近地星座异面遍历交会轨道全局优化

根据J 2摄动对轨道面的长期影响规律，提出了一种在近地低轨道星座卫星之间进行异面轨道交会的解析近似方法，并用于建立多星多约束遍历交会的混合整数规划模型，能够快速获得多星交会次序、交会时刻的最优解。仿真算例表明提出的方法适用于在轨服务、构型重建等类型任务的快速优化，计算速度和燃料消耗优于以往类似算法。     

An atmosphere-breathing propulsion system using inductively coupled plasma source

CubeSats have attracted more research interest recently due to their lower cost and shorter production time. A promising technology for CubeSat application is atmosphere-breathing electric propulsion, which can capture the atmospheric particles as propulsion propellant to maintain long-term mission at very low Earth orbit. This paper designs an atmosphere-breathing electric propulsion system for a 3 U CubeSat, which consists of an intake device and an electric thruster based on the inductively coupled plasma. The capture performance of intake device is optimized considering both particles capture efficiency and compression ratio. The plasma source is also analyzed by experiment and simulation. Then, the thrust performance is also estimated when taking into account the intake performance. The results show that it is feasible to use atmosphere-breathing electric propulsion technology for CubeSats to compensate for aerodynamic drag at lower Earth orbit.     

Land use and cover change (LUCC) impacts on Earth’s eco-environments: Research progress and prospects

Land use and cover change (LUCC) is one of the key variables dominating land–atmosphere interactions and strongly affects the Earth’s eco-environments by altering surface properties. Numerous studies have been carried out to assess the impact of LUCC. However, the Earth is a large, open and complex system characterized by complex interactions between its eco-environments and drivers. This study aimed to summarize previous studies of the impact of LUCC on the Earth’s eco-environments and discuss the progress and limitations in suggesting future directions. Previous studies have confirmed that LUCC has a wide range of impacts on the Earth’s eco-environments, which are represented by the alternation of climate (temperature, precipitation, wind, and humidity), hydrology (soil moisture, runoff, and evapotranspiration), ecology and environmental (air, water, and soil) pollution. Physically, the impacts were mainly attributed to the disturbance of the surface radiation budget and matter conservation caused by LUCC. Although great achievements have been made, several challenges remain because of the unavoidable uncertainties in data sources and methodologies and the complexity of eco-environmental evolution. Therefore, data assimilation, physical-based investigations, contribution isolation, and full-process analysis are required to overcome these challenges in future research. The results of this study helped to capture the impact of LUCC and its physical mechanisms, which provide useful clues for future research and support the relative land use management for sustainable development.     

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).