周期性复合材料构型及结构一体化优化

在传统双向渐进结构优化(BESO)方法基础上,充分考虑微观尺度和宏观尺度之间的相互耦合作用,通过等效弹性模量和灵敏度分析将复合材料微结构胞元设计和宏观结构拓扑优化相结合,建立周期性复合材料构型及结构一体化优化设计方法.为了消除“灰色区域”,假设了材料微结构0 1属性及在宏观结构空间排列的均一性,提高了优化结果的实际工程适用性.相关算例说明该方法可以有效地在宏观结构优化的同时得到与之相对应的材料微观最佳拓扑形状,也为不同给定宏观结构的微观周期性复合材料设计提供了有效手段.     

Topology Optimization Design of Heat Convection Problems With Variable-Density Cells北大核心CSCD

为获得优异的散热结构设计,发展了一种基于腐蚀-扩散算子的变密度胞元层级结构设计方法.通过腐蚀-扩散算子得到了一系列拓扑相似但体积分数不同的变密度微结构,计算并拟合得到变密度微结构等效热传导系数曲线.在此基础上,采用移动渐近线法更新宏观设计变量,将变密度微结构植入相应体积分数的宏观单元中完成装配.通过数值算例对不同优化方法下温度场的热柔顺度、平均温度、方差等参数进行了比较分析,结果表明,变密度胞元层级结构比传统单尺度胞元结构和周期胞元结构具有更好的散热性能.     

飞行器尾涡对的不稳定性建模

为了确定飞行器尾流的保持距离和诱导失稳运动性质,首先在一阶近似Biot-Savart定律的基础上,推导了任意多个涡对的诱导运动模型,进而利用线性组合方法得到涡系诱导运动的对称以及反对称模态,并结合模态矩阵特征值的性质描述对称分布涡系的稳定性.因为尾涡结构的不稳定性依赖于相应的模态矩阵特征值的取值,所以在利用对称分布的二涡对的模态验证所推导的模态矩阵理论的正确性的基础上,进一步给出了三涡对的模态矩阵对应的失稳模态.理论推导和特征值的计算显示随着涡丝数量的不断增加,三涡系的不稳定性增强,并且涡系对扰动的放大作用增强.     

基于变体积约束的阻尼材料微结构拓扑优化研究北大核心CSCD

阻尼复合结构的抑振性能取决于材料布局和阻尼材料特性.该文提出了一种变体积约束的阻尼材料微结构拓扑优化方法,旨在以最小的材料用量获得具有期望性能的阻尼材料微结构.基于均匀化方法,建立阻尼材料三维微结构有限元模型,得到阻尼材料的等效弹性矩阵.逆用Hashin-Shtrikman界限理论,估计对应于期望等效模量的阻尼材料体积分数限,并构建阻尼材料体积约束限的移动准则.将获得阻尼材料微结构期望性能的优化问题转化为体积约束下最大化等效模量的优化问题,建立阻尼材料微结构的拓扑优化模型.利用优化准则法更新设计变量,实现最小材料用量下的阻尼材料微结构最优拓扑设计.通过典型数值算例验证了该方法的可行性和有效性,并讨论了初始微构型、网格依赖性和弹性模量等对阻尼材料微结构的影响.     

基于LiToSim平台的海上风机过渡段优化软件开发

针对海上风机过渡段结构,考虑风机多尺度优化模型和所受环境荷载采取极端情况下,引入双向渐进结构拓扑优化方法,以全局应力最小化为目标、体积为约束,对风机过渡段进行优化设计;并在自主研发的LiToSim平台基础上,嵌入风机优化数值计算程序,最终形成一款关于海上风机过渡段拓扑优化的定制化软件TUR/TOPT.借助定制化软件TUR/TOPT平台,对比过渡段传统柔度优化与应力优化结果,突显出应力优化在减材设计过程中结构应力明显降低且能有效避免应力集中等方面的优势;TUR/TOPT软件的生成在风机建设选型过程中具有重要指导价值.     

