论文标题
深度学习以推进湍流模型不确定性定量的特征空间扰动方法
Deep Learning to advance the Eigenspace Perturbation Method for Turbulence Model Uncertainty Quantification
论文作者
论文摘要
Reynolds平均Navier Stokes（RANS）模型是湍流模拟中最常见的模型形式。它们用于计算雷诺应力张量，并为工程流提供强大的结果。但是rans模型预测具有较大的误差和不确定性。过去，有一些努力用于使用数据驱动的方法来提高其准确性。在这项工作中，我们概述了一种机器学习方法，以帮助使用特征空间扰动方法来预测湍流模型预测中的不确定性。我们使用训练有素的神经网络来预测RANS的形状的差异预测的雷诺应激椭球。我们将模型应用于许多湍流，并演示该方法如何正确识别与直接数值模拟（DNS），大涡模拟（LES）或以前工作的实验结果相比，建模误差发生的区域。
The Reynolds Averaged Navier Stokes (RANS) models are the most common form of model in turbulence simulations. They are used to calculate Reynolds stress tensor and give robust results for engineering flows. But RANS model predictions have large error and uncertainty. In past, there has been some work towards using data-driven methods to increase their accuracy. In this work we outline a machine learning approach to aid the use of the Eigenspace Perturbation Method to predict the uncertainty in the turbulence model prediction. We use a trained neural network to predict the discrepancy in the shape of the RANS predicted Reynolds stress ellipsoid. We apply the model to a number of turbulent flows and demonstrate how the approach correctly identifies the regions in which modeling errors occur when compared to direct numerical simulation (DNS), large eddy simulation (LES) or experimental results from previous works.
