基于RBF-HDMR模型与PSO算法的液力透平叶片优化

doi:10.3901/JME.2022.12.283

机械工程学报 ›› 2022, Vol. 58 ›› Issue (12): 283-292.doi: 10.3901/JME.2022.12.283

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基于RBF-HDMR模型与PSO算法的液力透平叶片优化

姜丙孝¹, 杨军虎^1,2, 王晓晖^1,2, 苗森春^1,2

1. 兰州理工大学能源与动力工程学院兰州 730050;
2. 甘肃省流体机械及系统重点实验室兰州 730050

收稿日期:2021-09-24 修回日期:2022-03-25 出版日期:2022-06-20 发布日期:2022-09-14
通讯作者: 杨军虎(通信作者),男,1962年出生,教授,博士研究生导师。主要研究方向为水力机械内部流动机理及设计理论。E-mail:lzyangjh@lut.cn
作者简介:姜丙孝,男,1986年出生,博士研究生。主要研究方向为水力机械流体动力学分析及现代优化理论。E-mail:jiangbingxiao0808@163.com
基金资助:
国家自然科学基金（52169019）、甘肃省高等学校产业支撑计划（2020C-20）和甘肃省杰出青年基金（20JR10RA203）资助项目

Blades Optimization of Pumps as Turbines Based on RBF-HDMR Model and PSO Algorithm

JIANG Bingxiao¹, YANG Junhu^1,2, WANG Xiaohui^1,2, MIAO Senchun^1,2

1. School of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou 730050;
2. Key Laboratory of Fluid Machinery and Systems of Gansu Province, Lanzhou 730050

Received:2021-09-24 Revised:2022-03-25 Online:2022-06-20 Published:2022-09-14

摘要/Abstract

摘要： 针对泵反转作透平时效率偏低的问题，提出将径向基函数与高维模型表示方法结合的混合模型用于液力透平性能优化。以液力透平叶轮叶片为研究对象，用Bezier曲线将叶片型线参数化，分离出代理模型的自变量并确定模型的训练区间，结合Matlab、Creo和Fluent软件构建基于叶片型线控制变量的液力透平效率预测代理模型，运用粒子群算法对构造的代理模型在训练区间内全域寻优得出液力透平的最优效率点及对应的叶片几何参数，分别用数值模拟和试验研究的方法验证代理模型的预测数据。结果表明，经代理模型优化后的液力透平数值模拟性能曲线与试验结果大体相符，在最优工况点，优化后的液力透平数值模拟效率值较原型液力透平提升了4.78%，轴功率提升了6.22%；试验效率值较原型液力透平提升了3.7%，轴功率提升了4.8%。

关键词: 液力透平, 叶片优化, 径向基函数, 高维模型表示方法, 粒子群算法

Abstract: In view of the low efficiency of pumps as turbines(PAT), a hybrid model combining radial basis function(RBF) and high-dimensional model representation(HDMR) is proposed to optimize the performance of PAT. The impeller blade of PAT is taken as the research object. The blade profile is parameterized with Bezier curves, the independent variables of the surrogate model are separated and the training interval of the model is determined. The surrogate model based on blade profile control variables for PAT efficiency prediction is constructed with Matlab, Creo and Fluent software. The particle swarm optimization(PSO) algorithm is utilized to optimize the surrogate model in the training interval by global optimization, the optimal efficiency point of the PAT and the corresponding blade geometry parameters are obtained. The prediction data of the surrogate model is verified through numerical simulation and experimental research. The results show that the performance curves of the numerical simulation of PAT optimized by the surrogate model are in good agreement with the test results. At the optimal operating point, the numerical simulation efficiency value of the optimized PAT is 4.78% higher than that of the prototype PAT, the shaft power is 6.22% higher, the test efficiency value is 3.7% higher than that of the prototype PAT, and the shaft power is 4.8% higher.

Key words: pumps as turbines, blade optimization, radial basis function, high-dimensional model representation, particle swarm optimization

中图分类号:

TK73
TH311

姜丙孝, 杨军虎, 王晓晖, 苗森春. 基于RBF-HDMR模型与PSO算法的液力透平叶片优化[J]. 机械工程学报, 2022, 58(12): 283-292.

JIANG Bingxiao, YANG Junhu, WANG Xiaohui, MIAO Senchun. Blades Optimization of Pumps as Turbines Based on RBF-HDMR Model and PSO Algorithm[J]. Journal of Mechanical Engineering, 2022, 58(12): 283-292.

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基于RBF-HDMR模型与PSO算法的液力透平叶片优化

Blades Optimization of Pumps as Turbines Based on RBF-HDMR Model and PSO Algorithm

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