Structural Optimization of Multi-Heat-Source Cold Plates Using Neural Network and Genetic Algorithm

doi:10.3969/j.issn.1008-0198.2026.05.010

Abstract

Abstract: For the structural optimization of multi-heat-source liquid-cooled plates, a multi-zone independent regulation and optimization method for straight rib cold plate flow channels is proposed, combining neural networks and genetic algorithms. Using the maximum temperature of heat sources and the inlet-outlet pressure difference as performance indicators, the limitations of conventional overall channel control are highlighted. Two optimization objectives are formulated and solved by combining via neural networks and genetic algorithms. The results show that minimizing the pressure difference reduces flow resistance by 47.4%, which is 34.9% lower than the result obtained via the conventional full-domain optimization. Under the comprehensive performance objective, flow resistance is reduced by 28% (12.2% lower than the overall optimization), while the temperature in critical regions decreases by 0.54 ℃, with all heat source temperatures maintained within reasonable ranges. This method provides a scalable framework for efficient design of multi-heat-source liquid-cooled cold plates, enabling flexible strategy formulation based on the weighting of thermal and hydraulic objectives, thereby effectively improving design efficiency and overall performance.

Key words: multi-heat source cold plate, neural network, genetic algorithm, structural optimization, parametric study

CLC Number:

TK124

ZHANG Yifei, ZHUANG Yubin, CUI Pan, LIU Zhichun. Structural Optimization of Multi-Heat-Source Cold Plates Using Neural Network and Genetic Algorithm[J]. Hunan Electric Power, 2026, 46(5): 76-84.

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