AI-Driven Aerospace Thermal–Fluid Engineering: CFD, Machine Learning, Reliability, and Digital Twins is a comprehensive and forward-looking guide that brings together aerospace thermal–fluid science, computational fluid dynamics (CFD), artificial intelligence, machine learning, and reliability engineering into a single authoritative resource.

Designed for engineers, researchers, students, and industry professionals, this book explores the convergence of heat transfer, fluid mechanics, turbulence modeling, and multiphysics simulation with modern AI and data-driven techniques. It provides deep coverage of CFD fundamentals, turbulence modeling, numerical methods, and thermal–fluid challenges in aerospace systems, while extending into cutting-edge applications such as AI-accelerated solvers, physics-informed neural networks (PINNs), digital twins, predictive maintenance, and autonomous engineering workflows.

Readers will learn how machine learning enhances CFD simulations, enables real-time thermal–fluid monitoring, improves fault detection, and supports remaining useful life (RUL) prediction. The book also emphasizes reliability engineering, uncertainty quantification, safety-critical AI, aerospace certification, and ethical considerations for intelligent engineering systems.

Packed with real-world aerospace case studies, computational workflows, Python and CFD tool examples, OpenFOAM/ANSYS projects, and practical design problems, this book serves both as an academic reference and an industry-ready handbook.