Advances in Automotive Technologies: Select Proceedings of ICPAT 2019 [1st ed.] 9789811559464, 9789811559471

Table of contents :
Front Matter ....Pages i-viii
CFD Analysis of Automotive Radiators (Swapnil Kumar, K. Sai Kiran, Thundil Karuppa Raj Rajagopal)....Pages 1-7
Ejector-Mechanical Compression Hybrid Air-Conditioning System for Automotives: System Configuration and Analysis (M. Anoop Kumar)....Pages 9-16
Investigation of the Combined Effect of Perforated Tube, Baffles, and Porous Material on Acoustic Attenuation Performance (Sandeep Kumar, K. Ravi)....Pages 17-27
Semi-autonomous Vehicle Transmission and Braking Systems (G. Paul Robertson, Rammohan A.)....Pages 29-38
Comparison of Gaseous and Liquid Fuel Cells for Automotive Applications (A. Thirkell, R. Chen)....Pages 39-50
Lane Monitoring System for Driver Assistance Using Vehicle to Infrastructure Connection (Akash Kalghatgi, A. Rammohan)....Pages 51-63
Integration of Area Scanning with PSO for Improving Coverage and Hole Detection in Sensor Networks (T. Shankar, Geoffrey Eappen, Shubham Mittal, Ramit Mehra)....Pages 65-82
Optimized Routing Algorithm for Wireless Sensor Networks (T. Shankar, Geoffrey Eappen, S. Rajalakshmi)....Pages 83-96
Survivability Technique Using Markov Chain Model in NG-PON2 for Stacked Wavelength (S. Rajalakshmi, T. Shankar)....Pages 97-112
Effects of Different Membranes on the Performance of PEM Fuel Cell (M. Muthukumar, A. Ragul Aadhitya, N. Rengarajan, K. Sharan, P. Karthikeyan)....Pages 113-125
Design Analysis and Fabrication of Race Car Seat to Increase Driver Comfort (K. Raja, C. D. Naiju, M. Senthil Kumar, N. Navin Kumar)....Pages 127-137
Design Optimization of Lubrication System for a Four-Cylinder Diesel Engine (J. Ramkumar, George Ranjit, Vijayabaskaran Sarath, V. Vikraman, Bagavathy Suresh, Namani Prasad Babu et al.)....Pages 139-155
Investigation on Turbocharger Actuator for LPG Fuelled SI Engine (K. Ravi, Jim Alexander, E. Porpatham)....Pages 157-168
Stress Analysis of Automotive Chassis Using Hypermesh and Optistruct (Vijay Sharma, D. Mallikarjuna Reddy, Shreekant Patil)....Pages 169-185
Development of Reaction Wheel Controlled Self-Balancing Bicycle for Improving Vehicle Stability Control (Omkar Patil, Sujay Jadhav, R. Ramakrishnan)....Pages 187-195
An Intelligent Energy Management Strategy for Electric Vehicle Battery/Ultracapacitor Hybrid Storage System Using Machine Learning Approach (Geetansh Mahajan, Abhinav, R. Ramakrishnan)....Pages 197-214
Low Velocity of Single and Multiple Impacts on Curved and Hybrid Curved Composite Panel for Aircraft Applications (D. Mallikarjuna Reddy, Shreekant Patil, Kiran S. Matti, Nemmani Abhinav)....Pages 215-223
Aerodynamic Study of a Three Wheeler Body (C. Bhaskar, Krishna Rawat, Muhammed Minhaj, M. Senthil Kumar, C. D. Naiju)....Pages 225-230
Evaluating the Hardness and Microstructural Analysis of Reinforcing the Nano Silicon Carbide and Nano Zirconium Oxide in Hybrid Al6061 Metal Matrix Composite (V. Deepakaravind, P. Gopal)....Pages 231-239
Exploratory and Performance Analysis of Solar Refrigeration System Using Nanofluids—A Review (M. Sivakumar, S. Mahalingam)....Pages 241-247

Citation preview

Lecture Notes in Mechanical Engineering

M. Razi Nalim R. Vasudevan Sameer Rahatekar   Editors

Advances in Automotive Technologies Select Proceedings of ICPAT 2019

Lecture Notes in Mechanical Engineering Series Editors Francisco Cavas-Martínez, Departamento de Estructuras, Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Francesco Gherardini, Dipartimento di Ingegneria, Università di Modena e Reggio Emilia, Modena, Italy Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia Vitalii Ivanov, Department of Manufacturing Engineering Machine and Tools, Sumy State University, Sumy, Ukraine Young W. Kwon, Department of Manufacturing Engineering and Aerospace Engineering, Graduate School of Engineering and Applied Science, Monterey, CA, USA Justyna Trojanowska, Poznan University of Technology, Poznan, Poland

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M. Razi Nalim R. Vasudevan Sameer Rahatekar •

•

Editors

Advances in Automotive Technologies Select Proceedings of ICPAT 2019

123

Editors M. Razi Nalim Department of Mechanical Engineering Purdue School of Engineering & Technology Indianapolis, IN, USA

R. Vasudevan School of Mechanical Engineering Vellore Institute of Technology (VIT) Vellore, Tamil Nadu, India

Sameer Rahatekar Enhanced Composites and Structures Centre Cranfield University Cranfield, UK

ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-15-5946-4 ISBN 978-981-15-5947-1 (eBook) https://doi.org/10.1007/978-981-15-5947-1 © Springer Nature Singapore Pte Ltd. 2021 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

CFD Analysis of Automotive Radiators . . . . . . . . . . . . . . . . . . . . . . . . . Swapnil Kumar, K. Sai Kiran, and Thundil Karuppa Raj Rajagopal

1

Ejector-Mechanical Compression Hybrid Air-Conditioning System for Automotives: System Configuration and Analysis . . . . . . . . . . . . . . . M. Anoop Kumar

9

Investigation of the Combined Effect of Perforated Tube, Baffles, and Porous Material on Acoustic Attenuation Performance . . . . . . . . . . Sandeep Kumar and K. Ravi

17

Semi-autonomous Vehicle Transmission and Braking Systems . . . . . . . . G. Paul Robertson and Rammohan A.

29

Comparison of Gaseous and Liquid Fuel Cells for Automotive Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Thirkell and R. Chen

39

Lane Monitoring System for Driver Assistance Using Vehicle to Infrastructure Connection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akash Kalghatgi and A. Rammohan

51

Integration of Area Scanning with PSO for Improving Coverage and Hole Detection in Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . T. Shankar, Geoffrey Eappen, Shubham Mittal, and Ramit Mehra

65

Optimized Routing Algorithm for Wireless Sensor Networks . . . . . . . . . T. Shankar, Geoffrey Eappen, and S. Rajalakshmi Survivability Technique Using Markov Chain Model in NG-PON2 for Stacked Wavelength . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Rajalakshmi and T. Shankar

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Contents

Effects of Different Membranes on the Performance of PEM Fuel Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 M. Muthukumar, A. Ragul Aadhitya, N. Rengarajan, K. Sharan, and P. Karthikeyan Design Analysis and Fabrication of Race Car Seat to Increase Driver Comfort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 K. Raja, C. D. Naiju, M. Senthil Kumar, and N. Navin Kumar Design Optimization of Lubrication System for a Four-Cylinder Diesel Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 J. Ramkumar, George Ranjit, Vijayabaskaran Sarath, V. Vikraman, Bagavathy Suresh, Namani Prasad Babu, and Malekar Amit Investigation on Turbocharger Actuator for LPG Fuelled SI Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 K. Ravi, Jim Alexander, and E. Porpatham Stress Analysis of Automotive Chassis Using Hypermesh and Optistruct . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Vijay Sharma, D. Mallikarjuna Reddy, and Shreekant Patil Development of Reaction Wheel Controlled Self-Balancing Bicycle for Improving Vehicle Stability Control . . . . . . . . . . . . . . . . . . . . . . . . . 187 Omkar Patil, Sujay Jadhav, and R. Ramakrishnan An Intelligent Energy Management Strategy for Electric Vehicle Battery/Ultracapacitor Hybrid Storage System Using Machine Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Geetansh Mahajan, Abhinav, and R. Ramakrishnan Low Velocity of Single and Multiple Impacts on Curved and Hybrid Curved Composite Panel for Aircraft Applications . . . . . . . . . . . . . . . . 215 D. Mallikarjuna Reddy, Shreekant Patil, Kiran S. Matti, and Nemmani Abhinav Aerodynamic Study of a Three Wheeler Body . . . . . . . . . . . . . . . . . . . . 225 C. Bhaskar, Krishna Rawat, Muhammed Minhaj, M. Senthil Kumar, and C. D. Naiju Evaluating the Hardness and Microstructural Analysis of Reinforcing the Nano Silicon Carbide and Nano Zirconium Oxide in Hybrid Al6061 Metal Matrix Composite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 V. Deepakaravind and P. Gopal Exploratory and Performance Analysis of Solar Refrigeration System Using Nanofluids—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 M. Sivakumar and S. Mahalingam

About the Editors

Dr. M. Razi Nalim is Executive Associate Dean for Research and Graduate Programs at the Purdue University School of Engineering & Technology in Indianapolis (currently on leave, and serving as Visiting Professor at Vellore Institute of Technology, Vellore, India). He has three decades of experience in higher education and professional practice – in industry, academia, and government. Working at NASA Glenn Research Center and Purdue University, he pioneered novel concepts for pressure-gain combustion engines and non-steady flow pressure-wave machines, aimed at efficiency, power and emissions improvement of aircraft and power generation engines. Recognized as an entrepreneurial ‘translational’ scholar at IUPUI, he helped establish multiple industry-university research consortia, especially with Rolls-Royce Corporation. His research has led to 7 patents, and over 100 publications, supported by over $10 million in grants from NASA, US National Science Foundation (NSF), Rolls-Royce, and other sponsors. He previously led R&D at two small start-up companies, and has launched a startup company to commercialize his research. He has received the IUPUI Bynum Faculty Mentor award for guiding undergraduate research, University Trustees teaching award for innovative learning contributions, and the highest honors of his school for research and service. He has conducted workshops on project-enhanced active learning in engineering education, supported by the NSF. Internationally, Dr. Nalim has given many keynote talks and served as NATO AGARD Scholar and twice as a Fulbright Scholar. He is an Associate Fellow of the American Institute of Aeronautics & Astronautics. Dr. R. Vasudevan is Professor & Dean of School of Mechanical Engineering and The Director- Centre for Innovative Manufacturing Research (CIMR) at Vellore Institute of Technology, Vellore, India.He obtained his Ph.D. from Concordia University, Canada. He has around 18 years of combined research and teaching experience in India and Canada. He secured University first rank and Gold medal during the Post Graduation. He was awarded International Tuition Fees Remission at Concordia University during 2007. He was also nominated for Governor General Gold Medal Award for Ph.D. thesis and best Ph.D. thesis Concordia University, vii

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About the Editors

Montreal, Canada. He has published around 45 research articles in international journals with high impact factors. He has also authored a monograph titled “Analysis of Smart Structure”. At present, he is working on seven research projects sponsored by various International and National funding agencies. He has also finished three funded projects sponsored by ARDB, VRDEand one consultancy project by Alvi Tech. Pvt. Ltd., Bangalore. He has guided 6 Ph.D. students and one M.S. (Research) scholar at VIT. His research focuses on broad range of problems in mechanics of composite structures, active and semi-active vibration control, structural health monitoring, with applications in aerospace and automotive industries. He is a life member of Indian Society of Technical Education, New Delhi, and a senior member of International Association of Computer Science and Information Technology, Singapore. Dr. Sameer Rahatekar earned his PhD at University of Cambridge where he worked on nano-composites modelling and manufacturing. He worked as a postdoctoral researcher at National Institute of Standards and Technology (NIST), USA where he worked on manufacturing strong and multi-functional natural polymer based fibers using ionic liquids as a benign solvent. He also worked on nano-particles dispersion, rheology and nano-composites manufacturing at NIST. He was a lecturer at the Advanced Composite Centre for Innovation and Science (ACCIS) at University of Bristol where he worked on manufacturing strong of cellulose fibres as precursors for carbon fibers and on nano-particles reinforced carbon/glass fiber composites for improved fracture toughness, erosion resistance and lightening strike protection of composites parts used in aerospace industry.

CFD Analysis of Automotive Radiators Swapnil Kumar, K. Sai Kiran, and Thundil Karuppa Raj Rajagopal

Abstract This paper of ours deals with the automotive radiators. We have shown a computational fluid dynamics (CFD) modelling simulation of mass flow rate of air passing through an automotive radiator. Modelling has been done in Solidworks and exported to ANSYS for CFD analysis. In our paper, the main implication that we have drawn is that the heat which is been transferred by a radiator is a function of the airflow at different air velocity. We undertook this experiment on a single radiator of constant geometry on the basis of some parameters like the material of the radiator and the vehicle’s speed. The thermal analysis is done for different velocities of air mixture passing through different tube materials such as aluminium and stainless steel. The numerical results were compared and results obtained served as a good database for the future investigations. Keywords Computational Fluid Dynamics · Thermal · Meshing · Radiators · Temperature drop

Nomenclature [1,2] Flow area Velocity of water Reynolds number for water Nusselt number for water Convective heat transfer coefficient of water Velocity of air Maximum velocity of air

((π /4) * (Di )2 ) m2 (ma/(ρ wa * Fa)) m/s ((ρ wa * V wa * Di )/μ) 0.023 * ((REwa )0.8 ) * ((PRwa )0.3 ) (NUwa * K wa )/(Di ) W/m2 K mair /(2 * ƥair * (π /4) * Df ) m/s (S t /(S t − Do )) * V air m/s

S. Kumar (B) · K. Sai Kiran Student, VIT University, Vellore 632014, India e-mail: [email protected] T. K. R. Rajagopal Professor, VIT University, Vellore 632014, India

© Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_1

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Reynolds number air Nusselt number for air Convective heat transfer coefficient of air Corrected fin length L c Coefficient for calculating efficiency m Efficiency of fin ïf Surface area of fin Af

S. Kumar et al.

(ρ air * Max V air * Df )/μair 0.664 * (REair )0.5 * (PRair )0.333 (NUair * K air /Do ) W/m2 K L f + (H f /2) m ((2 * hair)/(Kalu * H f ))0.5 (tan h (m * L c ))/(m * L c ) 2 * W f * L cm2

1 Main Text A lot of technology research work has been profoundly carried out on CFD Analysis of automobile radiator. Technical advances in the fields of automobile industry have led to a creation of different viewpoints regarding the improvement of the vehicle’s performance, its reliability and increasing pollution concerns. Automotive research industries are mainly giving an emphasis on one area which is the ability to rapid cooling of the engine for keeping in mind, its performance efficiency. Cooling is one of the important processes for maintaining and enhancing the operational performance of the system. Thus, researchers are starting to invest more on dealing with this issue for technological advancements. Automobile industries have high demands for high efficiency engines. A high efficiency engine is not only based on the performance of radiator but also depends on better fuel economy and less emission rate. Radiators play a crucial role in an automobile. They are the heat exchangers which are used to transfer thermal energy from one medium to another for the purpose of cooling and heating. The radiator is always a source of heat to its environment, although this may be for either the purpose of heating this environment, or for cooling the fluid or coolant supplied to it, as for engine cooling radiators transfer the bulk of their heat via convection instead of thermal radiation. The heat flow pattern in the working of the radiator is quite straight forward in the radiator’s operation; that is the engine heat flows through the coolant and then the coolant gets heated up [3]. The hot coolant is made to pass through the radiator from where the heat is taken up by the air.

2 Numerical Analysis Geometrical analysis of both the radiators is as follows Mass properties: Mass = 7.71 lb Volume = 213.29 cubic inches Surface Area = 6882.10 square inches

CFD Analysis of Automotive Radiators

3

(a)

(b)

Fig. 1 (a) Automotive radiator, made of aluminium (b) automotive radiator, made of stainless steel

Mass flow rate of coolant

Temperature drop of coolant in tube Aluminium

Stainless steel

0.23

27.2

23.3

0.33

22.8

18.8

2.1 Modelling We have done the modelling in the Solidworks. Figure 1a shows the radiator made of aluminium and Fig. 1b shows radiator made of stainless steel.

2.2 Meshing A pre-processing step for the computational field simulation is the discretization of the domain of interest and is called mesh generation. Meshing has their advantages and disadvantages in terms of both solution accuracy and the complexity of the mesh generation process. These provide physical preferences that help in automation. Figure 2a shows meshing for stainless steel and Fig. 2b shows meshing for aluminium.

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(a)

(b)

Fig. 2 (a) Meshing of automotive radiator, made of aluminium (b) meshing of automotive radiator, made of stainless steel

3 Results and Discussions It shows the overall heat transfer of coolant in the radiator for different mass flow rate of coolant for two different tube materials namely stainless steel and aluminium. The coolant heat is transferred from coolant to the air as the temperature of coolant decreases and temperature of air increases.

3.1 Thermal Analysis Here, we found that the performance of radiator depends upon the mass flow rate of coolant and air, temperature of coolant which could be altered so that to get the desired results for improving the performance of radiator [4]. Hence, computational fluid dynamic simulation approach is adopted to analyze the effect of different temperature distributions for coolant on the varying mass flow rate.

3.1.1

Temperature Distribution

Figure 3a shows temperature distribution for stainless steel and Fig. 3b shows for aluminium.

CFD Analysis of Automotive Radiators

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(a)

(b) Fig. 3 (a) Temperature distribution for stainless steel (b) temperature distribution for aluminium

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Fig. 4 (a) Velocity distribution, CFD analysis

4 CFD Analysis 4.1 Velocity Distribution 5 Conclusion In this work, theoretical calculations have been done. Using ANSYS, thermal analysis, meshing is done. Two different materials (aluminium and stainless steel) were chosen during modeling, and after that, analysis of radiators made of (aluminium and stainless steel) is done. In the temperature variation distribution, we can visualize the temperature variation of both the materials and corresponding to that performance of radiator with temperature distribution. Meshing is becoming difficult for CFD analysis of whole radiator CAD model. Optimization of model is done. Velocity distribution shows velocity profile.

CFD Analysis of Automotive Radiators

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References 1. Oliet C, Oliva A, Castro J (2007) Parametric studies on automotive radiator. Appl Thermal Eng 27 2. Lin C, Saunders J, Watkins S, The effect of changes in ambient and coolant radiator inlet temperatures and coolant flow rate on specific dissipation. SAE Technical paper 3. (Thomas)Wang T, Wagner J, Advanced automotive thermal management—nonlinear radiator fan matrix control 4. Maoa S, Cheng C, Li X, Michaelides EE, Thermal/structural analysis of radiators. Appl Thermal Eng

Ejector-Mechanical Compression Hybrid Air-Conditioning System for Automotives: System Configuration and Analysis M. Anoop Kumar

Abstract Air conditioning systems are essential for modern automotives. It is reported that the power drawn for functioning the air conditioning system affects the energy performance of the vehicle significantly. Vapour compression refrigeration systems are widely used for conditioning the vehicle in warm and humid climatic conditions. They draw the power from vehicle engine to run the compressor. This compressor power consumption can be reduced by making use of a hybrid mechanical compression refrigeration system which utilizes freely available waste heat in the vehicle to share a part of compression power. Ejector mechanical compression hybrid refrigeration systems are suitable for this application because of its compact size and simple operation. A hybrid ejector-mechanical hybrid airconditioning system is configured for a passenger vehicle. The thermodynamic analysis of the same is done to arrive at its coefficient of performance. Keywords Ejector-compression hybrid refrigeration · Ejector modeling · System configuration and analysis · COP

1 Introduction Air-conditioning system has become an essential component of modern automotive cars. In hot climatic conditions, systems are summer cooling systems that work on vapour compression refrigeration cycle. Even though there are many methods of refrigeration and air conditioning, vapour compression refrigeration (VCR) systems are used to condition a car as it is comparatively compact, reliable and cost effective. The essential components of VCR are the compressor, condenser, evaporator and the expansion valve. The compressor gets the power from car engine. It was found out that the current air-conditioning systems reduce the fuel economy of a conventional vehicle by at least 1.52 km per litre, and has an unacceptable impact on high fuel economy vehicle where the mileage was found to be reduced by 2.74 km per litre M. Anoop Kumar (B) VIT University, Vellore 632014, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_2

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[1]. It was also noticed that conventional air-conditioning loads can reduce electric vehicle range and hybrid electric vehicle fuel economy by nearly 40% depending on the size of the air conditioner and the driving cycle [2]. As the harmful effects of emissions from IC engine vehicles are increasingly felt in recent years, it is high time to have most energy efficient air-conditioning system in cars. We can think of any modification in standard vapour compression system to enable energy saving. Since waste heat is readily available in the form of engine cooling water and exhaust gas heat, a hybrid system based of heat energy will be possible. There are many refrigeration methods using input energy in the form of heat like vapour absorption system, thermo-electric systems, adsorption systems, ejector systems, etc. [3]. A hybrid system based on ejector will be suitable for the said application as ejectors are simple in operation, cheap and compact. The major disadvantages of ejector refrigeration system are poor COP and inadequate performance at off-design operating conditions. These demerits can be overcome with a suitable system configuration of a vapour compression-ejector hybrid air-conditioning system for the passenger car application.

2 Air Conditioning System for IC Engine Cars A car air conditioning (AC) system has the same components of a domestic AC system but in a miniature form. The essential components are shown in Fig. 1. The working is based on the standard vapour compression refrigeration system. Liquid refrigerant passes though the evaporator and get phase changed into vapour. The latent heat of vaporization is taken from the air which is being supplied to the car cabin. Thus, cool air can be produced and supplied to the cabin as long as there is liquid refrigerant vaporizing in the evaporator. In order to use the same refrigerant that is evaporated again to produce refrigeration effect, it has to be condensed and converted into liquid. Since condensation heat has to be rejected to ambient, which Fig. 1 Schematic of car AC

Condenser

Expansion valve

Compressor

Filter drier

Evaporator

Ejector-Mechanical Compression Hybrid Air-Conditioning System …

11

at higher temperature than the evaporator temperature, the refrigerant vapour from evaporator has to be pressurized to saturation pressure corresponding to the one at ambient temperature. A compact refrigerant compressor driven by engine power is used for this purpose [4]. Usually, the car AC compressors come with a clutch connected to the engine through a belt drive, which enable on–off working of the compressor to maintain the set temperature in the cabin.

3 Ejector Theory The ejector is a flow device consisting of nozzle sections so that it can allow a high pressure fluid, termed the primary fluid, to entrain a low pressure fluid (the secondary fluid) into the flow path, and discharge the mixed flow at an intermediate pressure that is higher than the secondary flow pressure. In effect the ejector can act like a compressor or a pump which can pressurize a fluid flow [5]. Its main advantages are that it is without any moving parts, lubricants and maintenance. The main two parameters that define the performance of the ejector are the entrainment ratio, which is defined as the ratio of the mass flow rates between the secondary flow and the primary flow, and the pressure lift ratio, which is the ratio of the ejector outlet pressure to the secondary flow pressure at the ejector inlet. Figure 2 shows a typical ejector geometry. It has mainly four parts. First, the nozzle through which the primary fluid expands and create a low pressure section at its outlet which facilitates the entrainment of the secondary flow. Second, a suction chamber where the secondary flow gets into the motive flow. Third, a mixing chamber where the two flows mix together to form a single flow. The mixing chamber consists of a convergent section and a constant-area part. Fourth part is a diffuser which reduces the velocity of mixed flow and increases the pressure to the required backpressure value. The main geometry is characterized by the area ratio, which is defined as the area of the constant-area part in the mixing chamber divided by the nozzle throat area.

Fig. 2 Ejector geometry (Courtesy [5])

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As mentioned above, the primary flow acts as motive fluid for the ejector while the secondary flow is the driven fluid. Both the primary and secondary flows can be in any of the flowing state-liquid ,vapour or two-phase. Ejectors have two main applications in refrigeration systems: the ejector refrigeration system (ERS), which use a vapour ejector to fulfil the function of a compressor, and the ejector-enhanced vapour compression refrigeration system. The later can be in varying system configurations. The familiar configuration is the one which applies a two-phase ejector as the expansion device for system performance improvement. It is most suited for trans-critical CO2 refrigeration as the pressure lift is high enough to produce substantial ejector effect. Another configuration is the hybrid systems employing ejector and the mechanical compressor like the one described in this literature.

4 Hybrid System Configuration IC engine cars have more than one source of waste heat. Engine cylinder cooling water and exhaust gas are typical examples. A compact heat exchanger can be designed to heat the refrigerant in a hybrid mechanical compression-ejector air-conditioning system. The system can be designed to have the required compression shared by a mechanical compressor as well as ejector installed in series as shown in Fig. 3. The refrigerant from the compressor is entrained by an ejector operated by motive steam from a heater. The compressor will be experiencing lesser head pressure and thereby

1 8

2 7 3

4

6 5

1. Engine cylinder, 2. Compressor, 3. Condenser, 4. liquid receiver 5. Pump, 6. Heater(Generator), 7. Ejector, 8. Car cabin Evaporator Fig. 3 Hybrid system components

Ejector-Mechanical Compression Hybrid Air-Conditioning System … Fig. 4 Hybrid system configuration

Condenser 4

Heater Pump 5

13

3 Ejector

6

6' 3'

2 Expansion valve 7

1 Compressor

Evaporator

lower power drain from the engine. The heater will be maintained hot water in the range of 80–90 °C using waste heat from the engine cylinders. The liquid refrigerant from the liquid receiver is pumped to the heater pressure. In the heater, hot water is maintained in a shell and the refrigerant is passed through the coil and it get its phase changed to saturated vapour. The saturated vapour at high pressure is expanded in the primary nozzle of the ejector and it will entrain the relatively low pressure vapour from the compressor. The compressor power saving depends on the intermediate pressure at state 2 in Fig. 4. The hybrid system components can be designed to operate the compressor in such way that compressor will pressurize the vapour to half of the required pressure ratio even though it is capable of pressurizing to the full. An electronic pressure and temperature control can be incorporated to sense the temperature of ambient, heater and the entraining fluid. If any off-design condition of the ejector is reached, the pump will stop working and there will not be any ejector effect. The entire compression load will be taken by mechanical compressor. In this way, reliability of operation of the air-conditioning system can be achieved.

5 Thermodynamic Analysis An analysis of the hybrid vapour compression-ejector system can be made based on energy and momentum balance in different system components. Referring to Fig. 4, mathematical equations can be developed for the various system components.

5.1 Ejector Ejector is the sensitive component in the hybrid system. The process of entraining the secondary flow by the primary flow is very complex. However, very effective mathematical models have been developed by researchers over the years. They could predict

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the performance of ejectors in good agreements with the experimental values. A very early profound analysis of the ejector was conducted by Keenan et al. (1950), who proposed two feasible methods to describe the ejector mixing process: the constantpressure mixing model, in which the pressure of the mixing process was assumed constant, and the constant-area mixing model, where the mixing process was assumed to occur in the constant-area part of the ejector [5]. For modelling the ejector, a constant-pressure mixing theory with the following assumptions is employed. 1. Steady flow the ejector components 2. Refrigerant heat loss in the ejector is negligible 3. Constant ejector efficiency values The various energy and momentum balance equations for the flow through different sections of the ejector can be written [6]. The most important parameter of the ejector operation is the entrainment ratio (μ) and is defined as the ratio of mass flow rate of secondary fluid to that of primary fluid. μ = m 2 /m 6 Applying steady flow energy balance to the primary nozzle flow, the velocity of primary fluid at nozzle exit can be written as V p6 =



  2ηn h p6 − h p6 s

where h p6 is the specific enthalpy at the inlet of the primary flow and h p6 is that at its exit after an isentropic expansion. The pressure at section 6 is usually taken as the pressure of secondary flow at inlet. ηn is the isentropic efficiency of nozzle and can be taken as 0.9. The primary flow entrains the secondary flow and both the streams get mixed at the section 3 of constant-area part of the ejector. The velocity of mixed flow can be written as Vm3 =

v p6 √ ηm 1+μ

where ηm is the mixing efficiency accounting for frictional losses in mixing chamber. Applying energy balance, the enthalpy of mixed flow at section 3 can be written as  h p6 + μh s2 V2  − m3 = 1+μ 2 

h m3

h s2 is the inlet specific enthalpy of the secondary flow at its inlet. Enthalpy of mixed refrigerant at diffuser exit or inlet to the condenser is written as

Ejector-Mechanical Compression Hybrid Air-Conditioning System …

h d3 = h m3 +

15

(h d3 s − h m3 ) ηd

where h d3 s is the exit enthalpy of refrigerant at diffuser outlet after isentropic compression and attaining condenser pressure. ηd is the diffuser isentropic efficiency which is in the tune of 0.9. Considering the above equations and neglecting the velocity of the outgoing stream from the ejector, the following expression can be written for ejector entrainment ratio.   ηn ηm ηd h p6 − h p6 s −1 μ= (h d3s − h m3 )

5.2 Other System Components Apart from the ejector, remaining of the system components can be analyzed with simple energy balance equations as follows Refrigeration effect produced Q e = m 2 .(h 1 − h 7 ) Compressor power consumption Wc = m2 .(h2 – h1 ) Where, Mass flow rate through the compressor m 2 = TQRe , TR is the total system capacity Ton of refrigeration expressed in kW. Mass flow through ejector m6 = m2 /entrainment ratio, µ Heat rejected in condenser Q c = (m 6 + m 2 ).(h 3 − h 4 ) Power consumed by pump W p = m 6 .(h 5 − h 4 ) Heat added to refrigerant in the heater Q h = m 6. (h 6 − h 5 ) Power saved Ws = m 2 .(h 3 − h 2 ) Overall system COP = C O P = Q e /(Wc + W p ) The above set of equations can be solved to analyze the performance of the hybrid system.

6 Conclusion Air Conditioning systems in automotive cars are essential and they consume substantial share of engine power and thus fuel input. In this contest, air-conditioning systems have to be operated at most energy efficient manner. IC engine cars have waste heat readily available and a hybrid system that can utilize this waste heat is configured. Out of the various heat operated refrigeration systems, ejector refrigeration is compact and cheap and thus suitable for the automotive application studied here. The demerit of poor performance of the ejector system at off-design operating conditions of ejector can be overcome by hybridizing it with a vapour compressor and employing

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a suitable control system. The proposed configuration is mathematically modelled for thermodynamic analysis.

References 1. Shete K (2015) Influence of automotive air-conditioning load on fuel economy of IC engine vehicles. Int J Sci Eng Res 6(8):1367–1372 2. Farrington R, Rugh J (2000) Impact of vehicle air- conditioning on fuel economy, tailpipe emissions, and electric vehicle range. Earth Technologies Forum, Washington, D.C. 3. Ghafoor A, Munir A (2015) Worldwide overview of solar thermal cooling technologies. Renew Sustain Energy Rev 43:763–774 4. Shah RK, Automotive air conditioning system-Historical developments and state of the technology and future trends. In: BSME-ASME international conference on thermal engineering, 20–22 December 2006, Dhaka, Bangladesh 5. Chen J, Investigation of vapour ejectors in heat driven ejector refrigeration systems. Doctoral Thesis, Department of Energy Technology, Royal Institute of Technology, KTH, Sweden 6. Xing M et al (2015) Performance evaluation of an ejector sub cooled vapour-compression refrigeration cycle. Energy Convers Manag 92:431–436

Investigation of the Combined Effect of Perforated Tube, Baffles, and Porous Material on Acoustic Attenuation Performance Sandeep Kumar and K. Ravi

Abstract In this work, muffler with perforated baffles and pipes has been studied for noise attenuation characteristics. Transmission loss parameter is used to quantify the acoustic performance in the muffler. The purpose of our study is to find the optimum arrangement for getting the best optimized effects for absorptive material on porosity of baffle plates and length of perforation in the pipes. The computational acoustic simulation tool COMSOL Multiphysics is used for modeling transmission and predicting acoustics absorption behavior through the porous pipe of the muffler. In the present study, two different configurations have been analyzed. Out of these, one configuration is structurally different in respect of the effect of porosity of the perforated pipes at inlet and outlet on the TL performance. Effect of porosity of the perforated pipe with absorptive material and the other configurations differs regarding the presence or absence of absorptive material in third chamber. The most important and significant outcomes of this study facilitate an optimum design of the muffler in 3D, which having optimum absorption for the exhaust gases induced noise generated in the muffler. It had been shown that perforated baffles play a crucial role in making the configuration of very robust. It was observed that 5% porosity is higher counterproductive in comparison with the other values of the porosity. The presented muffler is effective in attenuating the low–medium frequency as well as higher frequency band noise. The analyzing of the intake and exhaust lines of the muffler helps in reducing the vibrational losses.

S. Kumar · K. Ravi (B) School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, India e-mail: [email protected] S. Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_3

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1 Introduction IC engine exhaust line is the predominant source of noise in automobile, which is suppressed by muffler. The challenges in muffler design were attaining maximum acoustic transmission loss without increasing the back pressure which is a trade-off between the acoustic performance and engine power [1]. For acoustic analysis, the finite element methods (FEMs) [2, 3] and boundary element methods (BEMs) [4–6] are used as a standard tool in 3D analysis of muffler, and these are the methods adaptable for any geometries. However, having good results, these methods are not advisable for acoustic network having elements which admit one-dimensional. In this sense, transfer matrix is derived for different cascade elements of muffler [7– 9]. Back pressure is a larger problem with the cascade elemental muffler and poor acoustic performance, and this problem was resolved by means of multiply-connected muffler which calls for special analytical system along with the reactive elements [10– 14]. The invention of long strand fiber absorptive materials began its entry in the exhaust system along with the reactive elements. This muffler helps in managing the impedance of perforated plates subjected to grazing gas flow and backed by porous media, and helps in development of the hybrid mufflers or combination of mufflers [15–17]. The integrated transfer matrix (ITM) approach and the lumped flow resistance network theory are used for the flow acoustic analysis of a three-chamber combination muffler with perforated intermediate baffles [18–21]. The predicted value of transmission loss (TL) gives support against the finite element simulations. The muffler with perforated baffles and intermediate tubes connected to the consecutive chambers can be best analyzed by means of intermediate transfer matrix approach. Recently, this approach was successfully implemented by Verma and Munjal, and they have connected three chambers having perforated baffle hybrid muffler with and without a curved duct. They have done the parametric study of perforated baffles and absorptive materials for good acoustic performance, having length-to-diameter ratio 2.311 with offset inlet and outlet with circular in shape. The elliptical muffler is with 0.6 eccentricity and length-to-diameter ratio of 0.67. The compactness is increased by adding the impervious baffles in the upstream and perforated plate in the downstream. They observed that porosity of intermediate baffles should be in order of 5%; higher porosity is counterproductive. The transmission loss for medium and high frequencies was increased by adding the absorptive material in the third chamber of the muffler and also help to absorb the aerodynamic noise as there are discontinuities in the second chamber [22]. Herrin et al. shown that plane-wave models are useful for better understanding mufflers including the effect of adding elements or making dimensional changes that can be assessed quickly. Over time, designers develop better intuition for understanding the effect of changes and how to design more effective, less expensive mufflers, and also studied the advantages of plane-wave models in better understanding the design of muffler for various effects like as, sidlab with and without leak, with and without baffles [23].

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Fig. 1 Front view and side view of reactive muffler configuration

Kaneda, T. et al. validated the variation in TL by using 3D finite element method (FEM) and experimental results obtained in terms of sound pressure level at inlet and outlet of muffler by using speaker test. As well as in terms of exhaust temperature on the TL, they concluded that TL has good coincidence for each frequency [24]. Cherng, J. et al. studied the two muffler configurations, first is reactive muffler without absorptive material, and second one is of dissipative muffler with absorptive fiber glass, using ANSYS and commercial FEA software (CFEA) for different parameters, like as effect of absorbent material, effect of additional partition and absorption, effect of absorbent material effect of perforation holes, and effect of partition and extra perforation. Lastly, they concluded that adding the numbers of holes did not show the significant amount of transmission loss for both mufflers A and B. As well both numerical software tools have shown good agreement with the experimental results [25].

2 Details of Muffler Configurations A schematic diagram of the commercial muffler investigated here is shown in Fig. 1. This muffler consists of three chambers separated by perforated baffles. Chamber 3 is an acoustically lined duct (a perforated tube surrounded by a cavity filled with acoustically absorptive material). There are two small diameter (d) pipes (pipe 1 and pipe 2); pipe 1 passes through chamber 1 and opens in chamber 2, and pipe 2 starts from chamber 3 and opens in chamber 2; in our study, we have taken the perforation on pipe 1 and pipe 2 throughout the length of chamber 1 and chamber 2, respectively, as shown in Fig. 1. These three chambers are not separated from each other by the help of baffles in between them; the sound waves in these chambers interact with each other through pipes as well as perforations in the baffles plates.

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3 Parameters Property

Value

Description

Dh

1.5 mm

Plate thickness

dh

5 mm

Hole diameter in perforates

μ

1.8 × 10−5 Pa s

Dynamic viscosity

σp

0.056, 0.10, 0.15, 0.20

Porosity, pipe perforates

σbi

0.07, 0.14, 0.21 0.28

Porosity, baffle perforates on inlet side

σbo

0.07, 0.14, 0.21, 0.28

Porosity, baffle perforates on outlet side

Rf

10,000, 16,000, 20,000, 25,000 [Pa s/m2 ]

Flow resistivity

4 Methodology 4.1 3D FEM Analysis The muffler is divided into five different domains which are then discretized into small finite elements. By using the acoustic pressure as the independent variable, the wave propagation is solved in the frequency domain using the time harmonic pressure acoustic mode in COMSOL Multiphysics. The governing equation is the modified version of the 3D Helmholtz equation:   ∇p − . − ρc   ∇p − . − ρc

ω2 p =0 ρc2 ω2 p =0 ρc2

where ρ, c, and ω are the density, speed of sound, and angular frequency, respectively. The following boundary conditions are used to simulate the system. 1. At the solid boundaries, the outer walls of the muffler, the baffles plates between the chambers and the walls of the inner pipes are considered to be the sound hard (walls) boundary conditions:   ∇p .n = 0 − ρ Here, n is the unit normal vector of propagation,

Investigation of the Combined Effect of Perforated Tube …

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2. At the inlet boundary, waves are assumed to be combination of two waves, i.e., inlet wave and outlet wave,   ∇p iω 2iω − .n = p− p0 ρ ρc ρc where p0 = 1 Pa is the applied pressure at entrance to the inlet pipe and i is the imaginary unit. 3. At the outlet boundary, the model specifies a radiation condition for an outgoing plane wave.   iω ∇p .n = p − ρ ρc The transmission loss (TL) is expressed in terms of the ratio of the incident acoustic power at the inlet of the muffler, W incident , using the applied pressure, p0 , and the transmitted acoustic power at the outlet of the muffler, W transmitted , using the computed pressure, pc , at the outlet as  T L = 10 log10

 Wincident dB Wtransmitted

where  Wincident =

p02 and Wtransmitted = 2ρc



pc2 2ρc

where p0 is the pressure associated with the incident wave at the inlet and pc is the pressure associated with the wave transmitted into an anechoic termination.

4.2 Case Validation Validation of the method has been done with the published experimental results got by Verma and Munjal [22], for transmission loss of a single-pass perforated absorbing silencer. Figure 2 shows the comparison between the TL values calculated here by FEM reported by Verma and Munjal [22]. They may be seen to be in reasonably good agreement with each other, thereby validating the FEM, which in turn is used here to corroborate the plane-wave model developed for the two muffler configuration 1 and configuration 2 are shown in figure.

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a. Configuration 1

b. Configuration 2 Fig. 2 Different muffler configurations investigated in this paper

Configuration 1: This is a basic three-chamber configuration shown in Fig. 2a. Configuration 2: This configuration, shown in Fig. 2b, is similar to configuration 1, except that there is absorptive material in the third chamber, i.e., outlet side. The simulating conditions are as follows: exhaust gas temperature 293 K, density 1.2 kg/m3 , sound speed 343 m/s, and the flow resistivity of absorptive material is 16,000 Pa s/m2 are considered For the muffler shell diameter D of 193.52 mm and sound speed c0 of 343 m/s, the plane-wave cut-off frequency is given by: TL performance of the two configurations where the performance of the corresponding single large simple expansion chamber has also been superimposed for ready reference. The following observations may be made from Fig. 2. TL of all the three-chamber configurations of Fig. 2a is substantially better than that of corresponding single large simple expansion chamber muffler, which is as would be

Investigation of the Combined Effect of Perforated Tube …

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expected. The TL curve of configuration b is only marginally better than configuration a over the range of frequency. Therefore, it may not be worthwhile to include a bent tube in the muffler [22].

5 Results and Discussion In the present study, two different configurations have been analyzed. Out of these, one configuration is structurally different, and the other configurations differ regarding the presence or absence of absorptive material in third chamber. These configurations, shown in Fig. 5 are the absorptive material in the third chamber of the muffler. The transmission loss measurement has been done at a particular value of temperature 293 K. The TL value for Fig. 2a is taken at the particular value of flow resistivity 15,000 Pa s/m2 and baffles porosity of 0.07, and for the analysis of configuration (b), it is considered that the perforation in pipe is fixed to 0.056, the flow resistivity is taken as 16,000 Pa s/m2 , and porosity is also fixed during this analysis through particular ranges of values from 0.07 to 0.28 for baffle porosity.

6 Parametric Study 6.1 Effect of Porosity of the Perforated Pipe Without Absorptive Material Figure 3 shows the effect of porosity of the perforated pipes at inlet and outlet on the TL performance. Increasing the porosity design gives the higher TL at several TL peak values, as at 1800 and 1950 Hz, also giving the high number of peaks with proceeding toward the higher frequency. Higher porosity ratio produces more effectiveness in wave reflection and consequently generates higher TL at major frequencies. It may be observed that this effect which is prominent in the cascaded muffler configurations is only marginal better for the multiply-connected muffler configurations of Fig. 2a, b.

