과제정보
이 논문은 한화오션과 2024년도 정부(산업통상자원부)의 재원으로 한국산업기술진흥원의 지원을 받아 수행된 연구임(P0017006, 2024년 산업혁신인재성장지원사업).
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This study developed techniques for quantitatively evaluating the generative characteristics of flow fields in the ALS (Air Lubrication System) for ships through model experiments in a cavitation tunnel. The object was to clarify the similarity relations between the bubble layer flow fields of models and full-scale ships. Specifically, for the ALS system, which injects air to increase insertion loss and thereby reduce noise, the focus was on analyzing the bubble layer flow fields. An image segmentation algorithm based on OpenCV was developed to quantitatively assess the bubble layers, along with methods for analyzing the size and quantity of bubbles. Spatial densities of 10%, 50%, and 100% were established at extremely low, low, and high flow rates respectively with increasing flow speed leading to increased spatial density and individual bubble size.
이 논문은 한화오션과 2024년도 정부(산업통상자원부)의 재원으로 한국산업기술진흥원의 지원을 받아 수행된 연구임(P0017006, 2024년 산업혁신인재성장지원사업).