基于粒子群算法的强迫风冷散热器多目标优化

Multi-objective optimization of forced air-cooled heatsink based on particle swarm optimization algorithm

电力电子系统对强迫风冷散热器提出了低热阻、小型化和轻量化的要求,本文在考虑现有散热器设计方法的不足之后,通过散热器结构及其等效热阻网络构建热阻、压降和质量最小的多目标优化模型。针对当前粒子群算法求解多目标模型时存在的局限性,本文采用多种改进策略提高算法的性能。计算和仿真结果显示,优化后的强迫风冷散热器的热阻、压降、质量均有减小,功率器件表面温度明显降低,验证了多目标优化模型及算法的有效性。

The power electronic system requires forced air-cooled heatsink which has low thermal resistance,miniaturization and lightweight.In this paper,a multi-objective optimization model with minimum thermal resistance,pressure drop and mass is established based on the radiator structure and its equivalent thermal resistance network after considering the shortcomings of existing radiator design methods.In view of the limitations of the current particle swarm algorithm when solving multi-objective models,this paper adopts a variety of improved strategies to improve the performance of the algorithm.The calculation and simulation results show that the thermal resistance,pressure drop,and mass of the optimized forced air-cooled radiator are all reduced,and the surface temperature of the power device is significantly reduced,which verifies the effectiveness of the multi-objective optimization model and algorithm.