Journal article 407 views 57 downloads
Random matrix eigenvalue problems in structural dynamics: An iterative approach
Mechanical Systems and Signal Processing, Volume: 164, Start page: 108260
Swansea University Author: Sondipon Adhikari
-
PDF | Accepted Manuscript
©2021 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND)
Download (1.93MB)
DOI (Published version): 10.1016/j.ymssp.2021.108260
Abstract
Uncertainties need to be taken into account in the dynamic analysis of complex structures. This is because in some cases uncertainties can have a significant impact on the dynamic response and ignoring it can lead to unsafe design. For complex systems with uncertainties, the dynamic response is char...
| Published in: | Mechanical Systems and Signal Processing |
|---|---|
| ISSN: | 0888-3270 |
| Published: |
Elsevier BV
2022
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa57554 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
Uncertainties need to be taken into account in the dynamic analysis of complex structures. This is because in some cases uncertainties can have a significant impact on the dynamic response and ignoring it can lead to unsafe design. For complex systems with uncertainties, the dynamic response is characterised by the eigenvalues and eigenvectors of the underlying generalised matrix eigenvalue problem. This paper aims at developing computationally efficient methods for random eigenvalue problems arising in the dynamics of multi-degree-of-freedom systems. There are efficient methods available in the literature for obtaining eigenvalues of random dynamical systems. However, the computation of eigenvectors remains challenging due to the presence of a large number of random variables within a single eigenvector. To address this problem, we project the random eigenvectors on the basis spanned by the underlying deterministic eigenvectors and apply a Galerkin formulation to obtain the unknown coefficients. The overall approach is simplified using an iterative technique. Two numerical examples are provided to illustrate the proposed method. Full-scale Monte Carlo simulations are used to validate the new results. |
|---|---|
| Keywords: |
Random eigenvalue problem, Iterative methods, Galerkin projection, Statistical distributions, Stochastic systems |
| College: |
Faculty of Science and Engineering |
| Start Page: |
108260 |