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Fabric Tensor Characterization of Tensor-Valued Directional Data: Solution, Accuracy, and Symmetrization

机译:织物张量的张量定向数据表征:解决方案,准确性和对称化

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摘要

Fabric tensor has proved to be an effective tool statistically characterizing directional data in a smooth and frame-indifferent form. Directional data arising from microscopic physics and mechanics can be summed up as tensor-valued orientation distribution functions (ODFs). Two characterizations of the tensor-valued ODFs are proposed, using the asymmetric and symmetric fabric tensors respectively. The later proves to be nonconvergent and less accurate but still an available solution for where fabric tensors are required in full symmetry. Analytic solutions of the two types of fabric tensors characterizing centrosymmetric and anticentrosymmetric tensor-valued ODFs are presented in terms of orthogonal irreducible decompositions in both two- and three-dimensional (2D and 3D) spaces. Accuracy analysis is performed on normally distributed random ODFs to evaluate the approximation quality of the two characterizations, where fabric tensors of higher orders are employed. It is shown that the fitness is dominated by the dispersion degree of the original ODFs rather than the orders of fabric tensors. One application of tensor-valued ODF and fabric tensor in continuum damage mechanics is presented.
机译:织物张量已被证明是在光滑和帧淡漠形式统计学表征定向数据的有效工具。从微观物理和力学产生定向数据可以被概括为张量值取向分布函数(的ODF)。张量值的ODF的两个表征提出,分别使用非对称和对称织物张量。后来证明是nonconvergent并不太准确,但仍适用于需要在完全对称结构张量的可用的解决方案。这两种类型的织物张量表征中心对称和anticentrosymmetric张量值的ODF的解析解在二维和三维(2D和3D)的空间正交不可约分解的形式来呈现。是在正态分布的随机的ODF进行精度分析来评价两个表征,其中较高的订单织物张量被采用的近似质量。结果表明,健身是由原始的ODF而不是布张量的订单分散度为主。张量值ODF和连续损伤力学织物张量的一个应用被呈现。

著录项

  • 作者

    Kuang-dai Leng; Qiang Yang;

  • 作者单位
  • 年度 2012
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
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