Detection and classification of lean blow-out and thermoacoustic instability in turbulent combustors

Bhattacharya, Chandrachur; De, Somnath; Mukhopadhyay, Achintya; Sen, Swarnendu; Ray, Asok

doi:10.1016/j.applthermaleng.2020.115808

Cited by 18 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The FFT algorithm uses the knowledge of the physical process dynamics to distinguish between the stable and unstable regimes (Mondal et al, 2017;Bhattacharya, De, Mukhopadhyay, Sen and Ray, 2020). During the stable operation of the combustor, the signal energy is generally broadband and so no strong frequency components can be clearly identified.…”

Section: A Fast Fourier Transform (Fft)mentioning

confidence: 99%

“…A similar approach has also been shown by Mondal et al (2017) who introduced a more robust Fast Fourier Transform (FFT)-based method; this method relies on the fact that, during TAI, as the signal is sinusoidal, a major portion of the system's energy goes into a single frequency (and its harmonics), which shows up as a high-magnitude spike in the FFT. This method is further expanded upon by Bhattacharya, De, Mukhopadhyay, Sen and Ray (2020) where the method was shown to be effective at discriminating between lean blow-out, thermoacoustic instability, and stable operation by using a very simple scalar metric based approach. Recurrence analysis (Sen et al, 2018) has also been used as a means to distinguish stable signals from unstable ones.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Bhattacharya

O’Connor

Ray

2020

Combustion Science and Technology

Self Cite

View full text Add to dashboard Cite

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 lift-off, 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.

show abstract

Section: A Fast Fourier Transform (Fft)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bhattacharya

O’Connor

Ray

2020

Combustion Science and Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recurrence quantification analysis (RQA) is another technique providing precursor measures that show distinctive signatures toward LBO [35,36]. Recently, Bhattacharya et al [37] proposed a fast Fourier transform (FFT) based single scalar-valued measure to detect different operational regimes, namely, stable operation, thermoacoustic instability (TAI), and LBO based on the time series of acoustic pressure. Detailed discussions about the mechanism of LBO, its precursors, and control are summed up well in review articles [38,39].…”

Section: Introductionmentioning

confidence: 99%

Imprints of log-periodicity in thermoacoustic systems close to lean blowout

Banerjee¹,

Pavithran²,

Sujith³

2022

Preprint

View full text Add to dashboard Cite

In the context of statistical physics, critical phenomena are accompanied by power laws having a singularity at the critical point where a sudden change in the state of the system occurs. In this work, we show that lean blowout (LBO) in a turbulent thermoacoustic system can be viewed as a critical phenomenon. As a crucial discovery of the system dynamics approaching LBO, we unravel the existence of the discrete scale invariance (DSI). In this context, we identify the presence of logperiodic oscillations in the temporal evolution of the amplitude of dominant mode of low-frequency oscillations (A f ) exist in pressure fluctuations preceding LBO. The presence of DSI indicates the recursive development of blowout. Additionally, we find that A f shows a faster than exponential growth and becomes singular when blowout occurs. We then present a model that depicts the evolution of A f based on log-periodic corrections to the power law associated with its growth. Using the model, we find that blowout can be predicted even several seconds earlier. The predicted time of LBO in good agreement with the actual time of occurrence of LBO obtained from the experiment.

show abstract

“…The energy loss of acoustic signals in a closed chamber is little, because the furnace wall reflects sound waves. Therefore, the acoustic signals represent the combustion process in the whole furnace and are often used for the measurement of thermoacoustic instability [5]. However, the acoustic sources in industrial processes are complex [6] [7], and it is unreliable to monitor a flame only using acoustic sensors.…”

Section: Introductionmentioning

confidence: 99%

A Self-diagnostic Flame Monitoring System Incorporating Acoustic, Optical, and Electrostatic Sensors

Zhang

Yan

Bai

et al. 2022

2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

show abstract

Detection and classification of lean blow-out and thermoacoustic instability in turbulent combustors

Cited by 18 publications

References 27 publications

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

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

Imprints of log-periodicity in thermoacoustic systems close to lean blowout

A Self-diagnostic Flame Monitoring System Incorporating Acoustic, Optical, and Electrostatic Sensors

Contact Info

Product

Resources

About