CANCELED-I³MS - Chinesta Seminar
Prof. Dr. Francisco Chinesta - Hybrid-Twins: When Data-Driven Mechanics Joins Advanced Model Order Reduction to Define Real-Time Dynamic Data-Driven Application Systems
Department of Computational Mechanics, École Centrale de Nantes, France
Abstract
In the previous (third) industrial revolution digital twins (able to emulate a physical system from the accurate solution of the mathematical model expected describing it) were major protagonists, making accurate analyses and designs possible. Numerical simulation is nowadays present in most of scientific fields and engineering domains, making possible the virtual evaluation of systems responses and alleviating the number of experiences on the real system that the numerical model represents. However, usually, virtual models (digital twins) are static, that is, they are used in the analysis and/or design of complex systems, but they are not expected to accommodate or assimilate data so as to define dynamic data-driven application systems. The characteristic time of standard simulation strategies is not compatible with the real-time constraints compulsory for control purposes or augmented reality environments.
Moreover, in practice, significant deviations between the predicted and observed responses are noticed, limiting the use of digital twins in many applications requiring online adaptation. In that situation control-based approaches were and are usually retained, in which the system is condensed into a goal-oriented transfer-function for which, despite of its effectiveness, the amount of information manipulated remains quite limited to do not compromise the compulsory real-time feedback.
The origin of the just referred deviations between predictions and measurements is due to (i) inaccuracies in the employed models that sometimes continue to be crude approximations/descriptions of the real systems; (ii) to the fact that in many cases models evolve in time in an “a priori" almost unpredictable manner; and (iii) inaccuracies in the determination of the model parameters or in their time-evolution.
Thus, nowadays, it is generally accepted the urgent need of more reliable modeling approaches as well as the dynamic assimilation of collected data on running simulations, for defining efficient Dynamic Data-Driven Application Systems - DDDAS. DDDAS consist of three main ingredients: (i) a simulation core able to solve complex mathematical or data-driven models under real-time constraints; (ii) advanced strategies able to assimilate data; and (iii) a mechanism to adapt the model online to evolving environments (that could imply the model change and not only the change of the parameters that an “a priori" assumed model involves). Hybrid-Twin embraces these three functionalities into a new paradigm in simulation-based engineering, and more concretely a cyber-physical-system framework.