• 대한조선학회논문집
• /
• 제57권6호
• /
• pp.312-321
• /
• 2020

In this study, machine learning techniques were applied to predict the residual resistance coefficient (Cr) of low-speed full ships. The used machine learning methods are Ridge regression, support vector regression, random forest, neural network and their ensemble model. 19 hull form variables were used as input variables for machine learning methods. The hull form variables and Cr data obtained from 139 hull forms of KRISO database were used in analysis. 80 % of the total data were used as training models and the rest as validation. Some non-linear models showed the overfitted results and the ensemble model showed better results than others.

• 대한조선학회논문집
• /
• 제59권4호
• /
• pp.243-250
• /
• 2022

In the design stage of hull forms, a fast prediction method of resistance performance is needed. In these days, large test matrix of candidate hull forms is tested using Computational Fluid Dynamics (CFD) in order to choose the best hull form before the model test. This process requires large computing times and resources. If there is a fast and reliable prediction method for hull form performance, it can be used as the first filter before applying CFD. In this paper, we suggest the offset-based performance prediction method. The hull form geometry information is applied in the form of 2D offset (non-dimensionalized by breadth and draft), and it is studied using Convolutional Neural Network (CNN) and adapted to the model test results (Residual Resistance Coefficient; C_R). Some additional variables which are not included in the offset data such as main dimensions are merged with the offset data in the process. The present model shows better performance comparing with the simple regression models.

• 대한조선학회논문집
• /
• 제56권5호
• /
• pp.447-456
• /
• 2019

In the early stage of ship design, the rapid prediction of resistance of hull forms is required. Although there are more accurate prediction methods such as model test and CFD analysis, statistical methods are still widely used because of their cost-effectiveness and quickness in producing the results. This study suggests the prediction formula for the residual resistance coefficient (Cr) of the low-speed full ships. The formula was derived from the statistical analysis of model test results in KRISO database. In order to improve prediction accuracy, the local variables of hull forms are defined and used for the regression process. The regression formula for these variables using only principal dimensions of hull forms are also provided.

• 해양환경안전학회지
• /
• 제20권5호
• /
• pp.552-557
• /
• 2014

본 연구의 목적은 1인승 보트를 설계 제작하여 시운전 및 전산유체역학(CFD)를 이용하여 속도성능을 분석하는 것이다. 선형설계를 포함한 보트의 전반적인 설계과정을 설명하였고, 설계를 바탕으로 제작된 보트에 대하여 잠잠한 해상에서 시운전을 수행했다. 시운전을 통해 보트의 설계속도에서 제동마력은 1680 W가 계측하였다. 유동해석은 상용 CFD 코드인 STAR-CCM+를 이용하여 자유수면과 동적트림을 고려하여 수행되었다. 유동해석 결과 잉여저항 성분이 마찰저항 성분에 비해 크게 나타나는 것을 확인할 수 있었다. 시운전과 CFD 결과를 바탕으로 보트의 전체효율계수를 추정하였다. 전체효율계수는 전달효율과 준 추진효율로 나누었다. 준 추진효율은 동일 프로펠러를 사용하는 솔라보트의 속도성능 추정 시 사용될 수 있다. 연구결과은 향후 개발될 보트의 선형설계, 성능분석 및 개발에 정보를 제공할 수 있다.