” and this shows the continuous exploration on natural fibers by the researchers. Since the past 15–20 years, many researchers have investigated few bio-wastes for various applications. Characterizations of Alfa fiber [5], betel nut leaf fiber [6], sansaveriacynlindrica fiber [7], Albiziaamara bark fiber [8], fiber extracted Ipomoea staphylina plant [9] and Prosopisjuliflora bark fibers [10] reflects the serious research on various species plants which are simply garden plants or plants for no use grown in wastelands. The present work mainly intended to introduce and characterize one more new natural fiber for increasing the input of raw fibers for various applications. Furcraea Foetida Mediopicto (ffm), is a garden plant native to the Caribbean and northern South America. It is a perennial evergreen-succulent plant belongs to Asparagaceae family, consists of a dark green rosette of lance shaped-leathery leaves. Each leaf can grow up to 150 cm tall and 15 cm wide are not tipped with a sharp spine as in case of most of the agave plants. Furcreo Foetedo Mediopicta plant will grow in rich welldrained soils, has a lifespan of 8–22 years and puts a flowering stem towards the end of its life. Furcraea Foetida Mediopicto is a tropical and subtropical plant, which will live and grow in the sunshine where the temperatures are above 25 °C [11, 12]. The authors of present work have observed the fibers in the leaves of Furcraea Foetida Mediopicto plant and investigated potentiality of the fiber for various applications.

2 Method The fibers are extracted from healthy leaves of ffm plant; the leaves are retted in water for 21 days. From there on, juicy ffm leaves were taken from the water, delicately crushed and beaten to expel the greenish flesh from fiber strands. Each well-grown leaf of ffm plant approximately weighs 300 gms and contains around 600 fibers of length 0.8–1 m, which are ivory in color and with very less glazing unlike henequen and agave angustufolia fibers. These fibers have been sun-dried for two days and put in polythene cover and used for further investigations. ffm plant, identified fibers in the leaf, retting of leaves, extracted fibers and sundried fibers are shown in Fig. 1.

Fig. 1. Furcraea Foetida Mediopicto plant and the extracted fibers

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The extracted fibers are subjected to following tests to find: Density, Micro structural analysis through SEM, Thermo Gravimetric Analysis, Fourier Transform Infrared Spectroscopy for finding organic or inorganic material (chemical group), and tensile properties.

3 Results and Discussion 3.1

Density

Density of raw ffm fiber has been determined using Digital Density Meter. Weighed ffm fibers were cut in to small pieces and introduced in to toluene and acetone separately to find out the density. The density of five different ffm fiber samples along with working liquids is obtained. The average of all these five givens was given the density of the ffm fibers to be 891.3 kg/m3. It is observed that the density of fft fibers is lower than several commercially available fibers as shown in Table 1. This indicates that ffm fibers can be used for light-weight applications due the low density. Table 1. ffm fiber density is listed in comparison with other natural fibers Fibre ffm Date (L) Bamboo (M) Jute sisal flax Rectophyllumcamurunense

3.2

Density (kg/m3) 891.3 990 910 1300 1500 1500 947

Micro Structural Characteristics

Longitudinal and transverse views of SEM images of ffm fiber are shown in Fig. 2a. It is evident from the figure that, the surface of the ffm fiber has grooves and slots. This indicates the potentiality of ffm fiber, which facilitates good mechanical interlocking with polymeric matrices and thus made the ffm fibers suitable as reinforcement candidate for composite materials application. Two different flaky-honeycombs crosssectional cell walls have been observed in ffm fiber. One was circular one at the root end of the leaf and the other at the tail of the leaf was elliptical approximately. The area of the cross-section of the ffm fiber along with commercially available natural fibers is shown in Table 2. Figure 2b shows that ffm fiber might have primary and secondary walls, middle lamellae and lumens. From the literature [13] it is widely known that Primary and secondary cell walls are made up of cellulose and lignin respectively, basically gives the hardness to the fiber strands. It also is known [7] middle lamellae are the intercellular layer which joins the nearby cells made of hemicelluloses and lignin and every cell contains free space (lumen) to store water. From the SEM analysis, it can

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be assured that ffm fiber contains hemicelluloses, cellulose and lignin contents. Beakou et al. [14] have investigated the microstructure of a Rhectophyllumcamerunense plant fiber. It is widely known from the literature [15] that, the presence of trichrome can enhances the mechanical bonding of fiber with polymeric matrices and this fact assures ffm fibers are good embedment for polymeric composites. Table 2. ffm fiber geometrical properties Fiber ffm

Area of cross section (mm2) 0.015014

Length of the fiber (m) 0.8–1

Diameter of the fiber (lm) 181.95–80.525

Fiber Lumen area (lm2) 659.732–136.034

Fig. 2. SEM images of ffm fiber

3.3

FTIR Analysis

Fourier Transform Infrared Spectroscopy (FTIR) is an analytical technique used to identify organic, polymeric, and, in some cases, inorganic materials. The FTIR analysis method uses infrared light to scan test samples and observe chemical properties. The FTIR instrument sends infrared radiation of about 10,000 to 100 cm−1 through a sample, with some radiation absorbed and some passed through. The absorbed radiation is converted into rotational and/or vibrational energy by the sample molecules. The resulting signal at the detector presents as a spectrum, typically from 4000 cm−1 to 650 cm−1, representing a molecular fingerprint of the sample. Each molecule or chemical structure will produce a unique spectral fingerprint, making FTIR analysis a great tool for chemical components identification. Extracted FTIR spectrum of ffm fiber is shown in Fig. 3. The vibrational FTIR spectrum is captured between wave numbers 650 cm−1 to 4000 cm−1. Notable sharp peaks were observed in the spectra within the wavelength range of 666 cm−1 to 3477 cm−1. The peaks at 1037 cm−1, 1244 cm−1, 2853 cm−1, 2922 cm−1 and 3354 cm−1 in the spectra of raw ffm fiber, are attributed to polysaccharides components and alcohol groups which largely contains cellulose [10, 16]. The bands at 3389 cm−1 and 1750 cm−1 in the spectrum can be attributed cellulose and lignin + carbonyl band, respectively. The peak at 1750 cm−1 wavelength indicates

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the stretch of C-O of the acetyl group of hemicelluloses [17, 18]. The peak at 2853 cm−1 shows the alkyl saturated C-H and CH2 (methylene group) stretching vibrations. The degradation of C-H stretch of alkyl and methylene (CH2) group can be observed from the peak at 2922 cm−1 [5]. The COOH bending band was present at 666 cm−1 band. The presence of peak at 1372 cm−1 denotes the CH2 symmetric bending. The peak at 1244 cm−1 shows the stretch of C-O of acetyl group of lignin [18]. This extracted spectra from FTIR, confirm the existence of hemicelluloses, lignin and cellulose chemical components in the ffm fiber.

