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Investigating the interaction parameters on ventilation supercavitation phenomena: Experimental and numerical analysis with machine learning interpretation by Hossein Ali Kamali; Mahmoud Pasandidehfard is a Engineering article available to read on EtoBox.

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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

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Author

Hossein Ali Kamali; Mahmoud Pasandidehfard

Publisher

AIP Publishing

Published

2023

Language

EN

Field

Engineering (Physical Sciences)