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 $\mathrm{Re}=40,000$, and a cylinder at $\mathrm{Re}=140,000$. 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 complete flow domain.

中文翻译：

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数据驱动的RANS闭合用于钝体周围的三维流

在此简短说明中，我们应用了Schmelzer等人最近提出的数据驱动的RANS闭包建模框架。（2020）到完全三维的高雷诺数流：即壁挂式立方体和长方体$\mathrm{关于}=40，000$和一个圆柱体在 $\mathrm{关于}=140，000$。对于每个流，使用基于LES或DES参考数据的稀疏符号回归来生成新的RANS闭包。此新模型​​在CFD求解器中实现，然后应用于其他流量的预测。与基准相比，我们看到了持续改进$ķ-\omega$ SST模型用于预测完整流域中的平均速度。