Adaptively sampled particle fluids

doi:10.1145/1239451.1239499

Adaptively sampled particle fluids

2007-07-29

265

OA

AI

DOI：10.1145/1239451.1239499

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

SIMULATION

ANIMATION

Adaptively sampled particle fluids

Abstract

Journal

Organization

Cited Papers