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Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition
Computer Methods in Applied Mechanics and Engineering, Volume: 366, Start page: 113060
Swansea University Authors: Tanmoy Chatterjee, Sondipon Adhikari , Michael Friswell
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DOI (Published version): 10.1016/j.cma.2020.113060
Abstract
This paper addresses computational aspects in dynamic sub-structuring of built-up structures with uncertainty. Component mode synthesis (CMS), which is a model reduction technique, has been integrated within the framework of domain decomposition (DD), so that reduced models of individual sub-systems...
Published in: | Computer Methods in Applied Mechanics and Engineering |
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ISSN: | 0045-7825 |
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Elsevier BV
2020
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URI: | https://cronfa.swan.ac.uk/Record/cronfa54130 |
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2022-11-15T16:12:31.8310974 v2 54130 2020-05-05 Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition 5e637da3a34c6e97e2b744c2120db04d Tanmoy Chatterjee Tanmoy Chatterjee true false 4ea84d67c4e414f5ccbd7593a40f04d3 0000-0003-4181-3457 Sondipon Adhikari Sondipon Adhikari true false 5894777b8f9c6e64bde3568d68078d40 Michael Friswell Michael Friswell true false 2020-05-05 ACEM This paper addresses computational aspects in dynamic sub-structuring of built-up structures with uncertainty. Component mode synthesis (CMS), which is a model reduction technique, has been integrated within the framework of domain decomposition (DD), so that reduced models of individual sub-systems can be solved with smaller computational cost compared to solving the full (unreduced) system by DD. This is particularly relevant for structural dynamics applications where the overall system physics can be captured by a relatively low number of modes. The theoretical framework of the proposed methodology has been extended for application in stochastic dynamic systems. To limit the number of eigen-value analyses to be performed corresponding to the random realizations of input parameters, a locally refined high dimensional model representation model with stepwise least squares regression is presented. Effectively, a bi-level decomposition is proposed, one in the physical space and the other in the stochastic space. The geometric decomposition in the physical space by the proposed model reduction-based DD reduces the computational cost of a single analysis of the system and the functional decomposition in the stochastic space by the proposed meta-model lowers the number of simulations to be performed on the actual system. The results achieved by solving a finite-element model of an assembled beam structure and a 3D space frame illustrate good performance of the proposed methodology, highlighting its potential for complex problems. Journal Article Computer Methods in Applied Mechanics and Engineering 366 113060 Elsevier BV 0045-7825 Domain decomposition, Model reduction, Component mode synthesis, Schur complement, Functional decomposition, Stochastic space 1 7 2020 2020-07-01 10.1016/j.cma.2020.113060 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2022-11-15T16:12:31.8310974 2020-05-05T15:41:07.1762195 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Tanmoy Chatterjee 1 Sondipon Adhikari 0000-0003-4181-3457 2 Michael Friswell 3 54130__17465__2715b3fa69da470398c6da07cfac4288.pdf 54130.pdf 2020-06-10T18:35:12.0301862 Output 2867517 application/pdf Accepted Manuscript true 2021-04-30T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng |
title |
Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition |
spellingShingle |
Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition Tanmoy Chatterjee Sondipon Adhikari Michael Friswell |
title_short |
Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition |
title_full |
Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition |
title_fullStr |
Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition |
title_full_unstemmed |
Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition |
title_sort |
Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition |
author_id_str_mv |
5e637da3a34c6e97e2b744c2120db04d 4ea84d67c4e414f5ccbd7593a40f04d3 5894777b8f9c6e64bde3568d68078d40 |
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5e637da3a34c6e97e2b744c2120db04d_***_Tanmoy Chatterjee 4ea84d67c4e414f5ccbd7593a40f04d3_***_Sondipon Adhikari 5894777b8f9c6e64bde3568d68078d40_***_Michael Friswell |
author |
Tanmoy Chatterjee Sondipon Adhikari Michael Friswell |
author2 |
Tanmoy Chatterjee Sondipon Adhikari Michael Friswell |
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Computer Methods in Applied Mechanics and Engineering |
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This paper addresses computational aspects in dynamic sub-structuring of built-up structures with uncertainty. Component mode synthesis (CMS), which is a model reduction technique, has been integrated within the framework of domain decomposition (DD), so that reduced models of individual sub-systems can be solved with smaller computational cost compared to solving the full (unreduced) system by DD. This is particularly relevant for structural dynamics applications where the overall system physics can be captured by a relatively low number of modes. The theoretical framework of the proposed methodology has been extended for application in stochastic dynamic systems. To limit the number of eigen-value analyses to be performed corresponding to the random realizations of input parameters, a locally refined high dimensional model representation model with stepwise least squares regression is presented. Effectively, a bi-level decomposition is proposed, one in the physical space and the other in the stochastic space. The geometric decomposition in the physical space by the proposed model reduction-based DD reduces the computational cost of a single analysis of the system and the functional decomposition in the stochastic space by the proposed meta-model lowers the number of simulations to be performed on the actual system. The results achieved by solving a finite-element model of an assembled beam structure and a 3D space frame illustrate good performance of the proposed methodology, highlighting its potential for complex problems. |
published_date |
2020-07-01T07:53:57Z |
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11.04748 |