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Fuzzy finite element model updating of the DLR AIRMOD test structure

H. Haddad Khodaparast, Y. Govers, I. Dayyani, S. Adhikari, M. Link, M.I. Friswell, J.E. Mottershead, J. Sienz, Johann Sienz Orcid Logo, Michael Friswell, Sondipon Adhikari Orcid Logo, Hamed Haddad Khodaparast Orcid Logo

Applied Mathematical Modelling, Volume: 52, Pages: 512 - 526

Swansea University Authors: Johann Sienz Orcid Logo, Michael Friswell, Sondipon Adhikari Orcid Logo, Hamed Haddad Khodaparast Orcid Logo

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DOI (Published version): 10.1016/j.apm.2017.08.001

Abstract

This article presents the application of finite-element fuzzy model updating to the DLR AIRMOD structure. The proposed approach is initially demonstrated on a simulated mass-spring system with three degrees of freedom. Considering the effect of the assembly process on variability measurements, modal...

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Published in: Applied Mathematical Modelling
ISSN: 0307-904X
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa34866
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spelling 2020-06-16T08:44:01.2482370 v2 34866 2017-08-04 Fuzzy finite element model updating of the DLR AIRMOD test structure 17bf1dd287bff2cb01b53d98ceb28a31 0000-0003-3136-5718 Johann Sienz Johann Sienz true false 5894777b8f9c6e64bde3568d68078d40 Michael Friswell Michael Friswell true false 4ea84d67c4e414f5ccbd7593a40f04d3 0000-0003-4181-3457 Sondipon Adhikari Sondipon Adhikari true false f207b17edda9c4c3ea074cbb7555efc1 0000-0002-3721-4980 Hamed Haddad Khodaparast Hamed Haddad Khodaparast true false 2017-08-04 This article presents the application of finite-element fuzzy model updating to the DLR AIRMOD structure. The proposed approach is initially demonstrated on a simulated mass-spring system with three degrees of freedom. Considering the effect of the assembly process on variability measurements, modal tests were carried out for the repeatedly disassembled and reassembled DLR AIRMOD structure. The histograms of the measured data attributed to the uncertainty of the structural components in terms of mass and stiffness are utilised to obtain the membership functions of the chosen fuzzy outputs and to determine the updated membership functions of the uncertain input parameters represented by fuzzy variables. In this regard, a fuzzy parameter is introduced to represent a set of interval parameters through the membership function, and a meta model (kriging, in this work) is used to speed up the updating. The use of non-probabilistic models, i.e. interval and fuzzy models, for updating models with uncertainties is often more practical when the large quantities of test data that are necessary for probabilistic model updating are unavailable. Journal Article Applied Mathematical Modelling 52 512 526 0307-904X Fuzzy variable; Model updating; AIRMOD structure 31 12 2017 2017-12-31 10.1016/j.apm.2017.08.001 COLLEGE NANME COLLEGE CODE Swansea University 2020-06-16T08:44:01.2482370 2017-08-04T08:45:25.2383367 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised H. Haddad Khodaparast 1 Y. Govers 2 I. Dayyani 3 S. Adhikari 4 M. Link 5 M.I. Friswell 6 J.E. Mottershead 7 J. Sienz 8 Johann Sienz 0000-0003-3136-5718 9 Michael Friswell 10 Sondipon Adhikari 0000-0003-4181-3457 11 Hamed Haddad Khodaparast 0000-0002-3721-4980 12 0034866-21082017155123.pdf khodaparast2017v5.pdf 2017-08-21T15:51:23.5070000 Output 1278473 application/pdf Accepted Manuscript true 2018-08-12T00:00:00.0000000 true eng
title Fuzzy finite element model updating of the DLR AIRMOD test structure
spellingShingle Fuzzy finite element model updating of the DLR AIRMOD test structure
Johann Sienz
Michael Friswell
Sondipon Adhikari
Hamed Haddad Khodaparast
title_short Fuzzy finite element model updating of the DLR AIRMOD test structure
title_full Fuzzy finite element model updating of the DLR AIRMOD test structure
title_fullStr Fuzzy finite element model updating of the DLR AIRMOD test structure
title_full_unstemmed Fuzzy finite element model updating of the DLR AIRMOD test structure
title_sort Fuzzy finite element model updating of the DLR AIRMOD test structure
author_id_str_mv 17bf1dd287bff2cb01b53d98ceb28a31
5894777b8f9c6e64bde3568d68078d40
4ea84d67c4e414f5ccbd7593a40f04d3
f207b17edda9c4c3ea074cbb7555efc1
author_id_fullname_str_mv 17bf1dd287bff2cb01b53d98ceb28a31_***_Johann Sienz
5894777b8f9c6e64bde3568d68078d40_***_Michael Friswell
4ea84d67c4e414f5ccbd7593a40f04d3_***_Sondipon Adhikari
f207b17edda9c4c3ea074cbb7555efc1_***_Hamed Haddad Khodaparast
author Johann Sienz
Michael Friswell
Sondipon Adhikari
Hamed Haddad Khodaparast
author2 H. Haddad Khodaparast
Y. Govers
I. Dayyani
S. Adhikari
M. Link
M.I. Friswell
J.E. Mottershead
J. Sienz
Johann Sienz
Michael Friswell
Sondipon Adhikari
Hamed Haddad Khodaparast
format Journal article
container_title Applied Mathematical Modelling
container_volume 52
container_start_page 512
publishDate 2017
institution Swansea University
issn 0307-904X
doi_str_mv 10.1016/j.apm.2017.08.001
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
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description This article presents the application of finite-element fuzzy model updating to the DLR AIRMOD structure. The proposed approach is initially demonstrated on a simulated mass-spring system with three degrees of freedom. Considering the effect of the assembly process on variability measurements, modal tests were carried out for the repeatedly disassembled and reassembled DLR AIRMOD structure. The histograms of the measured data attributed to the uncertainty of the structural components in terms of mass and stiffness are utilised to obtain the membership functions of the chosen fuzzy outputs and to determine the updated membership functions of the uncertain input parameters represented by fuzzy variables. In this regard, a fuzzy parameter is introduced to represent a set of interval parameters through the membership function, and a meta model (kriging, in this work) is used to speed up the updating. The use of non-probabilistic models, i.e. interval and fuzzy models, for updating models with uncertainties is often more practical when the large quantities of test data that are necessary for probabilistic model updating are unavailable.
published_date 2017-12-31T07:08:55Z
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