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A Copula-based perturbation stochastic method for fiber-reinforced composite structures with correlations

机译:具有相关性的纤维增强复合材料结构的基于Copula的摄动随机方法

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Under most circumstances, there are correlations among random variables for composite structures, and these correlations can have a considerable effect on the reliability analysis result. Consequently, a Copula-based perturbation stochastic method is proposed to amend the disability of traditional perturbation stochastic method on correlation problems. As a statistical tool, Copula function meets the intrinsic demand of fiber-reinforced composite structures on variable's correlation and allows the choice of joint cumulative distribution functions (CDFs) to be separate from the marginal CDFs. With the aid of change-of-variable technique, Copula is introduced straightly into the framework to describe the relevance without any special transformation. Besides, the probability curves and reliability index of multi-variables of Gaussian and non-Gaussian can be achieved easily. Furthermore, in order to significantly improve the efficiency and capability to resist the mesh distortion compared with the traditional stochastic FEM, strain matrices are reconstructed by the edge-based smoothing technique. The overall uncertain condition coupled by stochastic fiber orientations, material parameters, and external loads is analyzed. Several examples are provided to verify the validity of the method. (C) 2017 Elsevier B.V. All rights reserved.
机译:在大多数情况下,复合结构的随机变量之间存在相关性,这些相关性会对可靠性分析结果产生重大影响。因此,提出了一种基于Copula的摄动随机方法,以解决传统摄动随机方法在相关性问题上的缺陷。作为一种统计工具,Copula函数可以满足纤维增强复合结构对变量相关性的内在需求,并允许将联合累积分布函数(CDF)与边际CDF分开进行选择。借助可变技术,Copula直接引入到框架中以描述相关性,而无需任何特殊转换。此外,可以容易地获得高斯和非高斯多元变量的概率曲线和可靠性指标。此外,与传统的随机有限元法相比,为了显着提高抗网格变形的效率和能力,通过基于边缘的平滑技术重建了应变矩阵。分析了随机纤维取向,材料参数和外部载荷共同作用的总体不确定性条件。提供了几个示例来验证该方法的有效性。 (C)2017 Elsevier B.V.保留所有权利。

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