Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks
This work addresses the computational bottleneck in fluid dynamics simulations for researchers and engineers, but it is incremental as it builds on super-resolution techniques with physics-based regularization.
The paper tackles the problem of reconstructing high-resolution turbulent flow data from computationally cheaper large eddy simulations (LES) by developing a physics-guided neural network method, achieving better reconstruction in terms of pixel-wise error and structural similarity compared to existing approaches.
Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alternative that is computationally less demanding, but is unable to capture all of the scales of turbulent transport accurately. Our goal in this work is to build a new data-driven methodology based on super-resolution techniques to reconstruct DNS data from LES predictions. We leverage the underlying physical relationships to regularize the relationships amongst different physical variables. We also introduce a hierarchical generative process and a reverse degradation process to fully explore the correspondence between DNS and LES data. We demonstrate the effectiveness of our method through a single-snapshot experiment and a cross-time experiment. The results confirm that our method can better reconstruct high-resolution DNS data over space and over time in terms of pixel-wise reconstruction error and structural similarity. Visual comparisons show that our method performs much better in capturing fine-level flow dynamics.