Drive innovation in thermal sciences with this essential book that leverages artificial intelligence and machine learning to transcend traditional computational methods and solve complex, real-time problems in heat transfer and fluid dynamics.
Traditionally, heat transfer and fluid dynamics have relied on classical computational methods like computational fluid dynamics, which employ numerical techniques to solve governing equations for fluid flow and thermal transport. However, these methods are often computationally intensive and limited in handling complex, real-time scenarios, especially in turbulence modeling, multiphase flows, and optimization tasks. This book explores the transformative impact of artificial intelligence in the fields of heat transfer and fluid dynamics. It covers a range of topics, including AI-based optimization techniques for thermal systems, machine learning applications in fluid dynamics, and the use of neural networks for modeling thermal systems. The book delves into advanced areas such as microfluidics, predictive maintenance, and real-time flow control, highlighting how AI enhances traditional computational fluid dynamics methods. It also presents case studies that illustrate successful implementations of AI in industrial processes, offering practical insights into its applications. By fostering an understanding of both theoretical and practical aspects, equips engineers and researchers with the tools necessary to leverage AI effectively in their work, ultimately driving innovation in thermal sciences.
Mukesh Kumar Awasthi, PhD is an Assistant Professor in the Department of Mathematics at Babasaheb Bhimrao Ambedkar University. He has published more than 125 research publications in journal and conference articles and book chapters, as well as ten books. His expertise lies in viscous potential flow, electro-hydrodynamics, magnetohydrodynamics, and heat and mass transfer.
Reshu Gupta, PhD is an Associate Professor in the Applied Science Cluster at the University of Petroleum and Engineering Studies with more than 20 years of teaching experience. She has published several papers in international journals and conference proceedings and three books. Her research areas include fluid dynamics, differential equations, heat and mass transfer, nanofluids, entropy, and artificial neural networks.
