基于遗传算法的贯流风机蜗壳型线优化研究  

Optimization of Rear Wall of Cross Flow Fan Using Genetic Algorithm

作　　者：黄驰 王达 杨昆[1] 王嘉冰[1] HUANG Chi;WANG Da;YANG Kun;WANG Jia-Bing(School of Energy and Power Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学能源与动力工程学院,武汉430074

出　　处：《工程热物理学报》2020年第5期1110-1115,共6页Journal of Engineering Thermophysics

基　　金：国家重点研发计划重点专项项目(No.2018YFB0606101);国家自然科学基金面上项目(No.51476063)。

摘　　要：建立了贯流风机蜗壳型线的自动优化平台,结合遗传算法和数值模拟,以贯流风机静压Ps作为优化目标,对贯流风机的蜗壳型线进行了优化,并研究了蜗壳周向位置θ0对风机性能的影响。结果表明,优化后的蜗壳型线与主流区气流更加贴合,风机静压略有提升。蜗壳周向位置θ0减小时,风机静压增加,效率先升后降;θ0越小,效率特性曲线越陡。蜗壳间隙下游涡流区随着θ0的减小而增大,随着流量的降低而增大。存在一个最优的θ0值获得较高的风机静压和效率。Combining genetic algorithm and numerical simulation,the rear wall of the cross-flow fan is optimized,while static pressure of the cross-flow fan is optimization target.The influence of the circumferential position of the rear wall(θ0)on the performance of the fan is studied.The results show that the optimized rear wall is more suitable for the airflow in the mainstream area,and the static pressure of the fan is slightly improved.Whenθ0 decreases,the static pressure of the fan increases,the efficiency rises at first then falls.Efficiency performance curve become steeper with smallerθ0.At the downstream of the rear wall gap,the eddy zone increases with the decrease ofθ0,it increases with the decrease of flow rate as well.There is an optimalθ0 value makes static pressure and efficiency both higher.

关 键 词：贯流风机 蜗壳型线 遗传算法 

分 类 号：TK432[动力工程及工程热物理—动力机械及工程]