Fig. 3. FTIR spectrum of ffm fiber

3.4

Tensile Behavior

Tensile properties of ffm fiber includes tensile strength, young’s modulus, percentage of elongation, specific strength have been evaluated according to ASTM D3379-75 standard [19]. Every specimen of ffm fiber was set up with an arrangement of 10 fiber strands of gauge length mounted on a bit of hard cardboard with a length of 50 mm. The finishes of the filaments were adhered on to the cardboard with epoxy gum and tried at a crosshead speed of 0.2 mm every moment. The test was done for 10 test samples to get substantial confirmation. The diameter of the ffm fiber was extracted from scanned electron microscopy and used for determining the mechanical properties. The tensile stress vs % of tensile strain graph of ffm fiber is shown in Fig. 4. It illustrates that the failure of the ffm fiber has undergone linear correspondence of stress to strain followed by non-linear variation phase. Similar kind of behavior is observed [7], in case of few sansaveria cylindrical fibers. According to Murherjee [20] investigations, this kind of behavior is mainly due to collapse of primary walls and delimitation of fiber cells. Tensile properties obtained from the experimentation are reported in Table 3. From the tensile test results, it can be concluded that ffm fibers are suitable for medium load application in making composites as reinforcement, specifically in polymeric matrices.

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Fig. 4. Tensile behavior of ffm fiber Table 3. ffm fiber tensile properties is listed in comparison with other natural fibers Fiber

Tensile strength (MP)

Tensile Modulus (GPa)

ffm

56 ± 12

Date (L) Bamboo (C) Jute Sisal RC average Palm fiber Glass-E

309 341 393–773 511–675 557.1 80–248 2000– 3500

3.5

% of elongation

Specific tensile strength (MPa/kg m−3)

Specific tensile modulus (MPa/kg m−3)

Reference

4.5 ± 1.85 16.05 ± 5.2 0.06282 ± 0.12

5.04 ± 0.57

11.32 19.67 26.5 9.4–22.0 5.8 0.5–3.2 70

11.44 22.10 20.38 6.3–14.7 6.1 0.333–4.571 28.0

Present work [20] [20] [21] [21] [18] [22] [18]

2.73 1.73 1.5–1.8 2.0–2.5 27.5 17–25 2.5

0.3121 0.3831 0.302–0.594 0.3407–0.4233 0.5883 0.05161–0.354 0.80–1.400

Thermal Resistance

In the present work, the thermal degradation parameters have been recorded using a thermo gravimetric analyzer. The experiment has been conducted within the controlled temperature starting from 0 °C and carried out till 620 °C. It detects the mass loss with resolution of 0.1 µg as a function of temperature. The primary and derivative thermo grams of raw ffm fiber are shown in the Fig. 5. From the existing literature, lignin is the first component which degrades, amongst the other cellulosic chemical components. Hemicelluloses, cellulose and lignin will degrade between the temperature ranges from 180–240, 230–310, and 300–400 °C respectively [21]. The Initial Degradation Temperature (IDT), Peak Degradation Temperature (PDT), Final Degradation Temperature (FDT) and percentage of Weight Loss is reported in Table 4. In the ffm fiber, a minor weight loss (5.9%) has been observed up to 250 °C due to moisture content present in it. Further increase in temperature, has been allowed to trace IDT and FDT, to be 280 °C with 14% weight loss and 385 °C with 80.3% weight loss, respectively. Yet, further increase in the temperature degraded almost complete sample slowly and the

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left-out fiber probably cellulose content was found to be 19.7% in terms of weight, at the temperature 620 °C. From the DTG peak, the peak temperature was determined which shows maximum decomposition rate with respect to time and the tailing region indicates end of fiber decomposition. From Fig. 5, it is also observed that, the peak degradation temperature (PDT) for the ffm fiber is 200 °C with the maximum mass decomposition to be 0.51 mg/min. 110.0

1.600

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350.0 Temp Cel

400.0

450.0

500.0

550.0

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0.000 650.0

Fig. 5. Primary and derivative thermo grams of raw ffm fiber

From the TGA analysis, it can be concluded that the ffm fibers are thermally stable till 360 °C. This value is higher than carao (190 °C), olive husk (200 °C), areca fruit (240 °C), grass (301 °C) and henequen (300 °C) [13, 22–25] and indicates outstanding thermal resistance of ffm against temperature loads. From the literature [26, 27], Net epoxy will be stable up to 335 °C and the addition of lingo cellulose fibers to the epoxy can further increase the composite’s thermal stability. This indicates, ffm fibers embedment in polymeric matrix also, could make the respective composite to sustain more thermal shocks than unreinforced matrix. Table 4. IDT, PDT and FDT of ffm fiber Fiber IDT PDT FDT Weight loss% Residue left after 620 °C ffm 280 °C 360 °C 385 °C 80.3 19.7

4 Conclusions Due to ecological concerns, it is necessary to find a viable alternative embedment for petroleum based-harmful-synthetic fibers in polymeric composites. In the present work, a new leaf fiber was extracted from ffm plant and investigated for the micro structural,

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physico-mechanical properties and thermal stability. From the investigation the following prominent conclusions can be made: The presences of lignin and cellulose have been confirmed through FTIR analysis ensures better rigidity and strength of ffm fiber. Rough surface of the ffm fiber confirmed through micro structural analysis offers mechanical interlocking with polymeric matrices and hence enhances the composite performance. Additionally, low density (890.3 kg/mm3) and ordinary tensile properties of ffm fiber gives sufficient specific strength for light weight-low to medium load applications. The nature of the ffm fiber found to be semi-crystalline and large crystallite size of fiber decreases the chemical and water absorption capacity. TG and DTA analysis revealed that ffm fibers are thermally stable up to 360 °C. By the present investigation, it can be bottom lined that ffm fibers are suitable as reinforcement agents in polymeric matrices for various light weight-medium load-thermal insulation applications.

References 1. Mohammed L, Ansari MN, Pua G, Jawaid M, Islam MS (2015) A review on natural fiber reinforced polymer composite and its applications. Int J Polym Sci 1:1–15 2. Lackey E, James GV, Kapil I (2008) Statistical characterization of pultruded composites with natural fiber reinforcements – part a: fabrication. J Nat Fibers 4:73–87 3. Shah DU, Schubel PJ, Clifford MJ (2013) Can flax replace E-glass in structural composites? A small wind turbine blade case study. Compos Part B: Eng 52:172–181 4. Popzyk, Klein (2017) DNFI awards. Discovery of natural fibres initiative 5. Paiva MC, Ammar I, Campos AR, Cheikh RB, Cunha AM (2017) Alfa fibers: mechanical, morphological and interfacial characterization. Compos Sc Tech 67:1132–1138 6. Arifuzzaman Khan GM, Shahrear Palash SR, Shamsul Alam M, Chakraborty AK, Gafur MA, Terano M (2012) Isolation and characterization of betel nut leaf fiber: its potential application in making composites. J Pol Comput 33:764–772 7. Sreenivasan VS, Somasundaram S, Ravindran D, Manikandan V, Narayanasamy R (2011) Microstructural physico-chemical and mechanical characterization of Sansevieriacylindricafibres – an exploratory investigation. J Mater Des 32:453–461 8. Senthamaraikannan P, Sanjay MR, Subrahmanya Bhat K, Padmaraj NH, Jawaid M (2018, in press) Characterization of natural cellulosic fibre from bark of Albiziaamara. J Nat Fibres. https://doi.org/10.1080/15440478.2018.1453432 9. Santhanam K, Kumaravel A, Saravanakumar SS, Arthanarieswaran VP (2016) Characterization of new natural cellulosic fibre from the Ipomoea staphylina plant. IJPAC 21:267–274 10. Saravanakumar SS, Kumaravel A, Nagarajan T, Sudhakard P, Baskarane R (2013) Characterization of a novel natural cellulosic fiber from Prosopisjuliflora bark. Carbohydr Polym 92:1928–1933 11. Villagenurseries, furcraea-foetida mediopicta. https://www.villagenurseries.com/product/ furcraea-foetida-medio-picta/ 12. My bageecha, furcraea-foetida-medio-picta. https://mybageecha.com/products/furcraeafoetida-mediopicta 13. Binoj JS, Edwin Raj R, Sreenivasan VS, Rexin Thusnavis G (2016) Morphological, physical, mechanical, chemical and thermal characterization of sustainable Indian Areca fruit husk fibres (Areca Catechu L.) as potential alternate for hazardous synthetic fibres. J Bionic Eng 13:156–165