Archimedean copula刻画的尺度比例失效率模型的极小次序统计量的随机序北大核心CSCD

在多元链式优化序下,该文研究了两组来自于不同相依尺度比例失效率分布的最小次序统计量的随机比较.在某种数学意义下,一个由尺度比例失效率分布的不同脆弱参数和尺度参数构成的矩阵变化到另一个矩阵时,该文研究了在一定的条件下,来自于第一个尺度比例失效率分布的最小次序统计量在普通随机序下小于变化到的参数矩阵对应的尺度比例失效率分布的最小次序统计量.该文也给出了一些数值例子来说明得到的结果的正确性.     

补图是树的图的距离拉普拉斯特征值（英文）

一个连通图的距离拉普拉斯矩阵定义为顶点传输度对角矩阵与距离矩阵的差,距离拉普拉斯矩阵的特征值称为这个图的距离拉普拉斯特征值.距离拉普拉斯伸展度定义为图的最大与次小距离拉普拉斯特征值的差.本文确定了补图的最大距离拉普拉斯特征值取得最小值和最大值的树及补图的次小距离拉普拉斯特征值取得最小值和最大值的树,也确定了补图的次大距离拉普拉斯特征值取得最小值的树,还确定了补图的距离拉普拉斯伸展度取得最小值和最大值的树.     

一类特殊矩阵的极值特征值反问题

针对矩阵特征值反问题,如何构造矩阵显得尤为重要,鉴于此,引入一种新的带比例关系矩阵.结果表明,只需利用其顺序主子阵的最小和最大特征值即可反构原矩阵,同时亦总结了矩阵元素与顺序主子阵特征值的关系.     

关于几类矩阵的特征值分布

<正> 在矩阵论中以及应用矩阵工具的各类问题中,估计矩阵的特征值大小与分布十分重要.在[1]中,我们给出了非负矩阵(元素全非负的矩阵)最大特征值的计算与估计方法,并将此结果推广到更广的一类矩阵.在本文中,我们将对实用中几类重要矩阵给出它们特征值分布的估计.     

以代数方法探讨结构系统再设计问题

利用矩阵修改理论探讨结构系统再设计问题,以等惯性转换求解动态劲度矩阵的隐根,并导出将特征值定位的计算方法;继而在隐根为已知下探讨隐向量的特质及解法,并确认修改后结构的振型必须区分成驻留性与非驻留性自然频率等两种状况处理.     

Finding the Maximum Eigenvalue of Essentially Nonnegative Symmetric Tensors via Sum of Squares Programming

Finding the maximum eigenvalue of a tensor is an important topic in tensor computation and multilinear algebra. Recently, for a tensor with nonnegative entries (which we refer it as a nonnegative tensor), efficient numerical schemes have been proposed to calculate its maximum eigenvalue based on a Perron–Frobenius-type theorem. In this paper, we consider a new class of tensors called essentially nonnegative tensors, which extends the concept of nonnegative tensors, and examine the maximum eigenvalue of an essentially nonnegative tensor using the polynomial optimization techniques. We first establish that finding the maximum eigenvalue of an essentially nonnegative symmetric tensor is equivalent to solving a sum of squares of polynomials (SOS) optimization problem, which, in its turn, can be equivalently rewritten as a semi-definite programming problem. Then, using this sum of squares programming problem, we also provide upper and lower estimates for the maximum eigenvalue of general symmetric tensors. These upper and lower estimates can be calculated in terms of the entries of the tensor. Numerical examples are also presented to illustrate the significance of the results.     

Reid-Zhi符号数值混合消元方法的一个应用

将Reid和Zhi提出的符号数值混合消元方法应用于求解多项式优化问题,将多项式优化问题转化为矩阵最小特征值求解问题,并在Maple软件中实现了算法.     

A dynamical method of DAEs for the smallest eigenvalue problem

This article gives a new method based on the dynamical system of differential-algebraic equations for the smallest eigenvalue problem of a symmetric matrix. First, the smallest eigenvalue problem is converted into an equivalent constrained optimization problem. Second, from the Karush–Kuhn–Tucker conditions for this special equality-constrained problem, a special continuous dynamical system of differential-algebraic equations is obtained. Lastly, based on the implicit Euler method and an analogous trust-region technique, we obtain a prediction-correction method to compute a steady-state solution of this special system of differential-algebraic equations, and consequently obtain the smallest eigenvalue of the original problem. We also analyze the local superlinear property for this new method, and present the promising numerical results, in comparison with other methods.     