6.2 Effect of Porosity of the Perforated Pipe with Absorptive Material We are considering the porosity values of thee perforated pipes are 0.056, 0.10, 0.15, and 0.20. Figure 4 shows the effect of porosity of the perforated pipe on the TL performance. It may be observed that this effect which is prominent in the cascaded

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Fig. 3 Effect of porosity of the perforated pipe without absorptive material

Fig. 4 Effect of porosity of the perforated pipe with absorptive material

S. Kumar and K. Ravi

Investigation of the Combined Effect of Perforated Tube …

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muffler configurations is only marginal for the multiply-connected muffler configurations. It is found that the low perforated pipe 0.056 is highly effective in the low frequency range (850–1050) in comparison with relatively high frequency band for high porosity, as we know that the muffler is effective for the particular values of length and diameter values.

6.3 Effect of Flow Resistivity of Absorptive Material This effect is taken place in Fig. 2b for the muffler configurations 2, and no significant differences are absorbed in the muffler having the absorptive material in the third chamber. The only significant improvement in transmission loss is noticed at middle range of frequency (550–1050 Hz). The flow resistivity values for materials are taken as, 10,000, 16,000, 20,000, and 25,000 Pa s/m2 for the absorptive material to visualize the effect on the muffler (Fig. 5).

Fig. 5 Effect of flow resistivity of absorptive material

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7 Conclusions The effect of perforated pipes with and without absorptive material at the back has been modeled by means of 3D FEM simulations. The corroboration has been shown to be consistently good. In particular, the interface between air and the absorptive lining has been modeled appropriately in 1D analysis. In this paper, the 3D FEM approach has been successfully applied to analyze the three-chamber multiplyconnected mufflers of the reactive as well as hybrid type. The plane-wave predictions have been corroborated against the FEM simulations. It has been shown that perforated baffles play a crucial role in making the configuration of Fig. 2a very robust. It is observed that 5% porosity is higher counterproductive in comparison with the other values of the porosity. The use of absorptive material in the third chamber raises the TL curve substantially at the medium and high frequencies. Incidentally, the dissipative material in the third chamber would also help to absorb the aerodynamic noise generated at the area discontinuities.

References 1. Munjal ML (2013) Recent advances in muffler acoustics. Int J Acoust Vib 18(2):71–85 2. Howard CQ, Cazzolato BS, Hansen CH (2000) Exhaust stack silencer design using finite element analysis. Noise Control Eng J 48(4):113–120 3. Tsuji T, Tsuchiya T, Kagawa Y (2002) Finite element and boundary element modelling for the acoustic wave transmission in mean flow medium. J Sound Vib 255(5):849–866 4. Seybert AF, Cheng CYR (1987) Application of the boundary element method to acoustic cavity response and muffler analysis. J Vibr Acoust Stress Reliab Des 109(1):15–21 5. Wu TW, Zhang P, Cheng CYR (1998) Boundary element analysis of mufflers with an improved method for deriving the four-pole parameters. J Sound Vib 217(4):767–779 6. Ih J-G (1992) The reactive attenuation of rectangular plenum chambers. J Sound Vib 157(1):93– 122 7. Ih J-G, Lee B-H (1985) Analysis of higher-order mode effects in the circular expansion chamber with mean flow. J Acoust Soc Am 77(4):1377–1388 8. Glav R, Åbom M (1997) A general formalism for analyzing acoustic 2-port networks, pp 739–747 9. Munjal ML, Galaitsis AG, Vér IL (2006) Passive silencers. Noise and vibration control engineering: principles and applications, 2nd edn, pp 279–343 10. Lal MM, Vorländer M, Költzsch P, Ochmann M, Cummings A, Maysenhölder W, Arnold W (2008) Formulas of acoustics. Springer Science & Business Media 11. Munjal ML (1997) Analysis of a flush-tube three-pass perforated element muffler by means of transfer matrices. Int J Acoust Vibr 2:63–68 12. Panigrahi SN, Munjal ML (2007) A generalized scheme for analysis of multifarious commercially used mufflers. Appl Acoust 68(6):660–681 13. Elnady T, Åbom M, Allam S (2010) Modeling perforates in mufflers using two-ports. J Vib Acoust 132(6):061010 14. Elnady T, Elsaadany S, Åbom M (2011) Flow and pressure drop calculation using two-ports. J Vib Acoust 133(4):041016 15. Vijayasree NK, Munjal ML (2012) On an Integrated Transfer Matrix method for multiply connected mufflers. J Sound Vibr 331(8):1926–1938

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16. Kirby R, Cummings A (1998) The impedance of perforated plates subjected to grazing gas flow and backed by porous media. J Sound Vibr 217(4):619–636 17. Selamet A, Lee IJ, Huff NT (2003) Acoustic attenuation of hybrid silencers. J Sound Vibr 262(3):509–527 18. Panigrahi SN, Munjal ML (2005) Combination mufflers-theory and parametric study. Noise Control Eng J 53(6):247–255 19. Munjal ML, Vijayasree NK, Chaitanya P (2013) Flow resistance network analysis of the backpressure of automotive mufflers. Indian J Eng Mater Sci 20(5):339–349 20. Yasuda T, Chaoqun W, Noritoshi N, Kazuteru N (2010) Predictions and experimental studies of the tail pipe noise of an automotive muffler using a one dimensional CFD model. Appl Acoust 71(8):701–707 21. Siano D (2011) Three-dimensional/one-dimensional numerical correlation study of a three-pass perforated tube. Simul Model Pract Theory 19(4):1143–1153 22. Abhishek V, Munjal ML (2015) Flow-acoustic analysis of the perforated-baffle three-chamber hybrid muffler configurations. SAE Int J Passeng Cars-Mech Syst 8 (2015-26-0131):370–381 23. Herrin D, Hua X, Zhang Y, Elnady T (2014) The proper use of plane wave models for muffler design. SAE Int J Passeng Cars-Mech Syst 7(3). https://doi.org/10.4271/2014-01-0016 24. Kaneda T, Oda M, Yamamoto M, Suwa J (1999) Prediction of transmission loss for motorcycle muffler. SAE Technical Paper 1999-01-3256. https://doi.org/10.4271/1999-01-3256 25. Cherng J, Wu W, Ding P, Hebbes M et al (2015) Design optimization of vehicle muffler transmission loss using hybrid method. SAE Technical Paper 2015-01-2306. https://doi.org/ 10.4271/2015-01-2306

Semi-autonomous Vehicle Transmission and Braking Systems G. Paul Robertson and Rammohan A.

Abstract In recent times, advancement in the automotive field has made major difference in the evolution of car over a decade. This evolution has made vehicles to move without a driver, and the vehicle performance has improved drastically. As autonomous vehicle is the future mode of transportation, nowadays, it plays a major role in research and development of an automotive industry. In this work, a semi-autonomous system which can be used in small electric vehicles has been built by operating a part of the transmission system and brake as automatic and rest as manual. In this system, the clutch, accelerator, and brake system are made to work automatically by predicting the obstacles using ultrasonic sensors. The shifting of gear alone is done manually. This system is built using a Bajaj CT100 engine, transmission systems, and drum brake. The engine power is transferred to the wheel through a modified transmission system. A Raspberry Pi controller and Python coding are used to control the system. By using an ultrasonic sensor, obstacles are detected, and according to the obstacle distance, the system will be made to start and run. Keywords Autonomous transmission · Gear · Drums braking system · Ultrasonic sensor

1 Introduction Research related to autonomous vehicle is the top priority for automotive industries as these are going to dominate the roads in the future. Recently, the top end vehicles sold by the premium automotive industries comes with functions like automatic cruise control (ACC) which maintains a relative speed of the vehicle with respect to the vehicles moving in the front and back side of the vehicle. With ACC “on,” the driver can remove the leg from accelerator and brake. The vehicle control system G. Paul Robertson (B) · Rammohan A. Vellore Institute of Technology, Vellore 632014, India e-mail: [email protected] Rammohan A. e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_4

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manages the level of acceleration and minimum distance that has to be maintained between the vehicles using brake and acceleration. This system will be very useful on the highways and reduces the strain of the driver. With ACC “on,” the vehicle maintains a constant speed if there is vehicle in front or backside of the vehicle. As the acceleration and braking systems are only made automatic, these are called as semi-autonomous vehicles. The semi name is used because steering control is still manual. When the ACC is on, the vehicle is made to run at constant speed till the driver steps on the brake or accelerator, and once stepped, the system migrates to manual mode. An electromagnetic clutch system engages transmission using electronic actuation, but the torque transmission from the engine to transmission is done mechanically. At times, transmission systems are also used for braking of the vehicle, and it is done by simply releasing the clutch at lower gears when the vehicle is moving at some velocity. The power required to run the engine without fuel is taken from the wheels and thereby decelerating the vehicle. This system assists the existing breaking system to improve the braking and stopping distance without locking the wheels. The electromagnetic clutch consumes electrical energy during engagement. With the help of an IR sensor (to detect the obstacle and its distance), breaks can be applied when driver is distracted and fails to apply in auto mode. Using a solenoid, compressed air, and plunger barrel assembly, this can be achieved [1]. To activate breaking, the air is passed through the pneumatic cylinder to expand the brake shoe. Breaking rate is adjusted using a flow control valve [2]. In a work, methods were explored using a sensor to monitor the automatic transmission system performance. It is used to find the fault in clutch, break, the pressure sensors, turbine speed sensors, and output shaft sensor. The engine is made to drive a hydraulic pump, which provides hydraulic pressure to shift the gears. The transmission shift controller is used to the gear selection bowl or vehicle speed, driver response, and operating condition [3]. Some researchers have used UV sensors to transmit and receive the rpm data to monitor and control the speed of the vehicle [4]. UV microcontroller is also used in braking system to detect the pulse and pedal push to stop the vehicle for safety [5]. The system is used to activate the protection of pedestrian in an advanced emergency breaking system that activates by considering the current distance between the host vehicle and obstacle in the critical breaking system [6]. The STC12CTA60S2 simple chip receives GPS vehicle, speed signal, reversing radar distance signal to determine the safety so obstacle that can reduce the acceleration and brake control. An automatic braking system is designed and implemented using a pneumatic principle [7]. Pneumatic principle is easy way to make a system with pressurized air brake. If there is obstacle at 4 feet, the centre signal is activated by pressing a switch which is connected to the bumper activation system. This bumper activation system can be activated only at 40–50 kmph. The speed of vehicle has been sensed by proximity sensor, and the control is by pneumatic bumper activation system. It is also used to protect the pedestrian and vehicle where compressed air is used for air braking system. The compressed air is supplied through the solenoid valve and left to atmosphere in a second solenoid valve [8]. The de-acceleration factor in a moving vehicle is not advisable and also risk when the vehicle is approaching in rear. The

Semi-Autonomous Vehicle Transmission and Braking Systems

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brake platform is shown as a smooth de-acceleration. It is a secured and risk-free pattern [9]. The transmission has been selected by traction values which can used in the vehicle gear ratio [10]. Braking system is calculated with weight, tyre, and rim dimension. It calculates torque which is needed for braking. Gear shifting is done by stepper motor, which has been monitored on steering wheel [11]. The friction braking system works on basis of the continuous braking for long distance braking in railways. It is used in high-speed trains. It is used to slow, maintain, constant speed, and also to prevent the deceleration [12]. This paper initiates the hydro-mechanical automatic transmission gear shifting operation system with oil pressure of clutch cylinder. There was no error in shifting and also no power interruption. The shifting parameters are to be in dynamic characteristics of a system to reduce the man power resource and time [13]. It also explains the braking power which is to be initiated in minimum time and distance with brake weight. The transmission is to provide the torque, acceleration, and speed to assist the motion of vehicle [14]. The brake disks, shift gear, and hub set are analyzed. Reduction of gears to reduce the system weight in transmission system is done. And also to improve the performance, weight reduction in braking system has been achieved [15]. The automated manual transmission is constructed using manual transmission with general clutch using actuator, hydraulic or electromechanical equipment. This is done to reduce the consumption of energy in vehicle. It can be used to reduce the exhaust emission and to increase the vehicle efficiency [16]. In this proposed work, using the sensor information, transmission and braking system are engaged by itself based on the requirement. Instead of driver acing on the transmission and braking, with the data of obstacle’s distance and its speed, clutch and brake are operated using a electromagnetic clutch and a solenoid. In case the obstacle is approaching, the system is decelerated using brake, and transmission system is disengaged. When there is no obstacle, the transmission will be engaged to transmit power. The semi-autonomous transmission and braking system is designed and validated in this work.

2 Experimental Setup The list of the components used is this experiment is listed below as shown in Fig. 1. The mechanical components are Bajaj CT100 engine and auto drum braking system. This engine is fitted on to a frame and also used for transmission purpose as clutch, gear, and accelerator. The engine is made to start on kick start basically. Drum brake system is a braking systems that works on basis of brake shoes that are being fitted with drum. The working principle of drum braking system is: when the pedal is pressed, the liquid is allowed to flow through the master cylinder and then to the wheel cylinder, the shoe is pressed on the drum, and the brake is applied. After the release of pedal, the brake is disengaged, and the wheel is made to rotate. The drum brake system needs few components for assembly:

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Fig. 1 Mechanical assembly

• • • • • •

Brake booster Master cylinder Brake shoe Brake disk Brake drum Wheel cylinder.

Brake booster is engaged with the master cylinder to give the high-power hydraulic fluid by the pressure from pedal to master cylinder, in which it is used to work on the high torque power. This booster is used to give the power to push the rod for drum brake. The rod in the brake shoe is applied to give pressure to the brake system. The booster is made to give the pressure whether it is low or high. The electronic components are listed below as shown in Figs. 2 and 3. The electronics are used on basis to make the functions by automations. The autonomous plays a vital role in the automotive field only by electronic components. The electronics used in mechanical components such as clutch, accelerator, and brake. The components used are Raspberry Pi-2 board, WiFi module, ultrasonic sensor, actuator, plunger, air compressor, LM3805, L293D, relay, resistors, and switch. A linear actuator is a device which is used to create the straight motion by circular motion. It is used by a conventional electric motor. It is probably used for machinery tools and industrial purpose. The cylinders are of hydraulic or pneumatic which is used to produce linear motion. The linear motion is derived from the rotating motor. It is of 12 V input voltage and works on principle of moving the clutch. The US-020 is an ultrasonic sensor model. It is commonly used for the detection of distance or an obstacle. It is an electronic component which is used in

Semi-Autonomous Vehicle Transmission and Braking Systems

33

Fig. 2 Electronic assembly

Fig. 3 Ultrasonic sensor location

Arduino/Raspberry Pi. It is a high-range sensor. It has a transmitter, receiver, and a control unit. It can be operated at any sunlight/black metal. It has four ports as Vcc, Gnd, TRIG, and ECHO. The placement of battery in the engine is shown in Fig. 4. It is a made of special design which is suitable to automotive and to accept all weather conditions. The design is rugged, and the exterior part is made up of polypropylene. The positive grid is a resistant that is rusted and also to present the reinforcement in corner. The negative grid is a larger area which is a ground phase. The specification of the battery is given in Table 1.

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G. Paul Robertson and Rammohan A.

Fig. 4 Battery in the engine

Table 1 Battery specification

Battery brand

Exide little champ

Battery type

EXLC35L lead acid

Dimension

197(L) * 129(W) * 227(H)

Weight

10.1 kg

Capacity

35 A

Voltage

12 V

Electrolytic volume

2.25 L

Charging current

3A

3 Design and Implementation The complete circuit design and implantation is shown in Fig. 5. The connections are made as per the circuit diagram. The devices are connected as per the circuit diagram shown. The actuator is made to get connected with clutch, and the plunger is made to get connected with accelerator. The air compressor is made to get connected to the auto drum brake system. The engine is made to start by kick start method based on the program executed in the Raspberry Pi. The UV sensor is started to detect the distance. Based on the programming while the engine starts: • The switch is pressed, and the ignition is started. • First, the clutch is pressed and held for 0.5 s. • On that time interval, the gear is made to change manually.

Semi-Autonomous Vehicle Transmission and Braking Systems

35

Fig. 5 Circuit diagram

• After the time interval, the clutch is slowly released as how the human legs released by actuator. • Then, the plunger is made to pull the accelerator, and the vehicle is moved at 20 kmph at an ideal speed. During low speed: • If the UV sensor detects 200–250 cm distance, the de-acceleration is done, whereas the plunger releases the accelerator by using relay. • So that in this case, the vehicle is made to run at 10 kmph. • After the distance is normal, the vehicle goes on to normal function. During engine braking: • If the UV sensor suddenly detects at 150–200 cm distance, the braking is applied. • The clutch is made to press in half mode, whereas the plunger is made to release the accelerator at same time by using relay. • Then, the air compressor is made to be on by the relay board, and then, the air is given to drum brake system, and the brake is applied. • After the normal distance at instant time, the air is made to be released manually, and then, the vehicle is made to move as per distance detected in sensor.

36

G. Paul Robertson and Rammohan A.

4 Results The result obtained during engine starting is shown in Fig. 6. Based on the programming in Raspberry Pi, the ECU waits for sensor signal. The distance of the nearest vehicle is 213.17 cm as shown. While starting the engine, the clutch press is detected, gear changed, clutch released, enabling acceleration, and vehicle moving is detected. During the vehicle moving, the sensor detects the obstacle and calculates the distance as shown in Fig. 7. Based on the testing scenario, the distance varies as shown.

Fig. 6 Engine start

Fig. 7 Vehicle moving

Semi-Autonomous Vehicle Transmission and Braking Systems

37

Fig. 8 De-acceleration

Fig. 9 After brake pedal pressed

During low-speed operation of the engine, the automatic deceleration is enabled, and the same has been shown in Fig. 8. At low speed, the vehicle distance is minimum. The initial vehicle distance is 331.98 cm, and due to low speed of the vehicle, the distance is 220.55 cm. The low speed and deceleration are shown in the display for driver attention. While brake pedal is pressed, the de-accelerating clutch press of the first half and second half is shown in Fig. 9. Similar to deceleration, the low-speed, distance, and clutch press position during first and second half are displayed during each operation.

5 Conclusion The semi-autonomous transmission and braking system is carried out in this work. Based on the experimentation, it is concluded that the automatic transmission in manual transmission car or bike can be done by using these methods. The algorithm clearly states that the following techniques can be used for automatic transmission of clutch and accelerator. It is proven that the braking system can be activated with the help of compressor air. The transmission of gear alone is made to be changed manually. The useful prototype model in real-time and collision detection and its control speed of the vehicle was implemented.

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References 1. Joshi SS, Jadhav VS, Bhoslae DP, Malave AC (2017) Design of automatic braking system for vehicles in hill station. Novateur Publications, IJIERT 4(4). ISSN: 2394-3696 2. Ahmed Q, Arasu M, Zhang J, Rizzoni G (2016) Sensor installation guide to monitor automatic transmission performance. IFAC Papers Online, pp 736–741 3. Kavatkar T, Salvi H, Rahate M (2017) Design and analysis of intelligent braking system. IJEDR 5(1). ISSN: 2321-9939 4. Kim H, Song B (2013) Vehicle recognition based on radar and vision sensor fusion for automatic emergency braking. In: 2013 13th international conference on control, automation and systems (ICCAS 2013) 20–23 Oct 2013 5. Lie G, Zejian R, Pingshu G, Jing C, Advanced emergency braking controller design for pedestrian protection oriented automotive collision avoidance system. Hindawi Publishing Corporation. Sci World J 2014, Article ID 218246, 11 pages. http://dx.doi.org/10.1155/2014/ 218246 6. Guo W, Qiao Y (2017) Design and development of vehicle reversing brake assist system. Open Access Library J 4:e4178. ISSN Online: 2333-9721. ISSN Print: 2333-9705 7. Kamble AA, Nalawade V, Patil SS, Automatic braking system. IJARIIT 3(2). ISSN: 2454-132X 8. Wada T, Doi S, Tsuru N, Isaji K, Kaneko H (2010) Characterization of expert drivers’ lastsecond braking and its application to a collision avoidance system. IEEE Trans Intell Transp Syst 11(2) 9. Wang Z, Zhao Z, Liu X, Performance testing of automatic transmission. In: 2011 International conference on electronic & mechanical engineering and information technology 10. Wadile C, Dubal R, Kolhe R, Rangaswamy V, Siddiqui A, Gurav N (2013) Selection, modification and analysis of power transmission and braking system of an ATV, 1(1). ISSN (Print): 2321-5747 11. Pandey SN, Khaliq A, Zaka MZ, Saleem MS, Afzal M (2015) Retarder used as braking system in heavy vehicles—a review. Int J Mech Eng Robot Res 4(2). ISSN 2278-0149. www.ijmerr. com 12. Zhang Y, Ma W (2013) Shift control system of heavy-duty vehicle automatic transmission. J Netw 8(12) 13. Patil T, Dange V, Desale S, Kulkarni A, Kharkar B (2018) Design, analysis and manufacturing of brake and transmission system for all terrain vehicle. Int J Adv Eng Res Dev 5(06) 14. Shi J, X. L, T. LU and J. ZHANG,” Development Of A New Traction Control System For Vehicles with Automatic Transmissions” International Journal of Automotive Technology, Vol. 13, No. 5, pp. 743–750 (2012) 15. Neil Roberts,Mike Dempsey,” Predicting the launch feel of automatic and dual clutch transmissions” Predicting the launch feel of automatic and dual clutch transmissions”, Proceedings of the 9th International Modelica Conference, September 3–5, 2012, Munich, Germany,https:// doi.org/10.3384/ecp12076295 16. M.S.Kumbhar,Dr.D,R.Panchagade,” A Literature Review on Automated Manual Transmission (AMT) A Literature Review on Automated Manual Transmission (AMT)” IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 03, 2014| ISSN (online): 2321- 0613

Comparison of Gaseous and Liquid Fuel Cells for Automotive Applications A. Thirkell and R. Chen

Abstract Elevating pressure on the automotive industry to significantly reduce harmful emissions has led to an increased focus on the research and development of alternative, ultra-low emission power sources, including batteries and fuel cells. To better understand how fuel cell systems could be integrated into automotive systems, it would be important to draw comparisons between different technologies. Two key fuel cell segments are compared for their suitability in automotive applications, gaseous and liquid fed fuel cells. Comparisons showed the inherent advantages and disadvantages of both technologies. Gaseous fuel cells, such as the increasingly popular polymer electrolyte membrane fuel cell, utilise hydrogen as a fuel and typically have very high-power densities. Liquid fuel cells are by comparison, less common. One up-and-coming technology is the direct methanol fuel cell. For use in automotive applications, this type of fuel cell shows potential as the storage of methanol is very similar to traditional internal combustion fuels such as petrol and diesel. Keywords Polymer electrolyte membrane fuel cell (PEMFC) · Hydrogen · Direct methanol fuel cell (DMFC) · Methanol · Electric vehicle

Nomenclature BEV CH3 OH CGH2 CO2 H2

Battery electric vehicle Methanol Compressed gaseous hydrogen Carbon dioxide Hydrogen

Peer-reviewed under responsibility of the scientific committee of the International Conference on Progress in Automotive Technologies, ICPAT—2019. A. Thirkell (B) · R. Chen Loughborough University, Loughborough LE11 3TU, UK e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_5

39

40

HEV FC ICE LH2 PEMFC

A. Thirkell and R. Chen

Hybrid electric vehicle Fuel cell Internal combustion engine Liquid hydrogen Polymer electrolyte membrane fuel cell

1 Introduction Pressure is increasing on the automotive industry to significantly reduce harmful emissions [1–4], in particular carbon dioxide (CO2 ). In the pursuit of reduced emissions, novel ultra-low emission powertrain designs must be developed. This has led to an increased focus on the research and development of alternative power sources, predominantly batteries and fuel cells. Hybrid electric vehicles (HEV) have proved an important stepping stone [4–6], both in the reduction of harmful emissions and the advancement of low-emission technologies. However, as these hybrid vehicles generally use an internal combustion engine as either the primary or secondary power provider, they can never eliminate all the harmful emissions. The next logical step based on the published roadmap [4] was the move from HEV to battery electric vehicles (BEV). Pure BEVs, although having the distinct advantage of producing zero emissions, do come with inherent disadvantages, predominantly, poor range and lengthy charge times. The average range of the three most popular BEVs on sale in the UK (Nissan Leaf [7], BMW i3 [8] and Renault ZOE [9]) is ≈160 miles. This is around half of the expected range of most fossil fuel cars. To overcome the challenge of range anxiety, the energy storage density of the system must be improved. This can either be done through research of novel highenergy-density battery technologies or implementing a secondary power source as a range extender. Fuel cells (FC) have been proposed as a viable, zero tailpipe emission technology for this purpose [10–14]. Fuel cells combine fuel with oxygen through an exothermic electrochemical reaction. The main by-products of this highly efficient reaction are heat and water [15, 16]. There are several FC technologies which show promise for use in automotive environments. These will be compared throughout this paper.

2 Fuel Cell Stack The component of a FC system in which the chemical redox reaction between fuel and oxidant takes place is commonly referred to as the ‘stack’. A FC stack is a solid-state component with no moving parts and is comprised of several individual cells. The individual cells are connected electrically in either series or parallel. It is possible to

Comparison of Gaseous and Liquid …

41

e-

Fuel / gaseous or liquid

Excess fuel & anode reactants

Heat

Anode

Cathode

O2 / air

Air & H2O

Gas Diffusion Layer

Polymer Electrolyte Membrane

Catalyst layer

Fig. 1 Typical architecture of a single-cell FC

vary the configuration of individual cells within a stack in order to meet the customer exact requirements for current and voltage. The possibility exists also have several smaller stacks connected electrically, allowing for distributed architectures. Figure 1 shows a typical architecture for a single cell of a FC stack. Fuel, either as a gas or liquid, is fed into the FC at the anode and the oxidant at the cathode. The first step of the reaction occurs at the anode catalyst layer where the fuel is broken down. The mobile ion, commonly H+ , can pass through the polymer membrane. This is then combined with the oxidant at the cathode catalyst layer to complete the reaction. Another benefit of FCs over traditional internal combustion engines (ICEs) is that they are highly efficient and not limited by Carnot limit. However, they are not 100% efficient. A form of the Nernst equation [15–18] (Eq. 1) can be used to combine the irreversible voltage losses associated with activation, ohmic resistance and mass transport as shown by [19]. Vc = E 0HHV

  i + in RT − iΩ − meni ln − 2α F i0

(1)

where Vc Average cell voltage (V) E 0HHV Thermodynamic reversible voltage based on the higher heating value of hydrogen (1.23 V) [15–18] R Universal gas constant (8.314 J/molK) [20] T Operating temperature (K) α Charge transfer coefficient (0.5) [15, 16] F Faraday constant (96,485 C/mol) [20]

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A. Thirkell and R. Chen

Fig. 2 Example polarisation and power curves for a PEMFC, produced in-house

I in i0  m n

Current density (A/cm2 ) Internal and fuel crossover equivalent current density (0.002 A/cm2 ) [17, 18] Exchange current density (3.0 × 10−6 A/cm2 ) Ohmic resistance (0.245  cm2 ) [15] Mass transport loss empirical constant 1 (3.0 × 10−5 V) [15] Mass transport loss empirical constant 2 (7 cm2 /A) [15]

Polarisation and power curves in Fig. 2 show how the irreversible voltage losses affect the performance of a FC. The power curve shows a drop-off in performance after peak power is reached. This corresponds to the fall in cell voltage as a result of mass transport losses in the FC. Fuel cell technologies which show promise in the automotive segment are the gaseous polymer electrolyte membrane fuel cell (PEMFC) and liquid-fuelled direct methanol fuel cell (DMFC). Other FC types such as solid oxide and alkaline fuel cells have substantial barriers to their implementation such as excessively high operating temperatures, long start-up times and low technology readiness levels.

2.1 PEMFC Polymer electrolyte membrane fuel cells are the most popular and highly developed form of gaseous FC. Equations (2a, 2b) show the half reactions for a PEMFC. These are important as they show that for each mole of hydrogen (H2 ) reacted, and two

Comparison of Gaseous and Liquid … Table 1 PEMFC information

43

Fuel

Hydrogen (H2 )

Operating temperature

30–100 °C [15, 16]

Power density

≈440 W/kg [21–28]

Cost

≈4000 $/kW [29]

electrons are made available. A summary of key details concerning PEMFCs is given in Table 1. H2 → 2H+ + 2e−

(2a)

1/2O2 + 2H+ + 2e− → H2 O

(2b)

Key advantages of PEMFCs over other FC types include: easy low-temperature start-up [30] and a comparatively high-power density. However, pure H2 must be used in low-temperature PEMFCs as any impurities in the fuel could irreversibly damage the catalyst layer in the cell. Although PEMFCs are able to operate over a fairly wide range of temperatures, literature shows that the current producing potential of the FC increases with temperature [31, 32]. Increased operating temperatures could be used in an automotive application as a heat source for cabin conditioning.

2.2 DMFC Direct methanol fuel cells can use liquid methanol (CH3 OH) fuel directly without pre-processing. They have proven to be the most popular direct liquid FC due to methanol’s exciting properties [33]. The half reactions for a DMFC (Eqs. 3a, 3b) show that for each mole of CH3 OH fuel consumed, six electrons are available. Theoretically, this means that for the same current, a DMFC would require a third of the fuel that a PEMFC would require (based on number of moles). CH3 OH + H2 O → 6H+ + 6e− + CO2

(3a)

3/2O2 + 6H+ + 6e− → 3H2 O

(3b)

Key details for DMFCs are summarised in Table 2. It is shown that both DMFCs and PEMFCs have similar operating temperatures. However, sluggish reaction kinetics and propensity for CH3 OH crossover [34] from anode to cathode hinder the performance available from existing DMFC stacks.

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A. Thirkell and R. Chen

Table 2 DMFC information

Fuel

Methanol (CH3 OH)

Operating temperature

20–90 °C [15, 16]

Power density

≈57 W/kg [35]

Cost

≈5250 $/kW [36, 37]

2.3 Summary Both PEMFCs and DMFCs have similar operating temperatures. However, DMFCs are ≈ 30% costlier for each kilowatt of desired power output. The main difference between the two described FC technologies is their power density. As demonstrated by Fig. 3, the power density of a typical DMFC stack is an order of magnitude lower than that of a PEMFC. The large disparity between power densities precludes the use of DMFCs in current automotive applications. Research is ongoing to improve the power density of this technology. If improved, DMFCs may prove to be an exciting option due to the benefits brought about by the liquid fuel type.

3 Fuel Storage Unlike batteries, FCs require a continuous supply of fuel to operate. This does add complexity to the overall system; however, the key benefit is that the fuel storage solution can be replenished in a fraction of the time it takes to charge a battery.

PEMFC 11kg 9litres

DMFC 88kg 63litres

Fig. 3 Comparison of stack mass and volume for two FC types. Assuming 5.0 kW at an operating voltage of 48 V

Comparison of Gaseous and Liquid …

45

Fuels typically used in gaseous or liquid FCs (H2 and CH3 OH) have low molecular weights. They can also be difficult to store, especially in the case of H2 . Combined with the desire for extended operation, these factors can lead to the fuel storage being the most substantial component of a FC system in terms of mass and volume.

3.1 Hydrogen Polymer electrolyte membrane FCs require pure hydrogen as a fuel. This is usually supplied to the FC at near-ambient temperatures and at a relatively low pressure in the region of 500 mbarg [27]. Hydrogen can be stored in various forms depending on user requirements. Key considerations include: mass and size of system, stability of the fuel and cost. Table 3 summarises the main hydrogen storage methods in use today. Cryo-compressed, liquid chemical and solid hydrogen storage solutions all show promise, however, are not yet ready for commercialisation. Commercial data was used to derive typical energy densities for compressed gas (CGH2 ) [38–41] and liquid (LH2 ) [42] storage options. These are given in Table 4. Table 3 Comparison of hydrogen storage methods [43–47] Method

Advantages

Disadvantage

Compressed (CGH2 ) • Mature technology • Relatively inexpensive • Different storage pressures available • Easy capacity scaling

• High storage pressures up to 700 bar required careful handling • Requires proper pressure regulation • Poor gravimetric and volumetric storage densities compared to other methods

Liquid (LH2 )

• Improved gravimetric and volumetric storage densities compared to CGH2

• Cryogenic temperatures require careful handling and enough insulation • Boil-off is an unavoidable phenomenon which must be managed to reduce waste

Cryo-compressed

• Virtual elimination of boil-off

• Technology still under development • Very expensive to implement

Liquid chemical

• Improved gravimetric and volumetric storage densities compared to LH2

• Toxic/harmful by-products possible • Recycling waste products is challenging

Solid (hydrides)

• Good volumetric storage density compared to LH2 • Potential for improved re-usability

• High mass leads to poor gravimetric storage density compared to CGH2 • Can be expensive

46 Table 4 Energy per unit mass and volume of total storage solution for different H2 storage methods

A. Thirkell and R. Chen Storage technology

Gravimetric (kWh/kg)

Volumetric (kWh/l)

CGH2 700 bar

2.11

2.47

LH2

7.37

2.80

3.2 Methanol Unlike hydrogen, CH3 OH is easily handled, transported and stored as a liquid. When calculating the amount of CH3 OH required, the concentration of the stored solution must be considered. The relationship between CH3 OH concentration and FC performance is well documented [48–53]. Figure 4 shows that as CH3 OH concentration is increased, peak current density increases allowing more power to be drawn from the FC. It also demonstrates the unfavourable effects of CH3 OH crossover [54–56]. This is demonstrated by the reduction in cell voltage at low current densities. Another factor to consider when determining CH3 OH concentration is the resulting energy density. Table 5 shows that a concentration of 15.0 M or above would be needed to obtain a similar energy density to that of H2 stored at 700 bar.

Fig. 4 Effect of CH3OH concentration on cell performance for a DMFC, adapted from [48]

Comparison of Gaseous and Liquid … Table 5 Energy per unit mass and volume of total storage solution for different CH3 OH concentrations

47

Methanol concentration

Gravimetric (kWh/kg)

Volumetric (kWh/l)

Concentrated

6.09

4.79

15.0 M

2.56

2.30

5.0 M

0.794

0.767

1.0 M

0.154

0.153

3.3 Summary Although the handling and storage of CH3 OH is far more straightforward than that of H2 , the overall mass and volume of any storage solution is a more important factor in the design process. The process outlined in [19] was used along with the FC stack design mentioned earlier to calculate the masses and volumes of the H2 storage solutions. A similar process was used to do the same for the CH3 OH options. The results are shown in Fig. 5. Masses and volumes of current commercial fuel storage solutions (CGH2 700 bar and 1.0 M CH3 OH) were found to be greatly in favour of hydrogen-fuelled PEMFCs.

Fig. 5 Comparison of total fuel system masses and volumes for H2 and CH3 OH storage solutions. Assuming FCs operating for 120 min at 5.0 kW and 48 V

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A. Thirkell and R. Chen

However, the results showed that significant weight savings could be made by using LH2 rather than CGH2 . The main reasons why this technology is not implemented in the current automotive systems are the wastage caused by boil-off and high cost of implementation [45]. The results also showed that for the designed systems, concentrated CH3 OH fuel storage has the lowest mass and volume when compared to the other storage solutions.

4 Conclusions The following conclusions were drawn from the work detailed in this paper: • Fuel cell technologies are a viable solution to the problem of range anxiety in current battery electric vehicles. • Gaseous PEMFCs show promise as their power density is an order of magnitude greater than that of liquid DMFCs. • Cheaper, easier handling and storage combined with the potential for higher energy storage density increase the favourability of DMFCs. • Current best solution is PEMFC with CGH2 unless: – Improvements in LH2 tank design to reduce boil-off and improve cost. – Significant DMFC stack performance improvement. Acknowledgements The authors would like to acknowledge the support of the EPSRC CDT in fuel cells and their fuels, grant number EP/L015749/1.

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34. McGrath KM, Surya Prakash GK, Olah GA (2004) Direct methanol fuel cell. J Ind Eng Chem 10(7):1063–1080 35. Kang K, Park S, Gwak G, Ji H, Ju H (2012) Development of a lightweight 200 W direct methanol fuel cell stack for UAV applications and study of its operating characteristics (II). Korean Hydrog New Energy Soc 23(3):243–249 36. Oorja Protonics Inc. (2016) Direct methanol fuel cells. Available: http://www.methanol.org/ wp-content/uploads/2016/06/Oorja-Corporate-Presentation-_4_27_16.pdf 37. Julich Forschungszentrum (2011) Costs of Direct Methanol Fuel Cells (DMFC). Available: http://www.fz-juelich.de/SharedDocs/Downloads/IEK/IEK-3/Flyer/Wirtschaftlichkeitsbetra chtung_DMFC (E).pdf?__blob = publicationFile 38. Horizon Fuel Cell Technologies (2017) Ultra-light composite cylinder (E-Series). Available: http://www.fuelcellstore.com/hydrogen-equipment/hydrogen-storage/composite-storage-cyl inders/ultra-light-composite-storage-cylinder-e-series. Accessed 05 Jan 2017 39. Luxfer Gas Cylinders (2015) G-StorTM H2 hydrogen-storage cylinders. Available: http://www. luxfercylinders.com/products/alternative-fuel/gstorh2. Accessed 05 Jan 2016 40. Luxfer Gas Cylinders (2015) L6X Composite inflation and aerospace cylinders. Available: http://www.luxfercylinders.com/products/l6x-composite-cylinder. Accessed 05 Jan 2016 41. Mahytec (2016) Hydrogen storage solutions. Available: http://www.mahytec.com/en/our-sol utions/. Accessed 05 Jan 2017 42. Lapesa, Horizontal cryogenic tanks. Available: www.lapesa.es/descargar.php?f=/sites/default/ files/documentos/gnli_1011.pdf. Accessed 05 Jan 2017 43. Ross DK (2006) Hydrogen storage: the major technological barrier to the development of hydrogen fuel cell cars. Vacuum 80(10):1084–1089 44. Gray EM (2007) Hydrogen storage—status and prospects. Adv Appl Ceram 106(1–2):25–28 45. Ahluwalia RK, Peng JK (2008) Dynamics of cryogenic hydrogen storage in insulated pressure vessels for automotive applications. Int J Hydrogen Energy 33(17):4622–4633 46. Schlapbach L, Züttel A (2001) Hydrogen-storage materials for mobile applications Nature 414(6861):353–358 47. Zuttel A (2003) Materials for hydrogen storage. Mater Today 6(9):24–33 48. Liu J, Zhao T, Chen R, Wong C (2005) The effect of methanol concentration on the performance of a passive DMFC. Electrochem Commun 7:288–294 49. Han J, Liu H (2007) Real time measurements of methanol crossover in a DMFC. J Power Sources 164(1):166–173 50. Colpan CO, Ouellette D (2018) Three dimensional modeling of a FE-DMFC short-stack. Int J Hydrogen Energy 43(11):5951–5960 51. Ko J, Lee G, Choi Y, Chippar P, Kang K, Ju H (2011) Comparison of numerical simulation results with experimental current density and methanol-crossover data for direct methanol fuel cells. J Power Sources 196(3):935–945 52. Zhao TS, Xu C, Chen R, Yang WW (2009) Mass transport phenomena in direct methanol fuel cells. Prog Energy Combust Sci 35(3):275–292 53. Nakagawa N, Tsujiguchi T, Sakurai S, Aoki R (2012) Performance of an active direct methanol fuel cell fed with neat methanol. J Power Sources 219:325–332 54. Gwak G, Kim D, Lee S, Ju H (2017) Studies of the methanol crossover and cell performance behaviors of high temperature-direct methanol fuel cells (HT-DMFCs). Int J Hydrogen Energy 1–13 55. Qi Z, Kaufman A (2002) Open circuit voltage and methanol crossover in DMFCs. J Power Sources 110(1):177–185 56. Hikita S, Yamane K, Nakajima Y (2001) Measurement of methanol crossover in direct methanol fuel cell. JSAE Rev 22(2):151–156

Lane Monitoring System for Driver Assistance Using Vehicle to Infrastructure Connection Akash Kalghatgi and A. Rammohan

Abstract Vehicular ad hoc networks have gained importance since their advent in 1998 and the technology has been having an exponential growth in contribution towards its betterment and stability. VANET allows local communication between vehicles and infrastructure along the road. Every vehicle shares critical information with every other vehicle in its vicinity for ensuring passenger safety and also allows access to Internet for multimedia entertainment. With all such applications and features, VANET has its own share of insecurities and limitations. Many successful attempts have been made in the last decade to solve the identified problems, but with rapid advancements in technology, the complexity of security solutions and network architectures increase despite having a solution for all kinds of problems. Therefore, it is necessary to simplify the existing VANET and begin working on alternate concepts that use existing technologies and protocols. A concept called lane monitoring system uses passive infrastructure along roadway and attempts to evolve them to fulfil the needs of evolving mobility solutions. Keywords ADAS · VANET · Internet of things · Lane monitoring system · Road telemetry

1 Introduction Vehicular ad hoc network (VANET) was created as an application of the principles of a mobile ad hoc network (MANET). The research began in the year 2000 after a team of engineers from Delphi-Delco Electronics [1] and IBM Corporation in Peer-review under responsibility of the scientific committee of the International Conference on Progress in Automotive Technologies, ICPAT—2019. A. Kalghatgi (B) Automotive Electronics, Vellore Institute of Technology (VIT), Vellore 632014, TN, India e-mail: [email protected] A. Rammohan Autonomous Vehicle Research Center, Vellore Institute of Technology (VIT), Vellore 632014, TN, India © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_6

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1998 proposed a concept of connecting every vehicle within a network, with an aim of providing many applications for vehicle safety. Over the years, various other applications of VANETS have been derived, few of them being electronic brake lights, platooning, traffic information systems, roadside emergency services, on-the-move services, etc. All such applications require the transfer of information from vehicle to vehicle (V2V) and vehicle to roadside units (RSUs), which can be achieved using a single-hop spread of cooperative awareness messages (CAM) and a multi-hop spread of messages over long distances. VANET uses short-range radio like WLAN along with cellular technologies like LTE. Researchers in universities and research labs worldwide have helped evolve VANET by working on each subsystem within it. The subsystems being worked upon are media access protocols, routing, warning message dissemination and VANET application scenarios. Many automobile companies are also working on V2V communication systems, those being General Motors, Toyota, BMW, Daimler, Honda, Audi, Volvo, Renault and the Car-to-Car communication consortium. VANET has many safety-related applications which have the potential to increase the safety on roads. These can be further categorized as follows: • Collision avoidance—A greater percentage of accidents can be prevented if the drivers are given a warning, a few seconds before a potential collision [2]. A driver may use this warning to vigilantly survey the vicinity and steer the vehicle towards a safe spot. • Cooperative driving—Drivers may get signals from other nodes about curves on the road, speed warning, sudden lane changes of vehicles, etc. These signals may help the drivers cooperate with others for safer driving. • Traffic monitoring—Traffic jams may be pre-intimidated to every vehicle approaching the spot so that alternate routes can be planned. Besides providing vehicle specific applications, VANET also provides user-based services as follows: • Peer-to-peer sharing for transferring music, movies among vehicles in the network. • Internet access for all people inside each vehicle, by having an uninterrupted connection to RSUs. • Location services for identifying nearest gas stations, restaurants and motels, toll booths, etc. Over the decade of continuous research and development, there have been new exciting applications for VANET such as an extension for driverless cars. However, there are major challenges that have come forward regarding the management and safety of the network. Various technical challenges have also been observed which are given below: • Network management—due to variations in speeds of vehicles, the network topologies and channel condition change rapidly. The connections are established and broken frequently, so the reliability of data transfer is doubtful.