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14. Beakou A, Ntenga R, Lepetit J, Ateba JA, Ayina LO (2008) Physico-chemical and microstructural characterization of ‘‘Rhectophyllumcamerunense’’ plant fibre. Compos: Part A 39:67–74 15. Yusriah L, Sapuan SM, Zainudin ES, Mariatti M (2014) Characterization of physical, mechanical, thermal and morphological properties of agro-waste betel nut (areca catechu.) husk fibre. J Cleaner Prod 72:174–180 16. Spinace MAS, Lambert CS, Fermoselli KKG, De Paoli M-A (2009) Characterization of lignocellulosiccurauafibres. J. Carbohydr Polym 77:47–53 17. Biagiotti J (2004) A systematic investigation on the influence of the chemical treatment of natural fibers on the properties of their polymer matrix composites. Polym Compos 25:470– 479 18. Liu W, Mohanty AK, Drzal LT, Askel P, Misra M (2004) Effects of alkali treatment on the structure, morphology and thermal properties of native grass fibers as reinforcements for polymer matrix composites. J Mater Sci 39:1051–1054 19. ASTM D3379-75 (1975) Test method for tensile strength and young’s modulus for highmodulus single-filament materials. ASTM standard 20. Murherjee PS, Satyanarayana KG (1984) Structure properties of some vegetable fibres, part 1. Sisal fibre. J Mater Sci 19:3925–3934 21. Zeriouh A, Belbirl L (1995) Thermal decomposition of a Moroccan wood under a nitrogen atmosphere. J Thermochimia Acta 258:243–248 22. D’Almeida ALFS, Barreto DW, Calado V, d’Almeida JRM (2008) Thermal analysis of less common lignocellulosicfibres. J Therm Anal Cal 91:405–408 23. Amar B, Salem K, Hocine D, Chadia I, Juan MJ (2011) Study and characterization of composites materials based on polypropylene load with olive husk flour. J Appl Polym Sci 122:1382–1394 24. Sgriccia N, Hawley MC (2007) Thermal morphological and electrical characterization of microwave processed natural fibre composites. Compos Sci Technol 67:1986–1991 25. Yang H, Yan R, Chen H, Lee DH, Zheng C (2007) Characteristics of hemicelluloses, cellulose and lignin pyrolysis. Fuel 86:1781–1788 26. Zhang XJ, Yi XS, Xu YZ (2008) Cure-induced phase separation of epoxy/DDS/PEK-C composites and its temperature dependency. J Appl Polym Sci 109:2195–2206 27. Li G, Li P, Zhang C, Yu Y, Liu H, Zhang S, Jia X, Yang X, Xue Z, Ryu S (2008) In homogeneous toughening of carbon fiber/epoxy composite using electro spun polysulfonenano fibrous membranes by in situ phase separation. J. Compos Sci Tech 68:987–994

Computational Analysis of Cavitation Structures on a Ship Propeller C. Syamsundar1(&) and P. Usha Sri2 1

2

Department of Mechanical Engineering, CMR Engineering College, Hyderabad 501 401, India [email protected] Department of Mechanical Engineering, University College of Engineering, Osmania University, Hyderabad 500 007, India

Abstract. Cavitation phenomenon is an unpredictable issue and intriguing subject in fluid dynamics and the investigation of cavitation structures around a ship propeller in a cavitation water tunnel for experimentation is a very complicated and consumes lot of time. In the present paper the consequences of cavitation structures at design and off design testing conditions are predicted by utilizing RANS equations in ANSYS CFX. A complete three dimensional ship propeller is demonstrated to simulate cavitation on screw propeller. From the literature, it is evident that, the most severe off design operating conditions is not accurately anticipated. In this paper computational analysis were carried out on various cavitation numbers at both designed and off designing conditions, to validate the experimental results. From these results, we have observed that, cavitating structures, and tip vortex formation on the blades were observed with good accuracy by competing with experimental results. Keywords: Cavitation structures Ship propeller


Computational fluid dynamics


1 Introduction Today the ships can be described in different methods, but most of them have fundamentally the same propulsion (Colley 2012; Chen 2015) as shown in Fig. 1. The back side of the blade, which is in motion direction is always at very low pressures (Colley 2012). We all know that, cavitation is a multiphase complex occurrence, due to this here three different definitions is given below (Knapp et al. 1970). • When the static pressure of liquid reaches to low pressures (vapor pressures) or below to it (Coutier-Delgosha et al. 2003). • Formation phase, growth and collapse phase of bubbles in liquid medium (Young 1989). • The collapse will take place in liquid at high pressures (Franc and Michel 2004). The repeated collapse of these cavitation bubbles on blade surface causes erosion, vibration and noise. Frank et al. (2007) studied the ship propeller and concluded that, by using CFD it is still difficult to predict cavitation at high pressure fluctuations. Chen © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 501–507, 2020. https://doi.org/10.1007/978-3-030-24314-2_60

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Fig. 1. Schematic diagram of a ship propeller (Chen 2015)

(2015) presented numerical simulation by using a commercial code STAR-CCM+. Kamal et al. (2017) modelled sheet cavitation at the suction side of the propeller and found that, they are in close with experimental results. The thrust force and torque produced by the propeller are represented in nondimensional number and it mainly depends on diameter of propeller (D), speed of rotation (n), advance velocity (VA), acceleration due to gravity (g), dynamic viscosity (µ), fluid density (q) (Chen 2015). Therefore, the thrust can be expressed as T ¼ kDa nb VAc qd le g f

ð1Þ

Where k is a proportionality constant, and a, b, c, d, e, and f are property index. The final expression is  T ¼ qn2 D4
f1

VA nD n2 D2 ; ; nD v gD

 ð2Þ

There are three non-dimensional numbers are in this equation Thrust coefficient KT is defined as kT ¼

  T VA nD2 n2 D2 ; ; ¼ f 1 qn2 D4 nD v gD 2

ð3Þ 2

2

VA is coefficient of advance (J), nDv is Reynolds number (Re) and ngDD is Where nD Froude number (Fr). In general, the ship propeller is generally operated far away from the free surface of liquid and doesn’t produce any surface waves, so Froude number can be ignored. Till now, the cavitation effects have been studied by many researchers by experiments. It involves high cost due to construction of cavitation water tunnels. Because of this, it is highly important to explore the CFD simulation techniques and cavitation models (Frank et al. 2007; Chen 2015). At the same time, in most of the papers, the most severe off design operating conditions are not properly studied. In this paper, the CFD simulations are performed at the same experimental conditions and results are compared.

