In this paper, for the first time, a three-step approach for the optimal design of stiffened panels accounting for the ultimate limit state due to welding residual stress is developed. First, authors rely on state-of-the-art analytical approaches coupled with recently data-driven nonlinear finite element methods surrogates characterized by functional which are computationally expensive to build but computationally inexpensive to use. Then, surrogates are used within a design optimization loop to find new optimal designs since nonlinear finite element methods are too computationally demanding for this purpose. Finally, the new designs are reassessed with the original nonlinear finite element methods to verify that substituting them with their surrogates in the optimization loop actually leads to better designs. Results obtained optimizing a series of parameters of a commonly used stiffened panel geometry under different scenarios will support the authors’ novel approach.

This study presents a numerical approach for modelling the Corrosion Fatigue Crack Growth (CFCG) in conventional casting and additively manufactured Ti6Al4V alloys. The proposed numerical model, based on Peridynamics (PD), combines the PD Fatigue Crack Growth (FCG) model and PD diffusion model in order to couple the mechanical and diffusion fields existing in the material due to the impact of environmental fatigue. The mechanical field is responsible for the characterisation of the changes to the structure due to the fatigue loading conditions. The diffusion field is based on the modelling of the adsorbed-hydrogen Stress Corrosion Cracking (SCC), in particular, the Hydrogen Embrittlement (HE) model is considered. The proposed approach has been validated using experimental data available in the literature showing the capability of the tool to predict the CFCG rates.

Crash stop maneuvering performance is one of the key indicators of the vessel safety properties for a shipbuilding company. Many different factors affect these performances, from the vessel design to the environmental conditions, hence it is not trivial to assess them accurately during the preliminary design stages. Several first principal equation methods are available to estimate the crash stop maneuvering performance, but unfortunately, these methods usually are either too costly or not accurate enough. To overcome these limitations, the authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance. Results on real-world data provided by the DAMEN Shipyards show the effectiveness of the proposal.