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Nowadays more and more in-depth study and continuous investigation is being carried out in the continuously variable transmission (CVT) field. A good continuously variable ratio changing action would greatly improve the performance of the transmission and offer a high fuel economy. So it would save energy and protect environment, furthermore it would reduce the working intensity and demands of driving skill on the driver. Therefore, a high efficiency and good performance continuously variable transmission (CVT) is urgently needed. This paper presents a new Satellite Star Gear (SSG) Continuously Variable Transmission System. It was created based on the Pulse Stepless Transmission with some improvements on the overrunning clutch, stepless speed change device etc. This paper introduces the basic mechanical structure and kinematical principle of a double eccentricity stepless speed change device, overrunning clutch and the whole mechanism (SSG).

Lagrangian approach has been widely adopted in the droplet impingement analysis for aircraft icing simulation. Some improvements were proposed, including: 1) The heat and mass transfer consideration in droplet dynamics; 2) More efficient droplet localization method, which could and facilitated to find the initial cell in Eulerian grid; 3) New computation method of impingement efficiency, which uses the cover ratio to transform the impingement efficiency of arbitrary impinged region to that of the cell element of body surface and avoids the iterative computation to find the trajectories reaching the corner of the panel or cell element. A numerical solver was built and integrated with the capabilities to deal with super-cooled large droplet (SLD) conditions by considering the splashing and bouncing of SLD. The computational results were validated with the experiment data, which shown good agreements in the impingement limitations and tendency.

An improved anti-icing design with film heating ejection slot and cover for the inlet part of aero-engine was brought out, which combines the interior jet impingement with the exterior hot air film heating and shows promising application for those parts manufactured with composite materials. A hybrid method based on the combination of the Back Propagation Neural Network (BPNN) and Genetic Algorithm (GA) is developed to optimize the anti-icing design for a typical aero-engine inlet vane in two dimensions. The optimization aims to maximize the heating performance of the hot air film, which is assessed by the heating effectiveness. The film-heating ejection angle and the cover opening angle are the two geometric variables to be optimized. Numerical model was established and validated to generate training and testing samples for BPNN, which was used to predict the objective function and find the optimal design variables in conjunction with the GA.

This paper raises a coupling system of aircraft environmental control and fuel tank inerting based on membrane separation. The system applies a membrane dehumidifier to replace water vapor removal unit of heat regenerator, condenser and water separator, which is widely used in conventional aircraft environmental control system (ECS) nowadays. Water vapor can travel across the membrane wall under its pressure difference without phase change, so the dehumidification process consumes no cooling capacity as traditional ECS and the cooling capacity of the new system increases. This paper first compares the thermodynamic properties of ECS based on membrane dehumidification and the traditional ECS based on condensation. The results show that the membrane dehumidification system has larger cooling capacity and lighter weight. For a given cooling capacity requirement, the membrane dehumidification system can use less bleed air since the enthalpy of the outlet air is lower.

Ice protection is important for aero-engine induction system, such as the inlet vanes. For the ice protection of such parts manufactured with low thermal conductivity polymer-based composite material, the combined heating method using interior jet impingement and exterior ejection film has certain advantages. The simulation model coupling CFD with solid heat conduction was developed and solved with the anisotropic thermal conductivities model to investigate the heat transfer enhancement in the stagnation region of aero-engine inlet vanes due to ejection slot and anisotropic heat conduction, which is related to the curved geometry, ejection slots and anisotropic heat conduction. The temperature distribution and heat flux ratio between the stagnation region on outside surface and the impingement region inside were calculated and analyzed for the configuration with different ejection angle and different materials.

The main objective of this work is to investigate, by means of numerical simulations, the performance of the engine nacelle ventilation cooling system of a helicopter under hover and forward flight conditions, and to propose a simplified method of evaluating the performance based on rotor downwash flow by taking the synthetical effect of engine nacelle, exhaust ejector and external flow of a helicopter into account. For the engine nacelle of a helicopter, an integrated model of the nacelle and exhaust ejector was set up including the domain of external flow. The unstructured grid and finite volume method were applied for domains and control equations discreteness, and the standard k-ε model was applied for solving turbulent control equations. Using the business CFD software, the flow field and the temperature field in the nacelle were calculated for single inlet scheme and double inlets scheme, total up to 9 schemes. The performance of the exhaust ejector was computed.

Unmanned Aerial Vehicles (UAVs) encounter various uncertainties, including unfamiliar environments, signal delays, limited control precision, and other disturbances during task execution. Such factors can significantly compromise flight safety in complex scenarios. In this paper, to enhance the safety of UAVs amidst these uncertainties, a control accuracy prediction model based on ensemble learning abnormal state detection is designed. By analyzing the historical state data, the trained model can be used to judge the current state and obtain the command tracking control accuracy of the UAV at that instant. Ensemble learning offers superior classification capabilities compared to weak learners, particularly for anomaly detection in flight data. The learning efficacy of support vector machine, random forest classifier is compared and achieving a peak accuracy of 95% for the prediction results using random forest combined with adaboost model .