합성곱 신경망을 이용한 선박의 잉여저항계수 추정

• Published in: 大韓造船學會 論文集 Vol. 59; no. 4; pp. 243 - 250
• Main Authors: 김유철, 김광수, 황승현, 연성모, Kim, Yoo-Chul, Kim, Kwang-Soo, Hwang, Seung-Hyun, Yeon, Seong Mo
• Format: Journal Article
• Language: English; Korean
• Published: 대한조선학회 01.08.2022
• Subjects: 조선공학
• ISSN: 1225-1143; 2287-7355
• DOI: 10.3744/SNAK.2022.59.4.243

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; CR). 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.