Integrating CFD, molecular dynamics, and AI techniques for thermal management of heat transfer devices in microelectronic chip cooling using nano-enhanced PCMs and hybrid cooling systems

Thermal management plays a key role in ensuring efficient operation across EVs, aerospace, power plants, and high-performance computing. Excessive heat flux and rising operating temperatures can significantly compromise the reliability and efficiency of devices such as CPUs, GPUs, and stretchable electronics. This review addresses thermal stress issues in modern electronic systems and highlights advanced cooling strategies, with a particular focus on microchannel heat exchangers (MCHS). Further this review emphasizes on evaluating cooling technologies for better energy efficiency and clean-energy support, highlighting nanofluids and PCMs for effective transient heat control. This study presents a recent progress in computational approaches including molecular dynamics simulations (MDS), computational fluid dynamics (CFD), and machine learning techniques such as genetic algorithms and artificial neural networks for optimizing microchannel geometries and enhancing heat transfer efficiency. Special attention is given to emerging microchannel designs, fluid flow behavior, and cooling performance, including concepts such as the Zwieback–Fung effect and fractal like branching channels. The review concludes by outlining future research directions toward the development of highly efficient and cost-effective cooling solutions for next-generation high-power electronic systems.