机载电子设备复合微通道液冷板智能优化设计

doi:10.7527/S1000-6893.2026.33030

Abstract

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.

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

CLC Number:

V245.3

Tongyong ZHANG, Mengxiang ZENG, Qiang CHEN, Qingguo FEI, Dahai ZHANG. Intelligent optimization design of composite microchannel liquid cold plate for airborne electronic devices[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(13): 133030.

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T_s/℃	ρ_s/（kg·m^-3）	λ_s/（W·（m·K）^-1）	c_p，s/（J·（kg·K）^-1）
0	2 700	162	917
93	2 700	177	978
201	2 700	192	1 028
316	2 700	207	1 078
428	2 700	223	1 133

T_f/℃	ρ_f/（kg·m^-3）	λ_f/（W·（m·K）^-1）	c_p，f/（J·（kg·K）^-1）	μ_f/（10^-3 Pa·s）
0	821.3	0.142 6	1 681	1.340
30	796.2	0.137 2	1 834	1.213
60	760.7	0.131 8	2 045	0.745
90	732.1	0.126 7	2 189	0.562
120	701.8	0.119 4	2 319	0.410
150	687.4	0.115 7	2 427	0.334

设计参数	数值范围	无量纲参数	数值范围
W_s/mm	1.1~1.9	α=W_s/W_c	0.55~0.95
W_r1/mm	0.3~1.2	β_r1=W_r1/W_f	0.15~0.60
L_r1/mm	0.3~1.2	δ_r1=L_r1/W_f	0.15~0.60
P_r1/mm	1.25~2.45	ε_r1=P_r1/L_fl	0.25~0.49
W_r2/mm	0.2~0.7	β_r2=W_r2/W_f	0.10~0.35
L_r2/mm	0.3~1.0	δ_r2=L_r2/W_f	0.15~0.50
P_r2/mm	0.1~0.9	ε_r2=P_r2/L_fs	0.04~0.36
q_m/（kg·s^-1）	0.01~0.08	Re	84~670

超参数	寻优范围
隐藏层层数	1~8
每层神经元数	2~64
激活函数	Sigmoid，ReLU，Leaky ReLU，Tanh
训练函数	Trainlm，Trainbfg，Trainrp，Trainscg
初始化函数	Initnw，Initlay，Initwb

设计参数	Pareto解
设计参数	Case 1	优化前	Case 3	TOPSIS	Case 5
α	0.949	0.750	0.941	0.840	0.737
β_r1	0.151	0.300	0.197	0.586	0.599
δ_r1	0.505	0.400	0.544	0.448	0.154
ε_r1	0.309	0.470	0.430	0.453	0.332
β_r2	0.103	0.200	0.104	0.349	0.350
δ_r2	0.491	0.300	0.319	0.200	0.351
ε_r2	0.121	0.240	0.127	0.358	0.359
Re	659.66	670.00	659.55	658.54	657.53

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

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Pareto解	性能指标	模型值	CFD值	误差/%
Case 1	Nu	34.431	35.689	3.52
Case 1	f	0.105	0.110	4.84
优化前	Nu		55.251
优化前	f		0.596
Case 3	Nu	55.408	57.355	3.40
Case 3	f	0.552	0.522	5.69
TOPSIS	Nu	70.755	75.544	6.34
TOPSIS	f	1.498	1.412	6.06
Case 5	Nu	83.714	81.654	2.52
Case 5	f	5.408	5.235	3.30

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