In this paper, the ventilated supercavities are studied both numerically and experimentally. A slender rod is considered as the solid body which has a sharp edged disk at the nose as a cavitator and special ports for air ventilation. The experiments are conducted in a recirculating water tunnel. The simulations are provided for two different algorithms in free-surface treatment, both using the VOF method but one using Youngs' algorithm in the advection of the free-surface and the other without. The comparison between numerical simulations and experiments show that the numerical method using Youngs' algorithm accurately simulates the physics of ventilated cavitation phenomena such as the cavity shape, the gas leakage and the re-entrant jet.

Investigating the impact of various parameters on the characteristics of supercavitation is an essential and continuously evolving matter. In the current study, the influence of the geometry behind the cavitator on the characteristics of ventilation supercavitation at different Froude numbers and at a constant ventilation coefficient has been investigated. For this purpose, at first, by using the experimental method on a model with a disk cavitator at different Froude numbers, the ventilation cavitation has been investigated and the appropriate numerical method has been validated based on the experimental results. Next, using the numerical method, the effect of filling the cavity volume by geometry with different dimensions and confined inside the cavity on the characteristics of the ventilated supercavity was investigated. Also, the geometry with unlimited length and variable diameter was investigated in order to determine the effect of increasing the diameter on the characteristics of the cavity. The results indicate that increasing the volume of the aft body confined within the supercavity does not have a significant effect on its characteristics. However, increasing the diameter of the aft body caused a significant reduction in the length of the supercavity for the geometry with an infinite body length.

Understanding the optimal values and interactions of parameters within each process is of highest importance. This study is dedicated to exploring the influence of various parameters and their interactions on ventilation supercavitation phenomena through interpretable machine learning (ML) models. In this study, the characteristics of supercavitation on a disk cavitator with enhanced ventilation at different Froude numbers have been examined through both experimental and numerical means. Subsequently, the data generated from the experimental and numerical methods have been employed to create the optimized ML model. Then, to investigate the behavior of important parameters, their interactions with each other, and the resulting impact of these interactions on conditioned cavitation, interpretable machine learning techniques, such as shapley additive explanations, partial dependence plots, and individual conditional expectations, were employed within an optimized ML model. The findings highlight that the ventilation coefficient is the most crucial parameter affecting the characteristics of supercavitation. Ventilation coefficient exhibits a non-linear behavior and performs effectively within the range of 0.06–0.12. Additionally, the water speed parameter and the ratio of the back-body's diameter significantly influence the cavity length and cavitation number. These parameters exhibit complex interactions, shaping the characteristics of blowing supercavitation.