论文标题
大型涡流模拟的各向异性网格上不变的数据驱动的亚电网应力建模
Invariant Data-Driven Subgrid Stress Modeling on Anisotropic Grids for Large Eddy Simulation
论文作者
论文摘要
我们提出了一种新方法，用于构建数据驱动的亚电网应力模型，以使用各向异性网格对湍流进行大型涡流模拟。我们方法的关键是旋转，反射和单位不变模型形式的伽利略，该模型还嵌入过滤器各向异性，以至于满足重要的亚网格应力身份。我们使用此模型表格仅使用少量各向异性过滤的DNS数据和简单且廉价的神经网络体系结构来训练数据驱动的子网格应力模型。先验和后验测试表明，受过训练的数据驱动的模型可以很好地推广以滤除各向异性比率，雷诺数数量和训练数据集外的流动物理学。
We present a new approach for constructing data-driven subgrid stress models for large eddy simulation of turbulent flows using anisotropic grids. The key to our approach is a Galilean, rotationally, reflectionally and unit invariant model form that also embeds filter anisotropy in such a way that an important subgrid stress identity is satisfied. We use this model form to train a data-driven subgrid stress model using only a small amount of anisotropically filtered DNS data and a simple and inexpensive neural network architecture. A priori and a posteriori tests indicate that the trained data-driven model generalizes well to filter anisotropy ratios, Reynolds numbers and flow physics outside the training dataset.
