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 proceedingConference contribution

23 Scopus citations


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 languageEnglish (US)
Title of host publicationProcedia Computer Science
PublisherElsevier BV
Number of pages10
StatePublished - Jun 1 2013
Externally publishedYes

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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