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This paper systematically compiles a series of high-quality research papers, delving into the intersection of artificial intelligence and thermal science, while thoroughly analyzing related research on micro/nanofluids and fractal thermal science. In the context of artificial intelligence applications in thermal science, it elaborates on how deep learning revolutionizes intelligent thermal management systems, optimizes heat exchanger performance, and constructs more accurate predictive models for thermal processes. In the domain of micro/nanofluids, it encompasses pivotal subjects such as the enhancement mechanisms of thermal conductivity in nanofluids, the design and implementation of microfluidic devices in precise temperature regulation, and the impact of nanoparticle dispersion and aggregation on the thermal properties of fluids. In the domain of fractal thermal science, the text delves into a range of subjects, including the fractal geometry of heat transfer in porous media, fractal analysis of thermal diffusion in complex materials, and the modeling and performance evaluation of fractal heat exchangers. This review serves as a valuable resource, offering researchers, engineers, and students in thermal science and related fields a comprehensive understanding of the subject matter. It is anticipated that this review will stimulate further research and innovation in this area, playing a pivotal role in guiding and inspiring advancements in thermal science.
Lei et al. (Wed,) studied this question.