基于螺旋理论的环形桁架式可展天线构型综合

基于螺旋理论针对环形桁架式可展天线机构的构型综合问题进行研究,首先概述基于螺旋理论的约束综合法,然后分析环形桁架可展天线机构的收展原理,将环形桁架机构分为上下环形边两部分,通过综合环形边机构并连接上下环形边便可得到环形桁架机构;采用移动副(P)来描述各个节点花盘的运动特性,通过基于螺旋理论的约束综合法针对环形边支链机构进行分析与综合,得到了16种约束支链机构,通过分析与优选得到了两种约束支链机构;采用综合得到的约束支链机构组合得到了环形边链路机构并通过连接组合与推演得到了四种环形桁架可展开机构;最后针对综合得到的四种环形桁架机构在Solidworks软件中进行了三维建模与运动仿真,校验了各机构的可展性。本文的研究为可展天线机构的构型综合提供了一种新思路,综合得到的几种环形桁架机构在空间可展开天线领域具有较好的应用前景。     

柔性特征的太阳电池翼在轨载荷分析

建立太阳电池翼地面模态试验修正后的较为精确的动力学模型,以此作为太阳电池翼的柔性特征提出基于混合坐标法的太阳电池翼在轨载荷分析方法,计算了航天器在轨机动、舱段分离、交会对接等典型工况下太阳电池翼的载荷变化情况,分析了不同工况下载荷与太阳电池翼动力学性能的相互关系,提出太阳电池翼为应对不同飞行工况应处于何种转角状态的结构设计建议。最后依据最大载荷工况开展了相关试验验证,试验结果表明本文的研究方法有效合理,可推广应用于其它型号太阳电池翼的在轨载荷设计。     

火星进入器作强迫震荡运动壁面脉动压力数值模拟

为研究超声速阶段进入器作强迫震荡运动对壁面脉动压力环境的影响规律,本文耦合进入器刚体运动方程与流体力学方程,采用动网格技术,对火星进入器模型开展非定常数值模拟,获取壁面不同位置处的脉动压力信息。研究表明：在超声速阶段,进入器作强迫震荡运动诱导的脉动压力远大于进入器保持相对静止时仅由非定常流动诱导的脉动压力。来流马赫数为1.2时,进入器作强迫震荡运动对脱体激波影响较小,脱体激波强度较弱且形态变化较小,攻角的震荡导致同一测点距离脱体激波的位置发生周期性改变,舱体迎风面及配平翼迎风面的脉动压力环境主要受攻角变化的影响;来流马赫数为3时,进入器作强迫震荡运动对脱体激波的影响较大,脱体激波震荡剧烈,诱导舱体迎风面及配平翼迎风面产生极其恶劣的脉动压力环境,功率谱分析表明激波震荡诱导的脉动压力能量主要集中在30 Hz左右。     

非工作状态下电液伺服机构运动分析

针对电液伺服机构安装到运载火箭后存在非工作状态下承受外力并产生被动运动的现状,分析伺服机构的受力情况并构建内部液体流动回路,研究非工作状态下伺服机构在外力作用时的运动特性。发现伺服阀零位特性以及伺服机构高压回路密封性能是影响伺服机构非工作状态下运动特性的主要因素。在外力作用下发生运动时油缸某一侧往往处于抽真空状态。使用零位特性良好的伺服阀以及高压回路泄漏小的伺服机构,在外力作用下将处于双向不动或单向可动的状况。利用典型产品开展了物理试验,验证了结论的有效性。     

对失控翻滚目标逼近的神经网络自适应滑模控制

针对失控航天器在空间中自由翻滚的情况,研究追踪器对失控翻滚目标逼近的位置和姿态六自由度耦合控制问题。建立追踪器与目标器相对运动的姿轨一体化动力学模型,设计追踪器逼近过程的标称轨迹和标称姿态。综合考虑系统不确定性和外部干扰,设计无抖振的神经网络自适应滑模控制器。将滑模控制与神经网络逼近相结合,采用径向基函数(RBF)神经网络对系统未知部分进行自适应逼近。由Lyapunov方法导出神经网络自适应律,通过自适应权重的调节保证整个闭环系统的稳定性。数值模拟实例说明了所设计的标称轨迹和标称姿态的合理性,同时验证了神经网络自适应滑模控制器的有效性。     

