Stochastic modeling and identification of an hyperelastic constitutive model for laminated composites

Abstract : 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.
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Submitted on : Monday, June 3, 2019 - 6:17:58 PM
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Brian Staber, Johann Guilleminot, Christian Soize, John Michopoulos, A. Iliopoulos. Stochastic modeling and identification of an hyperelastic constitutive model for laminated composites. Computer Methods in Applied Mechanics and Engineering, Elsevier, 2019, 347, pp.425-444. ⟨10.1016/j.cma.2018.12.036⟩. ⟨hal-02052852⟩

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