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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
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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 |
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ISSN: | 0888-3270 |
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Elsevier BV
2022
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URI: | https://cronfa.swan.ac.uk/Record/cronfa57554 |
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2021-09-09T14:28:35.6226004 v2 57554 2021-08-09 Random matrix eigenvalue problems in structural dynamics: An iterative approach 4ea84d67c4e414f5ccbd7593a40f04d3 0000-0003-4181-3457 Sondipon Adhikari Sondipon Adhikari true false 2021-08-09 ACEM 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. Journal Article Mechanical Systems and Signal Processing 164 108260 Elsevier BV 0888-3270 Random eigenvalue problem, Iterative methods, Galerkin projection, Statistical distributions, Stochastic systems 1 2 2022 2022-02-01 10.1016/j.ymssp.2021.108260 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2021-09-09T14:28:35.6226004 2021-08-09T09:57:00.5237339 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Sondipon Adhikari 0000-0003-4181-3457 1 S. Chakraborty 2 57554__20578__79c8fb4fab1e4a5c9126d7eb4b364b54.pdf 57554.pdf 2021-08-09T10:35:13.3379901 Output 2019221 application/pdf Accepted Manuscript true 2022-08-05T00:00:00.0000000 ©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) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/ |
title |
Random matrix eigenvalue problems in structural dynamics: An iterative approach |
spellingShingle |
Random matrix eigenvalue problems in structural dynamics: An iterative approach Sondipon Adhikari |
title_short |
Random matrix eigenvalue problems in structural dynamics: An iterative approach |
title_full |
Random matrix eigenvalue problems in structural dynamics: An iterative approach |
title_fullStr |
Random matrix eigenvalue problems in structural dynamics: An iterative approach |
title_full_unstemmed |
Random matrix eigenvalue problems in structural dynamics: An iterative approach |
title_sort |
Random matrix eigenvalue problems in structural dynamics: An iterative approach |
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4ea84d67c4e414f5ccbd7593a40f04d3_***_Sondipon Adhikari |
author |
Sondipon Adhikari |
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Sondipon Adhikari S. Chakraborty |
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Mechanical Systems and Signal Processing |
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10.1016/j.ymssp.2021.108260 |
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Elsevier BV |
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description |
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. |
published_date |
2022-02-01T14:12:04Z |
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1821415019991007232 |
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11.048085 |