In the downturn of the shipping industry, optimizing the speed of ships sailing on fixed routes has important practical significance for reducing operating costs. Based on the ship-engine-propeller matching relationship, this paper uses BP neural network to build main engine power model, and correction factors are introduced into the main engine power model to reflect the influence of wind and wave. The Kalman filter algorithm is used to filter the data collected by a river-sea direct ship during the voyage from Zhoushan to Zhangjiagang. The filtered data and the meteorological data obtained from the European Medium-Range Weather Forecast Center are used as the data set of the BP neural network to predict the main engine power. Based on the main engine power model, a multi-objective optimization model of ship speed under the influence of actual wind and waves was established to solve the conflicting goals of reducing sailing time and reducing main engine fuel consumption. This multi-objective model is solved by a non-dominated fast sorting multi-objective genetic algorithm to obtain the Pareto optimal solution set, thereby obtaining the optimal speed optimization scheme. Compared with the original navigation scheme, the navigation time is reduced by 8.83%, and the fuel consumption of the main engine is reduced by 12.95%. The results show that the optimization model can effectively reduce the fuel consumption and control the sailing time, which verifies the effectiveness of the algorithm.
Ship deck arrangement design is about determining the positions and dimensions of arranged objects. This paper presents the mathematical model for the ship deck arrangement optimization problem statement and how the individual’s objective and constraint functions are computed. Moreover, an improved multiobjective hybrid genetic algorithm is redesigned to solve this complex nondeterministic problem and generate a set of diverse and rational deck arrangements in the early stage of ship design. An adaptive crossover operator and a novel topological replace operator invoked in this algorithm are described. Finally, the proposed algorithm is tested on a main deck arrangement optimization of an underwater detection ship. In the validation tests, the proposed algorithm is compared to the standard NSGA-II to determine its ability to produce a set of diverse and rational deck arrangements. Subsequently, the performance tests are used to determine the ability of the algorithm to work with the highly constrained arrangement problems and the efficiency of the adaptive crossover and topological replace operators.
While passing through a navigable tunnel, a vessel usually undergoes a steady forward motion at low speed. Due to the limited size of the navigable tunnel, the restricted water conditions of small section-coefficient will have an adverse impact on vessel navigation safety. A bottom suction effect on vessels may occur for the risk of grounding and harming the maneuvering performance. Thus, it is particularly important to reveal the effect of the navigable tunnel scale on the vessel sinkage.
In this paper, a numerical model of the representative 1000-ton vessel of the Wujiang channel under construction is established. Numerical simulations of the vessel are conducted based on RANS equations in deep water, both in fixed condition and free condition (i.e. the trim and sinkage are allowed). And the validity of the method is verified by comparing the calculation results with the experimental ones. Subsequently, for diverse water depths, water widths and vessel speeds, the parameters such as the vessel resistance and sinkage are predicted and compared. The calculation results are analyzed to obtain the trend of vessel motion at different tunnel scales. In addition, the effect of the tunnel scale on vessel navigation performance is also investigated.
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