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In reacting flow systems, thermoacoustic instability characterized by highamplitude pressure fluctuations, is driven by a positive coupling between theunsteady heat release rate and the acoustic field of the combustor. When theunderlying flow is turbulent, as a control parameter of the system is variedand the system approach thermoacoustic instability, the acoustic pressureoscillations synchronize with heat release rate oscillations. Consequently,during the onset of thermoacoustic instability in turbulent combustors, thesystem dynamics transition from chaotic oscillations to periodic oscillationsvia a state of intermittency. Thermoacoustic systems are traditionally modeledby coupling the model for the unsteady heat source and the acoustic subsystem,each estimated independently. The response of the unsteady heat source, theflame, to acoustic fluctuations are characterized by introducing externalunsteady forcing. This necessitates a powerful excitation module to obtain thenonlinear response of the flame to acoustic perturbations. Instead ofcharacterizing individual subsystems, we introduce a neural ordinarydifferential equation (neural ODE) framework to model the thermoacoustic systemas a whole. The neural ODE model for the thermoacoustic system uses time seriesof the heat release rate and the pressure fluctuations, measured simultaneouslywithout introducing any external perturbations, to model their coupledinteraction. Further, we use the parameters of neural ODE to define an anomalymeasure that represents the proximity of system dynamics to limit cycleoscillations and thus provide an early warning signal for the onset ofthermoacoustic instability.

Neural ODE to model and prognose thermoacoustic instability