空间相机热平衡试验温度预测方法改进及应用

为实现对航天器热平衡试验平衡温度的预测,建立了热平衡温度预测的方程式,对影响预测结果的各参数进行了分析。首先,由节点网络法推导出平衡温度表达式,介绍了其基于非线性最小二乘法的求解过程,建立了平衡温度的迭代方程;其次,结合大量的试验数据,对影响迭代结果的3个因素进行了统计分析,并最终确定了各因素的合理取值范围;最后,利用温度预测程序对某空间相机热平衡试验中低温工况的平衡温度进行了预测,并与试验结束后的结果进行了对比。结果表明,预测温度与试验数据的误差为±1℃（在3%之内）,满足精度要求,对热平衡温度准确的预测有效的缩短了试验时间,节省试验费用。     

单轴台的大角度姿态快速机动联合控制方法

为验证小卫星大角度姿态快速机动与高精度稳定控制能力,基于单轴气浮台硬件仿真环境,提出了一种利用推力器与飞轮组合的联合控制策略.采用相平面控制技术与有限时间控制理论设计控制器,利用推力器实现无超调的快速机动控制,利用飞轮实现有限时间内的高精度稳定控制,使单轴台在有限时间内快速高精度稳定于目标姿态.物理仿真结果表明:该方法在有限时间内完成单轴台快速稳定控制的同时,可有效避免机动过程中的超调现象,且能有效规避推力器的频繁开关与飞轮的过快饱和等问题.     

基于自动有限元建模的民机机翼结构布局优化

针对民用飞机中广泛采用的双梁式机翼结构,以机翼结构纵向构件(长桁)和横向构件(翼肋)的数量为参数生成机翼CAD模型,然后基于Patran的PCL(Patran Command Language)语言,根据整体模型分割、局部模型编号、局部网格控制、整体有限元生成的思路,实现了以纵向和横向构件的数量变化为基础自动进行机翼结构有限元模型的构建及分析,并将上述流程集成到机翼结构的布局优化中.最后参照某大型民用飞机的机翼外形尺寸建立了CAD模型,以机翼的静强度、刚度和蒙皮稳定性为约束,以机翼结构质量最低为目标,对上、下蒙皮长桁和翼肋数量进行了优化.优化后的布局使机翼结构质量降低了10.1%,表明了布局优化方法的有效性和可行性.     

Al-Li合金搅拌摩擦焊搭接接头的疲劳性能

通过疲劳寿命试验、疲劳断口和金相组织分析,研究了Al-Li合金搅拌摩擦焊搭接接头的疲劳性能,分析了接头前进侧热机影响区(TMAZ, Thermal-Mechanically Affected Zone)出现的"钩状缺陷"对其疲劳性能的影响.结果表明,钩状缺陷容易引起裂纹萌生及扩展,减小接头的有效承载板厚,适当缩短搅拌针长度可以改善钩状缺陷,提高接头力学性能.母材与搭接接头的疲劳S-N曲线研究表明:在高疲劳应力下,接头的疲劳寿命接近母材,但随着疲劳应力水平降低,接头的疲劳寿命急剧下降,200万循环周次下的疲劳强度仅达到80 MPa,为母材的35%.     

显示界面字符编码工效设计与分析

针对人机显示界面中字符编码的工效设计问题,以封闭模拟驾驶舱为基础,采用32名被试开展静态模拟实验,选取反应时间和反应正确率为工效评价指标,得出不同光照水平、颜色匹配和字符高度下显示界面呈现的中文、英文、数字及符号的辨识绩效.实验结果表明:中/英文、数字和普通符号的辨识绩效差异显著;0~600 lx范围内4个水平光照变化对字符编码辨识绩效影响较小;颜色匹配方式对字符编码辨识绩效影响显著,黄/白、绿/青的字符/背景色匹配方式辨识绩效较差,在字符编码设计中尽量避免使用.通过实验结论及对实验数据的深入分析,建立显示界面字符编码设计的数学模型,为飞机驾驶舱人机显示界面编码设计提供参考.     

