3D visualization in complicated flow channel using deep learning-based bubble detection

Uesawa, Shinichiro  ; Ono, Ayako ; Yoshida, Hiroyuki  

This paper introduces a new measurement technique for visualizing the three-dimensional distribution of bubbles in a complex channel such as a nuclear reactor fuel assembly. Bubbly flow is important in many engineering fields, and especially in nuclear engineering, where bubble behavior significantly affects the performance and safety of nuclear reactors, and thus requires detailed understanding. Conventional rule-based image recognition has difficulty identifying bubbles overlapping in the line-of-sight direction, but in this study, deep learning (Mask R-CNN and Swin Transformer) is used to achieve highly accurate bubble detection with a small amount of training data. Furthermore, the tracking technique using ByteTrack made it possible to track many bubbles with complex motions, and by combining images taken from different viewpoints using two high-speed cameras and reconstructing the 3D shape of the bubbles using the ellipsoid approximation, 3D instantaneous local information such as bubble position, diameter, and velocity was obtained. To eliminate the effects of refraction and obstruction of vision by structures in the channel, a simulated fuel rod was made of a transparent material (PFA tube) with a refractive index similar to that of water, enabling distortion-free imaging and measurement even in channels with complex structures. This enabled 3D visualization of bubble behavior in complex channels, which had been difficult to achieve in the past. Since this technology enables highly accurate 3D visualization with a small number of cameras and a small amount of learning, it is expected to be applied to objects other than bubbles.