  This work evaluates the applicability of super-resolution generative
adversarial networks (SRGANs) as a methodology for the reconstruction of
turbulent-flow quantities from coarse wall measurements. The method is applied
both for the resolution enhancement of wall fields and the estimation of
wall-parallel velocity fields from coarse wall measurements of shear stress and
pressure. The analysis has been carried out with a database of a turbulent
open-channel flow with friction Reynolds number $Re_{\tau}=180$ generated
through direct numerical simulation. Coarse wall measurements have been
generated with three different downsampling factors $f_d=[4,8,16]$ from the
high-resolution fields, and wall-parallel velocity fields have been
reconstructed at four inner-scaled wall-normal distances $y^+=[15,30,50,100]$.
We first show that SRGAN can be used to enhance the resolution of coarse wall
measurements. If compared with direct reconstruction from the sole coarse wall
measurements, SRGAN provides better instantaneous reconstructions, both in
terms of mean-squared error and spectral-fractional error. Even though lower
resolutions in the input wall data make it more challenging to achieve highly
accurate predictions, the proposed SRGAN-based network yields very good
reconstruction results. Furthermore, it is shown that even for the most
challenging cases the SRGAN is capable of capturing the large-scale structures
that populate the flow. The proposed novel methodology has great potential for
closed-loop control applications relying on non-intrusive sensing.