整机产品失效机理危害度定量分析方法

针对整机产品失效机理多而复杂、失效数据少、主失效机理分析为定性方法的现状,提出了一种基于Bayes的失效机理危害度定量分析方法.首先利用Dirichlet分布描述产品的先验信息,结合现场失效数据确定失效频率的Bayes后验分布函数,然后通过Slice抽样方法获得失效频率的满条件分布,进而利用Gibbs抽样得到其Bayes估计,从而实现了各失效机理的定量分析.最后在某型光纤连接器失效机理已分析清楚的前提下,分别用FMMEA(Failure Modes,Mechanisms and Effects Analysis)及Bayes方法确定了其主失效机理,结果表明基于Bayes的失效机理危害度定量分析方法考虑了现场未出现的失效机理危害度,其确定的主失效机理比FMMEA结果更具有可信度.     

Comparison of ionosphere characteristic parameters obtained by ionosonde with IRI-2007 model over Southeast Asia

In this work, the foF2 and hmF2 parameters at the conjugate points near the magnetic equator of Southeast Asia are studied and compared with the International Reference Ionosphere (IRI) model. Three ionosondes are installed nearly along the magnetic meridian of 100°E; one at the magnetic equator, namely Chumphon (10.72°N, 99.37°E, dip angle 3.0°N), and the other two at the magnetic conjugate points, namely Chiang Mai (18.76°N, 98.93°E, dip angle 12.7°N) and Kototabang (0.2°S, 100.30°E, dip angle 10.1°S). The monthly hourly medians of the foF2 and hmF2 parameters are calculated and compared with the predictions obtained from the IRI-2007 model from January 2004 to February 2007. Our results show that: the variations of foF2 and hmF2 predicted by the IRI-2007 model generally show the similar feature to the observed data. Both parameters generally show better agreement with the IRI predictions during daytime than during nighttime. For foF2, most of the results show that the IRI model overestimates the observed foF2 at the magnetic equator (Chumphon), underestimates at the northern crest (Chiang Mai) and is close to the measured ones at the southern crest of the EIA (Kototabang). For hmF2, the predicted hmF2 values are close to the hmF2(M3000F2_OBS) during daytime. During nighttime, the IRI model gives the underestimation at the magnetic equator and the overestimation at both EIA crests. The results are important for the future improvements of the IRI model for foF2 and hmF2 over Southeast Asia region.     

Digital signal processing and numerical analysis for radar in geophysical applications

Numerical solutions for signal processing are described in this work as a contribution to study of echo detection methods for ionospheric sounder design. The ionospheric sounder is a high frequency radar for geophysical applications. The main detection approach has been done by implementing the spread-spectrum techniques using coding methods to improve the radar’s range resolution by transmitting low power. Digital signal processing has been performed and the numerical methods were checked. An algorithm was proposed and its computational complexity was calculated.     

Systematic examination of the geomagnetic storm sudden commencement using multi resolution analysis

Geomagnetic storm sudden commencements (SSC) contain a wealth of information which is useful in many applications. It is important to point out that the SSCs used in this study are sampled at the rate of one sample per second in order get use of such high resolution data. In this paper, two studies are made on the geomagnetic SSC and an SSC onset automatic detection algorithm is introduced. The first study is about finding the relationship between the SSC rise time and its amplitude. Where it is found that there is a positive correlation between the amplitude and the amplitude gradient which is the amplitude divided by rise time. The second study is checking the spectrum of the SSC, starting from its onset until the end of the SSC rise time. This check had proved that the SSC contains both low and high frequency regions. This led us to introduce a new term, namely the SSC variation rate (VR). This VR is defined as the maximum rate of change of the field in the higher-frequency region of the SSC. These two studies were the guide to build an SSC automatic detector of one sample per second data using multi resolution analysis (MRA) of the discrete wavelet transform (DWT). The data set contains 134 SSCs with different VRs that were collected from the Circum-pan Pacific Magnetometer Network (CPMN). It is found that the standard deviation of the detection error is 41 s and that the average error is 9 s. From the calculated error distribution function, it is found that the detection error is within the range of −1.5 to 3 min. The detection process, as will be shown in the article, takes 70 s for one station and 3 min if the decision is related to the detection(s) of other stations. These results demonstrate the superiority of the proposed algorithm over other algorithms in which the detection error ranges between −8 and 5 min and the detection process takes 2–10 min. In addition to being faster and more accurate than the other algorithms, the proposed algorithm is the first algorithm that automatically detects the SSC onset times from high-resolution data unlike previous studies that focused on determining the SSC times automatically using one-minute resolution data.