합성곱 신경망과 복셀화를 활용한 선박 저항 성능 예측

• Published in: 大韓造船學會 論文集 Vol. 60; no. 2; pp. 110 - 119
• Main Authors: 박종서(Jongseo Park), 최민주(Minjoo Choi), 송지수(Gisu Song)
• Format: Journal Article
• Language: Korean
• Published: 대한조선학회 2023
• Subjects: Deep learning(딥러닝); Convolutional neural network(합성곱 신경망); Resistance performance(저항 성능); Voxelization(복셀화); Computational fluid dynamics(전산유체역학)
• ISSN: 1225-1143; 2287-7355
• DOI: 10.3744/SNAK.2023.60.2.110

The prediction of ship resistance performance is typically obtained by Computational Fluid Dynamics (CFD) simulations or model tests in towing tank. However, these methods are both costly and time-consuming, so hull-form designers use statistical methods for a quick feed-back during the early design stage. It is well known that results from statistical methods are often less accurate compared to those from CFD simulations or model tests. To overcome this problem, this study suggests a new approach using a Convolution Neural Network (CNN) with voxelized hull-form data. By converting the original Computer Aided Design (CAD) data into three dimensional voxels, the CNN is able to abstract the hull-form data, focusing only on important features. For the verification, suggested method in this study was compared to a parametric method that uses hull parameters such as length overall and block coefficient as inputs. The results showed that the use of voxelized data significantly improves resistance performance prediction accuracy, compared to the parametric approach.