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Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

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A feature extraction method for hydrofoil attached cavitation based on deep learning image semantic segmentation

Yingyuan Liu¹, Yizhi Wang², Kang An²

¹The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, 200234, China. yyliu@shnu.edu.cn.

Scientific Reports

|February 5, 2025

Summary

This summary is machine-generated.

A new deep learning method automatically extracts hydrofoil cavitation features from images. This aids in understanding the transition from sheet to cloud cavitation for underwater vehicles.

Keywords:

Cavitation length Deep learning Hydrofoil Image semantic segmentation

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Area of Science:

Fluid Dynamics
Underwater Vehicle Technology
Artificial Intelligence

Background:

Cavitation presents a significant challenge for high-speed underwater vehicles like nuclear submarines and autonomous underwater vehicles.
Traditional methods for studying hydrofoil cavitation, such as water tunnel experiments and numerical simulations, generate vast amounts of image data.
Efficiently extracting meaningful features from these extensive image datasets is crucial for analysis.

Purpose of the Study:

To develop an automated feature extraction method for hydrofoil cavitation using deep learning semantic segmentation.
To investigate the transition mechanism between sheet cavitation and cloud cavitation.
To validate the accuracy and generalization capabilities of the proposed deep learning approach.

Main Methods:

Implementation of a deep learning-based semantic segmentation technique for image analysis.
Application of the method to extract cavitation features, including length, area, and position.
Validation of the method's performance on hydrofoil cavitation datasets.

Main Results:

The deep learning method accurately and automatically determines cavitation length.
The approach identifies more sensitive indicators, such as changes in cavitation area and position.
The method effectively pinpoints the transition from sheet to cloud cavitation.

Conclusions:

The proposed method streamlines the extraction of cavitation features from large image datasets.
Enhanced sensitivity in feature extraction provides deeper insights into attached cavitation development mechanisms.
This technique facilitates a more effective analysis of hydrofoil cavitation dynamics for improved underwater vehicle design and operation.