模拟月壤研制的初步设想

模拟月壤是月球样品的地球化学复制品，作者总结世界上已有的5种模拟月壤JSC-1，MLS．1，MLS-2，MKS-1和FJS-1的研制过程、方法与基本理化性质，认为系列化模拟月壤研制对中国首次月球探测有重要意义，在此基础上，作者提出系列化模拟月壤研制的基本思路．     

空间积分声光相关器——脉冲信号检测

论述了空间积分声光相关器对脉冲调制连续波信号的瞬时响应，分析了从实时光电检测器中检测出的波形，该波形是脉冲宽度，加权函数参量和与脉冲位置相关的空间衍射光分布的函数。进一步提出了脉冲序列相关时的光分布关系并通过实验加以论证。     

空间任意形状凸面的轨道空间外热流计算方法

采用蒙特卡罗（ＭｏｎｔｅＣａｒｌｏ）方法，进行了平板、半球面、抛物面的地球反照角系数和地球红外角系数计算，通过与文献数据的比较，证明了由此方法所编程序的可靠性和实用性。利用这种方法，可以计算空间任意形状凸表面的轨道空间外热流。     

OSIRIS-REx low-velocity particles during outbound cruise

We analyzed high-angular rate streaks first recorded by OSIRIS-REx’s MapCam during a 2017 search for Earth Trojan asteroids. We interpret them as water-ice particles that translated across the imager’s field of view, originating from the spacecraft itself. Their translation velocities approximated 0.1–1?m/s based on reasonable conclusions about their range. Pursuing several lines of investigation to seek a coherent hypothesis, we conclude that the episodic releases of the water ice particles are associated with spacecraft attitudes that resulted in solar illumination of previously shadowed regions. This correlation suggests that the OSIRIS-REx spacecraft itself possesses micro-climatic zones consisting of hot regions and cold traps that may temporarily potentially pass volatiles back and forth before losing most of them.     

对空间碎片近距随遇悬停的控制方法及悬停燃耗分析

对空间碎片检视或抓捕操控中的悬停控制及燃耗问题进行了研究。通过C W方程，建立任务星近距随遇定点悬停控制模型，通过设计状态反馈控制器分析定点悬停的可控性以及推控要求，结果表明近距悬停需要与悬停位置相关的两个正交方向上的常值连续推力控制量，以及用于抵抗扰动的三轴向上的反馈变推力控制量。分析了近距随遇悬停的推控分系统配置，建立了长时近距随遇悬停的燃耗及燃耗速率的数学模型，最后分析了既满足安全距离需求、又满足悬停方位需求的最小悬停燃耗模型。     

MIDACO software performance on interplanetary trajectory benchmarks

A numerical study of the MIDACO optimization software on the well known GTOP benchmark set, published by the European Space Agency (ESA), is presented. The GTOP database provides trajectory models of real-world interplanetary space missions such as Cassini, Messenger or Rosetta. The trajectory models are formulated as constrained nonlinear optimization problems and are known to be difficult to solve.     

卷簧式浅层月壤取样器设计及动力学分析

面向浅层月壤取样,文章设计了新颖的卷簧式可伸缩小型月壤取样器,它包括柔性取样臂和振动式取样头。对取样器进行了动力学有限元分析,研究了取样臂为不同长度时取样头的振动模态及频率响应,得出取样臂一阶横振频率与取样臂长度呈负幂函数关系;对取样器测控系统进行了振动试验,通过扫频方式测量取样臂为不同长度时的自振频率,给出了取样臂长...     

航天器安全防护保障探讨

把对航天器安全的威胁分为硬和软两大类，分析了对航天器产生威胁的类别，以及为保证航天器安全而需要采取的措施。介绍了研究的内容，需要建设的空间信息对抗系统和空间信息探测网。     

基于OpenFOAM的真实流体模型库及均相求解器开发

为了正确认识高压环境下的流体非理想性特征,及其对于低温射流喷雾过程的影响作用,将多种不同真实流体模型写入到开源CFD程序OpenFOAM仿真平台中,编译形成具有广泛通用性的真实流体热物理模型库;并将高压下真实流体特征的影响引入到压力修正算法中;开发出适用于描述超临界环境下低温流体流动过程的均相求解器。对高压下流体热物理性质的非理想性,以及超临界环境下的低温液氮射流喷注过程进行研究。结果表明:高压下的真实流体效应主要体现在低温区域,本文所建立的模型库可以在任意热力学状态范围内正确预测流体热物性;基于此模型库所开发的均相求解器可正确预测超临界环境下低温射流的详细流动特征和结构,而理想气体模型则会大幅低估射流核心区密度分布,中心线上误差高达68.9%;综合考虑计算精度和效率,PR EoS及相关模型更适合于这类超临界流动问题的求解;低温射流跨临界喷注中局部区域所发生的虚假沸腾现象会导致射流温升速率降低、体积膨胀速率加快;射流核心区压缩因子明显1,在数值仿真时必须考虑高压环境下的真实流体效应。     

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles (UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent collaboration. Then, an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources, execution time, social rules and costs. Besides, a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and control. Finally, a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s decisions. Experimental results demonstrate the effectiveness of the proposed methods.