Early detection of thermoacoustic instability in a staged single-sector combustor for aircraft engines using symbolic dynamics-based approach

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.