Blind benchmarking of seven longitudinal tensile failure models for two virtual unidirectional composites
Many models for prediction of longitudinal tensile failure of unidirectional (UD) composites have been developed in the last decades. These models require significant assumptions and simplifications, but their consequences for the predictions are often not clearly understood. This paper therefore pr...
| Autores: | , , , , , , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/366386 |
| Acesso em linha: | https://hdl.handle.net/2117/366386 https://dx.doi.org/10.1016/j.compscitech.2020.108555 |
| Access Level: | acceso abierto |
| Palavra-chave: | Composite materials Mechanical properties Computational mechanics Stress concentrations Longitudinal tensile failure Materials compostos Àrees temàtiques de la UPC::Enginyeria dels materials::Materials compostos |
| Resumo: | Many models for prediction of longitudinal tensile failure of unidirectional (UD) composites have been developed in the last decades. These models require significant assumptions and simplifications, but their consequences for the predictions are often not clearly understood. This paper therefore presents a blind benchmark of seven different models applied to two virtual materials. Reliably capturing the localisation of stress concentrations was vital in predicting the effect of matrix stiffness and strength on composite failure strain and strength as well as fibre break and cluster development. Although the models have different assumptions regarding stress re- distributions around fibre breaks, the 2-plet (clusters of two fibre breaks) development was similar. Distance- based criteria were shown to be inadequate for monitoring cluster development. The discussions provide detailed insight into how the model assumptions are linked to the differences in the predictions. |
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