Multi-Condition Aerodynamic Optimization of the Air Compressor Impeller Used in Fuel-Cell Vehicles

doi:10.7652/xjtuxb202109005

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Multi-Condition Aerodynamic Optimization of the Air Compressor Impeller Used in Fuel-Cell Vehicles

更新时间：2025-07-09

- Multi-Condition Aerodynamic Optimization of the Air Compressor Impeller Used in Fuel-Cell Vehicles
- Vol. 55, Issue 9, Pages: 39-48(2021)
- 作者机构：
  
  合肥通用机械研究院有限公司压缩机技术国家重点实验室,合肥,230031
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202109005
  CLC： V231.1
- Online First：10 September 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
Multi-Condition Aerodynamic Optimization of the Air Compressor Impeller Used in Fuel-Cell Vehicles[J]. 2021, 55(9): 39-48.
DOI：

Multi-Condition Aerodynamic Optimization of the Air Compressor Impeller Used in Fuel-Cell Vehicles[J]. 2021, 55(9): 39-48. DOI： 10.7652/xjtuxb202109005.

摘要

针对燃料电池离心空压机工况多变、过高压比导致燃料电池系统寄生功率过大的问题

基于参数化建模、拉丁超立方抽样、径向基函数神经网络和多目标灰狼优化算法

提出了多目标多工况带约束的燃料电池空压机叶轮气动优化设计方法

自主开发数值程序实现了气动优化的完整设计流程。以某两级燃料电池离心空压机叶轮为优化对象

采用拉丁超立方抽样获得叶轮关键设计变量的样本空间

基于自主流场分析程序计算叶轮样本对应的气动性能目标参数

在此基础上运用神经网络程序建立流场分析代理模型

以设计工况效率及非设计工况效率和压比为目标、设计压比为约束

运用多目标灰狼算法程序进行了叶轮气动设计的多工况多目标全局寻优。计算结果表明

拉丁超立方抽样实现了样本点在设计变量空间的均匀分布

运用神经网络建立的代理模型能够准确描述设计变量与性能目标间的映射关系

与流场计算所得性能目标的最大误差均小于1%

寻优计算获得了设计压比约束下的最优效率及相应的叶轮型线

优化后设计和非设计工况点的叶轮流场低速区减小、熵增降低

两级叶轮设计工况点的等熵效率分别提高2.2%和2%

非设计工况点的效率分别提高2.9%和2.2%。

Abstract

Aiming at variable working conditions and the problem of excessive parasitic power of fuel-cell system caused by over high pressure ratio of fuel-cell centrifugal compressor

a multi-objective and multi-condition aerodynamic optimal design method with constraints for fuel-cell air compressor impeller is proposed based on parametric design

Latin hypercube sampling

radial basis function neural network and multi-objective grey wolf optimization algorithm. The numerical programs are developed independently to realize the design process of aerodynamic optimization. Taking the impellers of a two-stage fuel-cell centrifugal air compressor as the optimization objects

Latin hypercube sampling is used to obtain the sample space of the key design variables of the impeller. Based on the internal code of flow-field analysis

the corresponding aerodynamic performance target parameters of the impeller samples are calculated. On this basis

the neural network program is used to establish the flow-field analysis surrogate model. Taking the efficiency of design point and the efficiency and pressure ratio of non-design point as objectives

and the design pressure ratio as constraint

the multi-objective grey wolf algorithm program is used to carry out the multi-condition and multi-objective aerodynamic optimization of impellers. The results show that the Latin hypercube sampling achieves the uniform distribution of sample points in the design variable space

and the surrogate model established by neural network can accurately describe the mapping relationship between the design variables and the performance targets

and the maximum error between the performance targets obtained by the surrogate model and flow-field calculation is less than 1%. The optimal efficiency and the corresponding impeller profile under the constraint of design pressure ratio are obtained by optimization calculation. After optimization

the low velocity region and entropy increment in the impeller flow field at the design and non-design operating points both reduced

and the isentropic efficiencies of the two impellers at the design operating point were increased by 2.2% and 2%

respectively

and the efficiencies of the two impellers at the non-design operating point were increased by 2.9% and 2.2%

respectively.

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