3D Visualization in Rod Bundle Flow Channel Using Deep Learning-based Bubble Detection

Abstract

Bubble visualization using a high-speed video-camera has been used as a measurement technique of bubble diameters and velocities. However, the bubble detection was difficult under the condition of the high void fraction because the overlapping bubbles for the sight direction of the camera increase with the void fraction. Additionally, the visualization for a system with objects, such as rod bundle flow channels, becomes more difficult. In this study, we applied a deep learning-based bubble detection technique with Shifted Window Transformer to bubble images shoot from two directions to identify the bubble size, three-dimensional (3D) positions of bubbles, 3D bubble trajectories in the rod bundle flow channel. Furthermore, we used perfluoroalkoxy alkane tubes with almost the same reflection as water in the channel to visualize the bubbly flow in the whole of the flow channel. We confirmed that the detection technique can segment individual bubbles in overlapping bubbles and bubbles behind the rod. By using the detection results, we estimated the diameter and velocity of each bubble and cross-sectional void fraction.