Particle Tracking in X-Ray and Optical Imaging for Cavitation Inception Studies on Rough Wall Surfaces

The dynamics of the flow leading to cavitation inception with a rough wall are investigated in a cavitation tunnel with a convergent-divergent section. Two types of measurement techniques, ultra-fast synchrotron X-ray imaging and optical imaging, are employed to acquire data on cavitation inception. The study aims to understand the effect of the free-stream nuclei population on inception and the flow dynamics leading to inception by seeding the flow. The diverging section of the Venturi is laser-etched to induce roughness varying from smooth (~3-5 micrometers) to rough (~100 micrometers). The etching geometry represents a checkerboard pattern to maintain consistent flow physics. Image processing techniques using machine learning are used to segment and track the particles in the flow field. Migration behavior is observed in the microbubbles generated from the breakup of incipient cavitation bubbles, leading to inception events, which aligns with previous studies. Particle tracking methods are used to further understand this phenomenon. The production of these microbubbles and the percentage that initiate cavitation events are investigated for various roughness cases.