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DTSTAMP:20211207T054759Z
LOCATION:220-221
DTSTART;TZID=America/Chicago:20211116T140000
DTEND;TZID=America/Chicago:20211116T143000
UID:submissions.supercomputing.org_SC21_sess157_pap564@linklings.com
SUMMARY:Scalable Adaptive PDE Solvers in Arbitrary Domains
DESCRIPTION:Paper\n\nScalable Adaptive PDE Solvers in Arbitrary Domains\n\
 nSaurabh, Ishii, Fernando, Gao, Tan...\n\nEfficiently and accurately simul
 ating partial differential equations (PDEs) in and around arbitrarily defi
 ned geometries, especially with high levels of adaptivity, has significant
  implications for different application domains. A key bottleneck in the a
 bove process is the fast construction of a 'good' adaptively-refined mesh.
  In this work, we present an efficient novel octree-based adaptive discret
 ization approach capable of carving out arbitrarily shaped void regions fr
 om the parent domain: an essential requirement for fluid simulations aroun
 d complex objects. Carving out objects produces an incomplete octree.  We 
 develop efficient top-down and bottom-up traversal methods to perform fini
 te element computations on incomplete octrees. We validate the framework b
 y (a)showing appropriate convergence analysis and (b)computing the drag co
 efficient for flow past a sphere for a wide range of Reynolds numbers  (1-
 10^6) encompassing the drag-crisis regime. Finally, we deploy the framewor
 k on a realistic geometry on a current project to evaluate COVID-19 transm
 ission risk in classrooms.\n\nTag: Reproducibility Badge, Applications, Co
 mputational Science\n\nRegistration Category: Tech Program Reg Pass\n\nRep
 roducibility Badges: Artifact Available
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