Elasticity Characterization of Particulate Composites: A Computational Evaluation of Topology and Polygon Mesh Finite Element Modelling

2022-28-0500

12/23/2022

Authors Abstract
Content
Predicting the elasticity based on phase characteristics in the area of composite material design is a critical challenge. In comparison with experimental and analytical techniques, there are several advantages of using finite element modelling based microstructure of composite Representative Volume Elements (RVE). Nevertheless; there are some drawbacks to RVE’s traditional geometry-based finite element modelling (GB-FEM), such as the time it takes to build usable modelling and produce substantial finite element meshes. To address these problems, we developed and improved a voxel-based finite element modelling (VB-FEM) method. VB-FEM, unlike GB-FEM, creates a homogeneous grid mesh first and then detects components that correspond to inclusions. The remainders of the stages are identical to those in GB-FEM. In two illustrative numerical instances, GB-FEM and VB-FEM were compared in terms of performance. GB-FEM and VB-FEM were compared in terms of performance. In terms of computational performance, GB-FEM is much more proficient than VB-FEM designed for composites with regularly scattered and large-size defects; however, VB-FEM is significantly more efficient for composites with irregularly scattered and small-size defects. VB-FEM, on the other hand, has a similarly or even greater performance than GB-FEM for composites with distributed evenly and small-size defects. VB-FEM has several of benefits over GB-FEM in addition to solving the aforementioned issues. The results suggest that VB-FEM is an effectual approach for forecasting particle composite elasticity.
Meta TagsDetails
DOI
https://doi.org/10.4271/2022-28-0500
Pages
7
Citation
R, G., N, B., K R, P., and R, S., "Elasticity Characterization of Particulate Composites: A Computational Evaluation of Topology and Polygon Mesh Finite Element Modelling," SAE Technical Paper 2022-28-0500, 2022, https://doi.org/10.4271/2022-28-0500.
Additional Details
Publisher
Published
Dec 23, 2022
Product Code
2022-28-0500
Content Type
Technical Paper
Language
English