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真实耦合偏差对压气机叶栅气动性能影响的不确定性分析

  • Zezhen Sun
  • , Wuli Chu
  • , Zhengtao Guo
  • , Yuqin Mo
  • , Kaiye Liu
  • Northwestern Polytechnical University Xian

科研成果: 期刊稿件文章同行评审

摘要

To investigate the effect of real coupling deviation on the aerodynamic performance of a blade cascade,chord length deviation,thickness deviation,and leading edge radius deviation were applied to a high load compressor blade cascade for research based on the arbitrary polynomial chaos method of moments. The research results showed that compared with the prototype,the probability of an increase in total pressure loss coefficient and a decrease in static pressure coefficient under negative angle of attack conditions was about 79.47% and 92.83%, respectively; under positive angle of attack conditions, the probability of an increase in total pressure loss coefficient was about 91.11%, and the probability of a decrease in static pressure coefficient was about 86.42%. The aerodynamic performance under different operating conditions was most sensitive to the deviation of the leading edge radius. Combined with the analysis of the loss source,it was found that the leading edge loss played a dominant role. Therefore,strict control of the leading edge machining accuracy was required during machining. Compared with the prototype blade,under negative attack angle conditions,the degree of separation in the blade angle region with added coupling error changed significantly, and the separation point, recirculation area, and axial length of the recirculation area increased significantly; under positive angle of attack conditions,the degree of separation in the blade angle region with added coupling error was not significant.

投稿的翻译标题Uncertainty analysis of effects of real coupling deviations on aerodynamic performance of compressor cascades
源语言繁体中文
文章编号20240605
期刊Hangkong Dongli Xuebao/Journal of Aerospace Power
40
11
DOI
出版状态已出版 - 11月 2025

关键词

  • cascades
  • coupling deviations
  • junction flow
  • sparse approximation of moment-based arbitrary polynomial chaos
  • uncertainty quantification

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