Preserving Fluid Sheets with Adaptively Sampled Anisotropic Particles

Abstract

This paper presents a particle-based model for preserving fluid sheets of animated liquids with an adaptively sampled Fluid-Implicit-Particle (FLIP) method. In our method, we preserve fluid sheets by filling the breaking sheets with particle splitting in the thin regions, and by collapsing them in the deep water. To identify the critically thin parts, we compute the anisotropy of the particle neighborhoods, and use this information as a resampling criterion to reconstruct thin liquid surfaces. Unlike previous approaches, our method does not suffer from diffusive surfaces or complex remeshing operations, and robustly handles topology changes with the use of a meshless representation. We extend the underlying FLIP model with an anisotropic position correction to improve the particle spacing, and adaptive sampling to efficiently perform simulations of larger volumes. Due to the Lagrangian nature of our method, it can be easily implemented and efficiently parallelized. The results show that our method can produce visually complex liquid animations with thin structures and vivid motions.

Highly adaptive liquid simulations on tetrahedral meshes

“…This approach, however, suffers from numerical diffusion and an inconsistent discretization near the tree's T-junctions. Targeting a similar direction as our work, Hong et al [2009] and Ando et al [2012] have demonstrated methods to adapt the resolution of FLIP particles in a simulation. Both methods, in contrast to ours, focus on static computational grids and are restricted to smaller differences in particle size.…”

“…We combat these problems by directly manipulating particle positions. During each time step, we apply the position correction algorithm of Ando et al [2012]; this algorithm essentially pushes each particle away from its neighbors to prevent clustering. We also introduce two special behaviors when the particles are close to the liquid surface (less than a distance of six times the particle radius).…”

“…For this, we modify the strategy of Ando et al [2012] to work within our framework: at each remeshing step, we loop through the particles and determine whether the resolution needs to be changed. If a particle is too small, then we merge it with its nearest neighboring particle, resulting in a particle whose radius is given by the combined volume of the two original particles.…”

Physically-based droplet interaction

“…the trajectories, sizes and numbers of droplets, to avoid costly iterative detection, resolution and advancement of the system within a single timestep ∆t, we choose to constrain each droplet to at most one resolvable collision per timestep. We then use a deterministic, trajectory-based scheme that combines Taskiran and Ergeneman [2014]'s use of sphere-moving sphere intersection tests with a pairing mechanism inspired by Ando et al [2012]. This is able to nd all possible collisions and results in a set of collision pairs where each particle is included at most once.…”

“…lower root t − . This is followed by a subsequent pairing step, as in the particle merging scheme of Ando et al [2012], to check each particle and its preferred collision pair. If both particles refer to the other and their collision time is within the substep ∆t, a binary collision is successfully found and the pair are marked and removed from the next iteration of detection.…”