The presented research aims to examine local fatigue failure modes of Glass Fiber-polymer Composite (a.k.a. GFRP), particularly how the bending of the Web-to-Flange Junctions (WFJ) and tensile loading of the material affects the composite's fatigue resistance. By conducting experimental analyses, the study investigates GFRP's efficacy both as a material and as a component element, drawing parallels with Eurocode standards for steel and concrete. The coupon tests under static and fatigue loading conditions provided an empirical data on the Ultimate Limit State (ULS) and fatigue resistance of the GFRP composites, including a better understanding of the effects of variation in the facing’s thicknesses in the web-core composite on the fatigue response. Subsequent three-point bending tests on the WFJ provided the insights into the strength of WFJ with the first indications on the governing failure mechanisms in the WFJ.@en

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.

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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.

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The continuous increase of marine traffic and the entry of autonomous ships into the market is urging an improvement in safety measures to guarantee avoidance of collisions between moving objects at sea. This rise in automated maneuverability requires gaining further insight in the vessel’s behavior. The ship design has to ensure that the vessel is controllable and capable of maneuvering securely, even at critical operating conditions. Crash stop maneuvering performance is one of the key indicators of the vessel’s safety properties for designers and shipbuilders. Many factors affect this performance, from the hull design to the environmental conditions; hence, it is non-trivial to assess them accurately during the preliminary design stages. In this paper, the authors focus on predicting accurately and with minimal computational effort the crash stop characteristics of a vessel in the design stage, for the preliminary assessment of safety requirements imposed by the classification societies. The crash stop prediction model of the said vessel can be utilized in combination with collision avoidance algorithms. The authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stop maneuvering performance. Results from full-scale measured data show the effectiveness of the proposed method.@en

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.

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