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Early detection of thermoacoustic instability in a staged single-sector combustor for aircraft engines using symbolic
Kento Baba1, Sena Kishiya1, Hiroshi Gotoda1
1Department of Mechanical Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan.
Abstract:
We experimentally conduct an early detection of thermoacoustic instability in a staged single-sector combustor using a novel methodology that combines symbolic dynamics and machine learning. We propose two invariants in this study: the determinisms of the joint symbolic recurrence plots DJ and the ordinal transition pattern-based recurrence plots DT. These invariants enable us to capture the phase synchronization between acoustic pressure and heat release rate fluctuations associated with a precursor of thermoacoustic instability. The latent space consisting of DJ and DT, which is obtained by a support vector machine in combination with the k-means clustering method, can appropriately determine a transitional regime between stable combustion and thermoacoustic instability.
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