Identification of Ship Maneuvering Behavior Using Singular Value Decomposition-Based Hydrodynamic Variations

Recent efforts on the decarbonization, autonomy, and safety of the maritime vehicles required comprehensive analyses and prediction of the behavior of the existing vessels and prospective adaptations. To predict the performance of vessels, a better understanding of ship hydrodynamics is necessary. However, it is necessary to conduct dozens of experiments or computational fluid dynamics simulations to characterize the hydrodynamic behavior of the vessels, which require significant amounts of cost and time. Thus, system identification studies to characterize the hydrodynamics of ships have gained attention. The present study proposes a hybrid methodology that combines the existing hydrodynamic databases, and a prediction model of ship hydrodynamics based on motion indexes obtained by turning and zigzag tests. Firstly, singular value decomposition was applied to extract the main hydrodynamic variations, and an artificial yet realistic hydrodynamic behavior generation systematics was developed. Then, turning and zigzag tests were simulated to train artificial neural network models which predict how hydrodynamic behavior varies based on the motion indexes. Finally, the proposed methodology was applied to two vessels to predict the hydrodynamic behaviors of the target ships based on given motion indexes. It was found that the motion obtained via the predicted hydrodynamics showed a high correlation with the given motion indexes.