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2 Computational Analysis of a Ship Propeller The ship propeller (Marine Trinket propeller) having 6 number of blades is modelled by using CATIA V5 R 19. The blade is modelled by taking several sections at various radii, and are rotated through their respective pitch angles as shown in Fig. 2(a). The computational domain consider for analysis is a cylindrical domain of length and diameter 23 m, which is ten times the propeller diameter and having a rotational speed of 1500 RPM as shown in Figs. 2(b) and 3.

Fig. 2. (a) Modelled ship propeller, and (b) Generation of mesh refinement around the blades

In mesh generation, nearly 3 million hexahedral cells are generated as shown in Fig. 3. Near wall y+ is very sensitive, and it is maintained as y+ < 5.

Fig. 3. Computational fluid domain considered and boundary conditions for CFD analysis

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At the inlet boundary, a uniform velocity of 6.22 m/s was prescribed and at the outlet, the atmospheric pressure was considered. Table 1 clearly shows the detailed solver control parameters for both non cavitating and cavitating test conditions. Table 1. Details of solver control parameters Parameters Pressure and Velocity coupling: Discretization scheme: Turbulence model: Solver control:

Non cavitating flow SIMPLE Upwind - Quadratic K-e Steady state

Cavitating flow SIMPLE First order upwind K-e Unsteady state Multiphase-Mixture 1. Water 2. Water vapor

3 Computational Results and Analysis Computational simulations have been carried out at six different cavitation conditions (r) i.e., 7.2, 5.1, 3.7 (design condition), 2.9, 2.3, and 1.9 respectively. The corresponding inlet velocities are 5.2, 6.2, 7.2 (design condition), 8.2, 9.2 and 10.2 m/s. The experimental and computational results are compared at cavitation number 3.7 (Paik et al. 2013) as shown in Fig. 4, which shows a good agreement with experiments. Figures 5 and 6(a–f), shows the variation of pressure on the front and back side of ship propellers. From these figures, we can clearly visualize that, water got vaporized at particular low pressure regions, which causes cavitation and flow separation. By clear observation, it is also concluded that cavitation and flow separation mainly happens at back side of the ship propeller and also at an outer blade location as shown in Fig. 7 (a)–(f).

Fig. 4. Comparison between (a) Kwang-Jun Paik et al. 2013, experimental results and (b) Computational simulation outcomes at cavitation number 3.7

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Fig. 5. Total pressure distribution at propeller inlet of cavitation numbers (a) 7.2, (b) 5.1, (c) 3.7 (design condition), (d) 2.9, (e) 2.3 and (f) 1.9 at a rotational speed of 1500 RPM

Fig. 6. Total pressure distribution on the propeller back side of cavitation numbers (a) 7.2, (b) 5.1, (c) 3.7 (operating condition), (d) 2.9, (e) 2.3, and (f) 1.9, at a rotational speed of 1500 RPM

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By using experiments, to calculate the cavity thickness is very complicated, because we have to use image processing on the cavity surface. Using computational fluid dynamics, we can also obtain the cavity length for different cavitation numbers as shown in Fig. 8(a)–(c). At higher cavitation numbers, there is a small attached cavitation is observed compared with lower cavitation numbers.

Fig. 7. Velocity stream lines of cavitation numbers (a) 7.2, (b) 5.1, (c) 3.7 (design condition), (d) 2.9, (e) 2.3, and (f) 1.9 at rotational speed of 1500 RPM

Fig. 8. Different types of cavitation structures on blades of a propeller operating at cavitation numbers (a) 7.2, (b) 3.7 (design condition), and (c) 1.9 at rotational speed of 1500 RPM

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4 Conclusions From CFD simulations for different cavitation numbers (design and off design condition), the main observations are; 1. Computational results are shown a very encouraging, good agreement with experiments and they were reproducible. 2. At higher cavitation numbers, there is a small attached cavitation is observed compared with lower cavitation numbers.

References Morgut M, Nobile E (2012) Numerical predictions of cavitating flow around model scale propellers by CFD and advanced model calibration Sato K, Oshima A, Egashira H, Takano S (2009) Numerical prediction of cavitation and pressure fluctuation around marine propeller Kamal IM et al (2017) A CFD RANS cavitation prediction for propellers Ghose JP, Gokarn RP (2004) Basic ship propulsion. Allied Publishers Pvt. Limited Knapp RT, Daily JW, Hammitt FG (1970) Cavitation. McGraw-Hill Book Company, London Coutier-Delgosha O, Reboud JL, Delannoy Y (2003) Numerical simulation of the unsteady behaviour of cavitating ows. Int J Numer Methods Fluids 42:527–548 Young F (1989) Cavitation. Imperial College Press, London Franc JP, Michel JM (2004) Fundamentals of Cavitation. Kluwer Academic Publisher, Dordrecht Colley E (2012) Analysis of flow around a ship propeller using open FOAM, pp 1–8 Sipilä T (2012) RANS analyses of cavitating propeller flows, Thesis for the degree of Licentiate of Science in Technology, Aalto University School of Engineering Frank T, Lifante C, Jebauer S, Kuntz M, Rieck K (2007) CFD simulation of cloud and tip vortex cavitation on hydrofoils. In: 6th international conference on multiphase flow, ICMF 2007, Leipzig, Germany, 9–13 July 2007, pp 1–5 Chen Z (2015) CFD investigation in scale effects on propellers with different blade area ratio. Master thesis, Aalesund University College, Norway

Heat Transfer in Food Crop Dryer Using Halogen Lamp Sowjanya Madireddi(&), V. Siddharth, Mohd Amaan, and M. Adithya CVR College of Engineering, Hyderabad 510501, Telangana, India [email protected]

Abstract. Drying of food crop during post harvesting is essential to remove the moisture content. Moisture removal is required to preserve the food crop from spoilage due to micro-organisms. Moisture removal depends on the design of the dryer and the heat distribution inside the dryer. Present work is the experimental investigation to obtain uniform temperatures in a crop dryer by distribution of heat using hot air at various positions in the chamber and using halogen lamp at different wattage. Usage of two halogen lamps in the opposite sides of the chamber is observed to get more variable and higher temperatures in the chamber with lower power input. Keywords: Heat transfer Pulse dryer


