Structure-Aware U-Net for Deep-Learning Super-Resolution Reconstruction of Turbulent Flows

Abstract

High-resolution flowfield data are essential for turbulence research and engineering applications, yet their acquisition from numerical simulations and experiments remains prohibitively expensive. Super-resolution reconstruction offers a promising alternative, but conventional interpolation-based and generic image super-resolution methods often fail to recover turbulence-specific fine-scale features, such as coherent vortical structures, thin shear layers, and high-wavenumber fluctuations. To address this limitation, this study proposes a structure-aware deep-learning framework, termed the Coordinate-Attention Residual Super-Resolution Network (CARSR-Net), for reconstructing turbulent flows from severely underresolved inputs. Here, structure-aware means that the model is designed to preferentially preserve physically important flow structures and their multiscale statistical signatures. In particular, a turbulence-oriented composite loss function is introduced to emphasize high-gradient and vortical regions so that the reconstruction is constrained not only by pointwise field accuracy but also by the recovery of physically relevant small-scale structures. Lightweight attention and multiscale feature aggregation modules are further incorporated to improve the extraction of directional and multiscale flow information. The model is evaluated on wall-bounded turbulent channel flow and forced isotropic turbulence using both instantaneous fields and representative statistical quantities. The results show that CARSR-Net reconstructs high-resolution flowfields with lower relative [Formula: see text] error and more accurate energy spectra and vorticity probability density functions than baseline methods. In particular, it better preserves high-frequency content and the physical consistency of the reconstructed fields.