Three-dimensional ESRGAN for super-resolution reconstruction of turbulent flows with tricubic interpolation-based transfer learning

Turbulence is a complicated phenomenon because of its chaotic behavior with multiple spatiotemporal scales. also has irregularity and diffusivity, making predicting reconstructing turbulence more challenging. This study proposes deep-learning approach to reconstruct three-dimensional (3D) high-resolution turbulent flows from spatially limited data using 3D enhanced super-resolution generative adversarial networks (3D-ESRGAN). In addition, novel transfer-learning method based on tricubic interpolation employed. Turbulent channel flow at friction Reynolds numbers [Formula: see text] = 180 500 were generated by direct numerical simulation (DNS) used estimate the performance model as well that interpolation-based transfer learning. The results, including instantaneous velocity fields statistics, show reconstructed agree reference DNS data. findings indicate proposed 3D-ESRGAN can even training