Year: 2026
Type: article
Abstract: Early-stage ship design demands efficient exploration of diverse hull forms under tight geometric and performance constraints. Existing data-driven approaches often rely on a single geometric representation, limiting their ability to capture both global proportions and local shape detail. This paper presents a representation-aware generative framework that learns a shared latent encoding of hull geometry from multiple modalities: surface point clouds, waterline sections, and buttock curves. A conditional multimodal autoencoder embeds these complementary descriptors into a unified latent space, enforcing cross-modal consistency and preserving geometric fidelity. To enable the generation of novel hull forms, a latent diffusion model is trained to sample hull representations conditioned on high-level design parameters, such as principal dimensions and form coefficients. The proposed approach produces watertight and geometrically coherent hull forms that align with user-specified design parameters. Experiments on a synthetic hull dataset demonstrate improved geometric coherence and controllability. The method provides a scalable foundation for performance-aware, data-driven exploration in early-stage ship design.
DOI: 10.3390/jmse14070642
Institution: University of Strathclyde
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funder: University of Strathclyde
OpenAlex ID: W7143984402