2022
DOI: 10.1098/rsta.2021.0336
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Computational modelling of the crushing of carbon fibre-reinforced polymer composites

Abstract: The use of lightweight carbon fibre-reinforced polymer (CFRP) composites in transportation vehicles has necessitated the need to guarantee that these new materials and their structures are able to deliver a sufficient level of crashworthiness to ensure passenger safety. Unlike their metallic counterparts, which absorb energy primarily through plastic deformation, CFRPs absorb energy through a complex interaction of damage mechanisms involving matrix (polymer) cracking, fibre/matrix debonding, fibre pull-out/ki… Show more

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Cited by 7 publications
(4 citation statements)
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“…Figure 17 shows that the resultant force of the ogival impactor is 18.5 kN. The nonuniformities prove the stages of delamination and start with delamination onset [20]. After that, it enters into delamination growth, where significant damage is displayed.…”
Section: Ogival Impactormentioning
confidence: 98%
“…Figure 17 shows that the resultant force of the ogival impactor is 18.5 kN. The nonuniformities prove the stages of delamination and start with delamination onset [20]. After that, it enters into delamination growth, where significant damage is displayed.…”
Section: Ogival Impactormentioning
confidence: 98%
“…Experimental methods, however, are time consuming and expensive. That is why researchers are getting more interested in FE Models [18][19][20][21][22] in recent years. Advances in commercial finite element analysis (FEA) software have enabled researchers to realistically simulate the performance of composite structures; however, computational cost is an issue to overcome.…”
Section: Introductionmentioning
confidence: 99%
“…This issue has recently been discussed by another author. 21 Due to composite crushing being highly sensitive to external factors, numerical modelling of crashworthy FRP structures can be unreliable such that designs are often based on costly experiments. Therefore, to increase understanding, it is crucial to reduce the number of parameters or be able to quantify parameter influences.…”
Section: Introductionmentioning
confidence: 99%