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Journal of Thermal Science

, Volume 28, Issue 5, pp 837–849 | Cite as

Application of Fast Wavelet Analysis on Early Stall Warning in Axial Compressors

  • Yang Liu
  • Jichao LiEmail author
  • Juan Du
  • Fan Li
  • Hongwu Zhang
Special column for the memory of Professor CHEN Naixing
  • 46 Downloads

Abstract

The timely detection of stall inception is of great significance for safe operation and stability control of axial compressor. In the current study, a fast wavelet tool was selected to predict stall precursor in axial compressors with spike-type and modal-wave stall inception. Dynamic pressure was measured in the casing wall by using a collection of time-resolved pressure transducers with circumferential and chord-wise spatial resolution. Fast wavelet analysis with low frequency reconstruction results demonstrate that the initial inception can be detected 110 rotor revolutions prior to stall for modal-wave stall inception in a 1.5 stage axial compressor. For spike-type stall inception, despite the failure of early stall warning via low frequency reconstruction, an increase amplitude frequency band of 0.2-0.8 blade passing frequency was identified using high frequency reconstruction in an isolated-rotor axial compressor. Fast wavelet method can predict two kinds of stall inceptions simultaneously in advance and realize the early stall warning in axial compressors through a reasonable selection of reconstructed frequency.

Keywords

axial compressor stall inception pre-stall inception stall warning fast wavelet 

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Notes

Acknowledgements

The authors are thankful for the support of the National Natural Science Foundation of China with Project No. 51676183 and No. 51727810 and the National Science and Technology Major Project (2017-II-0004-0017). The authors also acknowledge the Special Fund for the Member of Youth Innovation Promotion Association of CAS (2018173). The authors are grateful to Professor NIE Chaoqun, Dr. GENG Shaojuan, and Mr. ZHANG Qianfeng for the insightful discussion.

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Copyright information

© Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yang Liu
    • 1
    • 2
  • Jichao Li
    • 1
    • 2
    Email author
  • Juan Du
    • 1
    • 2
  • Fan Li
    • 1
    • 2
  • Hongwu Zhang
    • 1
    • 2
  1. 1.Advanced Gas Turbine Laboratory, Institute of Engineering ThermophysicsChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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