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• Congestion control—the network allows many vehicles to connect at once, thus leaving the network size unbounded. This can be observed during traffic jams and rush hours. Every vehicle has to assemble the data from other vehicles, interpret the information and correlate the same along with the readings that it has obtained by itself (using various sensors). • Safety—as VANET handles life critical information, the security issues must be identified before they occur and solved at earliest. If such a network is used for an autonomous vehicle, then it must be made sure that incorrect data is not inserted or critical data is not manipulated by an attacker. The automakers have physically implemented the network in a controlled environment and also simulated the same on computers. Upon close inspection of the results, the following potential problems and risks related to the network safety were identified: 1.1 Network attack—A successful network attack may cause the entire network to collapse [3]. Few popularly known attacks are being the Denial of Service (DoS) and Sybil attack. 1.1.1 Denial of Service—The attacker can fill any vehicle’s receive buffer with irrelevant data, processing which may require the onboard computer of a vehicle to utilize all of its computational resources. In that period of time, the vehicle might miss the important messages that could be critical for safety. 1.1.2 Sybil—The attacker can create a large number of non-existing or fake vehicle profiles and send a large amount of data from each to the target vehicles. The existing vehicles may interpret the situation to be a traffic jam and/or behave abnormally while taking the necessary actions (Fig. 1).

Fig. 1 Sybil attack on VANET [4]

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1.2 Application attack—The attackers may send application specific messages that can affect the behaviour of the vehicle, its driver and the passengers. A subclassification of such attacks is a fabrication, alteration and social attack. 1.2.1 Fabrication—This attack includes false speed and lane warnings, vehicle identities, traffic signal conditions, toll fare, etc [5]. 1.2.2 Alteration—The attacker can alter an existing data by delaying the transmitted information, resending the current transmission, changing the data within a message before it reaches a receiver node, etc. 1.2.3 Social—This attack contains immoral and emotional messages that could manipulate the behaviour of driver and passengers. Many researchers within educational institutions and automotive companies have contributed their efforts and have come up with solutions to solve the problems mentioned ahead. 1.3 DFPAV algorithm for congestion control—The Distributed Fair Transmit Power Adjustment (DFPAV) is a criterion for improving the safety and is built upon the concept of fairness [6]. Which means, it is very important that a good estimation of the state of all vehicles in its close surrounding is made by every vehicle to make safety applications capable of detecting any unsafe situation and take the right decisions to avoid danger in case of emergencies. In other words, if a vehicle has itself not assigned a fair portion of its resources, then it cannot broadcast itself to its closest neighbours but become a danger itself. To solve this problem, Vyas and Dandekar [7] have proposed another congestion control algorithm based on adaptive beacon rate scheme that can reduce the channel load drastically and thereby reduce congestion. 1.4 Mobility management—In early VANET, the mobile IPv6 (RFC3775) was used, but its functionality was limited to single-hop networks thereby failing to handle multi-hop networking. Unlike multi-hop, the single-hop networks require a direct link layer connection between gateway and nodes. Therefore, approaches were proposed to integrate multi-hop ad hoc networks into the Internet using mobile Internet protocol. In order to handle the mobility of vehicles, Bechler and Wolf [8] developed a mobility management protocol called MMIP6 based on the Mobile IPv4 principle. In MMIP6, the delivery of data has to be determined by the VANET routing protocol. The VANET routing protocol has to deliver the IP packets locally if the receiver being a vehicle located in the VANET can be reached via multi-hop communication. Otherwise, the data will be delivered to other nodes, i.e. Foreign Agents (FA) that the sending vehicle is currently registered with. Other advanced protocols are [9] Fast Mobile IPv6 (FMIP6) or Hierarchical Mobility management IPv6 (HMIP6), etc. For efficient network management of VANET, the IEEE 802.11a [10] WLAN standard was formed. The IEEE 1609 Wireless Access in Vehicular Environments (WAVE) [11] protocol stack is built on IEEE 802.11p and operates on seven reserved channels in the 5.9 GHz frequency band.

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2 Problem Statement With respect to all the applications of a VANET, the advantages, critical issues and their solutions discussed before, and there is one problem that only increases with every issue and its solution. This problem is the increasing complexity of the network and the receiver end (vehicles), the computation cost and latency at every node and the actual cost of physical implementation. This problem exists because of the fundamental idea of a VANET that every vehicle can connect to and share data between every other vehicle. Consider an ideal scenario in the real environment or a virtually simulated one, where there are fifteen vehicles in the vicinity. Each vehicle has a piece of critical information to share which could be as follows: i. ii. iii.

Few trees having collapsed on the road occupy two lanes. A series of shallow and deep potholes along all the lanes at irregular distances. If any vehicle has broken down in the middle of the road and is a bottleneck for the fast lane. iv. Vehicles with a drunk driver. v. Multiple vehicles speedily approaching from behind in the slow lane or taking an unexpected reverse. vi. Animals and pedestrians crossing the road. vii. An accident on one of the lanes or in between two lanes. viii. The flow of vehicles just before and after the traffic lights change. Every vehicle may use the sensors that it has onboard for detecting all kinds of abnormalities on the road in comparison with an ideal environment for driving. Of the many sensors like ultrasound, RADAR, LIDAR and vision cameras made available to it, the vehicle may use RADAR and ultrasound to keep itself within safe distances from all the vehicles in its proximity to prevent crashes. The LIDAR and vision cameras are used to scan the environment for other potential obstacles or threats. Once detected, the vehicle’s onboard computer has to use that information to navigate itself from the situation with maximum safety and passenger comfort. Simultaneously, the vehicle has to encode the same information, segment into data packets and transmit to other vehicles within absolute and virtual proximity using single and multi-hop principle, respectively. During this process, it has to carry out another important task of receiving packets from every other node. Being an ideal environment, the packets are true and unmanipulated by an attacker, frequency of connection establishment and breaks are within maximum limits, and all packets are received with unity probability in the first attempt. Even so, every participating vehicle has to receive data from every other vehicle node, restructure it into usable information and arrange the information into a driver understandable pattern on the application layer, where the driver may be a human or an AI. The complexity of computation and latency up to this point is mostly in the restructuring and application layer because the computer has to convert the instantaneous positional coordinates and orientation of obstacles detected by other nodes into the same with respect to itself. This task demands mathematical operations dealing with matrices, trigonometry and vector calculus for obtaining the position of

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Fig. 2 Pedestrians crossing an active lane [12]

an object, the displacement and velocity of its approach along with the orientation of the ego vehicle towards every target. The computer has to repeatedly perform these calculations on every data that is received from every node and represent every smallest change accurately for the application to leverage (Fig. 2). However, in practical scenarios, the data might be incomplete and incorrect for proper restructuring before passing it into the processing layer. This may lead to an increase in latency for the data of that particular instance. Indeed, with advancing research on this topic, there could be many solutions for solving all problems, but these solutions also demand computation from the computer for themselves. Therefore, the complexity increases not only for securing the network from potential threats as discussed earlier but also for achieving a reliable communication between vehicle to vehicle, as the name suggests. There cannot be two versions of the same technology, namely: VANET-lite and VANET-pro, to provide a cheaper alternative for budgetfriendly vehicles, but only one robust system for all vehicles. With the increasing requirement of processing capability, the cost of system development, its implementation on each vehicle tailored to its physical size and performance (both, engine and driver) and network maintenance might increase near to the cost of the vehicle itself. Therefore, the author has proposed a newer and better system for achieving nearly all the tasks of V2V communication but in the shortest manner with much lesser computation and latency.

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3 Proposed Solution The proposed solution is a system that actively monitors every lane on the road at regular distances. With the vehicles evolving from gasoline to electric and from driver dependent to autonomous, it is also necessary for the existing technologies surrounding the vehicles to evolve. The existing technologies being the stationary but passive ground units namely: the street light and the traffic signal poles. The concept makes use of these units as a continuous monitoring and updating node. Instead of giving out lights in fixed periodic intervals or otherwise collecting dust, these poles may use cameras to monitor all the critical problems mentioned within the problem statement section. Every pole shall be equipped with sight sensors (cameras) along with a low-cost computer that performs image processing, object classification and object behaviour detection using the neural network and machine learning. Considering the computational capabilities of today’s low-cost mobile computers, performing such tasks is not a burden. The real advantage of this system lies with the drastically simplified networking protocol. Once the stationary units have processed each sample frame from the sensors and extracted information, it may upload all the information to a server on an IoT cloud. Each stationary unit shall have its unique identifier that is strictly un-maskable along with its exact location on the road, set within the ID at the time of installation. Therefore, every chunk of data arriving and leaving the IoT server shall be equipped with the unique identifier that has the location information encoded within it. Now, every vehicle having the privilege to connect and access this private IoT server shall receive this data. The data may or may not have the information ready to be used directly at the application level. The vehicle may then decode the same accordingly and pass on to the application. Here, the application is the offline map pre-downloaded onboard the vehicles. Usually, a map is required only for navigating from point A to B along a route, but once the information received from the server is plotted on the detailed version of the map with every lane identified explicitly, the driver or AI can be alerted about the dangers (obstacles), ongoing situations (traffic or mishaps) and road conditions (wet and slippery, rough roads, shattered pieces of glass), at least 1 km away from the actual point. Using a V2V network, a vehicle may know the situation of roads nearby, when the ego vehicle is unable to detect the same due to blind spots (like being surrounded by trailers). Even though multi-hop technology is used, the distance and the number of hops allowed are limited for keeping the network congestion within limits. However, using this concept, the ego vehicle may increase its scan radius for surveying along a larger length of its route, demand information uploaded by adjacent nodes situated more further and plot all the information in its onboard map. Thus, the driver or AI may focus on manoeuvring the vehicle while planning the path and navigating safely out of every situation. The computer at each vehicle end has to perform no additional complex task apart from perceiving information from its onboard sensors, unlike V2V connection which requires a separate computer to perform all the tasks mentioned previously in the problem statement section. This concept may add a plethora of features to the existing model of Advanced Driver

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Assistance System (ADAS) and autonomous vehicles. Both, VANET–V2V and the LMS concept follow the same idea: ‘to observe the environment by itself using eyes, but also to use the ears and let the surroundings inform its situation’. The LMS performs this task in a more simplified and faster rate therefore efficient manner.

4 Implementation Major road safety applications are the primary measures taken to prevent (or reduce) the traffic accidents and loss of life. Traffic accidents that occur annually across the globe are as a result of intersection, rear-end, head-on and lateral collisions. In order to inform the drivers about every situation on the path ahead of the vehicle, the messages sent by each stationary unit must have a fixed packet length and the data within each packet must be refreshed at a constant frequency. The various types of information inside each packet are mentioned in Table 1 ahead. The active stationary unit applications offer assistance to drivers through the provision of time-sensitive, life-saving information about the road and traffic which enables drivers to avoid collisions with other vehicles on the road. This is achieved through the timely and reliable exchange of kinematic information amongst vehicles via LTE connection within a cellular structure designed for mobile phones. An added advantage of using this improvement over V2V ad hoc is that the dependency on GPS is reduced. VANET requires GPS for tracking the exact location of every vehicle at every speed so that the data received with each message can be properly arranged on the map using relative coordinates. In the case of LMS, the data obtained has the location of obstacles with respect to ego vehicle’s coordinates and does not require GPS for actively tracking the location of either stationary nodes or the receiving vehicles. In the blind spot zones like tunnels, a vehicle may request data from all the stationary nodes inside the tunnel while itself being inside. Connection to the wide area network (WAN) can be maintained inside tunnels using repeaters so that every vehicle may obtain refreshed copy of data from the server without its GPS position inside blind spots.

5 Results Figure 3 [13] describes a typical scenario where road blocks are created due to slow moving vehicles, in this case a bus which indicated using a red bounding box. The stationary monitoring unit at either one side of the road or over a divider in the middle detects everything from vehicles in each lane on both directions, indicated using light green and yellow colour for each direction, to obstacles such as pedestrians crossing the lanes and vehicles taking a U-turn. Using this information, it may trace the direction of flow and the amount of congestion created by it. The results of such detections uploaded to server can be downloaded by connected vehicles and present

Lane Monitoring System for Driver Assistance … Table 1 Messages from stationary unit to cloud

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Detections

Message

Refresh rate

Unique Identifier

Value

No (permanent)

Number of lanes

Value

No

Min Speed at lane 1 Value

No

Min Speed at lane 2 Value

No

Max Speed at lane 1 Value

No

Max Speed at lane 2 Value

No

No. of vehicles in lane 1

Value

2 Hz

No. of vehicles in lane 2

Value

2 Hz

Average speed in lane 1

Value

2 Hz

Average speed in lane 2

Value

2 Hz

Special use cases in each lane Drunk driver

Lane number/s

5 Hz

Reversing vehicle

Lane number/s

5 Hz

Stationary vehicle

Lane number/s

5 Hz

Speed breakers and potholes

Yes/no, lane number/s

5 Hz

Wet/slippery road

Yes/no

5 Hz

Glass shrapnel

Yes/no

5 Hz

Active directions

12 encoded directions

2 Hz

Signal light (RGY)

Three colours

1 Hz

Signal timer

Count value

Traffic signal statuses

Junction congestion Yes/no

1 Hz 1 Hz

this information as a telemetry to driver. The various ways a software application onboard any vehicle may present this information is as shown in Figs. 4 and 5 ahead. Every vehicle connected to the IoT cloud may request the server to send data from nodes far away from location of vehicle. This distance depends on the scan radius decided by vehicle’s driver assistance algorithm, which is itself dependent on the speed of vehicle. The information obtained can be represented in a user understandable format using an appropriate application. This may be expressed graphically on the Heads-Up Display (HUD) projected on the front windshield or on an LCD/LED display along with vehicle’s telemetry information. An example of such expression is the BMW i8 ‘s holographic HUD and the Tesla Model S ‘s LCD central console. Figure 4 [14] shows a holographic HUD where data from three nodes ahead of ego vehicle is obtained from server. The numerals 93/80 indicate the average speed in

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Fig. 3 Congestion due to physical obstacles [13]

lane 1 with respect to the maximum speed (limit) for that lane. These numbers are pre-programmed or detected by every stationary node (refer Table 1). The driver may now interpret the following: • Average speed (measured by unit 1) in lane 1 is higher than limit (80 km/h), while in lane 2, it is within the limits (50 km/h). • This speed is decreasing along the length and is measured by later units 2, 3, whereas unit 4 indicates a crash (shown in red). • This implies that the vehicles in lane 2 are moving into lane 1, which is why the average speed as detected by respective units is decreasing in both lanes. The driver may now take a decision accordingly. The monitoring unit clamped on a pole of traffic signal provides basic information like the state of signal, count on the timer before change of lights but also provides some predictive information based on analysis, such as the flow of traffic before and after the lights change. Figure 5 [15] shows the basic information of traffic lights. The information conveyed is as follows: • There are three lanes at the junction ahead—left, straight, right. • Signal at left lane is about to change from green to yellow in 35 s, signal at centre lane from red to green in 38 s and signal at right lane from red to green in 38 s.

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Fig. 4 Active lane speed and events [14]

Fig. 5 Traffic signal information [15]

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Thus, by knowing the signal state and the traffic density at each lane, the driver may decide if it is worth accelerating in order to cross the junction before the signal timeout, otherwise decelerate earlier and slower for passenger comfort. Driver may also switch to the required lane before reaching a tight spot. This system helps with pre-planning a route to avoid last minute changes when stuck inside a congested road.

6 Conclusion By studying the advantages, limitations, problems and the challenges of using a V2V and V2I connected system, a solution has been put forth that uses existing widely accepted protocols like LTE to simplify the process of obtaining road telemetry information. The architecture of this proposed network is much simpler to implement, configure and troubleshoot. The computation complexity on vehicle’s end is drastically reduced by using this model, therefore leading towards a faster, more responsive and a reliable mobility of the future. The drivers actively using ADAS or an AI for autonomous drive can make use of the rapidly refreshed traffic updates provided by this model for obtaining alerts about critical events such as storms, road mishap, rowdy/rogue drivers. The driver and passengers may experience a safer and worry-free journey over long and short distances. Acknowledgements I thank Prof. Rammohan A. for teaching us about the advanced systems for driver assistance, for explaining every concept in detail and imparting all the knowledge of this subject as well as for sharing his experiences while working in the field.

References 1. Dinesh D, Deshmukh M (2014) Challenges in vehicle ad hoc network. Int J Eng Technol Manage Appl Sci 2(7) 2. Eze EC, Zhang S, Liu E (2014) Vehicular ad hoc networks (VANETs): current state, challenges, potentials and way forward. In: 20th International Conference on Automation and Computing, September 2014 3. Raw RS, Kumar M, Singh N (2013) Security challenges, issues and their solutions for VANET. Int J Netw Secur Appl 5(5) 4. Sybil Attack, figure [13]. http://www.eurekaselect.com/136495/article 5. Samara G, Al-Salihy WAH, Sures R (2010) Security analysis of vehicular ad hoc networks. In: International Conference on Network Applications, Protocols and Services 6. Torrent-Moreno M, Santi P, Hartenstein H, Distributed fair transmit power adjustment for vehicular ad hoc networks 7. Vyas IB, Dandekar DR (2014) An efficient congestion control scheme for VANET. Int J Eng Res Technol 3(8) 8. Bechler M, Wolf L (2005) Mobility management for vehicular ad hoc networks. IEEE 9. Fu S, Atiquzzaman M (2005) Handover latency comparison of SIGMA, FMIPv6, HMIPv6 and FHIMPv6, IEEE Globecom

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10. IEEE Standard for Wireless Access in Vehicular Environments (WAVE) (2016) Multi-Channel Operation, IEEE Std. 1609.4TM 11. a—1999, 802.11b—1999, 802.11e—2005, 802.11n—2009, 802.11p—2010 Standards 12. Pedestrians crossing an active lane, figure [14]. https://www.newindianexpress.com/cities/ben galuru/2016/dec/12/delay-in-constructing-mg-road-skywalk-puts-pedestrians-lives-in-peril1548125.html 13. Road blocks due to traffic congestion, figure 1. https://upload.wikimedia.org/wikipedia/com mons/thumb/a/a4/Moscow_traffic_congestion.JPG/1200px-Moscow_traffic_congestion.JPG 14. Vehicles on a highway, figure 2. http://3.bp.blogspot.com/-q5HRDq9VAiM/VFjPk-nAcOI/ AAAAAAAAOTE/buDVAc6xsxw/s1600/20141021_145812.jpg 15. Vehicles in the city, figure 3. https://il1.picdn.net/shutterstock/videos/9453719/thumb/1.jpg 16. Sybil Attack on VANET. http://www.eurekaselect.com/images/graphical-abstract/swcc/6/1/1. jpg

Integration of Area Scanning with PSO for Improving Coverage and Hole Detection in Sensor Networks T. Shankar, Geoffrey Eappen, Shubham Mittal, and Ramit Mehra

Abstract Random deployment of wireless sensor network (WSN) leads to holes and coverage problem. Thus, the detection and healing of coverage holes have become major issues and obstacles for achieving the satisfactory coverage of the WSNs. For this purpose, comparison is made among tree-based coverage hole detection, Delaunay triangulation and Voronoi algorithm in terms of their performances. It helps to detect the coverage holes in WSNs. The area that is checked for holes may be more than necessary. A method is proposed that will reduce the number of sensors, so that only the desired area is checked for holes. The area that is checked for holes may be more than necessary. A method is proposed that will reduce the number of sensors, so that only the desired area is checked for holes. The proposal is to do this using image recognition technique. The image will be scanned and using the pixel occurrence of black and white colour in the image, and we should be able to identify the desired area and deploy the required number of additional sensors accordingly. We propose to compare the performance of existing tree-based algorithm and triangulation algorithms in this domain, with particle swarm optimization (PSO). Keywords Hole · Healing · Tree-based coverage hole detection · Delaunay triangulation · Voronoi algorithm · Pixel · Particle swarm optimisation

1 Introduction In WSNs, sensor nodes are spread randomly across the region of interest (ROI) for the detection of events and collection of information [1]. The range of sensor nodes in a sensor network is a circular disc with a fixed range. In this paper, the main focus is on the problem of coverage of holes, detection, and healing in WSNs (Voronoi, Delaunay, and tree-based). What is desirable is the need for a less number of additional sensors for covering the uncovered area. Few sensor networks are required to cover the uncovered regions. This is done by deciding T. Shankar · G. Eappen (B) · S. Mittal · R. Mehra School of Electronics and Communication Engineering, VIT University, Vellore 632014, Tamil Nadu, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_7

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roughly in which area to deploy the sensors, by differentiating the colours based on the intensity. We use particle swarm optimization to improve coverage area and reduce number of additional sensor deployed [2].

2 Literature Survey Wireless sensor networks have turned into a huge territory for examination. There has been a substantial assemblage of research on discovery of scope openings in WSNs throughout the last few a long time. Senouci et al. [3] aimed at addressing the problem of hole detection and healing in sensor nodes. High node density is desired although the cost of coverage should be less. Qiu and Shen [4] propose a Delaunay-based co-ordinate-free mechanism, and it detects coverage holes without the requirement of accurate location. But global view of holes is not provided. Bejerano [5] proposes a greedy-based algorithm, which activates some inactive nodes to patch/fill holes, but it has very high computational complexity and thus consumes more energy. Ma et al. [6] discussed a geometry base approach to detect holes, in a postdeployment region, every hole can be detected, but the actual location of the sensor is required. Zhang et al. [7] presented identify holes on a coverage boundary, and based on novel geometric techniques, localized Voronoi and polygons require the position of one-hop neighbours.

3 Methodology The main idea is to improve the way we deploy sensors in order to save energy, cost and improve throughput general flow. We deploy random nodes in the canvas (whole area) and plot it on the graph using MATLAB. We introduce the concept of hole [8]. A hole is any area on the canvas that is left uncovered when sensors are deployed [9, 10]. Then we plot the sensing radius of the each node (assuming a value) and identify the coverage of hole area and try to minimize it. We proceed towards getting an area without any hole [11]. Using the technique where we merge two sensing areas, so that we can reduce the number of sensors in order to save up on the energy consumption and cost. Using tree-based approach, we try to patch the holes and compare it with Delaunay and Voronoi-based algorithms [1]. Comparison of the above algorithms is hence plotted, and performance is measured. Figure 1a, b depicts general flow.

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Fig. 1 a General Flow of the deployment of the sensor nodes. b General Flow for the PSO based hole detection

(a) General Flow

(b) Generalflow

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Fig. 2 Random deployment of nodes

3.1 Algorithm 1: Delaunay Triangulation Start connecting the nodes to create a series of triangles. The triangles created here are referred to as Delaunay triangulation, which states that the three points from the triangle create a vertex when they touch the path of a circle. This circle is the circumcircle of the triangle. These triangles are such that the circumcircle of the three vertices of a triangle does not enclose any other node of the network. The figure gives a vivid description. Figure 2 shows random deployment of nodes.

3.2 Algorithm 2: Voronoi Method Here, in Fig. 4, the ‘+’ symbol signifies a node. The Voronoi diagram is made from the Delaunay triangles. Mark the mid-points of all the lines in the Delaunay triangles structure [12]. Now draw long perpendicular lines based on the mid-points. Do this for each triangle and mark these points separately. Join these points on top of the lines to get the Voronoi diagram. Each node gets its own area. If a node is able to cover its own area with its sensing circle, then its task is accomplished. If this condition is not met, which is true in most cases, we need additional sensors to optimize the coverage hole area. Figure 4 shows the individual regions of all nodes.

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Fig. 3 Delaunay triangles with respective circum circles

Fig. 4 Voronoi—individual regions of all nodes [1]

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3.3 Sensing Radius—Rs The radius of the circular sensing disc is denoted by Rs, and similarly, the radius of the empty circle is Re . Now, the uncovered regions of interest are to be detected with the help of empty circle’s concept [1]. Now, we will make use of circumcircles of the Delaunay triangles, which are called as empty circles. The determination of an empty circle is based on the comparison of Re and Rs . Perform comparison between Re and Rs for each uncovered region. Value of Re greater than Rs shows the presence of regions that are uncovered in vacant circle. Thus for sensing radius greater than Rs , priority is given to hole determination instead of hole detection.

3.4 Concept of IEC (Inscribed Empty Circle) Empty circles do not assure complete similarity with coverage holes [4, 13]. It is only giving us a broad idea. Figure 5 depicts grey regions in the green-coloured empty circles which shows empty circles with Re > Rs . Here, the proposed scheme is introduced as:

Fig. 5 Hole detection through empty circles [1]

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Fig. 6 IEC explained conceptually

Inscribed empty circles (IECs) for the estimation of coverage holes’ sizes [1]. An inscribed empty circle is a circle which is concentric with its corresponding empty circle. Its radius is Rie , which is given by the difference between empty circle radius and sensing radius, i.e. Re − Rs , where Re is greater than Rs . Here, black-coloured circle is the empty circle of radius Re. Red-coloured circles are the circles with radius as sensing radius (Rs ), and they are identical in size and called as sensing circles. The green-coloured circle is the IEC, which has the radius = Re − Rs [1]. The centres of the sensing circles are outside the empty circle, as the empty circle is a circum circle of a Delaunay triangle (Fig. 6). Steps: 1. If this length turns out to be more than 2 Rs , then the two behave to be in the same coverage hole (e.g.—[C1 , C2 ) and (C2 , C3 ) and (C3 , C4 )] [1]. 2. Else, if it turns out to be less than 2Rs and the holes are merged as one (e.g.—C4 , C5 ). 3. Else, the holes are said to be different holes (C1 , C2 , C3 , C4 , C5 and C6 ) To reduce the number of sensors deployed, energy and cost, a method is proposed to merge the empty circles, which belong to the same coverage hole, according to the concept of IEC [5]. Here, the merged circles are denoted with the same colour. 0020, and merging of isolated IECs are shown in Fig. 7. Here, in Fig. 8 certain empty circles are merged, and hence, we get a lesser number of red-coloured circles, which are sufficient to cover the coverage holes as shown in Fig. 9.

3.5 Hole Detection Here, in Fig. 10 we do hole detection through IEC with tree-based method [6]. A tree is formed by joining the centres of all the IECs [7]. Here, the blue line segments

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Fig. 7 Merging of isolated IECs [1]

join the centres of all the IECs, which forms a tree. Hence, the blue lines together form a tree.

3.6 Tree-Based Healing Method Figure 11 depicts the hole dissection—here, the green and blue lines indicate two different sub-trees [1, 14]. The proposal of dissection of a large coverage is done as [1]. In a particular tree, IECs are stored in a list according to their sizes. The IEC with the peak value is assigned as the origin, for the formation of a new sub-tree and is removed from the list. The sub-tree is created alongside of IECs and is removed from the list. These become the boundary IECs. Until sub-tree construction, no additional IEC is added.

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Fig. 8 Merged circles denoted with same colour

3.7 Optimal Patch Position Determination The image shown in Fig. 12 depicts a conceptual patch position. The blue, red, and brown circles are the optimum patches here, which will optimize our additional sensor requirement [15]. The optimum position to place a patch is on the originator IEC of a sub-tree, which is also the largest IEC of a sub-tree [1].

3.7.1

Sub-Trees Due to Hole Dissection

Sometimes, the radius of even the largest IEC in a sub-tree is small [1]. If that position is patched, it will cover only a small coverage hole area, which will be according to

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Fig. 9 Isolated circles with hole’s detected

the algorithm; but we will have to deploy additional sensors to cover the remaining coverage holes, which will result in an increase in resource consumption. Hence, we set a maximum value to avoid such cases. This value can be a constant or a variable.

3.8 Proposed Technique The area that is checked for holes may be more than necessary. A method is proposed that it will reduce the number of sensors, so that only the desired area is checked for holes. The image will be scanned, and using the particular pixel occurrence in the image, we should be able to identify the desired area and deploy the required number of additional sensors accordingly, in that area [16, 17]. PSO will enhance our output as it involves mobile nodes, which are capable of self-adjusting [12, 18].

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Fig. 10 Hole detection through IEC with a tree

Step 1: Take an image. Step 2: To determine the boundary of the map use a threshold function. Step 3: Obtaining the boundary of the map, user-defined decisions can be made. Step 4: Using the co-ordinates of the affected area, a rectangular/square grids can be applied to affected areas. Step 5: Now, the area is narrowed and the random deployment can be done in the given area.

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Fig. 11 Hole detection with dissection

3.8.1

System Model

Wireless sensor nodes are randomly deployed as {Q1 , Q2 , Q3 , …, Qq } and divide equally then apply PSO for finding the best position [2, 19–21].

3.8.2

Coverage Rate Calculation

The sensing rate p(gi , p) covered by gi is given by Eq. (1) [2]:  1, d(h i , p) ≤ r p(h i , p) 0, d(h i , p) > r

(1)

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Fig. 12 Conceptual diagram depicting sub-trees with deployment of sensors [1]

where r is sensor’s sensing radius, sensors are deployed as node set H = {h1 ; h2 ; h3 ; …; hn }, I depicts a node i whose co-ordinates are given by {x i , yi } [22].

3.8.3

Fitness Value

The combination of both the moving distance and the coverage rate forms our fitness function f (x), as depicted in Eq. (2) [2]. The start point of mobile nodes is (x ini yini ), and its target final position is (x fin yfin ) [23–25]. f (x) = 

α (xini − xfin ) 2 + (yini − yfin )2

+ β · (cov)(α + β = 1)

(2)

Upon the selection of candidate set C, position of mobile sensor in motion needs to be determined [26]. We make the use of PSO for calculating optimum value of mobile position, which computes the optimal placement of sensor nodes and move to the optimal location [2]. Fig. 13 shows the basic flowchart of PSO. Declaration of k particles, the speed of particle x i is vi , individual optimal solution for particle x i is pi = (pi1 , pi2, …, pin ), and the global optimum solution is pg = (pg1 , pg2 , …, pgn ).

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Fig. 13 PSO algorithm basic flow

4 Simulation Results Here, number of nodes is 50, network size is 100 × 100, number of iteration is 20, personal acceleration coefficient is 1.4962, social acceleration coefficient is 1.4962, and inertia coefficient is 0.7298. In Fig. 14 a, comparative analysis of tree-based, Delaunay and Voronoi algorithms based on the number of additional sensors required and the coverage rate [1]. The graph here shows that the tree-based method produces better result than Delaunay triangulation and Voronoi method.

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Fig. 14 Relationship between additional sensors and coverage rate

The proposed method of area scanning with PSO is implemented. The deployment of nodes, for depiction, is done on a geographical area as shown in Fig. 15. Simulation results show that no additional sensors are required via PSO method, whereas additional sensors are required in tree-based method, Delaunay triangulation method, and Voronoi method, for the coverage of holes. For instance, for a random deployment of 50 sensor nodes, to get a coverage of 95%, 18 additional sensors, with multiple iterations are required in tree-based method, and the same result can be achieved with no additional sensors and only 6 iterations in area scanning with PSO method. Figure 16 shows as the more the number of iterations, and the more will be the coverage; because in each iteration, the nodes will satisfy its fitness function which in turn will improve the coverage area.

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Fig. 15 Deployment of nodes in a particular region by area scanning method

5 Conclusion The energy consumption is reduced as the number of iterations is less in the proposed method. The comparison results of tree-based, Voronoi, Delaunay, and area scanning with PSO are depicted. From the proposed algorithm, we can achieve better coverage with less number of sensors.

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Fig. 16 Relationship between number of iterations and coverage rate

References 1. Li W, Wu Y (2016) Tree-based coverage hole detection and healing method in wireless sensor networks 2. Wang J, Ju C, Kim H-J, Simon Sherratt R, Lee S (2018) A PSO based coverage hole. Springer Nature Singapore Pvt Ltd 3. Jerbi M, Senouci SM, Rasheed T, Ghamri-Doudane Y (2009) Towards efficient geographic routing in urban vehicular networks. IEEE Trans Veh Technol 58(9):5048–5059 4. Qiu C, Shen H (2014) A delaunay-based coordinate-free mechanism for full coverage in wireless sensor networks. IEEE Trans Parallel Distributed Syst 25(4):828–839 5. Bejerano Y (2012) Coverage verification without location information. IEEE Trans Mobile Comput 11(4):631–643 6. Ma H, Sahoo PK, Chen YW (2011) Computational geometrical based distributed coverage hole detection protocol for the wireless sensor networks. J Netw Comput Appl 34:1743–1756 7. Zhang C, Zhang Y, Fang Y (2009) Localized algorithms for coverage boundary detection in wireless sensor networks. Wireless Netw 15(1):3–20 8. Khan I, Mokhtar H, Merabti H (2010) An overview of holes in wireless sensor network. In: Proceedings of the 11th annual postgraduate symposium on the convergence of telecommunications, networking and broadcasting, Liverpool, UK 9. Wang G, Cao G, Berman P, La Porta TF (2007) Bidding protocols for deploying mobile sensors. IEEE Trans. Mobile Comput. 6(5):563–576 10. Li F, Zhang B, Zheng J (2011) Geographic hole-bypassing forwarding protocol for wireless sensor networks. IET Commun 5(6):737–744

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11. Lee T, Qiao C, Demirbas M, Xu J (2010) ABC: a simple geographic forwarding scheme capable of bypassing routing holes in sensor networks. Ad Hoc Netw 8:361–377 12. Kumar M, Gupta V (2017) Benefits of using particle swarm optimization and Voronoi diagram for coverage in wireless sensor networks 13. Li W (2014) A novel graphic coverage hole description in wireless sensor networks. IEEE Commun Lett 18(12):2205–2208 14. Shankar T, Eappen G, Suresh V, Rajesh A, Mageshvaran R (2019, March) Contrast enhancement using quantile separation and bi-histogram equalization. In: 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), vol 1. IEEE, pp 1–4 15. Liu J, Feng Y, Xu H, et al. (2012) Greedy approximation algorithm of patching hole in wireless sensor networks. In: 2nd IEEE international conference on consumer electronics, communications and networks, pp 2604–2608 16. Karthikeyan A, Shankar T, Srividhya V, Sarkar S, Gupte A (2012) Energy efficient distributed image compression using Jpeg2000. In: Wireless Sensor Networks (WSN), vol 42, Issue 2, pp 227–236. ISSN: 1992-8645 17. Navada BR, Santhosh KV, Prajwal S, Shetty HB (2014) An image processing technique for color detection and distinguish patterns with similar color: an aid for color blind people. Manipal Institute of Technology, Manipal 18. Eappen G, Shankar T (2018) Energy efficient spectrum sensing for cognitive radio network using artificial bee colony algorithm. Int J Eng Technol 7(4):2319–2324 19. Karthikeyan A, Sanyukta, Gupta S, Shankar T (2014) A multi-objective approach for energy efficient clustering using comprehensive learning particle swarm optimization in mobile ad-hoc network. J Theor Appl Inf Technol 65(3):723–730 20. Karthikeyan A, Jindal Falak, Bumrah Neeraj Kaur, Pamecha S, Shankar T (2014) Threshold sensitive assistant aided clustering protocol for heterogeneous WSNS using niching particle swarm optimization. J Theor Appl Inf Technol 65(3):731–737 21. Eappen G, Shankar T (2020) Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network. In: Physical Communication, p 101091 22. James T, Shankar T, Karthikeyan A, Rajesh A (2015) Sensor node localization in wireless sensor networks using flower pollination metaheuristic algorithm. Int J Appl Eng Res 10(20):16404– 16408. ISSN 0973-4562 23. Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evolut Comput 30(2016):1– 10 24. Shankar T, James T, Mageshvaran R, Rajesh A (2016) Lifetime improvement in WSN using flower pollination meta heuristic algorithm based localization approach. Ind J Sci Technol 9(37). Article number 102117 25. Eappen G, Shankar T (2018) Optimization of two area AGC based power system using PSO tuned Fuzzy PID controller and PSO trained SSSC and TCPS. Int J Eng Technol 7(4.10):163– 168 26. Reddy A, Shankar T, Lavnya N, Mageshvaran R, Muthiah-Nakarajan V (2018) Self-deployment in wireless sensor networks using ant colony optimization method. ARPN J Eng Appl Sci 13(3):990–997

Optimized Routing Algorithm for Wireless Sensor Networks T. Shankar, Geoffrey Eappen, and S. Rajalakshmi

Abstract Communication systems have been progressed tremendously in the past two decades. A large part of this success can be attributed to the discovery of various new algorithms in the wireless sensor networks. This paper proposes three new algorithms for routing by modifying the existing algorithm known as A-star algorithm to find the optimal path between the source and the destination nodes. This paper places a special emphasis on reducing the path length between the source and destination nodes which in turn reduce the execution time as well as the resource spent in finding the optimal path between the source and the destination node. The new algorithms proposed are named as diagonal A-star (DA*) which gives the diagonal path search ability to the existing A-star (A*) algorithm, bidirectional A-star (BIDA*) which gives the ability of traversing from both source and destination nodes at the same time hence reduces the execution time and the third algorithm known as diagonal-bidirectional combined which combines the ability of both; the above newly proposed algorithms propose a more optimized routing solution between the source and destination nodes. Keywords Routing A-star (A*) · Diagonal A-star (DA*) · Bidirectional A-star (BIDA*) · Diagonal-bidirectional A-star combined (DBIDA*) · Path length · Execution time

1 Introduction Routing is one of the most pivotal aspects in the wireless sensor networks with efficient routing algorithm and a robust wireless network is established. Routing is defined as finding the path between the source and the destination nodes [1, 2]. There are end numbers of routing algorithms present already which provide optimal path between the source and the destination; still we are short of optimal routing algorithms for various environments and conditions hence three new robust algorithms, namely T. Shankar · G. Eappen (B) · S. Rajalakshmi School of Electronics Engineering, VIT University, Vellore 632014, Tamil Nadu, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_8

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(1) Diagonal (2) Bidirectional (3) Diagonal-bidirectional combined are proposed in this paper which provide optimal path between the source and the destination. The paper follows the content as follows: Sect. 2: describes the algorithm which is already present which has been modified to give more optimized path between the source and the destination Sect. 3: describes the proposed algorithm Sect. 4: Compares the results of all three proposed algorithm with the existing A-star algorithm and concludes.

2 Related Work A* algorithm is a widely used path finding algorithm to choose the shortest path between source and destination [3, 4]. It computes the overall cost using the following formula given in Eq. (1) as [5–7], f (n) = g(n) + h(n)

(1)

where, g(n) denotes the actual path from source to destination. h(n) represents the heuristic cost value (straight value) node n to goal node Algorithm contains open list and close list. In open list, set of all possible nodes (visited nodes) will be maintained and in the close list, the node which paves the path will be maintained. The main drawback of this algorithm is its efficiency [8, 9], and to increase this, an enhancement has been made to A* in the proposed concept. More optimality is provided by refining the existing A*. The proposed system contains three new concepts, namely • Diagonal A* • Bidirectional A* • Diagonal and bidirectional combined A*.

2.1 Diagonal A* Search To minimize the number of nodes, present in the open list, diagonal concept has been proposed. Instead of the looking out for four neighbouring nodes, it checks out all the neighbour nodes in the networks. Thus, the diagonal traverse path is used to reduce the distance between the source and the destination [10, 11, 20]. Figure 1 represents the flowchart for A* algorithm.

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Fig. 1 Flowchart for bidirectional A* search [3, 12–14]

The algorithm is defined as follows: (1) First step is to place the source node in open list. (2) If open list is empty, then return false and stop. (3) Select the successor from the eight possible neighbouring nodes and place it in the open list. (4) If the node in the open list has small value of f (n), remove it from open and place in closed list. If it is the goal node, return success and stop. (5) If it is not the goal node, then expand all the successors of the node in close list and maintain them in the open list. (6) Return to step 2.

2.2 Bidirectional A* Search Figure 1 represents the flowchart for diagonal A* search. Here, to reduce the time of path finding, bidirectional concept is deployed. In bidirectional A*, traversing of nodes starts from both source and destination simultaneously. Bidirectional A* is more time efficient when compared to both diagonal A* and A* [15, 16].

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The algorithm is defined as follows: (1) First step is to place the source node and the destination in open list. (2) If open list is empty, then return false and stop. (3) If the node in the open has small value of f (n), remove it from open and place in closed list for both source and destination traversal nodes. If both the source traversal and destination traversal meet, then return success and stop. (4) If not, then expand all the successors of the node in close list and maintain them in the open list. (5) Return to step 2.

2.3 Diagonal and Bidirectional Combine To increase the efficiency furthermore, the precious algorithms, namely diagonal and bidirectional have been combined to form a new algorithm which can give better results than both the algorithms proposed earlier. Figure 2 represents the flowchart for diagonal-bidirectional A-star algorithm.

