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A PINN approach for identifying governing parameters of noisy thermoacoustic systems

Published online by Cambridge University Press:  01 April 2024

Hwijae Son Open the ORCID record for Hwijae Son [Opens in a new window]  and
Minwoo Lee Open the ORCID record for Minwoo Lee [Opens in a new window]
Hwijae Son
Affiliation:
Department of Mathematics, Konkuk University, 120 Neungdongro, Gwangjin, Seoul 05029, South Korea
Minwoo Lee*
Affiliation:
Department of Mechanical Engineering, Hanbat National University, 125 Dongseodaero, Yuseong, Daejeon 34158, South Korea
*
†Email address for correspondence: [email protected]
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Abstract

Identifying the governing parameters of self-sustained oscillation is crucial for the diagnosis, prediction and control of thermoacoustic instabilities. In this paper, we propose and validate a novel method for computing the parameters of thermoacoustic oscillation in a stochastic environment, which exploits a physics-informed neural network (PINN). Specifically, we introduce a negative log-likelihood loss function that integrates the stochastic samples and the solution of the Fokker–Planck equation. The proposed framework is validated using the numerically generated signal and the experimental data obtained from an annular combustor, both before and after the supercritical Hopf bifurcation. The results of PINN-based system identification show good agreement with the actual system parameters and the original stochastic signal, with improved accuracy compared to established methods. To the best of our knowledge, this study constitutes the first demonstration of the PINN-inverse approach that uses the noise-induced dynamics of thermoacoustic systems, opening up new pathways for diagnosing and predicting the thermoacoustic behaviour of various combustion systems.

JFM classification

Mathematical Foundations: Machine learning Nonlinear Dynamical Systems: Low-dimensional models Reacting Flows: Combustion
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 984 , 10 April 2024 , A21
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Copyright
© The Author(s), 2024. Published by Cambridge University Press

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