Data-driven Detection and Early Prediction of Thermoacoustic Instability in a Multi-nozzle Combustor

Chandrachur Bhattacharya; Jacqueline O’Connor; Asok Ray

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Data-driven Detection and Early Prediction of Thermoacoustic Instability in a Multi-nozzle Combustor

机译：Data-driven Detection and Early Prediction of Thermoacoustic Instability in a Multi-nozzle Combustor

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ABSTRACT Thermoacoustic instability (TAI) is a critical issue in modern lean-burn gas-turbine combustors, which is induced by a strong coupling between the resonant combustor acoustics and fluctuations in the heat release rate. This instability may lead to high-amplitude pressure waves that generate undesirable noise levels as well as fatigue stresses in mechanical structures of the combustor. The intense pressure fluctuations due to TAI may also cause large flow perturbations and possibly flow reversal that may lead to flame oscillations, flame liftoff, and even flame blow-out. Hence, there is a strong need for exercising control actions in a timely fashion to mitigate the TAI phenomena. Anomaly detection is an essential prerequisite to the design of a good controller and such a detector must be able to reliably predict a forthcoming TAI. To detect and predict the onset of a TAI from an ensemble of pressure time series, this paper investigates three data-driven methods: Fast Fourier transform (FFT), symbolic time series analysis (STSA), and hidden Markov modeling (HMM). The main focus of the paper is to make a comparative evaluation of these three anomaly detection methods for classification of the current regime of operation into stable and unstable categories as well as for real-time identification of precursors to impending instabilities with short-length time series of measured variables (e.g., pressure oscillations). The results, generated on experimental data from a multi-nozzle combustor apparatus, have been compared to evaluate the performance of FFT, STSA, and HMM methods for TAI analysis.

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《Combustion science and technology》 |2022年第8期|1481-1512|共32页
作者
Chandrachur Bhattacharya; Jacqueline O’Connor; Asok Ray;
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The Pennsylvania State University||The Pennsylvania State University;

The Pennsylvania State University;

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正文语种英语
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Thermoacoustic instability; symbolic time-series analysis; hidden Markov modeling; data-driven anomaly detection; instability onset prediction; multi-nozzle combustor;

Data-driven Detection and Early Prediction of Thermoacoustic Instability in a Multi-nozzle Combustor

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