Fig. 2 Flowchart of diagonal-bidirectional A-star algorithm [3, 17, 18]

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The algorithm is defined as follows: (1) First step is to place the source node and the destination in open list. (2) If open list is empty, then return false and stop. (3) Select the successor from the eight possible neighbouring nodes for both the source and destination traversal nodes and place it in the open list. (4) If the node in the open has small value of f (n), remove it from open and place in closed list for both source and destination traversal nodes. If both the source traversal and destination traversal meet, then return success and stop. (5) If not, then expand all the successors of the node in close list and maintain them in the open list. (6) Return to step 2.

2.4 Module Description 2.4.1

Diagonal A* Search

Diagonal A* search looks for the eight neighbouring nodes instead of the four neighbouring nodes which usually happens in A* search hence can search in all directions and find the efficient shortest node which could be placed in the closed list. Consider the following example. The operation of diagonal A* search was described and shown in Fig. 3a–f, 7 × 7 matrixes are chosen. Here, (7, 4) is chosen as the source node S and the destination node D is (2, 2). Diagonal search is similar to A* search, where in A* traversing successor is chosen from the four neighbouring nodes but in diagonal search, successor is chosen from the eight possible neighbouring nodes. When compared to A*, the diagonal A* is more efficient, as it saves time to choose the path.

2.4.2

Bidirectional A* Search

To reduce the time of path finding, bidirectional concept is deployed. In bidirectional A*, traversing of nodes starts from both source and destination simultaneously [1, 19]. Consider the following example. The operation of bidirectional A* search was described and shown in Fig. 4a–d, 7 × 7 matrix is chosen. Here, (7, 4) is chosen as the source node S and the destination node D is (2, 2). Bidirectional search is similar to A* search, where in A*, traversing is done on one side (Source). In bidirectional search, traversing is done on both the sides (source and destination). When compared to A*, bidirectional is efficient such it saves time to choose the path.

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S D (a). An example for Diagonal A* search

S

S

S

D

D

D

c.Choosing of successor nodes

b.Source traversing

d. Source traversing

S

S

D

D

e. Choosing next successor nodes

f. Path from source to destination

Fig. 3 a An example for diagonal A* search. b Source traversing. c Choosing of successor nodes. d Source traversing. e Choosing next successor nodes. f Path from source to destination

S D a. An example for Bidirectional A* search

S D b. Source and Destination

S

S D

c. Choosing of successor nodes both the sides

D

d.Choosing of successor nodes both the sides and path is chosen

Fig. 4 a An example for bidirectional A* search. b Source and destination. c Choosing of successor nodes both the sides. d Choosing of successor nodes both the sides and path is chosen

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3 Simulation Outputs Figure 5 shows path from source to destination by applying diagonal A* algorithm, Fig. 6 shows path from source to destination by applying bidirectional A* algorithm and Fig. 7 shows path from source to destination by applying diagonal-bidirectional A* combined algorithm computer simulations in the project are discussed. The below figure’s red colour indicates source and green colour indicated destination.

Fig. 5 Path from source to destination by applying diagonal A* algorithm

Fig. 6 Path from source to destination by applying bidirectional A* algorithm

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Fig. 7 Path from source to destination by applying diagonal-bidirectional A* combined algorithm

4 Results Analysis Table 1 shows the difference between the diagonal A-star (DA*) and A-star (A*). This comparison is done for the path length for both the algorithm to find the shortest path between the source and the destination node. The difference between the path length also the percentage decrease in the path length compared to the A-star is also tabulated. Table 1 Comparison of path length between DA* and A* DEST

A*

DA*

Difference

%Decrease

D1

SOURCE 7.9

2.10

47

41.4

5.6

11.91

D2

3.10

1.8

61

53.97

7.03

11.52

D3

1.5

5.7

28

24.49

3.51

12.54

D4

10.5

5.15

49

49

0

0

D5

1.2

3.3

32

23.8

8.2

25.63

D6

6.5

4.1

59

51.38

7.62

12.92

D7

2.2

7.5

59

48.46

10.54

17.86

D8

13.1

2.1

62

54.38

7.62

12.29

D9

5.2

10.4

74

60.53

13.47

18.2

D10

12.2

1.9

57

45.28

11.72

20.56

Average

7.531

14.343

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Table 2 Comparison of path length between BIDA* and A* DEST

A*

BIDA*

Difference

%Increase

D1

SOURCE 7.9

2.10

47

51

4

8.51

D2

3.10

1.8

61

63

2

3.28

D3

1.5

5.7

28

30

2

7.14

D4

10.5

5.15

49

49

0

0

D5

1.2

3.3

32

32

0

0

D6

6.5

4.1

59

61

2

3.39

D7

2.2

7.5

59

59

0

0

D8

13.1

2.1

62

62

0

0

D9

5.2

10.4

74

74

0

0

D10

12.2

1.9

57

57

0

0

Average

1

2.232

Difference

%Decrease

Table 3 Comparison of path length between DBIDA* and A* SOURCE

DEST

A*

DBIDA*

D1

7.9

2.10

47

42.8

4.2

8.94

D2

3.10

1.8

61

55.63

5.37

8.8

D3

1.5

5.7

28

26.14

1.86

6.64

D4

10.5

5.15

49

49

0

0

D5

1.2

3.3

32

25.46

6.54

20.44

D6

6.5

4.1

59

53.04

5.96

10.1

D7

2.2

7.5

59

50.11

8.89

15.07

D8

13.1

2.1

62

56.04

5.96

9.61

D9

5.2

10.4

74

62.8

11.2

15.14

D10

12.2

1.9

57

46.94

10.06

17.65

Average

6.004

11.239

Table 2 shows the difference between the bidirectional A-star (BIDA*) and A-star (A*). This comparison is done for the path length for both the algorithms to find the shortest path between the source and the destination nodes. The difference between the path length also the percentage decrease in the path length compared to the A-star is also tabulated. Table 3 shows the difference between the diagonal-bidirectional A-star (DBIDA*) and A-star (A*). This comparison is done for the path length for both the algorithms to find the shortest path between the source and the destination nodes. The difference between the path length also the percentage decrease in the path length compared to the A-star is also tabulated.

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Table 4 Comparison of execution time between DA* and A* DEST

A*

DA*

Difference

%Decrease

D1

SOURCE 7.9

2.10

2.67

0.92

1.75

65.54

D2

3.10

1.8

6.895

0.495

6.4

92.82

D3

1.5

5.7

3.595

1.62

1.975

54.94

D4

10.5

5.15

1.41

1.88

0.47

33.33

D5

1.2

3.3

0.88

0.66

0.22

33.33

D6

6.5

4.1

6.05

0.37

5.68

93.88

D7

2.2

7.5

4.86

0.86

4

82.3

D8

13.1

2.1

1.22

0.19

1.03

84.43

D9

5.2

10.4

1.205

0.7

0.505

41.91

D10

12.2

1.9

1.035

0.18

0.855

82.61

Average

2.2885

66.509

Table 5 Comparison of execution time between BIDA* and A* SOURCE

DEST

A*

BIDA*

Difference

%Decrease

D1

7.9

2.10

4.67

3.875

0.795

17.02

D2

3.10

1.8

6.895

2.62

4.275

62

D3

1.5

5.7

3.595

2.78

0.815

22.67

D4

10.5

5.15

1.41

0.255

1.155

81.91

D5

1.2

3.3

1.88

0.815

1.065

56.65

D6

6.5

4.1

6.05

2.61

3.44

56.86

D7

2.2

7.5

0.86

0.375

0.485

56.4

D8

13.1

2.1

3.72

1.535

2.185

58.74

D9

5.2

10.4

5.205

2.27

2.935

56.39

D10

12.2

1.9

1.035

0.395

0.64

61.84

Average

1.779

53.048

Table 4 shows the difference between the diagonal A-star (DA*) and A-star (A*). This comparison is done for the time taken for both the algorithms to find the shortest path between the source and the destination node. The difference between the time taken also the percentage decrease in the time taken compared to the A-star is also tabulated. Table 5 shows the difference between the bidirectional A-star (BIDA*) and A-star (A*). This comparison is done for the time taken for both the algorithms to find the shortest path between the source and the destination node. The difference between the time taken also the percentage decrease in the time taken compared to the A-star is also tabulated.

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Table 6 Comparison of execution time between DBIDA* and A* DEST

A*

DBIDA*

Difference

%Decrease

D1

SOURCE 7.9

2.10

2.67

2.205

0.465

17.42

D2

3.10

1.8

6.895

0.605

6.29

91

D3

1.5

5.7

3.595

0.21

3.385

94.16

D4

10.5

5.15

1.41

0.475

0.935

66.31

D5

1.2

3.3

0.88

0.34

0.54

61.36

D6

6.5

4.1

6.05

0.46

5.59

92.4

D7

2.2

7.5

8.86

0.805

8.055

90.91

D8

13.1

2.1

1.22

0.28

0.94

77.05

D9

5.2

10.4

1.205

0.275

0.93

77.18

D10

12.2

1.9

8.035

0.79

7.245

90.17

Average

3.4375

75.796

Table 7 Comparison of number of operations done between DA* and A* SOURCE

DEST

A*

DA*

Difference

%Decrease

D1

7.9

2.10

574

175

399

69.51

D2

3.10

1.8

1269

227

1042

82.11

D3

1.5

5.7

368

107

261

70.92

D4

10.5

5.15

999

203

796

79.68

D5

1.2

3.3

439

107

332

75.63

D6

6.5

4.1

1109

217

892

80.43

D7

2.2

7.5

953

207

746

78.28

D8

13.1

2.1

1403

229

1174

83.68

D9

5.2

10.4

1255

224

1031

82.15

D10

12.2

1.9

1505

190

1315

87.38

Average

798.8

78.977

Table 6 shows the difference between the bidirectional diagonal A-star (DBIDA*) and A-star (A*). This comparison is done for the time taken for both the algorithms to find the shortest path between the source and the destination nodes. The difference between the time taken also the percentage decrease in the time taken compared to the A-star is also tabulated. Table 7 shows the difference between the diagonal A-star (DA*) and A-star (A*). This comparison is done for the number of operations done for both the algorithms to find the shortest path between the source and the destination nodes. The difference between the total numbers of operations also the percentage decrease in the operations done compared to the A-star is also tabulated.

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Table 8 Comparison of number of operations done between BIDA* and A* A*

BIDA*

Difference

%Decrease

D1

SOURCE 7.9

DEST 2.10

1190

706

484

40.67

D2

3.10

1.8

1269

890

379

29.87

D3

1.5

5.7

368

289

79

21.47

D4

10.5

5.15

199

198

1

0.5

D5

1.2

3.3

439

263

176

40.09

D6

6.5

4.1

1109

662

447

40.31

D7

2.2

7.5

953

574

379

39.77

D8

13.1

2.1

1403

719

684

48.75

D9

5.2

10.4

1255

820

435

34.66

D10

12.2

1.9

1505

674

831

55.22

Average

389.5

35.131

Table 9 Comparison of number of operations done between DBIDA* and A* SOURCE

DEST

A*

DBIDA*

Difference

%Decrease

D1

7.9

2.10

574

318

256

44.6

D2

3.10

1.8

1269

422

847

66.75

D3

1.5

5.7

368

183

185

50.27

D4

10.5

5.15

399

205

194

48.62

D5

1.2

3.3

439

115

324

73.8

D6

6.5

4.1

1109

403

706

63.66

D7

2.2

7.5

953

383

570

59.81

D8

13.1

2.1

1403

427

976

69.57

D9

5.2

10.4

1255

450

805

64.14

D10

12.2

1.9

1505

338

1167

77.54

603

61.876

Average

Table 8 shows the difference between the bidirectional A-star (BIDA*) and Astar (A*). This comparison is done for the number of operations done for both the algorithms to find the shortest path between the source and the destination nodes. The difference between the total numbers of operations also the percentage decrease in the operations done compared to the A-star is also tabulated. Table 9 shows the difference between the bidirectional diagonal A-star (DBIDA*) and A-star (A*). This comparison is done for the number of operations done for both the algorithms to find the shortest path between the source and the destination nodes. The difference between the total numbers of operations also the percentage decrease in the operations done compared to the A-star is also tabulated.

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Out of all three, diagonal gives 14.343% path efficiency, 66.509% execution time efficiency and 78.977% efficiency in resource spent, bidirectional gives 2.232% decrease in path efficiency, 53.048% execution time efficiency and 35.131% efficiency in resource spent, diagonal-bidirectional combined gives 11.239% path efficiency, 75.796% execution time efficiency and 61.876% efficiency in resource spent. The decrease in the path efficiency of the bidirectional algorithm can be justified as both the source and destination nodes traverse at the same time hence covers more number of neighbouring nodes. Overall, each of the modified algorithm has its own applications depending on the location of the source and the destination nodes.

5 Conclusion Out of proposed algorithms, diagonal gives 14.343% path efficiency, 66.509% execution time efficiency and 78.977% efficiency in resource spent, bidirectional gives 2.232% decrease in path efficiency, 53.048% execution time efficiency and 35.131% efficiency in resource spent, diagonal-bidirectional combined gives 11.239% path efficiency, 75.796% execution time efficiency and 61.876% efficiency in resource spent. Proposed algorithms applied here have its own unique applications depending on the condition of the environment, i.e. depending on the location of the source and the destination nodes. Based on the nature of the environment, anyone of the three algorithms can be chosen to find the optimal path between source and destination. For certain criteria, A* search will be efficient like simple and short distance. In case of no blocks or disturbances, bidirectional search can be applied such that execution time can be minimized.

References 1. Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks, swarm and evolutionary computation. Elsevier Publisher, vol 30, pp 1–10, October 2016 (Article in Press) 2. Shankar T, Shanmugavel S (2014) Energy optimization in cluster based wireless sensor networks, Journal of Engineering Science and Technology, School of Engineering, Taylor’s University, vol 9, no 2, pp 246–260. ISSN 18234690 3. Rana K, Zaveri M (2011) A-star algorithm for energy efficient routing in wireless sensor network, trends in network and communications. Springer, Berlin, pp 232–241 4. AlShawi IS, Yan L, Pan W, Luo B (2012) Lifetime enhancement in wireless sensor networks using fuzzy approach and A-star algorithm. Sens J IEEE 3010–3018 5. Eappen G, Shankar T (2020) Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network. Phys Commun 101091 6. Eappen G, Shankar T (2018) Energy efficient spectrum sensing for cognitive radio network using artificial bee colony algorithm. Int J Eng Technol 7(4):2319–2324 7. Eappen G, Shankar T (2018) Optimization of two area AGC based power system using PSO tuned fuzzy PID controller and PSO trained SSSC and TCPS. Int J Eng Technol 7(4.10):163– 168

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8. Yao J, Lin C, Xie X, Wang A, Hung C-C (2010) Path planning for virtual human motion using improved A* star algorithm. In: 2010 Seventh International Conference on Information Technology: New Generations (ITNG), pp 1154–1158 9. Cui SG, Wang H, Yang L (2012) A simulation study of A-star algorithm for robot path planning. 16th international conference on mechatronics technology, pp 506–510 10. Shankar T, Shanmugavel S (2013) Hybrid approach for energy optimization in cluster based wireless sensor networks using Energy balancing clustering protocol. J Theor Appl Inf Technol (JTAIT) 49(3):906–921, 31 Mar 2013. ISSN: 1992-8645 11. Shankar T, Shanmugavel S, Karthikeyan A (2013) Modified harmony search algorithm for energy optimization in WSN. J Int Rev Comput Softw (IRECOS) 8(6) 12. Yao J, Lin C, Xie X, Wang AJ, Hung C-C (2010) Path planning for virtual human motion using improved A* star algorithm. In: 2010 Seventh International Conference on Information Technology: New Generations (ITNG), pp 1154–1158, 12–14 Apr 2010 13. Khantanapoka K, Chinnasarn K (2009) Pathfinding of 2D & 3D game real-time strategy with depth direction A* algorithm for multi-layer. SNLP ‘09. Eighth International Symposium on Natural Language Processing, 2009, pp 184–188, 20–22 Oct 2009 14. Zeng W, Church RL (2009) Finding shortest paths on real road networks: the case for A*. Int J Geogr Inf Sci 23(4):531–543 15. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern SSC4. 4(2):100–107 16. Ye X, Han S-P, Lin A (2010) A note on the connection between the primal-dual and the A* Algorithm. Int’l J Oper Res Inf Syst 1(1):73–85 17. Likhachev M, Gordon G, Thrun S (2003) ARA*: Anytime A* search with provable bounds on sub-optimality. In: Thrun S, Saul L, Schölkopf B (eds), Proceedings of conference on Neural Information Processing Systems (NIPS), Cambridge, MA, 2003. MIT Press 18. Shankar T, Eappen G, Suresh V, Rajesh A, Mageshvaran R (2019) Contrast enhancement using quantile separation and Bi-Histogram equalization. In 2019 innovations in power and advanced computing technologies (i-PACT), vol 1. IEEE, pp 1–4 19. Shankar T, Karthikeyan A, Sivasankar P, Rajesh A (2017) hybrid approach for optimal cluster head selection in wsn using leach and monkey search algorithms. Journal of Engineering Science & Technology (JESTEC). The paper has been reviewed and accepted for publication in volume 12, issue 2 (February 2017) 20. Shankar T, Shanmugavel S, Karthikeyan A (2013) Hybrid approach for energy optimization in wireless sensor networks using PSO. Int Rev Comput Softw (IRECOS) 8(6)

Survivability Technique Using Markov Chain Model in NG-PON2 for Stacked Wavelength S. Rajalakshmi and T. Shankar

Abstract Survivability is the predominant major criteria for any fibre optical link during failure condition since it occurs frequently to disconnect several wavelength channels in the NG-PON2 system. The article proposes resilience in the protection mechanism for NG-PON2 network recommended by ITU-T G. 983.1 using Markov chain model. We analysed average downstream data loss for a 7:1 stacked NGPON2 architecture where seven working lines can be protected by one protection line. The protection scheme allows protection to an architecture capable of a network throughput of 80 Gbps. Further, there is a comparison between the performance of 3:1 and 7:1 architecture for 40 and 80 Gbps data rate. Keywords Optical line terminal · Optical network unit · Next generation-passive optical network stage 2

1 Introduction Next generation passive optical network stage 2 (NG-PON2) has been designed to co-exist with previous GPON architectures to ease deployment into existing optical distribution networks. At this date, NG-PON2 optical network finds applications in a lot of fields, so it is essential to develop protection architectures that can enable maximum throughput without much data loss. According to ITU-T standard, 50 ms or less than that must be the definite interruption time for data service. A dynamic method is proposed to configure all CSM parameters and time boundary limitations with optimal values for both heavy and low data traffic in DBA scheme. This scheme escalates the energy saving during heavy traffic load with the delay time margin of 56 ms for both DS and US links [1]. An efficient approach using optical switches was suggested for three-stage protection scheme to conserve both feeder and distribution fibres with the added advantage of a single mode fibre contiguous ring topology [2]. S. Rajalakshmi (B) · T. Shankar ECE, SENSE, VIT Vellore, Vellore, Tamil Nadu, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_9

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For the AoD-based network, an efficient routing algorithm is designed to provide dedicated path protection with enforced fibre switching which provides self-reliant of 1:1 protection for device failure at the high-end user level [3]. An innovative dynamic energy saving design with survivable routing algorithm for path protection is introduced for green and resilient network planning. It gives a total picture of various methods of protection methods by consuming minimum power and more capacity [4]. This research work illustrates a complete scenario of network survivability, which explains the service acceptance level during faulty network condition and to resume back to normal condition [5]. A novel system design is introduced in this article to reduce the effect of Rayleigh backscattering both in upstream and downstream data transfer examined in various ring topologies. They also provided the protection solution during multi-faults occurrence by the novel implementation of optical switches [6]. A dynamic efficient dual home protection scheme is proposed for 1:1 network path connection for severe network failure occurring in feeder fibre during extended reach. The secondary MC node takes over the position in case of primary MC node failure [7]. A PON system is designed using novel architecture for super fast and huge data traffic density in high-end access systems for Type B, NG-PON2 protection system. The system is designed for fast switching protection time of 50 ms for a speedy recovery from failures according to FSAN standards [8]. To ensure the PON system reliability, a protection technique is required either for residential or business application. So, the PON system is designed using Time and Wavelength Division Multiplexing architecture with proposed application to reduce the service outage duration [9]. The best method for committed successful protection connection is hardware redundancy for huge traffic transmission in the organized network. It duplicates the fibre in some failure conditions and devices in some another failure condition, which is present at the working optical light path from the central office (CO) to the Optical Network Terminal (ONT). Mainly, Type B and Type C protection are done in the majority of PON network. Similarly, NG-PON2 is also protected by taking the Time and Wavelength Division Multiplexing technique [10, 11]. N:1 fast protection mechanism design is proposed for four stacked wavelengths based on the wavelength tuning property of ONT [12]. This article discusses the proposed survivable strategy mechanism for eight stacked wavelengths in which one acts as a protection line for the rest of them. Section 2 is a study of the protection architecture of 7:1 architecture. Section 3 analyses the mathematical model used to implement the mechanism for a 7:1 architecture. Section 4 presents a comparison between 3:1 and 7:1 architecture performance for same data rate 40 Gbps.

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2 Protection Terminology 2.1 Protection Techniques Protection technique is used to replace a failure wavelength by an existing working wavelength in an NG-PON2 network. It is also used to pre-assign the capacity between nodes during a failure condition. The types of protection architecture are classified as 1 + 1 protection, 1:1 protection and finally 1:N protection. Linked protection is one method of the technique applied where the end node of the failed fibre initiated and directs the data traffic to the standby protection fibre. Here, both the faulty fibre and the protection fibre are connecting from the central office to the end subscriber. Path protection is another method of the technique applied which toggles the data traffic from the failed path to a suggested protection path. The suggested path should be connected with the working path from the central office to the end users.

2.2 Protection Architecture Types The major demand for huge bandwidth service tremendously increases for the broadband Internet access network application, where it is more important to protect the equipment and the fibre against failure. To meet the above crucial requirement, many protection architectures have been recommended for NG-PON2 networks. The ITUT G.983.1 has defined four main types of standard network protection architecture such as Type ‘A’, ‘B’, ‘C’ and ‘D’. Type ‘A’ Protection The feeder fibre is protected between the OLT and RN, by duplicating the feeder fibre. Protection switching is applied only to optical fibres. One fibre is working fibre, in case of any fault, it switches from working fibre to protection fibre. Type ‘B’ Protection OLT and feeder fibres both are protected, by doubling the transceivers in OLT. One OLT is working, and another OLT is used as a cold standby. In case of a fibre cut or OLT problem, the backup is activated to restore the services. Type ‘C’ Protection OLT, feeder fibres and ONU all are protected, by duplicating their components. Secondary protection circuits are placed at the OLT and ONUs, which enabling continuous switching the data to resume the backup facilities in the event of failure of fibre or equipment.

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Type ‘D’ Protection OLT, feeder fibres, splitter and ONU all are protected, by duplicating their components in case of primary component failure. In addition, another optical power splitter is inserted in a remote node.

2.3 NG-PON2 Operation This section explains the architecture without protection and with protection.

2.3.1

Basic Architecture Without Protection

Figure 1 shows the basic architecture consists of NG-PON2 consists of four OLTs and eight wavelengths, four each for upstream and downstream. If the data is transferred from OLT to ONU, then it is called as downstream, and if data is transmitted from ONU to OLT, then it is called as upstream transmission. Here, in this article, we have explained about two architecture and protection provided for the same. In the first design of extended architecture, we have two OLT and eight ONUs as a staking arrangement and evaluated the performance for the protection arrangement. In the second design, we have normal NG-PON2 arrangement.

Fig. 1 Shows basic NG-PON2 architecture without protection

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3 Proposed Protection Architecture for 7:1 The proposed NG-PON2 protection architecture has been designed to support eight pairs of up/downstream wavelengths. One of the eight wavelengths is selected by OLT as a protection wavelength pair which is shown in the proposed protection architecture with detailed in Fig. 2 as 7:1 architecture. Whenever a new ONU user is registered, the OLT is reconfigured with the protection wavelength pair and also assigns it a different working wavelength. Whenever there is a detection of loss of signal by ONU, it tunes the wavelength to the protection pair, and at the same time, the OLT switches the traffic to the protection wavelength fibre pair. It is not necessary for the protection wavelength pair to remain standstill during the normal working condition. The wavelength pair is used to carry a low amount of data traffic until failure is detected in one of the wavelengths. Major steps involved in the protection architecture: Step 1 One of the eight wavelength pairs from OLT is selected as the protection wavelength pair. Step 2 Whenever a new ONU registers, it is assigned with a working normal wavelength pair, and it is synchronized with the common protection wavelength pair. Step 3 Whenever there is a loss of signal or disconnectivity of the signal in any one of the active wavelength pairs, the ONU tunes the wavelength to protection wavelength pair. Step 4 Then, OLT transfers its traffic to protection wavelength pair. Step 5 Normal operation is resumed in the OLT and ONU as soon as the ONU finishes the wavelength tuning.

Fig. 2 Proposed protection architecture for 7:1 model

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The whole protection scheme easily decreases the disconnection time as there is no need for ONUs to go back to the initial state of re-registration to NG-PON2 system. All the ONUs have tunable transmitter and receiver which enable it to tune its wavelength to any pair in case of any OLT wavelength module breaks. In the proposed mechanism, the response time of ONU is equal to the summation of detection signal loss time added with ceasing time and wavelength ONU tuning time which is generally much less than the whole re-registration process.

3.1 Design Procedure for 7:1 Protection Model NG-PON2 protection is analysed by using a Markov chain model. Failure of the distribution of the inter-arrival time in NG-PON2’s is considered to be a Poisson process which has a memoryless property. The processing time for the protection rate to occur does not dependent on the faulty inter-arrival time, and thus, the protection wavelength pair has the following states: (1) When j = 0, F(j) states with normal operation, without any failure in the system, and the protection wavelength pair are either in an idle state or are carrying low data traffic. (2) When 1 ≤ j ≤ 7, F(j) is the state that jth pairs of wavelength are affected due to faults in fibre, and then, the affected ONU is the shift to a single protection pair. Here, ‘f’ is the failure rate, ‘p’ is the protection rate, and Fig. 3 shows the protection scenario by using the wavelength pair in the state space diagram. The protection wavelength pair states are characterized by a Markov chain model [12]. When there is failure occurring, the state transition happens to cut off the working fibre transmission signals present in the wavelength pair. The affected wavelength pairs are up to 7, which is pictured as the state transition diagram shown in Fig. 3 featured with the M/M/1/7 model properties. In Fig. 3, P(j) signifies the steady-state probability of the protection wavelength pair of state F(j). The steady-state probabilities are derived separately for 7:1 architecture and 3:1 architecture, which is discussed in the below section.

Fig. 3 Protection scenario using state space diagram

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3.2 Design Steps for 7:1 Model The steady-state probabilities can be derived as 1 when f = p 7    j f ∗ (1 − ( f / p)) p   p( j) = when f = p 1 − ( f / p)7 p( j) =

(1)

(2)

Assuming D(j) as the downstream data loss in bit/s when the protection wavelength pair is in state F(i): D( j) = j ∗ D R ∗ TR

(3)

where DR is downstream data lost and T R is traffic effective ratio. The average downstream data lost due to fibre failure (i.e., DSavg ) equals to (DS)avg =



 D R ∗ TR ∗ (1 − ( f / p)) ∗ ( f / p) + 2( f / p) ∧ 2 + 3( f / p) ∧ 3

+ 4( f / p) ∧ 4 + 5( f / p) ∧ 5 + 6( f / p) ∧ 6     +7( f / p) ∧ 7 / 1 − ( f / p) ∧ 4

(4)

3.3 Simulation and Numerical Results The protection design for NG-PON2 7:1 model using mark chain is designed, the steady-state probabilities are derived for downstream signal, and the average data loss is also derived in the above section. In this section, using the above derivation and equations, simulation is done using the MATLAB code. The simulation is done for input specifications of 80 Gbps data rate for 40 km distance with the wavelength of 15960 nm. The data loss is calculated and analysed for different effective traffic ratio starting from low load data traffic of 20% to up to full load data traffic 100% data traffic. The results obtained are discussed in the below paragraph for a different failure rate of 70 and 80%. Figure 4 shows the data loss in bps vs providing protection rate under the condition of failure rate 10−7 . The graph has been plotted for different values of effective traffic ratio from 20 to 100%. It has been seen that data loss decreases from the maximum value of 60 bps to less than 10 bps when the protection is varied from 10 to 100 s. It shows that data loss decreases as the protection rate increases. Figure 5 shows the data loss in bps vs providing protection rate under the condition of failure rate 10−8 . The graph has been plotted for different values of effective traffic

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Fig. 4 Data loss versus protection rate (f = 10 ˆ (− 7))

Fig. 5 Data loss versus protection rate (f = 10 ˆ (− 8))

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Fig. 6 Data loss vs protection rate for TR = 0.2

ratio from 0.2 to 1. It has been seen that data loss decreases from the maximum value of 5 bps to less than 1 bps when the protection is varied from 10 to 100 s. It depicts that the data loss decreases as the protection rate increases when the failure rate is varied from 10−7 to 10−8 . Figure 6 shows the data loss in bps vs providing protection rate under the condition of effective low data traffic rate 20%. The graph has been plotted for different values of failure ratio from 10−7 to 10−10 . It has been seen that data loss decreases from the maximum value of 4 bps to less than 1 bps when the protection rate is varied from 10 to 100 s. It depicts that the data loss decreases as the protection rate increases. Figure 7 shows the data loss in bps versus providing protection rate under the condition of effective heavy data traffic rate 80%. The graph has been plotted for different values of failure ratio from 10−7 to 10−10 . It has been seen that data loss decreases from the maximum value of 1.2 bps to less than 0.2 bps when the protection rate is varied from 10 to 100 s. Figures 4, 5, 6 and 7 illustrate the numerical analysis results of downstream data loss. For a constant value of failure rate, the data loss reduces with increase in protection rate for any value of effective traffic ratio. The data loss also increases with an increase in effective traffic ratio. For any value of protection rate, any increase in failure rate increases the average data loss.

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Fig. 7 Data loss versus protection rate for T R = 0.8

4 Design Steps for 3:1 Model Now that we have successfully derived the formulation of protection probability and protection rate for 7:1 architecture of NG-PON2, and our next step is to design a proposed model for 3:1 architecture.

4.1 Proposed Protection Architecture for 3:1 Model The below Fig. 8 shows the proposed protection model for 3:1 architecture. The operation of NG-PON2 protection 3:1 model remains the same as above 7:1 model. In the above design of model 3:1 shown in Fig. 8, we have designed for stacking four wavelength for data transmission out of which one wavelength is allotted for protection of data, so that whenever a data line shows disturbances or failure, we observe a loss in data. The protection wavelength at this disturbance or failure moment takes over the protection wavelength and sends the data itself, hence preventing any possible data loss.

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Fig. 8 Proposed protection architecture for 3:1 model

4.2 Design Steps for 7:1 Model Steady-state probabilities for 3:1 architecture are derived as follows: 1 when f = p 4    j f ∗ (1 − ( f / p)) p   p( j) = when f = p 1 − ( f / p)4 p( j) =

(5)

(6)

The average downstream data loss due to NG-PON2 failure (i.e., DSAVG ) equals to  2       3  f f f + 2 p + 3 pf D R ∗ TR ∗ 1 − p ∗ p  (DS)avg =  4  1 − pf

(7)

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4.3 Simulation and Numerical Results The protection design for NG-PON2 3:1 model using mark chain is designed, the steady-state probabilities are derived for downstream signal, and the average data loss is also derived in the above section. In this section using the above derivation and equations, simulation is done using the MATLAB code. The simulation is done for input specifications of 40 Gbps data rate for 40 km distance with the wavelength of 1596 nm. The data loss is calculated and analysed for different effective traffic ratio starting from low load data traffic of 20% to up to full load data traffic 100% data traffic. The results obtained are discussed in the below paragraph for a different failure rate of 70% and 80%. Figure 9 shows the data loss in bps vs providing protection rate under the condition of failure rate 10−7 . The graph has been plotted for different values of effective traffic ratio from 20 to 100%. It has been seen that data loss decreases from the maximum value of 40 bps to less than 10 bps when the protection is varied from 10 to 100 s. Figure 10 shows the data loss in bps vs providing protection rate under the condition of failure rate 10−8 . The graph has been plotted for different values of effective traffic ratio from 20 to 100%. It has been seen that data loss decreases from a maximum value of 5 bps to less than 1 bps when the protection is varied from 10 to 100 s. Figure 11 shows the data loss in bps vs providing protection rate under the condition of effective data traffic rate 0.2. The graph has been plotted for different values

Fig. 9 Data loss versus protection rate (f = 10 ˆ (− 7))

Survivability Technique Using Markov Chain …

Fig. 10 Data loss versus protection rate (f = 10 ˆ (− 8))

Fig. 11 Data loss versus protection rate for T R = 0.2

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Fig. 12 Data loss versus protection rate for T R = 0.8

of failure ratio from 10−7 to 10−10 . It has been seen that data loss decreases from the maximum value of 0.8 bps to less than 0.1 bps when the protection rate is varied from 10 to 100 s. Figure 12 shows the data loss in bps vs providing protection rate under the condition of effective data traffic rate 0.8. The graph has been plotted for different values of failure ratio from 10−7 to 10−10 . It has been seen that data loss decreases from the maximum value of 1.2 bps to less than 0.2 bps when the protection rate is varied from 10 to 100 s. On obtaining the results for 40 Gbps data transmission in a 3:1 architecture, we realize the protection rate for it. As we here send the data at 10Gbps per data line and we can clearly see that there are four lines, hence, total data transmitted is 10 * 4 = 40 Gbps.

5 Summary of the Results The data loss reduces as the protection recovery rate increases for any value of failure rate. The average data loss increases as the network failure rate increases. When the effective traffic ratio (E) is high, more data is transmitted in the downstream, and data loss from the network failure is high. Fast protection improves the protection

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Table 1 Summary of the results Scenario

7:1 architecture (80 Gbps) (bps)

3:1 architecture (40 Gbps) bps

r(failure rate) = 10 ˆ (− 7) E = 0.2, 0.5, 0.8, 1

Data loss = 0–100

Data loss = 0–50

r(failure rate) = 10 ˆ (− 7), E = 0.2, 0.5, 0.8, 1

Data loss = 0–20

Data loss = 0–5

r(failure rate) = 10 ˆ (− 8), 10 ˆ (− 9), 10 ˆ (− 10) E = 0.2

Data loss = 0–10

Data loss = 0–1

r(failure rate) = 10 ˆ (− 8), 10 ˆ (− 9), 10 ˆ (− 10) E = 0.8

Data loss = 0–10

Data loss = 0–4

rate ‘s’. A higher protection rate with wavelength tuning would reduce data loss. Table 1 shows the summary of the results.

6 Conclusion The following results are obtained on comparing the two protection architectures for a different data rate of 40Gbps. For a fixed value of effective traffic ratio E, the data loss is higher in 7:1 architecture for the lower value of the protection rate. For the higher value of protection rate, the data loss is higher in the case of 3:1 architecture. Results confirm this observation for any fixed value of failure rate, the data loss is higher in the case of 7:1 architecture for the lower value of protection rate at any given value of E.

References 1. Butt RA, Idrus SM, Qureshi KN, Shah PMA, Zulkifli N (2018) An energy efficient cyclic sleep control framework for ITU PONs. Opt Switch Netw 27:7–17 2. Gou K, Gan C, Zhang Y, Hua J (2018) A novel tangent-ring TWDM metro-access optical network featuring reconfiguration and reliability. Opt Switch Netw 29:27–38 3. Džanko M, Mikac B, Furdek M (2018) Dedicated path protection for optical networks based on function programmable nodes. Opt Switch Netw 27:79–87 4. Jalalinia SS, Cavdar C (2017) Green and resilient design of telecom networks with shared backup resources. Opt Switch Netw 23:97–107 5. Rak J, Papadimitriou D, Niedermayer H, Romero P (2017) Information-driven network resilience: research challenges and perspectives. Opt Switch Netw 23:156–178 6. Zhang S, Ji W, Li X, Huang K, Yan Z (2016) Efficient and reliable protection mechanism in long-reach PON. IEEE/OSA J Opt Commun Netw 8(1):23–32 7. Nag A, Payne DB, Ruffini M (2016) N:1 protection design for minimizing OLTs in resilient dual-homed long-reach passive optical network. J Opt Commun Netw 8(2):93–99

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8. Nishitani T, Hirano Y, Noda M, Motoshima K (2017) Protection systems for optical access networks. J Lightwave Technol 35(6):1197–1203 9. Nishitani T, Mizuguchi J, Mukai H (2015) Experimental study of type B protection for a TWDM-PON system. J Opt Commun Netw 7(3):A414–A420 10. ITU-T Rec. G.984.1, Gigabit-capable passive optical networks (GPON): General characteristics, 03/2008 11. ITU-T Recommendation G.984.2 Am1, Series G: physical media dependent (PMD) layer specification, Amendment 1: New Appendix III—Industry best practice for 2.488 Gbit/s downstream, 1.244 Gbit/s upstream G-PON, Feb 2006 12. ITU-T Recommendation G.984.2, Series G: Gigabit-capable Passive Optical Networks (GPON): Physical Media Dependent (PMD) layer specification, March 2003

Effects of Different Membranes on the Performance of PEM Fuel Cell M. Muthukumar, A. Ragul Aadhitya, N. Rengarajan, K. Sharan, and P. Karthikeyan

Abstract Nowadays, air pollution prevails as one of the major problems all over the world. Fuel cell is the recently developed technology to counteract air pollution. Fuel cells are electrochemical devices that produce electricity by the reaction of two gases such as hydrogen and oxygen. Proton exchange membrane (PEM) fuel cell is the most economical one. The advantage of using PEM fuel cell is that they can operate at low temperature of about 50 °C to 80 °C, and there is no emission of harmful gases to the atmosphere, thereby maintaining eco-friendly environment. The performance of the fuel cell is mainly influenced by various factors like material properties of components (like gas diffusion layer, membrane, catalyst layer), flow channel designs, operating conditions and water management. The main function of membrane which is made of polytetrafluoroethylene is to allow only the protons from anode to cathode and not allows electrons. So the membrane is called as PEM. The performance of the fuel cell is affected by different types of membranes. In this paper, the performance of PEM fuel cell with two different membranes such as Nafion 117 and Nafion 212 is analyzed. The serpentine flow field is chosen on both cathode and anode sides. The PEM fuel cell having active area of 11.6 cm2 is designed and analyzed with best-operating conditions. The results show that the PEM fuel cell with Nafion 212 membrane generates more power. Keywords Emission · Eco-friendly · Membrane · Nafion · Power

M. Muthukumar (B) · A. Ragul Aadhitya · K. Sharan Department of Mechanical Engineering, Nandha Engineering College, Erode 638052, India e-mail: [email protected] N. Rengarajan Department of Electrical and Electronics Engineering, Nandha Engineering College, Erode 638052, India P. Karthikeyan Department of Automobile Engineering, PSG College of Technology, Coimbatore 641004, India © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_10

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Nomenclature CL DEA GDL M-GDL OCV PAN PEM PEMFC RH L×C H2 O2

Catalyst Layer Dead End Anode Gas Diffusion Layer Metal Gas Diffusion Layer Open-Circuit Voltage Polyacrylonitrile Proton Exchange Membrane Proton Exchange Membrane fuel cell Relative Humidity Landing and Channel widths Hydrogen Oxygen

1 Introduction The global warming is increasing day to day, and the ozone layer got depleted to a large scale. The root cause for these phenomena is the emissions from automobiles, refrigerators, air coolers, etc. Various studies have been going on in order to develop alternative sources to reduce the air pollution. With the increase in the global needs for alternative sources of energy, fuel cell is one of the most promising sources of renewable energy. Fuel cell differs from battery. A battery obtains energy from chemicals previously stored in the battery, whereas a fuel cell can continuously supply power, if the gases are supplied continuously. Among various types of fuel cells, the PEM fuel cell is widely used. When compared with the other types, PEM fuel cell has the highest efficiency range, minimal maintenance, very long lifetime, very compact in size and low operating conditions. The efficiency of the PEM fuel cell is also greater than the conventional IC engines. The most common application of PEM fuel cell is uninterrupted power supplies (UPS) for household and commercial purposes. They are also used in the stationary power sources. The performance of the PEM fuel cell is influenced by various factors such as water clogging, design of the flow channels, operating conditions, materials of the catalyst layer (CL) and membrane. After various researches, it has been found that the serpentine design is the most economical one in terms of efficiency and power output. Bipolar plates, catalyst layer and gas diffusion layer (GDL) are made up of graphite, carbon paper and platinum carbon alloy, respectively. Different types of Nafion membrane are Nafion HP, Nafion 1110, Nafion 212, Nafion 211 and Nafion 117, etc. In this paper, the performance of fuel cells with Nafion 212 and Nafion 117 membranes is analyzed in detail.

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1.1 Functions of Membrane The membrane in the proton exchange membrane fuel cells is made up of polytetrafluoroethylene (PTFE). PTFE is a combination of the synthetic polymer, called as ionomer. The main function of the membrane is to split the electrons from the hydrogen gas and allows only the flow of protons through it, from which the name proton exchange membrane was derived. The separated electrons flow through the external circuit and reach the cathode side to combine with the oxygen gas. Then, all these are combined together to form water, and small amount of heat is generated. The reaction is also influenced by the catalytic activity. The overall reactions taking place in the fuel cell are as follows. H2 → 2H+ + 2e− 1/2O

2

+ 2e− + 2H+ → H2 O + Heat

(1) (2)

Water flows through the membrane from anode side to cathode side or vice versa. Based on the thickness of the membrane, the amount of proton conductivity and water cross flow is varied. The effective performance of fuel cell is greatly influenced by the moisture content of the membrane. If more water is formed on cathode side and on membrane, it will lead to water flooding and performance drop of fuel cell. If water is removed completely on cathode side and on membrane, it dehydrates the membrane and totally burns out the membrane and MEA. As the membrane and catalyst are costlier, they should be made into more durable. So, it is very important to maintain the optimum level of water in the membrane.

