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PhysX Research: Eulerian Water Simulation and Solids through Oriented Particles

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Two new research papers have landed on a homepage of Dr. Matthias Müller-Fischer, PhysX SDK Research Lead in NVIDIA and NovodeX co-founder.

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.

We badly want to see this one in further releases of PhysX SDK 3 or APEX.

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.

Update: Oriented Particles solver through CUDA


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

Written by Zogrim

May 19th, 2011 at 10:54 am

4 Responses to 'PhysX Research: Eulerian Water Simulation and Solids through Oriented Particles'

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  1. Are there any videos of the Solid Simulation with Oriented Particles content?


    David Black

    19 May 11 at 6:32 pm

  2. David Black:

    Are there any videos of the Solid Simulation with Oriented Particles content?

    Haven’t saw any so far



    19 May 11 at 8:04 pm

  3. Some video showcasing both the papers:
    Solid Simulation with Oriented Particles: 28s
    Real-Time Eulerian Water Simulation Using a Restricted Tall Cell Grid: 37s

    I actually completely overlooked the Solid Simulation with Oriented Particles paper, good thing you picked that up.



    20 May 11 at 9:00 am

  4. The “Solid Simulation with Oriented Particles” actually seems like the more useful paper.

    I can imagine thin sheets and poles with plastic deformation being added to a game very easily(eg lamp posts which bend when crashed into or car body panels with dents).


    David Black

    21 May 11 at 11:23 am

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