Structure preserving eigenvalue embedding for undamped gyroscopic systems

This paper concerns the eigenvalue embedding problem of undamped gyroscopic systems. Based on a low-rank correction form, the approach moves the unwanted eigenvalues to desired values and the remaining large number eigenvalues and eigenvectors of the original system do not change. In addition, the symmetric structure of mass and stiffness matrices and the skew-symmetric structure of gyroscopic matrix are all preserved. By utilizing the freedom of the eigenvectors, an expression of parameterized solutions to the eigenvalue embedding problem is derived. Finally, a minimum modification algorithm is proposed to solve the eigenvalue embedding problem. Numerical examples are given to show the application of the proposed method.     

陀螺特征值问题的并行子空间迭代法

<正>1引言陀螺系统特征值问题是转子动力学中的基本问题,是一类特殊的二次特征值问题.假设M和K是n阶对称矩阵,C是n阶反对称矩阵,则二次特征值问题(λ~2M+λC+K)x=0(1)     

Penalized Covariance Matrix Estimation Using a Matrix-Logarithm Transformation

For statistical inferences that involve covariance matrices, it is desirable to obtain an accurate covariance matrix estimate with a well-structured eigen-system. We propose to estimate the covariance matrix through its matrix logarithm based on an approximate log-likelihood function. We develop a generalization of the Leonard and Hsu log-likelihood approximation that no longer requires a nonsingular sample covariance matrix. The matrix log-transformation provides the ability to impose a convex penalty on the transformed likelihood such that the largest and smallest eigenvalues of the covariance matrix estimate can be regularized simultaneously. The proposed method transforms the problem of estimating the covariance matrix into the problem of estimating a symmetric matrix, which can be solved efficiently by an iterative quadratic programming algorithm. The merits of the proposed method are illustrated by a simulation study and two real applications in classification and portfolio optimization. Supplementary materials for this article are available online.     

Unified Gradient Approach to Performance Optimization Under a Pole Assignment Constraint

General closed-loop performance optimization problems with pole assignment constraint are considered in this paper under a unified framework. By introducing a free-parameter matrix and a matrix function based on the solution of a Sylvester equation, the constrained optimization problem is transformed into an unconstrained one, thus reducing the problem of closed-loop performance optimization with pole placement constraint to the computation of the gradient of the performance index with respect to the free-parameter matrix. Several classical performance indices are then optimized under the pole placement constraint. The effectiveness of the proposed gradient method is illustrated with an example.     

A note on “Methods for constructing distance matrices and the inverse eigenvalue problem”

In this paper, a symmetric nonnegative matrix with zero diagonal and given spectrum, where exactly one of the eigenvalues is positive, is constructed. This solves the symmetric nonnegative eigenvalue problem (SNIEP) for such a spectrum. The construction is based on the idea from the paper Hayden, Reams, Wells, “Methods for constructing distance matrices and the inverse eigenvalue problem”. Some results of this paper are enhanced. The construction is applied for the solution of the inverse eigenvalue problem for Euclidean distance matrices, under some assumptions on the eigenvalues.     

On minimax eigenvalue problems via constrained optimization

In this paper, we consider the problem of minimizing the maximum eigenvalues of a matrix. The aim is to show that this optimization problem can be transformed into a standard nonlinearly constrained optimization problem, and hence is solvable by existing software packages. For illustration, two examples are solved by using the proposed method.     

On inverse eigenvalue problems for block Toeplitz matrices with Toeplitz blocks

We propose an algorithm for solving the inverse eigenvalue problem for real symmetric block Toeplitz matrices with symmetric Toeplitz blocks. It is based upon an algorithm which has been used before by others to solve the inverse eigenvalue problem for general real symmetric matrices and also for Toeplitz matrices. First we expose the structure of the eigenvectors of the so-called generalized centrosymmetric matrices. Then we explore the properties of the eigenvectors to derive an efficient algorithm that is able to deliver a matrix with the required structure and spectrum. We have implemented our ideas in a Matlab code. Numerical results produced with this code are included.