A real-time digital twin of azimuthal thermoacoustic instabilities
arxiv(2024)
摘要
When they occur, azimuthal thermoacoustic oscillations can detrimentally
affect the safe operation of gas turbines and aeroengines. We develop a
real-time digital twin of azimuthal thermoacoustics of a hydrogen-based annular
combustor. The digital twin seamlessly combines two sources of information
about the system (i) a physics-based low-order model; and (ii) raw and sparse
experimental data from microphones, which contain both aleatoric noise and
turbulent fluctuations. First, we derive a low-order thermoacoustic model for
azimuthal instabilities, which is deterministic. Second, we propose a real-time
data assimilation framework to infer the acoustic pressure, the physical
parameters, and the model and measurement biases simultaneously. This is the
bias-regularized ensemble Kalman filter (r-EnKF), for which we find an
analytical solution that solves the optimization problem. Third, we propose a
reservoir computer, which infers both the model bias and measurement bias to
close the assimilation equations. Fourth, we propose a real-time digital twin
of the azimuthal thermoacoustic dynamics of a laboratory hydrogen-based annular
combustor for a variety of equivalence ratios. We find that the real-time
digital twin (i) autonomously predicts azimuthal dynamics, in contrast to
bias-unregularized methods; (ii) uncovers the physical acoustic pressure from
the raw data, i.e., it acts as a physics-based filter; (iii) is a time-varying
parameter system, which generalizes existing models that have constant
parameters, and capture only slow-varying variables. The digital twin
generalizes to all equivalence ratios, which bridges the gap of existing
models. This work opens new opportunities for real-time digital twinning of
multi-physics problems.
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