Efficient uncertainty quantification in stochastic finite element analysis based on functional principal components

Raffaele Argiento, Ilaria Bianchini, Ferdinando Auricchio, Ettore Lanzarone

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

6 Citazioni (Scopus)

Abstract

The great influence of uncertainties on the behavior of physical systems has always drawn attention to the importance of a stochastic approach to engineering prob- lems. Accordingly, in this paper, we address the problem of solving a Finite Element analysis in the presence of uncer- tain parameters. We consider an approach in which several solutions of the problem are obtained in correspondence of parameters samples, and propose a novel non-intrusive method, which exploits the functional principal component analysis, to get acceptable computational efforts. Indeed, the proposed approach allows constructing an optimal basis of the solutions space and projecting the full Finite Element problem into a smaller space spanned by this basis. Even if solving the problem in this reduced space is computationally convenient, very good approximations are obtained by upper bounding the error between the full Finite Element solution and the reduced one. Finally, we assess the applicability of the proposed approach through different test cases, obtaining satisfactory results.
Lingua originaleEnglish
pagine (da-a)533-549
Numero di pagine16
RivistaComputational Mechanics
Volume56
DOI
Stato di pubblicazionePubblicato - 2015

Keywords

  • Finite element analysis, Stochastic input parameters, Output uncertainty quantification, Functional, principal component analysis, Reduced basis

Fingerprint Entra nei temi di ricerca di 'Efficient uncertainty quantification in stochastic finite element analysis based on functional principal components'. Insieme formano una fingerprint unica.

Cita questo