Severe storms have gained more attention in recent years. Improved metocean data have led to new insight into severe wave conditions for marine design. Therefore, there exists an industrial demand for fast and accurate numerical tools to estimate the hydrodynamic loads during e.g. green water events.

Model tests generally play an important role in these studies. In the recent past, several practical engineering tools have also been developed, based on the experience from the experimental data bases in combination with simplified but still theoretical formulations. One such tool is Kinema2, which is based on non-linear random wave modeling combined with 3D linear diffraction theory to initially identify green water events, and then finally apply a simplified water-on-deck and slamming load estimation. This forms the background for the work presented in this paper which shows the feasibility of a new technique based on the Smoothed Particle Hydrodynamics (SPH). This method can give more detailed forecast of the hydrodynamics on the deck than the simplified water-on-deck estimation. SPH uses a Lagrangian framework (particles) to describe the fluid dynamics. The water propagation and kinematics of the green water events are, in this introductory stage of the study, reproduced by using a SPH inlet condition where particles are injected with given velocity from a curved rectangular area against the deck and the deckhouse. The relative wave height and water particle velocities found from KINEMA2. Numerical results for water elevation and velocity on deck are compared against model test time series and previous results from other numerical simulation methods. The present Lagrangian nature (compared to traditional Eulerian-VOF methods) can in principe significantly reduce the CPU demand and increase the simulation speed. Slamming pressures can then be calculated e.g. from simple slamming formula calculations. In principle, pressures can also be found directly from the SPH calculations, while this would demand a significantly larger number of particles which increases CPU demand of the SPH method.

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