Abstract
A statistical analysis of the yarn parameters of a plain woven carbon fiber reinforced polymer composite was conducted using X-ray micro-computed tomography data. An algorithm based on the correlated Gaussian random sequence was proposed to construct statistically equivalent yarns, which were introduced into a numerical multiscale model. A representative volume element was created to evaluate the macroscopic elastic properties of the composite. The predicted elastic constants showed a good agreement with experimental data obtained from tensile, compressive, and shear tests. This showed the importance of considering internal geometric variability for obtaining accurate simulation results. Finally, the performance of an electric vehicle back door made of the composite material was calculated by finite element analysis. The weight of the back door system was reduced by 47.45%, and performance results showed an excellent prospect of using lightweight composites.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 147-157 |
| Number of pages | 11 |
| Journal | Automotive Innovation |
| Volume | 1 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 1 2018 |
Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 11372181, 11772191, 51705312) and the China Postdoctoral Science Foundation (Grant No. 2017M61156). The authors acknowledge the support provided by Shanghai Jiao Tong University to Prof. Wei Chen.
Keywords
- CFRP back door
- Multiscale modeling
- Plain woven CFRP
- Statistical reconstruction
- X-ray micro-CT
ASJC Scopus subject areas
- Automotive Engineering