论文标题
基于神经网络的墙模型，用于周期性山丘上的湍流
A wall model based on neural networks for LES of turbulent flows over periodic hills
论文作者
论文摘要
在这项工作中，使用前馈神经网络（FNN）和壁溶解的LES（WRLES）数据开发了用于周期性山上湍流的数据驱动墙模型。为了开发适用于不同流程度的墙模型，所有流式位置的近壁区域中的流数据都将其分组为训练数据集。在开发的FNN壁模型中，我们分别采用壁正常距离，近壁速度和压力梯度作为输入特征和壁剪应力作为输出标签。通过将预测的壁剪应力与WRLE数据进行比较，可以检查受过训练的FNN壁模型的预测准确性和概括能力。对于瞬时壁剪应力，FNN预测显示了与WRLE数据的总体良好一致性，并且在山顶附近的位置观察到了一些差异。在大多数流式位置，FNN预测与WRLS预测之间的相关系数大于0.7。对于平均壁剪应力，FNN预测与WRLE数据非常吻合。更重要的是，对于不同的雷诺数字，观察到FNN壁模型的总体良好性能，证明其良好的概括能力。
In this work, a data-driven wall model for turbulent flows over periodic hills is developed using the feedforward neural network (FNN) and wall-resolved LES (WRLES) data. To develop a wall model applicable to different flow regimes, the flow data in the near wall region at all streamwise locations are grouped together as the training dataset. In the developed FNN wall models, we employ the wall-normal distance, near-wall velocities and pressure gradients as input features and the wall shear stresses as output labels, respectively. The prediction accuracy and generalization capacity of the trained FNN wall model are examined by comparing the predicted wall shear stresses with the WRLES data. For the instantaneous wall shear stress, the FNN predictions show an overall good agreement with the WRLES data with some discrepancies observed at locations near the crest of the hill. The correlation coefficients between the FNN predictions and WRLES predictions are larger than 0.7 at most streamwise locations. For the mean wall shear stress, the FNN predictions agree very well with WRLES data. More importantly, overall good performance of the FNN wall model is observed for different Reynolds numbers, demonstrating its good generalization capacity.
