Subspace Reduction for Stochastic Planar Elasticity

Author:

Hakula HarriORCID,Laaksonen MikaelORCID

Abstract

Stochastic eigenvalue problems are nonlinear and multiparametric. They require their own solution methods and remain one of the challenge problems in computational mechanics. For the simplest possible reference problems, the key is to have a cluster of at the low end of the spectrum. If the inputs, domain or material, are perturbed, the cluster breaks and tracing of the eigenpairs become difficult due to possible crossing of the modes. In this paper we have shown that the eigenvalue crossing can occur within clusters not only by perturbations of the domain, but also of material parameters. What is new is that in this setting, the crossing can be controlled; that is, the effect of the perturbations can actually be predicted. Moreover, the basis of the subspace is shown to be a well-defined concept and can be used for instance in low-rank approximation of solutions of problems with static loading. In our industrial model problem, the reduction in solution times is significant.

Publisher

MDPI AG

Reference21 articles.

1. Uncertainty Assessment of Large Finite Element Systems;Schenk,2005

2. Spectral Power Iterations for the Random Eigenvalue Problem

3. Approximate methods for stochastic eigenvalue problems

4. Sparse Tensor Approximation of Parametric Eigenvalue Problems;Andreev,2012

5. Stochastic analysis of moderately thick plates using the generalized polynomial chaos and element free Galerkin method

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