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Uncertainty propagation in dynamic sub-structuring by model reduction integrated domain decomposition / Tanmoy Chatterjee; Sondipon Adhikari; Michael Friswell

Computer Methods in Applied Mechanics and Engineering, Volume: 366, Start page: 113060

Swansea University Authors: Tanmoy, Chatterjee, Sondipon, Adhikari, Michael, Friswell

  • Accepted Manuscript under embargo until: 30th April 2021

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...

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Published in: Computer Methods in Applied Mechanics and Engineering
ISSN: 0045-7825
Published: Elsevier BV 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa54130
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spelling 2020-06-10T18:45:18.9101078 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 EEN 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 Engineering COLLEGE CODE EEN Swansea University 2020-06-10T18:45:18.9101078 2020-05-05T15:41:07.1762195 Tanmoy Chatterjee 1 Sondipon Adhikari 0000-0003-4181-3457 2 Michael Friswell 3 Under embargo Under embargo 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
author_id_fullname_str_mv 5e637da3a34c6e97e2b744c2120db04d_***_Tanmoy, Chatterjee
4ea84d67c4e414f5ccbd7593a40f04d3_***_Sondipon, Adhikari
5894777b8f9c6e64bde3568d68078d40_***_Michael, Friswell
author Tanmoy, Chatterjee
Sondipon, Adhikari
Michael, Friswell
author2 Tanmoy Chatterjee
Sondipon Adhikari
Michael Friswell
format Journal article
container_title Computer Methods in Applied Mechanics and Engineering
container_volume 366
container_start_page 113060
publishDate 2020
institution Swansea University
issn 0045-7825
doi_str_mv 10.1016/j.cma.2020.113060
publisher Elsevier BV
document_store_str 0
active_str 0
description 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 2020-07-01T04:18:02Z
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score 10.79001