Data-driven GENERIC modeling of poroviscoelastic materials
Article dans une revue avec comité de lecture
Auteur
GONZÁLEZ, DavidCUETO, Elías G.ALFARO, Icíar 
GHNATIOS, Chady CHINESTA SORIA, FranciscoRésumé
Biphasic soft materials are challenging to model by nature. Ongoing efforts are targeting their effective modeling and simulation. This work uses experimental atomic force nanoindentation of thick hydrogels to identify the indentation forces are a function of the indentation depth. Later on, the atomic force microscopy results are used in a GENERIC general equation for non-equilibrium reversible-irreversible coupling (GENERIC) formalism to identify the best model conserving basic thermodynamic laws. The data-driven GENERIC analysis identifies the material behavior with high fidelity for both data fitting and prediction.
Fichier(s) constituant cette publication
Cette publication figure dans le(s) laboratoire(s) suivant(s)
Documents liés
Visualiser des documents liés par titre, auteur, créateur et sujet.
-
Article dans une revue avec comité de lectureCHINESTA SORIA, Francisco; ; ; ; ; ; ; AMMAR, Amine; (Springer Verlag, 2013)
In this paper we are addressing a new paradigm in the field of simulation-based engineering sciences (SBES) to face the challenges posed by current ICT technologies. Despite the impressive progress attained by simulation ...
-
Article dans une revue avec comité de lecture; ; ; ; ;AMMAR, Amine; CHINESTA SORIA, Francisco (Springer Verlag, 2014)
Simulation of all phenomena taking place in a surgical procedure is a formidable task that involves, when possible, the use of supercomputing facilities over long time periods. However, decision taking in the operating ...
-
Article dans une revue avec comité de lecture; ; ; ; ;CHINESTA SORIA, Francisco (Wiley, 2019)
While modern CFD tools are able to provide the user with reliable and accurate simulations, there is a strong need for interactive design and analysis tools. State-of-the-art CFD software employs massive resources in terms ...
-
Article dans une revue avec comité de lecture; ; ; ;CHINESTA SORIA, Francisco (Elsevier, 2018)
In this work we explore the possibilities of reduced order modeling for augmented reality applications. We consider parametric reduced order models based upon separate (affine) parametric dependence so as to speedup the ...
-
Article dans une revue avec comité de lecture; ; ; ;CHINESTA SORIA, Francisco (Public Library of Science, 2020)
In this paper, a novel self-learning digital twin strategy is developed for fluid sloshing phenomena. This class of problems is of utmost importance for robotic manipulation of fluids, for instance, or, in general, in ...