Journal
JOURNAL OF COMPUTATIONAL PHYSICS
Volume 471, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2022.111621
Keywords
Kalman Filter; Data Assimilation; Multigrid algorithms
Funding
- Direction Generale de l'Armement [2018-0054 REPUB DGA]
- Region Nouvelle-Aquitaine [2018-0042 NAQ]
- French Agence Nationale de la Recherche (ANR) [ANR-17-CE22-0008, ANR-21-CE50-0031]
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This article investigates and assesses the essential features of the Multigrid Ensemble Kalman Filter proposed for Data Assimilation of fluid flows, with a focus on the improvement in performance due to the inner loop. The results indicate that the contribution of the inner loop is essential in obtaining accurate flow reconstruction and global parametric optimization.
Essential features of the Multigrid Ensemble Kalman Filter (Moldovan et al. (2021) [24]) recently proposed for Data Assimilation of fluid flows are investigated and assessed in this article. The analysis is focused on the improvement in performance due to the inner loop. In this step, data from solutions calculated on the higher resolution levels of the multigrid approach are used as surrogate observations to improve the model prediction on the coarsest levels of the grid. The latter represents the level of resolution used to run the ensemble members for global Data Assimilation. The method is tested over two classical one-dimensional problems, namely the linear advection problem and the Burgers' equation. The analyses encompass a number of different aspects such as different grid resolutions. The results indicate that the contribution of the inner loop is essential in obtaining accurate flow reconstruction and global parametric optimization. These findings open exciting perspectives of application to grid-dependent reduced-order models extensively used in fluid mechanics applications for complex flows, such as Large Eddy Simulation (LES).(c) 2022 Elsevier Inc. All rights reserved.
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