2 Influence of Design and Operating Parameters on the Performance of Fuel Cell Kahveci and Taymaz [1] developed the PEM fuel cell model with single serpentine flow channel by using computational fluid dynamics. They developed a threedimensional polymer electrolyte membrane with an active area of 25 cm2 . The 3D model was analyzed in the temperature range of 333–335 K, the pressure level of 1–3 atm, GDL thickness of 0.3 mm and relative humidity range (RH) of 10–100%. After successful experiments, it was found that the performance of the PEM fuel cell increased with optimum rise in temperature, but the temperature should not exceed 365 K. It was also found that performance was raised with increase in porosity of the GDL. Ghanbarian et al. [2] developed and investigated the design of parallel serpentine flow field with an active area of 5 × 5 cm2 . Various parameters like the numbers of serpentine turns, number of parallel channels, the rib between two adjacent channels and the channel width and height were considered. Six filtering constraints were introduced to trim down number of possible configurations for proper flow fields.

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The result produced the best serpentine-type flow field configuration for the given operating condition with geometrical specification as (L, n, s, w, d, h) = (249, 5, 4, 1, 1, and 0.4). Karthikeyan et al. [3] developed 3D model of PEM fuel cell to optimize the ten parameters including operating and design parameters by Taguchi method using L27 orthogonal array with three levels. The best-optimized grouping of input parameters was determined for back pressure, cell temperature, GDL porosity, channel depth and width, rib width, porous electrode thickness, GDL thickness, anode inlet velocity, cathode inlet velocity and cathode inlet water content. Muthukumar et al. [4] analyzed the performance of the PEM fuel cells with a fixed channel length of 20 mm and with varying landing to channel width (L × C) of 0.5 × 0.5, 1.0 × 1.0, 1.5 × 1.5, 2.0 × 2.0 mm2 . The results confirmed that PEM fuel cell with landing to channel width of 0.5 mm × 0.5 mm had generated the good performance with power density of 0.4473 W/cm2 and current density of 1.1183 A/cm2 among four designs. Even though the other three flow channel designs had more active area, they showed less performance. Hence, it was concluded that 0.5 × 0.5 mm2 had the best performance and efficiency. The influence of pressure and temperature on the taper and serpentine channeled PEM fuel cells was studied in [5, 6]. Ashrafi et al. [7] examined a modified Z-type flow field of 26.52 cm2 active area to improve the two-phase flow uniformity of parallel flow fields. Many experiments were conducted to find the unsteady distribution of water coverage, cathode stoichiometry ratio and power with z-flow fields. The results showed that power and efficiency are stable at high cathode stoichiometric ratios, but magnitude is low. The flow field was then modified with a 3D numerical model. The simulation results illustrated that the parasitic power for the air supply system of modified Z-type flow field was less than the simple flow field. Also, its overall efficiency was higher. Wen et al. [8] investigated an intersectant flow field design of active area 15 mm × 15 mm, diffusion layer of 1 mm, catalyst layer of 1 mm and membrane of 0.1 mm thickness numerically. The current density distribution, oxygen distribution, polarization curve and water mass distribution were introduced as the criteria to help the optimization of the proposed flow field. The single serpentine flow field type was taken as a reference. The results showed that the optimal conditions of the channel porosity and width were 0.5 mm and 0.3 mm, respectively. The optimal parameters were found on experimental results as hydrogen flow of 300 ml/min, air flow 500 ml/min and operating temperature 80 °C. Heidary et al. [9] investigated the cathode catalyst layer (CL) of the PEM fuel cell under different operating conditions of pressure, temperature and water saturation. The catalyst layer was modeled by using a macro-homogenous model. The results showed that level of saturation influenced the parameter on CL performance. The performance of CL improved by 19%, 30% (at 0.11 V) and 9% (at 0.05 V), when the temperature of the cell changed from 40 to 90 °C, the saturation level changed from 75% to 25% and the operating pressure of cell increased from 3 to 7 atm, respectively. Yang et al. [10] investigated the performance of dead-end anode (DEA) characteristics of PEM fuel cells with three different anode flow fields (parallel, serpentine and interdigitated) with or without anode exit reservoir at different operating modes.

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The result showed that the serpentine flow field with DEA had stable operation with an anode exit reservoir and pressure swing operation, whereas the parallel flow field was not suitable with DEA for PEM fuel cells. Zehtabiyan-Rezaie et al. [11] investigated the performance of the PEM fuel cells by using a converging- and diverging-type flow channels. The flow field had an operating area of 67 × 20 mm2 , with divergence and convergence angles of 0.3° and 0.2°, respectively. The results showed that 16% increase in electrical power output of 0.3° inclined channels in comparison with the conventional parallel flow channels. Wang et al. [12] investigated variety of cathode flow channel geometries for PEM fuel cells. On the basis of comparison with rectangular channel, cells having semicircle, trapezoid and triangle shapes were considered. The cells had the channel height, diffusion layer thick, catalyst layer thick and membrane thick of 0.5 mm, 0.4 mm, 0.005 mm and 0.035 mm, respectively. The results showed that the performance and water removal of triangle channel were better than trapezoid, semicircle and rectangular channels.

3 Influence of Materials of Components on the Performance of Fuel Cell Palaniswamy et al. [13, 14] investigated the performance of fuel cells with an active area 70 and 25 cm2 of various channels such as serpentine, zigzag and uniform pin types. Also, carbon inserts made of Vulcan carbon were placed in the landing of zigzag pin type. From the results, it was found that the carbon inserts with 80–90% porosity improved the power density and current density by 11.5% and 7%, respectively. Also, the carbon inserts have resulted in better water removal due to its high porosity, high electrical conductivity and high water absorption and transmission. Hu et al. [15] investigated Cr2 N-coated bipolar plates in PEM fuel cell by pack cementation. Cr2 N coating was made by packing powder mixture of 30Cr2 N-2NH4 Cl68Al2 O3 at 1100 °C for 4 h which gave outer Cr2 N layer and Cr-rich interdiffusion zone thickness of 18 µm and 13 µm, respectively. As a result, the immersion test in H2 SO4 solutions at 60 °C confirmed the pinhole-free feature and corrosion resistance of Cr2 N-coated plates. Salahuddin et al. [16] investigated polyacrylonitrile (PAN) nanofibers for gas diffusion layer in PEM fuel cells. The new GDL gave better performance than the conventional GDL. The hydrophilic area absorbed and drained the water from the carbonized GDL in the fuel cell with the aid of air flow for controlling water flooding. Havaej et al. [17] studied about the effects of catalyst loading distributions which vary in longitudinal and lateral directions under different non-uniform conditions. The results revealed that non-uniform catalyst loading distribution in both lateral and longitudinal direction had better cell performance. For better oxygen distribution and cell performance, the ratio of platinum between the inlet and the outlet of gas channels should be equal to 1.857. Wu et al. [18] investigated the performance of the PEM

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fuel cell for the best arrangement of protrusive gas diffusion layer. Taguchi design of experiments method was employed to find better pattern arrangement for protrusive GDL. Alshorman [19] investigated a new design for PEM fuel cell having an active area of 100 cm2 using a biological cellular membrane of thickness 0.0006 cm. The usage of biomembrane with current density of 0.4–1.2 A/cm2 increased the output power by 33% of the PEM fuel cell. Moreover, 17% more power was produced by biomembrane than the conventional one for the same hydrogen pressure. Yurtcan and Das [20] investigated a carbon black (CB) hybrid catalyst which was chemically synthesized under reduced graphene oxide (rGO) for PEM fuel cells. Depending on the comparison of their plain materials, rGO to CB weight ratios were changed from 90:10 to 50:50. While comparing all hybrid supported catalyst, the catalyst with rGO:CB ratio of 70:30 yielded the higher PEM fuel cell performance. Shiro Tanaka and Arnaud G. Malan [21] numerically investigated the performance of the PEM fuel cell with perforated metal gas diffusion layer (M-GDL) and conventional GDL under various selected parameters. This study gave the effective frame width and pore size for the perforated metal GDL. The optimized metal GDL gave 9.4% increase in performance of fuel cell. Öztürk and Yurtcan [22] investigated porous N-doped carbon nanotubes with the synthesis of polypyrrole (PPy) as support catalyst for PEM fuel cells. The results showed that 12 h activated N-CNTs produce higher surface area of 1607.2 m2 /g, smaller micropore volume of 0.355 cm3 /g and better catalyst supports than 18 h activated N-CNTs. Lin et al. [23] investigated the high-temperature PEM fuel cell using protic ionic liquid (PIN) graphene oxide hybrid membranes. The result showed a good mechanical properties and excellent thermal stability by the hybrid membrane. The addition of appropriate quantity of [APMIm][Br]-GO on membrane increased its proton conductivity. From the above, it is clear that the performance of PEM fuel cell is affected by various designs, operating and material properties. In this paper, the effect of different membranes on the performance of PEM fuel cell is analyzed numerically.

4 Modeling and Simulation 4.1 Modeling The geometry model for the PEM fuel cell is created using ANSYS 15.0. Among various flow field types, the serpentine-type flow field design gives good power output in PEM fuel cells. So, the serpentine flow channel design is used in the bipolar plates. The graphite plate of 50 mm width, 50 mm height and 10 mm thick is taken. The serpentine channel having a width and height of each 34 mm is drawn on the graphite plate. The total area of the serpentine channel on the plate is 11.6 cm2 in which the entire electrochemical reactions are carried out. So, this is known as active area of

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Fig. 1 Geometry of flow field design used on anode and cathode

the fuel cell, i.e., the active area of this fuel cell is 11.6 cm2 . The channel is modeled with a width and height of 2 mm each. The landing–to-channel (L:C) ratio of flow field is 2:2. The geometry of serpentine flow field used on anode and cathode sides of PEM fuel cell is shown in Fig. 1. A total of nine bodies are created for the full 3D model of PEM fuel cell which includes a single membrane and the pair of bipolar plates, flow channels, gas diffusion layers and catalyst layers for anode and cathode sides. To carry out the CFD analysis in ANSYS FLUENT 15.0, the inlet and outlet for the gases are to be assigned. So the surfaces are created at both inlet and outlet pathways of hydrogen and oxygen of anode and cathode side, respectively. The thicknesses of the catalyst layer, gas diffusion layer and Nafion 117 membrane are 0.08 mm, 0.3 mm and 0.157 mm, respectively. Another full 3D geometry for the PEM fuel cell is created with the Nafion 212 membrane of thickness 0.0508 mm by keeping all other components bipolar plates, catalyst layer gas diffusion layers are of same sizes. The complete geometry of PEM fuel cell is shown in the Fig. 2

4.2 Meshing The meshing of the PEM fuel cell is carried out in ANSYS ICEM CFD 15.0. The parts are created for inlet and outlet of hydrogen and oxygen, cathode and anode wall terminals. Then, separate bodies are assigned for bipolar plates, gas diffusion layer, catalyst layer, membrane and flow channels. Blocking is initialized, and each edge is divided into several numbers of nodes. To obtain a high-quality meshing, various sizing values are assigned for each part of the fuel cell. The thickness of membrane is divided into 10 divisions, anode gas diffusion layer is divided into six divisions,

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Fig. 2 Full 3D model of PEM fuel cell

cathode gas diffusion layer is divided into eight divisions, anode and cathode catalyst layer are divided into six divisions, anode and cathode channels are divided into six, and finally, the bipolar plates are divided into 11 divisions. Then, the meshing for PEM fuel cell is carried out using the Cartesian grid method. A total of 1.5 million elements are created through the mesh. The post-processing of meshing is done with FLUENT solver. The boundary conditions are assigned for inlet, outlet, wall and all other parts of the PEM fuel cell. The meshed model of the PEM fuel cell is shown in the Fig. 3.

Fig. 3 Meshed geometry of PEM fuel cell

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4.3 Simulation The PEM fuel cell simulation is carried out using the most commonly used computational fluid dynamics software tool ANSYS FLUENT 15.0. The add-on model for fuel cell is manually loaded. In the PEM fuel cell, add-on model joules heating, reaction heating, electrochemistry sources, Butler-Volmer rate and membrane water transport options are enabled to carry out the simulation. The open-circuit voltage (OCV) is set to be 0.95 V. In the anode zone-type selection, the anode plate is assigned to be the current collector along with the assigning of the zones for catalyst, porous electrode and channel. The membrane is selected to be the electrolyte. In the cathode zone-type selection, the cathode plate is assigned to be the current collector. Also the channel, catalyst and porous electrode zones are also assigned for cathode. In the materials section, the density and specific heat of membrane are set to be 1980 kg/m3 and 2000 J/kg-K, respectively. The density and specific heat of the catalyst and gas diffusion layer are set to be 2719 kg/m3 and 871 J/kg-K, respectively. By default, the fuel cell add-on model itself creates many shadow walls between the layers of the fuel cell. These shadow walls must be changed to interior in the boundary condition section. On doing this, the adjacent walls get deleted and both combined to form an interior wall. For the anode inlet, the mass flow rate of hydrogen is set to be 9.58 kg/s, and the direction of flow is set to normal to boundary. The temperature is set to be 333 K. The species transport of H2 is set to be 0.8, and the species transport of H2 O is set to be 0.2. In the user-defined scalar boundary condition, the water saturation is assigned to be specified value. For the cathode inlet, the mass flow rate of oxygen is set as 7.67e-7 kg/s. The temperature is set to be 333 K. The species transport of O2 is set to be 0.2, and the species of H2 O is 0.1. In the user-defined scalar boundary condition, the water saturation is assigned to be the specified value. For the anode and cathode outlet, the temperature is set at 333 K and water saturation is changed to specified value. For the anode wall, the temperature is set to be 333 K in the thermal section. In the user-defined scalar boundary condition, the electric potential is changed to specified value. For the cathode wall, the temperature is set to be 333 K in the thermal section. The electric potential is set to be 0.45 V. In the solution control, the under relaxation factor for pressure, momentum, protonic potential and water content is set to be 0.7, 0.3, 0.95 and 0.95, respectively. In the advanced solution controls, the cycle type for pressure, X-momentum, Y-momentum, Z-momentum, H2 , O2 , H2 O, energy, electric potential, protonic potential and water content are changed to F-cycle. The termination restriction for H2 , O2 , H2 O and water saturation is kept at 0.001. The termination restriction for protonic and electrical potential is kept at 0.0001. The maximum cycle is changed to 50. The operating pressure is set as 2 bar. The standard initialization is done with a temperature of 333 K. Then, the simulation is carried out.

122 Table 1 Power outputs from Nafion 117

M. Muthukumar et al. Voltage (V) Current density (A/cm2 ) Power density (W/cm2 ) 0.35

1.20

0.421

0.45

1.17

0.527

0.55

1.12

0.616

0.65

0.82

0.533

0.75

0.67

0.503

0.85

0.24

0.204

0.95

0

0

5 Results and Discussion The 3D full model of PEM fuel cell is analyzed with two different membranes, namely Nafion 117 and Nafion 212, under standard operating conditions. The results obtained are discussed below.

5.1 Results of Nafion 117 Membrane The PEM fuel cell with the Nafion 117 membrane of 0.157 mm thickness is analyzed under different operating parameters. At the cell voltage of 0.35 V, the current density of 1.20 A/cm2 and the power density of 0.42 W/cm2 are obtained. At the cell voltage of 0.45 V, the current density of 1.17 A/cm2 and the power density of 0.527 W/cm2 are obtained. Further, the peak values of power density of 0.616 W/cm2 for PEM fuel cell are obtained with current density of 1.12 A/cm2 at a cell voltage of 0.55 V. After this cell voltage, the value of power density is gradually reducing. The power density values at various cell potentials are given in Table 1.

5.2 Results of Nafion 212 Membrane The PEM fuel cell with the Nafion 212 membrane of 0.0508 mm thickness is analyzed under different operating parameters. At the cell voltage of 0.35 V, the current density of 1.27 A/cm2 and the power density of 0.445 W/cm2 are obtained. At the cell voltage of 0.45 V, the current density of 1.19 A/cm2 and the power density of 0.5355 W/cm2 are obtained. Further, the peak values of power density of 0.682 W/cm2 for PEM fuel cell are obtained with current density of 1.24 A/cm2 at the cell voltage of 0.55 V. After this cell voltage, value of power density starts reducing. The power density values at various cell potentials are given in Table 2. The performance of the fuel cell with different membranes is compared using polarization curve (V-I curves) and power density curves (P-I curves) as shown in

Effects of Different Membranes on the Performance … Table 2 Power output from Nafion 212

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Voltage (V) Current density (A/cm2 ) Power density (W/cm2 ) 0.35

1.27

0.445

0.45

1.19

0.536

0.55

1.24

0.682

0.65

0.94

0.611

0.75

0.69

0.517

0.85

0.32

0.272

0.95

0

0

Fig. 4 Polarization and power density curves

Fig. 4. From the curves, it is clear that the power output of fuel cell with Nafion 212 is more than Nafion 117.

6 Conclusion The full three-dimensional models of PEM fuel cell with two different membranes like Nafion 117 and Nafion 212 are modeled, meshed and analyzed using ANSYS 15.0 software package. Both the models of active area 11.6 cm2 are analyzed with same operating conditions (temperature, pressure, relative humidity, water content)

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and design parameters (widths, heights and thicknesses). From the numerical analysis, it is found that the PEM fuel cell with Nafion 212 membrane showed considerably better results when compared with Nafion 117. The PEM fuel cell with Nafion 212 yields 10.7% of more power density than the Nafion 117. The thickness and density of Nafion 212 are lesser than Nafion 117 and allow the smooth flow of photons through it. Due to less photonic resistance, the flow of photons from anode to cathode is being easier through Nafion 212 membrane. Also, the cross flow of water through the membrane keeps the membrane in optimum water content. So, Nafion 212 membrane can be preferred for high power requirements and higher efficiency in PEM fuel cells. Acknowledgements This work is supported by AICTE-RPS project with File No. 833/RIFD/RPS/POLICY-1/2016-17.

References 1. Kahveci EE, Taymaz I (2018) Imdat Taymaz, Assessment of single serpentine PEM fuel cell model developed by computational fluid dynamics. Fuel 217:51–58 2. Ghanbarian A, Kermani MJ, Scholta J, Abdollahzadeh M (2018) Polymer electrolyte membrane fuel cell flow field design criteria—Application to parallel serpentine flow patterns. Energy Convers Manage 166:281–296 3. Karthikeyan P, Muthukumar M, Vignesh Shanmugam S, Pravin Kumar P, Murali S, Senthil Kumar AP (2013) Optimization of operating and design parameters on the proton exchange membrane fuel cell by using Taguchi method. Procedia Eng 64:409–418 4. Muthukumar M, Karthikeyan P, Vairavel M, Loganathan C, Prveenkumar S, Senthil Kumar AP (2014) Numerical studies on PEM fuel cell with different landing to channel width of flow channel. Procedia Eng 97:1534–1542 5. Praveenkumar S, Muruganantham S, Premkumar M, Muthukumar M, Vetrivel A (2015) Influence of pressure and temperature on the performance of PEM fuel cell with taper flow channel design. Int J Appl Chem 11:505–513 6. Muthukumar M, Karthikeyan P, Eldho M, Nagarathinam P, Panneer Selvam EP, Prasanna R (2017) Impact of pressure on the performance of proton exchange membrane fuel cell. J Adv Chem 13:6462–6467 7. Ashrafi M, Kanani H, Shams M (2018) Numerical and experimental study of two-phase flow uniformity in channels of parallel PEM fuel cells with modified Z-type flow-fields. Energy 147:317–328 8. Wen D, Yin L, Piao Z, Lu C, Li G, Leng Q (2018) Performance investigation of proton exchange membrane fuel cell with intersectant flow field. Int J Heat Mass Transf 121:775–787 9. Heidary H, Kermani MJ, Khajeh-Hosseini-Dalasm N (2016) Performance analysis of PEM fuel cells cathode catalyst layer at various operating conditions. Int J Hydrogen Energy 41:22274– 22284 10. Yang Y, Zhanga X, Guo L, Liu H (2018) Different flow fields, operation modes and designs for proton exchange membrane fuel cells with dead-ended anode. Int J Hydrogen Energy 43:1769–1780 11. Zehtabiyan-Rezaie N, Arefian A, Kermani MJ, Noughabi AK, Abdollahzadeh M (2017) Effect of flow field with converging and diverging channels on proton exchange membrane fuel cell performance. Energy Convers Manage 152:31–44

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12. Wang X-D, Gui L, Duan Y-Y, Lee D-J (2012) Numerical analysis on performance of polymer electrolyte membrane fuel cells with various cathode flow channel geometries. Int J Hydrogen Energy 37:15778–15786 13. Karthikeyan P, Vasanth RJ, Muthukumar M (2015) Experimental investigation on uniform and zigzag positioned porous inserts on the rib surface of cathode flow channel for performance enhancement in PEMFC. Int J Hydrogen Energy 40:4641–4648 14. Palaniswamy K, Marappan M, Jothi VR (2016) Influence of porous carbon inserts on scaling up studies for performance enhancement on PEMFC. Int J Hydrogen Energy 41:2867–2874 15. Hu YQ, Chen F, Xiang ZD (2019) Cr2N coated martensitic stainless steels by pack cementation process as materials for bipolar plates of proton exchange membrane fuel cells. J Power Sources 414:167–173 16. Salahuddin M, Uddin MN, Hwang G, Asmatulu R (2018) Superhydrophobic PAN nanofibers for gas diffusion layers of proton exchange membrane fuel cells for cathodic water management. Int J Hydrogen Energy 43:11530–11538 17. Havaej P, Kermani MJ, Abdollahzadeh M, Heidary H, Moradi A (2018) A numerical modeling study on the influence of catalyst loading distribution on the performance of polymer electrolyte membrane fuel cell. Int J Hydrogen Energy 43:10031–10047 18. Wu HW, Shih G-J, Chen Y-B (2018) Effect of operational parameters on transport and performance of a PEM fuel cell with the best protrusive gas diffusion layer arrangement. Appl Energy 220:47–58 19. Alshorman AA (2016) Characteristic study of bio-membrane PEM fuel cell for performance upgrading. Procedia Comput Sci 83:839–846 20. Yurtcan AB, Das E (2018) Chemically synthesized reduced graphene oxide carbon black based hybrid catalysts for PEM fuel cells. Int J Hydrogen Energy 43:18691–18701 21. Tanaka S, Malan AG (2019) Investigating design parameters of a perforated metal gas diffusion layer in a polymer electrolyte membrane fuel cell. J Power Sources 413:198–208 22. Öztürk A, Yurtcan AB (2018) Synthesis of polypyrrole (PPy) based porous N-doped carbon nanotubes (N-CNTs) as catalyst support for PEM fuel cells. Int J Hydrogen Energy 43:18559– 18571 23. Lin B, Yuan W, Fei X, Chen Q, Zhu H, Li X, Yuan N, Chu F, Ding J (2018) Protic ionic liquid/functionalized graphene oxide hybrid membranes for high temperature proton exchange membrane fuel cell applications. Appl Surf Sci 455:295–301

Design Analysis and Fabrication of Race Car Seat to Increase Driver Comfort K. Raja, C. D. Naiju, M. Senthil Kumar, and N. Navin Kumar

Abstract The driver’s comfort and position are factors of utmost importance in motorsports, and these are key aspects in deciding the overall performance of a race car. This paper presents a strategy to increase overall driver comfort in a race car by providing design proposals for a comfortable seat. A prototype was built, as per norms and cockpit constraints, from which the design considerations for the driver’s position were decided. Vertebral and thigh impressions of four drivers were taken by using expandable two-part urethane foam. Three-dimensional scanning technique was used to convert these impressions into point cloud data. The data were refined to form surfaces of respective impressions. These scanned surfaces were converged to a single optimized seat design which was comfortable for all drivers. Using data acquisition (DAQ), a comparative study of lap timings was done on the experimental seat and existing seat design for validation. This methodology of seat design was implemented to improve driver performance. It was observed that from a graphical comparison of car’s timings on both seats, the timings of the car with existing seat design showed a steeper increase in timings than the car with new seating design. Keywords 3D scanning · Data acquisition

1 Introduction Interaction between humans and machines and factors affecting interaction are the key factors in designing a product. Driver comfort ensures the handling of the vehicle with ease and without applying any excessive effort while inside the car so to concentrate and stay alert while driving on a racetrack, thus increasing driver comfort and improving driver performance on the track. A driver-specific comfort seat enhances

K. Raja · C. D. Naiju (B) · M. Senthil Kumar School of Mechanical Engineering, VIT University, Vellore 632014, India e-mail: [email protected] N. Navin Kumar Hyundai Motor India, Sriperumbudur 602117, India © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_11

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driver safety and, at the same time, provides enough room within the cockpit to maneuver the car with utmost efficiency. A key design goal was to comfortably accommodate all drivers inside a race car cockpit. All aforementioned design procedures are to be taken into consideration and their flaws duly noted while designing. A single seat that fits all drivers without the use of any inserts or making replacements is the need of the industry. The existing strategies followed failed due to their inability to accommodate different drivers perfectly in the cockpit. Most of the race car needs a driver-specific seat design. It is reported that a seat using anthropometry is based on a “design to fit everyone” strategy which does not ensure individual driver comfort. For example, if a seat was to be made based on anthropometry, then the 95th percentile male and 5th percentile female [1] will be considered for dimension which can be greater than drivers’ dimensions. The extra free space will be a design flaw in terms of motorsports as that can lead to inefficient driver packaging and body movements within the seat. This should be restricted to avoid awkward posture and discomfort. The design of the comfort seat is becoming increasingly important. Statistical analysis is carried out to find the correlations between measurable and subjective comfort [2]. For an improved seat comfort design, driving posture measurement is needed. The body pressure distribution was measured, and data were analyzed for contact area, average pressure, and body part pressure ratio [3]. There are major issues in reverse engineering for the ability of effectively using point cloud data by different scanning devices [4]. A study on driving posture for the evaluation of occupant packaging is studied and report where a method of reconstructing posters of a driver in a real vehicle is carried out using a 3D laser scanner [5]. The photogrammetric technique is used to analyze photographs or video frames features from multiple angles to create dense surface models or point clouds [6]. A seat insert, though made according to the dimensions of a driver, is only capable of supporting some areas of the body rather than to provide full body support. Moreover, it is not feasible to change inserts for every driver change during dynamic events. Thus, a new design process was required to fulfill the need for a single seat that perfectly fits the body structures of different drivers.

2 Methodology A CAD model of a seat is made using anthropometry which is then manufactured. The anthropometry of the driver is considered as the shortest driver’s height of 155 cm and the highest measurement of 195 cm for this study. Such seats may or may not be comfortable as they are not designed driver specific. Expanding foam is used to obtain back the impression of the largest driver. The surface of the foam is smoothened by machining and then glass fiber or carbon fiber is laid for manufacturing. The foam underneath the composite material makes seat heavy. Driver comfort is compromised as well since the dimensions of the seat are based on the largest driver only.

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Expanding foam is used to get drivers’ back impressions which are then used as molds to make individual seats. The process requires a lot of time and capital. Foam inserts (made out of drivers’ back impressions) are made for individual drivers that sit between driver and seat. Even though the inserts are personalized, they fail to ensure a firm seating position for drivers. Also, these inserts have to be changed depending upon driver, and as such, the process poses a hindrance for smooth driver change during dynamic events. A prototype was constructed in order to get critical cockpit dimensions. For this, a comfortable position for the hip point of any anthropometrical profile was considered from the data of a population of drivers who participate in a race. Once the apparatus was constructed, drivers were seated and adjustments were made until a suitable setup of comfort and visibility was reached. In order not to compromise the car’s dynamic performance, trade-offs between driver comfort, aerodynamics, and center of gravity were considered during the setup process. The final setup not only provided a good and comfortable seating system but was a key feature for packaging parameters on a car and acted as a building block for the chassis design. Chassis was manufactured considering steering wheel position and other control positions like pedals, safety belt, etc. The vertebral and thigh impressions of four drivers were taken by placing a big polybag filled with two parts urethane foam inside the cockpit and making all drivers sit on such a bag individually one after another. These impression molds were 3D scanned using photometry. The point cloud data obtained from 3D scanning were refined and processed to create tessellated surfaces. These surfaces were used as a reference to create the CAD model of a single seat that was optimized to fit all the four drivers.

3 Seating Position Mapping and Manufacturing A reclined driver position was required to lower the center of gravity of car as the average weight of all drivers was around 20% of the total weight of the car. A lower center of gravity increases lateral acceleration (provided there are changes accordingly in the suspension). Moreover, the reclined driver position provides an aerodynamic advantage as well as reduces the frontal surface area which plays a major role in producing drag. These avoid rollover stability while cornering. An apparatus was made to measure critical cockpit dimensions. These dimensions are listed: main hoop and front hoop height; seatback angle and thigh angle; steering location; dash height and clearance to the floor; width of the cockpit at seat, legs, and shoulders; pedal height and position and shifter location. The back angle and thigh angle play a crucial role in determining seat and cockpit dimensions. The drivers were asked to sit in the apparatus and give their feedback on the positioning. All the drivers were satisfied with the positioning, and thus, the seat was decided to be reclining with a back angle of 45° from vertical and thigh angle of 55° from the horizontal axis. These parameters were taken into account for designing and manufacturing of seat. After the manufacturing of chassis, impression molds were created. A simple jig was

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created to support drivers in the required seating angle within chassis to create their respective molds. A jig is manufactured with a three-part structure seating area and a fixed back and thigh support that were adjusted according to required angles. In order to obtain vertebral and thigh impressions of all four drivers, four polybags filled with expandable two-part urethane foam were used. The bag contained one liter of each liquid of the polyurethane foam which was mixed thoroughly as shown in Fig. 1. Polybag was laid on jig, and the driver was seated in the required position on bag. The foam expanded around the body surface of the driver developing the required impression as shown in Fig. 2. This process was repeated for all four drivers to obtain their impressions. Finally, the mold was cleaned by removing unwanted materials as shown in Fig. 3. Fig. 1 Curved plates with single impact with meshed model

Fig. 2 Impression mold is formed when hardened

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Fig. 3 Final mold obtained after cleaning

4 3D Scanning and Point Cloud Data Processing The molds obtained were scanned using photometry which is a non-contact passive 3D scanning technique. The process uses a camera to take multiple images under different lighting conditions. The lightings differ in luminosity. The difference in illumination levels of different images helps the computer to calculate the depth at each pixel by estimating surface normal present in an object. The solutions are given by computer by detecting visible light because it is a readily available ambient radiation. Photometry was used because of the simplicity involved with the scanning process. Passive imaging is inexpensive because it does not need any particular hardware but simple digital cameras. Since photometry uses the difference in light to determine depth, polybags were painted black to avoid unnecessary reflection. The scanning was done on a Lambertian surface (black paint) so that surface luminance is isotropic for accurate scanning. The impression molds were scanned at FARO’s Chennai facility. The point cloud data of all molds were obtained and processed in Geomagic Wrap 3D Imaging Software [6]. As driver position remained constant for all four drivers, i.e., back and thigh angle, the scanned data only varied according to driver body contour which allowed effective merging of data. The point cloud data files of all molds were aligned and merged together using software as shown in Fig. 4. This gives the areas of high point population. These areas were filtered from merged data which contained noise, areas of low point population. The merged data were then converted into a surface by creating tessellations over resultant data as shown in Fig. 5. This surface file was exported into a standard IGES file format for further designing of the final seat.

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Fig. 4 Merged point cloud data of all four impression molds

Fig. 5 Surface formed over the merged point cloud data

5 Surface Development Using CAD Further processing of the scanned image was done in SolidWorks software. This image file was used as a reference for creating the CAD model of the final seat as shown in Fig. 6. Perpendicular planes were created along the seat contour to develop intersection curves of scanned seats and planes at different points. The location and number of planes to be created were decided depending on the ergonomic importance of different regions, for example, shoulder support, lower back, and hip support, thigh support, arm support, etc. The impression mold formed was asymmetric as

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Fig. 6 Planes created along the seat contour of the impression mold

foam expanded in a random fashion. Some areas had a better impression on one side as compared to another side. For example, the thigh impression of the left leg had a much better contour as compared to the right side, whereas shoulder contour on the right side was better than that on the left side. These irregularities on the scanned surface were rectified by using a better contour for both sides of the seat. The curves obtained were further smoothened as the surface of the scanned mold was uneven. The final grid of curves was used to create a loft surface as shown in Fig. 7. The lofted surface was then inserted in the assembly of the car to further improve its design and packaging. Issues such as clearance with firewall and steering column were resolved by small adjustments in the former or latter. Required elbow room for shifting was identified and incorporated. Mounting points for a seat on chassis members were created by extending ends as flanges for resting over chassis members. This helped achieve a complete cockpit closeout and improve aesthetics. Provisions for a six-point driver harness system were also cut out of the seat. Thus, the final CAD model of the seat was developed and designed and properly packaged inside the car as shown in Fig. 8.

6 Fabrication and Testing Using the final CAD model, a mold was contrived using medium density fiber (MDF) wood which was machined in layers using CNC milling. These layers were joined together to create a single mold for the seat. This mold was used to make a fiberglass mold for laying carbon fiber [7]. This multiple mold building process is used to achieve a better surface finish in the final product. Layers of carbon fiber were stacked and reinforced to manufacture a seat that provides required stiffness as shown in Fig. 8.

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Fig. 7 Planes created along the seat contour of the impression mold

Fig. 8 Manufacturing of the seat

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The manufactured seat was then tested by all drivers on track. Positive feedback was provided by drivers regarding comfort and drivability. The seat contour effectively supported the key regions, as discussed earlier, for all the drivers. Further, in an effort to quantify driver comfort, the most experienced driver drove the car in seat used for the previous season and new seat on two different days to increase driver comfort generated because of constant testing on the same vehicle dynamics and engine setup. The effects induced by the change of seat on the driver’s performance were studied. The timings of twenty laps, ten for each seat were noted using a data acquisition system (Figs. 9 and 10). A lap time graph was plotted for each seat to compare the change in lap timings. As observed from graphical comparison as shown in Fig. 11 of the car’s timings on both the seats, the timings of the car with the old seat saw a steeper increase in timings than timings of the car with a new seat. A lap-to-lap comparison of both runs shows that except for lap number two, all the laps taken in the new seat had recorded a lesser time than ones taken in the old seat.

Fig. 9 Lap timings of car with new seat

Fig. 10 Lap timings of car with new seat

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Fig. 11 Lap time graphs for both the seats

7 Conclusions The design procedure was executed as planned which successfully produced a driverspecific comfort seat for a race car. The use of 3D scanning technique and CAD modeling resulted in a single seat for all the drivers. This design approach can be revised and incorporated in the manufacturing of mold. The final seat was manufactured and finished using carbon fiber. The manufactured seat was then tested by all the drivers on the track. The data were analyzed, and positive feedback was obtained from all the drivers. This result was also compared with the old seat design. It was concluded that the new seat design gave better comfort to drives and more lap timings were achieved by them. The digitization technique played a major role in creating the seat design, and the final product obtained gave better comfort in driving. Acknowledgements The authors would like to convey their heartfelt thanks to management of Vellore Institute of Technology, Vellore, India, and Mr. Raman Baskaran of FARO Business Technologies India Pvt. Ltd., Chennai, India, for providing the authors with valuable inputs which were instrumental in completing this paper.

References 1. Mariotti E, Jawad B (2000) Formula SAE race car cockpit design an ergonomics study for the cockpit. In: SAE technical paper 2000-01-3091. https://doi.org/10.4271/2000-01-3091 2. Lee J, Ferraiuolo P (1993) Seat comfort. In: SAE technical paper 930105. https://doi.org/10. 4271/930105 3. Park S, Min S, Subramaniyam M, Lee H (2015) Driving posture measurement using 3D scanning measuring technique. SAE Int J Passeng Cars Mech Syst 8(2):600–605. https://doi.org/10.4271/ 2015-01-1392 4. Lerch T, Harder D, MacGillivray M, Domina T (2009) The body silhouette: a new data processing technique for point cloud data generated by a 3D laser body scanner. In: SAE technical paper 2009-01-2299. https://doi.org/10.4271/2009-01-2299

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5. Chen J, Jiang B, Song S, Wang H (2016) Vehicle driving posture reconstruction from 3D scanning data using a 3D digital human modeling tool. In: SAE technical paper 2016-01-1357. https:// doi.org/10.4271/2016-01-1357 6. Carter N, Hashemian A, Rose N, Neale W (2016) Evaluation of the accuracy of image based scanning as a basis for photogrammetric reconstruction of physical evidence. In: SAE technical paper 2016-01-1467. https://doi.org/10.4271/2016-01-1467 7. Rajamanickam U, Singhal A, Jothi M (2015) FRP mold and panel manufacturing for FSAE body panel and driver seat. In: SAE technical paper 2015-01-0727. https://doi.org/10.4271/2015-010727

Design Optimization of Lubrication System for a Four-Cylinder Diesel Engine J. Ramkumar, George Ranjit, Vijayabaskaran Sarath, V. Vikraman, Bagavathy Suresh, Namani Prasad Babu, and Malekar Amit

Abstract Lubrication system plays a very vital role in engine durability. Engine designers show utmost care in designing and validating the lubrication system. If not properly designed engine components will be subjected to pre-mature failure in the field incurring loss to the manufacturer pulling down the “brand image” which is considered sacrosanct. In the current work, the authors carry design exercise of the lubrication system for a four-cylinder diesel engine. Concept design, design optimization and thorough validation activities are carried out and captured in detail. Oil pressure distribution is simulated for the concept design. Based on the simulated results, design optimization is carried out through simulation and extensive validation under various boundary conditions. Different design iterations are done to alter the oil pressure levels and validate it at various oil galleries. The results of oil pressure at various oil galleries are neatly captured to optimize the lubrication system design, the process of which is explained in this paper. Keywords Lubrication system design · Oil pressure distribution · Oil gallery pressure · Engine durability

Abbreviations CO2 HLA CFD MOG FIP DR lpm

Carbon dioxide Hydraulic lash adjuster Computational fluid dynamics Main oil gallery Fuel injection pump Drive ratio Litres per minute

J. Ramkumar (B) · G. Ranjit · V. Sarath · V. Vikraman · B. Suresh · N. P. Babu · M. Amit Mahindra Research Valley, Mahindra & Mahindra, Chennai, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_12

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1 Introduction Ensuring the durability of the engine is top most priority for any engine designer since the functional requirements of the engine are a must and are taken for granted. Designers take painstaking efforts to make the engine durable. Each and every engine has to pass the claimed warranty life and the engine end of life. Customers are very knowledgeable and sensitive nowadays and are ready to sue the manufacturer if his engine/vehicle fails within the warranty period. Also, the manufacturer has to incur heavy losses to replace parts which fail within the warranty period. The losses are multi-fold when the manufacturer is sued. Hence, engine designers take the extra mile to prevent any durability related concern upfront during the design, simulation and validation stage itself. There are several parts of an engine which have relative motion amongst each other. All these parts experience excessive wear and tear if not properly lubricated [1]. Even though wear and tear of relative moving parts cannot be eliminated, mitigation of the same is possible with proper lubrication [2, 3]. Hence, these parts need to be lubricated properly which results in controlled wear. Designers ensure that the parts experience such controlled wear till the end of validation trials, i.e. end of engine life. Validation trials are planned in such a way that it mimics the usage pattern of the engine life span fitted onto the field vehicle. Test bed validation trials for engines are planned for worst case scenarios which would result in the actual vehicle driven by the customer under any harshest condition. During such validation trials, the relative moving engine parts experience considerable wear which must be within the wear limits (controlled wear) set by the designer. After the trial is completed, the engine is dismantled and wear values of the parts are measured and checked if they are within the controlled wear limits. If the wear values are within the limits, it is co-related to the qualification of entire engine life at the hands of the customer. Lubrication system must be properly designed to aid the controlled wear. Uncontrolled wear leads to pre-mature engine failure. Lubrication system helps in keeping this wear and tear within the specified limits. Therefore, it is important to properly design the lubrication system and do extensive simulations and validations. In the current work, the authors capture the intricate details of design optimization after touching upon the concept design of the lubrication system for a diesel engine. Proper lubrication system design not only influences the engine life but also affects the engine friction and performance [4, 5]. Several efforts have been made to reduce the engine fuel consumption/CO2 emission [5, 6]. Some studies reveal the importance of oil temperature [7]. Oil temperature plays an important role not only in controlling wear but also in controlling engine friction. Extensive simulation and validation works have been carried out in the prior papers. The prediction by simulation has come a long way. Optimization of simulation inputs to match the simulated values closer to the validated values is always a difficult task [8, 9]. Literature reveals that maintaining oil pressure is very important which needs to be thoroughly simulated which helps in reducing a lot of time-consuming

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Table 1 Engine specifications and boundary conditions Description

Specification

Displaced volume

1.5 L

Number of cylinders

4

Engine type

Diesel

Minimum engine speed

800 rpm

Maximum engine speed

4000 rpm

Oil pump drive

Internal drive via crankshaft

validation efforts. But a gap usually exists between simulated and validated results. Hence, the design finalization has to be done only after thorough validation. The authors have focused on validation activities in the current work. Emphasis is given on oil pressure build up to the required limit at all operating conditions. Several prior works reveal optimization for pressure build up [10] and also for downsizing the pump [11]. As the pressure drop across the filter is usually high, optimization of the same also gives benefits [12, 13]. Overall, prior work indicates importance of oil retention for reducing friction [14] and friction reduction and fuel economy [15]. Hence, current work captures the details of lubrication design optimization.

