Artificial Intelligence and Computational Modeling in Heat Transfer and Fluid Dynamics

Mukesh Kumar Awasthi, Reshu Gupta

• Publisher: Wiley-Scrivener
• Year: 2026
• Print ISBN: 9781394433575
• eBook ISBN: 9781394433582
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Preface
• 1 Artificial Intelligence in Heat Transfer and Fluid Dynamics: Innovations, Applications, and Future Directions
• 2 Machine Learning Applications in Fluid Mechanics
• 3 Artificial Intelligence-Enhanced Developments in Computational Fluid Dynamics
• 4 Artificial Neural Network-Based Analysis of Natural Convection in Ag-TiO2/H2O Hybrid Nanoliquids
• 5 Artificial Intelligence-Based Optimization of Heat Transfer in Gyrotactic-Nanofluid Flow
• 6 Artificial Intelligence-Based Heat Exchanger Design and Optimization
• 7 Artificial Intelligence-Driven Energy Optimization in Heating, Ventilation, and Air Conditioning Systems
• 8 Artificial Neural Network Model for Radiative Heat Transfer in a Magnetized Tapered Stenosed Artery
• 9 Artificial Intelligence-Driven Flow Optimization in Renewable Energy Systems
• 10 Artificial Intelligence-Driven Flow Optimization for Enhanced Efficiency in Renewable Energy Systems
• 11 Artificial Intelligence for Flow Optimization in Renewable Energy Systems
• 12 Physics-Informed Neural Networks for Exothermic Reactions in Porous Media
• 13 Machine Learning for Magnetohydrodynamic Nanofluid Flow: Artificial Neural Networks vs. Traditional Methods
• 14 Case Studies of Artificial Intelligence in Industrial Fluid and Thermal Processes
• 15 Artificial Intelligence in Microfluidics and Nanofluidics
• About the Editors
• Index
• Also of Interest
• End User License Agreement