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Model Updating Strategy of the DLR-AIRMOD Test Structure

Patelli, E. and Broggi, Matteo and Govers, Yves and Mottershead, J.E. (2017) Model Updating Strategy of the DLR-AIRMOD Test Structure. Procedia Engineering, 199, pp. 978-983. Elsevier. ISSN 1877-7058

Full text not available from this repository.

Official URL: https://doi.org/10.1016/j.proeng.2017.09.221

Abstract

Considerable progresses have been made in computer-aided engineering for the high fidelity analysis of structures and systems. Traditionally, computer models are calibrated using deterministic procedures. However, different analysts produce different models based on different modelling approximations and assumptions. In addition, identically constructed structures and systems show different characteristic between each other. Hence, model updating needs to take account modelling and test-data variability. Stochastic model updating techniques such as sensitivity approach and Bayesian updating are now recognised as powerful approaches able to deal with unavoidable uncertainty and variability. This paper presents a high fidelity surrogate model that allows to significantly reduce the computational costs associated with the Bayesian model updating technique. A set of Artificial Neural Networks are proposed to replace multi non-linear input-output relationships of finite element (FE) models. An application for updating the model parameters of the FE model of the DRL-AIRMOD structure is presented.

Item URL in elib:https://elib.dlr.de/115963/
Document Type:Article
Title:Model Updating Strategy of the DLR-AIRMOD Test Structure
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Patelli, E.UNSPECIFIEDUNSPECIFIED
Broggi, MatteoInstitut für Risiko und Zuverlässigkeit, Leibniz University HannoverUNSPECIFIED
Govers, Yvesyves.govers (at) dlr.dehttps://orcid.org/0000-0003-2236-596X
Mottershead, J.E.liverpool university, liverpool, ukUNSPECIFIED
Date:2017
Journal or Publication Title:Procedia Engineering
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:199
Page Range:pp. 978-983
Publisher:Elsevier
ISSN:1877-7058
Status:Published
Keywords:model updating, artificial neural Networks, Bayesian; simulation
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
Location: Göttingen
Institutes and Institutions:Institute of Aeroelasticity > Structural Dynamics and System Identification
Deposited By: Grischke, Birgid
Deposited On:14 Dec 2017 11:43
Last Modified:14 Dec 2017 11:43

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