Simulation-based Verification with Experimentable Digital Twins in Virtual Testbeds

  • Ulrich Dahmen
  • Jürgen Roßmann
Conference paper


As modern systems become more and more complex, their design and realization, as well as the effective management of engineering projects evolve to increasingly challenging tasks. This explains the continuous need for appropriate cross-domain methodologies in order to handle complexity and thereby create reliable systems. One important aspect is the substantiation that a specific system design is suitable for its intended use, which is usually achieved by testing. Unfortunately, those tests are carried out after the system has been produced so that the elimination of possible errors and defects causes high efforts and costs. This paper introduces a systematic approach for a simulation-based verification and validation support by using experimentable digital twins during the entire product life cycle. It allows to test the system under development in various virtual scenarios before it is implemented and tested in reality and thus reduces the risk of lately detected system design errors which increases the reliability of the development process.


Systems Engineering Modeling and Simulation Virtual Testbed V-Model Verification and Validation Digital Twin 


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  1. 1.
    H. Ulrich and G. J. B. Probst, Anleitung zum ganzheitlichen Denken und Handeln. Bern: P. Haupt, 1991.Google Scholar
  2. 2.
    A. G. Sephenson, “Mars climate orbiter: Mishap investigation board phase i report,” tech. rep., Mishap Investigation Board, Nov. 1999.Google Scholar
  3. 3.
    K. Forsberg and H. Mooz, “The relationship of system engineering to the project cycle,” in Proceedings of the 12th Internet World Congress on Project Management, 1994.Google Scholar
  4. 4.
    T. Weilkiens, Systems Engineering with SysML/UML. Elsevier Science & Technology, 2008.Google Scholar
  5. 5.
    International Council on Systems Engineering, “Systems engineering vision 2020,” Tech. Rep. INCOSE-TP-2004-004-02, INCOSE, 2007.Google Scholar
  6. 6.
    VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik: Fachausschuss Industrie 4.0, “Industrie 4.0 - technical assets,” tech. rep., Verein Deutscher Ingenieure e.V., D¨usseldorf, 2015.Google Scholar
  7. 7.
    Wissenschaftliche Gesellschaft f¨ur Produktionstechnik WGP e.V., “WGPStandpunkt Industrie 4.0,” tech. rep., WGP e.V., 2015.Google Scholar
  8. 8.
    M. Grieves, “Digital twin: Manufacturing excellence through virtual factory replication: A white paper,” 2014.Google Scholar
  9. 9.
    National Academy of Science and Engineering, and Communication Promoters Group of the Industry-Science Research Alliance, “Recommendations for implementing the strategic initiative industire 4.0,” tech. rep., acatech and CPG, Frankfurt/Main, 2013.Google Scholar
  10. 10.
    VDI 3633 Blatt 1, “Simulation of systems in materials handling, logistics and production,” 2014.Google Scholar
  11. 11.
    J. Rossmann and M. Schluse, “Virtual robotic testbeds: A foundation for e-robotics in space, in industry—and in the woods,” in Proceedings of the 4th International Conference on DeSE, (Dubai), pp. 496–501, 6th - 8th December 2011.Google Scholar
  12. 12.
    J. Rossmann, M. Schluse, C. Schlette, and R. Waspe, “Control by 3d simulation a new erobotics approach to control design in automation,” in Proceedings of the 5th ICIRA, vol. Part II of LNAI 7507, pp. 186–197, Montreal, Quebec, Canada: Springer, October 3-5, 2012 2012.Google Scholar
  13. 13.
    SCS Technical Committee on Model Credibility, “Terminology for model credibility,” SIMULATION, vol. 32, no. 3, pp. 103–104, 1979.Google Scholar
  14. 14.
    W. L. Oberkampf and C. J. Roy, Verification and validation in scientific computing. Cambridge: Cambridge University Press, 2010.Google Scholar
  15. 15.
    Department of Defense (DoD), DoD Directive No. 5000.59: Modeling and Simulation (M&S) Management, 1994.Google Scholar
  16. 16.
    American Society of Mechanical Engineers, Guide for verification and validation in computational solid mechanics : ASME V&V 10-2006. New York: ASME, 2006.Google Scholar
  17. 17.
    J.Weise, K. Brieß, A. Adomeit, H.-G. Reimerdes, M. G¨oller, and R. Dillmann, “An intelligent building blocks concept for on-orbit-satellite servcing,” in Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space (iSAIRAS), 2012.Google Scholar
  18. 18.
    ECSS-M-ST-10C Rev. 1, “Space project management - project planning and implementation,” 2009.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Man-Machine-InteractionRWTH Aachen UniversityAachenDeutschland

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