A method for early-stage design current loads determination on drill-ships

The increasing demand for offshore operations in deep water implies the necessity to predict station-keeping ability of offshore vessels since the early stages of design. To this end, besides developing sufficiently fast and accurate methodologies for the equilibrium resolution of the forces acting on the ship, it is of utmost importance to estimate, in a reliable way, the external forces acting on the vessel. This work focuses on the current loads, aiming at developing a model for fast current load prediction based on high-fidelity Computational Fluid Dynamics (CFD) computations. Selecting the drill-ships as reference vessel-type for the study, starting from the actual fleet operating worldwide, a systematic series of hulls has been generated varying the main hull-form parameters inside the database, according to a Box-Behnken scheme. CFD calculations based on RANS equations have been performed on the whole ship set, for a set of incidence angle varying from 0 to 180 degrees considering the hull symmetric. As numerical analyses are not suitable for fast calculations the results on the systematic series have been used as input for developing a surrogate model based on Multiple Linear Regressions (MLR). The method allows for scaling the results as a function of the Reynolds number, allowing for general and flexible applicability among different vessel dimensions. The results obtained with the developed model are compared with the conventional current loads estimation methods, and the obtained results are compared on the capability plot, highlighting the higher reliability of the proposed model for early-stage predictions. •CFD current loads simulations on a systematic series of drill-ships.•Employment of multiple linear regressions to determine surrogate model for current loads.•Impact of different current loads modelling on capability plots.•Identification of a suitable methodology for fast and reliable estimation of current loads on ships.