基于多目标遗传算法的屏蔽泵叶轮水力优化

Hydraulic optimization of canned-motor pump impeller based on multi-objective genetic algorithm

为改善屏蔽泵叶轮综合水力性能,搭建了ANSYS-Workbench与iSIGHT联合优化平台,采用优化拉丁立方设计对叶片骨线、前后盖板及叶缘厚度等共28个备选参数进行敏感性分析.基于各参数对目标函数影响程度,确定叶片前盖板及骨线处等9个参数作为最终优化输入参数,选取Kriging代理模型与非支配排序遗传算法NSGA-Ⅱ对效率及扬程迭代寻优,最终依据不同权重分配给出2种叶片优化方案.通过数值模拟验证,优化方案1与方案2在额定工况下效率分别提升1.98%和2.83%,扬程分别提升15.73 m和13.39 m,运行区间水力外特性均有明显提升.研究结果表明:前盖板参数z 3对效率及扬程影响最大,分别达到-18.99%与-30.10%;使用Kriging代理模型的预测精度最高,总误差E 0值为3.393%;在0.83 Q BEP~1.12 Q BEP运行区间,方案1与方案2的扬程明显高于原方案,方案1在最优流量工况的优化效果最为显著,达13.89%.

In order to improve the comprehensive hydraulic characteristic of the canned-motor pump,a joint optimization platform of ANSYS-Workbench and iSIGHT was built.The sensitivity analysis of 28 alternative parameters,such as the blade bone line,hub and shroud of impeller and blade thickness et al were carried out by optimized Latin cube design.Based on the influence degree of various parameters on the objective function,9 parameters were determined at the blade bone line and the shroud of impeller as the final input parameters.Kriging surrogate model and NSGA-Ⅱwere selected to iteratively optimize the efficiency and head.Finally,two blades optimization schemes were obtained according to different weight distribution.Through the verification of numerical simulation,the efficiency of Scheme 1 and Scheme 2 under rated working condition was increased by 1.98%and 2.83%,respectively,and the head was increased by 15.73 m and 13.39 m,respectively.The hydraulic characteristics in the operation range were significantly improved.The results show that the hub parameter z 3 has the greatest influence on the efficiency and head,reaching-18.99%and-30.10%,respectively.Kriging surrogate model has the highest prediction accuracy,with the total error of 3.393%.In the operating range of 0.83 Q BEP-1.12 Q BEP,the head of Scheme 1 and Scheme 2 is significantly higher than that of the original scheme,and Scheme 1 has the most significant optimization efficiency under the optimal flow condition,reaching 13.89%.