4.7 Article

Development of RVE-embedded solid elements model for predicting effective elastic constants of discontinuous fiber reinforced composites

Journal

MECHANICS OF MATERIALS
Volume 93, Issue -, Pages 109-123

Publisher

ELSEVIER
DOI: 10.1016/j.mechmat.2015.10.011

Keywords

Embedded solid elements; Representative volume element (RVE); Discontinuous fiber reinforced composite (DFRC); Random sequential adsorption (RSA) algorithm; Effective elastic constants

Funding

  1. Ford University Research Program

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In this work, an RVE model with embedded solid elements (RVE-ESE) is developed to predict the effective elastic constants of discontinuous fiber reinforced composites (DFRC). Unlike the traditional finite element (FE) modeling, the matrix and fibers can be meshed separately and independently due to the implementation of the embedded element technique, which enables the ease of RVE meshing without strictly satisfying the mesh conformity at the interfaces. Since the structured meshes can be used for the matrix, it becomes quite easy to impose the periodic boundary conditions on the RVE. In addition, a modified random sequential adsorption (RSA) algorithm based on the Boolean operation is proposed to generate the RVE by using the Python script in the ABAQUS/CAE. This algorithm can determine whether a new generated fiber overlaps with the pre-existing ones without looping calculation. Some important issues related to the modeling accuracy, such as the RVE size, the fiber separation effect and the mesh sensitivity of both matrix and fibers, are also investigated. It is found that the proposed model agrees very well with the traditional FE direct meshing approach when the element sizes of the matrix and fibers in the RVE-ESE model are sufficiently fine. Compared with commercial software Digimat, the current model yields relatively higher prediction on the elastic moduli of the composites with randomly distributed fibers. However, for the composites with aligned fibers, Digimat shows lower result for the transverse Young's modulus while a bit higher prediction for the longitudinal one. (C) 2015 Elsevier Ltd. All rights reserved.

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