Post harvest drying
Controlled drying


1 Introduction Post harvesting of food crops involves removal of excess moisture from the grains to protect from spoilage due to the growth of micro-organisms. Farmers usually try to achieve this by doing open sun drying (OSD). During open sun drying the fresh grains from the field are dried by spreading either on mud plates or on concrete slabs. By doing this, continuous monitoring of the drying process is required which further need man hours and increase in post harvesting costs. Hence, the concept of controlled drying is introduced in the industry. The drying takes place in a controlled environment by using special equipment called dryer to remove the moisture content in the fresh food grains by sending hot air. There are number of industrial dryers but are very expensive for the farmers and availability is limited at the farm fields. Hence, most often farmers do only open sun drying and loose a considerable quantity of food crop due to spoilage [1]. Sahadev reviewed drying of food crops by open sun drying and green house drying [2]. It was observed that the losses of agriculture products due to OSD are remarkable and can be reduced by adapting to green house drying techniques. However, green house drying is also climate dependent. Hence, drying in controlled environment is most recommended. In open sun drying the solar radiation and convection heat transfer phenomena are principal in drying. The heat required for drying except in open sun is produced by heating the air using heating element, halogen lamp or by Infra Red rays. Forced convection phenomenon is employed in the case of heating element as air is blown over the hot surface. Tolmac et al. studied the © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 508–516, 2020. https://doi.org/10.1007/978-3-030-24314-2_61

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convection dryer with material transport and observed that the heat transfer during drying involves loss due to air leaving the cabin, loss due to radiation and loss due to conduction [3]. The overall convection heat transfer due to losses is found to be 100 W/m2K. Heat and mass transfer coefficients were reviewed [4] for various food crops and dryers. 1. Heat transfer as per Newton’s law of cooling is given by Q = hADT

ð1Þ

Where Q is the heat transfer rate from solid surface to the ambient fluid (W) A is the surface area of the solid surface (m2) h is the heat transfer coefficient w/m2K ΔT is the difference in temperature of the solid wall and surrounding fluid (K or °C) 2. Heat transfer by radiation phenomenon Q=A ¼ r T4s  T4a




ð2Þ

Where Q/A is the radiation heat flux (W/m2) r is the Stephan-Boltzman constant 5.68  10−8 W/m2K4 Ts is the temperature of the radiation source (K) Ta is the temperature of the chamber air (K) 3. Nusselt number for heating of foods in the form of slices and cylinders by hot air is given by [5] NuD ¼ 0:249 Re0:64 D

ð3Þ

Where NuD is the Nusselt number ReD is the Reynolds number based 4. Heat gained by the air in the chamber is given by Q ¼ m Cp D T Where m = mass of air = q/v q = density of air v = volume of the chamber Cp = specific heat of air ΔT = rise in temperature of chamber air

ð4Þ

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5. Heat absorbed by the chamber material is given by Q ¼ m Cp D T

ð5Þ

Where m = mass of chamber material Cp = specific heat of chamber material ΔT = rise in temperature of chamber surface 6. Heat energy from halogen lamp ¼ Rated power  96:5 (6) Halogen lamp converts for every 100 W put in 3.5 W to light and 96.5 W to heat [7] 7. Heat load from lamp = wattage  usage factor  ballast factor  loadfactor (7) for halogen lamps usage factor = 1, Ballast factor = 0.85 and load factor = 1 Wattage is the power input

2 Experimentation A cubical chamber of 2 ft by 2 ft is employed for the purpose. Figure 1(a) shows the space volumes in the chamber. The chamber is spatially divided as left, middle and right planes considered vertically. Figure 1(b) shows chamber with thermocouples placed in the left plane. Volumes V1 to V9 are the volumes in the right plane. Volumes V1, V2, V3 are in the front side of right plane. Volumes V4, V5, V6 are in the middle of the right plane. Volumes V7, V8, V9 are in the backside of the right plane. The volumes are considered similarly for the middle and left planes. Heating of the space volumes is done with case(i) hot air at 60 °C and 2.5 m/s speed, case (ii) halogen lamp. Hot air inlet is once from the backside and once from the top of the chamber. For case(ii), two halogen lamps are placed opposite to each other near volume V5 on each side of the left and right vertical planes. The wattage of the lamps is changed from 150 W to 500 W each. Hot air generated by a heater at 1000 W is placed at the back of the cabin. Temperatures T1 to T9 are measured in all these cases for every 10 min in one hour duration. The values are plotted to find the distribution of temperature in the entire chamber.

3 Results and Discussion 3.1

Heating by Hot Air from the Center of the Back Surface of the Chamber

Figure 2 shows the temperatures in the chamber when the hot air is sent into the chamber from the center of the back surface. The steady state is reached at 60 min for the vertical middle plane and at 70 min for the right and left planes. Due to symmetry of the chamber, the temperatures at the right and left planes are found to be same. The

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Fig. 1. (a) Space volumes considered in the chamber showing vertical left, middle and right planes (b) Chamber with thermocouples placed in the left plane.

temperatures are found to be nearly uniform. As the food products are not placed currently in the chamber the air is free to circulate inside the chamber and escapes through the 5 mm holes made in the top surface as shown in Fig. 1. The average chamber temperature is observed as around 55 °C as there is a leakage of air from the front door due to small gaps near the closing surface. Hence, we can conclude that the position of hot air from the chamber back surface can create uniform temperatures. If the chamber is made completely leak proof and let the air escapes only through the vents in the top surface, higher temperatures can be created. By this the hot air takes more amount of moisture from the products to be dried which are kept inside the chamber.

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Figure 3 shows the rise in temperatures in the chamber when the hot air is blown into the chamber from the center of the top surface. 100

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Fig. 3. Temperatures in the chamber when hot air from the center of the top surface

The temperatures are measured for time duration of 1 h. Temperatures in the mid plane are slightly higher than that at the right/left planes. This is because of the direct flow of hot air initially into the middle plane and the diffusion of hot air later into the other planes or space volumes. However, the average temperature in the chamber obtained is 62 °C which is higher than the earlier case of sending the hot air from the back surface of the chamber. The power consumed in 1 h is 1 kW and the heat absorbed by air is 367.75 W (Eq. 4). 3.3

Heating by Halogen Lamps Each One at Left and Right Surfaces

Temperatures are measured using thermocouples at the defined volumes in the chamber space varying power supply to each bulb as 150 W, 200 W, 250 W and 500 W. Figure 4 shows the temperatures T1 to T9 in space volumes V1 to V9 in the Mid and Right/Left vertical planes at 500 W and 250 W. The temperature near to the halogen lamp in the right/left planes shows the highest value. The temperatures in the mid plane show slightly less than the right or left planes. However, the average temperatures are 81.55 °C and 89 °C respectively for mid plane and right/left plane at 500 W respectively with a difference of approximately 7 °C. The average temperatures for 250 W are 61 °C and 66 °C with a difference of 5 °C. For 200 W and 150 W the average temperatures for mid and right planes are 53 °C and 59 °C, and 45 °C and 55 °C respectively. The difference in temperatures is 6 °C and 5 °C respectively for 200 W and 150 W. Hence with the halogen lamps on each side of the chamber near uniform temperatures can be obtained. Figure 5 shows the average temperatures in the planes. As the halogen lamp is near the right/left plane the temperatures are slightly higher in the right-middle plane. And also the temperatures in the

