High-flexibility reconstruction of small-scale motions in wall turbulence using a generalized zero-shot learning

TL;DR

This paper introduces EC-SRGAN, a flexible super-resolution framework that accurately reconstructs small-scale wall turbulence structures from low-resolution data, leveraging zero-shot transfer and energy cascade modeling.

Contribution

The study presents a novel EC-SRGAN framework combining super-resolution GANs with energy cascade modeling, enabling zero-shot transfer and multi-scale turbulence reconstruction.

Findings

EC-SRGAN accurately reproduces turbulence spectra compared to DNS.

The framework generalizes well across different TBL datasets.

It efficiently reconstructs fields at multiple super-resolution ratios.

Abstract

This study proposes a novel super-resolution (or SR) framework for generating high-resolution turbulent boundary layer (TBL) flow from low-resolution inputs. The framework combines a super-resolution generative adversarial neural network (SRGAN) with down-sampling modules (DMs), integrating the residual of the continuity equation into the loss function. DMs selectively filter out components with excessive energy dissipation in low-resolution fields prior to the super-resolution process. The framework iteratively applies the SRGAN and DM procedure to fully capture the energy cascade of multi-scale flow structures, collectively termed the SRGAN-based energy cascade framework (EC-SRGAN). Despite being trained solely on turbulent channel flow data (via "zero-shot transfer"), EC-SRGAN exhibits remarkable generalization in predicting TBL small-scale velocity fields, accurately reproducing wavenumber spectra compared to DNS results. Furthermore, a super-resolution core is trained at a specific super-resolution ratio. By leveraging this pre-trained super-resolution core, EC-SRGAN efficiently reconstructs TBL fields at multiple super-resolution ratios from various levels of low-resolution inputs, showcasing strong flexibility. By learning turbulent scale invariance, EC-SRGAN demonstrates robustness across different TBL datasets. These results underscore EC-SRGAN potential for generating and predicting wall turbulence with high flexibility, offering promising applications in addressing diverse TBL-related challenges.