Intelligent optimization design of composite microchannel liquid cold plate for airborne electronic devices

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 the NSGA-Ⅱ algorithm, the surrogate is employed to conduct multi-objective optimization. The primary factors governing thermal-hydraulic performance are 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 the original design, the optimized MCH achieves an average Nusselt number of 70.755 and an overall performance factor (PEC) of 2.223, yielding enhancements of 36.7% in heat transfer and 9.2% in comprehensive performance.