2 Engine Details Simulation and experimental work are carried out on a four cylinder turbocharged diesel engine of 1.5 L cubic capacity. First, the lubrication system layout concept design is made and simulated to check the oil pressure distribution at several critical locations in the oil galleries in the engine. Experimental work is then carried out to substantiate the simulated results and also to optimize the design. Table 1 depicts the engine specifications.

3 Lubrication System Layout Lubrication system consists of various parts such as oil sump, oil pump, oil filter, piston cooling jet, oil galleries and throttle plugs. Concept design of the lubrication system layout is done in the first step based on benchmarking other engines and also based on prior experience. The lubrication system specifications used for the concept design is shown in Table 2. Figure 1 depicts the oil pump and drive components. As shown, oil pump is driven by the crankshaft through chain and sprocket drive mechanism. Power is taken from the crankshaft. Figure 2 depicts the lubrication oil circuit for the entire engine. Internal

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J. Ramkumar et al. Description

Specification

Oil pump type

G-rotor type

Oil pump displacement

22.7 cm3 /revolution

Oil pressure limit (max)

3.95 bar (Gauge)

Oil specification

SAE5W30

Oil pump drive method

Chain and sprocket drive

Oil pump drive ratio

1:0.76

Displacement

Fixed displacement

Fig. 1 Oil pump drive system layout

oil passage lines/oil galleries of the engine are shown in detail highlighting the key components of lubrication system. Figure 3 depicts the oil circuit of cylinder head along with key dimensions of the oil circuit. As shown in Fig. 3, two lengthy oil galleries are provided in the cylinder head along its length for the lubrication of both the camshafts (intake and exhaust). Lubrication lines are also extended to the rear side of the cylinder head behind intake camshaft, where the vacuum pump and fuel injection pump are placed, for the lubrication of both these pumps. The oil pressure and flow in the cylinder head lubrication lines are controlled by a throttle plug placed at the entry of the cylinder head oil gallery. As the name suggests, oil passing through the throttle plug experiences a drop in pressure as the plug throttles the oil flow, thereby limiting the quantity of oil and also reducing the oil pressure in the cylinder head oil gallery. This is done deliberately during the lubrication system design itself. Throttling the oil gallery reduces the oil consumption by the hydraulic lash adjuster (HLA), camshaft, vacuum pump and fuel pump. These components do not require very high oil pressure for lubrication;

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Fig. 2 Lubrication system layout for entire engine

Fig. 3 Lubrication system layout for cylinder head

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hence, benefit can be taken out in reducing the oil pump size by reducing the flow to these oil galleries. Considerable friction and cost can be reduced due to a smaller oil pump. Employing a smaller oil pump is also beneficial for packaging and it also aids in reducing the engine weight. Current work majorly focuses on the lubrication of cylinder head oil gallery called as HLA oil galleries. Authors have captured the various activities taken in design and validation stage for sizing the throttle plug oil hole diameter and for downsizing the oil pump amongst other design optimization activities.

4 Results and Discussion As mentioned, the first activity is the concept design based on benchmarks and previous experience. Once the concept design is ready, simulation is carried out using commercially available CFD software “FlowMASTER”. Various input data is fed into the simulation software ranging from gallery dimensions, volume, bearing clearances at several locations, etc. Since it is 1D simulation tool, data fed is as simple as length and diameter to calculate the volume. Other inputs such as oil temperature, oil characteristics like viscosity, density, etc., are also fed into the software. First level of design evaluation is done from the simulated results. Figure 4 shows a typical graph plotted from the simulated results for the initial concept design. The graph shows the oil pressure (gauge) at various locations of the engine at different engine speeds. By plotting the oil pressure requirements of the

Fig. 4 Typical pressure versus engine speed data predicted by FlowMASTER

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Fig. 5 Typical flow rate versus engine speed data predicted by FlowMASTER

components in the same graph, it can be checked whether the simulated pressure values are meeting the requirements/limits. Figure 5 shows a typical result simulated by FlowMASTER depicting the oil flow rate across different engine speeds. Based on the input data for the concept engine design, the software predicts the oil consumption at each and every location. Concept design can be optimized by modifying the oil consumption of the critical components and the selected pump delivery can be evaluated for sufficiency. Optimization of oil pump design (sizing the pump) can be done by modifying the volumes and clearances at critical locations based on experience and standards. After completing the above-mentioned design, exercises concept design is established. Once the concept design is frozen, drawings of various components are released for the first prototype manufacturing. The first set of prototype engines are built with these proto components. Once the proto engines are built, they are prepared with several adaptations and instrumentations for measuring pressure at critical locations. The proto engines are then mounted onto the test bed for several validation trials. Critical locations which are identified for pressure measurements are captured in Table 3. The need for the measurement at that location is also described. The instrumented proto engines are built according to these tabulated requirements identified as critical locations. Suitable pressure, temperature and other measuring instruments are selected based on experience and design requirements. More the data better is the understanding of engine oil pressure mapping, thereby higher the possibility for design optimization. The instrumented engine is tested at various engine speeds and loads. Normally, the oil pressure distribution does not get affected by the engine load. Since oil pump

146 Table 3 Critical locations for pressure measurement

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Criticality description

Oil pump out pressure

To ensure pump delivery

Pressure before oil filter

To measure pressure drop across filter

Pressure after oil filter Mail oil gallery pressure

To ensure sufficiency for crankshaft bearings

Cylinder head oil gallery

To check pressure for intake and exhaust camshaft

Fuel injection pump

To ensure lubrication of fuel pump and vacuum pump

Oil temperature

To check variations in pressure across temperature ranges

is a positive displacement type pump, it delivers oil pressure proportional to the speed of the engine. Figure 6 shows the oil pressure at different locations across various engine speeds. It can be seen that higher the engine speed, greater the oil pressure delivered by the oil pump. Conventional oil pumps have a pressure relief valve to control the oil pressure in the circuit. A spring inside the valve can be pre-compressed to any given pre-load according to the spring stiffness and deflection characteristics. This spring setting acts as the pressure relief setting. This pressure relief valve spring can be set based on the engine requirements. It can be seen in Fig. 6 that the oil pump delivery is linear till a certain engine speed till which the valve is closed by this spring setting. Once the pressure setting is achieved, the relief valve opens up by-passing the oil pumped by the pump directly back into the oil sump. Hence, only a portion of the oil quantity travels to the oil galleries. Remaining quantity is by-passed back to the sump in order to limit pressure in the oil gallery. The delivery characteristics of the pump would be linear throughout the entire engine speed range if the pressure relief valve is not employed. The spring is set such

Fig. 6 Oil pressure measured at critical locations on instrumented prototype engine

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that at about 4 bar pressure the relief valve opens allowing a portion of the oil to be by-passed back to the oil sump. Hence, the slope of the oil pressure line drops as the engine speed increases further. This can be clearly seen in Fig. 6 where up to a speed of 2600 rpm, the pump delivery characteristics are linear and beyond 2600 rpm, the pump delivery is slightly dwarfed due to the by-pass actuation. It can also be inferred from Fig. 6 that the highest pressure is measured at the location immediately at the exit of the pump (at pump delivery). As the oil travels further into the galleries, pressure drop occurs, and hence the pressure curves at other locations are below the pressure measured at the oil pump delivery location. The turbocharger is a very important functional part of the engine, and hence it is prioritized above other locations. It can be seen that the pressure measured at the entry of the turbocharger bearing housing is higher than the pressure measured at other locations. Similarly, main oil gallery (MOG) pressure also shows higher values over other locations as MOG is placed immediately after the pump delivery location. From MOG, all other oil galleries branch out. Crankshaft bearings and connecting rod bearings require higher lubricating oil pressure than that required for other parts due the higher loads experienced as one can imagine. Hence, it is the onus of the designer to provide the MOG with higher oil pressure due to the importance of lubricating crankshaft bearings over other bearings, which is clearly visible in Fig. 6. Oil has to travel a certain distance to reach the cylinder head oil galleries, and hence the drop in pressure is experienced at the HLA oil gallery. Also, due to the presence of the throttle plug at the entry of the gallery, the drop in pressure is noticed. The fuel injection pump and vacuum pump are the farthest placed components in the lube oil circuit, and hence they experience the lowest pressure levels amongst all locations in the circuit. Design target is to have a pressure level of 0.4 bar gauge at this location which is selected based on experience. The FIP tappet has reciprocating motion relative to the FIP bore. The loads carried by this pump are considerably high, and hence assumes importance in maintaining the pressure levels of 0.4 bar gauge. Any pressure below this limit has a high probability of damaging the parts due to wear and tear. Figure 7 depicts the oil pressure measured at the exit of oil pump at various oil temperatures. It can be seen that the pressure build up is not only proportional to the engine speed but also inversely proportional to the oil temperature. Higher the oil temperature lower is the pressure. This phenomenon of drop in pressure at higher temperature is predominant at lower speed ranges. At high speeds, the drop in pressure is not as high as that experienced in lower speeds. At the validation stage, it is ensured that sufficient oil pressure is maintained at all critical locations even at peak operating temperature of oil. In worst case, the oil temperature can go as high as 140 °C but rarely. Engine does not operate continuously at this high oil temperature. Peak continuous operating temperature of oil is less than 120 °C. Hence, pressure plots at critical locations are mapped at these temperatures also and the oil pressure measurement values are verified against the design limit. Figure 8 depicts the differential/pressure difference/delta pressure between the inlet to the oil filter and outlet from the oil filter. It can be understood that when

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Fig. 7 Oil pressure measured at different oil temperatures at the exit of oil pump

Fig. 8 Oil pressure difference measured before and after oil filter

oil passes through the filter a certain pressure drop is expected. The pressure drop can go as high as 1.6 bar as can be seen in the graph. Amongst several unwanted pressure drops in the lubrication circuit, the filter pressure drop is one significant drop. Based on the filter design, the pressure drop varies but it cannot be eliminated. This differential pressure is also proportional to the engine speed and oil pressure. Higher the engine speed, higher is the oil pressure at outlet of the oil pump and higher is the pressure drop across the oil filter. Pressure drop is also inversely proportional to the oil temperature. Higher the oil temperature lower is the pressure drop across the filter. This can be attributed to the oil pressure at the exit of the pump itself. As lower oil temperature has higher engine oil pressure at the pump delivery, the pressure drop across the filter is also higher.

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Any drop in pressure in the oil circuit is an unwanted event to the engine designer. Even though the pressure drop across the circuit is inevitable, the designer takes utmost care to keep the pressure drop levels to a lower limit. Higher pressure drop forces the designer to employ a higher capacity pump which results in increased engine power consumption and therefore reduced engine performance. During the design and validation stage, it was found that the pressure limits at each and every critical location was met comfortably except at the extreme end of the oil circuit. Fuel injection pump and vacuum pump received lesser oil pressure. This is due to the minor mismatch between the simulation and actual engine validation and also due to some design optimization. While the pressure levels were within the design limit for vacuum pump, the pressure levels were considerably lower for the fuel pump when compared against the design limits. Since fuel pump operates at higher loads compared to vacuum pump, maintaining oil pressure in the fuel pump assumes top priority, especially at higher temperatures and lower engine speeds (in the idling speed range) where the oil pressure is drastically low. Figure 9 shows the oil pressure levels which drop to as low as 0.34 bar during the lower speeds of about 750–800 rpm (during engine idling) and higher engine oil temperature of 130 °C. The pressure levels improved considerably when the oil temperature was lower than 100 °C. Hence, the oil pressure value at the FIP was to be improved by design optimization, especially for lower oil temperatures and lower engine speeds. Increasing the oil pressure at FIP is possible in several ways. Few of the options are listed below: 1. Increasing the throttle plug hole diameter. 2. Increasing the oil pump discharge/delivery/size. 3. Increasing the oil pump speed/drive ratio (DR).

Fig. 9 Oil pressure measured at the fuel injection pump (FIP)

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Table 4 Validation trial iterations’ matrix Description

Concept design Base design (first proto engine) (option 1)

Increased pump Increased drive delivery (option 2) ratio (option 3)

Oil pump capacity (lpm)

11.5

12.9

11.5

11.5

Drive ratio

0.76

0.76

0.76

0.86

Throttle plug hole diameter (mm)

2.3

2.8

2.8

2.8

There are few other methods to increase the oil pressure at FIP but the above stated three are simpler, convenient and quickly implementable. Table 4 depicts the different lubrication circuit options considered to carry out the oil pressure mapping validation trials in order to finalize and optimize the lubrication circuit layout design. Out of the three options stated, increasing the throttle plug hole diameter was the cheapest and easiest method for increasing the pressure in the HLA oil gallery. The initial throttle plug hole diameter for the concept design stage was 2.3 mm which was increased to 2.8 mm. Figure 10 depicts the pressure results of both the throttle plug hole diameters. It can be seen that increasing the throttle plug hole diameter helped in increasing the HLA oil gallery pressure but only at higher speeds. At lower speeds, the pressure rise was not very high as expected. Both the throttle plugs of 2.3 and 2.8 mm hole diameter gave almost similar results, especially at the lower speed regions like idling. The increase in pressure due to the diameter increase of the throttle plug hole was very marginal and not convincing. Hence, it was decided to carry out the other changes to the lubrication system layout as mentioned in the earlier section—increasing the oil pump discharge and

Fig. 10 Oil pressure at FIP at different oil temperatures oil temperature

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Fig. 11 Oil pressure mapping at 90 °C with different pump size and drive ratios

increasing the oil pump speed. Oil pump size increase results in increasing the oil pump discharge. A bigger pump is packaged in the same engine packaging layout. But the drive ratio is kept the same as previously used (0.76). This is achieved with a chain drive of 25 teeth driver sprocket mounted on the crankshaft and 33 teeth driven sprocket mounted onto the oil pump. Figures 11 and 12 depict the oil pressure mapping exercise done with three different lubrication system designs. First is the mapping done with existing oil pump of 11.5 litres per minute (lpm) flow rate driven by a chain and sprocket drive having a ratio of 0.76. This is referred to as base design (option 1) finalized after the simulations and initial validation of the first proto engines which showed reduced oil pressure at the FIP even after increasing the throttle plug diameter to 2.8 mm. Modified layout design included a higher flow rate pump of 12.9 lpm but with the same drive ratio of 0.76 (option 2). After thoroughly mapping the oil pressure at different locations like main oil gallery (MOG) of the crankcase and at FIP location, it was decided that 11.5 lpm pump gave better results with an increased drive ratio of 0.86 (option 3) instead of 0.76 when compared to pressure developed by a higher flow rate pump of 12.9 lpm. This is evident from Figs. 13 and 14 depicting the pressure mapping at FIP. 12.9 lpm pump with the drive ratio of 0.76 results in the same pressure levels as that of 11.5 lpm pump with a drive ratio of 0.76. The improvement is very marginal, and hence it was decided to scrap this proposal (option 2). Increasing the drive ratio to 0.86 from 0.76 with the same 11.5 lpm pump (option 3) showed far better results. Pressure values increased with this combination as can be seen in Figs. 13 and 14.

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Fig. 12 Oil pressure mapping at 130 °C with different pump size and drive ratios

Fig. 13 Oil pressure mapping at FIP for 90 °C with different pump size and drive ratios

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Fig. 14 Oil pressure mapping at FIP for 130 °C with different pump size and drive ratios

The increase in pressure is even better in the lower speed ranges which were the requirement. It can be seen that at 90 °C oil temperature and 800 rpm, the oil pressure at FIP increased by about 20% (0.5–0.61 bar). Even at higher temperatures like 130 °C, there was substantial increase in the FIP pressure of about 20% (from 0.34 to 0.41 bar). Hence, it was decided to finalize the design with this iteration of 11.5 lpm. This design showed better pressure values at higher oil temperatures. It was above the limit of 0.5 bar till 100 °C oil temperature which is the peak continuous oil temperature. The pressure values were higher than 0.4 bar even at intermittent peak temperatures of 130 °C as shown in Fig. 14. The final design specifications which were frozen for mass production are shown in Table 5. Table 5 Optimized design (final lubrication system layout design) specifications

Description

Specification

Oil pump capacity

11.5 lpm

Drive ratio

0.86

Throttle plug hole diameter

2.8 mm

Driver sprocket no. of teeth

25

Driven sprocket no. of teeth

29

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5 Summary and Conclusion The design optimization phase of the lubrication system is neatly captured with elaborate explanation of graphs which depicts the various iterations carried out. Concept design and simulation are touched upon but emphasis is given to the design optimization through extensive validation trials which are captured in this paper. Pressure mapping is done all over the lube oil circuit of the engine at several locations majorly at critical locations like HLA oil gallery, main oil gallery, fuel pump location, etc. The concern of low oil pressure at the critical location of FIP found which was not found during simulation but found only during validation trials is addressed. Freezing/finalizing the lube oil circuit design after the step by step evaluation by simulation and validation through iterative trials carried out for solving the concerns is explained in the current paper. Wear of all parts were within the controlled wear limit, hence, with the optimized design, and hence all prototype engines successfully completed validation trials without any abnormal wear. Acknowledgements Many thanks to Mahindra Research Valley for providing this opportunity to carry out this research work. Our gratitude to all my colleagues who have participated in discussions and given suggestions for this work. Special thanks to Mr. P. D. Kulkarni and Mr. Vishnu Patade who guided us in the whole endeavour of oil pressure mapping and design optimization.

References 1. Ren Y et al Research on the lubrication performance of crankshaft bearing under different engine operating conditions, 2018-01-0979 2. Poonia S et al Parametric study and prediction of lubrication oil film thickness in sliding and rolling interface valve trains, 2018-01-0931 3. Gokhale NP et al Solving valve train wear problems in medium speed high BMEP diesel engines, 2011-01-2217 4. Shayler PJ et al A modified oil lubrication system with flow control to reduce crankshaft bearing friction in a litre 4 cylinder diesel engine, 2016-01-1045 5. Srinivasan S et al Performance improvement of automotive oil pump to operate at high temperatures employed in modern diesel engines, 2012-01-0428 6. Loganathan S et al Design and development of vane type variable flow oil pump for automotive application, 2011-28-0102 7. Schnelder EW, Blossfeld DH Effect of lubricant properties and lubricant degradation on piston ring and cylinder bore wear in a spark-ignition engine, 2006-01-3413 8. Takagishi H et al Establishment of engine lubrication oil pressure and flow rate distribution prediction technology using 3D-CFD and multi body dynamics, 2009-01-1349 9. Klingebiel F et al Simulating engine lubrication systems with 1-D fluid flow models, 2000-010284 10. Thakur A et al Performance modification of three cylinder diesel engine Ge-rotor oil pump through rotor and PRV system, 2017-28-1934 11. Tao W et al Robust optimization of engine lubrication system, 2007-01-1568 12. Neveu CD et al Engine oil pumpability study in a heavy duty diesel truck engine, 2000-01-1988 13. Cehreli ZN et al Lubrication system development on 5-cylinder engine, 2007-01-2577

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14. Moritani H et al Challenge to the diesel engine lubrication with fuel, JSAE 20077101 15. Tamura K et al Impact of boundary lubrication performance of engine oils on friction at piston ring—cylinder liner interface, 2014-01-2787

Investigation on Turbocharger Actuator for LPG Fuelled SI Engine K. Ravi, Jim Alexander, and E. Porpatham

Abstract The effects of turbocharger wastegate actuator upon engine performance and emission were experimented on a twin-cylinder spark-ignited engine fuelled by gas phase LPG. The load was given by setting the throttle wide open and the speed was varied from 1000 to 3400 rpm. Taking the base reference as naturally aspirated conditions, various performance and emission parameters were compared for the conventional wastegate system as well as electronic version focusing on manifold absolute pressure, knock and exhaust resistance. The manifold absolute pressure and wastegate position were calibrated for the electronic wastegate to optimally perform based on engine requirements. There was appreciable improvement in brake thermal efficiency as well as reduction of unburnt hydrocarbon emissions for the turbocharger with electronic wastegate. Keywords Turbocharger · Wastegate actuator · LPG injection · Performance and emission

Nomenclature CNG CO ECM HC LPG MAP

Compressed natural gas Carbonmonoxide Engine control module Hydrocarbon Liquefied petroleum gas Manifold absolute pressure

Peer-review under responsibility of the scientific committee of the International Conference on Progress in Automotive Technologies, ICPAT – 2019. K. Ravi (B) · J. Alexander · E. Porpatham Automotive Research Center, School of Mechanical Engineering, VIT University, Vellore 632014, India e-mail: [email protected]

© Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_13

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Nitricoxide Spark ignition

1 Introduction The ever usage of conventional engine technologies has led to new product development which aims towards improvising engine performance with effective emission reduction. The choice of engine design is determined through the downsizing techniques which is an obvious choice for spark ignition (SI) engines. Turbocharging is one of the novel techniques of inducting excess air that attains gain in thermal efficiency and reduces emissions [1], which is unlikely not in case of naturally aspirated engine. Recent usage of alternative fuels has benefitted in several ways through energy resource balance, and in terms of automotive applications, it increases the engine efficiency [2]. One of the renowned alternative species that benefits SI engine operation is the liquefied petroleum gas (LPG), it has low carbon content having higher octane rating that reducing engine knock, and its higher hydrogen to carbon structure lowers the emissions [3]. The properties of LPG fuel are shown in Table 1. LPG benefits towards increasing thermal efficiency also adapting with higher compression ratio environment with optimum control of spark timing [4], this makes it compatible for multicylinder SI engine that share the overall cylinder volume along with the equipped turbocharger. The existing turbocharger technologies have pneumatic actuators such as vacuum or boost type wastegate systems that works based on intake manifold pressure differentials; however, these are limited towards performance when it is related to nature of air flow rate happening with respect to intake geometry. Studies have also shown that variations of the intake plenum geometry have significant effects both towards engine performance as well as delayed responses occurring during transient operations [6]. Research has been carried to determine vehicle performance and better drivability through control of actuator present over the turbine of the turbocharger, it is seen that an adaptable wastegate actuator minimizes the associated pumping losses during load variations as well as minimizes fuel consumption Table 1 Properties of LPG fuel [5]

Properties

LPG

Composition (% vol)

Propane C3 H8 —30%

Lower heating value (MJ/kg)

45.7

Butane C4 H10 —70% Density (kg/m3 )

2.26

Flame velocity (cm/s)

38.25

Research octane number

103–105

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[7]. In the midst of the internal plenum resistance arising to turbo-lag effects, suggestions have shown that the use of electronic wastegate control takes accurate control when the engine is at starting conditions subjected under low-pressure operation [8]. Adding to these issues, in the conventional method, there are certain limitations while operating at loads of medium speed ranges, where maximum boost pressure is attained that forms knock effects that dominates the control over actuation of wastegate resulting as loss of engine power [9]. Several model-based approaches have been implemented along with the engine control module (ECM) that has instant synchronous communication through sensors and actuators regulating the wastegate. Tests were carried out to estimate various engine parameters and the use of proportional integral derivative control the maximum manifold absolute pressure (MAP) values were set at peak torque zone of 7200 rpm where knocking intensities are also present [10]. In terms of engine performance studies have suggested that the closed-loop turbocharger control has greater improvement towards engine performance compared to the conventional type wastegate system [11–13]. In this study, the existing boost type wastegate system was upgraded with an electromechanical type actuator that performs based on calibrated map. Experimental comparisons were made between naturally aspirated, boost type and electromechanically controlled turbocharger system at full throttle position.

2 Experimental Setup and Methodology A twin-cylinder SI engine TATA Ace compressed natural gas (CNG) having cubic capacity of 704cc consist of port fuel injection system, cylinders cooled by liquid coolant and the present fuel used was a gas phase LPG. The default peak torque set for naturally aspirated conditions was 49 Nm@2200 rpm and maximum power was 15.5 kW@3400 rpm. The maximum engine performance and emission characteristics were estimated incorporating a KP35 turbocharger. The LPG fuel was regulated to an operating line pressure of 2 bar from the storage tank and was supplied to the common rail where the injectors were mounted near the port region. The flow rate of air was measured using a dresser make air flow meter and the fuel flow rate was estimated knowing the equivalence ratio from the emission analyser considering the stoichiometric air fuel ratio for LPG as 15.3:1. Figure 1 shows the layout of the experimental setup. An engine control module having closed-loop feedback system was used to monitor all functions such as throttle response, injection pulse width, spark timing, and wastegate control. The graphic user interface offers flexible calibration over various performance maps, Fig. 2 shows the variation of boost pressure and wastegate position with engine speed. The exhaust was coupled with a turbocharger that has suitable matching performance provided with an intercooler for stable combustion, since the compression ratio is higher version for SI engine a suitable type of wastegate actuation system was to be determined. Full throttle performance was carried out with speed ranging from 1000 to 3400 rpm for base readings at naturally aspirated and turbocharged conditions.

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Fig. 1 Layout of the experimental setup

Fig. 2 Intake boost and wastegate position with engine speed

The engine loading with variable speed control was done using the Dynalec make eddy current dynamometer. The emissions namely hydrocarbon, nitric oxide, and carbon monoxide were measured using a Horiba make five gas analyse. Initial base readings were conducted for the naturally aspirated conditions, and then tests were done with turbocharging with conventional and electronic wastegate.

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3 Result and Discussion Various engine performance and emission parameters at full throttle conditions were determined such as brake power, brake torque, brake thermal efficiency, brake specific fuel consumption, exhaust gas temperature and hydrocarbon, nitric oxide and carbon monoxide emissions.

3.1 Brake Power The engine output work which is denoted as brake power is compared between naturally aspirated and turbocharged conditions as shown in Fig. 3. The effect of electronic wastegate has appreciable gain towards brake power through all speed ranges up to 14.3 kW@3400 rpm which is superior to naturally aspirated and conventional boost type turbocharging. The electronic wastegate has sustained performance through entire speed ranges as per the calibrated table values.

Fig. 3 Variations of brake power with engine speed

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With the increase towards speed the conventional actuation system experiences knocking at 2800 rpm which is at the mid-ranges. This effect arises due to pumping losses that causes in loss of enthalpy transfer to the turbine and as a result of which the intake manifold suffers from insufficient pressure leading to no actuation of wastegate.

3.2 Brake Torque The torque available at the output is the measure of load capacity which is shown in Fig. 4. The conventional wastegate control has higher spring tension by deign that retains the valve which indirectly acts as exhaust back pressure that causes power drop and hence torque losses. Once the turbine work is overcome this later on prolongs to knock formation which occurs at speed range 2800 rpm, this is because of the initial delay and reduced compressor work this results in depletion of adequate intake manifold pressure that is required for actuation. The use of electronic wastegate produces maximum torque about 47.5 Nm@2200 rpm as well as was stable at higher speed ranges, this indicates accurate response to torque demands through ECM communication between MAP sensor and the motor actuator.

Fig. 4 Variations of brake torque with engine speed

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Fig. 5 Variations of brake thermal efficiency with engine speed

3.3 Brake Thermal Efficiency The amount of fuel energy converted to work output is determined by the brake thermal efficiency as shown in Fig. 5. A maximum brake thermal efficiency of 28.3%@2200 rpm was observed for the electronically controlled turbocharger. The presence of excess air in the mixture lowers the compression work which otherwise is required for rising the temperature of fuel present in the mixture. Now taking account of the air-cooled intercooler, it has added benefits much more towards reducing the overall charge temperature of the incoming compressed air, which altogether has improved brake thermal efficiency compared to naturally aspiration and conventional turbocharging.

3.4 Brake Specific Fuel Consumption The comparison of fuel consumption among naturally aspirates and turbocharged conditions is shown in Fig. 6. At regions of lower and medium speed ranges, the engine vacuum is higher resulting in throttling loss that leads to more fuel consumption. Whereas the turbocharger provided with the electronic wastegate system accurately monitors the MAP values and correspondingly determines the position of

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Fig. 6 Variations of brake specific fuel consumption with engine speed

the wastegate that minimizes exhaust resistance and operating at regions of higher vacuum.

3.5 Hydrocarbon Emission The hydrocarbon (HC) emissions arise generally due to insufficiency of air which is required during the combustion process, in some cases, when misfiring happens the mixture tends to get quenched on to cylinder wall regions as deposits forms unburnt hydrocarbon layers. The variations of HC emission with respect to speed are shown in Fig. 7. It is noted that the turbocharger having conventional wastegate control tends to have sources of HC emissions at lower speed and after the peak torque speed ranges, which is due to exhaust loading. As mentioned earlier, the pumping losses also cause unburnt charge to escape through the exhaust region, crevices, and past through piston rings. This affects the overall work done on the turbocharger causing turbolag intervals. However, switching to electronic wastegate control system at lower speed ranges the valves are open to have free flow of gases imitating the work on the turbine later on the valve tends to close achieving the required boost pressure.

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Fig. 7 Variations of HC emission with engine speed

3.6 Nitric Oxide Emission and Exhaust Gas Temperature The formation of nitric oxide (NO) emission is due to rise in combustion temperature in the presence of oxygen. The variations of NO emission and exhaust gas temperature with engine speed are shown in Figs. 8 and 9. The turbocharger with conventional wastegate system shows similar trend with increase towards speed, the presence of excess air beyond the limiting MAP value causes oxidation of atmospheric nitrogen to nitric oxide. The difference being that knock free operation occurs for the electronic wastegate system that provides necessary control over the limiting ranges of pressure. The peak combustion temperature is substantially reduced by the effects of inter-cooler and lean mixture.

3.7 Carbon Monoxide Emission The carbon monoxide (CO) emissions are caused due to reduced presence of air that is required for the oxidation of CO. This also arises from the flame quenching effects due to in complete combustion process which is shown in Fig. 10.

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Fig. 8 Variations of NO emission with engine speed

Fig. 9 Variations of exhaust gas temperature with engine speed

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Fig. 10 Variations of CO emission with engine speed

As a result of blow-by losses and other residuals of unburnt hydrocarbon leads to more CO in case of the conventional wastegate turbocharger. There was reduction in CO levels overall for the electronic wastegate type turbocharger.

4 Conclusion The experimental analysis was made for naturally aspirated, turbocharged conditions at full throttle position. There was significant improvement towards engine performance having a maximum brake 28.3%@2200 rpm with reduced HC and CO emissions for the turbocharger with the electronic wastegate actuator compared to the pneumatic type. Acknowledgements The authors wish to thank the Vellore Institute of Technology (VIT) and Science and Engineering Research Board (SERB), for their support to carry out the research work.

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References 1. Strunk W Jr, White EB (1979) The elements of style, 3rd edn. Macmillan, New York 2. Mettam GR, Adams LB (1999) In: Jones BS, Smith RZ (eds) Introduction to the electronic age. E-Publishing Inc., New York, pp 281–304 3. Bandel W, Fraidl GK, Kapus PE, Sikinger H, Cowland CN (2006) The turbocharged GDI engine: boosted synergies for high fuel economy plus ultra-low emission, SAE Technical Paper 4. Bae C, Kim J (2017) Alternative fuels for internal combustion engines. In: Proceedings of the Combustion Institute, pp 3389–3413 5. Karamangil MI (2007) Development of the auto gas and LPG-powered vehicle sector in Turkey: a statistical case study of the sector for Bursa, Energy policy, pp 640–649 6. Lawankar SM (2013) Influence of compression ratio and ignition timing on the performance of LPG fuelled SI engine, SAE Technical Paper 7. Porpatham E, Ramesh A, Nagalingam B (2013) Effect of swirl on the performance and combustion of a biogas fuelled spark ignition engine. Energy Convers Manag, pp 463–471 8. Vichi G, Romani L, Ferrari L, Ferrara G (2015) Development of an engine variable geometry intake system for a Formula SAE application. Energy Proced, pp 930–941 9. Eriksson L, Frei S, Onder C, Guzzella L (2002) Control and optimization of turbocharged spark ignited engines. IFAC Proceedings Volumes, pp 283–288 10. Romani L, Vichi G, Bianchini A, Ferrari L, Ferrara G (2016) Optimization of the performance of a formula SAE engine by means of a wastegate valve electronically actuated. Energy Proced 654–661 11. Rakopoulos C, Giakoumis E (2009) Diesel ENGINE TRANSIENT OPERATION. SpringerVerlag, London, UK 12. Wakeman RJ, Wright DO (1986) Closed loop turbocharger control with transient wastegate functions, SAE Technical Paper 13. Azzopardi JP, Farrugia JP, Caruana C, Grech N, Farrugia N, Chircop M, Farrugia M (2018) Testing and Implementation of a Turbocharged Formula SAE Vehicle, SAE Technical Paper

Stress Analysis of Automotive Chassis Using Hypermesh and Optistruct Vijay Sharma, D. Mallikarjuna Reddy, and Shreekant Patil

Abstract The automotive chassis is the fundamental and essential structure usually made of material like steel to hold the vehicle body and support all the subsystems of the vehicle, the passengers and aids in driver safety at all time. The different types of loads viz. static loads like payloads and mass of the vehicle, dynamic loads from wheel–road interface, braking, acceleration, etc., are stressed on the chassis structure. This work is focused to scrutinize the design of the chassis structure by carrying out stress analysis. The loads are calculated for various scenarios and applied on the chassis structure with proper constraints. For modelling of the chassis, Creo Parametric modelling software is utilized, for meshing the finite element pre-processor. Hypermesh is used and the structural analysis solver. Optistruct is the postprocessor used to obtain the solution. Keywords Automotive chassis · Static analysis · Hypermesh · Optistruct

1 Introduction The automotive chassis is an important part of an automobile. The chassis is a pillar for reinforcing the body and other parts of the automobile. The chassis of an automobile gives strength and steadiness to the vehicle under different conditions, and it also decides the overall shape of the vehicle. The components of the vehicle are transmission system, axles, suspension, braking, steering, wheels and tyres, etc., which are mounted on the chassis frame. In this work, the static analysis of automotive chassis has been carried out. Chassis is basically manufactured from steel, and other materials used are aluminium. Here, the ladder chassis of Mitsubishi FUSO FE 84G HDL is considered. The chassis has been modelled in Creo 3.0 using the most of the actual dimensions. Finite element analysis was done using Hypermesh and Optistruct.

V. Sharma · D. M. Reddy (B) · S. Patil Vellore Institute of Technology, Vellore 632014, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_14

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2 Literature Review Tomar et al. [1] have modelled an Eicher truck chassis frame in CAD software and have analyzed the performance of the chassis for the steel-52 material and carbon fibre and E-glass epoxy as the two composite materials. Abrya et al. [2] have studied on weld model to analysis the structural stress analysis. Seam welding is used to build the steel sheet assembly for the vehicle structure. The vehicle structure is mainly built with steel sheets, and the assembly is performed by seam welding. Shell element model is selected for finite element analysis. Fatigue analysis is performed for the whole chassis model. Ren et al. [3] have used ABAQUS to analyze the vibration analysis of SX360 dump trucks resulting for optimal design of the frame. With the help of analysis, they identified the maximum stress and its deflection distribution at various point and position of frame. Kwasniewski et al. [4] have evaluated the crashworthiness of paratransit bus through impact analysis to simulate the model behaviour, and LS-DYNA FE software is used under different impact condition such as front and side impact at various velocities. Nor et al. [5] evaluated the stress analysis of an I-beam structure for 35 tons trailer. CATIA V5R18 is used as the modelling software. When the beam is loaded with uniform loading distribution, the theoretical location of the maximum stress and maximum deformation does agree with the analyzed results. Patil et al. [6] have studied on the analysis of ladder type low loader truck chassis structure which includes C-beams performed by finite element method. Satish Kumar et al. [7] have performed the modelling and analysis of an Innova car frame. In the analysis, different cross-sections are considered. The results obtained are compared between the rectangular cross-section and C cross-section chassis frame in ANSYS for steel. The work concludes that the C cross-section is better to reduce area and production time. Karaoglu et al. [8] have performed the analysis of truck chassis including the rivert joint with the help of finite element model with using the iso-parametric elements. Ingole et al. [9] have presented the work on to modify the tractor trailer by using finite model to reduction of weight and manufacturing cost. Wang et al. [10] have established a finite element model of semi-trailer tractor where they analyzed the stress and Eigen frequencies. Rajesh et al. [11] have validated chassis frame model using finite element method and here validated the modal parameters against the static loading condition. In the present work, chassis was generated with the help of scanning and cloud point data conversion technique. Modelling has completed using finite model and validated to real-time data collected from experimental setup. The parameters like lateral bending, vertical bending, model testing and torsional bending test were replicated on the finite element model.

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Chassis frame selecƟon

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Fig. 1 Methodology flow chart

3 Methodology The work was started by conducting the literature survey, finding the research papers related to chassis. Then, after analyzing different research papers, the ladder frame type chassis is nominated as it is mostly used in heavy vehicles. Then accordingly, the chassis of Mitsubishi FUSO FE 84G HDL model is selected. The different load cases such as impact case in which front impact, half frontal impact and side impact were chosen. Then, the load case while in a bump is selected which involved simple bump, diagonal bump and bump and droop combination. The last load case selected is the vertical bending load case when the truck is fully loaded. The calculations for these real road loads acting on the chassis is then completed for analysis purpose. The design of the chassis is then modelled in Creo 3.0 software. The design of the chassis is a surface. The parasolid format of this model is imported in Hypermesh where the model is meshed and the thickness of the C cross-section is defined. Next step involved applying the boundary conditions like constraints and loads according the load cases. Then, the Optistruct solver is used for solving the load cases and interpreting the results by deformation and stress values. Figure 1 represents the flow chart of the methodology used.

4 Basic Calculation for Chassis Frame For automotive chassis or frame design, the understanding of loads acting on it is utmost important. The different real road cases when the automobile is manoeuvring are needed to be considered, and calculation of force acting on it is shown below. Material of the chassis is structural steel, having ultimate tensile strength = 560 MPa and Poisson ratio = 0.29. • For front impact, half frontal impact and side impact, the force is calculated using the change in momentum equation.

Change in momentum = F × t

(1)

The Change in momentum = m × v − m × u

(2)

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Hence, m × v − m × u = F × t

(3)

where m v u t F

Mass of whole vehicle = 8000 kg Final velocity of vehicle = 0 m/s Initial velocity of vehicle = 13.89 m/s Impulse time = 0.5 s Force Using Eq. (1), we get, F = 222,240 N.

• For bump, diagonal bump, bump and droop combination, a force equivalent to 3G is applied on the vehicle. where G = m × g m Total weight of vehicle = 8000 kg g Acceleration due to gravity Therefore, F = 78,480 N will be applied for this loading condition. • For vertical bending As max gross vehicle weight of truck is 8 tons, the two longitudinal beams of the automotive chassis are made from C channel cross-section having dimension 195 mm × 60 mm. Hence, the load on a single beam is 39,240 N.

5 CAD Modelling and Finite Element Analysis of Chassis Frame The modelling of the Mitsubishi FUSO chassis is done in Creo Parametric 3D modelling software. Figure 2 shows the CAD surface model of the chassis. For finite element analysis of the automotive chassis, according to the standard procedure, the modelled part is imported in Hypermesh, and then, the model is discretized into Fig. 2 CAD model of the chassis

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Fig. 3 Meshed chassis model

finite number of elements which means meshing is completed. Next, the boundary conditions like constraints and loads are applied according to the different load case. The material structural steel is assigned for the analysis. For output, the Optistruct solver is used to get the plot of the stress and deformation on the chassis model. Figure 3 shows the 2D meshing done in Hypermesh as it gives more uniform mesh and better element quality check criteria. The tria element is used in meshing of the chassis, and then, the thickness of 6 mm is given. The element length used is 6 mm.

5.1 Boundary Conditions (a) Front impact, half frontal impact and side impact For front impact, half frontal impact and side impact load cases, the leaf springs suspension act as simply supported beam for the chassis. Therefore, these four suspensions mounting points are constrained in all directions as shown in Fig. 4. The impact force of 222,240 N is applied on the whole front member for front impact analysis as shown in Fig. 5, while for half frontal impact analysis, the same force is applied only on the half area of the front member as shown in Fig. 6. For side impact analysis, the side members of the chassis are acted by the side impact force of 222,240 N as shown in Fig. 7. (b) Bump analysis For bump analysis, the rear mountings of leaf spring suspension are constrained. Figure 8 shows the boundary conditions applied on the chassis. The bump force of 78,480 N is applied on the front suspensions points when both front wheels are on a simple bump as shown in Fig. 9.

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Fig. 6 Half frontal impact force

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Fig. 7 Side impact force

Fig. 8 Constrained points for bump analysis

Fig. 9 Bump force

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(c) Diagonal bump analysis Diagonal bump analysis is for a road condition when front left tyre and rear right tyre are on level road which are constrained as show in Fig. 10. On the other hand, the front right tyre and rear left tyre are on a bump, and therefore, the force of 78,480 N is applied in vertical direction on these tyres as shown in Fig. 11. (d) Bump and droop combination This case is considered as a worse condition when one tyre is on a bump while other is in droop. Therefore, the rear suspension mounting points are constrained as shown in Fig. 12 while in front one suspension mounting point is acted by a force of 78,480 N in upward direction and other by the force in downward direction as shown in Fig. 13.

Fig. 10 Diagonal bump constraints

Fig. 11 Diagonal bump force

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Fig. 12 Bump and droop constraints

Fig. 13 Bump and droop force

(e) Vertical bending analysis For vertical bending analysis, all the four leaf spring suspension mounting points are constrained as shown in Fig. 14. Figure 15 shows the applied load of 39,240 N on the chassis in vertically downward direction.

6 Results and Discussions The result of the analysis for different load case acting on the chassis is shown in the below figures which shows the stress and deformation of each load case. (a) Front impact, half frontal impact and side impact For front impact load case, the standard FMVSS 208 is used. In this test, the vehicle is simulated with full frontal impact by hitting a solid, immovable object

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Fig. 14 Vertical bending constraints

Fig. 15 Vertical bending force

at a speed of 50 km/h. The impulse-momentum theorem is considered for this load case. The front-most member of the chassis gets deformed by 7.28 mm as shown in Fig. 16. The stress obtained from the analysis is 409.9 MPa which is concentrated at the middle portion of the front member can be seen in Fig. 17. For half frontal impact load case, the test simulates the vehicle hitting a solid, immovable object at a speed of 50 km/h, but only half of the vehicle gets hit in this case. The impulse-momentum theorem is also considered for this load case. The front most lateral member of the chassis gets deformed by 17.8 mm as shown in Fig. 18. From Fig. 19, the stress obtained from the analysis is 495.1 MPa which is concentrated between the middle and joint portion of the front member. For side impact case, the vehicle under study gets strike by another vehicle on the side beam of the chassis by the speed of 50 km/h. From Fig. 20, the deformation in this load case is of 4.47 mm. The stress obtained is 168.1 MPa which is concentrated mostly behind the front leaf spring suspension on the chassis as shown in Fig. 21.

