Convolutional neural network based reconstruction of flow-fields from concentration fields for liquid-droplet coalescence

Abstract

The coalescence of liquid droplets and lenses is of great practical and theoretical importance in fluid dynamics and the statistical mechanics of multiphase flows. During such coalescence, there is an interesting and intricate interplay between the shapes of the interfaces, separating two phases, and the background flow field. In experiments, it is easier to visualize concentration fields than to obtain the flow field. We demonstrate that two-dimensional (2D) encoder-decoder CNNs, 2D U-Nets, and three-dimensional (3D) U-Nets can be used to obtain flow fields from concentration fields here. To train these networks, we use concentration and flow fields from our numerical simulations of the Cahn-Hilliard-Navier-Stokes equations. We show that, given test images of concentration fields, our trained models accurately predict the flow fields. Finally, we use data from recent experiments on droplet coalescence to show how our method can be used to obtain the flow field from measurements of the concentration field.