Category: Fluids

Christopher Batty, Ben Houston

We present the first spatially adaptive Eulerian fluid animation method to support challenging viscous liquid effects such as folding, coiling, and variable viscosity. We propose a tetrahedral node-based embedded finite volume method for fluid viscosity, adapted from popular techniques for Lagrangian deformable objects. Applied in an Eulerian fashion with implicit integration, this scheme stably and efficiently supports high viscosity fluids while yielding symmetric positive definite linear systems. To integrate this scheme into standard tetrahedral mesh-based fluid simulators, which store normal velocities on faces rather than velocity vectors at nodes, we offer two methods to reconcile these representations. The first incorporates a mapping between different degrees of freedom into the viscosity solve itself. The second uses a FLIP-like approach to transfer velocity data between nodes and faces before and after the linear solve. The former offers tighter coupling by enabling the linear solver to act directly on the face velocities of the staggered mesh, while the latter provides a sparser linear system and a simpler implementation. We demonstrate the effectiveness of our approach with animations of spatially varying viscosity, realistic rotational motion, and viscous liquid buckling and coiling.

A Simple Finite Volume Method for Adaptive Viscous Liquids

We propose a two-scale method for particle-based fluids that allocates computing resources to regions of the fluid where complex flow behavior emerges. Our method uses a low- and a high-resolution simulation that run at the same time. While in the coarse simulation the whole fluid is represented by large particles, the fine level simulates only a subset of the fluid with small particles. The subset can be arbitrarily defined and also dynamically change over time to capture complex flows and small-scale surface details. The low- and high-resolution simulations are coupled by including feedback forces and defining appropriate boundary conditions. Our method offers the benefit that particles are of the same size within each simulation level. This avoids particle splitting and merging processes, and allows the simulation of very large resolution differences without any stability problems. The model is easy to implement, and we show how it can be integrated into a standard SPH simulation as well as into the incompressible PCISPH solver. Compared to the single-resolution simulation, our method produces similar surface details while improving the efficiency linearly to the achieved reduction rate of the particle number.

Two-Scale Particle Simulation

We present a new Eulerian fluid simulation method, which allows real-time simulations of large scale three dimensional liquids. Such scenarios have hitherto been restricted to the domain of off-line computation. To reduce computation time we use a hybrid grid representation composed of regular cubic cells on top of a layer of tall cells. With this layout water above an arbitrary terrain can be represented without consuming an excessive amount of memory and compute power, while focusing effort on the area near the surface where it most matters. Additionally, we optimized the grid representation for a GPU implementation of the fluid solver. To further accelerate the simulation, we introduce a specialized multigrid algorithm for solving the Poisson equation and propose solver modifications to keep the simulation stable for large time steps. We demonstrate the efficiency of our approach in several real-world scenarios, all running above 30 frames per second on a modern GPU. Some scenes include additional features such as two-way rigid body coupling as well as particle representations of sub-grid detail.

Real-Time Eulerian Water Simulation Using a Restricted Tall Cell Grid

Art direction of high resolution naturalistic liquid simulations is notoriously hard, due to both the chaotic nature of the physics and the computational resources required. Resimulating a scene at higher resolution often produces very different results, and is too expensive to allow many design cycles. We present a method of constraining or guiding a high resolution liquid simulation to stay close to a finalized low resolution version (either simulated or directly animated), restricting the solve to a thin outer shell of liquid around a guide shape. Our method is generally faster than an unconstrained simulation and can be integrated with a standard fluid simulator. We demonstrate several applications, with both simulated and hand-animated inputs.

Guide Shapes for High Resolution Naturalistic Liquid Simulation

We present a novel approach to fluid simulation, allowing us to take into account the surface energy in a precise manner. This new approach combines a novel, topology-adaptive approach to deformable interface tracking, called the deformable simplicial complexes method (DSC) with an optimization-based, linear finite element method for solving the incompressible Euler equations. The deformable simplicial complexes track the surface of the fluid: the fluid-air interface is represented explicitly as a piecewise linear surface which is a subset of tetrahedralization of the space, such that the interface can be also represented implicitly as a set of faces separating tetrahedra marked as inside from the ones marked as outside. This representation introduces insignificant and controllable numerical diffusion, allows robust topological adaptivity and provides both a volumetric finite element mesh for solving the fluid dynamics equations as well as direct access to the interface geometry data, making inclusion of a new surface energy term feasible. Furthermore, using an unstructured mesh makes it straightforward to handle curved solid boundaries and gives us a possibility to explore several fluid-solid interaction scenarios.

Optimization-based Fluid Simulation on Unstructured Meshes

We develop a new Lagrangian primitive, named Langevin particle, to incorporate turbulent flow details in fluid simulation. A group of the particles are distributed inside the simulation domain based on a turbulence energy model with turbulence viscosity. A particle in particular moves obeying the generalized Langevin equation, a well-known stochastic differential equation that describes the particle’s motion as a random Markov process. The resultant particle trajectory shows self-adapted fluctuation in accordance to the turbulence energy, while following the global flow dynamics. We then feed back Langevin forces to the simulation based on the stochastic trajectory, which drive the flow with necessary turbulence. The new hybrid flow simulation method features nonrestricted particle evolution requiring minimal extra manipulation after initiation. The flow turbulence is easily controlled and the total computational overhead of enhancement is minimal based on typical fluid solvers.

Langevin Particle: A Self-Adaptive Lagrangian Primitive For Flow Simulation Enhancement

Cem Yuksel, Texas A&M, 2010: Real-Time Water Waves with Wave Particles