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front and back side space inside chamber are slightly lower in right/left/middle planes than the middle space due to the effect of location of lamps. i.e. volume V5 in all planes. However, when the material to be dried is placed in the chamber heat will be dissipated by the air in the entire chamber. Natural convection takes place between the wet material and nearby air leading to movement of air. Hence, the small difference in temperatures (5 °C) can be neglected. Safe moisture content is different for different food crops [6]. The required temperatures can be obtained based on the quantity of the material to be dried. In the present work, the chamber size is designed for a batch dryer with a capacity of 5 kg. Table 1 gives the heat gained by the air in the chamber in one hour duration using the Eq. 4. Energy from halogen lamp is estimated using Eq. 6. Heat transfer to the cabin material is estimated using Eq. 5. It is interesting to note that the heat gained by air is nearly equal to the total energy from the two lamps. With increase in time of heating this value for 1000 W power input would have been reached to the total energy from two lamps as the steady state temperatures are not reached in the 1 h duration considered.

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Heat energy from halogen lamp (W) 965 482.5 386 289.5

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Comparison of Heating by Halogen Lamps and Hot Air

Figure 6(a) shows the temperatures obtained in the two cases of 1000 W hot air heat source and two halogen lamps as heat source at 500 W each on either side of the chamber. Temperatures in all the planes for the case of halogen lamp as heat source are found to be higher than the temperatures with hot air at the same wattage (1000 W) located centrally at the top surface. The % reduction in average temperatures in the vertical planes right, center and left plane with fan as heat source is 30.96%, 23.43% and 30.96% respectively (Fig. 6(b)). Moreover, the cost of the hot air source is 86.66% more than that of two halogen lamps. The temperature usually employed for seed drying is between 40 °C to 60 °C and for commercial drying it is from 60 °C to 80 °C for most of the food crops viz. wheat, Jowar, Maize, Ragi etc. At much lower wattages of 500 W of power, the temperatures obtained are between 50 °C to 65 °C using halogen heat source. Thus the power consumption is reduced by 50% compared to hot air at 1000 W. As the cost of industrial dryers is in lakhs, the present equipment can be used at filed level by the farmers for production of 5 kgs of seed per batch with low cost. Further investigation is being conducted to find the rate of moisture removal by placing the wheat grains.

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Fig. 6. (a) Temperatures in Left-Center-Right vertical plane space volumes (b) Average temperatures in the vertical planes with heat source as hot air at top-center and 2 halogen lamps on either side

4 Conclusions Dryer cabin for food crops is fabricated and tested for distribution of heat using hot air at 60 °C with 2.5 m/s velocity and with halogen lamp at different wattage. Hot air heater at 1000 W could produce a maximum temperature of around 60 to 65 °C where as by using halogen lamp higher temperatures (80 to 90 °C) are obtained for the same power input. By varying the power input to halogen lamps the rise in chamber temperatures observed are from 60 to 25 °C than the initial cabin air. As different food crops need different drying temperatures, the results obtained from the present work can be used to design the dryer with required capacity. The present chamber can also be used for seed/commercial drying of different food crops like wheat, Ragi etc. Acknowledgments. Authors sincerely thank the management of CVR College of Engineering for the continuous support for the research work in the Department of Mechanical Engineering.

References 1. Government of Telangana Agriculture and cooperation department, Agriculture challenges and way forward, A task Force report - NITI Aayog (2016) 2. Sahdev RK (2014) Open sun and greenhouse drying of agricultural and food products: a review. Int J Eng Res Technol 3(3):1053–1066 3. Tolmac D, Prvulovic S, Radovanovic L (2008) Effects of heat transfer on convection dryer with pneumatic transport of material. FME Trans 36:45–49 4. Krokida MK, Maroulis ZB, Marinos-Kouris D (2002) heat and mass transfer coefficients in drying: compilation of literature data. Drying Technol: Int J 20(1):1–18

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5. Ratti C, Crapiste GH (1995) Determination of heat transfer coefficients during drying of foodstuffs. J Food Process Eng 18:41–53 6. Ofor MO, Nwufo MI, Ibeawuchi II (2010) Solar drying for value addition and to minimize post-harvest losses. Ann Arid Zone 49(3&4):259–265 7. Web source Bob MacCargar. https://www.reptileuvinfo.com/html/watts-heat-lights-lampheat-output.html

Progressive Damage Analysis of Laminated Composites Yashasvi Achanta1(&), P. Ramesh Babu2, and D. Sandeep2 1

2

UCE, OU, Hyderabad, India [email protected] MED, UCE, OU, Hyderabad, India

Abstract. Laminate composites are used widely in diverse fields because of the ubiquitous advantages it provides compared to others. These materials are susceptible to developing of micro cracks which will change the global response of material thus creating high stresses and heightened damage in the other part. To precisely predict its life and practical applications, considering material strength and its capability to resist damage is important. This paper intends in finding out optimum conditions for minimum damage progression. A flat plate with stress concentrations in form of cut-outs made up of Epoxycarbon UD (Prepreg) and [45 0 −45 90]s layup has been modelled; progressive failure analysis under uni-axial tension loading is performed. Comparison of failure criteria is done and ultimately Hashin failure criteria is chosen for static analysis with different layups, cut-out shapes, sizes, orientations and multiple cut-outs. The observations are noted and outcomes are compared. Keywords: Laminate composites Hashin failure criteria


Progression
Static analysis


1 Introduction Laminates have many practical applications as engineering materials in many different fields as it has the ability to manage the fibre alignment. Unlike metals, where trade-offs has to be made between strength and toughness, these can be optimized to offer high strength, stiffness and also robustness. This paper seeks a comparison of the observations of six failure criteria which are Maximum strain [1–3], Maximum stress [1–3], Hashin [1–3], Puck [1–3], LaRC03 [1–3], LaRc04 [1–3] on progression [4] of damage on a flat plate made from Epoxy Carbon UD (Prepreg) with a circular hole [5] on it, and a uni-axial tension load is applied. A comparison is performed by plotting LoadDisplacement graphs for all the selected criteria and because of the numerous advantages, Hashin failure theory is selected for the later part of the problem. The second part of analysis includes progression of damage for circular cut-outs with increasing radius and also increasing the number of notches. Also, different shaped cut-outs were analysed and compared with the usual circular cut-outs. Load-Displacement graphs are plotted and optimum conditions for minimum damage progression are mentioned. These observations are connected to practical uses involving flat plates with cut-outs. This research is not intended to develop a model for finding out composites damage © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 517–525, 2020. https://doi.org/10.1007/978-3-030-24314-2_62

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and failure, rather it will review the failure criteria and progression damage models and specify the optimum shapes which can be used in the real world. The scope of this paper is limited to laminate composites under pure tensile stress.

