| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Soize, Christian
Université Gustave Eiffel
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (22/22 displayed)
- 2023Sensitivity of a granular homogeneous and isotropic second-gradient continuum model with respect to uncertainties
- 2023Sensitivity with respect to uncertainties of a particle-based homogeneous and isotropic second-gradient continuum model
- 2019Stochastic modeling and identification of an hyperelastic constitutive model for laminated compositescitations
- 2016Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocompositecitations
- 2015Modélisation stochastique continue et identification inverse d'interphases aléatoires à partir de simulations atomistiques
- 2015Stochastic modeling for statistical inverse identification in mechanics of materials
- 2015Stochastic representations and statistical inverse identification for uncertainty quantification in computational mechanics
- 2013On the statistical dependence for the components of random elasticity tensors exhibiting material symmetry propertiescitations
- 2009Computational elastoacoustics of uncertain complex systems and experimental validation
- 2009Robust updating of computational models with uncertainties for dynamical systems
- 2009Mesoscale probabilistic models for the elasticity tensor of fiber reinforced composites: Experimental identification and numerical aspectscitations
- 2008Inverse problems in stochastic computational dynamics
- 2007Computational elastoacoustics of uncertain complex systems and experimental validation
- 2007Robust updating of computational models with uncertainties for dynamical systems
- 2007A class of tensor-valued random fields for random anisotropic elastic microstructure modeling and stochastic homogenization
- 2006A class of tensor-valued random fields for random anisotropic elastic microstructure modeling and stochastic homogenization
- 2005Vibroacoustics of a cavity coupled with an uncertain composite panel
- 2005Uncertainties in structural dynamics for composite sandwich panels
- 2005Identification et validation expérimentale d'un modèle stochastique des incertitudes en vibroacoustique d'un panneau composite.
- 2005Probabilistic models for computational stochastic mechanics and applications
- 2005Modèle thermomécanique à haute température et à rupture pour les plaques multicouches carton-plâtre-carton soumises au feu. Expériences et simulations numériques
- 2004Uncertainties in structural dynamics for composite sandwich panels
Places of action
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article
Stochastic modeling and identification of an hyperelastic constitutive model for laminated composites
Abstract
International audience ; In this paper, we investigate the construction and identification of a new random field model for representing the constitutive behavior of laminated composites. Here, the material is modeled as a random hyperelastic medium characterized by a spatially dependent, stochastic and anisotropic strain energy function. The latter is parametrized by a set of material parameters, modeled as non-Gaussian random fields. From a probabilistic standpoint, the construction is first achieved by invoking information theory and the principle of maximum entropy. Constraints related to existence theorems in finite elasticity are, in particular, accounted for in the formulation. The identification of the parameters defining the random fields is subsequently addressed. This issue is attacked as a two-step problem where the mean model is calibrated in a first step, by imposing a match between the linearized model and nominal values proposed in the literature. The remaining parameters controlling the fluctuations are next estimated by solving an inverse problem in which principal component analysis and the maximum likelihood method are combined. The whole framework is illustrated considering an experimental database where multi-axial measurements are performed on a carbon-epoxy laminate. This work constitutes a first step towards the development of an integrated framework that will support decision making under uncertainty for the design, certification and qualification of composite materials and structures.