Artificial Intelligence Techniques for Modeling and Simulation in Heat Transfer

Adding artificial intelligence (AI) to the modeling and simulation of heat transfer represents a major advancement for thermal engineering. Heat transfer is important for many applications, including anything from designing industrial processes to cooling systems in electronics, nuclear reactors, and supercomputers. Heat transfer represents a broad spectrum of phenomena, encompassing conduction, convection, radiation, and phase change, that occur at several scales, from molecular to macroscale. These heat transfer processes are intrinsic to the efficiency and functionality of all types of energy systems, both traditional and emerging technologies, including combustion engines, thermal power plants, solar thermal, and geothermal technologies. The traditional approach to heat transfer analysis often employs methods utilizing numerical tools, with computational fluid dynamics and finite element analysis being common methods used. These methods, although providing quite accurate predictions of thermal processes, are computationally intensive and time consuming, especially with highly nonlinear, transient, or multiscale problems. AI methods, such as machine learning and deep learning, present a computationally efficient and scalable alternative. Learning patterns directly from data, AI models offer extremely fast predictions of heat transfer processes, and they can support real-time system monitoring and adaptive controls that would significantly reduce the computational burden from traditional means. This chapter explains potential solutions to some of these challenges, including using transfer learning to utilize pretrained models, employing explainable AI methods to increase model interpretability, and creating synthetic data to increase existing small datasets. Last, this chapter foreshadows new areas of AI thermal management that include the creation of digital twins to replicate and control states of a system in real time, utilizing quantum machine learning for decreased computational time, and including moral principles to vet responsible use of AI. Overall, these advances suggest a path toward a future where methods for overcoming heat transfer problems will become energy efficient, smart, and scalable in all domains.