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Multilevel Decomposition Framework for Reliability Assessment of Assembled Stochastic Linear Structural Systems / Tanmoy Chatterjee, Sondipon Adhikari, Michael Friswell

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, Volume: 7, Issue: 1, Start page: 04021003

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

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DOI (Published version): 10.1061/ajrua6.0001119

Abstract

To reduce the computational cost of assembled stochastic linear structural dynamic systems, a three-staged reduced order model-based framework for forward uncertainty propagation was developed. First, the physical domain was decomposed by constructing an equivalent reduced order numerical model that...

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Published in: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
ISSN: 2376-7642 2376-7642
Published: American Society of Civil Engineers (ASCE) 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56122
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first_indexed 2021-01-25T10:57:35Z
last_indexed 2021-02-18T04:20:35Z
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spelling 2021-02-17T17:36:10.8108498 v2 56122 2021-01-25 Multilevel Decomposition Framework for Reliability Assessment of Assembled Stochastic Linear Structural Systems 5e637da3a34c6e97e2b744c2120db04d Tanmoy Chatterjee Tanmoy Chatterjee true false 4ea84d67c4e414f5ccbd7593a40f04d3 Sondipon Adhikari Sondipon Adhikari true false 5894777b8f9c6e64bde3568d68078d40 Michael Friswell Michael Friswell true false 2021-01-25 AERO To reduce the computational cost of assembled stochastic linear structural dynamic systems, a three-staged reduced order model-based framework for forward uncertainty propagation was developed. First, the physical domain was decomposed by constructing an equivalent reduced order numerical model that limited the cost of a single deterministic simulation. This was done in two phases: (1) reducing the system matrices of the subcomponents using component mode synthesis and (2) solving the resulting reduced system with the help of domain decomposition in an efficient manner. Second, functional decomposition was carried out in the stochastic space by employing a multioutput machine learning model that reduced the number of eigenvalue analyses to be performed. Thus, a multilevel framework was developed that propagated the dynamic response from the subcomponent level to the assembled global system level efficiently. Subsequently, reliability analysis was performed to assess the safety level and failure probability of linear stochastic dynamic systems. The results achieved by solving a two-dimensional (2D) building frame and a three-dimensional (3D) transmission tower model illustrated good performance of the proposed methodology, highlighting its potential for complex problems. Journal Article ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 7 1 04021003 American Society of Civil Engineers (ASCE) 2376-7642 2376-7642 1 3 2021 2021-03-01 10.1061/ajrua6.0001119 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2021-02-17T17:36:10.8108498 2021-01-25T10:56:47.2132964 College of Engineering Engineering Tanmoy Chatterjee 1 Sondipon Adhikari 2 Michael Friswell 3
title Multilevel Decomposition Framework for Reliability Assessment of Assembled Stochastic Linear Structural Systems
spellingShingle Multilevel Decomposition Framework for Reliability Assessment of Assembled Stochastic Linear Structural Systems
Tanmoy, Chatterjee
Sondipon, Adhikari
Michael, Friswell
title_short Multilevel Decomposition Framework for Reliability Assessment of Assembled Stochastic Linear Structural Systems
title_full Multilevel Decomposition Framework for Reliability Assessment of Assembled Stochastic Linear Structural Systems
title_fullStr Multilevel Decomposition Framework for Reliability Assessment of Assembled Stochastic Linear Structural Systems
title_full_unstemmed Multilevel Decomposition Framework for Reliability Assessment of Assembled Stochastic Linear Structural Systems
title_sort Multilevel Decomposition Framework for Reliability Assessment of Assembled Stochastic Linear Structural Systems
author_id_str_mv 5e637da3a34c6e97e2b744c2120db04d
4ea84d67c4e414f5ccbd7593a40f04d3
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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 ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
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container_issue 1
container_start_page 04021003
publishDate 2021
institution Swansea University
issn 2376-7642
2376-7642
doi_str_mv 10.1061/ajrua6.0001119
publisher American Society of Civil Engineers (ASCE)
college_str College of Engineering
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hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Engineering{{{_:::_}}}College of Engineering{{{_:::_}}}Engineering
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description To reduce the computational cost of assembled stochastic linear structural dynamic systems, a three-staged reduced order model-based framework for forward uncertainty propagation was developed. First, the physical domain was decomposed by constructing an equivalent reduced order numerical model that limited the cost of a single deterministic simulation. This was done in two phases: (1) reducing the system matrices of the subcomponents using component mode synthesis and (2) solving the resulting reduced system with the help of domain decomposition in an efficient manner. Second, functional decomposition was carried out in the stochastic space by employing a multioutput machine learning model that reduced the number of eigenvalue analyses to be performed. Thus, a multilevel framework was developed that propagated the dynamic response from the subcomponent level to the assembled global system level efficiently. Subsequently, reliability analysis was performed to assess the safety level and failure probability of linear stochastic dynamic systems. The results achieved by solving a two-dimensional (2D) building frame and a three-dimensional (3D) transmission tower model illustrated good performance of the proposed methodology, highlighting its potential for complex problems.
published_date 2021-03-01T04:13:43Z
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