深度学习推进湍流模型不确定性量化的特征空间扰动方法
Deep Learning to advance the Eigenspace Perturbation Method for Turbulence Model Uncertainty Quantification
摘要：
雷诺平均Navier Stokes（RANS）模型是湍流模拟中最常用的模型形式。它们被用来计算雷诺应力张量，并给出工程流动的鲁棒结果。但RANS模型预测存在较大的误差和不确定性。在过去，已经有一些关于使用数据驱动的方法来提高其准确性的工作。在这项工作中，我们概述了一个机器学习的方法，以帮助使用的特征空间扰动方法来预测湍流模型预测的不确定性。我们使用一个训练有素的神经网络来预测的RANS预测雷诺应力椭球的形状的差异。我们将该模型应用到一些湍流，并展示了如何正确识别的方法相比，直接数值模拟（DNS），大涡模拟（LES）或实验结果从以前的作品时，发生建模误差的区域。
英文摘要：
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.
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