Fisrst one, called “Real-Time Eulerian Water Simulation Using a Restricted Tall Cell Grid“, presents further impovements to the real-time hybrid fluid solver, that we were able to see in recent demos like Lighhouse and Raging Rapids Ride.
We present a new Eulerian fluid simulation method, which allows real-time simulations of large scale three dimensional liquids. Such scenarios have hither to 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.
Second paper – Solid Simulation with Oriented Particles – describes universal solver, based on Position Based Dynamics and Shape Matching approach, that can be used to simulate rigid, plastic, cloth or soft body objects.
We propose a new fast and robust method to simulate various types of solid including rigid, plastic and soft bodies as well as one, two and three dimensional structures such as ropes, cloth and volumetric objects. The underlying idea is to use oriented particles that store rotation and spin, along with the usual linear attributes, i.e. position and velocity. This additional information adds substantially to traditional particle methods. First, particles can be represented by anisotropic shapes such as ellipsoids, which approximate surfaces more accurately than spheres.
Second, shape matching becomes robust for sparse structures such as chains of particles or even single particles because the undefined degrees of freedom are captured in the rotational states of the particles. Third, the full transformation stored in the particles, including translation and rotation, can be used for robust skinning of graphical meshes and for transforming plastic deformations back into the rest state.
Another interesting idea, that is welcomed in future PhysX SDK versions.
Thanks for AquaGeneral for a hint