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。     

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.     

载人航天器太阳翼多姿态输出能力建模及分析

载人航天器太阳翼在轨运行期间除正常对日定向转动外，根据飞行任务要求可能需要工作在α轴水平归零或垂直归零等特殊状态。在特殊工作模式下，太阳入射角和太阳翼的输出能力变化曲线更加复杂。建立太阳入射角与轨道角、太阳高度角、β轴转角等变量的数学模型，分析太阳入射角的变化规律，并结合光照区时长，建立太阳翼在轨输出能力的预测模型。与实际飞行数据相比较，表明该模型可较为准确地评估特殊工作模式下太阳翼的输出能力，可用于在轨整舱能量平衡分析，为飞行任务计划安排提供参考。     

卫星蜂窝夹层结构新型埋件工艺

针对卫星蜂窝夹层结构，研究了新型后埋件及其自动化后埋工艺，并与我国卫星领域传统后埋技术对比，考察了两种埋件技术对埋件承载力与承载系数的影响。结果表明，两种埋件系统承载模式基本一致，胶黏剂的分布状态对其承载性能有重要影响。新型后埋系统承载能力已经达到传统后埋系统的158.2%。新型后埋件技术更适合自动化方式，保证工艺稳定性一致性可靠性。