Data Assimilation and Optimal Calibration in Nonlinear Models of Flame Dynamics
Journal of Engineering for Gas Turbines and Power
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Yu, H., Jaravel, T., Ihme, M., Juniper, M., & Magri, L. (2019). Data Assimilation and Optimal Calibration in Nonlinear Models of Flame Dynamics. Journal of Engineering for Gas Turbines and Power, 141 (12)https://doi.org/10.1115/1.4044378
We propose an on-the-fly statistical learning method to take a qualitative reduced-order model of the dynamics of a premixed flame and make it quantitatively accurate. This physics-informed data-driven method is based on the statistically optimal combination of (i) a reduced-order model of the dynamics of a premixed flame with a level-set method, (ii) high-quality data, which can be provided by experiments and/or high-fidelity simulations, and (iii) assimilation of the data into the reduced-order model to improve the prediction of the dynamics of the premixed flame. The reduced-order model learns the state and the parameters of the premixed flame on the fly with the ensemble Kalman filter, which is a Bayesian filter used, for example, in weather forecasting. The proposed method and algorithm are applied to two test cases with relevance to reacting flows and instabilities. First, the capabilities of the framework are demonstrated in a twin experiment, where the assimilated data is produced from the same model as that used in prediction. Second, the assimilated data is extracted from a high-fidelity reacting-flow direct numerical simulation (DNS), which provides the reference solution. The results are analyzed by using Bayesian statistics, which robustly provide the level of confidence in the calculations from the reduced-order model. The versatile method we propose enables the optimal calibration of computationally inexpensive reduced-order models in real time when experimental data becomes available, for example, from gas-turbine sensors.
The authors are grateful to the 2018 Center for Turbulence Research Summer Program for having partly sponsored this project. L.M. gratefully acknowledges support of (i) the Royal Academy of Engineering Research Fellowship Scheme and (ii) the visiting fellowship of the Technical University of Munich– Institute for Advanced Study, funded by the German Excellence Initiative and the European Union Seventh Framework Programme under grant agreement no. 291763.
Royal Academy of Engineering (RAEng) ()
External DOI: https://doi.org/10.1115/1.4044378
This record's URL: https://www.repository.cam.ac.uk/handle/1810/288956