Refining Flow Structures with Deep Learning and Super Resolution Methods

Fluid mechanics’ simulations are widely incorporated in place of expensive and complex experiments, providing accurate property fields in a dense grid fashion. It is a fact that experimental measurements due to their discrete and, most of the times, sparse data representation, cannot fully define velocity, pressure, temperature, vorticity, which are common properties of interest, especially in turbulent flows. The need to upscale flow features from coarse-grained data has been successfully addressed during the past years with aid of novel artificial intelligence -based methods. This upscaling refers either to sparse experimental data or to coarse simulation data, bypassing computationally intensive classical direct numerical simulations. This study presents a deep learning approach that accepts low resolution vorticity fields, from an open channel flow simulation, and reconstructs the field to resemble a fine grid result. This super resolution approach shows that reconstruction is possible as long as the network training is performed using pairs of low/high resolution fields. The proposed model is based on the U-Net architecture and is investigated over the effect of the resolution scale factor of the input images.