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Fig. 17 Front impact stress

Fig. 18 Half frontal impact deformation

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Fig. 21 Side impact stress

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(b) Bump analysis When the vehicle manoeuvres on the road, there are many times cases in which it has to go over a bump. According to the study by Eicher Motors Ltd., when a heavy vehicle goes over a bump for different speed ranging from 10 to 60 km/h, the results obtained were about 3 g load. Therefore, for this case, 3 g load of 78,480 N is applied. The deformation is of 3.5 mm in upward direction as shown in Fig. 22. The maximum stress value obtained is 118.8 MPa, which is near the rear point of the front leaf spring suspension mounting that can be seen in Fig. 23. (c) Diagonal bump analysis The diagonal bump analysis is one of the worse loading cases, and here, the vehicle’s one diagonal tyres are on a bump while other diagonal tyres are on the plane road. The deformation obtained in this case is 33.51 mm from Fig. 24, and the maximum stress of 187 MPa from Fig. 25 is acting near the diagonal suspension mounting points.

Fig. 22 Bump analysis deformation

Fig. 23 Bump analysis stress

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Fig. 24 Diagonal bump deformation

Fig. 25 Diagonal bump deformation

(d) Bump and droop combination For the above case, a combined load case is implemented when one front tyre is on a bump and another tyre is in a droop. This is a combined loading condition acting on the chassis of the vehicle. In this case, from Fig. 26, the deformation is of

Fig. 26 Bump and droop deformation

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Fig. 27 Bump and droop stress

Fig. 28 Vertical bending deformation

12.78 mm, and the stress is of 210 MPa acting near the front suspension mounting points as shown in Fig. 27. (e) Vertical bending analysis The vertical loading case is due to the vehicle components and pay load acts vertically on chassis frame causing vertical deflection which represents like simply supported beam. The deformation is 4.5 mm in downward direction as shown in Fig. 28. From Fig. 29, the stress obtained is 206.66 MPa acting on the two longitudinal beams.

7 Conclusion In the present study, static characteristics of the automotive chassis like equivalent stress and deformation are successfully evaluated for front impact, half frontal impact, bump, diagonal bump, and bump and droop combination and vertical loading case by

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Fig. 29 Vertical bending stress

using Hypermesh and Optistruct. The deformation and the stress values obtained in front impact analysis are in good agreement with the permissible stress limit. For half frontal impact analysis, the deformation obtained is large, and therefore to avoid it, additional member can be added for better support. In side impact analysis, the stress values are within the permissible stress limits. In bump and diagonal bump analysis, the deformation and stress values are in safe limit, while for combined bump and droop load case, the deformation is large. To avoid this, diagonal members can be added between the longitudinal beams to resist the torsion in the chassis. The design of chassis is robust in vertical loading case.

References 1. Tomar A, Singh D (2016) Modelling and analysis of a chassis frame by using carbon fiber and E-glass epoxy as composite material: a comparative study. Int Res J Eng Tech 3(4) 2. Abry J, Mittelhaeuser C, Wolf S, Turlier D (2018) Enhanced fatigue structural stress analysis of a heavy vehicle seam welded steel chassis frame: FEA model preparation, weld model description, fatigue stress calculation and correlation with 10 year operating experience. Proced Eng 213:539–548 3. Ren Y, Yongchang Y, Zhao B, Fan C, Li H (2017) Finite element analysis and optimal design for the frame of SX360 dump trucks 13th global congress on manufacturing and management, GCMM 2016. Proced Eng 174:638–647 4. Kwasniewski L, Li H, Nimbalkar R, Wekezer J (2006) Crashworthiness assessment of a paratransit bus. Int J Impact Eng 32:883–888 5. Nora MAM, Rashida H, Mahyuddin WMFW (2012) Stress analysis of a low loader chassis. Sci Verse Sci Direct Procedia Eng 41:995–1001 6. Patil HB, Kachave SD, Deore KR (2013) Strees analysis of automotive chassis with various thicknesses. IOSR J Mech Civil Eng (IOSR-JMCE), e-ISSN: 2278-1684 6(1):44–49 7. Siva Nagaraju N, Satish Kumar MVH (2013) Modeling and analysis of an innova car chassis frame by varying cross-section. Int J Eng Res Tech (IJERT) 2:105–112 8. Karaoglu C, Kuralay NS (2002) Stress analysis of a truck chassis with riveted joints. Finite Elements Anal Des 38:1115–1130 9. Ingole KN, Bhope VD (2011) Stress analysis of tractor trailer chassis for self weight reduction. Int J Eng Sci Tech 3(9) September 2011, ISSN: 0975-5462

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10. Wang G, Zhao C, Pan P (2012) Strength analysis of a semitrailer tractor frame. SAE Technical Paper 2012-01-0526. https://doi.org/10.4271/2012-01-0526 11. Rajesh H, Patwardhan MA, Karanth NV, Bhatkhande M, Ramkumar R, Pachhapurkar N, Saraf M (2017) LCV chassis frame optimization using combined simulation and experimental approach. SAE Technical Paper 2017-26-0289, https://doi.org/10.4271/2017-26-0289

Development of Reaction Wheel Controlled Self-Balancing Bicycle for Improving Vehicle Stability Control Omkar Patil, Sujay Jadhav, and R. Ramakrishnan

Abstract This paper focuses on building a bicycle prototype which is can balance itself with the help of a reaction wheel. The bicycle prototype does not employ a rider for this task. This concept can be extremely helpful as a safety mechanism for future motorcycles wherein the rider can save himself from a crash, if he/she loses control over the motorcycle. The bicycle uses a controlling mechanism to prevent itself from falling on either sides in static condition. It uses an inertial measurement unit (IMU) sensor for detecting changes in the roll angle and reacts to maintain a constant vertical position. Reaction involves rotation of the reaction wheel by the motor which gets its input signal from a controller which works in conjunction with the sensor to analyze the data. Final aim is to build a prototype of the self-balancing bicycle and get an overview of the recent developments in the field of autonomous driving. Keywords Reaction wheel · Inertial measurement unit · Roll angle · Arduino

1 Introduction Bicycle is a very common form of transportation, recreation, and a medium of exercise for many people. Cycling helps in providing physical therapy by training body and improving strength, stamina, and coordination [1]. It might seem to be a simple task, but it is not the case for everyone, like for young children, or adults who never learned to ride a bicycle, or people with physical or cognitive disability. In such a scenario if a balancing assist system is developed, it can prove to be a great benefit to such individuals. Till now substantial research and investigative work has been done on two-wheeled self-balancing bicycle robot, which is widely taken into consideration in the field of intelligent vehicles and autonomous robotics. The timeline for this field started way back in 1998 where gyroscopic stabilization was used for balancing of robot bicycle, O. Patil · S. Jadhav · R. Ramakrishnan (B) Department of Design and Automation, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_15

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then again in 1999 and 2002, mass balancing or movement of center of gravity (COG) technique was used for balancing. Next in 2004, steering control technique was introduced in order to tackle this problem. A major breakthrough in the field of self-balancing platform was in the form of Murata Boy in 2005 [2], which was a self-balancing robot that could drive forwards, backwards or stay still and still maintain balance without putting its feet down. This feat was followed by the advent of Murata Girl. Yamaha unveiled their autonomous motorcycle riding robot called MOTOBOT in 2015 at Tokyo Motor Show [3]. This robot rides the Yamaha R1M bike in a similar fashion as a human. Honda also showcased their concept of self balancing in the form of riding assist technology, at Consumer Electronics Show (CES), which uses steering control for assisting the rider in balancing while moving at slow speeds or even at stationary position. After going through the references, few basic and important points were drawn out. There are four ways in which self-balancing can be achieved: • • • •

Control moment gyroscope (CMG) Mass balancing Steering control Reaction wheel

In control moment gyroscope (CMG) method, large amount of torque can be obtained, but energy consumed in this method is very high because the flywheel is continuously spinning. The CMG system is made up of a spinning rotor that is placed on a gimbal, whose angular momentum vector direction can be altered spinning of rotor. When the orientation of gyroscope changes, the spinning rotor applies torque on gimbal to produce gyroscopic reaction torque and precession to both gimbal and rotor spin axes [4]. Mass balancing technique is fairly simple and easy to understand, wherein a mechanical structure is used to balance the bicycle by shifting mass along the width of the bicycle, but the torque provided in this method is not large enough. Steering control technique is quite recent on the timeline, and in this technique, a controller controls the amount of torque that is to be applied to the handlebar in order to maintain the vertical position. Positives of this technique are low consumption of energy and lower mass of the system, while its negatives are that it needs ground reaction forces and cannot tolerate large roll angles [1, 3]. Reaction wheel technique is implemented by varying the speed of a reaction wheel is to generate a reactionary torque to the bicycle’s frame and about its spin axis (rolling). As the bicycle begins to tilt on one side, torque is applied to the reaction wheel via the motor that generates a reactionary torque on the bicycle, which helps bicycle regain its balance. Advantages of this system are that it is low cost in comparison with CMG and no ground reaction forces are required, while disadvantages are that it cannot produce large amount of torque [2]. Bicycle experiences various internal and external forces when dynamics is concerned. Some of them are stated below:

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Gravitational while leaning Inertial or centrifugal while taking a turn Gyroscopic when steered Aerodynamic if it is subjected to cross winds

These forces give us an idea in what way a bicycle should be designed and built so that it can resist all these forces and avoid failure while in motion.

2 System Configuration According to law of conservation of momentum, if no external torque is exerted on an object or system, the net angular momentum of that object will be conserved. This concept is used in satellite stability control wherein if the attitude of the satellite gets altered, reaction wheels get activated and they start applying torque so as to satellite use this concept for attitude control by the use of reaction wheels. Similar principle can be used for balancing the bicycle. This principle is inverted pendulum (Fig. 1). An inverted pendulum can be understood as a mechanical arrangement which has its mass above a pivot point. The pendulum can be a simple mass and rod, or a combination of many components put together to form an inverted pendulum system. Standard pendulum is stable, but inverted pendulum is unstable and must be balanced actively to remain stable (upright). After calculation, final torque equation obtained is,   T 2 = I 2 θ¨ 2 + θ¨ 1 where T 2 is the torque of the reaction wheel corresponding to the roll angle

Fig. 1 Inverted pendulum model

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Fig. 2 Subsystems of self-balancing bicycle

θ¨2 θ¨1

is the angular acceleration of the wheel is the angular acceleration of the bicycle

In case of a bicycle, it is a rigid body and can rotate around its contact point, i.e., rolling motion. It is a well-known fact that bicycle motion has multiple degrees of freedom, but here in this project the aim is to stabilize the roll angle around the point of contact with the ground relative to the direction of gravity. The self-balancing bicycle consists of two subsystems. The first subsystem is responsible for maintaining the balance of the vehicle, whereas the second subsystem controls the mobility (Fig. 2).

3 Approach The bicycle will be balanced with the help of two processes, data acquisition and actuation of reaction wheel. Data acquisition will be done with the help of an inertial measurement unit (IMU) sensor: MPU-6050 which has two inbuilt sensors embedded into it, and they are accelerometer and gyroscope. With the help of this sensor, the roll angle will be acquired (Figs. 3 and 4). Actuation of the reaction wheel will be done in proportion to the roll angle. Actuation of wheel will be done by the motor which will get its input signal from the motor driver. Motor driver will be controlled from the Arduino microcontroller. For the entire data acquisition and actuation process, Arduino IDE will be used. The main aim behind building the bicycle was to keep it simple in construction and keep space for accommodation of Arduino, battery, sensor, motor, and reaction wheel, without compromising the strength of the framework. The basic framework of the bicycle has been made by 3D printing technique. The placement of the IMU sensor (MPU 6050) plays a key role in the balancing of the bicycle. The sensor is placed

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Fig. 3 Cyclic process of self balancing

Fig. 4 CATIA model of the bicycle

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coinciding with the center of gravity (CoG) of the bicycle. For the determination of the CoG, the modeling software is used. The CoG point lies at Gx = 142.212 mm, Gy = (−3.21e−009) mm, and Gz = 55.22 mm. Reaction wheel is made out of commercial aluminum. The reaction wheel was the most important component of the mechanical design. A careful balance of mass versus moment of inertia was also very critical. In order to attain a high moment of inertia while minimizing the overall mass, the wheel needed to have the majority of its mass on the periphery of the wheel.

4 Working Process As the bicycle rolls by some angle, reaction torque will be applied to return to vertical position. In real time, reaction wheel will deliver required force to balance vehicle at its vertical position and maintain the same. The calculated torque equation is used by the Arduino to ascertain the amount torque and consequently the speed at which the motor needs to rotated. Torque requirements at various angles were plotted on graph: torque vs roll angle. This gives us the torque values for particular angle. When some angle is given by the MPU6050, the reaction wheel will deliver the required torque to restore its position (Figs. 5 and 6). Fig. 5 Flowchart of working

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Fig. 6 Real-time implementation

5 Conclusion This is a two wheeler vehicle that has been built with keeping safety as the main motive. This paper primarily focuses on the development bicycle using reaction wheel. Controlling of the bicycle involved data acquisition, data processing, calculation of torque requirement equations, and actuation of the reaction wheel. The bicycle can balance itself when any form of support is removed, but the one drawback that this bicycle has is it cannot move at a faster pace. This concept can be implemented to build a real-life bicycle that is capable of balancing itself.

6 Future Scope Optimization of the bicycle framework for more enhanced control of the bicycle and subjecting the bicycle to disturbances in order to understand the response time of the controller while coming back to steady vertical position are future scopes. This balancing technique can be used for motorcycles too, wherein the rider, if loses control, the vehicle can take control and prevent accidents.

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Appendix 1 The modeling of the bicycle is based on the inverted pendulum model and uses Lagrangian approach of dynamics. This approach results in a torque output which is required for the balancing of the bicycle. l 1 : Length of Pendulum. l 2 : Distance of the center of mass. θ 1 : Angle of the Pendulum. θ˙ 1 : Angular velocity of pendulum. θ¨ 1 : Angular acceleration of pendulum. θ 2 : Angle of Reaction Wheel. τ : Torque applied by motor on the wheel. V A : Linear velocity at point ‘A’. V B : Linear velocity at point ‘B’. Solution: → XB = l2 Sin θ 1 X˙ B = l 2 Sin θ˙ 1 X A = l 1 Sin θ 1 X˙ A = l 1 Cos1 θ˙ 1 Y B = l 2 Cos θ 1 Y˙ B = l 2 Cos θ˙ 1 Y A = l 1 Cos θ 1 Y˙ A = l 1 Sin1 θ˙ 1 2 2 Now, add ( X˙ B + Y˙ B)

2 2 So, equation will be l 22 Si n2 θ˙ 1 + l 22 C os2 θ˙ 1   But, we know Sin θ 2 + Cos θ 2 = 1



2 2 2 2 2 V 2B = X˙ B + Y˙ B = l 22 θ˙ 1 V˙ A = l 21 θ˙ 1

where XA YA XB YB

Position of point A in ‘X’ co-ordinate. Position of point A in ‘Y ’ co-ordinate. Position of point B in ‘X’ co-ordinate. Position of point B in ‘Y ’ co-ordinate

K .E = Kinetic Energy. K = K .E 1 + K .E 2

 2 2 = 1/2 I 1 θ˙ 1 + 1/2 I 2 2θ˙ 1     2 = 1/2 I 1 θ˙ 1 + 1/2 I 2 θ˙ 1 + θ˙ 2 + 1/2 M 2 V 2B + 1/2 M V 2A  2 2 = 1/2 I 1 θ˙ 1 + 1/2 I 2 θ˙ 1 + θ˙ 2 + 1/2 M 2 θ˙ I 2 1 2

2 2 2 = 1/2 I 1 θ˙ 1 l 22 + 1/2 I 2 θ˙ 1 + 1/2 I 2 θ˙ 2 + I 2 θ˙ 1 θ˙ 2 + 1/2 I 2 θ˙ 2

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   2 K = 1/2 M 2 l 22 + I 1 + I 2 + M 1 l 1 Cos θ 11 + 1/2 I 2 θ˙ 2 + I 2 θ 1 θ 2

(1)

P.E = Potential Energy. P.E = gCos θ 1 (M 1 l 1 + M 2 l 2 )

(2)

[Lagrangian–Euler equation = Kinetic Energy − Potential Energy.] Put Eqs. (1) and (2) in above formula. L = K .E − P.E    2 = 1/2 M 2 l 22 + I 1 + I 2 + M 1 l 21 + 1/2 I 2 θ˙ 2 + I 2 θ 1 θ 2 − gCos θ 1 (M 1 l 1 + M 2 l 2 )    δL = M 2 l 22 + I 1 + I 2 + M 1 l 21 θ˙ + I 2 θ˙ 2 δ θ˙ 1    d δL = M 2 l 22 + I 1 + I 2 + M 1 l 21 θ¨ 1 + I 2 θ¨ 2 d t δ θ˙ 1 δL = −gSin θ 1 (M 1 l 1 + M 2 l 2 ) δθ 1 By Lagrangian–Euler Equation. d δL δL − = T1 dt δ θ˙ 1 δθ 1   M 1 l 21 + I 1 + I 2 + M 1 l 21 θ¨ 1 + I 1 θ¨ 2 + 9g 1 (M 1 l 1 + M 2 l 2 ) = T 1 Similarly for T 2 derivation w.r.t. θ˙ 1 Final torque equation   T 2 = I 2 θ¨ 2 + θ¨ 1 .

References 1. Prasad M, Nirwan NW (2016) Design and fabrication of automatic balancing bicycle. Int J Sci Eng Tech Res (IJSETR), 5(2) 2. Balanced and Controlled, By Team Vidyut (IIT Kanpur) 3. Tamaldin N, Yusof HIM, Abdollah MFB, Omar G, Rosley MIF (2017) Design of self -balancing bicycle. Proc Mech Eng Res Day 2017:160–161 4. Almujahed A, Deweese J, Duong L, Potter J (2009) Auto-balanced robotic bicycle (ABRB), Electrical and Computer Engineering Department, Volgenau School of Engineering, George Mason University, Fairfax VA, Senior Design Project Spring

An Intelligent Energy Management Strategy for Electric Vehicle Battery/Ultracapacitor Hybrid Storage System Using Machine Learning Approach Geetansh Mahajan, Abhinav, and R. Ramakrishnan Abstract Due to reduction in the non-renewable sources such as petrol, diesel and increase in pollution levels, we need to find an alternate way to drive the automotive. One of the best alternatives is to use pure electric vehicle which has zero emission and requires electricity as a power source instead of non-renewable sources. These electric vehicles get the required power from batteries but they face challenges like lower range and less battery life, and they fail to provide the same acceleration as of IC engines. If batteries are used with ultracapacitor, then they can meet the power requirements required by the driver. Regenerative braking is a way to restore power at the time of deceleration but it provides a lot of charge in a short period of time, but battery cannot accommodate higher amount of charge in less charge. Ultracapacitors, on the other hand, can be charged using this regenerative braking method while the automobile is in motion, and excess charge can then be transferred to battery for charging. An intelligent energy management system is necessary to take these decisions of discharging and charging of this ultracapacitor and battery HESS, in order to increase the range of vehicle and battery life. Machine learning will be used to design and train the controller for the vehicle to take decision of its own when facing real-time situations. Keywords Energy management system · Regenerative braking · Ultracapacitors · Battery life · Range of vehicle · Machine learning

1 Introduction In today’s scenario, HEVs and EVs have proved to be captivating revolution in the field of automobiles. It provides a firm future of green travel in which fuel-dependent engines are replaced with electric motors, thereby lessening our complete dependence on non-renewable source of energy such as fossil energy and at the same time helping in producing less harmful emissions [1]. It has been recorded that transportation has G. Mahajan · Abhinav · R. Ramakrishnan (B) School of Mechanical Engineering (SMEC), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_16

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turned into the most developing variable of the world’s fuel utilization taking up 49% of oil assets and around 33% of the country’s ozone harming substance discharges originate from it [1, 2]. By considering climatic changes and energy crises, one should think for a more clean and secure option for transportation. In such a sense, electrified transportation is the best possible solution to way out [3]. As people are using gasoline car from way back 1900s, it is necessary to make newer technology such that they will have an interest in buying it. In recent world scenario, electrical vehicles are the best possible solution to have where they can charge this electric vehicle anywhere, in a daytime it can be at the office, at the centralized power stations or even by using renewable sources at the parking space [4]. For the efficient electric vehicle with the electrical systems, it is important to have a proper energy storage system and that can be done by using battery technology, which is environment friendly too, although for having clean energy vehicles energy storage is being the ultimate challenge [2]. It is a better contrivance to utilize the otherwise wasted energy through storing it into battery by incorporating the regenerative braking which will also improve the performance [5]. Enormous amount of power is getting generated in short time period during regenerated braking but as battery is having low power density it is difficult to store generated power and eventually during peak power demands as battery contains lower efficiency, it cannot handle that power so energy will be wasted [6]. There are some essential requirements of an energy management system which will be assuredly fulfilled by using supercapacitor with battery [7]. The supercapacitor has attributes that will make it store a large amount of power for shorter period of time and even withstand greatly during peak power demands [8]. The inclusive cost will be reduced by combining it with battery so that results will be improved by utilizing all the energy obtaining the essential motion [9]. There are plentiful researches that are going on hybrid energy storage system which gives improved potential in lessening the power loss [10, 11]. Intelligent control system will provide an appropriate stable way of using battery and supercapacitors [12, 13]. It is expected that a better and effective output performance by training the control system uses numerous data values. Control plans can be improved further for rapidly growing computational power on-board vehicles by implementing AI which has an astonishing potential [14]. AI is an implementation of cognitive human immitted intellectual ability that imparts in and improves instead of being unambiguously altered [15]. Testing the setups of hybrid vehicles reduces the cost and overall cycle time due to PC displaying before actual model development can be started [16].

2 Dynamic Model of Electric Vehicle Electric vehicle model specifies the value for the power and energy profiles needed to drive the electric vehicle as required [17]. These two profiles are gained by the simulation of the power model taking speed variation with time as input value, taking into account different conditions of various driving cycles. To estimate the

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speed profile as output from required power value, drivetrain model is simulated in MATLAB-Simulink. The proposed model figures power request dependent on a few powers, just as the speed and street profiles. [18] The resultant power F res is the whole of the considerable number of powers on vehicle and it is expressed as: Fres = Faccn + Faero + Froll + Fgx

(1)

where F aero represents aerodynamic drag force, F acc is acceleration force, F gx is gravitational force, F roll represents the force due to rolling resistance, E cons is the energy consumed in a trip, and P is the power required by the vehicle. 2 Faero = 0.5 × ρ × S × C x × Vveh

(2)

Froll = μMg

(3)

Fgx = Mg sin(α)

(4)

t E cons (t) =  P=

P(t) · dt

(5)

0

 d Vveh M + Faero + Froll + Fgx · Vveh dt

(6)

Where ρ represents the air density (kg/m3 ), S represents the frontal surface area of vehicle (m2 ), C x is the air penetration coefficient, α represents the inclination of roadway, and M is the mass of vehicle. Figure 1 shows the configuration of the proposed HESS incorporated within a vehicle model shown in Fig. 2. It comprises of different subsystems, for example, driving cycle which contains the data from various driving cycles, and a slight modification inside this subsystem can change the input driving cycle, power and energy calculation subsystem calculates the power and energy requirements from the driving cycle data available using the above equations. The ESS subsystem contains the supercapacitor and battery connected in parallel using DC–DC convertor to achieve power demand of the vehicle. Output power from the ESS is considered as an input to the drivetrain model which gives the final speed of vehicle. For the calculation of power and energy requirement, various parameters were used, which are mentioned in Table 1.

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Fig. 1 Configuration of the proposed HESS

3 Model of Energy Storage System In order to be in a position to deal with energy management system, complete study of working of different parts of EVs must be done. The battery work and the management system and its requirement should meet the power demand of the vehicle [19]. It basically ensures the security, reliability and lengthens the battery’s service life. The EMS can be split into the following tasks, namely: high voltage management, data acquisition, and SOC estimation [20, 21]. One of the concerns for the drivers is the lack of confidence in ensuring range of the EVs. The vagueness of a battery’s performance stances is a great challenge to forecast the extended range of EVs [22]. Figure 3 shows the configuration of the proposed hybrid energy storage system which contains a battery and ultracapacitor connected in parallel to a DC bus link. Two DC–DC convertors, one for each battery and supercapacitor are used to maintain voltage for both the units independently. Since battery cannot meet high power density requirement, we have connected a supercapacitor in parallel with battery to provide high power density whenever required. When the vehicle is cruising the power, demand is met by battery. During deceleration, electric motor at each wheel starts to rotate in reverse direction, hence, behaving like a generator and producing power which can be used to charge the supercapacitor and battery simultaneously. The controller designed above determines the power to be taken from battery and supercapacitor based on the power demand and also SoC of both storage units.

201

Fig. 2 Vehicle model

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202 Table 1 Vehicle characteristics

G. Mahajan et al. Parameters

Value

Mass of vehicle (kg)

860

Frontal surface area (m2 )

2.75

Density of air

(kg/m3 )

1.2

Penetration air coefficient

0.3

Coefficient of rolling resistance C x

0.008

4 Sizing of Battery and Supercapacitor Li–ion batteries sudden rise in demand parallels with a viable solution to supply EVs because of their capabilities such as providing high energy density, high voltage, fast charging, low self-discharge, and their durability [23]. Complementing to the drawbacks of batteries, supercapacitors have great ability to store and release this energy very quickly reflecting its high power density [24]. As compared to Li–ion battery, they have less capability to store the energy [25, 26]. To accelerate the vehicle and recover the otherwise wasted energy during the braking, these devices are appropriate for high-power vehicle applications. Nbat_ cells =

E v_ cons (E cel_ batt − αbatt_ cons Wcel_ batt (1.4)) Ubus Ucel_ sc   8(E sc ) Nsc_ s =  2   3 Usc_ max Ccel_ sc Nsc_ s =

Nsc_ p

(7) (8)

(9)

where N bat_cells are the no. of battery cells required in the battery pack to meet the energy demand, α bat_cons is variation of energy spent, E v_cons represents the maximum energy produced by battery pack, E cell_batt is the energy of one battery cell, and W cell_batt is the weight of one battery cell. N SC_S and N SC_P are the no. of ultracapacitor cells in series and parallels, U bus is voltage of DC link, U cel_SC represents cell voltage of the ultracapacitor, U SC_max represents maximum voltage of the DC Link, and C cel_SC is the capacity of an ultracapacitor cell (Tables 2 and 3) [27].

5 Result and Conclusions The simulation has been carried on two driving cycles namely Indian driving cycle and ARTEMIS driving cycle. Power requirements for both cycles are computed with the help of power model in Simulink considering almost all the forces that effect vehicle motion. The output of the model provides with the apt speed value as

203

Fig. 3 HESS model

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204 Table 2 Lithium-ion battery characteristics

Table 3 Ultracapacitor characteristics

G. Mahajan et al. Battery

Value

Nominal voltage (V)

3.7

Capacity (Ah)

40

Specific energy (Wh/kg)

133.8

Max current charge/discharge (A)

40/40

Weight (kg)

0.935

Volume (l)

0.42

Supercapacitor

Value

Nominal voltage(V)

2.7

Capacity (F)

350

Power density (W/kg)

4300

Energy (Wh)

5.062

Weight (kg)

0.063

Volume (l)

0.053

required with the aid of drivetrain model. Different such cycles power calculations and optimum speed calculations can be generated in order to provide an efficient energy management system comprising of battery and supercapacitor. For Indian driving cycle (Fig. 4): The above graph depicts the power requirement by the vehicle and output power from the working of HESS comprising of supercapacitors and battery. The requirement of power along with the availability of SoC of the storage systems together considers the amount of charge provided in order to meet the requirements (Fig. 5). During acceleration phase in the beginning as shown in Fig. 6 when there is a sudden rise in power demand, supercapacitors become active in providing most of the required power as it has property to work efficiently in cases where there is sudden power demand or high power to intake as charge. From the graph, it can be interpreted that around 3000 watts of power were asked by the vehicle from the storage system, out of which almost 83% of the required power was provided by supercapacitor and rest by the battery. This distribution of power makes the vehicle more efficient and increases the range while maintaining the quality of battery. The power demand is met by battery when vehicle is moving with negligible acceleration which is parallel to characteristics of battery. Also, the regenerative effect in order to charge the storage system also works similarly by providing charge to supercapacitors when there is high value of slope. At the end of the cycle during deceleration of vehicle, the rise in SoC level of supercapacitors can clearly be seen (Figs. 7 and 8). For ARTEMIS driving cycle: The ARTEMIS driving cycle power requirements are met by the hybrid storage system depending upon the conditions of the system during acceleration, cruising, and deceleration phase as follows (Figs. 9, 10, and 11).

205

Fig. 4 Power demand and power output for IDC

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Fig. 5 Battery power requirements and state of charge

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Fig. 6 Supercapacitor power requirements and SOC

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Fig. 7 Speed input and speed output

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209

Fig. 8 Power demand and power output for IDC

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Fig. 9 Battery power requirements and SOC

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Fig. 10 Supercapacitor power requirements and SOC

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Fig. 11 Speed input and speed output

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The effective power requirement met by the working of battery and supercapcitor can be utilized to increase the range of the electric vehicle and make it more feasible for general masses. The optimized distribution of power demands for the different cycles can be incorporated into many such cycles and generates efficient results. In this article, designing and modeling of an intelligent energy management system for a HESS of EV using a battery and ultracapacitor by the help of machine learning have been carried out. This approach could help us to increase the life of battery and the range of vehicle by designing a new EMS. Currently, the EVs have a range of 75 miles whereas only Tesla S is providing 335 miles. However, which is unaffordable by middle-class families. By implementing machine learning and training the model on various driving cycles, we can get a well-trained intelligent control system which can work efficiently to maintain the state of health and state of charge of ultracapacitor and battery. This control system can be further tested experimentally in real life to check the effectiveness of the model formed.

References 1. Jaguemont J, Boulon L, Dubé Y (2016) A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures. Appl Energy 164:99–114 2. Wang Q, Jiang B, Li B, Yan Y (2016) A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles. Renew Sustain Energy Rev 64:106–128 3. Sakhdari B, Azad NL (2015) An optimal energy management system for battery electric vehicles. IFAC-PapersOnLine 48(15):86–92 4. Khaligh A, Zhihao Li Z (2010) Battery, ultracapacitor, fuel cell, and hybrid energy storage systems for electric, hybrid electric, fuel cell, and plug-in hybrid electric vehicles: state of the art. IEEE Trans Veh Technol 59(6):2806–2814 5. Lv C, Zhang J, Li Y, Yuan Y (2014) Regenerative braking control algorithm for an electrified vehicle equipped with a by-wire brake system. In: SAE Technical Paper Apr 2014 6. Axsen J, Burke A, Kurani K (2008) Batteries for plug-in hybrid electric vehicles (PHEVs): goals and state of technology circa. UCD-ITSRR-08-17, May 2008 7. Zhao H, Burke AF (2010) Fuel cell powered vehicles using supercapacitors-device characteristics, control strategies, and simulation results. Fuel Cells 10(5):879–896 8. Choi M, Kim S, Seo S (2012) Energy management optimization in battery/supercapacitor hybrid energy storage system. IEEE Trans Smart Grid 3(1):463–472 9. Lesiuta E (2016) Optimization of a hybrid energy storage system for electric vehicles using machine learning methods 10. Golchoubian P, Azad NL (2015) An optimal energy management system for electric vehicles hybridized with supercapacitor. In: VASME dynamic systems and control conference 11. Tie SF, Tan CW (2013) A review of energy sources and energy management system in electric vehicles. Renew Sustain Energy Rev 12. Gao L, Dougal A, Liu S (2005) Power enhancement of an actively controlled battery/ultracapacitor hybrid. IEEE Trans Power Electron 20(19):236–243 13. Schupbach RM, Balda JC, Zolot M, Kramer B (2003) Design methodology of a combined battery-ultracapacitor energy storage unit for vehicle power management. Proc IEEE Power Electron Spec Conf 1:88–93 14. Caratti A, Catacchio G, Gambino C, Kar NC (2013) Development of a predictive model for regenerative braking system. In: IEEE Transp Electrif Conf Expo, pp 1–6

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15. Ferreira AA, Pomilio JA, de Araujo Spiazzi G, Silva L (2008) Energy management fuzzy logic supervisory for electric vehicle power supplies system. IEEE Trans Power Electron 23(1):107– 115 16. Butler KL, Ehsani M, Kamath P (1999) A Matlab-based modeling and simulation package for electric and hybrid electric vehicle design. IEEE Trans Veh Technol 48(6):1770–1778 17. Gao DW, Mi C, Emadi A (2007) Modeling and simulation of electric and hybrid vehicles. Proc IEEE 95(4):729–745 18. Mesbahi T et al (2013) Li-ion battery emulator for electric vehicle applications. In: 2013 IEEE vehicle power and propulsion conference (VPPC) IEEE 19. Qiang J, Yang L, Ao G, Zhong H (2006) Battery management system for electric vehicle. In: IEEE international conference on vehicular electronics and safety. Print ISBN: 1-4244-0758-3, pp 134–138 20. Cheng KWE, Divakar BP, Wu HJ, Ding K, Ho HF (2011) Battery-management system (BMS) and SOC development for electrical vehicles. IEEE Trans Veh Technol 60:76–88 21. Lukic SM, Jian Cao J, Bansal RC, Rodriguez F, Emadi A (2008) Energy storage systems for automotive applications. IEEE Trans Ind Electron 55(6):2258–2267 22. Salehen PMW, Suait MS, Razali H, Sopian K (2017) Development of battery management systems (BMS) for electric vehicles (EVs) in Malaysia. In: The 2nd international conference on automotive innovation and green vehicle (AiGEV 2016), vol 90 23. Hu R Battery management system for electric vehicle. Thesis 24. Cao J, Xiong R (2017) Reinforcement learning-based real-time energy management for plug-in hybrid electric vehicle with hybrid energy storage system. In: 9th international conference on applied energy, ICAE2017, 21–24 Aug 2017, Cardiff, UK. vol 142, pp 1896–1901 25. Stamps AT, Holland CE, White RE, Gatzke EP (2005) Analysis of capacity fade in a lithium ion battery. J Power Sour 150:229–239 26. Ehsani M, Gao Y, Emadi A (2010) Modern electric, hybrid electric, and fuel cell vehicles: fundamentals, theory, and design. CRC Press, Boca Raton 27. Peterson SB, Apt J, Whitacre JF (2010) Lithium-ion battery cell 79 degradation resulting from realistic vehicle and vehicle-to-grid utilization. J Power Sour 195(8):2385–2392

Low Velocity of Single and Multiple Impacts on Curved and Hybrid Curved Composite Panel for Aircraft Applications D. Mallikarjuna Reddy, Shreekant Patil, Kiran S. Matti, and Nemmani Abhinav Abstract The use of composites from the past few decades has been outstanding. Composites are becoming very popular due to their high specific strength. The main aim of this research is to analyze the behavior of the composite structure under lowvelocity impact. In this paper, curved composite panels were studied under single and multiple impacts where impact analysis is done numerically. Numerical analysis includes finite element modeling and finite element analysis of the structure through Abaqus software. Different parameters were obtained, and the results of which include deformation, stress distribution, contact force, and energy absorbed by the structure. Numerical results were compared for different materials as well as impacts and are validated with literature data. Keywords Curved composite panel · Low-velocity impact · Multiple impact · Hybrid composite panel

1 Introduction The composite structures are being widely used in many manufacturing industries such as aircraft, windmills, ships, automotive, sports, health care, and bridges. Even though they are expensive compared to other materials, their use has become a necessity in some areas where safety, less weight, high resistance, etc., are more vital when compared to their high cost. Composite materials are known for high energy absorbing capacity and high resistance to damage. Out of all the material types, CFRP and GFRP plates are mostly preferred in aviation industry. Although these composites are very strong than other materials, they offer poor resistance to impact loads.

D. M. Reddy (B) · S. Patil · K. S. Matti · N. Abhinav School of Mechanical Engineering, VIT University, Vellore 632014, India e-mail: [email protected] S. Patil e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_17

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The impact due to low velocity can generate a noteworthy damage and minimize the stiffness by over fifty percent [1, 2]. It becomes very difficult to inspect damage in composite material; this is why investigation was done on it. Until now the research work was largely focused on low-impact behavior on flat composite plates [4–8], while there were very few studies on curved composite panels subjected to lowvelocity multiple impacts. Kastler et al. [3] investigated on experimental as well as numerical analysis of low-velocity impact on composite curved plates subjected to single impact. In reality, it was observed that bird strikes which are low-velocity impacts may not always be a normal impact but it might be an oblique impact with a combination of normal impact [9–13]. At times, few research works become very cumbersome to carryout experimentally, so these can be solved by utilizing FEM. These models can symbolize static conditions, and results received were found close to the literature data [14, 15]. It has also been observed that composite structures are very good absorbers of energy and hence study of displacement and energy absorption by the curved composite panel was studied in this work. The hemispherical impactor was used for the current study. In this paper, when curved composite panel and hybrid curved composite panel are subjected to low-velocity impact for four different cases by choosing hemispherical impactor, various parameters are studied and validated with the literature data. The parameters obtained are displacements, contact forces, contact stresses, and energy absorption with respect to time. The composite plates are made by CFRP, GFRP, aluminum and combination of them are modeled and studied under different impacts. The entire FE model is carried out in Abaqus software.

2 Details of Impact Tests 2.1 Dimensions of Composite Plate The test specimen consists of different laminated fiber reinforcement composite panels and aluminum panel with epoxy resin. All the plates are cut to size 127 × 254 mm having thickness of 0.125 mm each. The composite plates used are oriented with angles as per ASTM standards as 0°/−45°/+45°/90°. Every specimen is made of seven plates bonded together by epoxy matrix. Specimen 1: GFRP having four layers with laminate stacking sequence of [+45/0/−45/90]s subjected to single and multiple impacts. Specimen 2: GFRP and CFRP alternate laminas with stacking sequence [+45G /0C /−45G /90C ]s subjected to multiple impacts. Specimen 3: GFRP of stacking sequence of [0/45/−45/90/−45]s sandwiched between aluminum plates. Specimen 4: Graphite-reinforced composite (AS4) with a stacking sequence of [+45/0/−45/90]s .

Low Velocity of Single and Multiple Impacts on Curved … Table 1 Glass fiber properties

Table 2 Aluminum 3003 alloy properties

Parameter

217

GFRP

CFRP

kg/m3

1600 kg/m3

P

2089

E1

45.6 GPa

220 GPa

E2

8.2 GPa

14 GPa

E3

8.2 GPa

14 GPa

Ÿ12

0.278

0.2

Ÿ13

0.278

0.2

Ÿ23

0.365

0.25

G12

5.83 GPa

9 GPa

G13

5.83 GPa

9 GPa

G23

3 GPa

4.6 GPa

Parameter

Value

Young’s modulus

69 GPa

Density, ρ

2800 kg/m3

Poisson’s ratio, Ÿ

0.3

Yield stress

150 MPa

In Table 1, GFRP and CFRP properties are mentioned. The aluminum alloy properties are represented in Table 2. The mass of the impactor is 1.13 kg and has a radius of 6.35 mm.

3 Methodology The first step involves the selection of appropriate material for the research. CFRP and GFRP are costly materials compared to conventional materials but because of excellent properties, they are widely used in aerospace industry where intricate parts are widely susceptible to low-velocity damage. The numerical model is created by considering standard ASTM specifications for impact testing. In this paper, FE model Abaqus CAE software is used for modeling and simulation. Discrete rigid impactor is modeled. The model is properly meshed and appropriate boundary conditions are applied and then simulated in Abaqus. The results obtained for different cases are studied.

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Table 3 Nomenclature Parameter

Symbol

SI unit

Mass of impactor

mi

kg

Acceleration due to gravity

g

m/s2

The gap between the impactor and upper plate

hd

m

Final velocity

V

m/s

Time

t

s

Linear acceleration

a

m/s2

Contact force

F

N

Initial velocity

u

m/s

4 Mathematical Formulations √ Velocity of impactor, V = 2gh d Potential energy of impactor, P.E = mi .g.hd Kinetic energy of impactor, K .E = 21 m i V 2 To calculate time, h d = V × t To calculate acceleration, v = u + at To calculate force, F = m × a (Table 3).

5 Modeling of Curved Plate Numerical simulations are executed by modeling a 3D model of curved plate under single impactor as shown in Fig. 1. The meshing is done by quad elements. To analyze the failure of the plate, Hashin’s failure criteria are used in the simulation. The 3D model of the curved plate under multi-impactor is shown in Fig. 2. The impactor is made to contact the upper curved plate at different locations. The curved plate and different impactor parts are assembled as shown in Fig. 2. In the simulation,

Fig. 1 Curved plates with single impact with meshed model

Low Velocity of Single and Multiple Impacts on Curved …

219

Fig. 2 Curved plates with multiple impacts meshed model

the surface-to-surface contact is considered between the impactor and the upper curved plate. The impact simulation is carried out by varying impact energy and for different materials.

6 Results and Discussion In this paper, the curved plate is subjected to low-velocity impact was modeled by Abaqus. In Fig. 3, the 3D counter of deformation of the impactor is presented. From the Fig. 3, it is observed that amount of variation of deformation by curved plate is impacted by both single and multiple impacts. Deformation of the curved plate under multi-impactor is high because the amount of impact energy on the top of the surface is high compared to single impact.

