A Cascaded Convolutional Neural Network for Two-Phase Flow PIV of an Object Entering Water

Abstract

In the two-phase flow particle image velocimetry (PIV) experiment of an object entering water, the mask of the noncomputed area and the calculation of velocity field in particle images are two key stages. Due to the complexity of the edge of the object in the particle image, the mask calibration is usually performed manually and then the PIV estimation is carried out. We propose a cascaded convolutional neural network (CNN) in this article to implement end-to-end two-phase flow fluid motion estimation. In the first stage, the image segmentation network U-Net is used to mask the noncomputational area of the image and extract the liquid phase. In the second stage, we adopt the improved deep optical flow network, which is known as recurrent allpairs field transforms (RAFT) to calculate the velocity field. What is more, the corresponding datasets are generated for training model parameters. Finally, our approach is tested on synthetic and experimental images. The experimental results indicate that our approach not only reaches accurate segmentation of the calculated liquid phase region but also achieves a high-precision velocity field calculation. Meanwhile, this cascade CNN model has high efficiency toward real-time estimation.