Numerical Investigation of Cavitation Models Combined with RANS and PANS Turbulence Models for Cavitating Flow Around a Hemispherical Head-Form Body

Accurate prediction of cavitating flows is essential for improving the performance and durability of marine and hydrodynamic systems. This study investigates the influence of different cavitation models—Kunz, Merkle, and Schnerr–Sauer—on the numerical prediction of cavitation around a hemispherical head-form body using computational fluid dynamics (CFD). Additionally, the effects of turbulence modeling approaches, including Reynolds-averaged Navier–Stokes (RANS) and partially averaged Navier–Stokes (PANS), are examined to assess their capability in capturing transient cavitation structures and turbulence interactions. The results indicate that the Schnerr–Sauer model, which incorporates bubble dynamics based on the Rayleigh–Plesset equation, provides the most accurate prediction of cavitation structures, closely aligning with experimental data. The Merkle model shows intermediate accuracy, while the Kunz model tends to overpredict cavity closure, limiting its ability to capture unsteady cavitation dynamics. Furthermore, the PANS turbulence model demonstrates superior performance over RANS by resolving more transient cavitation phenomena, such as cavity shedding and re-entrant jets, leading to improved accuracy in pressure distribution and vapor volume fraction predictions. The combination of the PANS turbulence model with the Schnerr–Sauer cavitation model yields the most consistent results with experimental observations, highlighting its effectiveness in modeling highly dynamic cavitating flows.