一种鲁棒的概率核主成分分析模型

大数据时代面临的数据维数越来越高,对数据降维处理越发显得重要。经典的主成分分析模型已被证明是一种有效的数据降维方法。但它在处理非线性、存在噪声和异常点的数据时存在效果较差的问题。对此,文章提出了一种鲁棒概率核主成分分析模型。该模型将核方法与基于高斯隐变量模型的极大似然框架相结合,用多元 t分布作为先验分布,以同时解决主成分分析在这 3个方面的弊端。提出混合鲁棒概率核主成分分析模型,使其可直接用于对混合的非线性数据进行降维和聚类分析。在不同数据集上进行的实验结果表明,与标准的混合概率核主成分分析模型相比,文中模型在数据聚类方面有更高的准确率。     

后掠翼三维鼓包串激波控制参数的影响

针对跨声速后掠翼,三维鼓包串作为一种有效的减阻方式具有结构简单、高效及鲁棒性好等优点.利用全局优化算法探索了鼓包设计参数空间的整体特性,并对鼓包长度、三维鼓包展向设计参数对鼓包减阻效果的影响进行了研究,发现鼓包顶点位置和高度对阻力系数最敏感,三维鼓包的展向设计参数则对阻力系数不敏感,而鼓包长度和鼓包相对展长越长越有利于减阻.在此基础上开展了小后掠角自然层流机翼加3种不同类型鼓包串的优化研究,通过优化结果发现,增加优化后的三维鼓包串,可将小后掠角自然层流机翼阻力发散马赫数向后推移,并且鼓包平均长度和控制区越大,效果越好.三维鼓包串具有良好的局部控制特性,可用于局部较强激波的抑制.三维鼓包串对常规后掠翼波阻具有良好的控制效果,同时能够抑制激波诱导的机翼后缘气流分离.      

共轴式直升机桨毂阻力特性与减阻试验

共轴式直升机桨毂阻力占全机废阻的50%以上,因此有必要对桨毂阻力特性和减阻设计进行研究。 通过对某共轴式直升机桨毂模型、减阻方案及减阻方案加装涡流发生器进行风洞试验,研究轴式直升机桨毂的 阻力特性,验证减阻方案及减阻方案加装涡流发生器的减阻效果。结果表明:桨毂支臂方位角、转速和攻角变 化对桨毂及其减阻方案的阻力影响很小;上下桨毂整流罩与中间轴整流罩之间的缝隙对阻力影响比较大;减阻 方案可以降低约33%的桨毂阻力,而加装涡流发生器的减阻效果并不明显。     

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。     

Shock protection of penetrator-based instrumentation via a sublimation approach

It is often necessary for space-borne instrumentation to cope with substantial levels of shock acceleration both in the initial launch phase, as well as during entry, descent and landing in the case of planetary exploration. Current plans for a new generation of penetrator-based space missions will subject the associated on-board instrumentation to far greater levels of shock, and ways must therefore be found to either ruggedize or else protect any sensitive components during the impact phase. In this paper, we present an innovative method of shock protection that is suited for use in a number of planetary environments, based upon the temporary encapsulation of said components within a waxy solid which may then be sublimated to return the instrument back to its normal operation. We have tested this method experimentally using micromachined silicon suspensions under applied shock loads of up to 15,000g, and found that these were able to survive without incurring damage. Furthermore, quality factor measurements undertaken on these suspensions indicate that their mechanical performance remains unaffected by the encapsulation and subsequent sublimation process.     

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.     

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。     

基于特征空间变换的运载火箭Pogo模型降阶方法

针对运载火箭Pogo状态空间模型存在维数高和奇异性的问题,开展了Pogo模型的降阶方法研究。基于特征空间变换理论,导出了一般形式的Pogo状态空间模型的解耦形式。该形式与推进系统和结构系统的模态频率相对应,从而通过模态截断或保留感兴趣的模态实现有效的模型降阶。同时,还给出了降阶方法的实数运算公式。在两种不同型号推进系统的火箭Pogo问题中进行了仿真校验。结果表明所提出的降阶方法对奇异和非奇异Pogo状态空间模型都能给出正确的降阶模型,显著地提高计算效率,具有很好的通用性,为Pogo时域仿真和主动抑制提供了合适的模型。     

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.     

An artificial neural network approach for aerodynamic performance retention in airframe noise reduction design of a 3D swept wing model

With the progress of high-bypass turbofan and the innovation of silencing nacelle in engine noise reduction, airframe noise has now become another important sound source besides the engine noise. Thus, reducing airframe noise makes a great contribution to the overall noise reduction of a civil aircraft. However, reducing airframe noise often leads to aerodynamic perfor-mance loss in the meantime. In this case, an approach based on artificial neural network is intro-duced. An established database serves as a basis and the training sample of a back propagation (BP) artificial neural network, which uses confidence coefficient reasoning method for optimization later on. Then the most satisfactory configuration is selected for validating computations through the trained BP network. On the basis of the artificial neural network approach, an optimization pro-cess of slat cove filler (SCF) for high lift devices (HLD) on the Trap Wing is presented. Aerody-namic performance of both the baseline and optimized configurations is investigated through unsteady detached eddy simulations (DES), and a hybrid method, which combines unsteady DES method with acoustic analogy theory, is employed to validate the noise reduction effect. The numerical results indicate not merely a significant airframe noise reduction effect but also excel-lent aerodynamic performance retention simultaneously.