Deep learning-assisted design of slip-coupled microstructures for next-generation microfluidic cooling systems

Microfluidic cooling is a promising technology for thermal management in high power-density electronic chips and compact energy conversion devices. While prior research has focused on single design parameters, both the coupling effects of multiple factors and effective optimization strategies are still insufficiently explored. In this study, we develop a data-driven framework to design slip-coupled microstructures for advanced microfluidic cooling systems. The results indicate that the slip boundary contributes to both improved heat transfer and reduced flow resistance across different rib configurations. Synergistic effects between the rib location ratio X and height ratio Y become especially pronounced at high flow rates and large slip lengths. A deep learning model is subsequently employed to accurately capture the non-monotonic interactions and predict key performance metrics. The best configuration is pinpointed at X  = 0.375 and Y  = 0.291 across the whole parameter space, corresponding to the maximum performance evaluation criteria of 2.871. Integrating with a genetic algorithm, a localized optimization strategy is developed to mitigate thermal hotspots under multiple constrains, which effectively reduces the maximum temperature rise by 65.1 %. This study delivers a scalable and generalizable methodology for co-optimizing microstructures and slip boundary, offering practical guidance for intelligent thermal regulation in next-generation power electronics and energy systems.