Jianyu Zhuo

With the continuous development of the shipping market, bow thrusters have become more important for ship maneuvering. Therefore, the performance of bow thrusters is studied in this paper. In order to obtain an unsteady performance of the bow thruster under different ship speed conditions, the SST k-ω turbulence model is adopted to predict the hydrodynamics of the bow thruster. With the ship’s speed increasing gradually, the variation characteristics of hydrodynamic coefficients and the flow field distribution at key positions are analyzed. The results show that with an increase in ship speed to three knots, the thrust coefficient and torque coefficient of the bow thruster decrease by 2.69~4.07% and 2.34~3.08%. In addition, the blade vibration amplitude intensifies. In the departure direction, the propeller load is more susceptible to being influenced and decreases by an additional 2.34~4.16% compared with that in the berthing direction. Meanwhile, it is found that the velocity distribution is asymmetrical. The inlet velocity at the bow side is faster, which results in the maximum peak pressure being about three times the minimum peak pressure. In addition, the pressure’s nonuniformity in the tunnel increases gradually with the increase in ship speed. Compared with the pressure distribution in the berthing direction, the pressure distribution before and after the propeller is more uniform, which is consistent with the results of hydrodynamic change and velocity distribution. The research in this paper has a certain reference significance for understanding the hydrodynamic performance of bow thrust operation.

With the further establishment of relevant regulations on ship emissions by countries worldwide and the IMO, and the increasing frequency of severe sea conditions in shipping routes, optimizing ship energy efficiency under high wind and wave conditions has become an important research direction. This study establishes a grey-box model for optimizing ships’ energy consumption under severe sea conditions, with wave heights above two meters and a Beaufort scale score above five, based on the principle of ship–engine–propeller matching and a non-dominated sorting optimization algorithm. Using historical navigation data from a case ship under severe sea conditions, a white-box model and a black-box model for ship fuel consumption were established. These models were combined to create a grey-box model for ship fuel consumption. The K-Medoids clustering algorithm was used to cluster severe sea conditions. The optimization variables were the main engine’s speed, with the fuel consumption per nautical mile and the ship’s speed being used as optimization objectives. The non-dominated sorting genetic algorithm was optimized for each sea condition, resulting in the best speed for each sea state. The results indicate that the model developed in this paper reduced the main engine’s fuel consumption per nautical mile by 21.9% and increased the speed by 16.7% under the most severe sea conditions. Therefore, the proposed model effectively optimizes ship energy efficiency and reduces navigation time under severe sea conditions, providing an effective solution for operations in actual severe sea conditions.