Abstract:

Conceptual design refers to the process from the proposal of original requirements to the generation of initial schemes during the preliminary design phase. In response to frequent changes in requirements for ship design at this stage, strong dependence on parent models, and the high cost of concurrent design of multiple schemes, a conditional generative adversarial model for hull forms was proposed for scenarios lacking approximate parent models. This model used resistance performance as the conditional label to generate hull-form geometric schemes that met the performance index requirements. Firstly, contour feature curves of the bare hull were constructed using parametric modeling methods; geometric parameters characterizing the hull form were selected, and corresponding three-dimensional meshes were obtained. Secondly, based on the integral theory, the characteristic surface areas and hull-form coefficients were obtained from the three-dimensional mesh, and a consistency dataset of geometric parameters and performance characteristics was constructed via the automated interface of the developed performance-prediction platform. Finally, based on a traditional generative adversarial network (GAN), a multilayer perceptron was employed to encode conditional features represented by resistance, which were then combined with geometric features in the hidden layers of the generator. This approach guided the learning of hull-form sample distributions under varying resistance conditions and generated hull-form geometric parameters that satisfied the specified constraints. The initial realization of a forward-design process for hull forms without reliance on parent-model information and based on overall performance of typical types provided a design basis for the rapid generation and iterative optimization of conceptual schemes under uncertain requirements.

Key words: ship design, conceptual design, hull design concept, forward design, intelligent generation, conditional generation model, generative adversarial networks