Smart Heat Transfer and Thermal Management: Leveraging AI, Machine Learning, and Soft Computing revolutionizes heat transfer engineering by integrating artificial intelligence (AI), machine learning (ML), and soft computing. This groundbreaking book delves into state-of-the-art research and practical applications, providing a holistic approach to optimize thermal management. By deepening the understanding of heat transfer principles while explaining AI, ML, and soft computing methodologies, it offers innovative solutions for heat transfer challenges across various industries. The synergy between these disciplines results in enhanced predictive modeling, system optimization, and thermal control for improved energy efficiency and cost-effectiveness.
Soft computing techniques, including fuzzy logic and neural networks, expand traditional heat transfer methods, allowing for adaptive and intelligent thermal systems. Through case studies, simulations, and real-world examples, the book demonstrates how AI and ML-driven algorithms can lead to sustainable and eco-friendly thermal management solutions, making it a valuable resource for engineers, researchers, and students alike.
1. Chapter 1. Introduction to heat transfer for digital era - p. 3 2. Chapter 2. Heat transfer mechanisms: Conduction, convection, radiation, and extended surfaces - p. 57 3. Chapter 3. Convection heat transfer correlations - p. 89 4. Chapter 4. Advanced methods in heat transfer analysis: Mathematical modeling, numerical techniques, finite element analysis, and computational fluid dynamics - p. 107 5. Section 2. AI and machine learning applications in heat transfer - p. S2 6. Chapter 5. Foundation and introduction to artificial intelligence in heat transfer - p. 143 7. Chapter 6. Soft computing: An introduction to intelligent problem-solving techniques - p. 161 8. Chapter 7. Hybrid AI-ML algorithms for heat transfer - p. 191 9. Section 3. Advanced computational approaches in heat transfer - p. S3 10. Chapter 8. Simulation of different types of heat exchangers using ASPEN PLUS and ASPEN EDR, along with the comparative analysis and overview of different types of heat exchangers - p. 223 11. Section 4. Heat exchangers - p. S4 12. Chapter 9. Validation of experimental results of shell and tube heat exchanger using simulation software - p. 259 13. Chapter 10. Plate heat exchangers and their versatile applications—Artificial neural networks - p. 273 14. Chapter 11. Air-cooled heat exchangers - p. 297 15. Section 5. Advanced heat exchangers - p. S5 16. Chapter 12. Advancements in microchannel heat exchangers: Design, performance, and applications - p. 327 17. Chapter 13. Finned-tube heat exchangers for automotive application - p. 363 18. Chapter 14. Regenerative heat exchangers - p. 391 19. Chapter 15. Heat transfer through composite wall - p. 409 20. Section 6. Case studies in heat transfer and heat exchangers - p. S6 21. Chapter 16. Case studies in energy-efficient HVAC systems: Examples of success and innovation - p. 441 22. Chapter 17. Case study of AI and machine learning technology in thermal exchanger - p. 471 23. Chapter 18. Harnessing machine learning and artificial intelligence for improved thermal operations - p. 487 24. Index - p. 509
Smart Heat Transfer and Thermal Management: Leveraging AI, Machine Learning, and Soft Computing revolutionizes heat transfer engineering by integrating artificial intelligence (AI), machine learning (ML), and soft computing. This groundbreaking book delves into state-of-the-art research and practical applications, providing a holistic approach to optimize thermal management. By deepening the understanding of heat transfer principles while explaining AI, ML, and soft computing methodologies, it offers innovative solutions for heat transfer challenges across various industries. The synergy between these disciplines results in enhanced predictive modeling, system optimization, and thermal control for improved energy efficiency and cost-effectiveness.
Soft computing techniques, including fuzzy logic and neural networks, expand traditional heat transfer methods, allowing for adaptive and intelligent thermal systems. Through case studies, simulations, and real-world examples, the book demonstrates how AI and ML-driven algorithms can lead to sustainable and eco-friendly thermal management solutions, making it a valuable resource for engineers, researchers, and students alike.
1. Chapter 1. Introduction to heat transfer for digital era - p. 3 2. Chapter 2. Heat transfer mechanisms: Conduction, convection, radiation, and extended surfaces - p. 57 3. Chapter 3. Convection heat transfer correlations - p. 89 4. Chapter 4. Advanced methods in heat transfer analysis: Mathematical modeling, numerical techniques, finite element analysis, and computational fluid dynamics - p. 107 5. Section 2. AI and machine learning applications in heat transfer - p. S2 6. Chapter 5. Foundation and introduction to artificial intelligence in heat transfer - p. 143 7. Chapter 6. Soft computing: An introduction to intelligent problem-solving techniques - p. 161 8. Chapter 7. Hybrid AI-ML algorithms for heat transfer - p. 191 9. Section 3. Advanced computational approaches in heat transfer - p. S3 10. Chapter 8. Simulation of different types of heat exchangers using ASPEN PLUS and ASPEN EDR, along with the comparative analysis and overview of different types of heat exchangers - p. 223 11. Section 4. Heat exchangers - p. S4 12. Chapter 9. Validation of experimental results of shell and tube heat exchanger using simulation software - p. 259 13. Chapter 10. Plate heat exchangers and their versatile applications—Artificial neural networks - p. 273 14. Chapter 11. Air-cooled heat exchangers - p. 297 15. Section 5. Advanced heat exchangers - p. S5 16. Chapter 12. Advancements in microchannel heat exchangers: Design, performance, and applications - p. 327 17. Chapter 13. Finned-tube heat exchangers for automotive application - p. 363 18. Chapter 14. Regenerative heat exchangers - p. 391 19. Chapter 15. Heat transfer through composite wall - p. 409 20. Section 6. Case studies in heat transfer and heat exchangers - p. S6 21. Chapter 16. Case studies in energy-efficient HVAC systems: Examples of success and innovation - p. 441 22. Chapter 17. Case study of AI and machine learning technology in thermal exchanger - p. 471 23. Chapter 18. Harnessing machine learning and artificial intelligence for improved thermal operations - p. 487 24. Index - p. 509
