Matching optimization of impeller and volute in a multistage centrifugal pump

被引:0
作者
Zhao, Jiantao [1 ]
Pei, Ji [1 ]
Yuan, Jianping [1 ]
Wang, Wenjie [1 ,2 ]
机构
[1] National Research Center of Pumps, Jiangsu University, Zhenjiang
[2] Key Laboratory of Fluid and Power Machinery, Xihua University, Ministry of Education, Chengdu
来源
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2024年 / 45卷 / 09期
关键词
backpropagation neural network; energy efficiency optimization; genetic algorithm; matching optimization; multiobjective optimization; multistage centrifugal pump; numerical simulation; surrogate model;
D O I
10.11990/jheu.202206094
中图分类号
学科分类号
摘要
To solve the problem of the narrow high-efficiency operation zone and low overall energy efficiency of a multistage centrifugal pump, an optimal design study of the impeller and volute of a multistage double-suction centrifugal pump with a specific speed of 64 was conducted. The applicability of different surrogate models in the optimization of the hydraulic performance of the multistage centrifugal pump was investigated and compared, and a GA-BP neural network was selected as the optimal surrogate model. Nine main design parameters were chosen as optimization variables, and the efficiency of the pump under partial-load and nominal conditions 0. 6Qd and 1. 0Qd were set as the optimization objectives. The Pareto-optimal solution of the multiobjective optimization problem was obtained using the NSGA-II algorithm with the Latin hypercubic sampling method and an automatic numerical analysis program to construct a sample database, and appropriate combinations of parameters were selected according to actual engineering requirements. The analysis results showed that the efficiency of the model pump was increased by 2. 49% and 3. 09% under partial-load and nominal conditions, respectively, and the issue of the steep drop in the head under overload conditions was alleviated. This method can be a reference for the positive design of multistage centrifugal pumps. © 2024 Editorial Board of Journal of Harbin Engineering. All rights reserved.
引用
收藏
页码:1670 / 1678
页数:8
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