论文标题
通过雷诺的发散张力张量，一种数据驱动的方法，用于关闭RANS模型
A data-driven approach for the closure of RANS models by the divergence of the Reynolds Stress Tensor
论文作者
论文摘要
在本文中，提出了一个新的数据驱动模型来关闭和提高rans方程的准确性。 Reynolds应力张量（RST）的差异是通过神经网络（NN）获得的，该神经网络（NN）的体系结构和输入选择可以保证Galilean和坐标框架旋转。前者源自NN的输入选择，而后者是从第一个差异扩展为向量的。该方法已被广泛用于针对各向异性RST或第一个差异的数据驱动模型，在这里提出了第一个差异。因此，提出了第一个与平均数量差异的构成关系以获得这种扩展。此外，一旦训练了提出的数据驱动方法，就无需运行任何经典的湍流模型来关闭方程。与标准的湍流模型相比，使用正方形管和周期性丘陵的流动测试来显示本方法的优势。
In the present paper a new data-driven model is proposed to close and increase accuracy of RANS equations. The divergence of the Reynolds Stress Tensor (RST) is obtained through a Neural Network (NN) whose architecture and input choice guarantee both Galilean and coordinates-frame rotation. The former derives from the input choice of the NN while the latter from the expansion of the divergence of the RST into a vector basis. This approach has been widely used for data-driven models for the anisotropic RST or the RST discrepancies and it is here proposed for the divergence of the RST. Hence, a constitutive relation of the divergence of the RST from mean quantities is proposed to obtain such expansion. Moreover, once the proposed data-driven approach is trained, there is no need to run any classic turbulence model to close the equations. The well-known tests of flow in a square duct and over periodic hills are used to show advantages of the present method compared to standard turbulence models.
