Abstract
In this short note we apply the recently proposed data-driven RANS closure modelling framework of Schmelzer et al.(2020) to fully three-dimensional, high Reynolds number flows: namely wall-mounted cubes and cuboids at Re=40,000, and a cylinder at Re=140,000. For each flow, a new RANS closure is generated using sparse symbolic regression based on LES or DES reference data. This new model is implemented in a CFD solver, and subsequently applied to prediction of the other flows. We see consistent improvements compared to the baseline k−ω SST model in predictions of mean-velocity in complete flow domain.
Highlights
Reynolds averaged Navier-Stokes (RANS) models are notoriously inaccurate in the presence of massive flow separation, for example in the wake of bluff bodies
We examine three flows: a wall-mounted cube in a channel (Flow A), a wall-mounted cuboid in a channel (Flow B), and an infinite circular cylinder (Flow C)
The RANS part of the DES is based on the Spalart-Allmaras oneequation model [27], in contrast to our use of k − ω SST in our enhanced RANS
Summary
Reynolds averaged Navier-Stokes (RANS) models are notoriously inaccurate in the presence of massive flow separation, for example in the wake of bluff bodies. Rection fields are solved for by injecting DES, LES or DNS data into the RANS equations; and a model is obtained by regressing these corrections against mean-flow quantities available to RANS. This model can be applied to predict a flow for which no reference data is available. We wish to demonstrate that our framework has the capability of constructing RANS closures that generalize acceptably for massively detached flows. As such they may be useful as components of larger, general purpose models. The 3D LES data source means that optimization of the symbolic regression for large data-sets is required, and we introduce a practical technique for library reduction
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