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Data Assimilation and Optimal Calibration in Nonlinear Models of Flame Dynamics

Accepted version
Peer-reviewed

Type

Article

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Authors

Yu, Hans 
Jaravel, Thomas 
Ihme, Matthias 
Juniper, Matthew P 

Abstract

jats:titleAbstract</jats:title> jats:pWe 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 are produced from the same model as that used in prediction. Second, the assimilated data are 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 become available, for example, from gas-turbine sensors.</jats:p>

Description

Keywords

Journal Title

Journal of Engineering for Gas Turbines and Power

Conference Name

Journal ISSN

0742-4795
1528-8919

Volume Title

141

Publisher

ASME International

Rights

All rights reserved
Sponsorship
Royal Academy of Engineering (RAEng)