基于BP神经网络的立式离心泵导叶与蜗壳优化设计
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金项目(51979125)、江苏省重点研发计划项目(BE2019089)和江苏省普通高校研究生实践创新计划项目(SJCX21_1682)


Optimization Design of Vane Diffuser and Volute in Vertical Centrifugal Pump Based on Back Propagation Neural Network
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    立式离心泵是大型灌溉和长距离调水工程的核心动力装备,单机配套功率能够达到40MW级。为了降低立式离心泵的运行能耗,以效率指标为优化目标,基于BP(反向传播)神经网络模型与多岛遗传算法对其多个过流部件进行优化设计。考虑到各过流部件的匹配性,采用Plackett-Burman试验设计从导叶与蜗壳的10个设计参数中筛选出优化设计变量。运用最优拉丁超立方采样方法设计了106组方案,并搭建了立式离心泵自动数值模拟优化平台。基于BP神经网络模型构建了优化设计变量和优化目标之间的高精度非线性关系,最终通过多岛遗传算法得到导叶与蜗壳的最优参数组合。研究结果表明,运用SST k-ω湍流模型能够准确地预测立式离心泵的性能参数;BP神经网络是映射泵设计参数和性能参数间内在联系的有效方法;优化后模型设计工况下效率达到90.21%,较原始模型提高了3.61个百分点;优化后的导叶与蜗壳对立式离心泵设计工况和小流量工况下的性能影响更为显著;优化后导叶与其他过流部件匹配性提高,导叶与蜗壳内部流动特性得到明显改善。

    Abstract:

    Vertical centrifugal pump is a high specific speed centrifugal pump, which is usually with radial vane diffuser structure. As the core power equipment for large-scale irrigation projects and long-distance water transfer, the matching motor power for vertical centrifugal pump is huge and can reach 40MW,and the efficiency directly determines its operating energy consumption. In order to reduce the energy consumption of vertical centrifugal pumps, an optimization on multi-components was proposed based on back propagation neural network (BPNN) and multi-island genetic algorithm (MIGA) . The matching of the hydraulic components was taken into account and the Plackett-Burman test design was used to screen out the optimal design variables from the 10 design parameters of the vane diffuser and the volute. Then, totally 106 sets of cases were sampled by using optimal Latin hypercube sampling (OLHS), and an automatic numerical simulation optimization platform for the vertical centrifugal pump was built to quickly obtain the optimization objective values corresponding to each set of case. The BPNN was used to construct the high-precision nonlinear relationship between the optimization variables and the optimization objective. Finally, the optimal parameter combination of vane diffuser and volute was obtained through MIGA. The results showed that the performance parameters of vertical centrifugal pump could be more accurately predicted by using SST k-ωturbulence model. BPNN was an effective method to construct high-precision nonlinear relationship between pump design parameters and performance parameters. The efficiency of the optimized model under design condition reached 90.21%, which was 3.61 percentage points higher than that of the original model. The optimized vane diffuser and volute had a more obvious influence on the performance of vertical centrifugal pumps under design condition and part-load conditions. The matching between the vane diffuser and other hydraulic components was better, and the internal flow pattern of the vane diffuser was significantly improved after optimization. The optimization method proposed could provide a certain reference for the optimization design of centrifugal pumps.

    参考文献
    相似文献
    引证文献
引用本文

张德胜,杨港,赵旭涛,杨雪琪,高雄发.基于BP神经网络的立式离心泵导叶与蜗壳优化设计[J].农业机械学报,2022,53(4):130-139. ZHANG Desheng, YANG Gang, ZHAO Xutao, YANG Xueqi, GAO Xiongfa. Optimization Design of Vane Diffuser and Volute in Vertical Centrifugal Pump Based on Back Propagation Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(4):130-139.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-05-17
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2021-06-30
  • 出版日期: