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To improve the cost-effectiveness of modelling of wave interactions, a “numerical wavetank” is presented whose distinctive novel feature is its ability to couple both deep-water potential-flow and shallow-water models to controllable, prespecified wavemaker motion and beach topography. The coupling is in part obtained via a variational principle approach that guarantees important conservation properties and numerical stability. The model presented is the first fully nonlinear model to couple deep-water (discretised as finite elements) and shallow-water equations (discretised as finite volumes). Resulting simulations of wave generation, propagation and absorption by shallow-water-wave breaking are presented and analysed. A discussion is given on the efficacy of the novel approach.
A blind full-scale CFD resistance prediction workshop was held in 2024, with the Lucy Ashton paddle steamer as its test case. Results from forty participants were received for the three different parts in which the workshop was organised, which consisted of a grid refinement study with common grids, full-scale simulations for varying Froude number, and model-scale simulations at a constant Froude number for varying model sizes. This paper presents a summary of the results gathered for the workshop along with its main findings, and the comparison with the results available from the experimental campaign carried out for the Lucy Ashton in the 1950s. The computational results led to lower ship resistance than the experimental data for all conditions, due to the simulations considering the ship to be hydrodynamically smooth and to not heave or pitch. The scatter of the resistance at full-scale showed a decreasing trend as the Froude number was increased with a median absolute deviation of at most 2.3 %. The spread in the numerical results obtained for the full-scale conditions was equivalent to that observed for the model-scale cases, building further confidence in full-scale CFD.
Running from end of 2021 to early 2025, the TopTier Joint Industry Project, with 40 participants, focused on enhancing cargo securing safety on container ships. Many aspects were covered in the project, ranging from container and lashing strength, stowage, stack dynamics, operational guidance to rules and regulations.This paper focuses on parametric rolling ship motion. It is of key importance to know in which wave conditions large roll might occur. For this reason, seakeeping model tests were performed for two large container vessels with a capacity of 10,000 and 15,000 containers. 190 tests in regular waves and 42 tests in irregular seas (most of them with 3-h duration) were performed, most of which were performed in stern and bow quartering seas. Both synchronous and parametric roll were studied in wave conditions around the boundary of in- and off-design motions. Together with several other participants, calculations were done to compare and validate tools and their ability to predict the wave conditions in which off-design roll motions are likely to occur. The tools are detailed and compared against each other as well as the experimental results, revealing both capabilities and challenges in the assessment of parametric rolling.
Solving the pressure-Poisson equation remains the primary computational bottleneck in incompressible unstructured flow solvers primarily due to the inherent sensitivity of traditional linear solvers to mesh irregularities. This work introduces a data-driven algebraic multigrid (AMG) smoother that uses a modified graph convolutional isomorphism network (GCIN). The graph neural network predicts optimal polynomial coefficients to construct a sparse pseudo-inverse operator across diverse grid topologies. The coefficients are optimized to reduce the residual after each V-cycle iteration. By directly capturing the algebraic structure of the system from the sparse coefficient matrix, the proposed method maintains the solver's linearity while adapting to local anisotropies in unstructured grids. Our framework demonstrates significant performance gains by reducing the number of V-cycles required for a given tolerance and delivering wall-clock speedups from 4
Structural condition-based maintenance for naval vessels combines onboard monitoring to sense and evaluate the current structural health of a vessel and make projections about the future state, plus a decision framework to determine the optimal moment when maintenance activities should be performed. This paper explores the implementation of structural condition-based maintenance aboard naval vessels in an end-to-end perspective, meaning from conceptualization to defining a baseline ship structural health monitoring sensor system and to linking with a decision framework for making informed maintenance decisions. Beginning with considering the motivations and necessities when operating naval vessels, possible maintenance strategies are defined. Competing motivations based on the relevant stakeholders are discussed, especially related to the desired outcomes of implementing a condition-based maintenance system for hull structural integrity. The major components of a condition-based maintenance system are outlined, focusing on structural damage due to fatigue, corrosion, and extreme loading. Special attention is paid to structural health monitoring and the different sensing techniques required, along with the state-of-the-art research supporting such techniques. Multiple monitoring concepts are explored, focusing on the different ways to monitor the wave environment exciting the vessel and the resulting structural damage. Three selected previous monitoring campaigns and resulting lessons learned offer guidance on a resulting baseline sensor system for structural condition-based maintenance for naval vessels. Specific areas of needed future research are pointed out to further advance structural condition-based maintenance onboard naval vessels.
