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A ship’s fuel consumption can be significantly increased when sailing in harsh sea conditions. Any measures to increase ship energy efficiency must rely on accurate description of the ship’s performance. Current theoretical physical models always contain large uncertainties to describe a ship’s energy performance especially in the mechanical system models. Some black-box performance models have been constructed by machine learning methods based on ship performance data. But the black box models can be only useful for a specific ship with data inputted for the model construction. 

This project first investigates the enhancement of ship manoeuvring models through the integration of prior knowledge embedded in parametric model structures and semiempirical formulas. The study begins with a pre-study focusing on one degree of freedom in ship roll motion, aiming to develop parameter identification techniques and propose a parametric model structure with good generalization. This knowledge is then extended to the manoeuvring problem, with objectives including the development of parameter identification techniques for ship manoeuvring models, proposing a generalizable parametric model structure, mitigating multicollinearity, and identifying added masses. Methodologically, the research employs various parametric model structures for roll motion and manoeuvring, investigated through free running model tests and virtual captive tests (VCT). A novel parameter identification method combining inverse dynamics with an extended Kalman filter (EKF) is proposed. Additionally, a deterministic semi-empirical rudder model is introduced to address multicollinearity issues. The implications of this research suggest that integrating semi-empirical rudder models and utilizing VCT can significantly enhance the accuracy and generalization of ship manoeuvring models, contributing to more reliable and physically accurate simulations in maritime engineering. 

Based on the experiences of building various gray-box models for ship dynamics, the knowledge is used to study ship energy performance models with the applications of two case study ships, one on unconventional doubled-ended vessel, and the other on short sea shipping. Based on their data analytics and gray-box models describe those ship’s energy performance, a Bayesian based ship voyage planning decision support system was developed during the project. It is demonstrated that the data analysis enhanced decision support system can reduce fuel consumption from 5-10% dependent on the voyages used for those two case study vessels.