基于机器学习的改型歧管式微通道热沉流动传热特性多目标优化OA

To address the heat dissipation requirements of ultra-high heat flux electronic devices,numerical simulations were conducted to study the flow and heat transfer characteristics of a modified microchannel heat sink.The influence of channel height and width on its overall performance was analyzed.A prediction model based on the genetic algorithm-optimized least squares support vector machine was established,the multi-objective particle swarm optimization algorithm was employed to optimize the geometric configuration of the heat sink,and the grey relational analysis-vlsekriterijumska optimizacija kompromisno resenje(GRA-VIKOR)and entropy-weighted technique for order of preference by similarity to ideal solution(TOPSIS)methods were utilized to screen out the globally optimal design.The results show that:compared with the original MMCHS benchmark model established in this study(geometric parameters:hm1=25 μm,hm2=25 μm,wm1=200 μm,wm2=200 μm),the optimized scheme effectively reduces the peak temperature by enhancing the secondary vortex effect,resulting in a 20.2％increase in the Nusselt number(Nu)and a 10.2％decrease in the pressure drop(Δp).The optimization strategy proposed in this study provides new insights for high-heat-flux thermal management;while ensuring a significant improvement in performance,it greatly reduces the computational cost required by traditional parametric methods and can offer theoretical guidance for the development of novel and high-efficiency microchannel heat sinks.