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Crashworthiness of bionic fractal hierarchical structures
Abstract
Abstract This paper presents a fractal hierarchical hexagon structure inspired by bionic structures, such as spider webs, as a novel lightweight energy absorber. Experimental testing and computational modeling are carried out to characterize the effects of fractal order and geometrical shapes on the crashworthiness of this new structure compared to a simple hierarchical structure. The computational results reveal that both simple hierarchical and fractal structures can present significant improvement in energy absorption over single wall nonhierarchical structure. Furthermore, fractal configuration, geometrical parameters and order have important effect on the energy absorption efficiency of fractal hierarchical structures, with the 2nd order design being the optimal for a given mass. The findings of this research offer a new route for designing novel lightweight energy absorbers with improved crash protection against impact.
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Citations
Cited by
Ha, N. S., Pham, T. M., Hao, H., & Lu, G. (2021) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2021.106464 · Cited by 266
Mathiazhagan, N., Palaniyappan, S., & Sivakumar, N. K. (2023) · Journal of the Mechanical Behavior of Biomedical Materials · doi:10.1016/j.jmbbm.2022.105611 · Cited by 48
Metadata
- Authors
- Zhang, Y., Wang, J., Wang, C., Zeng, Y., & Chen, T.
- Venue
- Materials & Design
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 158
- Pages
- 147-159
- ISSN
- 0264-1275 (ISSN-L)
- OpenAlex
- W2886163300
- Cited by
- 221
- References
- 0
- FWCI
- 7.67
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Zhang, Y., Wang, J., Wang, C., Zeng, Y., & Chen, T. (2018). Crashworthiness of bionic fractal hierarchical structures. Materials & Design. https://doi.org/10.1016/j.matdes.2018.08.028
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
