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dc.contributor.authorDu, Qiang
dc.contributor.authorChen, Miaomiao
dc.contributor.authorXie, Jianxin
dc.date.accessioned2021-08-16T11:43:23Z
dc.date.available2021-08-16T11:43:23Z
dc.date.created2021-03-26T08:43:40Z
dc.date.issued2021
dc.identifier.citationComputational Materials Science. 2021, 186 .en_US
dc.identifier.issn0927-0256
dc.identifier.urihttps://hdl.handle.net/11250/2768003
dc.description.abstractThe multi-phase Kampmann-Wagner Numerical (KWN) model, which had been successfully applied for modeling particle precipitation (i.e. Ostwald ripening), is extended toward grain growth modeling. The extensions consist of adopting grain topology- and size-dependent growth rate equation obtained from either the reported parameterization of phase field simulations results or theoretical analysis. Global volume conservation equation is imposed in the extended model to reflect the spacing filling constraints that grains do not overlap and no voids appear. The extended model has been applied to simulate ideal grain growth, grain growth with the initial states of lognormal/normal/Weibull distributions, bi-modal grain size distribution and arbitrary Voronoi tessellations, and the simulation results have been verified by Hillert's asymptotic solution and phase field simulation results. The extension has enabled the KWN approach applicable to model grain growth thus reducing the threshold for the establishment of an efficient Integrated Computational Materials Engineering modeling framework.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectPhase field methoden_US
dc.subjectKampmann-Wagner Numerical modelen_US
dc.subjectGrain growth modellingen_US
dc.titleModelling grain growth with the generalized Kampmann-Wagner numerical modelen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2020 The Author(s). Published by Elsevier B.V.en_US
dc.source.pagenumber8en_US
dc.source.volume186en_US
dc.source.journalComputational Materials Scienceen_US
dc.identifier.doi10.1016/j.commatsci.2020.110066
dc.identifier.cristin1901212
dc.source.articlenumber110066en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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