管翅式换热器遗传算法优化  

Optimization of genetic algorithm for finned tube heat exchanger

作　　者：高乙栋 董震 吕明新[1,2] 赖艳华 GAO Yidong;DONG Zhen;Lü Mingxin;LAI Yanhua(School of Energy and Power Engineering,Shandong University,Jinan 250061,China;Suzhou Institute,Shandong University,Suzhou 215123,China)

机构地区：[1]山东大学能源与动力工程学院,山东济南250061 [2]山东大学苏州研究院,江苏苏州215123

出　　处：《热科学与技术》2021年第4期318-323,共6页Journal of Thermal Science and Technology

基　　金：苏州市重点产业技术创新-前瞻性应用研究资助项目(SYG201834)。

摘　　要：管翅式换热器作为一种高效的换热设备,提升其换热效率和降低投资成本显得至关重要。通过单目标遗传算法(GA)和多目标非支配排序遗传算法(NSGA-Ⅱ)对管翅式换热器进行优化设计,设置翅片高度、翅片间距、管长、横向管数和纵向管数5个自变量的合理设计范围,单目标优化选用换热器效率、压降熵产和最大收益3个目标函数,根据热力学第一、二定律以及经济分析进行目标优化,比较三者的优化结果从而得到最佳优化目标函数,以及在特定运行条件下换热器的最佳设计方案,其中运行净利润能直接反应出换热器效益;多目标采用单目标优化中换热器传热效率和总投入为目标函数,进一步验证优化的合理性和经济性。It is essential to further improve the heat transfer efficiency of the tube-fin heat exchanger and reduce its investment costs. Using single-object genetic algorithm(GA) and multi-objective non-dominated sorting genetic algorithm(NSGA-Ⅱ) optimizes the tube-fin heat exchangers, and setting the reasonable design range of the five independent variables as fin height, fin spacing, tube length, transverse tube number and longitudinal tube number. For single-objective optimization, three objective functions of heat exchanger efficiency, pressure drop entropy production, and maximum return are used. Based on the first and second laws of thermodynamics and economic analysis, the data of the three factors are compared to obtain the optimal optimization objective function, the optimized heat exchanger design method at certain opertation conditions. Multi-objective uses the heat exchanger efficiency of the single objective optimization and total investment under non-dominated conditions as the objective functions, thus further verifying the rationality and economy of the optimization.

关 键 词：翅片管换热器 单目标遗传算法 多目标非支配排序遗传算法 优化 

分 类 号：TK172[动力工程及工程热物理—热能工程]