基于孪生支持向量回归的多级离心泵外特性曲线预测及设计方法

doi:10.3901/JME.2024.14.364

机械工程学报 ›› 2024, Vol. 60 ›› Issue (14): 364-377.doi: 10.3901/JME.2024.14.364

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基于孪生支持向量回归的多级离心泵外特性曲线预测及设计方法

童哲铭^1,2, 马鑫航^1,2

1. 浙江大学流体动力基础件与机电系统全国重点实验室杭州 310063;
2. 浙江大学机械工程学院杭州 310063

收稿日期:2023-08-03 修回日期:2024-04-01 出版日期:2024-07-20 发布日期:2024-08-29
作者简介:童哲铭,男,1988年出生,研究员,博士研究生导师。主要研究方向为旋转机械设计理论及方法。E-mail:tzm@zju.edu.cn
基金资助:
国家自然科学基金资助项目(52075481，52375274)。

Prediction and Design of the External Characteristic Curve of Multistage Centrifugal Pump Based on Twin Support Vector Regression

TONG Zheming^1,2, MA Xinhang^1,2

1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310063;
2. School of Mechanical Engineering, Zhejiang University, Hangzhou 310063

Received:2023-08-03 Revised:2024-04-01 Online:2024-07-20 Published:2024-08-29

摘要/Abstract

摘要： 准确快速地获取多级离心泵的外特性曲线，对设计过程中泵性能指标的判断具有重要的意义。现有基于计算流体力学(Computational fluid dynamics, CFD)数值模拟方法依赖设计人员经验、计算耗时以及偏工况点精度差，提出一种基于孪生支持向量回归(Twin support vector regression, TSVR)的离心泵外特性曲线预测方法。针对多级离心泵流道复杂的特点，筛选出10个结构参数和流量作为模型输入。通过TSVR方法建立输入变量与外特性值之间的复杂非线性关系，对多级离心泵的外特性曲线进行预测。与试验结果相比，所提方法可以较好地预测性能指标，对扬程、效率和轴功预测的平均相对误差分别为1.71%、3.56%、3.36%。此外，与BP神经网络相比，TSVR对扬程和效率预测的方均根误差显著降低。同时，在整个工况范围内，TSVR模型对比传统采用RNG湍流模型的CFD方法有更高的精度以及计算效率。对比表明，所提方法可提高多级离心泵性能曲线预测的速度和精度，为实现多级泵的快速预测及高效性能设计提供重要的支撑。

关键词: 离心泵, 支持向量回归, 数值模拟, 多工况, 性能预测

Abstract: Obtaining the external characteristic curve of a multistage centrifugal pump accurately and quickly is of great significance to the judgment of the pump performance index in the design process. The existing methods based on computational fluid dynanics(CFD) numerical simulation rely on the experience of designers, calculation time, and low accuracy of off-design points. Therefore, a prediction method of centrifugal pump external characteristic curve based on twin support vector regression(TSVR) is proposed. According to the complex characteristics of the multistage centrifugal pump flow channel, 10 structural parameters and flow are selected as the model input. The complex nonlinear relationship between structural parameters and external characteristic values is established by the TSVR method, and the external characteristic curve of the multistage centrifugal pump is predicted. Compared with the test results, the proposed method can better predict the performance indicators, and the average relative errors of head, efficiency, and axle work are 1.71%, 3.56%, and 3.36% respectively. In addition, compared with BP neural network, the root-mean-square error of TSVR for head and efficiency prediction is significantly reduced. At the same time, the prediction speed and accuracy of the TSVR model are far better than that of traditional CFD numerical calculation in the whole operating range. The comparison results show that the proposed method can improve the speed and accuracy of performance curve prediction of multistage centrifugal pumps, and can provide technical support for the fast prediction and efficient design of multistage pumps.

Key words: centrifugal pump, support vector regression, numerical simulation, multiple working conditions, performance prediction

中图分类号:

TH311

童哲铭, 马鑫航. 基于孪生支持向量回归的多级离心泵外特性曲线预测及设计方法[J]. 机械工程学报, 2024, 60(14): 364-377.

TONG Zheming, MA Xinhang. Prediction and Design of the External Characteristic Curve of Multistage Centrifugal Pump Based on Twin Support Vector Regression[J]. Journal of Mechanical Engineering, 2024, 60(14): 364-377.

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基于孪生支持向量回归的多级离心泵外特性曲线预测及设计方法

Prediction and Design of the External Characteristic Curve of Multistage Centrifugal Pump Based on Twin Support Vector Regression

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