Lean blow-out (LBO) and thermoacoustic instability (TAI) are common undesirable occurrences in modern lean-burn turbulent combustion systems, such as fossil-fuel furnaces for gas turbines and in both land-based power generation and marine or aviation propulsion applications. While LBO causes loss of power due to flame extinction, TAI leads to loud noise, vibrations and mechanical fatigue-failure. Timely detection and classification of the operating conditions are important for both open-loop and closed-loop control of combustion systems to ensure their long service life, high efficiency, and reliability & availability. Data-driven techniques already exist for detection of these phenomena; however, most of these techniques require high training and/or processing times. This paper presents a fast Fourier transform (FFT)-based method to generate, in real time, a single scalar-valued measure for detection and classification of operational regimes; this measure can also be used to identify precursors (i.e., for prediction of impending LBO and TAI). This FFT-based method utilizes prior knowledge of the combustion system acoustics; and the measure acts as a classifier to distinguish different operational regimes. The underlying algorithms have been validated on time series data, collected from a (commercially available) microphone sensor that is external to the laboratory-scale experimental apparatus.
|Original language||English (US)|
|Journal||Applied Thermal Engineering|
|State||Published - Nov 5 2020|
All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Industrial and Manufacturing Engineering