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The potential for data-driven applications to scale-resolving simulations ofturbulent flows is assessed herein. Multigrid sequential data assimilationalgorithms have been used to calibrate solvers for Large Eddy Simulation forthe analysis of the high-Reynolds-number flow around a rectangular cylinder ofaspect ratio 5:1. This test case has been chosen because of a number ofphysical complexities which elude accurate representation using reduced-ordernumerical simulation. The results for the statistical moments of the velocityand pressure flow field show that the data-driven techniques employed, whichare based on the Ensemble Kalman Filter, are able to significantly improve thepredictive features of the solver for reduced grid resolution. In addition, itwas observed that, despite the sparse and asymmetric distribution ofobservation in the data-driven process, the data augmented results exhibitperfectly symmetric statistics and a significantly improved accuracy also farfrom the sensor location.

Multigrid sequential data assimilation for the large-eddy simulation of a massively separated bluff-body flow