高超声速飞行器脆性透波材料大热流冲击下断裂性能试验

高超声速飞行器在俯冲段、高机动变轨或瞬间外露定位探测设备时,快速变化的高热流密度气动热会对天线窗、天线罩等部件产生强烈的热冲击。判断脆性材料透波部件在大热流密度冲击下是否出现断裂破坏及确定断裂时间点,对于高超声速飞行器能否最终锁定并击中目标具有极为重要的意义。本文建立石英灯红外辐射式大热流冲击试验系统,最大冲击热流密度可达1.5 MW/m²,并对SiO₂和Al₂O₃2种脆性材料进行了高速热冲击试验。热流冲击模拟准确,控制结果与预设热流的相对误差小于1.0%。同时,采用数字图像相关方法实时采集热冲击过程中脆性材料表面散斑图像的动态变化,成功捕捉并获得了断裂时间点这一重要关键参数。通过对散斑图像的分析计算,得到了脆性试验件断裂前的表面应变的变化。试验结果为高超声速飞行器透波天线窗等信号探测定位部件在高速大热流热冲击下的安全可靠性设计提供了重要依据。     

Online condition diagnosis for a two-stage gearbox machinery of an aerospace utilization system using an ensemble multi-fault features indexing approach

China manned space station is designed to operate for over ten years. Long-term and sustainable research on space science and technology will be conducted during its operation. The application payloads must meet the ‘‘long life and high reliability" mission requirement. Gearbox machinery is one of the essential devices in an aerospace utilization system, failure of which may lead to downtime loss even during some disastrous catastrophes. A fault diagnosis of gearbox has attracted attentions for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. A novel fault diagnosis method based on the Ensemble Multi-Fault Features Indexing(EMFFI) approach is proposed for the condition monitoring of gearboxes. Different from traditional methods of signal analysis in the one-dimensional space, this study employs a supervised learning method to determine the faults of a gearbox in a two-dimensional space using the classification model established by training the features extracted automatically from diagnostic vibration signals captured. The proposed method mainly includes the following steps. First, the vibration signals are transformed into a bi-spectrum contour map utilizing bi-spectrum technology,which provides a basis for the following image-based feature extraction. Then, Speeded-Up Robustness Feature(SURF) is applied to automatically extract the image feature points of the bi-spectrum contour map using a multi-fault features indexing theory, and the feature dimension is reduced by Linear Discriminant Analysis(LDA). Finally, Random Forest(RF) is introduced to identify the fault types of the gearbox. The test results verify that the proposed method based on the multi-fault features indexing approach achieves the target of high diagnostic accuracy and can serve as a highly effective technique to discover faults in a gearbox machinery such as a two-stage one.     

Experimental investigation on dynamic response of flat blades with underplatform dampers

One test rig with three blades and two Under-Platform Dampers (UPDs) is established to better understand the dynamical behavior of blades with UPDs. A pre-loaded spring is used to simulate the centrifugal load acting on the damper, thereby achieving continuous adjustment of the pressing load. UPDs with different forms, sizes and materials are carefully designed as experimental control groups. Noncontact measurement via a laser Doppler velocimeter is employed and contact excitation which is performed by an electromagnetic exciter is adopted to directly obtain the magnitude of the excitation load by a force sensor mounted on the excitation rod. Particular attention is paid to the influence of the contact status of the contact surfaces, e.g. the pressure-sensitive paper is used to measure the effective contact area of the UPDs. The experimental variables are selected as the centrifugal force, the amplitude of the excitation force, the damper mass, the effective contact area, and the damper material. The Frequency Response Function (FRF) of the blade under different experimental parameters is obtained by slow frequency sweep under sinusoidal excitation to study the influence of each parameter on the dynamic characteristics of the blade and the mechanism analysis is carried out combined with the experimental results.     

Effect of drag reducing riblet surface on coherent structure in turbulent boundary layer

The characteristics of turbulent boundary layer over streamwise aligned drag reducing riblet surface under zero-pressure gradient are investigated using particle image velocimetry. The formation and distribution of large-scale coherent structures and their effect on momentum partition are analyzed using two-point correlation and probability density function. Compared with smooth surface, the streamwise riblets reduce the friction velocity and Reynolds stress in the turbulent boundary layer, indicating the drag reduction effect. Strong correlation has been found between the occurrence of hairpin vortices and the momentum distribution. The number and streamwise length scale of hairpin vortices decrease over streamwise riblet surface. The correlation between number of uniform momentum zones and Reynolds number remains the same as smooth surface.     

基于粒子群优化算法的永磁电机热网络参数识别

监测永磁电机的永磁体温度对于保证电机的使用寿命至关重要,因为过高的温度会产生永磁体不可逆失磁现象。提出了一种基于粒子群优化算法的永磁电机热网络参数识别方法,实现用热网络监测永磁体的温度。该方法首先建立永磁电机的热网络模型,利用粒子群优化算法结合电机温升试验所得温度数据对热网络模型的主要热力参数进行识别;然后利用该热网络模型进行在线温度识别,识别过程能够快速收敛,具备良好的辨识精度;最后,通过对比仿真识别温度和电机温升试验数据,验证了该方法的准确性。