Objective Current ship hull design practices are still largely dependent on designers' expertise, parent ship data, and external commercial CAD software, which results in extended design cycles and restricted innovation capacity. This study aims to investigate the application of three-dimensional generative large-scale models in ship hull conceptual design by developing an intelligent design framework to address the limitations of conventional design paradigms.

Method First, four mainstream three-dimensional generative large-scale models (Hunyuan 3D, Meshy, Rodin, and Tripo) were selected to generate hull samples using dual-modal inputs, consisting of text prompts and contour sketches. A systematic evaluation was conducted to compare the accuracy of feature representation and geometric integrity under varying input conditions. Second, a quantitative surface quality assessment method based on slope detection was proposed. This method identifies abnormal curvature regions on hull surfaces by calculating half-breadth values at mesh element centroids, while incorporating multiple constraint conditions. Two quantitative indices, the special odd area ratio (SOA) and the normal odd area (NOA) ratio, were defined to enable an objective evaluation of the fairness of generated hull surfaces. Third, a combined smoothing optimization pipeline, integrating Laplacian and Taubin algorithms, was developed. The Laplacian algorithm reduces local noise and stepped artifacts by repositioning mesh vertices toward the geometric centers of their neighborhoods, while the Taubin algorithm effectively mitigates volume shrinkage through the alternating application of positive and negative smoothing factors. Together, these algorithms work synergistically to enhance surface quality, meeting the requirements for CFD analysis. Finally, hydrostatic calculations were performed to verify the engineering feasibility of the geometric models, followed by numerical simulations of resistance performance using STAR-CCM+.

Results A systematic analysis of the resistance coefficient variations across three candidate hull forms within the Froude number range of Fr=0.20~0.30 identified the optimal hull form, generated by Rodin. This hull form exhibited the lowest total resistance coefficients across all operating conditions. CFD results demonstrate that the AI-generated hull forms, after post-processing optimization, meet both mesh quality and hydrodynamic analysis requirements. These findings preliminarily validate the technical feasibility of using generative large-scale models in ship hull design.

Conclusion Three-dimensional generative large-scale models demonstrate significant potential for application in ship hull conceptual design. The proposed quantitative surface quality evaluation method and hybrid smoothing algorithms provide essential technical support for the engineering application of AI-generated geometries. The research outcomes effectively facilitate hydrodynamic performance analyses, such as resistance evaluation, and lay a foundation for the development of intelligent hull optimization design frameworks. This approach reduces reliance on parent ship data and designer experience, while fostering the development of innovative hull forms.