Publication:
On the accuracy assessment of Laplacian models in MPS

dc.citedby36
dc.contributor.authorNg K.C.en_US
dc.contributor.authorHwang Y.H.en_US
dc.contributor.authorSheu T.W.H.en_US
dc.contributor.authorid55310814500en_US
dc.contributor.authorid7402311620en_US
dc.contributor.authorid13302578200en_US
dc.date.accessioned2023-05-16T02:47:08Z
dc.date.available2023-05-16T02:47:08Z
dc.date.issued2014
dc.description.abstractFrom the basis of the Gauss divergence theorem applied on a circular control volume that was put forward by Isshiki (2011) in deriving the MPS-based differential operators, a more general Laplacian model is further deduced from the current work which involves the proposal of an altered kernel function. The Laplacians of several functions are evaluated and the accuracies of various MPS Laplacian models in solving the Poisson equation that is subjected to both Dirichlet and Neumann boundary conditions are assessed. For regular grids, the Laplacian model with smaller N is generally more accurate, owing to the reduction of leading errors due to those higher-order derivatives appearing in the modified equation. For irregular grids, an optimal N value does exist in ensuring better global accuracy, in which this optimal value of N will increase when cases employing highly irregular grids are computed. Finally, the accuracies of these MPS Laplacian models are assessed in an incompressible flow problem. © 2014 Elsevier B.V. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.cpc.2014.05.012
dc.identifier.epage2426
dc.identifier.issue10
dc.identifier.scopus2-s2.0-84904723233
dc.identifier.spage2412
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84904723233&doi=10.1016%2fj.cpc.2014.05.012&partnerID=40&md5=7b9976c4bbc94437c88739b9e1691514
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22072
dc.identifier.volume185
dc.publisherElsevieren_US
dc.sourceScopus
dc.sourcetitleComputer Physics Communications
dc.titleOn the accuracy assessment of Laplacian models in MPSen_US
dc.typeArticleen_US
dspace.entity.typePublication
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