Early warning detection of thermoacoustic instability using three-dimensional complexity-entropy causality space

Detection and prediction of thermoacoustic instabilities are major challenges in the development and operation of combustion systems. In this paper, we introduce a tool for categorizing dynamical states and detecting early warning signals of thermoacoustic instability in a combustor. This tool, named complexity-entropy causality space (CECS), exploits the permutation entropy and Jensen-Shannon complexity of both the pressure and heat release rate signal. We identify the early warning stage of thermoacoustic instability in both the experimental and numerical system by applying k-medoids clustering in CECS. This study constitutes the development and demonstration of a novel framework that can accurately detect the onset of impending thermoacoustic instabilities.

Automated summary

The study introduces a tool called CECS for categorizing dynamical states and detecting early warning signals of thermoacoustic instability in a combustor. By utilizing permutation entropy and Jensen-Shannon complexity, this tool can accurately identify the early warning stage of thermoacoustic instability in both experimental and numerical systems through k-medoids clustering. This framework demonstrates a novel approach for detecting the onset of impending thermoacoustic instabilities.