Multi-objective Route Optimization of Wind-assisted Ships Considering Sail Angle-of-attach Control

To address the challenges in the route optimization of wind-assisted ships, namely insufficient quantification of wind energy utilization efficiency, limited accuracy in fuel consumption prediction, and lack of multi-objective coordinated optimization mechanism, this study proposes a multi-objective route optimization method integrating dynamic sail control with hybrid propulsion prediction. A dynamic sail control strategy model based on aerodynamic characteristics is developed to achieve spatial vector analysis of auxiliary thrust from sails. This model overcomes the limitations of conventional static angle-of-attack configurations by enabling real-time dynamic adjustment of sail parameters, thereby maintaining a high level of wind energy conversion efficiency. To resolve the dual constraints of poor environmental adaptability in traditional physical models and weak physical interpretability in data-driven approaches, a physics-constrained hierarchical artificial neural network architecture is constructed. This architecture establishes feature space bases using ship kinematic equations and employs attention-guided neural networks for residual learning. The proposed method preserves the underlying physical principles of energy consumption while enabling bidirectional coupling between data features and fluid dynamics equations. Validation on North Atlantic routes demonstrates that the proposed method reduces the mean absolute percentage error (MAPE) of fuel consumption prediction by 21.9% compared to purely physical models, while offering significantly enhanced inter-pretability over purely data-driven methods. Furthermore, a multi-objective optimization model incorporating both time costs and fuel consumption is established. A coordinated optimization algorithm combining non-dominated sorting genetic algorithm (NSGA-Ⅱ) and technique for order preference by similarity to ideal solution (TOPSIS) is developed, which improves the convergence rates of the non-dominated solution sets compared to standard algorithms. An empirical study conducted on the wind-assisted vessel"NEW ADEN"demonstrates that, during typical voyages in the North Atlantic, the effective operational efficiency of the sail is improved. Compared with the traditional recommended routes, the optimized route reduces voyage time by approximately 5%, fuel consumption costs and fixed costs by 9.1% and 4.95%, respectively, and total operational costs by over 7.2%. This optimization improves the economic benefits of wind-assisted ships while effectively reducing environmental pollution.