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• W2980314983 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." @default.
• W2980314983 created "2019-10-18" @default.
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• W2980314983 date "2019-10-01" @default.
• W2980314983 modified "2023-10-16" @default.
• W2980314983 title "Early detection of thermoacoustic combustion oscillations using a methodology combining statistical complexity and machine learning" @default.
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• W2980314983 doi "https://doi.org/10.1063/1.5120815" @default.
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