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神经网络模型在压气机通流特性分析中的应用

费腾 季路成 周玲

费腾, 季路成, 周玲. 神经网络模型在压气机通流特性分析中的应用[J]. 航空动力学报, 2022, 37(6): 1260-1272. doi: 10.13224/j.cnki.jasp.20210105
引用本文: 费腾, 季路成, 周玲. 神经网络模型在压气机通流特性分析中的应用[J]. 航空动力学报, 2022, 37(6): 1260-1272. doi: 10.13224/j.cnki.jasp.20210105
FEI Teng, JI Lucheng, ZHOU Ling. Application of neural network model in compressor through-flow analysis[J]. Journal of Aerospace Power, 2022, 37(6): 1260-1272. doi: 10.13224/j.cnki.jasp.20210105
Citation: FEI Teng, JI Lucheng, ZHOU Ling. Application of neural network model in compressor through-flow analysis[J]. Journal of Aerospace Power, 2022, 37(6): 1260-1272. doi: 10.13224/j.cnki.jasp.20210105

神经网络模型在压气机通流特性分析中的应用

doi: 10.13224/j.cnki.jasp.20210105
基金项目: 国家自然科学基金(51676015)
详细信息
    作者简介:

    费腾(1993-),男,博士,主要从事叶轮机械气动热力学研究。

    通讯作者:

    周玲(1988-),女,副研究员,博士,主要从事叶轮机械气动热力学研究。E-mail:lingzhou@bit.edu.cn

  • 中图分类号: V231.3

Application of neural network model in compressor through-flow analysis

  • 摘要: 为了解决通流特性分析程序中原始模型对压气机性能预测精度不足的问题,提高压气机通流特性分析过程的可靠性,基于对大量多圆弧叶栅的数值模拟结果建立了压气机叶栅性能数据库,并以该数据库为依托,采用神经网络建模方法建立了压气机叶栅基准损失系数和基准落后角模型。结果显示:两模型对叶栅基准损失系数和基准落后角的预测精度均满足工程应用要求,其精度分别为±0.002和±1°。在对采用神经网络模型的通流特性分析程序校验过程中发现,其无论对压气机整机性能还是对流动细节的预测精度上都获得了显著提高,尤其是在主流区。此外从压气机整体特性上看,基准损失系数和基准落后角精度的提高对非设计工况损失系数和落后角的预测精度影响是积极的。

     

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出版历程
  • 收稿日期:  2021-03-09
  • 刊出日期:  2022-06-28

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