The University of Southampton
University of Southampton Institutional Repository

Comparative study of projection schemes for stochastic finite element analysis

Record type: Article

We present a comparison of subspace projection schemes for stochastic finite element analysis in terms of accuracy and computational efficiency. More specifically, we compare the polynomial chaos projection scheme with reduced basis projection schemes based on the preconditioned stochastic Krylov subspace. Numerical studies are presented for two problems: (1) static analysis of a plate with random Young’s modulus and (2) settlement of a foundation supported on a randomly heterogeneous soil. Monte Carlo simulation results based on exact structural analysis are used to generate benchmark results against which the projection schemes are compared. We show that stochastic reduced basis methods require significantly less computer memory and execution time compared to the polynomial chaos approach, particularly for large-scale problems with many random variables. For the class of problems considered, we find that stochastic reduced basis methods can be up to orders of magnitude faster, while providing results of comparable or better accuracy.

PDF sach_06a.pdf - Accepted Manuscript
Download (3MB)

Citation

Sachdeva, Sachin K., Nair, Prasanth B. and Keane, Andy J. (2006) Comparative study of projection schemes for stochastic finite element analysis Computer Methods in Applied Mechanics and Engineering, 195, (19-22), pp. 2371-2392. (doi:10.1016/j.cma.2005.05.010).

More information

Published date: 2006
Keywords: stochastic finite element analysis, projection schemes, polynomial chaos, stochastic reduced basis methods

Identifiers

Local EPrints ID: 23315
URI: http://eprints.soton.ac.uk/id/eprint/23315
ISSN: 0045-7825
PURE UUID: ec1e8d6f-8560-4514-aadb-e9ecec410c76

Catalogue record

Date deposited: 15 Mar 2006
Last modified: 17 Jul 2017 16:18

Export record

Altmetrics

Contributors

Author: Sachin K. Sachdeva
Author: Prasanth B. Nair
Author: Andy J. Keane

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×