
doi: 10.1002/nme.3097
AbstractThe main aim of this paper is a development of the semi‐analytical probabilistic version of the finite element method (FEM) related to the homogenization problem. This approach is based on the global version of the response function method and symbolic integral calculation of basic probabilistic moments of the homogenized tensor and is applied in conjunction with the effective modules method. It originates from the generalized stochastic perturbation‐based FEM, where Taylor expansion with random parameters is not necessary now and is simply replaced with the integration of the response functions. The hybrid computational implementation of the system MAPLE with homogenization‐oriented FEM code MCCEFF is invented to provide probabilistic analysis of the homogenized elasticity tensor for the periodic fiber‐reinforced composites. Although numerical illustration deals with a homogenization of a composite with material properties defined as Gaussian random variables, other composite parameters as well as other probabilistic distributions may be taken into account. The methodology is independent of the boundary value problem considered and may be useful for general numerical solutions using finite or boundary elements, finite differences or volumes as well as for meshless numerical strategies. Copyright © 2011 John Wiley & Sons, Ltd.
Finite element methods applied to problems in solid mechanics, finite element method, Stochastic and other probabilistic methods applied to problems in solid mechanics, homogenization method, non linear least squares method, Composite and mixture properties, Effective constitutive equations in solid mechanics, Gaussian random variables, probabilistic moments
Finite element methods applied to problems in solid mechanics, finite element method, Stochastic and other probabilistic methods applied to problems in solid mechanics, homogenization method, non linear least squares method, Composite and mixture properties, Effective constitutive equations in solid mechanics, Gaussian random variables, probabilistic moments
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