Journal article 1690 views
A Fourier-Karhunen-Loève discretization scheme for stationary random material properties in SFEM
,
Yuntian Feng ,
Roger Owen ,
D. F. Li,
Ian Davies
International Journal for Numerical Methods in Engineering, Volume: 73, Issue: 13, Pages: 1942 - 1965
Swansea University Authors:
Chenfeng Li , Yuntian Feng , Roger Owen , Ian Davies
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1002/nme.2160
Abstract
In the numerical modelling of a physical system involving random media, the firstkey step is usually to represent, with a finite set of deterministic primary functions andrandom variables, the associated stochastic field (e.g. random Young’s modulus andrandom Poisson’s ratio) which is often defined...
| Published in: | International Journal for Numerical Methods in Engineering |
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| ISSN: | 0029-5981 1097-0207 |
| Published: |
Wiley
2008
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa12322 |
| Abstract: |
In the numerical modelling of a physical system involving random media, the firstkey step is usually to represent, with a finite set of deterministic primary functions andrandom variables, the associated stochastic field (e.g. random Young’s modulus andrandom Poisson’s ratio) which is often defined by its statistical moments. In this paper,an efficient and accurate method is presented to represent a stochastic field given by itsexpectation and covariance functions. Based on the Karhunen-Loève expansion, thismethod represents stochastic fields in terms of multiple Fourier series and a vector ofmutually uncorrelated random variables. The result can be treated as a semi-analyticsolution of the Karhunen-Loève expansion, which is achieved by minimizing themean-squared error of the characteristic equation and solving a standard algebraiceigenvalue problem. To verify the proposed method, exponential covariance functionswith exact Karhunen-Loève expansion solutions are employed and good agreementsare observed on both eigenvalues and eigenfunctions. Representations of stochasticfields with Gaussian covariance functions are also performed to demonstrate theeffectiveness and robustness. As no meshing is required in this method, its efficiencyand accuracy are not sensitive to the dimensions or the correlation distance of thestochastic field under consideration. |
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| College: |
Faculty of Science and Engineering |
| Issue: |
13 |
| Start Page: |
1942 |
| End Page: |
1965 |