000149130 001__ 149130
000149130 005__ 20250125214313.0
000149130 0247_ $$2doi$$a10.1007/s00466-019-01705-3
000149130 0248_ $$2sideral$$a111497
000149130 037__ $$aART-2019-111497
000149130 041__ $$aeng
000149130 100__ $$0(orcid)0000-0001-5483-6012$$aMoya, B.$$uUniversidad de Zaragoza
000149130 245__ $$aLearning slosh dynamics by means of data
000149130 260__ $$c2019
000149130 5060_ $$aAccess copy available to the general public$$fUnrestricted
000149130 5203_ $$aIn this work we study several learning strategies for fluid sloshing problems based on data. In essence, a reduced-order model of the dynamics of the free surface motion of the fluid is developed under rigorous thermodynamics settings. This model is extracted from data by exploring several strategies. First, a linear one, based on the employ of Proper Orthogonal Decomposition techniques is analyzed. Second, a strategy based on the employ of Locally Linear Embedding is studied. Finally, Topological Data Analysis is employed to the same end. All the three distinct possibilities rely on a numerical integration scheme to advance the dynamics in time. This thermodynamically consistent integrator is developed on the basis of the General Equation for Non-Equilibrium Reversible–Irreversible Coupling, GENERIC [M. Grmela and H.C Oettinger (1997). Phys. Rev. E. 56 (6): 6620–6632], framework so as to guarantee the satisfaction of first principles (particularly, the laws of thermodynamics). We show how the resulting method employs a few degrees of freedom, while it allows for a realistic reconstruction of the fluid dynamics of sloshing processes under severe real-time constraints. The proposed method is shown to run faster than real time in a standard laptop.
000149130 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T24-17R
000149130 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000149130 590__ $$a3.459$$b2019
000149130 591__ $$aMECHANICS$$b25 / 136 = 0.184$$c2019$$dQ1$$eT1
000149130 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b13 / 105 = 0.124$$c2019$$dQ1$$eT1
000149130 592__ $$a1.612$$b2019
000149130 593__ $$aApplied Mathematics$$c2019$$dQ1
000149130 593__ $$aComputational Mathematics$$c2019$$dQ1
000149130 593__ $$aOcean Engineering$$c2019$$dQ1
000149130 593__ $$aComputational Theory and Mathematics$$c2019$$dQ1
000149130 593__ $$aMechanical Engineering$$c2019$$dQ1
000149130 593__ $$aComputational Mechanics$$c2019$$dQ1
000149130 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000149130 700__ $$0(orcid)0000-0003-3003-5856$$aGonzalez, D.$$uUniversidad de Zaragoza
000149130 700__ $$0(orcid)0000-0002-9135-866X$$aAlfaro, I.$$uUniversidad de Zaragoza
000149130 700__ $$aChinesta, F.
000149130 700__ $$0(orcid)0000-0003-1017-4381$$aCueto, E.$$uUniversidad de Zaragoza
000149130 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000149130 773__ $$g64 (2019), 511–523$$pComput. mech.$$tCOMPUTATIONAL MECHANICS$$x0178-7675
000149130 8564_ $$s1685437$$uhttps://zaguan.unizar.es/record/149130/files/texto_completo.pdf$$yPostprint
000149130 8564_ $$s2158390$$uhttps://zaguan.unizar.es/record/149130/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000149130 909CO $$ooai:zaguan.unizar.es:149130$$particulos$$pdriver
000149130 951__ $$a2025-01-25-20:56:56
000149130 980__ $$aARTICLE