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Cameron Talischi Glaucio H. Paulino Chau H. Le 《Structural and Multidisciplinary Optimization》2009,37(6):569-583
Traditionally, standard Lagrangian-type finite elements, such as linear quads and triangles, have been the elements of choice
in the field of topology optimization. However, finite element meshes with these conventional elements exhibit the well-known
“checkerboard” pathology in the iterative solution of topology optimization problems. A feasible alternative to eliminate
such long-standing problem consists of using hexagonal (honeycomb) elements with Wachspress-type shape functions. The features
of the hexagonal mesh include two-node connections (i.e. two elements are either not connected or connected by two nodes),
and three edge-based symmetry lines per element. In contrast, quads can display one-node connections, which can lead to checkerboard;
and only have two edge-based symmetry lines. In addition, Wachspress rational shape functions satisfy the partition of unity
condition and lead to conforming finite element approximations. We explore the Wachspress-type hexagonal elements and present
their implementation using three approaches for topology optimization: element-based, continuous approximation of material
distribution, and minimum length-scale through projection functions. Examples are presented that demonstrate the advantages
of the proposed element in achieving checkerboard-free solutions and avoiding spurious fine-scale patterns from the design
optimization process. 相似文献
104.
This paper consists of two parts. In the first, more theoretic part, two Wiener systems driven by the same Gaussian noise excitation are considered. For each of these systems, the best linear approximation (BLA) of the output (in mean square sense) is calculated, and the residuals, defined as the difference between the actual output and the linearly simulated output is considered for both outputs. The paper is focused on the study of the linear relations that exist between these residuals. Explicit expressions are given as a function of the dynamic blocks of both systems, generalizing earlier results obtained by Brillinger [Brillinger, D. R. (1977). The identification of a particular nonlinear time series system. Biometrika, 64(3), 509-515] and Billings and Fakhouri [Billings, S. A., & Fakhouri, S. Y. (1982). Identification of systems containing linear dynamic and static nonlinear elements. Automatica, 18(1), 15-26]. Compared to these earlier results, a much wider class of static nonlinear blocks is allowed, and the efficiency of the estimate of the linear approximation between the residuals is considerably improved. In the second, more practical, part of the paper, this new theoretical result is used to generate initial estimates for the transfer function of the dynamic blocks of a Wiener-Hammerstein system. This method is illustrated on experimental data. 相似文献
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106.
随着计算机和互联网的迅猛发展,面向互联网的社会网络挖掘和分析成为一个新的课题。从互联网挖掘的社会网络往往规模巨大,这对网络分析算法的性能提出了更高的要求 。介数值作为图的重要结构性质,广泛应用于基于图的聚类、分类算法,如何降低其计算的复杂性是急需解决的问题。目前,常用的方法是利用对最短路径长度的近似来降低低网络分析算法的复杂性,但已有的近似方法没有考虑现实大规模网络的复杂网络特性,对最短路径长度的近似方 近似计算方法,其基本思想是结合复杂网络的结构特性,利用通过网络中枢节点的路径来近似最短路径,以近似的最短路径求得介数的近似值。这为图的结构性质的近似估算算提供了一种新颖的思路。通过与传统的介数计算方法和近的分析得到了若干有益的结论,为进一步的研究工作奠定了基础。 相似文献
107.
本文基于正交函数逼近方法,借助于小波变换,并利用其运算矩阵及其运算性质,研究了分布参数系统的辨识问题。将Haar小波正交基应用于分布参数系统的辨识中,经正交小波逼近变换,将原偏微分描述的分布参数系统转化为代数矩阵方程,并且,考虑了初始条件和边界条件,获得了算法简单、计算方便、具有较高精度的辨识算法,简化了分布参数系统辨识的求解过程,应用在分布参数系统辨识中不失为一种有效的分析方法。仿真实例表明了本文所提出的算法的有效性。 相似文献
108.
