An Adaptive Sampling Approach to Incompressible Particle-Based Fluid

Abstract

We describe an adaptive particle-based technique for simulating incompressible fluid that uses an octree structure to compute inter-particle interactions and to compute the pressure field. Our method extends the hybrid Flip technique by supporting adaptive splitting and merging of fluid particles, and adaptive spatial sampling for the reconstruction of the velocity and pressure fields. Particle splitting allows a detailed sampling of fluid momentum in regions of complex flow. Particle merging, in regions of smooth flow, reduces memory and computational overhead. The octree supporting field-based calculations is adapted to provide a fine spatial reconstruction where particles are small and a coarse reconstruction where particles are large. This scheme places computational resources where they are most needed, to handle both flow and surface complexity. Thus, incompressibility can be enforced even in very small, but highly turbulent areas. Simultaneously, the level of detail is very high in these areas, allowing the direct support of tiny splashes and small-scale surface tension effects. This produces a finely detailed and realistic representation of surface motion.