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Modeling Fluid Phenomena -Vinay Bondhugula (25 th & 27 th April 2006)

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Presentation on theme: "Modeling Fluid Phenomena -Vinay Bondhugula (25 th & 27 th April 2006)"— Presentation transcript:

1 Modeling Fluid Phenomena -Vinay Bondhugula (25 th & 27 th April 2006)

2 Two major techniques Solve the PDE describing fluid dynamics. Simulate the fluid as a collection of particles.

3 Rapid Stable Fluid Dynamics for Computer Graphics – Kass and Miller SIGGRAPH 1990

4 Previous Work Older techniques were not realistic enough: –Tracking of individual waves –No net transport of water –Can’t handle changes in boundary conditions

5 Introduction Approximates wave equation for shallow water. Solves the wave equation using implicit integration. The result is good enough for animation purposes.

6 Shallow Water Equations: Assumptions 1)Represent water by a height field. Motivation: In an accurate simulation, computational cost grows as the cube of resolution. Limitation: No splashing of water. Waves cannot break.

7 Contd… 2) Ignore the vertical component of the velocity of water. Limitation: Inaccurate simulation for steep waves.

8 Contd… 3) Horizontal component of the velocity in a column is constant. Assumption fails in some cases: Undercurrent Greater friction at the bottom.

9 Notation h(x) is the height of the water surface b(x) is the height of the ground surface d(x) = h(x) – b(x) is the depth of the water u(x) is the horizontal velocity of a vertical water column. d i (n) is the depth at the i th point after the n th iteration.

10 The Equations F = ma, gives the following: The second term is the horizontal force acting on a water column. Volume conservation gives:

11 Contd… Differentiating equation 1 w.r.t x and equation 2 w.r.t t we get: From the simplified wave equation, the wave velocity is sqrt(gd). Explains why tsunami waves are high –The wave slows down as it approaches the coast, which causes water to pile up.

12 Discretization Finite-difference technique is applied:

13 Integration Implicit techniques are used:

14 Another approximation Still a non-linear equation! –‘d’ is dependent on ‘h’ Assume ‘d’ to be constant during integration –Wave velocities only change between iterations.

15 The linear equation: Symmetric tridiagonal matrices can be solved very efficiently.

16 The linear equation The linear equation can be considered an extrapolation of the previous motion of the fluid. Damping can be introduced if the equation is written as:

17 A Subtle Issue In an iteration, nothing prevents h from becoming less than b at a particular point, leading to negative volume at that point. To compensate for this the iteration creates volume elsewhere (note that our equations conserve volume). Solution: After each iteration, compute the new volume and compare it with the old volume.

18 The Equation in 3D Split the equation into two terms - one independent of x and the other independent of y - and solve it in two sub- iterations. We still obtain a linear system!

19 Rendering Rendered with caustics – the terrain was assumed to be flat. Real-time simulation!! –30 fps on a 32x32 grid

20 Miscellaneous Walls are simulated by having a steep incline.

21 Results Water flowing down a hill…

22 More Images Wave speed depends on the depth of the water…

23 Particle-Based Fluid Simulation for Interactive Applications -Matthias Muller et. al. SCA 2003

24 Motivation Limitations of grid based simulation: No splashing or breaking of waves Cannot handle multiple fluids Cannot handle multiple phases

25 Introduction Use Smoothed Particle Hydrodynamics (SPH) to simulate fluids with free surfaces. Pressure and viscosity are derived from the Navier-Stokes equation. Interactive simulation (about 5 fps).

26 SPH 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.

27 Contd… m j is the mass,  j is the density, A j is the quantity to be interpolated and W is the smoothing kernel

28 Modeling Fluids with Particles Given a control volume, no mass is created in it. Hence, all mass that comes out has to be accounted by change in density. But, mass conservation is anyway guaranteed in a particle system.

29 Contd… Momentum equation: Three components: –Pressure term –Force due to gravity –Viscosity term (  is the viscosity of the liquid)

30 Pressure Term It’s not symmetric! Can easily be observed when only two particles interact. Instead use this: Note that the pressure at each particle is computed first. Use the ideal gas state equation: p = k*  where k is a constant which depends on the temperature.

31 Viscosity Term Method used is similar to the one used for the pressure term.

32 Miscellaneous Other external forces are directly applied to the particles. Collisions: In case of collision the normal component of the velocity is flipped.

33 Smoothing Kernel Has an impact on the stability and speed of the simulation. –eg. Avoid square-roots for distance computation. Sample smoothing kernel: all points inside a radius of ‘h’ are considered for “smoothing”.

34 Surface Tracking and Visualization Define a quantity that is 1 at particle locations and 0 elsewhere (it’s called the color field). Smooth it out: Compute the gradient of this field:

35 Contd… If |n(r i )| > l, then the point is a surface point. l is a threshold parameter.

36 Results Interactive Simulation (5fps) Videos from Muller’s site:

37 Fluid-Fluid Interaction Results



40 References Rapid, Stable Fluid Dynamics for Computer Graphics – Michael Kass and David Miller – SIGGRAPH 1990 Particle-Based Fluid Simulation for Interactive Applications – Muller et. al., SCA 2003 Particle-Based Fluid-Fluid Interaction - M. Muller, B. Solenthaler, R. Keiser, M. Gross – SCA 2005

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