Statistical learning of interface dynamics
Reduced-order models of flame dynamics can be used to predict and mitigate the emergence of thermoacoustic oscillations in the design of  a²Ô»å . 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  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 . This leads to an optimally calibrated reduced-order model with quantified uncertainties, which accurately reproduces elaborate  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.
For further details
- Yu, H., Juniper, M. P., & Magri, L. (2021). A data-driven kinematic model of a ducted premixed flame. Proceedings of the Combustion Institute, 38(4), 6231-6239.
For further information, please contact dd-aerospace-eng-research-centre@imperial.ac.uk and l.magri@imperial.ac.uk.