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Artificial Intelligence and Machine Learning in Heat Transfer Optimization for Sustainable Energy Systems examines how to use AI/ML-driven methodologies to enhance heat transfer processes in energy systems, such as industrial heat recovery, HVAC systems, and renewable energy generation, with a focus on sustainability.
Exploring applications in sustainable energy systems, renewable resources, and smart grids, the book presents intelligent control methodologies, predictive modelling, real time data analysis, and thermal management with deep learning. It covers AI-driven heat transfer monitoring, which is critical for a variety of applications beyond sustainability, including industrial production, aerospace, automotive, and electronics cooling. The chapters feature numerous case studies of AI/ML implementation in heat exchangers, power plants, and renewable energy systems.
This book will interest researchers and graduate students studying the intersection of AI, ML, and heat transfer optimization as applied to energy systems.
List of contents
1. Introduction to Heat Transfer in Sustainable Energy Systems. 2. Fundamentals of Artificial Intelligence in Heat Transfer Optimization. 3. Thermal Systems and Energy Efficiency: Challenges and Opportunities. 4. AI Techniques for Modeling and Simulation in Heat Transfer. 5. Machine Learning Approaches for Heat Transfer Prediction. 6. Neural Network-Based Heat Transfer Optimization of Casson Nanofluid Flow for Sustainable Energy Applications. 7. CFD and Neural Networks for Combustion Systems. 8. AI-Driven Thermal Management in Solar Energy Systems. 9. AI-Driven Thermal Management in Wind and Hybrid Renewable Energy Systems. 10. Real-Time Control of HVAC Systems Using AI - Methodologies and Algorithms. 11. Real-Time Control of HVAC Systems Using AI - Applications. 12. Big Data and IoT for Heat Transfer Monitoring. 13. AI in Industrial Heat Recovery and Waste Heat Utilisation. 14. Deep Learning Architectures for Thermal Diagnostics. 15. Hybrid AI Techniques for Multiphysics Heat Transfer Problems. 16. Artificial Intelligence for Achieving Net-Zero Energy: Sustainability Pathways. 17. Future Trends: Quantum Computing and Reinforcement Learning in Heat Transfer.
About the author
Dr. Jatoth Heeraman is a member of the Research Faculty at Karnavati University, India. He has five years of teaching and research expertise in Mechanical Engineering. He completed his Ph.D. in April 2024 from Lovely Professional University, Punjab, India. His doctoral research focused on heat transfer and friction factor augmentation using twisted tape inserts with dimple configuration in a double-pipe heat exchanger through experimental investigations. Dr. Heeraman also holds a Master of Technology (M.Tech) in Thermal Engineering and a Bachelor of Technology (B.Tech) in Mechanical Engineering. His contributions include more than 17 SCI-indexed journal publications, multiple conference presentations, book chapters, and filed patents. His research interests lie in compressible turbulence-combustion diagnostics, high-speed flow systems, renewable energy systems, and advanced CFD algorithms integrated with AI/ML.
Dr. Praveen Barmavatu is an Assistant Professor in the Department of Mechanical Engineering, Universidad Tecnológica Metropolitana, Santiago, Chile. He has over 8 years of experience in teaching and research covering diverse subjects such as heat exchangers, renewable energy sources, automobile engineering, and refrigeration and air conditioning. Dr. Barmavatu received a Bachelor of Technology in Mechanical Engineering and a Master of Technology in Thermal Engineering from JNTU Hyderabad, India. He received his Ph.D. in Mechanical Engineering from Shri Jagdishprasad Jhabarmal Tibrewala University, Rajasthan, India. Dr. Barmavatu has published 5 book chapters, 6 patent grants, and over 60 papers in scientific journals.
