A ship hull offset feature cognition and generation method based on conditional deep convolutional generative adversarial networks

The hull form modelling progress in ship design is significantly relied on the parent hull database and the professional designers well trained with CAD software, and it is usually a time and experience costly work. The conditional generation of ship hull with both geometrical and locational features by training an artificial neural network was concerned by this paper. The geometrical feature means the overall shape variety of ship designs like bulbous bow, stern shaft, etc., the locational feature means the shape difference between stern, front and mid-body of ships. Firstly, a conditional deep-convolutional generative adversarial network (CDC-GAN) was constructed to distinguish the geometrical and locational features individually; Secondly, the CDC-GAN was well trained to learn and generate these features with different resolutions and categories, from easy to hard; In the end, the training cost and performance of networks were compared and concluded to prove the capability of CDC-GAN in solving ship hull form generating issues. This paper is based on authors’previous investigation with regular GAN, and it provides a further exploration about the potential of CDC-GAN in ship design. © 2024 China Ship Scientific Research Center. All rights reserved.