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Early detection of thermoacoustic combustion oscillations using a methodology combining statistical complexity and
Takayoshi Hachijo1, Shinga Masuda1, Takuya Kurosaka1
1Department of Mechanical Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan.
Abstract:
We conduct an experimental study on early detection of thermoacoustic combustion oscillations using a method combining statistical complexity and machine learning, including the characterization of intermittent combustion oscillations. Abrupt switching from aperiodic small-amplitude oscillations to periodic large-amplitude oscillations and vice versa appears in pressure fluctuations. The dynamic behavior of aperiodic small-amplitude pressure fluctuations represents chaos. The complexity-entropy causality plane effectively captures the subtle changes in the combustion state during a transition to well-developed combustion oscillations. The feature space of the complexity-entropy causality plane, which is obtained by a support vector machine, has potential use for detecting a precursor of combustion oscillations.
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