Implementation of Convolutional Neural Network to Enhance Turbulence Models for Channel Flows
Implementation of Convolutional Neural Network to Enhance Turbulence Models for Channel Flows
复制标题
实施卷积神经网络来增强河道流的湍流模型
DOI：
10.1109/s.a.i.ence50533.2020.9303178
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
S. Yakovenko
中科院分区：
文献类型：
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作者：
O. Razizadeh;S. Yakovenko
卷积神经网络（CNN）的实施，以提高湍流模型，这是需要关闭的雷诺平均Navier-Stokes（RANS）方程。机器学习技术使用的规范流测试用例的高保真度的可用数据集。这些数据来自大涡模拟或直接数值模拟，这需要巨大的计算资源。第一阶段以广泛使用的k-ω湍流模型作为RANS的基本模型，利用OpenFOAM软件对下壁有周期性凸起的平面通道和缩放通道内的湍流流动进行了计算。然后，将CNN算法应用于这些情况。与基线RANS模型相比，CNN应用于不同几何形状的壁的通道中的典型湍流流动中，与均方误差损失函数相比，CNN应力各向异性张量分量的预测被证明是改进的。
The convolutional neural network (CNN) is implemented to enhance a turbulence model which is needed to close the Reynolds-averaged Navier–Stokes (RANS) equations. The machine-learning technique uses the available data sets of high fidelity for canonical flow test cases. These data have been produced from large-eddy simulations or direct numerical simulations, which require huge computing resources. At the first stage, the widely used k-ω model is taken as a baseline RANS model, and computations are performed by means of OpenFOAM for turbulent flows in the plane channel having the periodic hills on the lower wall and in the converging-diverging channel. Then, the CNN algorithm is applied to these cases. The prediction of the Reynolds-stress anisotropy tensor components is shown to be improved after the application of CNN with the mean square error loss function in comparison with that for the baseline RANS model in the investigated canonical turbulent flows in channels with walls of different geometry.