A Novel One-Camera-Five-Mirror Three-Dimensional Imaging Method for Reconstructing the Cavitation Bubble Cluster in a Water Hydraulic Valve

Abstract

In order to study the bubble morphology, a novel experimental and numerical approach was implemented in this research focusing on the analysis of a transparent throttle valve made by Polymethylmethacrylate (PMMA) material. A feature-based algorithm was written using the MATLAB software, allowing the 2D detection and three-dimensional (3D) reconstruction of bubbles: collapsing and clustered ones. The valve core, being an important part of the throttle valve, was exposed to cavitation; hence, to distinguish it from the captured frames, the faster region-based convolutional neural network (R-CNN) algorithm was used to detect its morphology. Additionally, the main approach grouping the above listed techniques was implemented using an optimized virtual stereo vision arrangement of one camera and five plane mirrors. The results obtained during this study validated the robust algorithms and optimization applied.