有攻角细长旋成体极端地面效应分析

本文用匹配摄动方法研究了细长旋成体贴近地面运动时诱导的不可压缩势流问题,它是经典细长体理论的推广和发展。文中将流场分为内外两个渐近区域,建立了问题的匹配摄动理论,并利用文献[1]中的双圆诱导不可压缩势流的精确解,给出了该问题的首阶解析解。然后又导出了细长体上的吸力和俯仰力矩的计算公式。在此基础上,文中对细长旋成体极端地面效应的运动学规律和动力学规律作了定性和定量两个方面的分析。     

强海杂波背景下目标检测方法综述

高频地波雷达被广泛应用于海面目标的检测,而由于海杂波的分布散射具有很强的动态特性,通常情况下成为了海面目标检测的主要干扰成分.因此,在强海杂波背景下进行目标检测的关键在于如何有效抑制海杂波.从循环对消、子空间分解、模型预测以及分形特征这几方面对海杂波抑制技术进行综述、分析和总结,为后续对海面目标检测提供参考.     

直升机尾桨/尾梁耦合稳定性分析研究

单桨带尾桨直升机的尾旋翼设计成摆振柔软型后,带来了新的动力学问题:尾桨与尾梁耦合动不稳定性问题。本文简述了它与主旋翼/机体耦合动不稳定问题的差别,主要介绍了尾桨与尾梁耦合动不稳定性问题的分析方法,通过算例分析,为消除这种不稳定性,对一些关键设计参数提出了设计要求。     

卫星磁载荷现场校准技术研究

本文介绍了一种卫星磁场探测载荷现场校准技术,设计了拆分式磁场线圈、无磁支架等,研制了校准装置。利用该技术实现了对卫星磁载荷在整星测试、发射前等多个阶段的测试校准,解决了卫星载荷在星体装配完成后无法测试的难题,提高了磁载荷测试数据可靠度,保证了科学实验的有效性。该校准技术已服务于我国首颗地磁监测试验卫星(张衡一号卫星),对其磁场探测载荷在整星测试、出厂测试、发射场技术确认等多个阶段进行了校准,取得了很好的应用效果。     

小卫星非常规动力学分析与控制

针对小卫星研制中出现的一些非常规动力学问题进行研究,并提出解决办法。针对小卫星组件最大冲击谱量级超出常规冲击试验规范谱的分析与验证困难,通过研究提出了能够分析评估组件高量级冲击的方法。针对小卫星随机振动下强度设计方法还存在较大误差,分析了系统模态质量比与模态频率在一定频段的影响规律,认为忽略模态质量耦合会带来误差,提出了考虑模态质量耦合的准静态载荷计算方法。最后,针对小卫星整星开展了减振研究,分析了正弦和随机减振效果,对于对称安装减振系统,提出了减振系统的主频率计算公式。     

一种起落架载荷谱相似性判别方法

起落架结构的寿命监控对保障其安全性与经济性具有重要作用,但由于地面谱复杂的高低载荷非线性交互作用,难以准确计算单机谱的损伤。以当量损伤计算方法为基础,通过对寿命监控中的单机谱进行初步筛选,判别单机谱与基准谱的相似程度,进而分析损伤计算方法的适用性。提出了一种基于时间序列分析中的动态时间弯曲方法的起落架载荷谱相似性判别方法,进行了基准谱与4个单机谱下的疲劳实验,通过分析损伤计算误差与起落架载荷谱相似距离的关系,验证所提载荷谱相似性判别方法的合理性。      

仿生正弦前缘对翼面动态失速的影响

动态失速导致叶片气动载荷急剧变化，造成振动载荷激增，桨叶寿命大幅衰减。针对动态失速问题，从座头鲸胸鳍在动态倾转下取得良好的流动特性获得启示，据此模化出仿生正弦前缘翼面（包含3种波峰和2种波长），旨在实现动态失速控制。借助三维非定常数值模拟方法，采用运动网格技术，基于SC1095旋翼翼型，研究了仿生前缘动态失速流动控制机理及运动参数和来流速度的影响。结果表明：正弦前缘大幅度降低俯仰力矩系数峰值和阻力系数峰值；前缘波峰越大、波长越小，阻力系数峰值与俯仰力矩系数峰值的抑制效果越明显，虽然升力系数峰值减小，但其减小量远小于前两者，例如其中一种仿生翼使俯仰力矩系数峰值减小了47.7%，阻力系数峰值减小了36.4%，升力系数峰值减小14.1%；在最大迎角附近，正弦前缘能够缓和失速特性，使载荷变化更为平缓；在高平均迎角、低俯仰频率、低马赫数下，仿生翼动态失速控制效果更强，相比较而言迎角振幅的影响较小。     

