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

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.

KEYWORDS:

• Thermoacoustic instability
• symbolic time-series analysis
• hidden Markov modeling
• data-driven anomaly detection
• instability onset prediction
• multi-nozzle combustor

Acknowledgments

The work reported here has been supported in part by the U.S. Air Force Office of Scientific Research (AFOSR) under Grant Nos. FA9550-15-1-0400 and FA9550-18-1-0135 in the area of dynamic data-driven application systems (DDDAS). The data collection was funded by the U.S. Department of Energy University Turbine Systems Research Program under grant DE-FE0025495 with program monitor Mark Freeman. The first author is also thankful to Indo-US Science and Technology Forum (IUSSTF) for granting the Research Internship for Science and Engineering (RISE) scholarship to him for collaboration between Pennsylvania State University and Jadavpur University. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies.

Additional information

Funding

This work was supported by the Air Force Office of Scientific Research [FA9550-15-1-0400,FA9550-18-1-0135]; Indo-US Science and Technology Forum [Research Internship for Science and Engineering]; U.S. Department of Energy [DE-FE0025495].