A real-time digital twin of nonlinear azimuthal thermoacoustics

Predicting azimuthal thermoacoustic oscillations in real time is key to the safe operation of gas turbines and aeroengines. We propose a real-time digital twin of a hydrogen-fuelled laboratory annular combustor for different equivalence ratios. The digital twin is composed of (i) a deterministic physics-based low-order model, (ii) raw data from microphones at four azimuthal locations, and (iii) a data-driven tool that estimates biases. These three elements are statistically combined by the Regularized bias-aware Ensemble Kalman filter (r-EnKF) to infer states, parameters, and model errors (i.e., biases) in real time. The digital twin accurately predicts the azimuthal dynamics from raw acoustic data by leveraging data, physics, and estimates of the model bias, in contrast to the bias-unregularized ensemble Kalman filter. The proposed real-time digital twin generalizes existing low-order model methods because it enables the prediction of the fast-varying acoustic variables. Finally, the proposed framework infers all the system parameters simultaneously and allows the parameters to change over time as the acoustic dynamics vary. This work opens new opportunities for real-time digital twinning and low-order modelling, for example, turbulent flows, which are the subject of current research efforts.