• Solid Mechanics •

### RESEARCH ON MULTISCALE STOCHASTIC MECHANICAL PROPERTIES PREDICTION OF PLAIN WOVEN CARBON FIBER COMPOSITES1)

Xu Can*,†, Zhu Ping*,†,2), Liu Zhao**, Tao Wei*,†

1. *State Key Laboratory of Mechanical System and Vibration, Shanghai 200240, China;
Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, Shanghai 200240, China;
**School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
• Received:2020-01-02 Accepted:2020-02-24 Online:2020-05-18 Published:2020-03-17

Abstract: Plain woven carbon fiber composites have multi-scale characteristics and spatial randomness in structure. Meanwhile, the mechanical properties of the component materials vary due to different storage conditions, composition phase components and batches. When the stochastic mechanical properties of plain woven carbon fiber composites are predicted with considering of the parameter uncertainty at different scales, there are two main difficulties: first, the large number of random variables makes the accuracy and efficiency of the uncertainty propagation method required; second, a high-precision correlation model is needed to be established because of multi-dimensional correlations. To solve above problems, this paper proposes a multi-scale prediction method based on polynomial chaos expansion and vine Copula for the stochastic mechanical properties of plain woven composites. The random parameters of materials and structures at the microscopic and mesoscopic scales of the plain woven composites are taken into account, and the uncertainties of mechanical properties are studied scale by scale based on the bottom-up hierarchical propagation strategy. In this method, Vine Copula theory is used to construct the multi-dimensional joint probability distribution of correlated random variables, and the non-embedded polynomial chaos expansion is used to realize uncertainty propagation. Results show that the correlation coefficients of the dependence model constructed by the proposed method are almost the same as that of original data and the stochastic prediction of mechanical properties at different scales are realized efficiently and accurately.

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