  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.
