Dynamic data-driven prediction of instability in a swirl-stabilized combustor

What is it about?

This paper proposes a fast nonlinear tool for early detection of onsets of thermo-acoustic instability in combustion systems. An information-theoretic measure of combustion instability, which models the spatio-temporal codependence among heterogeneous sensors, is constructed to capture the precursors before visible appearance of instability. The proposed method exhibits robustness to varying level of sensor noise as demonstrated by experimentation on a a laboratory-scale swirl-stabilized combustor.

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Why is it important?

This paper proposes a fast dynamic data-driven method for detecting onsets of thermo-acoustic instabilities, where the underlying algorithms are built upon the concepts of symbolic time series analysis (STSA) and information theory . The proposed method captures the spatio-temporal co-dependence among time series from heterogeneous sensors (e.g. pressure and chemiluminescence) to generate an information-theoretic precursor, which is uniformly applicable across multiple operating regimes of the combustion process. The proposed method has been experimentally validated on the time-series data, generated from a laboratory-scale swirl-stabilized combustor, while inducing thermo-acoustic instabilities for various protocols (e.g. increasing Reynolds number at a constant fuel flow rate and reducing equivalence ratio at a constant air flow rate) at varying air-fuel premixing levels. The instability precursor measure is compared with those of the state-of-the-art techniques in terms of its performance of instability prediction, computational complexity, and robustness to sensor noise.

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