A machine learning strategy for computing interface curvature in Front-Tracking methods

dc.contributorUniversidade de São Paulo (USP)
dc.contributorUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-29T08:36:54Z
dc.date.accessioned2022-12-20T02:57:56Z
dc.date.available2022-04-29T08:36:54Z
dc.date.available2022-12-20T02:57:56Z
dc.date.created2022-04-29T08:36:54Z
dc.date.issued2022-02-01
dc.identifierJournal of Computational Physics, v. 450.
dc.identifier1090-2716
dc.identifier0021-9991
dc.identifierhttp://hdl.handle.net/11449/229983
dc.identifier10.1016/j.jcp.2021.110860
dc.identifier2-s2.0-85120343716
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5410117
dc.description.abstractIn this work we have described the application of a machine learning strategy to compute the interface curvature in the context of a Front-Tracking framework. Based on angular information of normal and tangential vectors between marker points, the interface curvature is predicted using a neural network. The Front-Tracking-Machine-Learning method is validated using a sine wave and then applied in combination with a Marker-And-Cell method for solving a complex free surface flow. Our results indicate that it is feasible to employ machine learning concepts as an alternative approach for computing curvatures in Front-Tracking schemes.
dc.languageeng
dc.relationJournal of Computational Physics
dc.sourceScopus
dc.subjectCurvature
dc.subjectFree surface flows
dc.subjectFront-Tracking
dc.subjectMachine learning
dc.subjectMarker-and-cell
dc.titleA machine learning strategy for computing interface curvature in Front-Tracking methods
dc.typeArtículos de revistas


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