Dynamic reconstruction of a numerical 2D cylinder wake flow using Data Assimilation

Nishant Kumar (1) , Franck Kerhervé (2) , Laurent Cordier (2)

1 PPrime [Poitiers] - Institut Pprime [UPR 3346]
2 TIC - Turbulence Incompressible et Contrôle [Institut Pprime]

The design of model-based flow controllers requires the knowledge of a dynamical model which can accurately predict the flow state. Real-time and robust estimation of the flow state however remains a challenging task when only limited spatial and temporal discrete measurements are available. In this paper, the objective is to draw upon the methodologies implemented classically in meteorology to develop dynamic observers for flow control applications. A well established data assimilation method based on Kalman filter is considered. This approach is here extended to both estimate model states and specific flow parameters. The strategy is numerically demonstrated on a POD Reduced-Order Model of a 2D-cylinder wake flow at low Reynolds number.

Mots clés: data assimilation; dual ensemble Kalman filter; POD Reduced-Order Model; 2D-cylinder wake flow

Domaines: Mécanique des fluides [physics.class-ph]; Sciences de l'ingénieur [physics]

Nishant Kumar, Franck Kerhervé, Laurent Cordier. Dynamic reconstruction of a numerical 2D cylinder wake flow using Data Assimilation. 5th Symposium on Fluid-Structure- Sound Interactions and Control (FSSIC2019), Aug 2019, Crete, Greece. ⟨hal-02411731⟩