多支点柔性转子系统临界转速稳健设计方法

基于响应面法和容差模型提出了定量考虑参数变差影响的转子系统临界转速稳健设计方法。该方法利用响应面模型获得多参数、多目标之间的函数关系,并以多阶临界转速分布为约束条件、以临界转速对支承刚度变差敏感度最低为设计目标。对小涵道比涡扇发动机低压转子系统临界转速稳健设计结果表明:在考虑各支点支承刚度变差情况下,采用多参数、多目标稳健设计方法,可保证多支点柔性转子系统的临界转速特性在满足设计要求的同时,转子系统临界转速对支承刚度变差的敏感度最低,验证了该方法的有效性。      

航空发动机双转子系统“临界跟随”现象的机理及影响

为了探明“临界跟随”现象导致航空发动机振动居高不下的产生机理,建立了考虑中介轴承的双转子动力学模型,推导了双转子发生“临界跟随”现象时参数之间的关系,分析了“临界跟随”状态下转子系统的动力学特性。结果表明:“临界跟随”现象会导致转子系统无法越过盘偏摆振型临界转速,造成转子振动对不平衡质量分布极其敏感,其主要影响因素为盘极转动惯量与直径转动惯量的比值(简称惯量比)以及双转子增速比；当高压盘等效惯量比等于1,或增速比与任意低压盘惯量比相等时,转子系统表现出高压激励条件下的“临界跟随”特征；当低压盘惯量比等于1,或增速比与高压盘等效惯量比的乘积等于1时,转子系统表现出低压激励条件下的“临界跟随”特征。提出了必须严格控制航空发动机叶盘的等效惯量比以及双转子转速控制律的设计建议。      

基于CMPSO算法的永磁同步电机转动惯量识别研究

永磁同步电机(PMSM)是一种具有很强非线性的动态系统,在工业驱动应用中发挥着重要作用。实时辨识转动惯量对于高精度PMSM系统控制和稳定性状态监测具有重要意义,但传统转动惯量识别方法精度较低。在传统离散模型参考自适应理论基础之上,用柯西变异粒子群优化(CMPSO)算法代替待辨识参数自适律设计环节,以实现PMSM转动惯量识别。该转动惯量辨识方法充分利用了CMPSO算法的快速高效收敛性,仿真结果和试验结果表明了可行性、正确性和准确性。     

Establishment of atmospheric weighted mean temperature model in the polar regions

Due to the special geographical location and extreme climate environment, the polar regions (Antarctic and Arctic) have an important impact on global climate change. Atmospheric weighted mean temperature (Tm) is a crucial parameter in the retrieval of precipitable water vapor (PWV) from the zenith wet delay (ZWD) of ground-based Global Navigation Satellite System (GNSS) signal propagation. In this paper, the correlation between weighted mean temperature and surface temperature (Ts) is studied firstly. It is shown that the correlation coefficients between Tm and Ts are 0.93 in the Antarctic and 0.94 in the Arctic. The linear regression Tm model and quadratic function Tm model of the Antarctic and the Arctic are established respectively using the radiosonde profiles of 12 stations in the Antarctic and 58 stations in the Arctic from 2008 to 2015. The accuracies of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model which is a state-of-the-art global Tm model are verified using the radiosonde profiles from 2016 to 2018 in the Antarctic and Arctic. Root Mean Square (RMS) errors of the linear regression Tm model, the quadratic function Tm model and GPT2w Tm model in the Antarctic are 3.07 K, 2.87 K and 4.32 K respectively, and those in the Arctic are 3.53 K, 3.38 K and 4.82 K, which indicates that the quadratic function Tm model has a higher accuracy compared to linear regression Tm model, and the accuracies of the two regional Tm models are better than that of GPT2w Tm model in the polar regions. In order to better evaluate the accuracy of Tm in the PWV retrieval, the PWV values of radiosondes are used for comparisons as the reference value. The RMS errors of PWV derived from the two Tm models are similar for 1.28 mm in the Antarctic and 1 mm in the Arctic respectively. In addition, the spatial and temporal variation characteristics of Tm are analyzed in the polar regions by spectral analysis of Tm data using fast Fourier transform. The results show that the Tm has obvious seasonality and annual periodicity in the polar regions, and the maximum difference between warm season and cold season is about 63 K. After comparing and analyzing the influences of latitude, longitude and elevation on the Tm in the polar regions, it is found that latitude and elevation have a greater influence on the Tm than the longitude. As the latitude and elevation increase, the Tm decreases, and vice versa in the polar regions.     

