A topology multi-objective optimization method and system for high-power electric cooling multi-channel single-phase heat transfer and energy efficiency

By employing a topological multi-objective optimization method for high-power electric cooling of multi-channel single-phase heat transfer, combined with optimal Latin hypercube design and surrogate model, the microchannel structure is optimized, solving the problems of large pressure drop, uneven temperature and high energy consumption in the cooling of high-power electronic devices, and achieving efficient performance trade-off and energy efficiency improvement.

CN122154220APending Publication Date: 2026-06-05CHONGQING UNIV OF TECH
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Patent Information

Application Number
CN202610280093.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-09
Publication Date
2026-06-05

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Abstract

The application provides a high-power electric power cooling multi-channel single-phase heat transfer and energy efficiency topology multi-objective optimization method and system, and the method comprises the following steps: obtaining a design scene input condition, establishing a topology optimization model, solving a model output fluid and solid material distribution and a two-dimensional topology model; reconstructing a two-dimensional model to obtain a three-dimensional microchannel area model; sampling three-dimensional topology model variables, obtaining an optimal Latin hypercube after optimization, constructing a surrogate model; selecting new filling points according to a criterion to calculate and update a sample set and the surrogate model; optimizing total thermal resistance and pumping power conflict targets to obtain a Pareto optimal solution set. In the topology optimization stage, a microchannel two-dimensional section is selected as a design domain, a density method is used, average temperature and energy consumption are minimized as targets, an optimal material distribution configuration is generated, and a three-dimensional model is reconstructed. In the multi-objective optimization stage, a sampling is performed to construct a surrogate model, an EGO-PEI algorithm is used for optimization, and a Pareto optimal solution set is obtained.
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