现有的多视角谱聚类算法大多只线性结合了各视角的基拉普拉斯矩阵,未考虑不同视角数据的差异性对最优拉普拉斯矩阵的影响,存在聚类性能受限的问题。提出一种基于黎曼几何均值与高阶拉普拉斯矩阵的谱聚类算法(RMMSC),挖掘多视角数据中的高阶连接信息与流形信息,提高最优拉普拉斯矩阵对各视角的信息利用率。按一定的权重线性结合数据单一视角的各阶拉普拉斯矩阵,得到每个视角的基拉普拉斯矩阵,通过低阶与高阶连接信息的结合使用,充分体现多视角数据集的全局结构。在此基础上,计算各视角基拉普拉斯矩阵的黎曼几何均值,将其作为最优拉普拉斯矩阵输入谱聚类算法,得到聚类结果。相比于传统矩阵算数均值的计算,基于黎曼流形的黎曼几何均值能够更好地恢复互补层数据的流形信息。实验结果表明,RMMSC在多组标准数据集上聚类效果优于ONMSC、MLAN、AMGL等算法。其中,在Flower17数据集上,精确度较基准算法ONMSC提高了2.14%,纯度提高了1.7%,且收敛性较好。 相似文献
109.
Jine Zhang Xiaobing Chen Mengqin Wang Qinghua Zhang Wenxiao Shi Xiaozhi Zhan Meng Zhao Zhe Li Jie Zheng Hui Zhang Furong Han Huaiwen Yang Tao Zhu Banggui Liu Fengxia Hu Baogen Shen Yuansha Chen Yue Zhang Yunzhong Chen Weisheng Zhao Jirong Sun 《Advanced functional materials》2023,33(41):2306434
Ferromagnetic materials with a strong spin-orbit coupling (SOC) have attracted much attention in recent years because of their exotic properties and potential applications in energy-efficient spintronics. However, such materials are scarce in nature. Here, a proximity-induced paramagnetic to ferromagnetic transition for the heavy transition metal oxide CaRuO3 in (001)-(LaMnO3/CaRuO3) superlattices is reported. Anomalous Hall effect is observed in the temperature range up to 180 K. Maximal anomalous Hall conductivity and anomalous Hall angle are as large as ∼15 Ω−1 cm−1 and ∼0.93%, respectively, by one to two orders of magnitude larger than those of the typical 3d ferromagnetic oxides such as La0.67Sr0.33MnO3. Density functional theory calculations indicate the existence of avoid band crossings in the electronic band structure of the ferromagnetic CRO layer, which enhances Berry curvature thus strong anomalous Hall effects. Further evidences from polarized neutron reflectometry show that the CaRuO3 layers are in a fully ferromagnetic state (∼0.8 μB/Ru), in sharp contrast to the proximity-induced canted antiferromagnetic state in 5d oxides SrIrO3 and CaIrO3 (∼0.1 μB/Ir). More than that, the magnetic anisotropy of the (001)-(LaMnO3/CaRuO3) superlattices is eightfold symmetric, showing potential applications in the technology of multistate data storage. 相似文献
110.
The performance of modern control methods, such as model predictive control, depends significantly on the accuracy of the system model. In practice, however, stochastic uncertainties are commonly present, resulting from inaccuracies in the modeling or external disturbances, which can have a negative impact on the control performance. This article reviews the literature on methods for predicting probabilistic uncertainties for nonlinear systems. Since a precise prediction of probability density functions comes along with a high computational effort in the nonlinear case, the focus of this article is on approximating methods, which are of particular relevance in control engineering practice. The methods are classified with respect to their approximation type and with respect to the assumptions about the input and output distribution. Furthermore, the application of these prediction methods to stochastic model predictive control is discussed including a literature review for nonlinear systems. Finally, the most important probabilistic prediction methods are evaluated numerically. For this purpose, the estimation accuracies of the methods are investigated first and the performance of a stochastic model predictive controller with different prediction methods is examined subsequently using multiple nonlinear systems, including the dynamics of an autonomous vehicle. 相似文献