关键词: 微通道换热器, 强化换热, 多目标优化, 神经网络, NSGA-II

Abstract: With the improvements in performance and integration of onboard electronics, traditional microchannel heat sinks (MCHs) are inadequate for the escalating thermal management requirements. A surrogate model for MCH with turbulence-promoting structures is established based on neural network. Combined with NSGA-II, the surrogate is employed to conduct multi-objective optimization. The primary factors governing thermal-hydraulic performance is identified by SHAP analysis. Additionally, the optimal nondimensional design parameters are obtained based on entropy-weighted TOPSIS. The results indicate that the neural network fully learns the complex mapping between features and performance, achieving good fitting and predictive ability, with a maximum mean absolute error of 0.383. The surrogate accelerates optimization process, with a maximum error of 6.34%. Compared with original design, the optimized MCH achieves an average Nusselt number of 70.755 and an overall performance factor (PEC) of 2.223, yielding a 9.2% improvement in comprehensive performance.

Key words: microchannel heat sinks, heat transfer enhancement, multi-objective optimization, neural network, NSGA-II