Fig. 3 3D view deformation of a curved plate subjected to single and multi impactor

Fig. 4 Internal energy versus time curve for different impacts

D. M. Reddy et al.

Internal Energy (mJ)

220 2000

Graphite single GF single

1500 1000

GF multiple

500 0 -500

GFCF multiple 0

0.005

0.01

0.015

Time (s)

6.1 Internal Energy Versus Time Figure 4 indicates a typical internal energy—time curve for both single and multiple impacts curve. Impact simulations are carried out by considering different materials for graphite, glass, and combination of both glass and graphite fibers. Maximum internal energy is observed for the panel with combination of graphite and glass fiber with multiple impacts, followed by graphite fiber and least internal energy is observed for GFRP subjected to single impact.

6.2 Displacement Versus Time Figure 5 indicates a typical displacement–time curve for both single and multiple impacts curve. Maximum displacement is observed for the panel of combination of aluminum and GFRP, where aluminum being an isotropic material offers less resistance than the composite materials, hence being displaced to a maximum extent. In the case of multiple impacts, the hybrid composite panel of GFRP and CFRP offers better resistance to deformation. Since CFRP has excellent mechanical properties, it is the preferable material to resist deformation due to impact loads. 0

Displacement (mm)

Fig. 5 Displacement versus time curve for different impacts

-2

0

0.005

-4 -6 -8

0.015

Graphite epoxy single GF single GF multiple

-10 -12

0.01

Time (s)

Low Velocity of Single and Multiple Impacts on Curved … 2000

Kinetic Energy (mJ)

Fig. 6 Kinetic energy versus time for different impacts

221

Graphite single GF single

1500 1000 500 0 0

0.005

0.01

0.015

GF multiple GFCF multiple

Time (s)

20

Contact force (N)

Fig. 7 Contact force versus time

0 -20 0

0.005

0.01

0.015

Graphite single GF single GF multiple

-40

GFCF multiple

-60 -80 -100 -120

Time (s)

6.3 Kinetic Energy Versus Time Figure 6 indicates a typical kinetic energy–time curve for both single and multiple impacts curve. Maximum kinetic energy is observed for the combination of GFRP and CFRP curved plate. The validation for graphite curved plate is done and observed that kinetic energy is more for hybrid composite panel.

6.4 Contact Force Versus Time Figure 7 indicates a typical contact force–time curve for both single and multiple impacts curve. The maximum contact force is observed for combination of GFRP and CFRP panel. Least contact force is observed for graphite composite subjected to single impact load because it is having the high resistance capacity against load.

6.5 Contact Stress Versus Time From Fig. 8, it can be observed that maximum contact stress occurs for the panel of GFRP and CFRP combination subjected to multiple loads. Least contact stress occurs for the panel consisting of only GFRP subjected to single impact load.

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Fig. 8 Contact stress versus Time Contact stress (MPa)

0.8

Graphite single GF single

0.6 0.4 0.2 0 0 -0.2

0.002 0.004 0.006 0.008

0.01

0.012

GF multiple GFCF multiple AlGF multiple

Time (s)

7 Conclusion Different combination of composite panels is studied under low impact velocity. In this paper, impact parameters like internal energy, kinetic energy, contact force, displacement, and contact stress are studied. CFRP combined with GFRP offers very good mechanical properties and comparatively less cost than CFRP when used alone. In this, present study observed that even the curved panels made entirely of GFRP and subjected to multiple impacts also offer good resistance to deformation and can be used in aerospace industry to counter practical troubles such as bird strike on fuselage, fuel tank, nose, and wings. The present study is focused on curved composite bodies in real-time applications of aerospace industry and hybrid composite curved plates proved to be the better choice. Acknowledgements The project presented in this article is supported by and Engineering Research Board and Department of Science and Technology, Government of India (File Number: ECR/2017/000512).

References 1. Sanchez-Saez S, Barbero E, Zaera R, Navarro C (2005) Compression after impact of thin composite laminates. Compos Struct Sci Technol 65(13):1911–1919 2. Sanchez-Saez S, Barbero E, Navarro C (2008) Compressive residual strength at low temperatures of composite laminates subjected to low-velocity impacts. Compos Struct 85(3):226–232 3. Kistler Laura S, Waas Anthony M (1998) Experiment and analysis on the response of curved laminated composite panels subjected to low velocity impact. Int J Impact Energy 21(9):711– 736 4. Zhou G, Hill M (2007) Damage characteristics and residual compressive strength of composite honeycomb sandwich panels. In: Proceedings of 16th international conference on composite materials (ICCM/16), Kyoto, Japan, pp 2817–26 [8–12 July] 5. Liu J (2018) Design of aircraft structures against threat of bird strikes. Chin J Aeron 6. Jun Liu, Yulong Li, Xiaosheng Gao (2014) A numerical model for bird strike on sidewall structure of an aircraft nose. Chin J Aeronaut 27(3):542–549 7. Hedayati R (2013) A new bird model and the effect of bird geometry in impacts from various orientations. Aerosp Technol 8. Foo CC, Chai GB, Seah LK (2008) A model to predict low velocity impact response and damage in sandwich composites. Compos Sci Technol 68:1348–1356

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9. Heimbs S, Fischer U, Theiler W (2017) Numerical analysis of bird strike resistance of helicopter searchlight. Procedia Struct Integr 5:689–696 10. Smojver I, Ivancevic D (2010) Numerical simulation of bird strike damage prediction in airplane flap structure. Compos Struct 92:2016–2026 11. Mao Y, Hong L (2016) Dynamic response and damage analysis of fiber-reinforced composite laminated plates under low velocity oblique impact, Nonlinear Dyn. https://doi.org/10.1007/ s11071-016-3130-5, Springer Science + Business Media Dordrecht 12. Smojver I, Ivancevic D (2011) Bird strike damage analysis in aircraft structures using abaqus/explicit and coupled Eulerian Lagrangian approach. Compos Sci Technol 71:489–498 13. Ivanez I, Moure MM, Garcia-Castillo SK (2015) The oblique impact response of composite sandwich plates. Compos Struct 133:1127–1136 14. Kumar KS, Patil S, Reddy DM (2018) Modeling and analysis of low velocity impact on composite plate with different ply orientations. In: International conference on innovation, engineering and entrepreneurship. Springer, Cham 15. Patil SD, Reddy M, Reddy M (2018) Low velocity impact analysis on composite structures—a review. In: AIP conference proceedings, vol. 1943. No. 1. AIP Publishing

Aerodynamic Study of a Three Wheeler Body C. Bhaskar, Krishna Rawat, Muhammed Minhaj, M. Senthil Kumar, and C. D. Naiju

Abstract A three-wheeled mode of transportation other than just in India is common in most developing countries. Proving to be a cheap and effective way of transportation, their small size, easy bike-like manoeuvrability, due to its single front tire control and low cost of both purchase and maintenance make it nearly ideal as a method of public transportation. Despite all their benefits, updates to this vehicle have been few and far between. Three wheeler faces many tough tasks with more demands being made of its usage in ergonomics, exhaust and performance parameters. New designs have to be made to take three wheeler into the twenty-first century. The original three wheeler used in India was introduced in 1956, and there have been few changes to that original design, and while it has proved good over the years, it fails to live up to the current consumer expectations. The aim of this paper is to study three wheeler design and validate its limitations using aerodynamic study of the drag and lift forces that it faces. It calculates the theoretical maximum velocity to be 77 km/hr with the coefficient of drag to be 0.4975. The design of three wheeler needs more than a face lift, and hopefully, this study acts as a guide to other research. Keywords Three wheeler · Aerodynamics · CFD

1 Introduction While to most normal consumers, problems with the three-wheeled phenomenon are apparent and they may not be to the one who is not so acquainted with the marvellous vehicle. While the inherent three-wheeled design commonly known as an auto rickshaw in developing nations helps in its drivability and manoeuvrability, driving or travelling in these vehicles is a harrowing experience. Vibrations, noise,

Peer reviewed under responsibility of the scientific committee of the International Conference on Progress in Automotive Technologies, ICPAT—2019. C. Bhaskar · K. Rawat · M. Minhaj · M. Senthil Kumar (B) · C. D. Naiju School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_18

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dust and some other problems related to their functioning are listed below, and they are the reason to first take into the challenge faced in the existing design. Since rickshaws do not have a single-shell design, the large amount of vibration felt during driving can build large seams/gaps in the rickshaw plates. Moreover, the rickshaws are also very prone to rust which compromises the integrity of the body further. Passenger leg space is not a lot in auto-rickshaw and can be improved upon. Same goes for the seat height of both driver and passengers. Due to the open design, the interior of the rickshaw is open to the elements and tends to become dirty very fast. Due to the advent of CNG in rickshaws, modern CNG rickshaws have no luggage space. While more rickshaws are converting to battery operation, the problem remains as battery cells take up a large amount of space and reduce the vehicles storage. These ergonomically problems seem numerous; however, apart from these, the very fact that the auto-rickshaw is not a very economical vehicle and is not built very aerodynamically makes it require a new body structure as well. Our paper hence looked at understanding the design and carrying out a drag force estimation and validation. This helped us to understand just how bad the current design is and what kind of limitations it forces on the vehicle. Studies conducted by various institutes on how to design and the parameters to keep in mind to get an optimized design for a product were viewed. A limit to ergonomic studies was maintained. Many studies were reviewed in order to best understand what the scope of our study would be. 21- Century ready auto rickshaws need to be developed. Suspension system of the auto rickshaw needs design changes. In their study, two different suspension types were chosen for the front and for the rear. These designs were then simulated and tested and reported [1]. Many issues related to three wheeler design and optimization need to be carried out. A study is carried out using a multi-body dynamic (MBD) model and with experiments conducted on a prototype three-wheeled vehicle (TWV) on a test track and reported [2]. A conceptual design study was carried out considering the driver, passenger and luggage space [3]. To improve vehicle handling, impact safety and passenger ergonomics, a digital model is developed, studied and reported for a hybrid auto rickshaw [4]. Looking into the problems faced by auto rickshaws, an estimation method of passenger car equivalence of auto rickshaws at signalized intersections by a macroscopic approach is studied and reported [5].

2 CFD Studies The design and analysis were carried out on the 3D model developed as per the available data. Measurements were taken and modelled using 3D modelling software. 2D dimensions are shown in Fig. 1. Commercial CFD analysis software was used for the CFD aerodynamic analysis of the three wheeler. The exterior of the three wheeler was modelled. Many elements were eliminated in the design to simplify the design and analysis. Elements like rear-view mirrors, indicators, mud pads, etc.,

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Fig. 1 2D model of three wheeler

were eliminated. This helped in a refined model for the FE analysis. A volumetric FE model was made from the CAD model and used for CFD studies. To make the volumetric FE model, it has been used the CAD. Different CAD models were required to be designed for the airflow analysis. The dimensions of an actual auto rickshaw model were used to replicate its design. The next step was fluid flow analysis on the body of the rickshaw. Fluid flow trajectory and surface plot, using which the analysis of the drag and lift force on the auto body at various speeds and graphs is plotted.

3 CFD Analysis An analysis was carried out on the body, and flow trajectories at 10 m/s free stream velocity are recorded. Figure 2 and 3 shows the analysis.

4 Results and Discussions Results obtained for the CFD analysis is shown in Table 1. The lift force, drag force, power consumed and the tabulated coefficient of drag are shown. The drag force, lift force and power consumed were plotted against speed. These graphs are shown in Fig. 4, 5 and 6. The drag coefficient for various stages of starting from 10–80 km/hr for an increment of 10 km/hr is found out.

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Fig. 2 Flow trajectories at 10 m/s free stream velocity

Fig. 3 Flow trajectories over the surface

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Table 1 CFD analysis results Speed (km/hr)

Lift force (N)

Drag force (N)

Power consumed (kW)

Coefficient of drag

10

0.119

6.113

0.01699414

0.50405268

20

1.119

17.042

0.09475352

0.351303197

30

4.502

56.847

0.47410398

0.520818705

40

2.236

96.107

1.06870984

0.495286591

50

2.71

144.528

2.0089392

0.476687229

60

10.646

228.05

3.803874

0.522334977

70

11.55

322.438

6.27464348

0.542589857

80

21.378

440.559

9.79803216

0.567604247

Drag Force vs Speed

Fig. 4 Drag force versus speed

500

Drag (N)

400 300 200 100 0

0

20

40

60

80

100

80

100

Speed (km/hr)

Lift Force vs Speed

Fig. 5 Lift force versus speed

25

Lift (N)

20 15 10 5 0

0

20

40

60

Speed (km/hr)

5 Conclusion In this paper, a CFD model simulation of three wheeler is carried out. From the analysis, it is evident that the theoretical maximum velocity of a three wheeler can attain if it only has to overcome drag force which is approximately 77 km/hr which

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Power Consumed vs Speed

Fig. 6 Power consumed Vs speed

12

Power (kW)

10 8 6 4 2 0

0

20

40

60

80

100

Speed (km/hr)

corresponds to the stated auto rickshaw maximum velocity of 80 km/hr. The average value for the coefficient of drag is calculated to be 0.4975. This validated the design and calculations of the drag force while also displaying the need of a better more aerodynamic shape of the three wheeler body due to the massive impact the current drag has on its performance.

References 1. Gyllendahl T, Tran D (2012) Development of an auto rickshaw vehicle suspension. Bachelor’s Thesis, Lulea University of Technology Department of Engineering Sciences and Mathematics, Sweden 2. Gore MM, Ronge BP, Misal ND (2015) A Review paper on design and analysis of system of three wheeler. Int J Appl Innovation Eng Manage 4(5) 3. Rane N, Bapat VP Design of the exterior for a three passenger auto rickshaw. Design Case study, IDC, IIT Bombay 4. Sveder Christoffer, Isaksson Martin, Cook Daniel, Winterquist Hanna, Gustafsson Anders (2011) Project auto-rickshaw. Lulea University of Technology, Sweden 5. Rahman MM, Okura I, Nakamura F (2004) Effects of rickshaws and auto-rickshaws on the capacity of urban signalized intersections. IATSS research, 28(1):26–33, ISSN 0386-1112

Evaluating the Hardness and Microstructural Analysis of Reinforcing the Nano Silicon Carbide and Nano Zirconium Oxide in Hybrid Al6061 Metal Matrix Composite V. Deepakaravind and P. Gopal Abstract Aluminium alloy (Al6061) matrix composite is reinforcing of β phase APS silicon carbide nanoparticle at scale range of 50 nm in the weight percentage of 2% along with β phase APS zirconium oxide of nanoparticle at 45 nm scale range in the weight % of 2.8, 3.0 and 3.2%. By progressing, the reinforcement element is carried out using the stir casting method to form the hybrid aluminium-based metal matrix nanocomposite samples. Microstructure and hardness properties are analysed in fabricated aluminium-based metal matrix nanocomposite samples. These metal matrix nanocomposites are characterized by scanning electron microscope (SEM). Hardness tests are carried out in order to identifying hardenability in the aluminium-based metal matrix nanocomposite. The results revealed that Al metal matrix nanocomposites are containing 2% of nano silicon carbide along with 3.2 wt% of nanoparticle of zirconium oxide (ZrO2 ) samples to improve the hardness strength among the other samples of hybrid aluminium (Al6061) metal matrix nanocomposite. Keywords Aluminium (Al6061) · Aluminium metal matrix nanocomposite(AMMNC) · Scanning electron microscope(SEM) · Zirconium oxide(ZrO2 ) · Nano metre (nm)

1 Introduction Advanced technology used for preparation of material with a familiar combination of properties, it could not be melted in the material, metal alloy, polymeric and ceramics materials. Generally, a composite was defined as combination of two or more similar and dissimilar materials to have a distinct interface between elements. Resulting properties were larger than specific components constituents. V. Deepakaravind (B) Department of Mechanical Engineering, Velammal Institute of Technology, Chennai, Tamil Nadu, India e-mail: [email protected] P. Gopal Department of Automobile Engineering, Anna University BIT Campus, Trichy, Tamil Nadu, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_19

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Metal matrix-based composite material was consisting of superior properties such as high strength, high stiffness, high thermal stability, high elastic modulus, high electrical and thermal conductivity and exhibits greater corrosion resistance, oxidation and wear comparable to the metal matrix material [1]. Composite material is composed of two or more constituent phase: matrix phase and reinforcement phase. The discontinuous phase in composites is usually harder and stronger than continues phase and is called reinforcing agents. The continuous phase is called as the matrix. Composite materials can be categorized based on the matrix material are summarized as follows: Metal matrix-based composites were broadly used in aerospace, marine, automobile, marine and structural application, etc., due to outstanding mechanical properties. In this, metal matrix composite stands for reinforcing in a ductile metal matrix. By improving, the strength of MMC has considerably better mechanical properties compared to the strength of base metal matrix material.

1.1 Literature Review Malhotra [2] observed that aluminium was influenced by varying weight percentage composition of zirconia (5 and 10%) with fixed as percentage fly ash (10%) reinforcing in Al6061 metal matrix-based composite material using stir casting method. Hence, it was identified with increased hardness and ultimate tensile strength increase to increase the weight fraction of reinforcement material in aluminium-based metal matrix composite. Therefore, a better hardness 94HV with tensile strength of 278 MPa for 10% zirconia and 10% fly ash was found in the reinforcement of aluminium-based composite material. Aluminium alloy 6061 has determined elongation of 21.66%, and it was significantly to reduce within a range of 85–90% due to the addition of reinforcement element in the aluminium-based matrix composite material. Girisha [3] investigated with the effect of different weight percentage fraction of nanoparticles of zirconium oxide as 0.5, 1, 1.5 and 2% were reinforced in aluminiumbased metal matrix composite using stir casting method. Hence, it was observed that particle agglomeration is present in casting of composite material due to large content of nanoparticles of zirconium oxide. The wear and hardness properties were increased as increase in the weight fraction of nanoparticles of zirconium dioxide. Jenix Rino [4] investigated the mechanical behaviour of aluminium Al6063 alloy composite was strengthened by adding zircon sand and alumina particle as reinforcing weight percentage as 8% using stir casting method. It observed homogenous distribution of the reinforcement in Al6063 matrix material. The hardness and tensile strength of the composite have the higher value at the composite sample having the reinforcement mixture of 4 wt%ZrSiO4 + 4wt% Al2 O3 . Meena [5] analysed that mechanical properties of developing SiC were reinforced in Al6063 metal matrix-based composite material using the stirring technique. The experiment was performed by varying the reinforced particle size as 200 meshes with

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Table 1 Chemical composition of aluminium alloy 6061 Constituents

Al

Mg

Si

Fe

Cu

Zn

Mn

Cr

Other

Weight percentage of composition.%

96.50

0.956

0.562

0.532

0.236

0.202

0.102

0.046

0.864

the different weight percentage composition as 5, 10, 15 and 20% of SiC particle were added reinforcement in composite material. The stirring process was operated at 200 rpm using a graphite impeller on behalf of 15 min time duration. A homogenous dispersion of silicon carbide particle elements was observed in the aluminium-based metal matrix element, hardness, tensile and yield strength were improved with an addition of reinforcing particulate size and percentage weight fraction of SiC particles. Percentage elongation, impact strength and percentage reduction area were decreased with rising reinforcement particle size of composite material. Maximum hardness 83 HRB and impact strength 37.01 Nm were achieved as 20% weight percentage of SiC particles in aluminium metal matrix-based composite material. Ravesh et al. [6] studied the effect of the different weight fraction of SiC (2.5, 5, 7.5 and 10%) and 5% fly ash reinforced 6061 aluminium matrix composite by stir casting technique. Tensile strength, hardness and impact strength increased with growth in weight fraction of SiC particles. A better tensile strength 115 N/mm2 , hardness 93 RHN and toughness value 7.8 for a 10% SiC and 5% Fly ash reinforced composite material were obtained.

1.2 Material Composition of Aluminium (Al6061) Aluminium metal matrix material is utilized in experimental study as well as aluminium alloy 6061 composite. The chemical composition of aluminium alloy 6061 is shown in Table 1. Aluminium 6061 ingot was purchased from Gokul Industry, Coimbatore, India. The aluminium alloy Al6061 is a precipitation of toughening aluminium alloy with an enclosing magnesium and silicon as its major alloying elements in the aluminium alloys components. The mechanical properties of aluminium alloy Al6061 grades are settled on tempering with the heat treatment of the material. In this, the material is compared to other materials, and it was offered relatively high strength, good workability, high machinability, high resistance to corrosion and is broadly available.

2 Experimental Procedure In this experiment [7, 8], we consider Al 6061 with zirconium oxide nanopowder (Zro2) as weight 0%, 5% and 10%, respectively. Composites are produced by using

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stir casting technique as shown in figure. Al 7075 is taken in the form of cylindrical rods for the experiments. Temperature about 6000–7500 °C is set in an electric furnace with control panel. The cylindrical rods are placed inside the graphite crucible. The graphite crucible containing rods is now placed inside the furnace, and it is heated until it reaches its melting point; once the metal reaches into the liquid state, the slag formed on the surface will be removed slowly. The reinforced material zirconium oxide nanopowder (Zro2) is preheated in the electric furnace at 800°C temperature in order to remove the moisture in Zro2 nanopowder. Now add the Zro2 nanopowder in the aluminium 6061 liquid state slowly by stirring the graphite rod at speed of 400 rpm. Stirring is done very slowly for 5–10 min because it will mix properly. Now pour the liquid metal in the required die dimensions of diameter 20 mm*150 mm length in order to conduct the experiment on mechanical properties. In this experiment, Al 6061 was reinforced with zirconium oxide nanopowder (Zro2) as weight 2.8, 3.0% and 3.2 and 2% weight of nano silicon carbide to form the hybrid nanocomposite sample as shown in Table 2. In this, nanocomposites are fabricated using stir casting technique as shown in Fig. 1. Al 6061 is taken in the form of cylindrical rods for these experiments. Temperatures are maintained about 600–750 °C and set in an electric furnace with control panel. The cylindrical rods are kept inside in the graphite crucible. This graphite crucible was contained with rods, it is placed inside the electric furnace, and it is heated until it reaches its melting point. Therefore, the metal is reached into the liquid molten state, and the slag formed on Table 2 Weight % of materials used in experiment S.No

Samples

% of Al 6061 by weight

%of β phase SiC by weight

% of β phase ZrO2 by weight

1

S0

100%

–

–

2

S1

2%

2.8%

3

S2

2%

3.0%

4

S3

2%

3.2%

95.2% 95% 94.8%

Fig. 1 Stir casting set-up for making of pure sample S0 and AMNC samples(S1, S2 and S3)

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Fig. 2 Pure sample(S0) and AMNC samples(S1, S2 and S3)

the surface will be removed slowly. Reinforced nanoparticle of zirconium oxide is preheated in the electric furnace at 800 °C temperature in the order of removing the moisture present in nanopowder of Zro2 . By adding nano zirconium oxide powder in to the molten aluminium alloy. Stirring was done very slowly for 5–10 min, due to which it was mixed properly. Then pouring liquid molten metal was required in the die with the dimension of diameter 20 mm*150 mm length. The mechanical properties are conducted experiment on Aluminium based Nano Composite Samples (AMNC) S1, S2 and S3 samples as well as pure aluminium 6061 composite samples S0 as shown in Fig. 2.

3 Results and Discussion A. X-ray diffractometer (XRD) studies X-ray diffraction studies are carried out using Phillips X-ray diffractometer (model PW 3710) with Cu Kλ radiation (λ = 1.5405 A˚). X-ray diffraction pattern of AMNC sample confirms the crystalline phase, and mean crystal size determined is around 40 nm scale range. In the XRD observations, three strongest peaks shown in Figs. 3, 4 and 5 are detected with Miller indices (223), (054), (122), (125) and (082) corresponding to Bragg angles 30°, 36°, 51°and 59°, respectively. The characteristic peaks are higher in intensity, which is indicated that the products are good in crystalline nature. No peaks corresponding with the impurities are detected, showing that the final product of AMNC samples S1, S2 and S3 in Figs. 3, 4 and 5. It is observed that intensity of the peaks increased with high thermal treatment due to agglomeration, It means that the crystalline growth has been improved. The full width at half maxima of major peaks decreases and confirms with the grain size growth. B. SEM analysis The size and morphology of the ZrO2 nanoparticles have been determined using scanning electron microscopy. Figure 3 shows the image random distribution of the

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Fig. 3 AMNC sample 1 (S1)

Fig. 4 AMNC sample 2 (S2)

Fig. 5 AMNC sample 3 (S3)

ZrO2 nanoparticles having non-spherical shape and diameter in the range of nano metre. The nanocomposites are found to be agglomerated, when it is analysed by scanning electron microscopy (SEM: JEOL, Japan, JSM 840A) studies as shown in Figs. 4, 5, 6, 7 and 8. It can be observed that the ZrO2 crystallites have no uniform

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Fig. 6 AMNC sample S1 at 50 μm scale range

Fig. 7 AMNC sample S2 at 50 μm scale range

shape. This is believed to be related to the non-uniform distribution of temperature and mass flow in the combustion flame, due to the high surface energy of the particles and from the SEM there is such difference which was observed for different wt% of ZrO2 dispersed aluminium powder. In the scanning electron microscope images are observed the presnce of aluminium and zirconium in Aluminium based nano composite samples.

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Fig. 8 AMNC sample S3 at 50 μm scale range

C. Microhardness test results The microhardness test results show on AMNC samples S1, S2 and S3 are revealed with an increasing trend in base matrix hardness as well as increasing reinforcement content of sample S3 as shown in Fig. 9. Trend analysis of microhardness measurements in AMNC sample results revealed that increasing the reinforcement content leads to be a vital increase in the hardness as well as attributing to the presence of harder ZrO2 nanomaterials within a large constraint to be localizing the deformation during indentation due to their presence and reduced in the grain size of AMNC samples. Results show that increase in the weight percentage of reinforcement with increased hardness value increases up to 3.2 weight percentage. Decreased the presence of porosity in the crystal structure of Aluminium based nano composite samples Al6061 aluminium alloy was reinforced with nanosized ZrO2 and nano silicon carbide of sample 3 is best once compared to other three sample as shown in Fig. 9

Fig. 9 Trend analysis of microhardness test results of pure sample and AMNC samples (S1,S2 and S3)

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4 Conclusion Al6061 aluminium alloy is reinforced with nanosized ZrO2 and nanosized silicon carbide are successfully fabricated using stir casting method as shown in Fig. 2. The nanoparticles of reinforcement elements are uniformly distributed in the base matrix composites of AMNC samples as shown in Figs. 6, 7 and 8. Therefore, reinforcement nanoparticle agglomeration is observed in hybrid AMNC samples composites with large content of ZrO2 as shown in Figs. 3, 4 and 5. From the experimental testing results of casted Aluminium based nano composite samples S1, S2 and S3 stir casting was found as suitable method for fabrication of this kind of AMNC Samples S1, S2 and S3. Mechanical characterization is revealed with the presence of nanoparticulates of zirconium oxide in aluminium-based matrix composite. From the microhardness test, the result is suggested to improve the microhardness properties of Al6061 as well as AMNC samples. AMNC sample S3 has higher microhardness value 102VHN compared to other three samples S0, S1 and S2.

References 1. Balakumar G (2013) Development and property evaluation of copper- chilled aluminum alloy reinforced with nano-ZrO2 metal matrix composites (NMMCS). Int J Networks Appl 4(1):1–11I. ISSN 0976-5859 2. Chernyshova TA, Kobeleva LI, Bykov PA, Bolotova LK, Kalashnikov IE, Volochko AT, Izobello AY (2013) Nanostructuring of dispersion_reinforcedaluminum_matrix composite materials. Inorganic materials: applied research, 4(3):247–255. ISSN 2075_1133 3. Koli DK, Agnihotri G, Purohit R (2013) Properties and characterization of Al-Al2O3 composites processed by casting and powder metallurgy routes (Review). Int J Latest Trends Eng Technol 2(4) 4. Nandipati G DR., kommineni R DR., Damera NR Dr., Nallu R Dr. (2013) Fabrication and study of the mechanical properties of AA2024 alloy reinforced with B4C nano-particles using ultrasonic cavitation method. IOSR J Mechanical Civ Eng 7(4):01–07 5. Kwon Hansang, Lee Gil-Geun, Leparoux Marc, Kawasaki Akira (2013) Functionally graded dual-nanoparticulate-reinforced aluminummatrixcompositematerials. J Phys: Conf Ser 419(2013):012004 6. Dixon J, Ghannam S (2013) Strengthening of aluminum matrix nano composite using Al2O3SiC. Eur J Appl Eng 10:2668–3792 7. Rasidhar L Dr., Rama Krishna A, Srinivas Rao Ch. Dr. (2013) Fabrication and investigation on properties of ilmenite (FeTiO3) based Al nanocomposite by stir casting process. Int J Bio-Sci Bio-Technol 5(4) 8. Suresh SM, Mishra D, Srinivasan A, Arunachalam RM, Sasikumar R (2011) Production and characterization of micro and nano Al2 O3 particle-reinforced LM25 aluminium alloy composites. ARPN J Eng Appl Sci 6(6)

Exploratory and Performance Analysis of Solar Refrigeration System Using Nanofluids—A Review M. Sivakumar and S. Mahalingam

Abstract In this day and age refrigeration, frameworks assume a crucial job to satisfy the human needs. A persistent research is being done by numerous specialists so as to enhance the execution of these frameworks. Directly utilized, vapor pressure refrigeration framework does not work effectively because of lack of electric power. This examination covers an expansive diagram of sun-based photovoltaic innovation, which utilizes effectively accessible sun-oriented vitality for refrigeration reason. It incorporates an engine, a blower, an inverter and battery, a photovoltaic controller, and boards. This should be possible by changing over sunlight-based vitality into power by methods for photovoltaic gadgets, which can be used by the electric engine to drive vapor weight refrigeration structure. The principle goal of the examination is dealing with the deficiency of electric power, in living situations by utilizing a cooling framework coupled to a sun-oriented establishment. In this sunlight-based refrigeration framework, when traditional refrigerants like (R22, HFCR134a, R600, and so forth.) are utilized, it prompts low warm conductivity, heat exchange rate, and COP level, and a portion of alternate effects is corrosive downpour, softening of ice sheets, ocean level raising, well-being impacts, air contamination, ozone consumption, which is exceptionally risky to the earth. To maintain a strategic distance from these dangers, one of the routes is to utilize nanofluids which are not destructive to nature. The utilization of nanofluids results in high warm conductivity and heat exchange rate and gives a better COP level. The accompanying three nanofluids Al2 O3 , ZrO2 , and Cu2 O have been now utilized in the refrigeration framework. A portion of the properties of given nanofluids will be changed to advance new nanofluids. The improved nanofluids will be utilized in refrigeration framework and a similar will be contrasted and different nanofluids like R22, R134a, R290, and R600a. Despite the fact that Al2 O3 , ZrO2 , and Cu2 O give great outcomes, the new nanofluids have been advanced for better outcomes. M. Sivakumar (B) Assistant Professor, Department of Mechanical Engineering, Annai Mathammal Sheela Engineering College, Erumapatty, Namakkal 637013, India e-mail: [email protected] S. Mahalingam Assistant Professor, Department of Mechanical Engineering, Sona College of Technology, Salem 636005, India © Springer Nature Singapore Pte Ltd. 2021 M. R. Nalim et al. (eds.), Advances in Automotive Technologies, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-15-5947-1_20

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Keywords Vapor compression refrigeration system · Nanofluids (Al2 O3 , ZrO2 , Cu2 O) · Refrigerants (R22, R134a, R290, and R600a) · Solar photovoltaic collector with battery and inverter · Coefficient of performance (COP)

1 Introduction Expansive number of populace in creating nations like India lives in the rustic or remote areas where network power is inaccessible. So the capacity of medications and sustenance is preposterous as a result of low-temperature prerequisites. In India, there are endless spots where the working of understood innovation vapor pressure refrigeration does not work proficiently because of the deficiency of electric vitality. In this situation, sun-powered vitality is the most bounteous of all vitality shapes. Sustainable wellsprings of vitality from sun are very non-dirtying and thought about clean. Sun-powered vitality as the green and ecological neighborly vitality has delivered vitality for billions of years. Sun-oriented vitality that achieves the earth is around 4 × 1015 MW, and it is multiple times as substantial as the worldwide usage. Sun-based power age became much more quickly (+86.3%), however from a little base. Inexhaustible types of vitality represented 2.1% of worldwide vitality utilization, up from 0.7% in 2001. Subsequently, the usage of sunlight-based vitality and the innovation of nanofluids charmed considerably more consideration. Nanofluids are set up by suspending nanomeasured particles (1–100 nm) in regular liquids which have higher warm conductivity than the base liquids. Nanofluids have the going with properties when appeared differently in relation to the common solid–liquid suspensions (I) higher warmth trade between the particles and fluids because of the high surface region of the particles, (ii) better scattering strength with transcendent Brownian movement, (iii) diminishes molecule obstructing, (iv) decreased siphoning power when contrasted with base liquid to acquire equal warmth exchange. Nanoparticles can be utilized in refrigeration frameworks in light of its unfathomable enhancement in thermo-physical and heat exchange capacities to upgrade the execution of refrigeration frameworks. In a vapor pressure refrigeration framework, the nanoparticles can be enhanced to the grease. At the point, when the refrigerant is circled through the blower, it conveys hints of grease + nanoparticles blend (nano-ointments) so alternate parts of the framework will have nanolubricant—refrigerant blend.

2 Literature Review Jwo et al. [1] directed examinations on a refrigeration framework supplanting R134a refrigerant and polyester oil with a hydrocarbon refrigerant and mineral oil. The mineral ointment included added Al2 O3 nanoparticles to enhance the grease and warmth exchange execution. Their examinations exhibit that the 60% R-134a and

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0.1 wt% Al2 O3 nanoparticles were flawless. Under these conditions, the power use was reduced by about 2.4%, and the coefficient of execution was expanded by 4.4%. Henderson et al. [2] led a test investigation on the stream bubbling warmth exchange of R134a-based nanofluids in a level cylinder. They found a great scattering of CuO nanoparticle with R134a and POE oil and the warmth exchange coefficient builds over 100% over standard R134a/POE oil results. Fatehmulla et al. (2011) planned and grew low power refrigeration framework utilizing PV modules, 2 modules every one of 36 sun-oriented cells. Yilanci et al. [3] contemplated the vitality of an examination of a fridge and fueled by a photovoltaic explored to acquire effective activity conditions dependent on exploratory information. Sobamowo et al. [4] structured and created photovoltaic-controlled DC vapor pressure refrigeration framework for creating nations, for example, Nigeria and demonstrated that its appropriateness to various climatic locales in Africa and could be utilized for transient nourishment stockpiling, enhancement in the well-being administrations and living conditions in remote and provincial zones which were not able to access power from the network. Kumar et al. [5] examined the impact of aluminum oxide put together nano-oil with respect to the COP of the framework and solidifying limit of the framework. The trial setup was worked according to Indian guidelines. Refrigerants like R12, R22, R600, R600a, and R134a were utilized as a refrigerant. The execution of the framework relies on the thermo-physical properties of the refrigerant. The expansion of nanoparticles to the refrigerant outcomes in enhancement is in the thermo-physical properties in this way enhancing the execution of the refrigeration framework. The exploratory investigations show that the refrigeration framework with nano-refrigerant works regularly. There was increment in the COP of the framework by 19.6%. Mineral oil with alumina nanoparticles oil blend was explored, and it was discovered that there is an expansion in solidifying limit and decrease in power utilization by 11.5% when contrasted with polyester. Aluminum oxide-based nano-oil in refrigeration framework was discovered working palatably. Sendil Kumar and Elansezhian [6] performed an investigation in his paper, and ZnO nanoparticles with refrigerant blend were utilized in HFC R152a refrigeration framework. The framework execution with nanoparticles was then explored. The centralization of nano-ZnO runs in the request for 0.1% v, 0.3% v, and 0.5%v with a molecule size of 50 nm and 150 g of R152a was charged and tests were directed. The blower pull pressure, release pressure, and evaporator temperature were estimated. The outcomes showed that ZnO nano-refrigerant works ordinarily and securely in the framework. The ZnO nanoparticle fixation is a significant factor considered for heat move improvement in the refrigeration framework. The presentation of the framework was altogether improved with 21% less vitality utilization when 0.5%v ZnOR152a refrigerant. Both the attractions weight and release pressure were brought down by 10.5% when nano-refrigerant was utilized. The evaporator temperature was diminished by 6% with the utilization of nano-refrigerant. Thus, ZnO nanoparticles could be utilized in the refrigeration framework to significantly diminish vitality

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utilization. The utilization of R152a with zero ozone exhausting potential (ODP) and exceptionally less GWP and subsequently gives a green and clean condition. Senthilkumara and Praveenb [7] explored in his paper, we report a technique that utilizes flammable gas to improve the vitality proficiency of refrigeration countering strategy utilizing CuO—R600a as interchange refrigerants. Along these lines, unwavering quality and execution of nano-refrigerant in the working liquid have been explored tentatively. Another nano-refrigerant is utilized in the local cooler. The exhibitions of the nano-refrigerant, for example, the cooling limit, vitality proficiency proportion were resolved. The outcomes show that the blend of R600a with nanoparticles (CuO) works regularly in the household cooler. The cooling limit of the residential cooler is expanded by 10–20% by utilizing nano-refrigerant.

3 Experimental Investigations 3.1 Use of Nanofluids in Refrigeration System There is parcel of information and yield parameters in the field of refrigeration framework. These parameters of refrigerants are identified with one another when we use in vapor pressure refrigeration framework. We can utilize nanofluids in the vapor pressure framework to improve the warm conductivity. The effectiveness of the framework relies on the properties of the refrigerant. Typically, R22, R134a, R290, and R600a are utilized in refrigeration framework as a refrigerant. The limit with regard to warm exchange isn’t all that great, and henceforth, it prompts increment in vitality utilization. In this way, the refrigeration framework with nano-refrigerants works skillfully. It is discovered that the solidifying limit is higher and decrease in power utilization.

3.2 Experimental Setup The exploratory setup comprises of an engine, a blower, an evaporator, a condenser, an extension valve, a battery, an inverter, a PV controller, and photovoltaic boards (Fig. 1).

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Fig. 1 Experimental setup of the experiment

4 Experimental Procedure The sun-based photovoltaic framework uses a sunlight-based controlled prime mover to drive a refrigeration framework. This should be possible by changing over sunoriented vitality into power by methods for photovoltaic gadgets, at that point using an electric engine to work a vapor blower. A hermetically fixed blower is utilized for nanofluid refrigerant, a constrained kind cool condenser, a development valve and an evaporator containing water were incorporated. Five thermocouples, two weight checks, and one vitality meter are given to quantify the channel and outlet weight of blower, temperature and the power utilization at required areas. The refrigeration framework execution test incorporates vitality utilization tests and solidifying limit tests. The sort of evaporator utilized in this framework is a water tank. To quantify the vitality expended amid refrigeration framework activity, perusing is noted from energy meter. The test is done for 20 min for every blend of nanofluids by taking note of down the normal drop in temperature of water from its underlying temperature. The solidifying limit is dictated by the mass of water put away in the evaporator.

5 Observation and Analysis For breaking down the execution of vapor pressure refrigeration framework, the accompanying perceptions were précised:

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1. The refrigerant’s warmth limit while utilizing R22, 134a, R290, and R600a leaves something to be desired and expands control utilization too. 2. The refrigeration framework with nano-refrigerant works skillfully. 3. It is discovered that the solidifying limit is higher and the power utilization diminishes. 4. When contrasted with ordinary refrigeration framework, the COP level is improved by the utilization of nanofluids. 5. While utilizing the nanofluids, different dangers to the earth are stayed away from. 6. Although Al2 O3 , ZrO2 , Cu2 O give great outcomes, the new nanofluids have been enhanced for better outcomes.

6 Conclusion The test examination of vapor pressure refrigeration framework was done with the accompanying ends: 1. The commitment of this work was to enhance the warm solace in living conditions by utilizing a cooling framework coupled with a sunlight-based establishment. 2. The photovoltaic board fulfills the power request of the blower. 3. The nanofluid refrigerant works capability in refrigeration framework and guaranteed that the reason will be the warm property of nanofluids is higher than regular refrigerant. 4. The coefficient of performance (COP) of the refrigeration framework is enhanced amid use of nanofluids while contrasted with traditional refrigerant. 5. Nanofluids will be eco-agreeable with conditions. 6. Cost of sun-powered power is conservative than electric power.

References 1. Jwo CS, Jeng LY, Teng TP, Ho Chang (2009) Effects of nanolubricant on performance of hydrocarbon refrigeration system. J Vac Sci Technol B Microelectron Nanometer Struct 27(3):1473–1477 2. Henderson K, Park YG, Liu L, Jacobi AM (2010) Flow-boiling heat transfer of R-134a-based nanofluids in a horizontal tube. Int J Heat Mass Transf 53(5-6):944–951 3. Ekren O, Yilanci A, Cetin E, Ozturk HK (2011) Experimental performance evaluation of a PV-powered refrigeration system. Electronics and Electrical Engineering-Elektronika Irelektrotechink 8(114):7–10 4. Sobamowo MG, Ogunmola BY, Ismail SO, Ogundeko IA (2012) Design and development of a photovoltaic-powered DC vapour compression refrigerator with an incorporated solar tracking system. Int J Mech Comput Manuf Res 1:19–28 ISSN: 2301-4148 5. Kumar RR, Sridhar K, Narasimha M (2013) Heat transfer enhancement in domestic refrigerator using R600a/mineral oil/nano-Al2O3 as working fluid. Int J Comput Eng Res 3(4):42–50

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6. Sendil Kumar D, Elansezhian R (2014) ZnO nano refrigerant in R152a refrigeration system for energy conservation and green environment. Front Mech Eng 9(1):75–80 7. Senthilkumar A, Praveen R (2015) Performance analysis of a domestic refrigerator using cuo –r600a nano – refrigerant as working fluid. J Chem Pharm Sci (ICRAMET’ 15) ISSN: 0974-2115