A data-driven kinematic model of a ducted premixed flame

Reduced-order models of flame dynamics can be used to predict and mitigate the emergence of thermoacoustic oscillations in the design of gas turbine and rocket engines. This process is hindered by the fact that these models, although often qualitatively correct, are not usually quantitatively accurate. As automated experiments and numerical simulations produce ever-increasing quantities of data, the question arises as to how this data can be assimilated into physics-informed reduced-order models in order to render these models quantitatively accurate. In this study, we develop and test a physics-based reduced-order model of a ducted premixed flame in which the model parameters are learned from high-speed videos of the flame. The experimental data is assimilated into a level-set solver using an ensemble Kalman filter. This leads to an optimally calibrated reduced-order model with quantified uncertainties, which accurately reproduces elaborate nonlinear features such as cusp formation and pinch-off. The reduced-order model continues to match the experiments after assimilation has been switched off. Further, the parameters of the model, which are extracted automatically, are shown to match the first-order behavior expected on physical grounds. This study shows how reduced-order models can be updated rapidly whenever new experimental or numerical data becomes available, without the data itself having to be stored. (c) 2020 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Automated summary

This study developed and tested a physics-based reduced-order model of a ducted premixed flame, where model parameters were learned from high-speed videos. By assimilating experimental data into a level-set solver using an ensemble Kalman filter, an optimally calibrated reduced-order model accurately reproduced complex nonlinear features, matching experiments even after assimilation was switched off. The automatically extracted model parameters were found to match the expected first-order behavior based on physics, showcasing how reduced-order models can be rapidly updated with new data availability without storing the data itself.