The matching optimization design of the blade geometry of the existing hydraulic model of the impeller and guide vane is carried out in order to improve the hydraulic performance of the waterjet pump. The Latin Hypercube sampling method is used for the space sampling of parameters that affect the performance of the waterjet pumps, such as the impeller blade load, the guide vane blade load and the stacking position. The performance parameters of the pump is obtained through steady numerical simulation of each sample by using the RANS equations. The mapping response model is built between the pump design parameters and the calculated performance parameters based on the artificial neural network. The genetic algorithm is adopted to optimize the waterjet pump hydraulic model with the aim of the maximum hydraulic efficiency. The iterative cycling of the optimization process is automatically completed, which can shorten the design and development period of the hydraulic model. The results show that after optimization, the internal flow field is significantly improved with the same head of the pump, and the efficiency of the calculation design point reaches 91.8%, while the efficiency validated by model test reaches 88.9%, as well as the range of the flow rate in high efficiency region is widened by 15%.

Stabilizing systems are necessary for the ship in wind and waves to ensure stable operations and improve the safety and comfort of the crew.As an effective stabilizer, gyrostabilizer acts entirely within the hull without requiring sufficient movable weight to generate a control moment. The nonlinear wave force disturbance model of the ship in random waves is firstly established. The combined dynamic model of the ship and the gyrostabilizer is then built together with the working principle of the gyrostabilizer. The corresponding MATLAB Simulink block diagramsof the ship motion control are constructed for two kinds of gyrostabilizer models, i.e., the natural-driven gyrostabilizer model and the controller-driven gyrostabilizer model. The results show that both the natural-driven and controller-driven gyrostabilizer are able to reduce the rolling motion through the gyroscopic effect of high-speed spinning and precession angel variation. The controller-driven gyrostabilizer can reduce the nonlinear rolling motion of the ship more effectively than the natural-driven gyrostabilizer.

Ship unsinkability is an important performance to evaluate the ship survivability, and it is also a key index for optimizing the subdivision strategy. However, the high cost is still a difficulty restricting the practical application of the unsinkability optimization. More effective methods will be provided with the continuous application of machine learning technology. The optimization of unsinkability subdivision is solved by using the particle swarm optimization (PSO) algorithm based on reinforcement learning. And the optimization system development and interface design is implemented for the integrated machine learning module and unsinkability design module. The effect of different parameter settings in the algorithm on the optimization efficiency is also discussed. The analysis of the optimal solution shows that this method can effectively find out the optimal subdivision scheme. It can provide a basis for formulating scientific subdivision strategies.

The automatic control model test platform based on 6-DoF optical system has been developed according to the model test demands for the automatic control of the two-ship lateral replenishment at sea. This article elaborates the composition, function and usage of this test platform, analyzes the characteristics and advantages of the platform compared with those of the conventional ones. The test platform is used to perform the two-ship lateral replenishment model tests in a large towing tank for the validation of the test platform. The current research lays a solid foundation for the application of the automatic control technology in the two-ship lateral replenishment at sea. It can also provide reference for the research and development of such advanced test platforms.