3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems

While analytical solutions of critical (phase) transitions in physical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical systems. A key example of such a physical system is thermoacoustic instability in combustion, where prediction or early detection of an onset of instability is a hard technical challenge, which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries. The instabilities arising in combustion chambers of engines are mathematically too complex to model. This complexity is due to high-dimensional, pseudo-periodic coupling from high pressure oscillations and vorticities at the flame fronts generated by the combustion instabilities. To address this issue in a data-driven manner instead, we propose a novel deep learning architecture to detect the evolution of self-excited oscillations using spatiotemporal data, i.e., hi-speed videos taken from a swirl-stabilized combustor (a laboratory surrogate of gas turbine engine combustor). Our deep learning architecture called 3D convolutional selective autoencoder (3D-CSAE) consists of filters to learn, in a hierarchical fashion, the complex visual and dynamic features related to combustion instability from the training videos. We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions. We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video. The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability compared to the ubiquitous 2D deep learning models, that lacks the ability to capture the temporal dynamics. The machine learning-driven results are verified with physics-based off-line measures. Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions.