Model Updating Strategy of the DLR-AIRMOD Test Structure

Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review

Autorschaft

  • Edoardo Patelli
  • Matteo Broggi
  • Yves Govers
  • John E. Mottershead

Organisationseinheiten

Externe Organisationen

  • The University of Liverpool
  • Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)978-983
Seitenumfang6
FachzeitschriftProcedia Engineering
Jahrgang199
PublikationsstatusVeröffentlicht - 12 Sept. 2017
Veranstaltung10th International Conference on Structural Dynamics, EURODYN 2017 - Rome, Italien
Dauer: 10 Sept. 2017 → 13 Sept. 2017

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.

ASJC Scopus Sachgebiete

Zitieren

Model Updating Strategy of the DLR-AIRMOD Test Structure. / Patelli, Edoardo; Broggi, Matteo; Govers, Yves et al.
in: Procedia Engineering, Jahrgang 199, 12.09.2017, S. 978-983.

Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review

Patelli, E, Broggi, M, Govers, Y & Mottershead, JE 2017, 'Model Updating Strategy of the DLR-AIRMOD Test Structure', Procedia Engineering, Jg. 199, S. 978-983. https://doi.org/10.1016/j.proeng.2017.09.221
Patelli, E., Broggi, M., Govers, Y., & Mottershead, J. E. (2017). Model Updating Strategy of the DLR-AIRMOD Test Structure. Procedia Engineering, 199, 978-983. https://doi.org/10.1016/j.proeng.2017.09.221
Patelli E, Broggi M, Govers Y, Mottershead JE. Model Updating Strategy of the DLR-AIRMOD Test Structure. Procedia Engineering. 2017 Sep 12;199:978-983. doi: 10.1016/j.proeng.2017.09.221
Patelli, Edoardo ; Broggi, Matteo ; Govers, Yves et al. / Model Updating Strategy of the DLR-AIRMOD Test Structure. in: Procedia Engineering. 2017 ; Jahrgang 199. S. 978-983.
Download
@article{9432e00429204df0bbf3fae23358bee0,
title = "Model Updating Strategy of the DLR-AIRMOD Test Structure",
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.",
keywords = "Artificial Neural Networks, Bayesian, Model updating, Simulation",
author = "Edoardo Patelli and Matteo Broggi and Yves Govers and Mottershead, {John E.}",
note = "Publisher Copyright: {\textcopyright} 2017 The Authors. Published by Elsevier Ltd. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 10th International Conference on Structural Dynamics, EURODYN 2017 ; Conference date: 10-09-2017 Through 13-09-2017",
year = "2017",
month = sep,
day = "12",
doi = "10.1016/j.proeng.2017.09.221",
language = "English",
volume = "199",
pages = "978--983",

}

Download

TY - JOUR

T1 - Model Updating Strategy of the DLR-AIRMOD Test Structure

AU - Patelli, Edoardo

AU - Broggi, Matteo

AU - Govers, Yves

AU - Mottershead, John E.

N1 - Publisher Copyright: © 2017 The Authors. Published by Elsevier Ltd. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2017/9/12

Y1 - 2017/9/12

N2 - 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.

AB - 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.

KW - Artificial Neural Networks

KW - Bayesian

KW - Model updating

KW - Simulation

UR - http://www.scopus.com/inward/record.url?scp=85029906780&partnerID=8YFLogxK

U2 - 10.1016/j.proeng.2017.09.221

DO - 10.1016/j.proeng.2017.09.221

M3 - Conference article

AN - SCOPUS:85029906780

VL - 199

SP - 978

EP - 983

JO - Procedia Engineering

JF - Procedia Engineering

SN - 1877-7058

T2 - 10th International Conference on Structural Dynamics, EURODYN 2017

Y2 - 10 September 2017 through 13 September 2017

ER -