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dc.contributor.authorChang, Jumyung
dc.contributor.authorPartono, Ruben
dc.contributor.authorAzevedo, Vinicius C.
dc.contributor.authorBatty, Christopher
dc.date.accessioned2024-04-22 14:44:48 (GMT)
dc.date.available2024-04-22 14:44:48 (GMT)
dc.date.issued2022-12
dc.identifier.urihttps://doi.org/10.1145/3550454.3555498
dc.identifier.urihttp://hdl.handle.net/10012/20466
dc.description©ACM, 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Graphics, https://doi.org/10.1145/3550454.3555498.en
dc.description.abstractWe propose to augment standard grid-based fluid solvers with pointwise divergence-free velocity interpolation, thereby ensuring exact incompressibility down to the sub-cell level. Our method takes as input a discretely divergence-free velocity field generated by a staggered grid pressure projection, and first recovers a corresponding discrete vector potential. Instead of solving a costly vector Poisson problem for the potential, we develop a fast parallel sweeping strategy to find a candidate potential and apply a gauge transformation to enforce the Coulomb gauge condition and thereby make it numerically smooth. Interpolating this discrete potential generates a point-wise vector potential whose analytical curl is a pointwise incompressible velocity field. Our method further supports irregular solid geometry through the use of level set-based cut-cells and a novel Curl-Noise-inspired potential ramping procedure that simultaneously offers strictly non-penetrating velocities and incompressibility. Experimental comparisons demonstrate that the vector potential reconstruction procedure at the heart of our approach is consistently faster than prior such reconstruction schemes, especially those that solve vector Poisson problems. Moreover, in exchange for its modest extra cost, our overall Curl-Flow framework produces significantly improved particle trajectories that closely respect irregular obstacles, do not suffer from spurious sources or sinks, and yield superior particle distributions over time.en
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada.en
dc.language.isoenen
dc.publisherAssociation for Computing Machineryen
dc.relation.ispartofseriesACM Transactions on Graphics;41(6)
dc.subjectcomputing methodologiesen
dc.subjectphysical simulationen
dc.subjectdivergence-freeen
dc.subjectstream functionen
dc.subjectvector potentialen
dc.subjectvelocity interpolationen
dc.subjectadvectionen
dc.titleCurl-Flow: Boundary-respecting pointwise incompressible velocity interpolation for grid-based fluidsen
dc.typeArticleen
dcterms.bibliographicCitationChang, J., Partono, R., Azevedo, V. C., & Batty, C. (2022). Curl-flow. ACM Transactions on Graphics, 41(6), 1–21. https://doi.org/10.1145/3550454.3555498en
uws.contributor.affiliation1Faculty of Mathematicsen
uws.contributor.affiliation2David R. Cheriton School of Computer Scienceen
uws.typeOfResourceTexten
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen


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