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Multi-Objective Optimization Method for Performance Prediction Loss Model of Centrifugal Compressors

  • ZHANG Lei ,
  • FENG Xueheng ,
  • YUAN Wei ,
  • CHEN Ruilin ,
  • ZHANG Qian ,
  • LI Hongyang ,
  • AN Guangyao ,
  • LANG Jinhua
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  • 1. Department of Power Engineering, North China Electric Power University, Baoding 071003, China
    2. Hebei Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, China
    3. Baoding Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, China

网络出版日期: 2025-03-05

基金资助

The authors would like to acknowledge the supports of National Natural Science Foundation of China (Grant No. 52076079), Natural Science Foundation of Hebei Province, China (Grant No. E2020502013), and Fundamental Research Funds for the Central Universities, China (Grant No. 2021MS079/Grant No. 2022MS081).

版权

Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2024

Multi-Objective Optimization Method for Performance Prediction Loss Model of Centrifugal Compressors

  • ZHANG Lei ,
  • FENG Xueheng ,
  • YUAN Wei ,
  • CHEN Ruilin ,
  • ZHANG Qian ,
  • LI Hongyang ,
  • AN Guangyao ,
  • LANG Jinhua
Expand
  • 1. Department of Power Engineering, North China Electric Power University, Baoding 071003, China
    2. Hebei Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, China
    3. Baoding Key Laboratory of Low Carbon and High Efficiency Power Generation Technology, North China Electric Power University, Baoding 071003, China

Online published: 2025-03-05

Supported by

The authors would like to acknowledge the supports of National Natural Science Foundation of China (Grant No. 52076079), Natural Science Foundation of Hebei Province, China (Grant No. E2020502013), and Fundamental Research Funds for the Central Universities, China (Grant No. 2021MS079/Grant No. 2022MS081).

Copyright

Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2024

摘要

损失模型的选择对一维平均流线分析中获得离心压缩机的性能有显著影响。本文基于Augier、Coppage和Jansen提出的经典损失模型,提出了一套优化的损失模型。讨论了在NASA低速离心压缩机(LSCC)的22-36 kg/s质量流量下,由三套模型预测的损失比例和变化规律。结果表明:表面摩擦损失、扩散损失、圆盘摩擦损失、间隙损失、叶片负荷损失、再循环损失和无叶扩压器损失权重均大于10%,在性能预测中占主导地位。因此,这些损失被考虑在新损失模型的组成中。此外,采用多目标优化方法(GA)对损失系数进行校正,以获得最终优化的损失模型。与实验数据相比,三种经典模型的绝热最大相对误差为7.22%,而优化损失模型计算的最大相对误差为1.22%,降低了6%。同样,与原始模型相比,总压比的最大相对误差也降低了。因此,当前优化的模型在趋势和精度方面都比经典损失模型提供了更可靠的性能预测。

本文引用格式

ZHANG Lei , FENG Xueheng , YUAN Wei , CHEN Ruilin , ZHANG Qian , LI Hongyang , AN Guangyao , LANG Jinhua . Multi-Objective Optimization Method for Performance Prediction Loss Model of Centrifugal Compressors[J]. 热科学学报, 2025 , 34(2) : 590 -606 . DOI: 10.1007/s11630-024-2081-2

Abstract

The selection of loss models has a significant effect on the one-dimensional mean streamline analysis for obtaining the performance of centrifugal compressors. In this study, a set of optimized loss models is proposed based on the classical loss models suggested by Aungier, Coppage, and Jansen. The proportions and variation laws of losses predicted by the three sets of models are discussed on the NASA Low-Speed-Centrifugal-Compressor (LSCC) under the mass flow of 22 kg/s to 36 kg/s. The results indicate that the weights of Skin friction loss, Diffusion loss, Disk friction loss, Clearance loss, Blade loading loss, Recirculation loss, and Vaneless diffuser loss are greater than 10%, which is dominant for performance prediction. Therefore, these losses are considered in the composition of new loss models. In addition, the multi-objective optimization method with the Genetic Algorithm (GA) is applied to the correction of loss coefficients to obtain the final optimization loss models. Compared with the experimental data, the maximum relative error of adiabatic the three classical models is 7.22%, while the maximum relative error calculated by optimized loss models is 1.22%, which is reduced by 6%. Similarly, compared with the original model, the maximum relative error of the total pressure ratio is also reduced. As a result, the present optimized models provide more reliable performance prediction in both tendency and accuracy than the classical loss models.

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