The design and commissioning of a fully elastic model of a uniform container ship

Experimental hydroelasticity has not followed the rapid evolution of its computational counterpart. Hydroelastic codes have changed significantly in the past few decades, moving to more detailed modelling of both the structure and the fluid domain. Physical models of ships are, even today, manufactured with a very simplified structural arrangement, usually consisting of a hollow rectangular cross section. Appropriate depiction of the internal structural details ensures that properties relevant to antisymmetric vibration are scaled accurately from the real ship to the model. Attempts to create continuous, ship-like structures had limited success, as manufacturing constraints did not allow for much internal structural detail to be included. In this investigation, the first continuous model of a ship with a detailed internal arrangement resembling a container ship is designed, produced using 3D printing and tested in waves. It is demonstrated that the global responses of the hull in regular head waves agree well with theory and past literature, confirming that such a model can represent the behaviour of a ship. Furthermore, it is found that the model is capable of capturing local responses of the structure, something that would be impossible with “traditional” hydroelastic ship models. Finally, the capability of the model to be used to investigate antisymmetric vibrations is confirmed. The methodology developed here opens a whole new world of possibilities for experiments with models that are tailored to the focus of the investigation at hand. Moreover, it offers a powerful tool for the validation of modern state-of-the-art hydroelastic codes. Ultimately, it creates the next step in the investigation of dynamic responses of ship structures, which contribute significantly to accumulating damage of the hull. Better understanding of these responses will allow designers to avoid over-engineering and use of big safety factors to account for uncertainties in their predictions.