Abstract
Engineering is evolving in the same way than society is doing. Nowadays, data is acquiring a prominence never imagined. In the past, in the domain of materials, processes and structures, testing machines allowed extract data that served in turn to calibrate state-of-the-art models. Some calibration procedures were even integrated within these testing machines. Thus, once the model had been calibrated, computer simulation takes place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints. This gives rise to the the family of so-called twins: the virtual, the digital and the hybrid twins. Moreover, as discussed in the present paper, not only data serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm.
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Notes
There seems to be no consensus on the definition of the concepts of virtual, digital and hybrid twins. In this paper we suggest one possible distinction, that seems feasible, attending to their respective characteristics. It is not the sole possibility, of course, nor do we pretend to create any controversy on it.
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Acknowledgements
This project has received funding from the Spanish Ministry of Economy and Competitiveness through Grants number DPI2017-85139-C2-1-R and DPI2015-72365-EXP and by the Regional Government of Aragon and the European Social Fund, research group T24 17R. Special thanks to several industrial groups and companies that contributed to the approches presented in this review article, among them, ESI, Gestamp, Volkswagen, Renault, APT, Vitirover, Airbus, Ariane, Saint Gobain, Michelin, AddUp, …
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Chinesta, F., Cueto, E., Abisset-Chavanne, E. et al. Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data. Arch Computat Methods Eng 27, 105–134 (2020). https://doi.org/10.1007/s11831-018-9301-4
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DOI: https://doi.org/10.1007/s11831-018-9301-4