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 modeling, 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.
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.
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.
Dr. Jatoth Heeraman is an Assistant Professor of Mechanical Engineering at Malla Reddy (MR) Deemed to be University, Hyderabad, India. He received his Ph.D. in Mechanical Engineering from Lovely Professional University, India in 2024 and earned his M.Tech (Thermal Engineering) and B.Tech (Mechanical Engineering) degrees from JNTUH, Hyderabad, India. With over eight years of academic and research experience, his doctoral work focused on experimental heat transfer enhancement in heat exchangers. He has contributed over 25 journal articles and holds 25 Indian patents (published) and one UK patent. His research interests include high-speed flows, renewable energy systems, and AI/ML-enabled CFD.
Dr. Praveen Barmavatu is an Assistant Professor of Mechanical Engineering at Universidad Tecnológica Metropolitana (UTEM), Santiago, Chile. He earned his Ph.D. in Mechanical Engineering in 2019 and holds M.Tech and B.Tech degrees from JNTUH, Hyderabad, India, along with a Diploma in Mechanical Engineering. His research focuses on heat exchangers, thermal energy storage, solar thermal systems, and AI/ML-based optimization. He has published over 110 journal articles, holds several patents, and has contributed to international projects funded by ANID FONDECYT (Chile), DRDO (India), and KFUPM (Saudi Arabia).
