基于径向基神经网络与粒子群算法的双叶片泵多目标优化  

Multi-objective optimization of double vane pump based on radial basis neural network and particle swarm

作　　者：王春林[1] 胡蓓蓓 冯一鸣[1] 刘轲轲 Wang Chunlin;Hu Beibei;Feng Yiming;Liu Keke(School of Energy and Power Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学能源与动力工程学院,镇江212013

出　　处：《农业工程学报》2019年第2期25-32,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目(51476070;51109094)

摘　　要：针对双叶片泵存在水力性能比相同比转速的多叶片离心泵低的缺陷,该文以一台型号为80QW50-15-4的双叶片污水泵作为研究对象,将其设计流量点的扬程和效率定为优化目标,运用ANSYS CFX(computational fluid dynamics x)进行数值模拟获得性能数据,采用径向基(radial basis function,RBF)神经网络建立结构参数与扬程、效率性能间的预测模型,并将其用作粒子群算法的适应值评价模型,在样本空间内进行最优值求解,获得扬程和效率的Pareto解。选取扬程最优个体和效率最优个体进行数值模拟,研究其在输运不同介质时的性能与内流场差异,并与初始模型的数值模拟数据相比较。经试验验证,清水介质中设计流量点扬程最优个体的扬程较初始个体增加0.96 m,增幅达到5.5%;效率最优个体的效率较初始个体提升了10.11个百分点。该优化方法改善了叶轮水力特性,使双叶片泵性能得到提高。The double vane pump is a special type of flow vane centrifugal pump.It adopts a design with less blades,which leads to a disadvantage that the performance of the double vane pump is inferior to that of the multi-blade pump at the same specific velocity.Its stability is 3%-8%lower than of a vane centrifugal pump.Therefore,it is necessary to improve the work efficiency by optimizing the hydraulic design.This article took a double-passage sewage pump model 80QW50-15-4 as the research object.The optimization objective was to design the head and efficiency of the flow point.ANSYS CFX(computational fluid dynamics x)was used to perform numerical simulation to obtain performance data.According to the two-dimensional hydraulic drawing of the initial model pump,the three-dimensional modeling software Pro/Engineer5.0 was used to simulate the water body of the impeller and the volute and to perform mesh division and irrelevance verification.The model pump was subjected to numerical simulation and experiment of clear water medium,and the performance curve was obtained and compared.The error analysis showed that the maximum error of head and efficiency was 3.9%and 1.7%,which meant that the performance prediction model established by this method had high accuracy.Partial initial model impeller structure parameters were selected for performance impact analysis.The Plackett-Burman screening test was used to determine the blade wrap angle,blade outlet angle and impeller outlet width were significant factors affecting head and efficiency of design flow.According to Fang Kaitai's unified design table,training samples of RBF(radial basis function)neural network were arranged,so as to establish important structural parameters and performance prediction models,and generated 5 groups of structural parameters random for neural network testing and error analysis.The head and efficiency performance prediction model trained by radial basis neural network was introduced into the particle swarm optimization algorithm as the fitness evaluatio

关 键 词：泵 算法 优化 数值模拟 径向基神经网络 

分 类 号：TH311[机械工程—机械制造及自动化]