دانلود کتاب هوش مصنوعی و مدل‌سازی محاسباتی در انتقال حرارت و دینامیک سیالات

Artificial Intelligence (AI) and computational modeling have transformed the fields of heat transfer and fluid dynamics, offering innovative solutions for complex engineering challenges. This book Artificial Intelligence and Computational Modeling in Heat Transfer and Fluid Dynamics presents a comprehensive exploration of AI-driven techniques, their applications, and the advancements in computational modeling to enhance efficiency and accuracy in thermal and fluid systems. The chapters in this book provide a detailed examination of various methodologies, from machine learning applications to AI-enhanced simulations, making this an essential resource for researchers, engineers, and industry professionals. Chapter 1 introduces the role of AI in heat transfer and fluid dynamics, highlighting its applications in aerodynamics, chemical processing, and power generation. Chapter 2 delves into machine learning techniques for fluid mechanics, addressing flow prediction, turbulence modeling, and optimization. Chapter 3 explores AI-driven computational fluid dynamics (CFD) to enhance simulation accuracy and reduce computation time. Chapter 4 investigates the application of artificial neural networks in analyzing natural convection in hybrid nanofluids. Chapter 5 presents an AI-based optimization framework for heat transfer in gyrotactic-nanofluid flow. Chapter 6 discusses AI-driven techniques for optimizing heat exchanger design and performance. Chapter 7 focuses on AI-powered energy optimization in HVAC systems using machine learning and game theory. Chapter 8 applies artificial neural networks to radiative heat transfer in magnetized stenosed arteries. Chapter 9 examines AI-driven flow optimization techniques in renewable energy systems. Chapter 10 expands on AI’s role in enhancing efficiency and reliability in renewable energy grids. Chapter 11 investigates deep learning approaches for optimizing energy management in sustainable power systems. Chapter 12 applies physicsinformed neural networks to model exothermic reactions in porous media. Chapter 13 compares artificial neural networks with traditional numerical methods for solving MHD hyperbolic tangent nanofluid flow. Chapter 14 provides case studies showcasing AI applications in industrial thermal and fluid processes. Chapter 15 explores AI-driven advancements in microfluidics and nanofluidics for precise flow control and diagnostics. This book serves as a bridge between AI methodologies and practical applications in heat transfer and fluid dynamics, offering valuable insights into emerging trends, challenges, and future directions. By integrating AI with traditional computational techniques, the chapters collectively present a roadmap for developing next-generation solutions for complex thermal and fluid systems. We hope this book will inspire researchers and practitioners to explore AI-driven innovations that enhance efficiency, sustainability, and technological progress in engineering applications.