Adaptively sampled particle fluids
We present novel adaptive sampling algorithms for particle-based fluid simulation. We introduce a sampling condition based on geometric local feature size that allows focusing computational resources in geometrically complex regions, while reducing the number of particles deep inside the fluid or near thick flat surfaces. Further performance gains are achieved by varying the sampling density according to visual importance. In addition, we propose a novel fluid surface definition based on approximate particle-to-surface distances that are carried along with the particles and updated appropriately. The resulting surface reconstruction method has several advantages over existing methods, including stability under particle resampling and suitability for representing smooth flat surfaces. We demonstrate how our adaptive sampling and distance-based surface reconstruction algorithms lead to significant improvements in time and memory as compared to single resolution particle simulations, without significantly affecting the fluid flow behavior. © 2007 ACM.
adams_2007_ASP.pdf
postprint
openaccess
copyright
7.04 MB
Adobe PDF
662ff8c635a664e1a3006643e506254d
adaptive_150x150.png
Thumbnail
openaccess
copyright
19.38 KB
PNG
d997ad300569483ba95d5a89872078fd