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A physics-infused strategy relying on the Ensemble Kalman Filter (EnKF) ishere used to augment the accuracy of a continuous Immersed Boundary Method(IBM). The latter is a classical penalty method accounting for the presence ofthe immersed body via a volume source term which is included in theNavier-Stokes equations. The model coefficients of the penalization method,which are usually selected by the user, are optimized here using an EnKFdata-driven strategy. The parametric inference is governed by the physicalknowledge of local and global features of the flow, such as the no-slipcondition and the shear stress at the wall. The C++ library CONES (CouplingOpenFOAM with Numerical EnvironmentS) developed by the team is used to performan online investigation, coupling on-the-fly data from synthetic sensors withresults from an ensemble of coarse-grained numerical simulations. The analysisis performed for a classical test case, namely the turbulent channel flow with$Re_\tau = 550$. The comparison of the results with a high-fidelity DirectNumerical Simulation (DNS) shows that the data-driven procedure exhibitsremarkable accuracy despite the relatively low grid resolution of the ensemblemembers.

A physics-infused Immersed Boundary Method using online sequential Data Assimilation