2017
DOI: 10.1016/j.compstruct.2016.10.114
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Virtual testing of composites: Imposing periodic boundary conditions on general finite element meshes

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Cited by 18 publications
(14 citation statements)
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“…Periodic boundary conditions were imposed on the non-periodic RVE mesh following the approach of Nguyen et al [45], using Lagrange polynomials of degree 9. The macroscopic stress was determined as the average of the corresponding microscopic quantity, computed with the simplified formula described in [46].…”
Section: Discussionmentioning
confidence: 99%
“…Periodic boundary conditions were imposed on the non-periodic RVE mesh following the approach of Nguyen et al [45], using Lagrange polynomials of degree 9. The macroscopic stress was determined as the average of the corresponding microscopic quantity, computed with the simplified formula described in [46].…”
Section: Discussionmentioning
confidence: 99%
“…The macroscopic effective uniaxial stress and strain ̅ were determined as the volume averages of the corresponding microscopic local quantities using the simplified formulas reported in ref. [27].…”
Section: Constituent Materials Modelsmentioning
confidence: 99%
“…However, this technique is not applicable 29 in the presence of discontinuities such as cracks, which is the second challenge in the present case. The method of Akpoyomare et al [27], which is based on decomposing a face into triangles over which linear interpolants are defined, does not suffer from this limitation. On the other hand, to decrease the impact of the non-smooth transition at the edges of the triangles, a high number of triangles should be used, thus increasing the computational complexity.…”
Section: Sensitivity To the Model Parametersmentioning
confidence: 99%
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“…PBCs were initially proposed by Michel et al., 22 Suquet 23 for the homogenization of the periodic materials. Later, many researchers 2430 have implemented them for the homogenization of composite materials containing elastic and inelastic constituents. Espadas-Escalante et al.…”
Section: Multi-scale Modellingmentioning
confidence: 99%