Super-resolution analysis with machine learning for low-resolution flow data

Machine-learned super-resolution is performed to reconstruct the high-resolution flow (HR) field from low-resolution (LR) fluid flow data. As preliminary tests, we use two-dimensional cylinder and NACA0012 airfoil wake flow fields and observe good agreement with reference HR data. Next, we apply two machine-learned architectures based on the convolutional neural network (CNN) for two-dimensional decaying isotropic turbulence. The HR data sets are obtained from direct numerical simulation (DNS) and LR data sets are generated by max and average pooling operations. In this work, we present the hybrid Down-sampled Skip-Connection Multi-Scale (DSC/MS) model, which can reconstruct the flow field accurately from coarse input flow field data. Towards the end of the paper, we discuss the possibility of a machine-learned model for super-resolution in experimental and computational fluid dynamics.