2 Problem Statement The objective of this research is to analyse the progressive damage on a plate made from EpoxyCarbon UD [15] with cut-outs in the form of stress concentrations. Dimensions of the specimen: 152  38 mm2 Radius of the notches: 4 mm Layup: [45° 0° −45° 90°]s No. of plies and thickness of the whole: 8 mm and 8 mm. This model is chosen because of its real-life applications in aerospace industries etc.; for example, in aircraft wings notches are needed for delivering fuel with the help of fuel lines and also to decrease the structural weight of the air-craft as a whole, they are also convenient for inspecting, maintaining and also Non-Destructive testing. 2.1

Comparison of Different Failure Criteria

MaximumStrain, MaximumStress, Hashin, Puck, LaRc03, LaRc04. Among all of these Hashin criteria is chosen for the second part of analysis as it is more advantageous in comparison with others and also it is more reliable. 2.2

Effect of Geometry of Cut-Outs

For every analysis varied shapes and sizes of the cut-outs are selected and the observations are compared.

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Effect of Lay-Up of Plies Symmetric lay-out of ply [45 0 −45 90]s Asymmetric lay-out of ply [45 0 −45 90 −45 0 45 −90] Anti-symmetric lay-out of ply [45 0 −45 90 −90 45 0 −45] Symmetric Cross lay-out of ply [0 90 0 90]s Symmetric Angle lay-out of ply [0 45 −45 0]s

3 Solution Methodology 3.1

Stress Analysis

Stress analysis is applied to laminates having stress concentrations so that it can analyse the distribution of stress in each and every layer of a ply in all modes i.e., tension, sheer and compression for fibre and matrix separately and also can find out the status of damage. Selecting a failure criteria being the most important part is done accurately to find out damage for different modes of damage. The Hashin theory [8] is applied to find failures because unlike any other criteria having been limited by the traditional predictions and its inability to find out the failure modes. The laminate will lose its capability of supporting any loadings when the damage accumulates and ultimately reaches a critical point, which will finally make it catastrophic (final failure) 3.2

Degradation Rules for Properties of the Material

The properties of the material will be degraded [9, 10] in the areas of damage once damage takes place in laminate. In this research, damage detected is analysed by continuously degrading the properties of the failed areas of the material by a factor of 0.75. The ply discounting method [11–13] is utilised as the laminate material property degradation factor. 3.3

Finite Element Analysis

A damage model [4, 14] is created in ANSYS WORKBENCH according to the geometric restraints as per the problem. The properties of the chosen material have been assigned ultimately creating a model with different sizes, layers and also properties. Meshing has been done denser around the hole intentionally to analyse distributions of stresses very accurately. The material degradation is applied to the laminate accordingly. The extreme left side of the plate is fixed i.e., given zero degrees of freedom and the right edge of the laminate has been given an incremental load of 50,000 N in X-Direction from 0 to 5 s with an increment of 10,000 N per second. Starting with 0 N to 10,000 N in the first step, if damage has not been occurred in all the elements, the load is increased by a factor of 10,000 N every time and a stress analysis is done in the further steps. If damage is found and the final failure has not occurred, the material is further degraded in the areas of the elements that have been damaged in regard to the property degradation criteria that have been mentioned above and an analysis of stress

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is further done to find out the damage yet again. This continues until a catastrophe has taken place.

4 Results 4.1

Damage Progression

Analysis is done for each and every case of the problem statement. Only the observations of elliptical, circular and square holes is mentioned, a complete analysis of all the other cases also done (Figs. 1, 2 and 3).

Fig. 1. Status of damage circular hole with radius of 4 mm

Fig. 2. Status of damage of an elliptical shaped hole.

Fig. 3. Status of damage of a square shaped cut-out

4.2

Load vs Displacement Plots

Load versus displacement graphs has been done for every case which further enables to give an accurate comparison of the optimum shape of all types of cut-outs on a scale common to all. The presence of a kink in the graph indicates failure and the stop of the graph indicates catastrophe (Fig. 4).

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Fig. 4. Load vs Displacement plot for Failure criteria.

First Ply failure: Maximum stress > Puck > Hashin > LaRc04 > LaRc03 > Maximum strain

Fig. 5. Load vs displacement plot for circular cut outs with radius 4 mm, 6 mm and 8 mm.

First Ply failure: Rsmall > Rbig

Fig. 6. Load vs displacement plots for multiple cut-outs.

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First Ply failure: Vertical > Inclined > Horizontal

Fig. 7. Load vs displacement plots for elliptical cut-outs.

First ply failure: Ellipse 1 > Ellipse 3 > Ellipse 2 > Ellipse

Fig. 8. Load vs displacement plots for different square cut outs.

First Ply failure: Horizontal > Horizontal (fillet) > Tilted > Tilted (Fillet)

Fig. 9. Load vs displacement plots for different shapes.

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First Ply failure: Ellipse > Square > Circle

Fig. 10. Load vs displacement plots for different layup of cut-outs.

First Ply failure: Symmetric > Anti-symmetric > Asymmetric

5 Conclusions In laminates failure begins at the most weakest link. By keenly observing the progression of failure in the model, it was found out that the matrix failure occurs first in this loading condition. Although, the model failure does not occur immediately because the fibres are intact and helps carrying and redistributing the load again and again. Catastrophe occurs when all the fibres fail. Different failure theories are used to analyse the model. Among all these, Hashin theory gave approximately an average prediction of all different theories. Among the ones with different radius of circular cut-outs, as the radius of the circle increases, the peak of the curve (Fig. 5) lowers i.e. the force that the laminate can withstand before failure decreases. As the radius of the circle increases, the peak of the curve shifts to the left i.e. the time after which it fails was less. From the Fig. 6, for multiple circular configurations it is observed that slope of the curve for all the three configurations are same till the first ply failure and also vertical configuration takes higher load than other two configurations. From Fig. 7, when the ellipse is in horizontal position, failure occurs at lower values of force. Comparing ellipses of same geometry, ellipse with major axis in vertical direction sustains more force than the other one. Comparing ellipses of same orientation (major axis horizontal), the ellipse with smaller major axis sustains more force before failure. Comparing ellipses of same orientation (major axis vertical), the ellipse with longer major axis sustains more force than the other before failure. From Fig. 8, when the side of the square was in line with loading axis, failure occurs at higher values of force also the square with fillet sustains higher load but the square without fillet sustains more force before failure. It was also observed that in squares with the same orientation, the second ply failure occurs later in the one with the fillet. From the Fig. 9, it is evident that an elliptical cut-out would take the maximum load when compared to a circle or a square. From Fig. 10, a symmetrically laid up laminate holds up more amount of load before failure of the first ply occurs while anti-symmetrically laid up plies sustain more even after the failure of the first-ply occurs. All these analysis performed in this paper states that the size,

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orientation and shape of the holes play a very important role in the progression of damage. Although the effect of taking different stacking sequences is over-shadowed by the other major factors, it still plays a major role in the failure envelope development. The effect of all these factors can be very large when combined and can lead to better designs when taken into consideration.

