The oil jet lubrication performance of a high-speed and heavy-load gear drive is significantly influenced and determined by the oil jet nozzle layout, as there is extremely limited meshing clearance for the impinging oil stream and an inevitable blocking effect by the rotating gears. A novel mathematical model for calculating the impingement depth of lubrication oil jetting on an orthogonal face gear surface has been developed based on meshing face gear theory and the oil jet lubrication process, and this model contains comprehensive design parameters for the jet nozzle layout and face gear pair. Computational fluid dynamic (CFD) numerical simulations for the oil jet lubrication of an orthogonal face gear pair under different nozzle layout parameters show that a greater mathematically calculated jet impingement depth results in a greater oil volume fraction and oil pressure distribution. The influences of the jet nozzle layout parameters on the lubrication performance have been analyzed and optimized. The relationship between the measured tooth surface temperature from the experiments and the corresponding calculated impingement depth shows that a lower temperature appears in a situation with a greater impingement depth. Good agreement between the mathematical model with the numerical simulation and the experiment validates the effectiveness and accuracy of the method for evaluating the face gear oil jet lubrication performance when using the impingement depth mathematical model.

The capability of path tracking largely determines the operational efficiency of deep-sea mining vehicles. In this paper, the relationships of vehicle–sediment mechanical interaction were obtained by sinkage and shear tests. Then, an overset grid method was used to establish the computational fluid dynamics (CFD) model of the vehicle, and the spatial hydrodynamic distribution was calculated in different motion states. Based on the above research, a multi-body dynamic (MBD) model of the mining vehicle was developed, which considered the spatial hydrodynamic effects and the mechanical interaction between vehicle and sediment. In addition, a path-tracking controller based on fuzzy logic control was proposed. A genetic algorithm optimized the fuzzy rules through co-simulation between the controller and the MBD model. Finally, the co-simulation results of the vehicle which moved along the expected path indicated that the performance of the optimized fuzzy controller was preferable to the original fuzzy controller.