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A multivariate interval approach for inverse uncertainty quantification with limited experimental data

Faes, Matthias and Broggi, Matteo and Patelli, Edoardo and Govers, Yves and Mottershead, John and Beer, Michael and Moens, David (2018) A multivariate interval approach for inverse uncertainty quantification with limited experimental data. Mechanical Systems and Signal Processing (MSSP), 118, pp. 534-548. Elsevier. doi: 10.1016/j.ymssp.2018.08.050. ISSN 0888-3270.

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Official URL: https://www.sciencedirect.com/science/article/pii/S0888327018305946


This paper introduces an improved version of a novel inverse approach for the quantification of multivariate interval uncertainty for high dimensional models under scarce data availability. Furthermore, a conceptual and practical comparison of the method with the well-established probabilistic framework of Bayesian model updating via Transitional Markov Chain Monte Carlo is presented in the context of the DLR-AIRMOD test structure. First, it is shown that the proposed improvements of the inverse method alleviate the curse of dimensionality of the method with a factor up to 105. Furthermore, the comparison with the Bayesian results revealed that the selection ofthe most appropriate method depends largely on the desired information and availability of data. In case large amounts of data are available, and/or the analyst desires full (joint)-probabilistic descriptors of the model parameter uncertainty, the Bayesian method is shown to be the most performing. On the other hand however, when such descriptors are not needed (e.g., for worst-case analysis), and only scarce data are available, the interval method is shown to deliver more objective and robust bounds on the uncertain parameters. Finally, also suggestions to aid the analyst in selecting the most appropriate method for inverse uncertainty quantification are given.

Item URL in elib:https://elib.dlr.de/123002/
Document Type:Article
Additional Information:Online-Publishing. Die gedruckte Version erscheint 2019.
Title:A multivariate interval approach for inverse uncertainty quantification with limited experimental data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Faes, MatthiasKU LeuvenUNSPECIFIED
Broggi, MatteoInstitut für Risiko und Zuverlässigkeit, Leibniz University HannoverUNSPECIFIED
Patelli, EdoardoUniversity of LiverpoolUNSPECIFIED
Govers, YvesYves.Govers (at) dlr.dehttps://orcid.org/0000-0003-2236-596X
Mottershead, JohnCentre for Engineering Dynamics, The University of Liverpool, United KingdomUNSPECIFIED
Beer, MichaelLeibniz University HannoverUNSPECIFIED
Moens, DavidKU LeuvenUNSPECIFIED
Journal or Publication Title:Mechanical Systems and Signal Processing (MSSP)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1016/j.ymssp.2018.08.050
Page Range:pp. 534-548
Keywords:multivariate interval uncertainty, uncertainty quantification, DLR-AIRMOD, Bayesian model, updating, limited data
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:fixed-wing aircraft
DLR - Research area:Aeronautics
DLR - Program:L AR - Aircraft Research
DLR - Research theme (Project):L - Flight Physics (old)
Location: Göttingen
Institutes and Institutions:Institute of Aeroelasticity > Structural Dynamics and System Identification
Deposited By: Govers, Dr.-Ing. Yves
Deposited On:27 Nov 2018 11:33
Last Modified:06 Sep 2019 15:28

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