The swash-plate axial piston pump is one of the most widely used pumps due to its simplicity and compactness in structure. In such a pump, the piston-cylinder system plays a crucial role, with its lubrication characteristics greatly affecting the overall pumping performance. A new numerical approach is proposed in this study for modeling mixed lubricated piston-cylinder interfaces of variable lengths in swash-plate axial piston pumps in the framework of multibody dynamics. The approach couples the hydrodynamic mixed lubrication model of the piston-cylinder interface with the multibody dynamics model of the piston pump. The lubrication model is established with a transient average Reynolds equation considering asperity contacts and is solved with the finite element method to derive the hydrodynamic forces of the lubricated pair, while the multibody dynamics model is established with Lagrangian formalism by considering hydrodynamic forces as external forces. Results for piston-cylinder interfaces of variable lengths in swash-plate axial piston pumps are presented, and the impacts of cylinder length and the tilt angle of the swash plate on the tribological performances of the interface are discussed. The results indicate that increasing the cylinder length can improve the stability and wear resistance of the piston, but it can exacerbate the frictional power loss. Moreover, although enlarging the tilt angle of the swash plate can effectively increase pump displacement, it can easily lead to serious friction, wear, and leakage problems. Consequently, the tilt angle of the swash plate should be carefully selected in practical applications.

A fast and accurate bauxite recognition method combining an attention module and a clustering algorithm is proposed in this paper. By introducing the K-means clustering algorithm into the YOLOv4 network and embedding the SE attention module, we calculate the corresponding anchor box value, enhance the feature learning ability of the network to bauxite, automatically learn the importance of different channel features, and improve the accuracy of bauxite target detection. In the experiment, 2189 bauxite photos were taken and screened as the target detection datasets, and the targets were divided into four categories: No. 55, No. 65, No. 70, and Nos. 72–73. By selecting the category volume balanced datasets, the optimal YOLOv4 network model was obtained after training 7000 times, so that the average accuracy of bauxite sorting reached 99%, and the reasoning speed was better than 0.05 s. Realizing the high-speed and high-precision sorting of bauxite greatly improves the mining efficiency and accuracy of the bauxite industry. At the same time, the model provides key technical support for the practical application of the same type of engineering.

Horizontal axis tidal current hydroturbines are widely used to extract tidal current energy. The blade is an important component of the hydroturbine, and its performance primarily depends on the airfoil pattern of the blade. Based on previous studies and elements of bionic technology, this study proposes a design method using bionic technology in the blade design to obtain a composite blade with NACA and shark airfoils and good hydrodynamic performance. An airfoil with a high lift-drag-ratio or a high torque coefficient is selected from multiple airfoil designs, and the optimal combination airfoil blade model is generated according to the blade element momentum theory and Wilson blade theory. The pressure and velocity fields of different combinations of the airfoil blades are investigated using numerical simulation and underwater experiments. The results demonstrate that the combined blade offers optimal performance when the pitch angle is 60°. Its torque coefficient and turbine speed are higher than those of conventional blades, and its energy coefficient is increased by approximately 14.5%.