论文标题
深度学习，以有效地重建高分辨率的湍流DNS数据
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data
论文作者
论文摘要
在计算流体动力学领域内，直接数值模拟（DNS）用于获得流体流的高度精确的数值解。但是，这种用于数值求解Navier-Stokes方程的方法在计算上非常昂贵，主要是由于需要大量精制的网格。大型涡流模拟（LES）提出了一种更高效的方法，用于求解低分辨率（LR）网格上的流体流量，但导致溶液保真度的总体降低。通过本文，我们介绍了一个新颖的深度学习框架SR-DNS NET，该网络旨在通过利用图像超级分辨率中使用的深度学习技术来减轻解决方案保真度和计算复杂性之间的固有权衡。使用我们的模型，我们希望学习从粗体LR解决方案到精制的高分辨率（HR）DNS解决方案的映射，以消除对高度精制的网格执行DNS的需求。我们的模型有效地从LES（如低分辨率解决方案）中有效地重建了高保真DNS数据，同时产生了良好的重建指标。因此，我们的实施提高了LR解决方案的解决方案的准确性，同时仅产生了部署训练有素的深度学习模型所需的计算成本的边际增加。
Within the domain of Computational Fluid Dynamics, Direct Numerical Simulation (DNS) is used to obtain highly accurate numerical solutions for fluid flows. However, this approach for numerically solving the Navier-Stokes equations is extremely computationally expensive mostly due to the requirement of greatly refined grids. Large Eddy Simulation (LES) presents a more computationally efficient approach for solving fluid flows on lower-resolution (LR) grids but results in an overall reduction in solution fidelity. Through this paper, we introduce a novel deep learning framework SR-DNS Net, which aims to mitigate this inherent trade-off between solution fidelity and computational complexity by leveraging deep learning techniques used in image super-resolution. Using our model, we wish to learn the mapping from a coarser LR solution to a refined high-resolution (HR) DNS solution so as to eliminate the need for performing DNS on highly refined grids. Our model efficiently reconstructs the high-fidelity DNS data from the LES like low-resolution solutions while yielding good reconstruction metrics. Thus our implementation improves the solution accuracy of LR solutions while incurring only a marginal increase in computational cost required for deploying the trained deep learning model.
