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基于多目标粒子群算法的矩形微通道结构优化
引用本文:陆辉山,王宁,卫红梅,赵守耀,李沛.基于多目标粒子群算法的矩形微通道结构优化[J].科学技术与工程,2022,22(3):1085-1090.
作者姓名:陆辉山  王宁  卫红梅  赵守耀  李沛
作者单位:中北大学机械工程学院
基金项目:超重力化工过程山西省重点实验室开放基金(CZL2020-06);校学科研究基金项目(110246);
摘    要:为提升矩形微通道的综合性能,通过多目标粒子群算法对矩形微通道进行数值优化,由响应曲面法拟合热阻函数,再以热阻与压降为目标函数,建立以矩形微通道结构参数为变量的多目标粒子群算法的数学模型。由多目标粒子群算法计算得到热阻与压降的pareto优化解集,用k-mean聚类法对优化解集进行聚类得到4个代表解,与未优化解进行对比,评价其综合性能。结果表明:响应曲面法拟合的热阻函数的相关系数R2分别为0.9981、0.9961均接近1,模型准确度高,点3与点0的仿真结果对比表明经过多目标粒子群算法优化后的通道的温度分布更加均匀,最高温度更低,压降更低,综合性能更优。可见该种方法可以提供一定工况范围内的优化解集,提升微通道的综合性能。

关 键 词:微通道  响应曲面法  多目标粒子群算法  结构优化
收稿时间:2021/4/22 0:00:00
修稿时间:2021/11/3 0:00:00

Structure optimization of rectangular microchannel based on multi-objective particle swarm optimization
Lu Huishan,Wang Ning,Wei Hongmei,Zhao Shouyao,Li Pei.Structure optimization of rectangular microchannel based on multi-objective particle swarm optimization[J].Science Technology and Engineering,2022,22(3):1085-1090.
Authors:Lu Huishan  Wang Ning  Wei Hongmei  Zhao Shouyao  Li Pei
Institution:School of Mechanical Engineering, North University of China
Abstract:In order to improve the comprehensive performance of rectangular microchannels, the numerical optimization of rectangular microchannels was carried out by using the multi-objective particle swarm optimization algorithm, and the thermal resistance function was fitted by the response surface method. Then, the mathematical model of the multi-objective particle swarm optimization algorithm was established with the structural parameters of rectangular microchannels as the objective functions, taking the thermal resistance and pressure drop as the objective functions. The Pareto optimal solution set of thermal resistance and pressure drop was calculated by the multi-objective particle swarm optimization algorithm. The K-mean clustering method was used to cluster the optimal solution set, and four representative solutions were obtained. The comprehensive performance of the optimal solution set was evaluated by comparing it with the unoptimized solution. The results show that the correlation coefficient R2 of the thermal resistance function fitted by the response surface method is 0.9981 and 0.9961, which are close to 1 respectively. The model accuracy is high. The comparison between the simulation results at point 3 and point 0 shows that the temperature distribution of the channel optimized by the multi-objective particle swarm optimization algorithm is more uniform, the maximum temperature is lower, the pressure drop is lower, and the comprehensive performance is better. It can be seen that this method can provide an optimal solution set within a certain range of working conditions and improve the comprehensive performance of microchannels.
Keywords:microchannel  Response surface method  Multi-objective Particle Swarm Optimization  Structure optimization
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