• 대한조선학회논문집
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• 제51권3호
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• pp.259-264
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• 2014

Tip vortex cavitation (TVC) is important for naval ships and research vessels that require raising the cavitation inception speed to maximum possible values. The concepts for alleviating the tip vortex are summarized by Platzer and Souders (1979), who carried out a thorough literature survey. Active control of TVC involves the injection of a polymer or water from the blade tip. The main effect of such mass injection (both water and polymer solutions) into the vortex core is an increase in the core radius, consequently delaying TVC inception. However, the location of the injection port needs to be selected with great care in order to ensure that the mass injection is effective in delaying TVC inception. In the present study, we propose a semi-active control scheme that is achieved by attaching a thread at the propeller tip. The main idea of a semi-active control is that because of its flexibility, the attached thread can be sucked into the low-pressure region closer to the vortex core center. An experimental study using a scale model was carried out in the cavitation tunnel at the Seoul National University. It was found that a flexible thread can effectively suppress the occurrence of TVC under the design condition for a model propeller.

• 한국음향학회지
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• 제40권4호
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• pp.261-269
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• 2021

공동 유동과 이로 인한 소음에 관한 대부분의 기존 연구들은 효율성이라는 장점 때문에 비압축성 가정의 검증 없이 비압축성 Reynolds averaged Navier-Stokes 방정식에 기반한 수치 해석 방법을 사용하고 있다. 하지만 지금까지 비압축성 가정이 공동 유동과 소음의 예측에 미치는 영향에 대한 연구가 전무한 실정이다. 본 연구에서는 날개 끝 와류공동 유동과 소음에 대한 유체의 압축성 영향을 고찰하기 위하여 날개 끝 와류 공동을 대상으로 비압축성 기반의 해석과 압축성 기반의 해석을 모두 수행하고, Ffowcs Williams and Hawkings(FW-H) 음향상사법을 적용하여 공동 소음을 예측하고 비교하였다. 상류 방향의 유동 영향을 고려하기 위하여, 스큐각이 17°인 수중 추진기를 장착한 DARPA Suboff 잠수함 몸체를 고려하였다. 해석 영역은 실험결과와의 비교를 위하여 선박해양플랜트연구소에서 보유하고 있는 대형 캐비테이션 터널의 시험부와 동일하게 설정하였다. 날개 끝 와류 공동을 정확하게 예측하기 위하여 고정확도의 비정상 상태 지연박리와류모사 해석방법을 적응형 격자 기법과 연계하여 사용하였다. 압축성 유동 해석기법을 이용하여 예측한 음향 스펙트럼이 실험결과와 더 일치하는 결과를 확인하였다.

• 대한조선학회논문집
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• 제58권2호
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• pp.105-111
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• 2021

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.

• 한국가시화정보학회지
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• 제20권3호
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• pp.128-135
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• 2022

Cavitation occurs inevitably in marine propellers rotating at high speed in the water, which is a major cause of underwater radiated noise. Cavitation-induced noise from propellers rotating at a specific frequency not only reduces the sonar detection capability, but also exposes the ship's location, and it causes very fatal consequences for the survivability of the navy vessels. Therefore cavity inception speed (CIS) is one of the important factors determining the special performance of the ship. In this study, we present a method using computer vision that can detect and quantitatively estimate tip vortex cavitation on a propeller rotating at high speed. Based on the model test results performed in a large cavitation tunnel, the effectiveness of this method was verified.