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<p>Thermoacoustic instability (TAI) in gas turbine or rocket combustors can cause significant structural damages and operational failure. Universally reliable or industry-standard method to early-detect an impending TAI does not exist to date. To bridge this gap, we study the transition to TAI using three well-known tools of dynamical science: the symbolic time series analysis (STSA), multi-scale permutation entropy analysis (MPEA), and topological data analysis (TDA). We find that TDA is able to capture the dynamical transition ahead of the occurrence of TAI. Further, we find that the sublevel set filtration-based TDA is computationally inexpensive and can be automated easily. Therefore, we propose sublevel set filtration-based TDA as a complimentary approach to existing tools for early detection of impending TAI in real time.</p>

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Early detection of thermoacoustic instability in a Rijke tube using topological data analysis

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Arijit Bhattacharya,
  • Somnath De,
  • Achintya Mukhopadhyay,
  • Swarnendu Sen

摘要

Thermoacoustic instability (TAI) in gas turbine or rocket combustors can cause significant structural damages and operational failure. Universally reliable or industry-standard method to early-detect an impending TAI does not exist to date. To bridge this gap, we study the transition to TAI using three well-known tools of dynamical science: the symbolic time series analysis (STSA), multi-scale permutation entropy analysis (MPEA), and topological data analysis (TDA). We find that TDA is able to capture the dynamical transition ahead of the occurrence of TAI. Further, we find that the sublevel set filtration-based TDA is computationally inexpensive and can be automated easily. Therefore, we propose sublevel set filtration-based TDA as a complimentary approach to existing tools for early detection of impending TAI in real time.