272 D. J. Wagg et al. 30.3 Conclusions In this paper the building blocks of a simulation digital twin have been briefly outlined. In order to fuse the building blocks together a series of workflow processes are required, and the process of data-augmented modelling was considered in more detail. This concept is a key defining characteristic of the digital twin idea, and it was shown using a simple numerical example how augmenting a model with data can be used to compensate for the inherent model discrepancy. There are multiple approaches for inferring model discrepancy, all of which use a data augmentation process, where model form errors are compensated for. The choice of a digital twin does not prescribe a single strategy for inferring model discrepancy, however it will not be possible to ignore this form of uncertainty and bias. General approaches may incorporate grey-box modelling via machine learning components, or fully statistical methods; this is a challenge to the implementation of a digital twin. Acknowledgement The support of the UK Engineering and Physical Sciences Research Council (EPSRC) through grant EP/R006768/1 is greatly acknowledged. References 1. Grieves, M., Vickers, J.: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Transdisciplinary Perspectives on Complex Systems, pp. 85–113. Springer, Cham (2017) 2. 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, 1–14 (2011). Article ID 154798 3. Datta, S.P.A.: Emergence of Digital twins—is this the march of reason? J. Innov. Manag. 5, 14–33 (2017) 4. Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., Liao, H.: Intelligent prognostics tools and e-maintenance. Comput. Ind. 57, 476–489 (2006) 5. Cerrone, A., Hochhalter, J., Heber, G., Ingraffea, A.: On the effects of modeling as-manufactured geometry: toward digital twin. Int. J. Aerosp. Eng. 2014, 1–10 (2014). Article ID 439278 6. Brenner, B., Hummel, V.: Digital twin as enabler for an innovative digital shopfloor management system in the ESB Logistics Learning Factory at Reutlingen-University. Procedia Manuf. 9, 198–205 (2017) 7. Knapp, G., Mukherjee, T., Zuback, J., Wei, H., Palmer, T., De, A., DebRoy, T.: Building blocks for a digital twin of additive manufacturing. Acta Mater. 135, 390–399 (2017) 8. DebRoy, T., Zhang, W., Turner, J., Babu, S.: Building digital twins of 3D printing machines. Scr. Mater. 135, 119–124 (2017) 9. 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) 10. Schleich, B., Anwer, N., Mathieu, L., Wartzack, S.: Shaping the digital twin for design and production engineering. CIRP Ann. 66, 141–144 (2017) 11. Söderberg, R., Wärmefjord, K., Carlson, J.S., Lindkvist, L.: Toward a digital twin for real-time geometry assurance in individualized production. CIRP Ann. 66, 137–140 (2017) 12. Uhlemann, T.H.J., Schock, C., Lehmann, C., Freiberger, S., Steinhilper, R.: The digital twin: demonstrating the potential of real time data acquisition in production systems. Procedia Manuf. 9, 113–120 (2017) 13. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S.C.Y., Nee, A.: Digital twin-driven product design framework. Int. J. Prod. Res. 1–19 (2018) 14. Iglesias, D., Bunting, P., Esquembri, S., Hollocombe, J., Silburn, S., Vitton-Mea, L., Balboa, I., Huber, A., Matthews, G., Riccardo, V., et al.: Digital twin applications for the JET divertor. Fusion Eng. Des. 125, 71–76 (2017) 15. Worden, K., Cross, E.J., Gardner, P., Barthorpe, R.J., Wagg, D.J.: On digital twins, mirrors and virtualisations. In: Proceedings of the 37th IMAC Conference (2019, to appear) 16. Kennedy, M., O’Hagan, A.: Bayesian calibration of computer models. J. R. Stat. Soc. Ser. B Stat. Methodol. 63, 425–464 (2001) 17. Brynjarsdóttir, J., O’Hagan, A.: Learning about physical parameters: the importance of model discrepancy. Inverse Problems 30, 114007 (2014)
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