基于粒子群算法的内外翅片管换热器优化

PSO-Based Optimization of Internally and Externally Finned Tube Heat Exchanger

粒子群优化算法(Particle Swarm Optimization,简称PSO)是一种新型的优化算法,今把PSO应用于内外翅片管换热器的结构尺寸优化,建立了物理数学模型,开发了C++程序。把体积作为优化目标函数,以换热面积和压降作为约束条件,对管子横向间距、纵向间距、管排数、外翅片间距、换热器在与热气流垂直方向的长度进行了优化,并与利用遗传算法的文献结果对比:在相同的设计参数和相同的优化变量搜索范围条件下,体积减小9.5%,重量减轻16%,优化计算时间减小一个量级,PSO应用于换热器优化设计优于遗传算法。

Particle Swarm Optimization （PSO） is a new type of optimization algorithm, and it was used here to optimize the structural dimensions of the internally and externally finned tube exchanger. The physical and mathematical model was established, and the C＋＋ Program was developed. The heat exchanger volume was considered as the optimization objective function; the heat transfer area required for the heat duty and the pressure drop were considered as the restrictive conditions. The transverse tube pitch, longitudinal tube pitch, the number of tube rows, fin pitch and the heat exchanger length along the direction perpendicular to the hot gas flow were taken as the optimization variables. Under the same design parameters and the same optimization variables scope of the search conditions, comparing with the results obtained by using optimization algorithm of genetic algorithm, the volume of heat exchanger obtained by using optimization algorithm of PSO algorithm decreases by 9.5%, its weight obtained reduces by 16% and the computing time needed reduces by one order of magnitude. It indicates that the PSO algorithm is superior to genetic algorithm for the optimization of heat exchanger design.