Holographic measurement of discrete bubbles in cavitating flow and optimization of a multiscale Eulerian-Lagrangian model

This study integrates experimental measurements and numerical simulations to systematically investigate multiscale cavitation bubble dynamics in cloud cavitating flow around a NACA66 hydrofoil. Full flow field high-speed imaging technology conducted under an inflow velocity of 8.5 m/s and a cavitation number of 1.4 shows that macroscopic cavity structures display typical periodic evolutionary behavior, including three evolution stages of growth of attached cavity, development of re-entrant jet and cloud cavity shedding. The observed shedding frequency is approximately 45.55 Hz, and the maximum attached cavity length reaches 0.8 times the chord length of hydrofoil. Quantitative measurements of cavitation nuclei in the incoming flow and discrete microbubbles within the cavitation zone are acquired via digital in-line holography (DIH) technology. In the inflow region, the number density and average size of cavitation nuclei exhibit minor temporal fluctuations. In contrast, the spatial number density of microbubbles in the cavity shedding region and the wake flow region shows periodic evolution in response to the development of cloud cavitation. Meanwhile, the bubble size distribution follows a dual-power-law pattern, with smaller bubbles adhering to a − 4/3 power-law scaling and larger ones conforming to a − 10/3 scaling. Additionally, a Eulerian–Lagrangian two-way coupled model is optimized based on the experimental holographic imaging data of microbubble in cloud cavitating flow. Large Eddy Simulation (LES) coupled with the Volume of Fluid (VOF) method is employed for macroscopic cavity dynamics in the Eulerian framework, while the dynamics of nuclei and microbubbles are addressed via a Discrete Bubble Model (DBM) in the Lagrangian framework. The model incorporates physically-based conversion criteria to couple the two frameworks and is validated against experimental observations of microbubble behavior. Validation results demonstrate that the improved model accurately predicts cavitation shedding frequency, cavity morphology, and bubble number density distribution, confirming its reliability and predictive capability for simulating multiscale cavitation dynamics.