Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data
This work addresses the problem of high computational expense in fluid flow simulations for researchers and engineers, offering an incremental improvement by applying existing super-resolution techniques to a specific domain.
The paper tackles the trade-off between computational cost and solution fidelity in Computational Fluid Dynamics by introducing SR-DNS Net, a deep learning framework that reconstructs high-resolution DNS data from low-resolution LES solutions, achieving good reconstruction metrics with only a marginal increase in computational cost.
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.