A GPU-Accelerated Reduced Order Modelling Framework for CFD-Based Ship Hull Design
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This work presents a proof-of-concept (PoC) numerical workflow for fast and accurate ship hull design and resistance prediction, based on the synergistic integration of three enabling technologies: CFD, advanced mesh morphing, and AI-based modelling. The proposed methodology targets the marine sector, leveraging approaches that have already demonstrated strong performance and industrial maturity in other domains such as automotive and aeronautics. The AI-based surrogate framework is built using NVIDIA’s DoMINO technology, which has proven effective in learning complex nonlinear mappings from high-dimensional CFD data while enabling real-time predictions on GPU architectures. Extending such AI-driven approaches to naval hydrodynamics represents a promising opportunity, provided that the training data generation pipeline is robust, scalable, and physically reliable. A key enabling factor for training data generation is the efficient creation of large families of geometrically consistent hull variants. In this context, radial basis function (RBF) mesh morphing is employed as a fast and flexible parametrization technique, allowing the generation of numerous high-quality geometric configurations from a single baseline hull while preserving mesh integrity without the need to guarantee the topological consistency. The data foundation is provided by ENGYS’ open-source CFD technology, based on Reynolds-averaged Navier–Stokes simulations with a linearized free-surface model, extensively validated in marine applications. CFD simulations are performed for a design of experiments built on the “Kriso Container Ship” hull, producing a comprehensive database of flow solutions. The resulting AI model enables real-time prediction of hull deformation effects, pressure and shear stress distributions, and calm-water resistance, achieving a maximum relative error below 7% on unseen configurations. The PoC, developed within the Italian AI4TwinShip initiative, demonstrates how the integration of validated CFD solvers, RBF-based geometric morphing, and GPU-accelerated AI models forms a solid foundation for future ship digital twin technologies.
