ABSTRACT

Model uncertainty is pervasive and inherent in the engineering field. It could bring potential risks in real applications, especially for ship behaviour prediction under environmental disturbances. The evaluation and quantification of model uncertainty are of importance for accurate ship motion prediction. This study applies model uncertainty analysis and sensitivity analysis methods to evaluate the ship motion model's level of uncertainty against environmental disturbances and ship manoeuvres. Firstly, three models are created based on the a dynamical model (Mariner) in Marine Systems Simulator. After that, models are tested on various predefined scenarios. The similarity of predicted trajectories and the reference is evaluated by Euclidean distance and used to quantify the uncertainty of models. Next, statistical analysis is used to analyze the uncertainty of models. Sensitivity analysis (SA) method called ‘PAWN’ and ‘UnivariateSpline’ interpolation technology are combined to identify which factors contribute the most to model's performance. The results suggest that the uncertainty caused by external factors varies from different models under different manoeuvres. SA can tell us which factors (wind angle, wind velocity, and surge speed) have a large influence to the model uncertainty given a ship maneuver. Such analyses, on the one hand, contribute operators to choosing the optimum model according to the current conditions for better ship motion prediction. On the other hand, they can pick up the most important factors for fast uncertainty modelling.

INTRODUCTION

Digital technology has become an enabler for making ship motion more intelligent and safe. A large variety of advanced ship motion prediction models have been developed and present a good performance. However, the model's robustness and stability have been a huge challenge that might lead to serious accidents. This problem results from model uncertainty. Uncertainty is ubiquitous in modelling-related engineering fields, especially for ship motion prediction [Arendt et al., 2012]. Therefore, it is of importance to evaluate model uncertainty for the support of model improvement and accurate ship motion prediction.

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