A progressive damage simulation algorithm for GFRP composites under cyclic loading. Part I: Material constitutive model - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Composites Science and Technology Année : 2011

A progressive damage simulation algorithm for GFRP composites under cyclic loading. Part I: Material constitutive model

Résumé

A life prediction algorithm and its implementation for a thick-shell finite element formulation for GFRP composites under constant or variable amplitude loading is introduced in this work. It is a distributed damage model in the sense that constitutive material response is defined in terms of meso-mechanics for the unidirectional ply. The algorithm modules for non-linear material behaviour, pseudo-static loading-unloading-reloading response, constant life diagrams and strength and stiffness degradation due to cyclic loading were implemented on a robust and comprehensive experimental database for a unidirectional Glass/Epoxy ply. The model, based on property definition in the principal coordinate system of the constitutive ply, can be used, besides life prediction, to assess strength and stiffness of any multidirectional laminate after arbitrary, constant or variable amplitude multi-axial cyclic loading. Numerical predictions were corroborated satisfactorily by test data from constant amplitude fatigue of Glass/Epoxy laminates of various stacking sequences.
Fichier principal
Vignette du fichier
PEER_stage2_10.1016%2Fj.compscitech.2011.01.023.pdf (638.49 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00730296 , version 1 (09-09-2012)

Identifiants

Citer

Elias N. Eliopoulos, Theodore P. Philippidis. A progressive damage simulation algorithm for GFRP composites under cyclic loading. Part I: Material constitutive model. Composites Science and Technology, 2011, 71 (5), pp.742. ⟨10.1016/j.compscitech.2011.01.023⟩. ⟨hal-00730296⟩

Collections

PEER
67 Consultations
260 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More