Adaptively sampled particle fluids

Abstract

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 distancebased 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.

Weighted linde-buzo-gray stippling

“…Here, an energy function is defined and points are dynamically inserted or deleted to match this energy on a local level. Adaptive fluid simulations adapt point distributions with time coherence (Adams et al 2007;Ando et al 2013;Springel 2010). None of these systems, however, use a unified method based on splitting and merging Voronoi cells.…”

Controlling shapes of air bubbles in a multi-phase fluid simulation

“…Müller et al [21] devised a particle system to animate elastic and plastic objects with specified material properties. Adams et al [1] proposed adaptively-sized SPH particles. Thürey et al [28] introduced a technique to control liquid, which preserves its details using high-pass and low-pass filters.…”

“…We use the particle level-set method [6] to represent complicated fluid surfaces, and octree structure [17] for fast grid simulation, and back and forth error compensation and correction (BFECC) [13] to achieve 2nd-order accuracy in preserving the fluid volume. We employ the adaptive SPH technique [1] to simulate small-scale air bubbles. The equation for the pressure force acting on an air bubble is…”

A survey on virtual reality

“…Yuksel and colleagues [85] proposed a wave particle model, which could simulate the spoondrift of a fluid surface in real-time and the interaction with floating objects of surface. Adams and colleagues [86] proposed a particle water wave simulation method based on adaptive sampling which could carry out adaptive particle sampling according to the local feature of fluid and visual interest to reduce the number of required particles for fluid simulation. Using this method for adaptive sampling could effectively guarantee the smoothness of interframe change and improve the efficiency at the same time.…”