基于IPSO-Elman神经网络的飞机客舱能耗预测

为了提高飞机客舱使用地面空调制冷时客舱能耗的预测精度，提出了一种改进的粒子群优化（IPSO）Elman神经网络的飞机客舱能耗预测模型。依据对算法中惯性权重与学习因子的收敛域分析，得出了二者合理的取值范围，将粒子到全局最优位置间距离与参数的取值范围相结合，构造了惯性权重与学习因子的动态调节函数，对其进行非线性的动态调节，并引入了变异因子，提出了一种跳出局部最优的策略，防止粒子群优化（PSO）陷入局部最优。将IPSO-Elman应用于Boeing738飞机客舱能耗预测中，与PSO-Elman、Elman算法进行性能比较，仿真结果表明基于IPSO-Elman的客舱能耗预测模型在预测精度和收敛速度方面均有一定的提升。该研究结果为飞机客舱能耗预测模型的建立提供了理论依据，对飞机地面空调的节能与机场电能合理调配提供了支持。     

Orbit determination of BDS-3 satellite based on regional ground tracking station and inter-satellite link observations

The BeiDou global navigation satellite system (BDS-3) has established the Ka-band inter-satellite link (ISL) to realize a two-way ranging function between satellites, which provides a new observation technology for the orbit determination of BDS-3 satellites. Therefore, this study presents a BDS satellite orbit determination model based on ground tracking station (GTS) observations and ISL ranging observations firstly to analyze the impact of the ISL ranging observations on the orbit determination of BDS-3 satellites. Subsequently, considering the data fusion processing, the variance component estimation (VCE) algorithm is applied to the parameter estimation process of the satellite orbit determination. Finally, using the measured data from China’s regional GTS observations and BDS-3 ISL ranging observations, the effects of ISL ranging observations on the orbit determination accuracy of BDS-3 satellites are analyzed. Moreover, the impact of the VCE algorithm on the fusion data processing is evaluated from the aspects of orbit determination accuracy, Ka-band hardware delay parameter stability, and ISL ranging observation residuals. The results show that for China’s regional GTSs, the addition of BDS-3 ISL ranging observations can significantly improve the orbit determination accuracy of BDS-3 satellites. The observed orbit determination accuracy of satellite radial component is improved from 48 cm to 4.1 cm. In addition, when the initial weight ratio between GTS observations and ISL ranging observations is not appropriate, the various indicators which include orbit determination accuracy, ISL hardware delay, and ISL observation residuals were observed to have improved after the adjustment of the VCE algorithm. These results validate the effectiveness of the VCE algorithm for the fusion data processing of the GTS observations and ISL ranging observations.     

A new bearing fault diagnosis method based on modified convolutional neural networks

Fault diagnosis is vital in manufacturing system. However, the first step of the traditional fault diagnosis method is to process the signal, extract the features and then put the features into a selected classifier for classification. The process of feature extraction depends on the experimenters’ experience, and the classification rate of the shallow diagnostic model does not achieve satisfactory results. In view of these problems, this paper proposes a method of converting raw signals into two-dimensional images. This method can extract the features of the converted two-dimensional images and eliminate the impact of expert’s experience on the feature extraction process. And it follows by proposing an intelligent diagnosis algorithm based on Convolution Neural Network (CNN), which can automatically accomplish the process of the feature extraction and fault diagnosis. The effect of this method is verified by bearing data. The influence of different sample sizes and different load conditions on the diagnostic capability of this method is analyzed. The results show that the proposed method is effective and can meet the timeliness requirements of fault diagnosis.