6 Future Scope The primary focus in this research has been to analyse the optimum factors to slow down the failure in progression in real-life structural models. Zhu et al. utilised a micromechanical approach to develop the failure envelope for a unidirectional composite laminate. They came to a conclusion that not even one of the existing failure theories are in comparison with their theory in the entire range and also that the combination of Maximum stress and Tsai–Wu theory is the best alternative for finding out failure in an uni-directional composites. It would further be interesting to analyse the effect of different combinations of different failure theories in analyzing the model. There are many ways to improve the strength of the laminate. As mentioned above, stacking sequence and also the geometry have an important effect on failure. The effect of properties of the material, volume fraction of the fiber and also the orientation of the fiber is not investigated. Hygrothermal stresses and other ambient conditions have not been taken into consideration. There is scope of analysis in above mentioned areas. Also, it would be very interesting to analyse the damage progression in composite structures with bolted connections and hybrid joints.

References 1. Hiton MJ, Soden PD (1998) Predicting failure in composite laminates: the background to the exercise. Compos Sci Technol (Impact Factor: 3.63) 58(7):1001–1010. https://doi.org/10. 1016/s0266-3538(98)00074-8 2. Sun CT (2008) Strength analysis of unidirectional composites and laminates. Compr Compos Mater Sci 1:641–666 3. Icardi U, Locatto S, Longo A (2007) Assessment of recent theories for predicting failure of composite laminates. Appl Mech Rev 60:76–86 4. Camanho PP, Matthews FL (1999) A progressive damage model for mechanically fastened joints in composite laminates. J Compos Mater 23:2248–2249 5. Nguyen BN (1997) Three-dimensional modeling of damage in laminated composites containing a central hole. J Compos Mater 31:1672–1693 6. American Society for Testing and Materials (2006) Standard test method for short beam strength of polymer matrix composite materials and their laminates. ASTM Standard D 2344/D 2344M, ASTM International 7. Tirkas K, Kortschot MT (1995) The relationship between critical strain energy release rate and delamination Mmde in mutidirectional carbon-fibre/epoxy laminates. ASTM STP 1285:283–304 8. Hashin Z (1980) Failure criteria for unidirectional fiber composites. J Appl Mech 47:329– 334

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9. Lessard LB, Chang FK (1991) Damage tolerance of laminated composites containing an open hole and subjected to compressive loadings: part II – experiment. J Compos Mater 25:44–64 10. Chen WH, Lee SS, Yeh JT (1995) Three-dimensional contact stress analysis of a composite laminate with bolted joint. Compos Struct 30:287–297 11. Ochoa OO, Reddy JN (1992) Finite element analysis of composite laminates. Kluwer Academic Publishers 12. Sleight DW. Progressive failure analysis methodology for laminated composite structures, NASA/TP-1999-209107 13. Chang FK, Lessard LB (1991) Damage tolerance of laminated composite containing an open hole and subjected to compressive loadings: part I – analysis. J Compos Mater 25:2–43 14. Tserpes KI, Labeas G, Papanikos P, Kermanidis T (2002) Stength prediction of bolted joints in graphite/epoxy composite laminates. Compos: Part B 33:521–529 15. Venkateshwar Reddy C (2018) J Eng Res Appl 8(7):21–24 (Part -III). www.ijera.com. ISSN 2248-9622

Optimization of Pecking Order Layout with Job Shop Scheduling as Constraint: An Approach of Metaheuristics K. Mallikarjuna1(&), V. Veeranna2, and K. Hemachandrareddy3 1

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ME Department, Mallareddy Engineering College, Misammaguda, Hyderabad, Telangana, India [email protected] Department of ME, St. John College of Engineering, Yemmiganoor, AP, India 3 Department of ME, JNTU Ananthapuram, Anantapur, AP, India

Abstract. Globally experts and researchers believe that litheness play a critical role in industrial sector. Exclusively connected with tiny lot size production because litheness flexible is a vital part to be include in arrangement of racks in layout design among the manufacturing segment. With regards to such conditions, considering NP hard dual objective issues is, commonly, a very cumbersome task. In this paper, authors addressed about a population based metaheuristics like differential evolution (DE) and sheep flock method (SFM) for cracking Pecking order layout design issues in flexible lot arrangement environment. The originators concentrated on double target advancement of which essential goal is worried about the adaptable occupation (FJSP) planning issue, the following goal concentrated on Pecking request Layout issues where removing the demand of machines with in lead-ins of ladder to restrain hard and fast transportation cost and amassing lead time of vocations on machines. The execution of the estimation (SFM and DE) is checked by benchmark issues. Finally, it is contemplated that SFM outfits perfect results when differentiated with DE. Keywords: Pecking order layout
FMS layout Differential evolution
Sheep flock method




Job shop scheduling




1 Introduction Flexibility in FMS is concern with dealing the machines, breakage of tools, changes in scheduling, part mix and alternative routing. Actually, this works relates Ladder layout design which is a plan of setup requirement for yielding products or rendering services with job shop scheduling as constraint. Problems concern with outline of a plant are commonly observed in industries linked to a position [1] of capacity in a plant. Abbas and Khan et al. presented a contextual analysis for planning of jobs during processing of chunks on existing machineries. Further they estimated the Throughput and motion stint and action stint in terms of entire dispensation time [2]. Chaudhry and Luo reviewed that during 1990–2001 nearly 21 major production journals published genetic algorithm related papers [3]. Ku, Hu, Wang et al. addressed on how to crack the © Springer Nature Switzerland AG 2020 S. C. Satapathy et al. (Eds.): ICETE 2019, LAIS 2, pp. 526–532, 2020. https://doi.org/10.1007/978-3-030-24314-2_63

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static FLP with matching areas by applying parallel simulated annealing (SA) and genetic algorithms (GAs) using a coarse grained model in retrieving the solution [4]. Ripon et al. focused about two major issues such as FLP and JSSP that affect the efficiency of manufacturing systems [5].

2 Problem Portrayal The issue plan received from Liu et al. [6] taken reference for this work. researcher’s accentuation on structure of Ladder format in adaptable arrangement of assembling with [FJSP] adaptable jobshop booking as limitation. 2.1

Multi Goal Mathematical Representations

In this area, creator acquaint the double target condition with break the adaptable variable cluster planning issues which are brought together with Ladder format configuration primes to reduce the make range, Throughput Goal of Work Diminish MAKSP, F (Smaxi) Minimize; FðS maxÞ ¼ Sn; m

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Sheep Flock Method

Sheep rush (SF) strategy is a developmental high level search method dependent on sheep advancement. This strategy [5, 6] recreates hereditary qualities of lamb runs basically. Give us a chance to expect of two runs of sheep’s in a structure which is being these two runs are taken consideration via two propels. At the point when propels occupied in chitchat, an opportunity of blending of herds been takes place, in such circumstance both the propels reach the blended group and attempt to isolate the lamb’s from blended rushes and keep the herds as already. In reality, the impact of legacy will blemish on particular rushes. In this, pairwise reverse transformation assume key job which is given beneath.

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Psuedo code for pair wise inverse mutation for sheep flock method //

Pair wise interchange mutation

for (int p = 1; p