Abstract
MBSE has made significant strides in the last decade. Today, its focus is on extending system life cycle coverage and improving system model accuracy by leveraging methods such as formal modeling, machine learning, and analytics in conjunction with exploiting digital engineering technology. The growing convergence between digital engineering and MBSE and the advent of digital twins has made it possible to enhance system modeling and increase life cycle coverage. Digital twins are computational models of systems that have bidirectional communication with the real-world systems and evolve in synchronization with them. This chapter presents how digital twin technology can be leveraged in MBSE and offers an example of digital twin creation for an unmanned aerial vehicle (UAV) that can dynamically replan its path in response to disruptions such as inflight damage or changes in environmental conditions. This chapter also presents results of early experimentation with a digital twin and describes how operational analysis and modeling can be enhanced by leveraging the digital twin construct.
References
Bone, M., Blackburn, M., Kruse, B., Dzielski, J., Hagedorn, T., Grosse, I., 2018. Toward an Interoperability and Integration Framework to Enable Digital Thread. Systems. 2018; 6(4):46.
Kinard, D., 2010. The Digital Thread–Key to F-35 Joint Strike Fighter Affordability, Aerospace Manufacturing and Design http://www.onlineamd.com/amd-080910-f-35-joint-strikefighter-digital-thread.aspx.
West, T.D., Pyster, A., 2015. Untangling the Digital Thread: The Challenge and Promise of Model-Based Engineering in Defense Acquisition, INSIGHT, 18(2), pp. 45-55, August.
Madni, A.M., 2018. Transdisciplinary Systems Engineering: Exploiting Convergence in a Hyper-connected World. New York, NY: Springer.
Madni, A.M., Erwin, D., 2018. Next Generation Adaptive Cyber Physical Human Systems, Year 1 Technical Report, Systems Engineering Research Center, September.
Madni, A.M., Madni, C.C., Lucero, D.S., 2019. Leveraging Digital Twin Technology in Model-Based Systems Engineering, MDPI Systems, special issue on Model-Based Systems Engineering, Publication, March.
Grieves, M., Vickers, J., 2017. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems, F.-J. Kahlen et al. (eds.), Transdisciplinary Perspectives on Complex Systems, Springer International Publishing, Germany.
Hoffenson, S., Brouse, P., Gelosh, D.S., Pafford, M., Strawser, L. D., Wade, J., Sofer, A., 2019. Grand Challenges in Systems Engineering education, in S.C. Adams et al. (eds.), Systems Engineering in Context, Proceedings of the 16th Annual Conference on Systems Engineering Research.
Madni, A.M., Purohit, S. 2019. Economic Analysis of Model-Based Systems Engineering. MDPI Systems, special issue on Model-Based Systems Engineering, Publication, February.
Madni, A.M., Sievers, M., 2018. Model-Based Systems Engineering: Motivation, Current Status, and Research Opportunities, Systems Engineering, Special 20th Anniversary Issue, vol. 21, issue 3.
Kraft, E.M., 2015. HPCMP CREATE-AV and the Air Force Digital Threat, 53rd AIAA Aerospace Sciences Meeting, Kissimmee, FL.
Datta, S.P.A., 2016. Emergence of Digital Twins, arXiv e-print (arXiv:1610.06467).
Datta, S.P A., 2017. Emergence of Digital Twins – Is this the march of reason? Journal of Innovation Management, 5(3), 14–33. https://doi.org/10.24840/2183-0606_005.003_0003
Folds D. J. and McDermott T. A., 2019. “The Digital (Mission) Twin: an Integrating Concept for Future Adaptive Cyber-Physical-Human Systems,” IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 2019, pp. 748-754, https://doi.org/10.1109/SMC.2019.8914324.
Ghosh, A.K., Ullah, S., Kubo, A., 2019. Hidden Markov model-based digital twin construction for futuristic manufacturing systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing. 1-15. https://doi.org/10.1017/S089006041900012X.
Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., 2018. Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine. 51. 1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474.
Marr, B., 2017. What Is Digital Twin Technology – And Why Is It So Important? Forbes, https://www.forbes.com/sites/bernardmarr/2017/03/06/what-is-digital-twin-technology-and-why-is-it-so-important/#78b97b8a2e2a.
Morton, S.A., McDaniel, D.R., Sears, D.R., Tillman, B., Tuckey, T.R., 2009. Kestrel—a fixed wing virtual aircraft product of the CREATE program, in Proceedings of the 47th AIAA Aerospace Sciences Meeting, Orlando, Fla, USA, January, AIAA 2009-338.
Ocampo, J., Millwater, H., Crosby, N., Gamble, B., Hurst, C., Reyer, M., Mottaghi, S., Nuss, M., 2020. An Ultrafast Crack Growth Lifing Model to Support Digital Twin, Virtual Testing, and Probabilistic Damage Tolerance Applications. https://doi.org/10.1007/978-3-030-21503-3_12.
Madni, A.M., Purohit, S., Madni, A., 2020. Digital Twin Technology-Enabled Research Testbed for Game-Based Learning and Assessment: Theoretical Issues of Using Simulations and Games in Educational Assessment, O'Neil, H. (Eds.), Taylor & Francis, Spring.
Kapteyn, M.G., Pretorius, J.V.R. & Willcox, K.E. A probabilistic graphical model foundation for enabling predictive digital twins at scale. Nat Comput Sci 1, 337–347 (2021). https://doi.org/10.1038/s43588-021-00069-0.
Chen, P.C., Baldelli, D.H., Zeng, J., 2008. Dynamic flight simulation (DFS) tool for nonlinear flight dynamic simulation including aeroelastic effects,” in Proceedings of the AIAA Atmospheric Flight Mechanics Conference and Exhibit, Honolulu, Hawaii, USA, AIAA 2008-6376.
Glaessgen, E.H., and Stargel, D.S., 2012. The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles.
Stojek, M., & Pietraszek, J., 2015. Simulation-Based Engineering Science Challenges of the 21st Century. Applied Mechanics and Materials, 712, 3–8. https://doi.org/10.4028/www.scientific.net/AMM.712.3
Tuegel, E.J., Ingraffea, A.R., Eason, T.J., Spottswood, S.M., 2011. Reengineering Aircraft Structural Life Prediction Using a Digital Twin, International Journal of Aerospace Engineering, https://doi.org/10.1155/2011/154798.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., … & Levine, S. (2018). Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905.
Torabi, F., Warnell, G., & Stone, P. (2018). Behavioral cloning from observation. arXiv preprint arXiv:1805.01954.
Ho, J., & Ermon, S. (2016). Generative adversarial imitation learning. arXiv preprint arXiv:1606.03476.
Juliani, A., Berges, V., Teng, E., Cohen, A., Harper, J., Elion, C., Goy, C., Gao, Y., Henry, H., Mattar, M., Lange, D. (2020). Unity: A General Platform for Intelligent Agents. arXiv preprint arXiv:1809.02627. https://github.com/Unity-Technologies/ml-agents.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this entry
Cite this entry
Madni, A.M., Purohit, S., Madni, C.C. (2022). Exploiting Digital Twins in MBSE to Enhance System Modeling and Life Cycle Coverage. In: Madni, A.M., Augustine, N., Sievers, M. (eds) Handbook of Model-Based Systems Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-27486-3_33-1
Download citation
DOI: https://doi.org/10.1007/978-3-030-27486-3_33-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27486-3
Online ISBN: 978-3-030-27486-3
eBook Packages: Springer Reference Intelligent Technologies and RoboticsReference Module Computer Science and Engineering