4.7 Article

Multiscale analysis for predicting the constitutive tensor effective coefficients of layered composites with micro and macro failures

期刊

APPLIED MATHEMATICAL MODELLING
卷 75, 期 -, 页码 250-266

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2019.05.031

关键词

Asymptotic homogenization method; Finite element analysis; Delamination; Debonding

资金

  1. National Council for Scientific and Technological Development (CNPq) [428591/2016-7, 310656/2018-4]
  2. Santa Catarina State Research and Innovation Funding Agency (FAPESC) [2017TR1747]
  3. PREI-DGAPA-UNAM
  4. MyM-IIMAS-UNAM

向作者/读者索取更多资源

The aim of the present work consists of predicting the effective coefficients of constitutive tensor for layered composites with delamination in macro-scale considering the influence of the debonding between fiber and matrix in micro-scale. Thus, a multiscale methodology is proposed to solve this problem. Firstly, the effective coefficients for the micro-scale level are calculated via the Finite Element Method (FEM), considering different degrees of micro failure. By using the micro-scale level homogenization results, the effective coefficients for layered composites are calculated by the Finite Element Method, as well as by Asymptotic Homogenization Method (AHM), considering different extensions of delamination (failures in macro-scale). Results show that the macro-scale analyses are affected by the micro failure, with the most influence of the micro failure in the coefficients C-11*, C-12* C-66*. In addition, when the thickness of the adhesive between layers increases, the effective coefficients decrease with the macro failure (delamination). Comparisons between homogenized and heterogeneous numerical models show that for almost all effective coefficients, there are excellent convergences. Only for the values of C-12*, there are relevant divergences for specific limit cases. Finally, the macro-scale results obtained via FEM and AHM are compared to evaluate the advantageous and disadvantageous of the proposed multiscale methodology. (C) 2019 Elsevier Inc. All rights reserved.

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