Optimizing Energy-Efficient jet impingement cooling using an artificial neural network (ANN) surrogate model for high heat flux Semiconductors

Abstract

As the demand for high-performance electronic devices and electric vehicles (EVs) increases, the importance of energy-efficient thermal management has become more important. This study presents the optimization of two high-heat flux (865.8 W/cm2) semiconductor thermal management systems targeting an EV inverter. The cooling system employs a jet impingement integrated with microposts. The thermal resistance (Rth.avg) and pumping power (Ppump) were evaluated through computational fluid dynamics (CFD) simulations by varying the jet nozzle parameters, micropost variables, and volume flow rate. To ensure the accuracy of the CFD model, experimental validation was also conducted, demonstrating less than a 5 % error. Then a surrogate model predicting Rth.avg and Ppump was developed using the artificial neural network (ANN) method. The sensitivity analysis subsequently identified the primary influencing factors of the system, and the Pareto optimal fronts were determined via the elitist non-dominated sorting genetic algorithm (NSGA-II). Through optimization, the approach provided multiple optimal designs for a broad spectrum of Ppump values, which was feasible within a brief period (less than 200 s) due to the rapid estimations by the ANN-based surrogate model. The experimentally validated design achieved a 65 % improvement in heat transfer coefficient (∼102.96 kW/(m2·K)) at similar pumping power levels (∼0.42 W) compared to previously reported jet impingement studies. Moreover, the design chosen through the optimization (Case B) projected a 140 % enhancement in heat transfer coefficient (∼150.2 kW/(m2·K)) with only a 63 % rise in Ppump (∼0.65 W). By exploring a more diverse range of multi-jet and micropost configurations, we were able to achieve higher performance. This study highlights the potential of combining jet impingement cooling with microposts as a highly attractive strategy for thermal management in high heat flux and multi-hot spot applications.