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TERATEC 2018 Forum
Workshop 1 - Wednesday, June 20 from 9h00 to 12h30


Hybrid TwinTM - A new paradigm for the industry and society4.0 : Enriching technology and humans
Prof. Dr. Francisco (Paco) CHINESTA, ESI Group Chair @ ENSAM ParisTech & ESI Group Scientific Department

Abstract : In the previous industrial revolution, virtual twins (emulating a physical system) were major protagonists. However, usually numerical models (virtual twins) are static, that is, they are used in the design of complex systems and their components, but they are not expected to accommodate or assimilate data. The reason is that the characteristic time of standard simulation strategies is not compatible with the real-time constraints mandatory for control purposes. Model Order Reduction techniques opened new possibilities for more efficient simulations.

A possible alternative within the MOR framework consists in extracting “offline" the most significant modes involved in the model solution, and then project the solution of “quite similar" problems in that reduced basis. Another technique, the so-called PGD, consists of calculating offline a parametric solution containing the solution of all possible scenarios.

The next generation of twins, the so-called digital twins, allowed for assimilating data collected from sensors with the main aim of identifying parameters involved in the model as well as their time evolution in real time, anticipating actions from their predictive capabilities. Thus, simulation-based control was envisaged and successfully accomplished in many applications; in many cases using low computing devices such as microprocessors and tablets. Despite an unexpected difficulty appeared immediately. Namely, in practice significant deviations between the predicted and observed responses were noticed, limiting or abandoning their use in many applications.

Indeed, DDDAS consist of three main ingredients: (i) a simulation core able to solve complex mathematical problems representing physical models under real-time constraints; (ii) advanced strategies able to proceed with data-assimilation, data-curation, data-driven modelling and finally data-fusion when using compatible descriptions for the physical and data-based models; and (iii) a mechanism to adapt the model online to evolving environments (control). Hybrid TwinTM embraces these three functionalities into a new paradigm in simulation-based engineering sciences - SBES -.

Biography : Francisco Chinesta is currently full Professor of computational physics at ENSAM ParisTech (Paris, France), He was (2008-2012) AIRBUS Group chair professor and since 2013 ESI-Group chair professor on advanced modeling and simulation of materials, structures, processes and systems. He is honorary fellow of the “Institut Universitaire de France” – IUF - and Fellow of the Spanish Royal Academy of Engineering. He received many scientific awards in four different fields: in particular three of them related to the present talk: the Maurice Couette prize awarded by the French association of rheology, the ESAFORM prize awarded by the European Association of Material Forming and the Zienkiewicz medal awarded by the International Association of Computational Mechanics. He is author of about 300 papers in peer-reviewed international journals and more than 600 contributions in conferences and books. He is president of the French association of computational mechanics (CSMA) and director of the CNRS research group (GdR) on model order reduction techniques for engineering sciences. He is editor and associate editor of many journals.


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