复合材料帽形加筋结构稳定性研究

建立了复合材料帽形加筋结构的参数化有限元模型,给出了两种进行层板结合处的模拟计算方法,通过计算得到了模型的屈曲载荷并总结了加筋结构几何参数对结构稳定性的影响,给出了模型的收敛性分析,对比了模型网格密度对于计算结果的影响,找到最佳的网格密度,提高了模型计算的效率和准确性,为实际的结构设计提供了重要参考,为结构分析提供了依据。     

民机结构维修大纲抽样检查原则与方法研究

民机结构维修中的抽样检查是保证飞机持续适航的关键措施。结构抽样检查是为了掌握同一时期下线飞机的结构损伤情况,以便更好地修订维修计划。针对民机结构维修的抽样是非随机抽样的特点,本文研究了结构维修抽样的原则,结合空客A320抽样经验,给出了我国自主设计大飞机结构维修抽样准则和抽样的具体步骤。     

柔性杆子杆模型参数的遗传算法优化求解

研究了应用实数型的遗传算法解决柔性机器人子杆法建模中,子杆模型参数的优化求解问题.子杆模型参数形成高度非线性的方程组,它的求解是子杆法能否实现的关键.普通的牛顿迭代法及其变形,要求初始向量足够靠近解,一些所谓大范围收敛的算法又有方程组的Jacobian矩阵非奇异等要求,在此都难以应用.本文基于遗传算法的解法可求得模型参数的最优解,实例仿真显示了这一方法的可行性和高效率.      

Investigation on the interface damage in drilling low-stiffness CFRP/Ti stacks

Carbon fiber reinforced plastic and titanium alloy (CFRP/Ti) stacks have been widely used as aerospace structures because of their excellent combination of physical properties. Interface damage caused by interface gaps, significantly different from that of metal/metal stacks, is a common problem in the through-hole drilling of CFRP/Ti stacks with low stiffness. In this study, a force–deformation coupling model was developed to further examine the formation mechanism and the control method of interface damage. Firstly, the coupling model was built considering the interaction between the thrust force and the deformation. To solve this model, a numerical method was proposed in which specific cutting coefficients were calibrated using only the thrust force of rigid stacks. Secondly, drilling experiments were performed with different feed rates and bending stiffness. Experimental results indicate that interface damage mainly includes interlayer chips and surface damage of CFRP layers. The surface damage, which is irreparable, is caused by the rotary extension of metal chips along the interlayer gap. Thirdly, variations of the interface gap were calculated with the coupling model that had been verified by measured thrust forces. The damage area was found to have a linear dependence relation with the interlayer gap. However, in conditions of large gap sizes, the interface damage areas increased with the interlayer gap at high feed rates, while decreasing slightly at low feed rates. This phenomenon was satisfactorily explained by the presented model. Finally, a method was proposed to determine the appropriate pressure exceeding which no interlayer damage will occur. Additional drilling experiments proved the method effective. This study leads to further understanding of the forming mechanism of interlayer damage and of selecting appropriate parameters in drilling low-stiffness composite/metal stacks.     

Classification of solar wind structures and intense geomagnetic storm alarms with self-organizing maps

Intense geomagnetic storms (D_st < −100 nT) usually occur when a large interplanetary duskward-electric field (with Ey > 5 mV m⁻¹) lasts for more than 3 h. In this article, a self-organizing map (SOM) neural network is used to recognize different patterns in the temporal variation of hourly averaged Ey data and to predict intense storms. The input parameters of SOM are the hourly averaged Ey data over 3 h. The output layer of the SOM has a total of 400 neurons. The hourly Ey data are calculated from solar wind data, which are provided by NSSDC OMNIWeb and ACE spacecraft and contain information on 143 intense storms and a fair number of moderate storms, weak storms and quiet periods between September 3, 1966 and June 30, 2002. Our results show that SOM is able to classify solar wind structures and therefore to give timely intense storm alarms. In our SOM, 21 neurons out of 400 are identified to be closely associated with the intense storms and they successfully predict 134 intense storms out of the 143 ones selected. In particular, there are 14 neurons for which, if one or more of them are present, the occurrence probability of intense storms is about 90%. In addition, several of these 14 neurons can predict big magnetic storm (D_st  −180 nT). In summary, our method achieves high accuracy in predicting intense geomagnetic storms and could be applied in space environment prediction.     

智能结构有限元模型的建立及静态形状控制

本文建立了含有分布压电传感元件和执行元件的板结构的有限元模型，提出了一种新的具有电自由度板弯曲单元，用于分析含有分布传感元件和执行元件的板结构。以此为基础，提出了智能结构静态形状控制的一般方法，即被动控制方法和主动控制方法。最后，提供了两个数值示例，说明本文提出的方法的应用。     

基于模糊神经网络的涡结构识别方法研究

研究气动光学传输效应产生的机理是红外成像末制导的共性基础技术之一,基于涡结构对光学传输效应进行建模是一种非常有效的方法,而涡结构的识别是其必要前提。文中提出一种新的涡结构识别方法,把折射率场经小波变换后的系数矩阵等效为具有一定纹理结构的图像,计算图像的共生矩阵及其统计量,由于涡结构模式复杂,特征量较多,设计了等价结构的模糊神经网络进行涡结构识别。与小波分解后直接提取特征量的识别方法相比,本文的方法从空、频角度更加准确全面地表征湍流涡结构模式,计算机仿真结果表明该方法优于神经网络的识别效率。     

基于压电智能结构飞机座舱振动噪声主动控制研究

针对在某些特殊情况下飞机座盘舱振动、噪声过大的实际情况，本文对其进行了振动、噪声抑制研究。以某飞机座舱模型为研究对象，利用压电智能结构，并结合振动主动控制方法和LMS自适应滤波算法，实现了一套完整的飞机座舱模型振动、噪声主动控制系统。最后本文给出实验结果，证明了此方法对飞机座舱模型振动、噪声抑制的有效性。     

结构随机跳变系统的条件滤波新方法

提出了不完全状态及控制信息的结构随机跳变系统的滤波方法，解决了结构随机跳变系统在控制入与状态的观测互为相关情况下，系统的状态以及不确知的控制输入的最优估计问题。并以线性高斯情况为例进行了数字仿真，证明了该方法的有效性。     

Cylindrical and spherical dust-electron-acoustic shock waves due to dust charge fluctuation

Cylindrical and spherical dust-electron-acoustic (DEA) shock waves propagating in a dusty plasma (containing cold inertial electrons, hot Maxwellian electrons, stationary and streaming ions, and charge fluctuating stationary dust) are theoretically investigated by reductive perturbation method. It is shown that the effect of the dust charge fluctuation introduces some new features in the nonlinear propagation of the DEA waves, particularly the dust charge fluctuation provides a source of dissipation, and is responsible for the formation of the DEA shock structures. It is also found that the basic features of the DEA nonlinear structures are significantly modified by the non-planar (viz. cylindrical and spherical) geometry, and that the height of the cylindrical DEA shock structures are larger than that of the planar DEA shock structures, but smaller than that of the spherical ones. The implications of these results in laboratory dusty plasmas are briefly discussed.