• Journal of the Society of Naval Architects of Korea
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• v.57 no.6
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• pp.312-321
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• 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.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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• pp.243-250
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• 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.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.5
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• pp.447-456
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• 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.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.5
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• pp.552-557
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• 2014

The objective of the This study is to access the speed performance employing the sea trial test and CFD with the our own designed and manufactured one-man boat. The overall design process including hull form design was explained. The sea trial was carried out with a manufactured boat in the clam sea. Brake power at the design speed of a boat through the sea trial was measured as 1680 W. The flow computation was conducted considering free surface and dynamic trim using a commercial CFD code(STAR-CCM+). The result of computation provided the information that residual resistance is bigger than fraction's at design speed. The total efficiency were predicted based on the sea trial and CFD. The Total efficiency was divided into shaft efficiency and quasi-propulsive efficiency. By using quasi-propulsive efficiency, it becomes possible to predict speed performance of boat in future. The results can provide information regarding hull form design, performance analysis and development of a boat in future.