Journal of the Society of Naval Architects of Korea (대한조선학회논문집)

The Society of Naval Architects of Korea (대한조선학회)
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A Study on Autonomous Cavitation Image Recognition Using Deep Learning Technology

딥러닝 기술을 이용한 캐비테이션 자동인식에 대한 연구

  • Ji, Bahan (Department of Naval Architecture and Ocean Engineering, Chungnam National University) ;
  • Ahn, Byoung-Kwon (Department of Naval Architecture and Ocean Engineering, Chungnam National University)
  • 지바한 (충남대학교 선박해양공학과) ;
  • 안병권 (충남대학교 선박해양공학과)
  • Received : 2020.02.29
  • Accepted : 2021.01.22
  • Published : 2021.04.20
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Abstract

The main source of underwater radiated noise of ships is cavitation generated by propeller blades. After the Cavitation Inception Speed (CIS), noise level at all frequencies increases severely. In determining the CIS, it is based on the results observed with the naked eye during the model test, however accuracy and consistency of CIS values are becoming practical issues. This study was carried out with the aim of developing a technology that can automatically recognize cavitation images using deep learning technique based on a Convolutional Neural Network (CNN). Model tests on a three-dimensional hydrofoil were conducted at a cavitation tunnel, and tip vortex cavitation was strictly observed using a high-speed camera to obtain analysis data. The results show that this technique can be used to quantitatively evaluate not only the CIS, but also the amount and rate of cavitation from recorded images.

Keywords

References

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