基于降维算法和等效杆长的可展结构精度分析

考虑铰链间隙和杆件尺寸误差的不确定性并通过概率统计方法对其进行研究,提出了一种基于单变量降维算法(UDRM)和等效杆长模型的可展结构精度分析方法。利用UDRM将可展结构的精度性能函数解耦为多个杆件尺寸误差的独立作用形式,建立精度分析模型。引入等效杆长模型,等效杆件替代原杆件进行精度计算。将铰链间隙与原始杆件尺寸误差合并到等效杆件的尺寸误差中,同时证明了等效杆长尺寸误差近似服从正态分布。以某卫星可展开天线为算例,结合高斯求积公式求解展开状态下精度指标的分布期望和方差。通过与蒙特卡罗模拟(MCS)和一次二阶矩(FOSM)法计算结果的对比验证了本文精度分析方法的正确性和高效性。     

金属膜盘联轴器轴向振动特性研究

为研究金属膜盘联轴器的轴向振动特性,分别通过有限元计算和试验方法分析了金属膜盘联轴器的轴向拉伸刚度、轴向压缩刚度和轴向固有频率。根据分析结果,对某型膜盘联轴器开展了在运转条件下的轴向振动测试。结果表明:金属膜盘联轴器的轴向拉伸刚度和压缩刚度均呈现明显的非线性特性。在某型联轴器的工作转速范围内存在轴向振动固有频率,但仅在使用转速激励,且在联轴器的轴向固有频率下运转,联轴器才不会发生轴向共振。     

基于强度退化的齿轮可靠性计算模型研究及应用

为了研究强度退化对齿轮可靠性的影响,针对一般工作环境下具有多种失效模式的齿轮,通过引入条件概率,考虑零件失效相关性和材料性能退化因素,利用动态应力-强度干涉模型建立基于强度退化的齿轮可靠性的计算模型。以某型齿轮可靠度计算为例,其计算结果表明:在考虑材料强度退化时,齿轮的可靠度随工作时间的增加不断降低,验证了该计算模型的准确性。     

基于神经网络模型的襟翼主动控制旋翼减振分析

基于径向基函数(Radial basis function,RBF)神经网络构建了一种带后缘襟翼主动控制(Active controlled flap,ACF)的旋翼振动载荷计算模型。采用正交试验方法确立RBF网络训练样本的输入,在CAMRAD II中计算前飞状态下与训练样本对应的旋翼桨毂六力素,并将主通过频率下的分量作为样本输出,对RBF网络进行离线训练。在此基础上采用多周控制器对被控模型进行振动载荷主动控制。随后以2桨叶4m直径ACF旋翼为例,构建了其桨毂减振分析方法,并对桨毂动载荷各分量的减振效果进行了分析。研究表明,采用正交样本训练的RBF网络能够精确映射襟翼偏角与桨毂振动载荷的非线性关系,施加多周控制后,桨毂垂向振动载荷降低接近50%,其他方向的振动载荷也有不同程度的降低。     

橡胶减振垫在航天器中的应用及验证

文章针对某航天器振动试验过程中出现的自锁阀及其安装支架振动响应放大问题,提出了一种采用橡胶减振垫的解决措施,给出了增加减振垫后设备安装的刚度模型及其分析,并通过振动试验验证了这种方法的有效性。试验结果表明:采用橡胶减振垫可以较好地解决部分航天器上仪器设备在振动环境条件下响应超标的问题,措施简单有效,满足使用要求,具有一定的推广应用价值。     

Bush有限单元原理及其在航天器结构建模中的应用

利用大量的数值计算确定了MSC.NASTRAN软件中广义弹簧单元（Bush）刚度阵的解析表达式,通过对比Bush元与剪切梁元的刚度阵,推导了Bush元参数与剪切梁刚度参数的对应关系,并利用展开状态的太阳翼结构和大型桁架结构的静力和动力分析验证了推导结果。该研究揭示了Bush有限单元的原理和所有输入参数的物理含义,有助于Bush元在结构有限元分析中的准确使用。     

基于变刚度变阻尼的柔性喷管动态模型

针对柔性接头动态迟滞曲线受控制系统控制位置精度和动态响应速度影响较大的问题,基于电液伺服机构和柔性接头变刚度变阻尼模型,构建了柔性喷管的电液伺服机构-变刚度变阻尼模型,将其和电液伺服机构-定刚度定阻尼模型进行了对比。分析了电液伺服机构主要参数、柔性接头工作参数等对电液伺服机构-柔性接头系统动态特性的影响。分析结果表明:电液伺服机构-变刚度变阻尼模型所构造的迟滞曲线可更准确地与实验结果相吻合,并符合迟滞曲线随频率变化的规律,反馈系数、放大器静态放大系数、电液伺服机构增益、滑阀流量增益等参数对系统动态特性的影响更为明显。该模型为固体火箭发动机电液伺服机构-柔性接头系统动态特性的调整提供理论依据。     

细长杆降低超声速客机气动噪声的数值分析

在超声速客机机头加装适当的细长杆可以降低飞行噪声。本文借助基于AUSM+格式的准三维数值模拟手段对7组13种细长杆方案做了分析比较,总结出了近场气体参数与远场气体参数之间的关系的规律。文章还从气体动力学理论和激波理论出发,对数值模拟结果作了解释,并揭示了细长杆降噪效果与细长杆外形的内在联系,以及近场、远场两者关系的经验公式。文章从各种方案中选取了降噪效果较好的细长杆方案做三维流场分析,并通过流固耦合计算对其结构强度做了校核,验证了方案的可行性。     

Global aerodynamic design optimization based on data dimensionality reduction

In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However, for complex problems in which large number of design variables are needed, the computational cost becomes prohibitive, and thus original global optimization strategies are required. To address this need, data dimensionality reduction method is combined with global optimization methods, thus forming a new global optimization system, aiming to improve the efficiency of conventional global optimization. The new optimization system involves applying Proper Orthogonal Decomposition (POD) in dimensionality reduction of design space while maintaining the generality of original design space. Besides, an acceleration approach for samples calculation in surrogate modeling is applied to reduce the computational time while providing sufficient accuracy. The optimizations of a transonic airfoil RAE2822 and the transonic wing ONERA M6 are performed to demonstrate the effectiveness of the proposed new optimization system. In both cases, we manage to reduce the number of design variables from 20 to 10 and from 42 to 20 respectively. The new design optimization system converges faster and it takes 1/3 of the total time of traditional optimization to converge to a better design, thus significantly reducing the overall optimization time and improving the efficiency of conventional global design optimization method.     

A salient edges detection algorithm of multi-sensor images and its rapid calculation based on PFCM kernel clustering

Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.