000121363 001__ 121363
000121363 005__ 20240319081014.0
000121363 0247_ $$2doi$$a10.1016/j.jcp.2022.111672
000121363 0248_ $$2sideral$$a131299
000121363 037__ $$aART-2022-131299
000121363 041__ $$aeng
000121363 100__ $$aSolán-Fustero, P.$$uUniversidad de Zaragoza
000121363 245__ $$aA POD-based ROM strategy for the prediction in time of advection-dominated problems
000121363 260__ $$c2022
000121363 5060_ $$aAccess copy available to the general public$$fUnrestricted
000121363 5203_ $$aThe use of reduced-order models (ROMs) for the numerical approximation of the solution of partial differential equations is a topic of current interest, being motivated by the high computational efficiency of ROMs when compared to full-order models (FOMs). To construct a ROM to approximate the solution of transport equations, the use of the proper orthogonal decomposition (POD) method is a common choice. POD-based ROMs rely on the snapshot method, which consists in the off-line computation of a set of values corresponding to the solution up to the training time by means of the FOM. Then, the ROM is constructed and solved, up to the training time. When considering parabolic equations, the method is able to compute the solution beyond the training time. However, when considering hyperbolic problems, POD-based ROMs fail when computing the solution beyond the training time, this being one of the strongest limitations of POD-based ROMs. In this work, a strategy in the framework of POD-based ROMs to predict solutions in time is introduced. This method, called CT-ROM, is based on a coordinate transformation and allows to compute the solution of advection-dominated problems beyond the training time. The performance of this strategy is assessed using a variety of test cases, showing promising results in all of them. The extension of the CT-ROM to higher spatial dimensions by means of the Radon transform is also presented. The results obtained are encouraging and motivate the application of this idea to more complex problems.
000121363 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000121363 590__ $$a4.1$$b2022
000121363 592__ $$a1.753$$b2022
000121363 591__ $$aPHYSICS, MATHEMATICAL$$b3 / 56 = 0.054$$c2022$$dQ1$$eT1
000121363 593__ $$aApplied Mathematics$$c2022$$dQ1
000121363 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b46 / 110 = 0.418$$c2022$$dQ2$$eT2
000121363 593__ $$aComputational Mathematics$$c2022$$dQ1
000121363 593__ $$aPhysics and Astronomy (miscellaneous)$$c2022$$dQ1
000121363 593__ $$aModeling and Simulation$$c2022$$dQ1
000121363 593__ $$aNumerical Analysis$$c2022$$dQ1
000121363 593__ $$aComputer Science Applications$$c2022$$dQ1
000121363 594__ $$a7.9$$b2022
000121363 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion
000121363 700__ $$aGracia, J.L.
000121363 700__ $$0(orcid)0000-0002-3465-6898$$aNavas-Montilla, A.$$uUniversidad de Zaragoza
000121363 700__ $$aGarcía-Navarro, P.
000121363 7102_ $$15001$$2600$$aUniversidad de Zaragoza$$bDpto. Ciencia Tecnol.Mater.Fl.$$cÁrea Mecánica de Fluidos
000121363 773__ $$g471 (2022), 111672 [26 pp.]$$pJ. comput. phys.$$tJournal of Computational Physics$$x0021-9991
000121363 8564_ $$s2028623$$uhttps://zaguan.unizar.es/record/121363/files/texto_completo.pdf$$yPreprint
000121363 8564_ $$s1782682$$uhttps://zaguan.unizar.es/record/121363/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint
000121363 909CO $$ooai:zaguan.unizar.es:121363$$particulos$$pdriver
000121363 951__ $$a2024-03-18-15:27:22
000121363 980__ $$aARTICLE