The multi-power face gear split flow system is a new type of transmission system, which has the advantages of stable and reliable transmission and strong carrying capacity. And it has great potential in the application of helicopter transmission systems. In this paper, the multi-power face gear split flow system was taken as the research object. Based on the lumped parameter method and Newton’s second law, the translational-torsional dynamic model of the system was established considering the translational vibration and the torsional vibration of the gears, and the meshing force curves and load-sharing coefficient curves were drawn. At the same time, the factors affecting the load-sharing characteristics of the transmission system were studied. The impacts of manufacturing errors, assembly errors, manufacturing error phases, assembly error phases, meshing damping, support stiffness, and the input power on the load-sharing coefficients were analyzed. The research shows that the errors and error phases of spur gears have small impacts on the load-sharing coefficients, while the support stiffness of spur gears has a great impact on the load-sharing coefficients. The errors and error phases of face gears have small impacts on the load-sharing coefficients, while the support stiffness of spur gears has a great impact on the load-sharing coefficients. The load-sharing coefficients increase constantly with the increase in the meshing damping between face gears and spur gears, whereas the load-sharing coefficients decrease constantly with the increase in the input power.

A kinematic equation of profiling float is nonlinear and has time-varying parameters. Traditional PD controllers not only demonstrate an inconsistent response to different depth controls but also face problems of overshooting and high power consumption. To realize the goal of depth control of profiling buoy under low power consumption, an improved double PD control method was proposed in this paper. The real-time prediction of position and low-power running of the sensor were realized through sparse sampling and depth prediction. The combination control over position, speed, and flow was realized by introducing the speed and flow expectation function. Then, a MATLAB/Simulink simulation model was constructed, and the proposed controller was compared with a single PD controller and an improved single PD controller. Among ten depth control tests, the proposed method was superior given its short response time, small overshooting, small steady-state error, and low power consumption. Moreover, it achieved a consistent control effect on different target depths. The simulation results demonstrated that a nonlinear and time-varying floating system controlled by the proposed method has favorable robustness and stability. This system will consume minimal power simultaneously.

Aiming at the asymmetric helical gear injection lubrication, simulation models for coast flank and drive flank injections were established by computational fluid dynamics (CFD). Firstly, the two oil injection methods of the asymmetric helical gear were explained. Then, a mathematical calculation model suitable for gear injection lubrication analysis was established. Finally, the model is simulated by CFD software. The oil volume fraction and pressure at the meshing point during the gear meshing process of the two injection models are obtained. The effects of gear speeds and spray velocities on the oil volume fraction and pressure are analysed. By comparing the two oil injection methods, when the spray velocity is more than 2.7 times the pitch line velocity, better lubrication is obtained by the drive flank injection method. When the spray velocity is less than 2.7 times the pitch line velocity, the coast flank injection method can achieve better lubrication.

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