高速异步电主轴电机的电磁噪声分析

采用有限元法分析了高速异步电主轴电机定子结构的动力学特性(振型、固有频率),分别采用解析法和有限元法分析了电机在额定转速60 000 r/min下空载和负载的电磁力波,利用傅里叶变换对径向气隙磁密和径向电磁力波进行了谐波分析,得到了不同阶次不同频率下的谐波幅值。还将得到的电磁力波加载到电机定子相应节点处,对高速异步电主轴电机进行了电磁振动和声场分析,并计算出电机在空载和负载情况下产生的电磁噪声。     

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。     

基于形貌优化法的无轴承开关磁阻电机减振降噪

脉振的径向电磁力作为激励源作用于12/8极单绕组宽转子齿无轴承开关磁阻电机（Bearingless switched reluctance motor with wider rotor teeth,BSRMWR）的定子齿面并传送至定子轭部及机壳,会引发较大的振动噪声,阻碍其推广应用。针对这一问题,本文从本体结构的角度入手对电机壳体进行优化改进。采用三维多物理场有限元模型,建立了BSRMWR电磁-结构-振动-声场耦合模型。通过对BSRMWR电磁场进行瞬态分析,得到径向电磁力。将模态应变能方法应用于BSRMWR的壳体得到电机外壳结构有较大的应变能,说明电机壳体结构的薄弱。基于此,通过形貌优化的方法对电机的壳体结构进行优化。结果表明,采用形貌优化后机壳结构的BSRMWR,其振动和噪声均有显著改善。     

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。     

基于循环平稳性的噪声调频干扰高分辨测向方法

根据噪声调频信号循环平稳特性分析,提出了基于阵列接收信号循环均值-空域处理的波达方向(DOA)估计算法。仿真结果表明:该算法的角度估计性能明显高于比幅比相单脉冲测角法,且在高信噪比环境中,该法的估计性能更好、运算量较小。     

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.     

风扇进口噪声预测模型的改进

针对Heidmann模型对风扇进口低频噪声(1k Hz)预测结果偏低的情况,通过引入风扇叶尖弦长雷诺数和相关几何参数改进了Heidmann模型中风扇进口宽频噪声的频谱修正函数,确定了其中频谱修正系数为0.085。对比改进模型和Heidmann模型的风扇进口噪声预测结果发现:在亚声速工况下,改进的模型很好地改善了风扇进口低频噪声的预测结果,改进幅值可以达到约45d B;而在超声速工况下,由于风扇进口产生的组合单音噪声成为风扇进口噪声的主要组成部分,除了在小于100Hz的频域内,改进的模型与Heidmann模型的预测结果差别不大。     

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.     

一种基于机动辨识预测的空空导弹导引律

现有的空空导弹导引头在有噪声和干扰的环境下获得目标精确信息存在时间延迟,且新一代目标的机动能力更强,不对导弹加以补偿会造成较大脱靶量,所以需对目标状态有效预测。针对新一代目标规避空空导弹常用的大机动模式,为满足新一代空空导弹发展需求,设计了一种新型复合导引律。从目标自身出发,研究高机动目标规避导弹采用的典型机动形式,对机动轨迹进行离线建模,构建具有扩展能力的目标机动模型库。设计自适应滤波器对测量噪声进行降噪。同时,利用模型库设计了机动辨识预测器,对目标实际机动进行在线辨识。基于在线辨识的结果对目标机动进行预测,并对时间延迟进行补偿和修正,实现对高机动目标的精确打击。仿真结果表明:该方法对不同类型的机动目标均有较高的预测精度和命中精度。