纳秒激光脉冲宽度对2024铝合金损伤特性的影响

飞机激光除漆对2024铝合金蒙皮可能造成潜在的损伤,需探究激光参数变化对基体材料表面的作用规律,其中脉冲宽度对激光-材料作用与材料损伤特性具有重要影响。本文采用COMSOL Multiphysics软件模拟分析了不同脉冲宽度时激光作用铝合金表面的温升特性,并借鉴ISO 11254 1-on-1激光损伤阈值测试方法研究了脉冲宽度对铝合金损伤阈值的影响,进一步分析了不同脉冲宽度下的烧蚀凹坑微观形貌、直径与深度变化规律。结果表明：铝合金表面峰值温度随脉冲宽度增加而降低;脉冲宽度从150 ns增加到240和330 ns时,铝合金损伤阈值从9.96 J/cm²分别增加到11.24和12.66 J/cm²,当激光能量密度达到损伤阈值时,3μm厚氧化膜被完全损伤,破坏了铝合金表面完整性;烧蚀凹坑直径和深度随脉冲宽度增加而增加,深度受影响程度更大。该研究可为激光与材料作用的脉冲宽度选择提供一定的参考。     

国内外深空探测器精密定轨软件研究综述及WUDOGS简介

深空探测器精密定轨软件系统的研制在深空探测活动中是一个非常重要的环节,一直受到各大航天机构的重视。针对国内外深空探测器精密定轨软件平台的研究现状,重点介绍了具有代表性的美国JPL(Jet Propulsion Laboratory,喷气推进实验室)的DPTRAJ/ODP(Double Precision TRAJectory program/Orbit Determination Program,双精度轨道程序/定轨程序)和MONTE(Mission analysis,Operations,and Navigation Toolkit Environment,任务分析、操作和导航工具箱环境),GSFC(Goddard Space Flight Center,戈达德航天飞行中心)的GEODYN-II以及法国CNES(Centre National dEtudes Spatiales,国家空间研究中心)的GINS(Géodésie par Intégrations Numériques Simultanées,同步数值积分大地测量)软件系统,对这些软件的结构与功能进行了总结。之后对武汉大学自主研制的深空探测器精密定轨软件系统WUDOGS(Wuhan University Deep space Orbit determination and Gravity recovery System,武汉大学深空探测器精密定轨与重力场解算软件系统)的主要模块与功能进行了介绍,通过与GEODYN-II的交叉对比验证,表明:对于探测器的轨道预报,WUDOGS与GEODYN-II的1个月位置差异小于0.3mm,2d位置差值小于5×10~(-3) mm;双程测距、双程测速的理论计算值和GEODYN-II的差值RMS(Root Mean Square,均方根)分别在0.06mm,0.002mm/s的水平;WUDOGS目前已初步具备了月球和火星探测器精密定轨能力。最后对WUDOGS的下一步发展方向进行了展望。     

软夹层场地隔震结构动力特性试验与数值计算对比分析

基于刚性地基和软夹层地基上基础隔震结构的振动台模型试验体系,建立了刚性地基和软夹层地基土-基础隔震结构动力相互作用的等尺寸三维有限元分析模型,对比分析了两者不同地基上模型结构体系的自振频率、加速度反应和模型地基的加速度反应规律。对比分析结果表明:数值模拟计算结果和模型试验结果虽然在数值上的差距较大,但两者反应的模型在地基地震反应特征、隔震层的隔震效果和模型隔震结构的地震反应等方面的规律基本一致。也就是说,采用振动台模型试验开展土-隔震结构动力相互作用的研究在定性规律的反应上是可行的,但在定量模拟方面还存在明显的不足。     

聚酰亚胺薄膜与碳网格共固化基板成型工艺研究

为满足未来深空探测对航天器太阳翼基板更高的耐温性需求,提出一种新型的太阳翼基板成型工艺方法：聚酰亚胺薄膜与碳网格面板共固化成型。重点研究了聚酰亚胺薄膜与网格面板的剥离性能、网格面板的拉伸性能、蜂窝夹层结构的弯曲性能、共固化基板的耐温性能,并与J-133胶粘贴聚酰亚胺薄膜的传统工艺进行对比。结果表明,共固化工艺的聚酰亚胺薄膜剥离强度约为传统工艺的2倍,且在140℃下的各项力学性能保持率均大于87%,具有很好的耐温性;共固化基板在经历-100~140℃热真空试验后,外观质量及各项性能均合格,满足太阳翼基板耐140℃及以下空间环境的使用需求。     

