Abstract
Cavitation is a technical challenge for high-speed underwater vehicles, such as nuclear submarines and underwater robots, et al. The cavitation phenomena of hydrofoils are typically studied through water tunnel experiments or numerical simulations, which yield extensive cavitation images. To conveniently extract cavitation features from the massive images, a feature extraction method for hydrofoil cavitation was proposed in this work based on deep learning image semantic segmentation techniques. This method is employed to investigate the mechanism of the transition process from sheet cavitation to cloud cavitation on hydrofoils. The accuracy and generalization ability of the proposed method have been validated. The results indicate that, in addition to accurately obtaining the cavitation length automatically, the method can also derive more sensitive indicators such as area and position changes of the cavitation regions. This heightened sensitivity is invaluable for precisely pinpointing the transition from sheet-like cavitation to cloud cavitation, thereby aiding in a more effective analysis of the development mechanism of attached cavitation. In summary, our proposed method not only streamlines the extraction of cavitation features from massive images but also enhances the understanding of development mechanisms of attached cavitation by providing additional data and more sensitive indicators for analysis.