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