Artificial Intelligence and Computational Modeling 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.