Data-driven RANS closures for three-dimensional flows around bluff bodies

TL;DR

This paper applies a data-driven RANS closure modeling framework to complex three-dimensional high Reynolds number flows around bluff bodies, demonstrating improved mean-velocity predictions over traditional models.

Contribution

It introduces a novel application of sparse symbolic regression for RANS closure modeling in 3D bluff body flows at high Reynolds numbers.

Findings

Consistent improvement in mean-velocity predictions over baseline models.

Successful implementation of data-driven closures in CFD simulations.

Application to multiple geometries and flow conditions.

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 $k-\omega$ SST model in predictions of mean-velocity in the complete flow domain.