  This work evaluates the applicability of super-resolution generative
adversarial networks (SRGANs) as an intermediate step for the reconstruction of
wall-parallel velocity fields from coarse wall measurements. 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 $y^+=[15,30,50,100]$. Even though lower resolutions make it more
challenging to achieve high accuracy predictions, the suggested SRGAN-based
network helps us to achieve acceptable results. The proposed novel DNN-based
methodology to reconstruct flow fields from coarse wall measurements in
turbulent flows has great potential for opposition-control applications relying
on non-intrusive sensing.
