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A comparison of two stochastic model updating methods using the DLR AIRMOD test structure

Y. Govers, H. Haddad Khodaparast, M. Link, J.E. Mottershead, Hamed Haddad Khodaparast Orcid Logo

Mechanical Systems and Signal Processing, Volume: 52-53, Pages: 105 - 114

Swansea University Author: Hamed Haddad Khodaparast Orcid Logo

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DOI (Published version): 10.1016/j.ymssp.2014.06.003

Abstract

The problem of stochastic model updating is addressed by means of the application of two methods (covariance and interval model updating) to the DLR AIRMOD structure which is repeatedly disassembled and reassembled to provide a database of modal variability due to uncertainty in joint and support st...

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Published in: Mechanical Systems and Signal Processing
ISSN: 0888-3270
Published: 2015
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URI: https://cronfa.swan.ac.uk/Record/cronfa18105
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first_indexed 2014-07-11T01:30:15Z
last_indexed 2020-06-16T12:28:29Z
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spelling 2020-06-16T08:42:49.7397913 v2 18105 2014-07-10 A comparison of two stochastic model updating methods using the DLR AIRMOD test structure f207b17edda9c4c3ea074cbb7555efc1 0000-0002-3721-4980 Hamed Haddad Khodaparast Hamed Haddad Khodaparast true false 2014-07-10 AERO The problem of stochastic model updating is addressed by means of the application of two methods (covariance and interval model updating) to the DLR AIRMOD structure which is repeatedly disassembled and reassembled to provide a database of modal variability due to uncertainty in joint and support stiffnesses and masses of cables and screws. The covariance method is based on an assumption of small uncertainty and implemented at each step of an iterative approach by forward propagation of uncertain parameters using a multivariate normal distribution. The interval approach is based on a Kriging meta-model, thereby providing a very efficient surrogate to replace the expensive full finite element model. This allows a mapping from multiple output measurements to define a hypercube bounded by intervals of parameter uncertainty. It is shown that the measured data is fully enclosed by the hyper-ellipses and hypercubes of the covariance and interval methods respectively. As expected, the interval method is found to be more conservative than the covariance approach but still provides useful estimates without restriction by any assumption of probability distribution. Journal Article Mechanical Systems and Signal Processing 52-53 105 114 0888-3270 28 2 2015 2015-02-28 10.1016/j.ymssp.2014.06.003 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2020-06-16T08:42:49.7397913 2014-07-10T09:34:53.7934377 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering Y. Govers 1 H. Haddad Khodaparast 2 M. Link 3 J.E. Mottershead 4 Hamed Haddad Khodaparast 0000-0002-3721-4980 5
title A comparison of two stochastic model updating methods using the DLR AIRMOD test structure
spellingShingle A comparison of two stochastic model updating methods using the DLR AIRMOD test structure
Hamed Haddad Khodaparast
title_short A comparison of two stochastic model updating methods using the DLR AIRMOD test structure
title_full A comparison of two stochastic model updating methods using the DLR AIRMOD test structure
title_fullStr A comparison of two stochastic model updating methods using the DLR AIRMOD test structure
title_full_unstemmed A comparison of two stochastic model updating methods using the DLR AIRMOD test structure
title_sort A comparison of two stochastic model updating methods using the DLR AIRMOD test structure
author_id_str_mv f207b17edda9c4c3ea074cbb7555efc1
author_id_fullname_str_mv f207b17edda9c4c3ea074cbb7555efc1_***_Hamed Haddad Khodaparast
author Hamed Haddad Khodaparast
author2 Y. Govers
H. Haddad Khodaparast
M. Link
J.E. Mottershead
Hamed Haddad Khodaparast
format Journal article
container_title Mechanical Systems and Signal Processing
container_volume 52-53
container_start_page 105
publishDate 2015
institution Swansea University
issn 0888-3270
doi_str_mv 10.1016/j.ymssp.2014.06.003
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 Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 0
active_str 0
description The problem of stochastic model updating is addressed by means of the application of two methods (covariance and interval model updating) to the DLR AIRMOD structure which is repeatedly disassembled and reassembled to provide a database of modal variability due to uncertainty in joint and support stiffnesses and masses of cables and screws. The covariance method is based on an assumption of small uncertainty and implemented at each step of an iterative approach by forward propagation of uncertain parameters using a multivariate normal distribution. The interval approach is based on a Kriging meta-model, thereby providing a very efficient surrogate to replace the expensive full finite element model. This allows a mapping from multiple output measurements to define a hypercube bounded by intervals of parameter uncertainty. It is shown that the measured data is fully enclosed by the hyper-ellipses and hypercubes of the covariance and interval methods respectively. As expected, the interval method is found to be more conservative than the covariance approach but still provides useful estimates without restriction by any assumption of probability distribution.
published_date 2015-02-28T03:21:07Z
_version_ 1763750621127639040
score 11.012924

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