基于MADDPG的多无人机协同任务决策

针对多无人机任务决策方法研究中传统优化算法难以在短时间内得到期望结果的问题,基于深度强化学习提出一种无人机多智能体深度确定性策略梯度(MADDPG)算法,通过允许无人机在学习时使用全局信息,在应用决策的时候只使用局部信息的方法,从网络结构、状态空间、动作空间和奖励函数设计了 MADDPG算法的模型结构.最后通过仿真实验...     

Kelvin半无限体内部作用竖向力时的解答与应用

选择Kelvin模型描述土体的粘弹性本构关系,利用半空间体内部受竖向集中力的Mindlin弹性解,根据粘弹性理论中的准静态弹性-弹粘性对应原理,推导了竖向力作用在Kelvin粘弹性半无限土体内部的理论解。通过对应力和位移解答进行Laplace逆变换,给出了应力与位移的时域解。作为解答的应用,建立了Kelvin粘弹性半无限体内部矩形面积上作用有三角形分布、均匀分布荷载时的粘弹性沉降计算公式。为了便于计算与工程应用,根据粘弹性理论解编制了计算程序。结果验证和实例分析表明,文中理论解是正确的,研究结果为工程应用提供了理论依据。     

An effective crack position diagnosis method for the hollow shaft rotor system based on the convolutional neural network and deep metric learning

In recent years, the crack fault is one of the most common faults in the rotor system and it is still a challenge for crack position diagnosis in the hollow shaft rotor system. In this paper, a method based on the Convolutional Neural Network and deep metric learning (CNN-C) is proposed to effectively identify the crack position for a hollow shaft rotor system. Center-loss function is used to enhance the performance of neural network. Main contributions include: Firstly, the dynamic response of the dual-disks hollow shaft rotor system is obtained. The analysis results show that the crack will cause super-harmonic resonance, and the peak value of it is closely related to the position and depth of the crack. In addition, the amplitude near the non-resonant region also has relationship with the crack parameters. Secondly, we proposed an effective crack position diagnosis method which has the highest 99.04% recognition accuracy compared with other algorithms. Then, the influence of penalty factor on CNN-C performance is analyzed, which shows that too high penalty factor will lead to the decline of the neural network performance. Finally, the feature vectors are visualized via t-distributed Stochastic Neighbor Embedding (t-SNE). Naive Bayes classifier (NB) and K-Nearest Neighbor algorithm (KNN) are used to verify the validity of the feature vectors extracted by CNN-C. The results show that NB and KNN have more regular decision boundaries and higher recognition accuracy on the feature vectors data set extracted by CNN-C, indicating that the feature vectors extracted by CNN-C have great intra-class compactness and inter-class separability.     

Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems (6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods. Finally, we conclude this paper with a discussion of open problems in this area.     

弱太阳闪烁对深空通信的影响

行星聚合和太阳闪烁是影响深空通信的重要因素. 分析了行星聚合和太阳闪烁的物理特性, 建立了弱太阳闪烁条件下深空通信信道的Rician 模型, 并得出了当Rician 因子大于10 dB 时近似为高斯信道的结论. 对弱太阳闪烁下X 波段和Ka 波段的通信性能进行了仿真, 这对建立可靠的深空通信链路具有十分重要的意义.     

深空探测低信噪比通信研究

与传统无线通信相比, 深空通信由于传输损耗巨大、探测器能量受限且轨道多变等因素, 须解决信噪比(Signal to Noise Ratio, SNR)极低、数据接收不连续及信道参数多变等难题. 针对这些问题, 论述了在深空探测极低SNR下需重点研究的深空天线组阵、编码与调制、联合解调译码、喷泉码以及其自主无线电实现等关键技术, 以有效协助解决上述深空通信难题. 同时, 讨论了这些关键技术的发展趋势及其在未来深空通信中的应用.