Evolutionary Methods Based Modeling and Analysis of Solar Thermal Systems: A Case Studies Approach

ISBN-13: 9783031276347 / Angielski

Biplab Das;Jagadish

Opis

This book presents insights into the thermal performance of solar thermal collectors using both computational and experimental modeling. It consists of various computational and experimental case studies conducted by the authors on the solar thermal collector system. The authors begin by developing thermal modeling using a case study that shows the effect of different governing parameters. A few more experimental cases studies follow that highlight the energy, exergy, and environmental performance of the solar thermal collector system and to examine the performance of a modified solar collector system, illustrating performance improvement techniques.Finally, application of different evolutionary optimization techniques such as soft computing and evolutionary methods, like fuzzy techniques, MCDM methods like fuzzy logic based expert system (FLDS), Artificial Neural Network (ANN), Grey relational analysis (GRA), Entropy-Jaya algorithm, Entropy-VIKOR etc. are employed.

Spis treści

Introduction .- Modeling and optimization of energetic and exergetic performance of solar air collector .- Expert system based thermal performance analysis of corrugated absorber plate based solar air collector .- Investigation of thermal performance of SAC variables using fuzzy logic-based expert system.- Sustainability assessment of solar air collector using deep learning.

O autorze

Dr. Biplab Das presently working as an Associate Professor in the Department of Mechanical Engineering, National Institute of Technology Silchar, India. Dr. Das completed his Ph.D. from NERIST, Itanagar, India, in the year of 2014. Later, he pursued his Post-Doctoral research from the University of Idaho, USA. He is the recipient of the prestigious Bhaskara Advanced Solar Energy (BASE) Fellowship from IUSSTF and DST, Govt. of India. He is also awarded a ‘DBT Associateship’ by the Department of Biotechnology, Govt. of India. He has 15 years of experience in teaching/research and published more than 120 nos. of referred International/National Journal/conference journal papers. Presently Dr. Das has been involved in 10 nos. of sponsored projects as PI/Co-PI, funded by SERB, DST, Ministry of Power, and the Ministry of Climate Change, Govt. of India. He has guided 09 nos. of Ph.D. and at present 06  Ph.D. scholars are working with him. Dr. Das serves as an editor of 3 nos. of books/proceedings.

Dr.Jagadish obtained his Ph.D. in Mechanical Engineering with specialization in Production Engineering from the National Institute of Technology Silchar and Post-graduation in Product Design and Development from the National Institute of Technology Warangal, India. He has over 3 years of industrial experience in the field of design and analysis and more than 8 years of teaching and research experience. Currently, he is working as an Assistant Professor in the SQC & OR Unit, Indian Statistical Institute, Bangalore Center, Bangalore, India since March 2023. Prior he worked as Assistant Professor at NIT Raipur and NIT Silchar also. He received an “Institutional Award (Gold Medal)” by Institution of Engineers India and “Best Innovative Award” by Springer for his outstanding research contribution and published more than 55 research papers, 4 books, and 17 book chapters. He is a regular reviewer of various Production Engineering and optimization-related SCI indexed journals. He is the life member of professional bodies like Associate Member of Institute of Engineers (India), Committee Member of Soft Computing Club SCILAZ at NIT Silchar, and Member of American Society of Mechanical Engineers (ASME). His areas of interests are Green Manufacturing, Advanced Manufacturing Process, Rapid Prototyping; Computer-Aided Design/Manufacturing, Composite Machining, Composite Material and Machining Characterization; Applied Soft Computing Techniques and Optimization; Renewable Energy; etc

Blurb

This book presents insights into the thermal performance of solar thermal collectors using both computational and experimental modeling. It consists of various computational and experimental case studies conducted by the authors on the solar thermal collector system. The authors begin by developing thermal modeling using a case study that shows the effect of different governing parameters. A few more experimental cases studies follow that highlight the energy, exergy, and environmental performance of the solar thermal collector system and to examine the performance of a modified solar collector system, illustrating performance improvement techniques.

Finally, application of different evolutionary optimization techniques such as soft computing and evolutionary methods, like fuzzy techniques, MCDM methods like fuzzy logic based expert system (FLDS), Artificial Neural Network (ANN), Grey relational analysis (GRA), Entropy-Jaya algorithm, Entropy-VIKOR etc. are employed.

• Covers improvement of solar thermal systems and advances in solar air collector systems, modeling, and optimization;
• Includes modeling and parametric optimization issues for the practitioners of solar thermal industries;
• Provides a new method for modeling and optimizing solar air collectors using actual case studies from the field.