拓扑数据分析在复杂脑网络分析中的应用

针对复杂脑网络分析中网络结构变化阈值选择中没有公认的标准确定合适阈值这一问题，基于拓扑分析中的持续同调性理论，本文提出一种多尺度大脑网络建模分析方法，该方法在大脑全尺度距离范围之内，通过不断增加阈值，运用Rips过滤算法捕获网络的动态持续拓扑特征，并用条形码和持续图对拓扑特征可视化，最后通过计算持续图之间的Bottleneck距离和Wasserstein距离分析持续特征的稳定性。实验结果表明，该方法能更准确地提取大脑网络的拓扑结构特征并提高诊断分类的准确性。     

ADS-B监视报文参数统计及其所需性能

基于广播式自动相关监视（ADS-B）报文的位置导航不确定性指标（NUC_P）的监视质量备受关注。针对位置导航不确定性参数好、监视质量却不高的问题，深入研究广播式自动相关监视报文数据项及其与基于性能的通信与监视的相关性。在充分考虑通信导航监视相关性及全球卫星导航系统（GNSS）广域增强等因素基础上，推导、构建了监视所需性能估计模型，并给予合理简化。在测试空域利用2个具有运行资质的生产商ADS-B接收站，连续采集近2亿条ADS-B报文，统计分析报文各信息项的状态参数，并应用到所需性能估计模型评估计算这两个ADS-B站的所需监视性能。此外还将参考文献报文参数代入模型评估所需性能。结果显示，当考虑报文各信息项统计状态及具备广域增强服务支撑时，ADS-B所需监视性能参数估计值未达到99.9%要求。若缺乏广域增强服务及相关性因素等，系统所需性能与国际民航组织（ICAO）监视要求的差距则更大。     

基于数字孪生的智能脉动管控

为提升飞机总装脉动生产线智能化水平,开展了基于数字孪生的智能脉动管控研究。基于数字孪生五维模型理论构建了飞机总装脉动生产线数字孪生应用架构,从反应式计划调度、物流精准配送、智能作业指导、产线健康监视、资源迭代优化配置5个方面开展了技术研究,为复杂产品装配的智能制造应用提供参考。     

工艺模型驱动的物料动态精准配送技术

针对飞机装配车间传统“领料式”物料配送存在的弊端,提出了一种工艺模型驱动的物料动态精准配送方法,构建了飞机装配车间物料精准配送管理系统,阐述了系统构建涉及的关键技术难点,采用基于模型的三维可视化消耗式BOM重构方式,建立与设计了模型动态同步精准的MBOM工艺模型,利用RFID技术的优点实时获取物料信息,通过iGPS系统引导AGV物料运输平台来实现装配站位物料的高精度配送。     

熔融沉积成型连续碳纤维增强尼龙蜂窝芯材的压缩特性

采用熔融沉积成型方法成形了连续碳纤维增强尼龙复合材料蜂窝芯材,并对不同测试方向的静态单轴压缩特性进行了表征分析,着重关注不同测试方向熔融沉积成形蜂窝芯材平面静态压缩破碎行为和能量吸收行为,并与纯聚合物基体进行对比。结果表明:X_1方向的压缩平台区域的力-位移曲线更加平滑稳定,且载荷值稍高于X_2方向,因此X_1方向更适用于能量吸收应用;此外,连续纤维的增强作用可使蜂窝芯材的平台载荷值得到明显提升。研究结果为连续纤维增强聚合物复合材料的空间增材制造提供理论基础。     

Numerical study of traveling ionosphere disturbances with vertical incidence data

High frequency ionosphere vertical sounding, as an important and representative application for detecting the ionosphere and studying the characteristics of radio propagation, can be utilized to monitor the ionosphere continuously variation and to acquire the ionosphere asymmetrical features of diverse scale above the ionosphere vertical sounding stations. This is a first article on real time application of numerical methods to obtain the parameters of traveling ionosphere disturbances (TIDs) using vertical incident ionograms. In this paper, the distribution of ionosphere electron density with TIDs is constructed using a background ionosphere model superimposed a perturbation theory model. The background ionosphere electron density is modelled by the inversion of vertical incident ionograms which are observed before the appearance of the disturbance. Based on the fourth order Adams-Bashforth-Moulton (the so-called ABM) predictor corrector method, instead of Runge-Kutta method, the fast digital ray tracing method is established. According to process of the disturbed trace simulation and parameters inversion, the characteristic parameters of ionosphere disturbance at different detection time can be obtained in real time. The numerical analysis of TIDs is then captured completely.