An Adaptive Time-Stepping Algorithm for the Allen-Cahn Equation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Chaeyoung | - |
dc.contributor.author | Park, Jintae | - |
dc.contributor.author | Kwak, Soobin | - |
dc.contributor.author | Kim, Sangkwon | - |
dc.contributor.author | Choi, Yongho | - |
dc.contributor.author | Ham, Seokjun | - |
dc.contributor.author | Kim, Junseok | - |
dc.date.accessioned | 2022-08-25T14:40:44Z | - |
dc.date.available | 2022-08-25T14:40:44Z | - |
dc.date.created | 2022-08-25 | - |
dc.date.issued | 2022-07-16 | - |
dc.identifier.issn | 2314-8896 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/143357 | - |
dc.description.abstract | In this paper, we present a simple and accurate adaptive time-stepping algorithm for the Allen-Cahn (AC) equation. The AC equation is a nonlinear partial differential equation, which was first proposed by Allen and Cahn for antiphase boundary motion and antiphase domain coarsening. The mathematical equation is a building block for modelling many interesting interfacial phenomena such as dendritic crystal growth, multiphase fluid flows, and motion by mean curvature. The proposed adaptive time-stepping algorithm is based on the Runge-Kutta-Fehlberg method, where the local truncation error is estimated by using fourth- and fifth-order numerical schemes. Computational experiments demonstrate that the proposed time-stepping technique is efficient in multiscale computations, i.e., both the fast and slow dynamics. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | HINDAWI LTD | - |
dc.subject | NUMERICAL SCHEME | - |
dc.subject | ERROR ESTIMATION | - |
dc.subject | MEAN-CURVATURE | - |
dc.subject | VARIABLE STEPS | - |
dc.subject | EFFICIENT | - |
dc.subject | SURFACES | - |
dc.subject | MOTION | - |
dc.title | An Adaptive Time-Stepping Algorithm for the Allen-Cahn Equation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Junseok | - |
dc.identifier.doi | 10.1155/2022/2731593 | - |
dc.identifier.scopusid | 2-s2.0-85135044630 | - |
dc.identifier.wosid | 000834903400001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF FUNCTION SPACES, v.2022 | - |
dc.relation.isPartOf | JOURNAL OF FUNCTION SPACES | - |
dc.citation.title | JOURNAL OF FUNCTION SPACES | - |
dc.citation.volume | 2022 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Applied | - |
dc.relation.journalWebOfScienceCategory | Mathematics | - |
dc.subject.keywordPlus | NUMERICAL SCHEME | - |
dc.subject.keywordPlus | ERROR ESTIMATION | - |
dc.subject.keywordPlus | MEAN-CURVATURE | - |
dc.subject.keywordPlus | VARIABLE STEPS | - |
dc.subject.keywordPlus | EFFICIENT | - |
dc.subject.keywordPlus | SURFACES | - |
dc.subject.keywordPlus | MOTION | - |
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