000095110 001__ 95110
000095110 005__ 20210902121743.0
000095110 0247_ $$2doi$$a10.1371/journal.pone.0234569
000095110 0248_ $$2sideral$$a118695
000095110 037__ $$aART-2020-118695
000095110 041__ $$aeng
000095110 100__ $$0(orcid)0000-0001-5483-6012$$aMoya, Beatriz$$uUniversidad de Zaragoza
000095110 245__ $$aPhysically sound, self-learning digital twins for sloshing fluids
000095110 260__ $$c2020
000095110 5060_ $$aAccess copy available to the general public$$fUnrestricted
000095110 5203_ $$aIn 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.
000095110 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T88$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R
000095110 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000095110 590__ $$a3.24$$b2020
000095110 591__ $$aMULTIDISCIPLINARY SCIENCES$$b26 / 73 = 0.356$$c2020$$dQ2$$eT2
000095110 592__ $$a0.99$$b2020
000095110 593__ $$aMultidisciplinary$$c2020$$dQ1
000095110 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000095110 700__ $$0(orcid)0000-0002-9135-866X$$aAlfaro, Iciar$$uUniversidad de Zaragoza
000095110 700__ $$0(orcid)0000-0003-3003-5856$$aGonzalez, David$$uUniversidad de Zaragoza
000095110 700__ $$aChinesta, Francisco
000095110 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, Elías$$uUniversidad de Zaragoza
000095110 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000095110 773__ $$g15, 6 (2020), e0234569 1-16$$pPLoS One$$tPloS one$$x1932-6203
000095110 8564_ $$s1784276$$uhttps://zaguan.unizar.es/record/95110/files/texto_completo.pdf$$yVersión publicada
000095110 8564_ $$s428420$$uhttps://zaguan.unizar.es/record/95110/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000095110 909CO $$ooai:zaguan.unizar.es:95110$$particulos$$pdriver
000095110 951__ $$a2021-09-02-09:43:28
000095110 980__ $$aARTICLE