Open AccessPhysically sound, self-learning digital twins for sloshing fluids
Author(s) -
Beatriz Moya,
Icíar Alfaro,
David González,
Francisco Chinesta,
Elías Cueto
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0234569
Subject(s) - slosh dynamics , container (type theory) , computer science , class (philosophy) , fluid–structure interaction , simulation , artificial intelligence , mechanics , physics , finite element method , mechanical engineering , engineering , thermodynamics
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 simulation-assisted decision making. The proposed method infers the (linear or non-linear) constitutive behavior of the fluid from video sequences of the sloshing phenomena. Real-time prediction of the fluid response is obtained from a reduced order model (ROM) constructed by means of thermodynamics-informed data-driven learning. From these data, we aim to predict the future response of a twin fluid reacting to the movement of the real container. The constructed system is able to perform accurate forecasts of its future reactions to the movements of the containers. The system is completed with augmented reality techniques, so as to enable comparisons among the predicted result with the actual response of the same liquid and to provide the user with insightful information about the physics taking place.