2020
DOI: 10.1016/j.ijplas.2019.11.014
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A gradient-extended two-surface damage-plasticity model for large deformations

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Cited by 63 publications
(30 citation statements)
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“…Furthermore, it would be worthwhile to examine the influence of potential finite element mesh dependencies, which might occur when using conventional, 'local' continuum damage models. A suitable approach for this was presented by Brepols et al [5] for small strains and has only recently been extended to finite deformations by Brepols et al [6]. The authors used a gradient-extension in order to obtain a non-local version of the model.…”
Section: Resultsmentioning
confidence: 99%
“…Furthermore, it would be worthwhile to examine the influence of potential finite element mesh dependencies, which might occur when using conventional, 'local' continuum damage models. A suitable approach for this was presented by Brepols et al [5] for small strains and has only recently been extended to finite deformations by Brepols et al [6]. The authors used a gradient-extension in order to obtain a non-local version of the model.…”
Section: Resultsmentioning
confidence: 99%
“…Inserting the discretisation (15) into the weak forms (11) and (12) allows us to separate the nodal values from the test functions, and, together with the definitions of the internal and external forces…”
Section: Discretisationmentioning
confidence: 99%
“…Therefore, plasticity can evolve even in highly damaged material states. The recently published model [11], which includes kinematic hardening, follows a conceptually similar multi-surface approach as established as this work proceeds. Analogous to the effective stress, a novel effective damage driving force is proposed.…”
Section: Introductionmentioning
confidence: 99%
“…Choosing damage itself for the gradient-extension is of course not a unique choice, however, the very good experience of our research group following this approach for different kinds of inelastic and anisotropic material effects (see e.g. [3,4]) motivates this choice.…”
mentioning
confidence: 99%