动力学缩聚模型修正的矩阵逼近法

为避免结构动力学分析中修正有限元模型时的大型稠密矩阵运算,研究了一种用基于矩阵逼近的有限元缩聚模型修正法。根据有限元模型的缩聚和修正,给出了试验模态的扩阶、物理模态到广义模态的映射和矩阵逼近,并讨论了结构存在重频时模态的相关性。算例表明,该法可行。     

具有终端角度和终端时间约束的闭环制导律及其可行性分析

研究了同时具有终端角度(攻击角度)和终端时间(攻击时间)约束的制导问题.通过将非线性运动学制导模型中的自变量由导引时间变换为速度方向角,可以利用最小值原理直接推导出一种闭环形式制导律,而不必引入任何的线性化处理.在该制导律的导引下,导弹能够精确击中目标并且精确满足终端时间和角度的约束.为了研究该制导律的可行性,本文定义并分析了若干重要参数的可行域.该闭环制导律及其可行性分析被应用于多弹齐射攻击的两种情况.数字仿真结果验证了所提出方法的有效性.     

不同密度弹丸对水冰的超高速撞击成坑实验

为了研究冰冻天体表面撞击坑的形成与演化,开展了水冰的超高速撞击成坑实验。使用二级轻气炮发射1.0 mm直径的球形弹丸,以3 km/s、5 km/s和7 km/s速度对圆柱状冰块进行撞击。弹丸材料包括聚碳酸酯和不锈钢两种,冰块温度为253 K。实验观察到了不同弹丸和不同速度条件下,冰块中撞击坑的形貌特征。对撞击坑直径、深度和剖面形状进行了测量,并与文献中铝弹丸对水冰的撞击坑进行了比较分析。获得了水冰撞击坑特征随撞击参数的变化规律,结果表明：撞击坑直径和深度的主导机制不同,坑深主要由弹丸侵彻作用形成,而坑径主要由冰块的剥落所致;坑深比坑径具有更强的对于弹丸密度的依赖性,高密度弹丸撞击坑直径具有比低密度弹丸更强的对于撞击速度的依赖性;撞击坑体积与撞击能量成正比,高密度弹丸形成的撞击坑直径表现出“能量缩比”行为,而低密度弹丸形成的撞击坑直径表现出“动量缩比”行为。     

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。     

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。     

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。     

基于改进Johnson-Cook模型的5083P-0铝合金动态本构关系研究

为研究5083P-0铝合金在高应变率下的力学行为及本构模型,通过RPL100材料试验机和分离式霍普金森压杆(SHPB),对5083P-0铝合金进行了准静态及应变率范围950～3000 s-1的冲击动态压缩实验.结果 显示随着应变率的增加,5083P-0铝合金的屈服强度、流动应力增加,应变硬化率减小,具有应变硬化效应,正...     

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.