A dynamic data driven application system for real-time monitoring of stochastic damage

E. E. Prudencio; P. T. Bauman; S. V. Williams; D. Faghihi; K. Ravi-Chandar; J. T. Oden

doi:10.1016/j.procs.2013.05.375

A dynamic data driven application system for real-time monitoring of stochastic damage

E. E. Prudencio, P. T. Bauman, S. V. Williams, D. Faghihi, K. Ravi-Chandar, J. T. Oden

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

24 Scopus citations

Abstract

In this paper we describe a stochastic dynamic data-driven application system (DDDAS) for monitoring, in real-time, material damage in aerospace structures. The work involves experiments, different candidate damage models, finite element discretization, Bayesian analysis of the candidate models, Bayesian filtering with the most plausible model, parallel scientific libraries, and high performance computing. Here we describe a low-degree-of-freedom model designed for proof-of-concept, in preparation for the development of the full DDDAS. The physical system involves fiber-reinforced composite plates subjected to quasi-static loading and enriched with distributed carbon nanotubes that act as sensors, signaling damage through changes on the voltage profile. We give an overview of the experimental data we collected, of the damage models we explored, and of the Bayesian methodology we applied in order to use uncertain experimental data for driving the stochastic system.

Original language	English (US)
Title of host publication	Procedia Computer Science
Publisher	Elsevier BV
Pages	2056-2065
Number of pages	10
DOIs	https://doi.org/10.1016/j.procs.2013.05.375
State	Published - Jun 1 2013
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-24
Acknowledgements: The support of this work under AFOSR contract FA9550-11-1-0314 is gratefully acknowledged. Authors Bauman, Oden, and Prudencio, were also partially supported by the DOE contract DE-FC52-08NA28615 in connection with the Predictive Science Academic Alliance Program. Author Oden was also partially supported by the DOE contract DE-FG02-05ER25701 in connection with the Multiscale Mathematics Program. Author Prudencio was also partially supported by the Academic Excellence Alliance program of KAUST.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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10.1016/j.procs.2013.05.375

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@inproceedings{6255f55b98a34118a63c4d148d4b9e9d,

title = "A dynamic data driven application system for real-time monitoring of stochastic damage",

abstract = "In this paper we describe a stochastic dynamic data-driven application system (DDDAS) for monitoring, in real-time, material damage in aerospace structures. The work involves experiments, different candidate damage models, finite element discretization, Bayesian analysis of the candidate models, Bayesian filtering with the most plausible model, parallel scientific libraries, and high performance computing. Here we describe a low-degree-of-freedom model designed for proof-of-concept, in preparation for the development of the full DDDAS. The physical system involves fiber-reinforced composite plates subjected to quasi-static loading and enriched with distributed carbon nanotubes that act as sensors, signaling damage through changes on the voltage profile. We give an overview of the experimental data we collected, of the damage models we explored, and of the Bayesian methodology we applied in order to use uncertain experimental data for driving the stochastic system.",

author = "Prudencio, {E. E.} and Bauman, {P. T.} and Williams, {S. V.} and D. Faghihi and K. Ravi-Chandar and Oden, {J. T.}",

note = "KAUST Repository Item: Exported on 2022-06-24 Acknowledgements: The support of this work under AFOSR contract FA9550-11-1-0314 is gratefully acknowledged. Authors Bauman, Oden, and Prudencio, were also partially supported by the DOE contract DE-FC52-08NA28615 in connection with the Predictive Science Academic Alliance Program. Author Oden was also partially supported by the DOE contract DE-FG02-05ER25701 in connection with the Multiscale Mathematics Program. Author Prudencio was also partially supported by the Academic Excellence Alliance program of KAUST. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

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doi = "10.1016/j.procs.2013.05.375",

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AU - Prudencio, E. E.

AU - Bauman, P. T.

AU - Williams, S. V.

AU - Faghihi, D.

AU - Ravi-Chandar, K.

AU - Oden, J. T.

N1 - KAUST Repository Item: Exported on 2022-06-24 Acknowledgements: The support of this work under AFOSR contract FA9550-11-1-0314 is gratefully acknowledged. Authors Bauman, Oden, and Prudencio, were also partially supported by the DOE contract DE-FC52-08NA28615 in connection with the Predictive Science Academic Alliance Program. Author Oden was also partially supported by the DOE contract DE-FG02-05ER25701 in connection with the Multiscale Mathematics Program. Author Prudencio was also partially supported by the Academic Excellence Alliance program of KAUST. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2013/6/1

Y1 - 2013/6/1

N2 - In this paper we describe a stochastic dynamic data-driven application system (DDDAS) for monitoring, in real-time, material damage in aerospace structures. The work involves experiments, different candidate damage models, finite element discretization, Bayesian analysis of the candidate models, Bayesian filtering with the most plausible model, parallel scientific libraries, and high performance computing. Here we describe a low-degree-of-freedom model designed for proof-of-concept, in preparation for the development of the full DDDAS. The physical system involves fiber-reinforced composite plates subjected to quasi-static loading and enriched with distributed carbon nanotubes that act as sensors, signaling damage through changes on the voltage profile. We give an overview of the experimental data we collected, of the damage models we explored, and of the Bayesian methodology we applied in order to use uncertain experimental data for driving the stochastic system.

AB - In this paper we describe a stochastic dynamic data-driven application system (DDDAS) for monitoring, in real-time, material damage in aerospace structures. The work involves experiments, different candidate damage models, finite element discretization, Bayesian analysis of the candidate models, Bayesian filtering with the most plausible model, parallel scientific libraries, and high performance computing. Here we describe a low-degree-of-freedom model designed for proof-of-concept, in preparation for the development of the full DDDAS. The physical system involves fiber-reinforced composite plates subjected to quasi-static loading and enriched with distributed carbon nanotubes that act as sensors, signaling damage through changes on the voltage profile. We give an overview of the experimental data we collected, of the damage models we explored, and of the Bayesian methodology we applied in order to use uncertain experimental data for driving the stochastic system.

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M3 - Conference contribution

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BT - Procedia Computer Science

PB - Elsevier BV

ER -

A dynamic data driven application system for real-time monitoring of stochastic damage

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