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Particle-based fluid simulation for interactive applications

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Presentation on theme: "Particle-based fluid simulation for interactive applications"— Presentation transcript:

1 Particle-based fluid simulation for interactive applications
Matthias Müller David Charypar Markus Gross 陳岳澤

2 Outline Introduction Navier-Stokes Equation
SPH (Smoothed Particle Hydrodynamics ) Smooth Kernel Marching Cubes Result

3 Introduction Navier-Stokes Equation describe the motion of fluid substances such as liquids and gases Use Smoothed Particle Hydrodynamics (SPH) to simulate fluids with free surfaces. Interactive simulation (about 5 fps).

4 Navier-Stokes Equation -1
Conservation of momentum equation Three components: Pressure term External force term Viscosity term v: velocity, : density, p: pressure, g: external force, : viscosity coefficient

5 Navier-Stokes Equation -2
The acceleration ai of particle i is (fi is body force) Using ai , we can get velocity and position of particle i

6 SPH -1 Originally developed for astrophysical problems (1977).
Interpolation method for particles. Properties that are defined at discrete particles can be evaluated anywhere in space. Uses smoothing kernels to distribute quantities.

7 SPH -2 Smoothing of attribute A mj: mass rj : density
Aj: quantity to be interpolated W: smoothing kernel h

8 Particle density Smoothing of attribute A Particle density

9 Pressure Term Navier-Stokes Equation Pressure Term

10 Viscosity term Navier-Stokes Equation Viscosity Term

11 External force term Other external forces are directly applied to the particles. Collisions: In case of collision the normal component of the velocity is flipped.

12 Smoothing Kernel -1 Has an impact on the stability and speed of the simulation. ex: Avoid square-roots for distance computation. Sample smoothing kernel:

13 Smoothing Kernel -2 all points inside a radius of ‘h’ are considered for “smoothing”. Thick line: the kernel Thin line: the gradient of kernel Dashed line: the laplacian of kernel

14 Smoothing Kernel -3 For n particles n2 potential interactions!
To reduce to linear complexity O(n2) define interaction cutoff distance h

15 Smoothing Kernel -4 Fill particles into grid with spacing h
Only search potential neighbors in adjacent cells

16 Marching Cubes -1 To visualize the free surface

17 Marching Cubes -2

18 Result Interactive Simulation (5fps) Marching Cubs 2200 particle
Point Splatting


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