Real-time digital twins of thermoacoustic instabilities in hydrogen-fuelled annular combustors

The dynamics of azimuthal thermoacoustic instabilities in annular combustors are intricate and changeable with respect to the operating conditions. To control thermoacoustic instabilities, we need quantitatively accurate low-order models that can infer the dynamics in real-time from sensor's data. In this work, we propose real-time digital twins of thermoacoustic instabilities by combining data from laboratory experiments and nonlinear low-order models. We employ the recently proposed regularized bias-aware ensemble Kalman filter to infer on the fly the thermoacoustic state, model parameters, and model errors from pressure measurements only. We validate the real-time digital twin by comparing the prediction with the state-of-the-art methods based on offline calibration. This research introduces new possibilities for safe operation of hydrogen-based aeroengines through real-time digital twinning.