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Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability

Mingming Zhang1,2,3, Pan Kong1, Aiguo Xia4

  • 1Faculty of Science, Beijing University of Technology, Beijing 100124, China.

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|April 24, 2023
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Summary
This summary is machine-generated.

This study introduces a novel WT-GWO-SVR model for predicting axial compressor stall precursors. The method enhances aeroengine safety by providing early instability warnings for active control.

Keywords:
gray wolf algorithmmachine learningphase space reconstructionspatial modesupport vector regressionwavelet singular spectrum entropy

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Area of Science:

  • Aerospace Engineering
  • Computational Fluid Dynamics
  • Machine Learning

Background:

  • Stable aeroengine operation relies on predicting stall precursors in axial compressors.
  • Early identification of system instability is crucial for safety and active control.

Purpose of the Study:

  • To propose an advanced aerodynamic system modeling method for predicting stall precursors.
  • To enhance the accuracy and reliability of instability prediction in aeroengines.

Main Methods:

  • A hybrid model combining Wavelet Transform (WT) and Gray Wolf Algorithm optimized Support Vector Regression (GWO-SVR) was developed.
  • The correlation-correlation (C-C) algorithm reconstructed phase space from chaotic data.
  • A wavelet singular information entropy algorithm was used for stall inception detection.

Main Results:

  • The WT-GWO-SVR model demonstrated superior prediction accuracy and reliability using wavelet reconstruction coefficients.
  • The proposed method achieved early instability warnings approximately 102 revolutions in advance.
  • The wavelet singular information entropy algorithm effectively identified stall inception.

Conclusions:

  • The WT-GWO-SVR model offers a robust approach for predicting stall precursors in axial compressors.
  • Early warning capabilities significantly aid in the active control and safety of aeroengines.
  • This research advances intelligent identification of system instability in complex aerodynamic systems.