大型网状天线展开过程温度及展开时机分析

大型网状天线在轨展开过程复杂,风险大。反射器在展开过程中的温度影响展开动力与碳纤维管件抗弯能力,是反射器展开安全的关键因素。通过测试得到反射器展开动力的传动效率、桁架管件抗弯能力随温度变化规律,即-65℃时管件抗弯能力最好,而传动效率随温度升高而提高。通过建立大型网状天线有限元模型与节点矩阵转换算法,模拟展开臂与反射器的展开过程,对管件与T型铰链在轨温度变化规律进行分析,确定最佳展开时机,降低展开过程中展开动力不足、管件强度过载等风险。分析结果表明：布置在地球静止轨道卫星东舱板的大型网状天线展开时机为6:00~9:00时,管件抗弯能力最强;展开时机为4:00时,T型铰链展开动力传动效率最高。     

停机位容量评估及其包络线分析

为客观评估停机位系统保障能力,探索机位容量提升方法,将进出港航班的地面作业时间、停机位缓冲时间及航班定位时间引入到容量模型,设计了一种改进型的停机位系统容量评估模型。以国内某机场为例,使用停机位容量包络线方法,分别从进出港航班比例变化、机型组合比变化、机位利用率和航班平均机位占用时间同时变化三个方面,对停机位系统的容量进行了评估分析。使用该模型和容量分析方法,可以为机场运行管理人员在制订机位容量提升计划、组织高峰时段航班运作及制订中远期机位规划等方面提供有益的决策支持。     

Individual Differences in Representational Precision Predict Spatial Working Memory Span

Perhaps the signature feature of working memory is that it is limited. In the same subjects, we used two different retrieval tasks to independently measure two different limits of spatial memory. Precision was measured by asking participants to localize a missing target item among a field of other targets and distracters. Capacity was measured with a similar task where participants identified, rather than localized, a set of remembered targets from within a larger set of identical items. Across participants, the precision of localization was positively correlated with the number of successfully retrieved items. These data suggest that an individual's representational capacity may ultimately be constrained by their ability to form precise representations of space.     

氧化锆热障涂层的组织和性能研究

通过研究发动机热端部件氧化锆热障涂层8YSZ （ZrO2＋8％Y2O3）的结合强度、隔热性能和抗热震性能,分析热障涂层性能和相应的失效机制,总结了传统热障涂层和纳米热障涂层之间性能的差异,研究结果表明：纳米热障涂层的力学性能、隔热能力和抗热震性能均明显优于传统热障涂层.     

航天用碳纳米管增强铝合金复合材料的力学与阻尼性能

碳纳米管(CNT)作为增强体的铝基复合材料(CNT/Al)具有轻质、高强、高模量、易加工的性能优势,用作轻量化材料在航天航空领域具有巨大的应用前景。为了获得兼顾其力学性能和阻尼性能的轻量化结构材料,采用叠片粉末冶金与合金化方法制备了质量分数为1.5% CNT/2A12复合材料,并研究了不同时效条件下的力学性能与阻尼性能。在130 ℃时效6~14 h时,复合材料具有最佳的拉伸强度与延伸率,抗拉强度最高可达595 MPa(时效12 h),延伸率最高可达14.0%(时效8 h)。复合材料的阻尼在0~180 ℃时变化不大,其在0.005左右,180~300 ℃时明显提高,300 ℃时可达0.05,阻尼性能受时效时间影响不大。复合材料的储存模量随测试温度升高而下降,在180~300 ℃时随振动频率升高而升高。时效条件为130 ℃-8 h时,质量分数1.5% CNT/2A12复合材料性能兼具良好的力学性能与阻尼性能。     

利用健康因子的卫星电池容量估计方法

卫星在轨工作期间,准确估计电池容量对提高电源系统的安全性与可靠性有重要意义。为此,针对电池在工作过程中实际容量难以直接测量的问题,提出一种利用间接健康因子估计电池容量的方法。首先,从锂电池恒压充电模式下的电流数据中提取平均充电电流,将其作为反映电池容量变化的特征参数。在实现了锂电池健康状态表征的基础上,对比分析容量和平均充电电流随着充放电循环周期的变化曲线。然后,讨论了容量和平均充电电流之间的相关性,进而建立了容量估计模型。最后,通过从NASA锂电池数据集中提取容量和平均充电电流,划分训练集和测试集,验证了用该方法估计容量的有效性和准确性。     

AWGN信道中一维调制与二维调制的信道容量

卫星通信和深空通信信道都可近似为加性高斯白噪声信道,所以研究这种信道中常用调制方式下的信道容量有重要意义.从香农信息论出发,推导出了一维调制和二维调制下的信道容量.在此基础上,阐明了在不同信噪比条件下应采用的调制方式,以及编码调制能够在不牺牲频谱利用率的前提下,得到编码增益的本质.讨论了无带宽约束下AM调制和PSK调制的香农限和在高信噪比条件下PSK的近似容量.      

Comparison Structure Forms Between Isogrid and Orthogrid of C/ E CompositeTrellis Wound Structure Based on Calculation of Load Carrying

This paper calculated load carrying of isogrid and orthogrid of carbon epoxy composite trellis wound
structure (C/ E CTWS) using non linear finite element method. Based on the analysis, test cases were designed and
tests of axial compression were carried. Analysis result and test result fit well. In order to be used in the project, this
kind of structure cut out repairing was calculated. The method presented in this paper has been proved and can be used
to solve complicated engineering problems. According to calculations and experimental results combined with applica
tion, a principle of choosing wound structure is obtained and principle could be applied to engineering.
     

一种舱段快速连接结构设计及承载能力分析

设计了一种新型的舱段间连接装置，分析了其在轴向力载荷作用下的受力情况，并以相对滑动作为失效判据给出了预紧力的计算方法，在此基础上分析了卡箍倾角、摩擦系数以及预紧力等对该结构承载能力的影响。研究结果表明：直径为1 200 mm的连接结构能够满足1 000 kN 轴拉载荷下的使用要求。卡箍与壳体配合面倾角、摩擦系数以及预紧力等均会对该连接结构的承载能力产生影响，其中预紧力的影响主要表现为预紧力增大，卡箍与上下壳体对接面的压力增大，产生的摩擦力增大，因此其轴向承载能力增大。此外，随着配合面倾角增大以及摩擦系数的减小，则会导致该连接装置承载能力产生显著下降。     

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems to fully exploit their computational performance. A locally generalised Multi-Agent Reinforcement Learning (MARL) for real-world DCB problems is proposed. The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management (ATFM) region to quickly obtain a satisfactory solution. In this method, agents of all flights in a scenario form a multi-agent decision-making system based on partial observation. The trained agent with the customised neural network can be deployed directly on the corresponding flight, allowing it to solve the DCB problem jointly. A cooperation coefficient is introduced in the reward function, which is used to adjust the agent’s cooperation preference in a multi-agent system, thereby controlling the distribution of flight delay time allocation. A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated. Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method. From a statistical point of view, it is proven that the proposed method is generalised within the scope of the flights and sectors of interest, and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods. The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.