Acceleration of the spectral stochastic FEM using POD and element based discrete empirical approximation for a micromechanical model of heterogeneous materials with random geometry. (English) Zbl 1441.74270
Summary: The spectral stochastic FEM with local basis functions in the stochastic domain (SL-FEM) is one of the most flexible and accurate stochastic methods, however, also the most computationally expensive. These expenses are traditionally associated with the extra large tangent stiffness matrix and a huge number of elements which need to be re-integrated in every iteration. In this work, we incorporate the proper orthogonal decomposition (POD) into the SL-FEM, thus performing a drastic reduction of the stiffness matrix. In order to reduce the integration costs by hyperreduction, a novel element-based modification of the discrete empirical interpolation, the so-called element-based empirical approximation method (EDEAM), is developed and combined with the POD. Particular advantages of the SL-FEM for order reduction and hyperreduction compared to other stochastic techniques are discussed. The new reduced-order SL-FEM is applied to the computational homogenization of materials with random geometry of the microstructure, i.e., to a general class of problems exhibiting strongly nonlinear, non-smooth and sometimes discontinuous dependency of the solution on some random parameters. The reduced-order SL-FEM demonstrates a high accuracy and a high solution speed, whereby the solution time for the reduced-order SL-FEM is comparable to the solution time of only one single Monte-Carlo sample.
MSC:
| 74S05 | Finite element methods applied to problems in solid mechanics |
| 65N30 | Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs |
| 74A40 | Random materials and composite materials |
| 74M25 | Micromechanics of solids |
Keywords:
stochastic FEM; reduced order modeling; POD; hyperreduction; computational homogenization; random geometrySoftware:
redbKITReferences:
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