@article{ZHANG2026, 
author = {Tongyong ZHANG and Mengxiang ZENG and Qiang CHEN and Qingguo FEI and Dahai ZHANG},
title = {Intelligent optimization design of composite microchannel liquid cold plate for airborne electronic devices},
year = {2026},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {47},
number = {13},
keywords = {microchannel heat sinks, heat transfer enhancement, multi-objective optimization, neural network, NSGA-Ⅱ algorithm},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2026.33030},
doi = {10.7527/S1000-6893.2026.33030},
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 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.}
}