4.7 Article

Blind benchmarking of seven longitudinal tensile failure models for two virtual unidirectional composites

期刊

COMPOSITES SCIENCE AND TECHNOLOGY
卷 202, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.compscitech.2020.108555

关键词

Strength; Mechanical properties; Computational mechanics; Stress concentrations; Longitudinal tensile failure

资金

  1. European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant [722626]
  2. Subprograma Estatal de Formacion del MICINN [BES-2016-078270]
  3. European Social Fund
  4. research programme of DPI [812T17]
  5. Royal Academy of Engineering
  6. FCT - Fundacao para a Ciencia e a Tecnologia [MITP-TB/PFM/0005/2013]
  7. [RTI2018-097880-B-I00]

向作者/读者索取更多资源

This paper presents a blind benchmark of seven different models applied to two virtual materials for prediction of longitudinal tensile failure of unidirectional composites. Capturing the localization of stress concentrations was vital in predicting the effect of matrix stiffness and strength on composite failure strain and strength as well as fiber break and cluster development. The discussions provide insight into how model assumptions are linked to the differences in predictions.
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 redistributions 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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