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Compressed dynamic mode decomposition for the analysis of centrifugal compressor volute

  • Xiaojian Yang
  • , Xiaocheng Zhu*
  • , Chenxing Hu
  • , Zhaohui Du
  • *Corresponding author for this work
  • Shanghai Jiao Tong University

Research output: Contribution to journalArticlepeer-review

Abstract

The compressed dynamic mode decomposition (compressed DMD) is used to analysis the unsteady characteristics of a centrifugal compressor. Firstly, to extract the unsteady flow structures of the volute under the mild surge condition, dynamic mode decomposition (DMD) method is applied to the flow field snapshots. The results indicate that the characteristic frequencies relating to the blade passing frequencies (BPFs) and corresponding modes are captured. Besides, the perturbation oscillating with the characteristic frequency (233.5 Hz), which is generated by the non-synchronic unsteady flow, is also obtained accurately. The non-synchronic unsteady flow is vividly shown by the reconstruction of the mild surge mode. Ten, the unsteady flow of the volute is also analyzed by the compressed DMD method, which is performed on the much compressed datasets and thus saves the calculation time. The dominant characteristic frequencies and corresponding modes can also be extracted accurately by the compressed DMD method when adopting an appropriate compression ratio. The compression ratio has an important influence on the results of the compressed DMD method. Considering the dominant frequencies and the energy ratio spectrum, a conservative recommendation of the compression ratio is 1% in this case. The energy ratio scaling method performs better than the amplitude scaling method.

Original languageEnglish
Pages (from-to)118-129
Number of pages12
JournalInternational Journal of Heat and Fluid Flow
Volume74
DOIs
Publication statusPublished - Dec 2018
Externally publishedYes

Keywords

  • Centrifugal compressor volute
  • Compressed dynamic mode decomposition
  • Dynamic mode decomposition
  • Numerical simulation
  • Unsteady flow

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