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Multiscale DDDAS Framework for Damage Prediction in Aerospace Composite Structures

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Handbook of Dynamic Data Driven Applications Systems

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Abstract

In recent years, there has been a significant increase in the use of Unmanned Aerial Vehicles (UAV). UAVs are expected to fly a large number of long (48 or more hours) missions, and operate without failure. Furthermore, in order to increase the durability of these vehicles and to decrease weight, composite materials are currently experiencing a widespread adoption in applications related both to military and civilian aerospace structures. As a result, in order to decrease costs associated with the operation, maintenance, and, in some cases, loss of these vehicles, it is desirable to have a Dynamically Data-Driven Applications Systems framework that can reliably predict the onset and progressions of structural damage in geometrically and materially complex aerospace composite structures operating in the environments typical of UAVs. In this chapter we present a multiscale DDDAS Interactive Structure Composite Element Relation Network (DISCERN) framework.The proposed multiscale DISCERN framework is successfully deployed on a full-scale laminated composite structure to predict the damage onset, evolution, and the structure remaining fatigue life.

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Notes

  1. 1.

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Acknowledgements

This work was supported by the AFOSR Grant FA9550-16-1-0131. The authors greatly acknowledge this support.

Author information

Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB, Canada

    A. Korobenko

  2. Department of Structural Engineering, University of California, San Diego, CA, USA

    M. Pigazzini

  3. Department of Civil Engineering, University of Hong Kong, Hong Kong, China

    X. Deng

  4. School of Engineering, Brown University, Providence, RI, USA

    Y. Bazilevs

Authors
  1. A. Korobenko
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  2. M. Pigazzini
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  3. X. Deng
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  4. Y. Bazilevs
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Corresponding author

Correspondence to A. Korobenko .

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Editors and Affiliations

  1. Air Force Office of Scientific Research, Air Force Research Laboratory, Arlington, VA, USA

    Erik Blasch

  2. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

  3. Information Directorate, Air Force Research Laboratory, Rome, NY, USA

    Alex Aved

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Korobenko, A., Pigazzini, M., Deng, X., Bazilevs, Y. (2018). Multiscale DDDAS Framework for Damage Prediction in Aerospace Composite Structures. In: Blasch, E., Ravela, S., Aved, A. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-95504-9_30

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  • DOI: https://doi.org/10.1007/978-3-319-95504-9_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95503-2

  • Online ISBN: 978-3-319-95504-9

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