Deep-learning-based liquid extraction algorithm for particle image velocimetry in two-phase flow experiments of an object entering water

Abstract

Particle image velocimetry (PIV) enables the study of instantaneous fluid kinematics through flow visualization, which promotes the study of an object entering a liquid surface. However, even with PIV, the accurate extraction of the liquid phase region remains elusive due to the unsteady hydrodynamic forces involved. Therefore, we present a deep-learning network based on U-Net to solve the problems. The network, named MRes-Att-Unet, combines the mechanisms of residual connectivity and attention, which is proven to be effective in medical image segmentation. Considering the use of a training of network in supervised learning, we created a corresponding dataset based on the PIV experiment of an object entering water in two-phase flow and tested the images of an object entering water in an isolated wave crushing experiment. The results showed that the MRes-Att-UNet improved the segmentation accuracy of the gas–liquid boundaries, with especially enhanced results in a wide range of laser scattering, fuzzy bubbles, splash, and other complex interferences. Moreover, a WIDIM method is used to process the segmented and original images, and the results show that a deep learning method is feasible for segmenting PIV images and can effectively reduce the error vector of two-phase flow boundary.