Computers & Fluids ( IF 2.5 ) Pub Date : 2021-05-04 , DOI: 10.1016/j.compfluid.2021.104997 Jasper P. Huijing , Richard P. Dwight , Martin Schmelzer
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 , and a cylinder at . 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 SST model in predictions of mean-velocity in complete flow domain.
中文翻译:
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和一个圆柱体在 。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 SST模型用于预测完整流域中的平均速度。