Skip to main content

A Hardware Testbed for Dynamic Data-Driven Aerospace Digital Twins

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12312)

Abstract

This paper presents a hardware testbed that furthers the development of a dynamic data-driven application system (DDDAS). In particular, the focus of this testbed is on enabling a self-aware unmanned aerial vehicle (UAV). Self-awareness in this context refers to the ability of the vehicle to collect information about itself and use this information to alter the way it completes missions via on-board dynamic decision-making. Prior work has focused on developing computational methods that enable a digital twin of this vehicle, and demonstration of the resulting self-aware capability via simulation. This work presents a hardware testbed and associated experimental methodology for data collection, analysis, and demonstration of the self-aware UAV concept. The hardware testbed includes custom-built carbon fiber wings, the design of which have been validated via flight test. A sensor suite composed of wireless high frequency dynamic strain sensors has been developed and demonstrated using benchtop experiments. The proposed DDDAS architecture, which includes previously developed computational methods, has the potential to enable two-way coupling between estimation of the UAV structural state and dynamic mission replanning; capability that is critical for realizing the self-aware UAV concept.

Keywords

  • DDDAS
  • Digital twin
  • Self-aware unmanned vehicle

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-61725-7_7
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-61725-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

References

  1. Allaire, D., Biros, G., Chambers, J., Ghattas, O., Kordonowy, D., Willcox, K.: Dynamic data driven methods for self-aware aerospace vehicles. Procedia Comput. Sci. 9, 1206–1210 (2012)

    CrossRef  Google Scholar 

  2. Blasch, E., Ravela, S., Aved, A. (eds.): Handbook of Dynamic Data Driven Applications Systems. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95504-9

    CrossRef  Google Scholar 

  3. Boschert, S., Rosen, R.: Digital twin—the simulation aspect. In: Hehenberger, P., Bradley, D. (eds.) Mechatronic Futures. Challenges and Solutions for Mechatronic Systems and their Designers, pp. 59–74. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32156-1_5

    CrossRef  Google Scholar 

  4. Darema, F.: Dynamic data driven applications systems: a new paradigm for application simulations and measurements. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 662–669. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24688-6_86

    CrossRef  Google Scholar 

  5. Glaessgen, E., Stargel, D.: The digital twin paradigm for future NASA and US air force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, p. 1818 (2012)

    Google Scholar 

  6. Kapteyn, M., Knezevic, D., Huynh, D., Tran, M., Willcox, K.: Data-driven physics-based digital twins via a library of component-based reduced-order models. Int. J. Numer. Methods Eng. (2020)

    Google Scholar 

  7. Kapteyn, M.G., Knezevic, D.J., Willcox, K.: Toward predictive digital twins via component-based reduced-order models and interpretable machine learning. In: AIAA Scitech 2020 Forum, p. 0418 (2020)

    Google Scholar 

  8. Li, C., Mahadevan, S., Ling, Y., Choze, S., Wang, L.: Dynamic Bayesian network for aircraft wing health monitoring digital twin. AIAA J. 55(3), 930–941 (2017)

    CrossRef  Google Scholar 

  9. Martins, B.L., Kosmatka, J.B.: Health monitoring of aerospace structures via dynamic strain measurements: an experimental demonstration. In: AIAA Scitech 2020 Forum, p. 0701 (2020)

    Google Scholar 

  10. Rasheed, A., San, O., Kvamsdal, T.: Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access 8, 21980–22012 (2020)

    CrossRef  Google Scholar 

  11. Reifsnider, K., Majumdar, P.: Multiphysics stimulated simulation digital twin methods for fleet management. In: 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, p. 1578 (2013)

    Google Scholar 

  12. Singh, V., Willcox, K.E.: Methodology for path planning with dynamic data-driven flight capability estimation. AIAA J. 55, 2727–2738 (2017)

    Google Scholar 

  13. Tuegel, E.J., Ingraffea, A.R., Eason, T.G., Spottswood, S.M.: Reengineering aircraft structural life prediction using a digital twin. Int. J. Aerosp. Eng. (2011)

    Google Scholar 

Download references

Acknowledgments

The authors thank Gray Riley and Alexander Vladimir Andersen of Aurora Flight Sciences for their contributions to the development of the testbed aircraft. This work was supported in part by AFOSR grant FA9550-16-1-0108 under the Dynamic Data-Driven Application Systems Program, the MIT-SUTD International Design Center, and a Cockrell School of Engineering graduate fellowship.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Stefanie J. Salinger , Michael G. Kapteyn , Cory Kays , Jacob V. R. Pretorius or Karen E. Willcox .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Salinger, S.J., Kapteyn, M.G., Kays, C., Pretorius, J.V.R., Willcox, K.E. (2020). A Hardware Testbed for Dynamic Data-Driven Aerospace Digital Twins. In: Darema, F., Blasch, E., Ravela, S., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2020. Lecture Notes in Computer Science(), vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61725-7_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61724-0

  • Online ISBN: 978-3-030-61725-7

  • eBook Packages: Computer ScienceComputer Science (R0)