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。     

基于欠采样的零阶优化算法

非平衡学习吸引了许多研究者的关注。一般情况下,少数类是更值得关注的,并且其误分类代价要远高于多数类。由于非平衡数据分布的非均衡性,标准的分类算法将难以适用。为了解决非平衡数据分类问题,给出了基于欠采样的零阶优化算法。首先,为了降低数据非平衡分布的影响,针对不同非平衡比的数据集给出了不同的两种采样策略。然后,采用了一种引入间隔均值项的支持向量机(Support vector machine,SVM)优化模型进行分类,并使用带有方差减小的零阶随机梯度下降算法进行求解,提高了算法的精度。在非平衡数据上进行了对比实验,实验证明提出的方法有效提高了非平衡数据的分类效果。     

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。     

转子系统不平衡故障的再现方法浅析

针对转子系统不平衡特征再现设计问题,提出1种基于动力学相似关系的模型再现方法。根据转子系统的振动微分方程,通过积分模拟法和量纲分析法建立转子系统中转轴、转盘、弹性支承和不平衡量的相似关系。根据相似关系,设计了原型不平衡转子系统的动力学相似模型,利用有限元方法仿真验证了所得相似关系的正确性。试验采用最小二乘三点法识别原型与模型不平衡转子试验台的不平衡量,给出再现原型不平衡特征的过程,验证了不平衡特征再现设计方法的有效性。总结了基于动力学相似关系的转子系统不平衡特征再现设计方法。     

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。     

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.     

M数缩尺法旋翼颤振动力相似模型设计

本文以延-2复合材料桨叶旋翼为例,用M数缩尺法尝试设计了缩尺比为1/5的颤振相似模型。设计从旋翼颤振微分方程开始,应用π定理得出10个独立相似准则数,列出各有关物理量缩尺因子后进行模型结构设计,从而得出了若干有益的结论。     

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.