DOI: 10.1615/TSFP8
4D-VARIATIONAL DATA ASSIMILATION FOR POD REDUCED-ORDER MODELS
SINOPSIS
In flow control, reduced-order models based on Proper Orthogonal Decomposition (POD ROM) are often used as surrogate model for deriving a control law. However, these models are in general too fragile to be used in closed-loop control where the dynamics is strongly modified by the control. Here, a 4D-Variational data assimilation approach (4D-Var) as classically used in meteorology is used to estimate at best the state of the system from inhomogeneous sources of information coming from a model, noisy observations and a background solution. Two complementary strategies (strong constraint 4D-Var and weak constraint 4D-Var) are assessed in the case of a cylinder wake flow with data coming from numerical simulation and PIV experiments.