The subject of this handbook lies at the confluence of thermal engineering, artificial intelligence (AI), and optimization science. It explores the development, analysis, and application of intelligent thermal systems—systems that leverage computational intelligence and data-driven methods to enhance design, performance, and decision-making in thermal domains. As global energy demands rise and sustainability becomes critical, improving the efficiency and intelligence of thermal systems is not just desirable—it is essential.
This handbook covers a wide range of topics central to the evolution of intelligent thermal systems. It begins with foundational thermal engineering concepts and progresses toward the integration of AI techniques such as machine learning, deep learning, reinforcement learning, fuzzy logic, and hybrid and ensemble methods. It also presents various optimization methods—particularly metaheuristic algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), Jaya algorithm, Rao algorithms, Grey Wolf Optimizer (GWO), Best-Worst-Random (BWR), and Best-Mean-Random (BMR)—applied to complex thermal systems. These include heat exchangers, refrigeration systems, HVAC systems, combustion devices, thermal power systems, thermal energy storage, solar and renewable thermal systems, industrial thermal processes, thermal control in aerospace and automotive fields, microchannel cooling and heat sinks, heat pipes, and thermal systems in electronics and data centers.
A key emphasis is placed on AI- and optimization-assisted modeling, prediction, and control of thermal systems and processes, demonstrating how these tools can significantly reduce experimental costs and accelerate design cycles. Readers will also gain insight into hybrid modeling strategies, digital twins, surrogate-based optimization, and real-time thermal management using AI-based controllers. The major benefit to readers is a comprehensive, interdisciplinary resource that combines theory, methodology, and real-world case studies. Researchers, practicing engineers, and advanced students will find value in the book’s unified treatment of AI and optimization in thermal contexts. Unlike traditional thermal engineering texts, this handbook presents cutting-edge tools capable of addressing the nonlinearity, uncertainty, and multi- and many-objective nature of modern thermal system challenges.
Furthermore, the book serves as a practical guide for implementation, including algorithmic frameworks, computational strategies, and software examples—making it suitable for both academic and industrial environments. Another strong selling point is its application-oriented focus: each chapter illustrates how intelligent methods lead to tangible improvements in performance, sustainability, and energy efficiency.
This handbook fills a crucial gap in the literature by presenting intelligent, AI-driven approaches to thermal system design and optimization. It reflects the paradigm shift occurring in thermal sciences and equips readers with the knowledge and tools to lead innovation in this fast-evolving field.
