  The potential for data-driven applications to scale-resolving simulations of
turbulent flows is assessed herein. Multigrid sequential data assimilation
algorithms have been used to calibrate solvers for Large Eddy Simulation for
the analysis of the high-Reynolds-number flow around a rectangular cylinder of
aspect ratio 5:1. This test case has been chosen because of a number of
physical complexities which elude accurate representation using reduced-order
numerical simulation. The results for the statistical moments of the velocity
and pressure flow field show that the data-driven techniques employed, which
are based on the Ensemble Kalman Filter, are able to significantly improve the
predictive features of the solver for reduced grid resolution. In addition, it
was observed that, despite the sparse and asymmetric distribution of
observation in the data-driven process, the data augmented results exhibit
perfectly symmetric statistics and a significantly improved accuracy also far
from the sensor location.
