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    Toward a detection approach of surge precursors using a semi-classical signal analysis method

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    Type
    Article
    Authors
    Deligant, Michael
    Pereira, Michael cc
    Laleg-Kirati, Taous-Meriem cc
    Bakir, Farid
    Khelladi, Sofiane
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Electrical and Computer Engineering Program
    Estimation, Modeling and ANalysis Group
    Date
    2022-06-24
    Embargo End Date
    2023-06-24
    Permanent link to this record
    http://hdl.handle.net/10754/679592
    
    Metadata
    Show full item record
    Abstract
    A semi-classical signal analysis (SCSA) method is presented in this paper to show the relevance of its use for the detection of compressors surge precursors. This approach provides some new spectral parameters from physical properties of flow that might be interesting for signal analysis of turbomachines unsteadiness in general and surge detection in particular. Spectral parameters are obtained by considering the studied temporal signal as a trap-potential for a semi-classical particle and are represented by discrete energy levels given by the discrete spectrum of a Schrödinger operator. In this paper, it is shown that the new parameters are very sensitive to rapid and very small disturbances and present actually an alternative or new indices for the analysis of unsteadiness in turbomachinery. To illustrate its potential and its usefulness, the SCSA method is applied for the analysis of surge early signs and operating range of a centrifugal compressor.
    Citation
    Deligant, M., Pereira, M., Laleg-Kirati, T.-M., Bakir, F., & Khelladi, S. (2022). Toward a detection approach of surge precursors using a semi-classical signal analysis method. The European Physical Journal Plus, 137(6). https://doi.org/10.1140/epjp/s13360-022-02923-0
    Publisher
    Springer Science and Business Media LLC
    Journal
    EUROPEAN PHYSICAL JOURNAL PLUS
    DOI
    10.1140/epjp/s13360-022-02923-0
    Additional Links
    https://link.springer.com/10.1140/epjp/s13360-022-02923-0
    ae974a485f413a2113503eed53cd6c53
    10.1140/epjp/s13360-022-02923-0
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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