A computationally efficient coupled multi-scale model for short fiber reinforced composites
Journal article, 2022

A coupled multi-scale (macro–micro) model is developed to predict non-linear elasto-plastic behavior of short fiber reinforced composites. At the microscopic level, a recently proposed micro-mechanical model, developed based on a two-step orientation averaging approach, is used. A wide range of micro-structural parameters, including matrix and fiber constitutive parameters, fiber volume fraction and fiber aspect ratio, can be accommodated in the model. Different interactions including Voigt, Reuss and a self-consistent assumption are considered in the model. This micro-mechanical model is then incorporated in a Finite Element model of the macro-scale problem, enabling coupled macro–micro simulations of real-life structures/specimens. Numerical examples and comparisons with experimental data, taken from literature, show that the model gives good predictions. Besides, several strategies and techniques are employed to improve the computational efficiency of the model. These techniques include replacing originally utilized trapezoidal integration (for fiber orientations and calculation of the Eshelby tensor) with more efficient integration schemes, and using a more efficient method for data storage. Comparisons of the computational efforts shows that these improvements substantially decreased the computational cost of the model.

Mechanical behavior

Coupled multi-scale modeling

Short fiber reinforced composites

Finite Element Method

Orientation averaging

Author

B. A. Castricum

Eindhoven University of Technology

Martin Fagerström

Chalmers, Industrial and Materials Science, Material and Computational Mechanics

Magnus Ekh

Chalmers, Industrial and Materials Science, Material and Computational Mechanics

Fredrik Larsson

Chalmers, Industrial and Materials Science, Material and Computational Mechanics

S. M. Mirkhalaf

University of Gothenburg

Composites Part A: Applied Science and Manufacturing

1359-835X (ISSN)

Vol. 163 107233

Subject Categories

Applied Mechanics

Bioinformatics (Computational Biology)

Control Engineering

DOI

10.1016/j.compositesa.2022.107233

More information

Latest update

10/26/2023