Vorticity Confinement for Eulerian Fluid Simulations

Eulerian MAC Fluid Simulation with Vorticity Confinement

Eulerian fluid simulations simulate the flow of fluids by tracking fluid velocity and density over a set of individual (discreet) evenly spaced grid locations. One downside to this approach is that the finer details in the fluid can be smoothed out, so you lose those little swirls and vortices.

Eulerian MAC Fluid Simulation with Vorticity Confinement

A simple fix for this is to add Vorticity Confinement. If you read the Wikipedia page on Vorticity Confinement you may be no wiser on what it is or how to add it into your fluid simulations.

Eulerian MAC Fluid Simulation with Vorticity Confinement

My explanation of vorticity confinement is that it looks for curls (vortices) in the fluid and adds in velocity to help boost the swirling motion of the fluid. Adding vorticity confinement can also give more turbulent looking fluid simulations which tend to be more aesthetically pleasing in simulations (unless you are a member of team laminar flow).

Eulerian MAC Fluid Simulation with Vorticity Confinement

The code for implementing vorticity confinement is relatively simple. For 2D I used the snippet provided by Iam0x539 in this video.

function Curl(x,y:integer):double;
     Curl:=xvelocity[x,y+1]-xvelocity[x,y-1] + yvelocity[x-1,y]-yvelocity[x+1, y];

procedure VorticityConfinement(vorticity:double);
var dx,dy,len:double;
     for y:=2 to _h-3 do
          for x:=2 to _w-3 do
               dx:=abs(curl(x + 0, y - 1)) - abs(curl(x + 0, y + 1));
               dy:=abs(curl(x + 1, y + 0)) - abs(curl(x - 1, y + 0));

Eulerian MAC Fluid Simulation with Vorticity Confinement

The VorticityConfinement procedure is called once per simulation step. It looks for local curl at each fluid grid point and then increases the local x and y velocities using the curl. This is what helps preserve the little vortices and helps reduce the smoothing out of the fluid.

Eulerian MAC Fluid Simulation with Vorticity Confinement

To demonstrate how vorticity confinement changes a fluid simulation, the images within this post and the following movie add vorticity confinement to my previous Eulerian MAC Fluid Simulations code.

Eulerian MAC Fluid Simulations with Vorticity Confinement is now included in the latest version of Visions of Chaos.


Eulerian Marker-and-Cell Fluids

Eulerian MAC Fluid Simulation

Benedikt Bitterli has a set of YouTube videos that have been an inspiration for years.

Eulerian MAC Fluid Simulation

He generously shares the source code to a series of programs on his Incremental Fluids GitHub that cover implementing a 2D fluid simulation. His code is based on Robert Bridson’s book, “Fluid Simulation for Computer Graphics”. I have seen that book mentioned all over the place and almost bought a copy, but reviews say it is focused more on the math (not so helpful to me) and not on the code (which I can follow much easier than math formulas).

Eulerian MAC Fluid Simulation

So far, I have converted Benedikt’s first and second programs for inclusion in Visions of Chaos. Calculations at 4K resolution were originally taking up to 10 minutes per frame, but with some multi-threading and code optimizations I got it down to around 10 seconds per 4K resolution frame on a relatively modern i7 CPU.

Eulerian MAC Fluid Simulation

The results so far are really nice. The resulting flows show very high detailed vortices and fluid behavior.

Eulerian MAC Fluid Simulation

Here is a sample 4K resolution movie showing these fluids in motion.

Eulerian Marker-and-Cell Fluid Simulations are now available in the latest version of Visions of Chaos.


Jos Stam’s Fluid Simulations in 3D

I am a huge fan of anything related to fluid simulations. This interest was once again sparked when Daniel Shiffman covered fluid simulation in his latest Coding Train video.

As always, I recommend Coding Train to any developer. Dan has a unique way of making his programming topics both interesting and entertaining.

The code converted during the Coding Train video (Mike Ash’s Fluid Simulation For Dummies) is based on Jos Stam’s Stable Fluids code.

Jos Stam

Jos Stam

Jos Stam wrote his seminal paper Real-Time Fluid Dynamics for Games back in March 2003. I have lost count of the times I seen the paper cited or linked to. It has had a huge influence on fluid simulation.

The original source code from the paper is provided here and here.

Going back 11 years, one of my first YouTube videos was this super low resolution example of 2D fluid simulation using Stam’s methods.

3D Fluid

My main objective when revisiting Stam’s stable fluids was to get a 3D version going.

A quick Google search led me to Blain Maguire’s implementation which I was able to translate into a working 3D fluid simulation.

By default Stam’s fluid method generates a fairly smoothed fluid/smoke flow. To make things more interesting you want to add a bit of turbulence. The key term here is “vorticity confinement”. Vorticity confinemement was first described in the paper Visual Simulation of Smoke by Fedkiw et al.

I found some 3D source code for adding vorticity confinement to Stam’s stable fluids here. See the fluid.cpp file inside the fire32.tar.gz archive.

At this stage I had 3D fluid working. Now comes the fun part, how to display the fluid.

Displaying 3D Fluid

Once you get the code working, the main issue becomes how to display the fluid.

Stam’s fluid uses what is known as an Eulerian approach to fluid simulation. Rather than track individual fluid particles (as in SPH simulations) the fluid is simulated by tracking velocity and density at fixed grid based locations in 3D space. This means that as the simulation runs you have a 3D grid of fluid properties that need to be displayed.

The method I use is to hide the cells that contain a fluid velocity lower than a threshold value. 0.01 seems to work OK for my tests. Then render the cells as spheres or cubes. Color shaded based on fluid velocities.

3D Jos Stam Stable Fluid

For a fake volume rendering like approach you can render the fluid using translucent billboard quads. If you run a pre-render pass over the array and strip any cell that is surrounded by other cells it results in only having the “shell” of the fluids rendering. Rendering these shells with the billboard particles gives a nice result.

3D Jos Stam Stable Fluid

Movie results


3D Jos Stam Stable Fluids are now included in Visions of Chaos.


3D Multiphase Smoothed-Particle Hydrodynamics

I have wanted to implement a 3D version of a fluid simulation in Visions of Chaos for years (3D was a planned feature since I first got 2D SPH working back in 2013). It took a lot longer than expected.

For the 3D code I extended the 2D SPH code I had into the third dimension. In the end it wasn’t too difficult (everything seems simple once you work out how to do it). After a week or so of hacking away I had multiphase fluids flowing in 3D space.

The code is based on Tom Madam’s SPH Code in this blog post. For 3D you just need to add in a Z dimension for particle positions etc.

3D is slower than 2D with the extra dimension and even with a decent multi-core CPU the simulation starts to crawl as the particle count increases. Using the awesome Mitsuba to render some nicely lit spheres got me the following movie.


Multi-threading support in Visions of Chaos

A long overdue and much requested feature in Visions of Chaos has been better support for multi core CPUs. Watching Visions of Chaos churn away calculating a long series of frames for a movie and only seeing one of your CPU cores in use can be frustrating.

Converting single threaded code into multi-threaded capable code is not exactly easy (depending on the algorithm some code is easier than others) but I have started converting some of the easier modes into multi-thread capable code.

Smoothed Particle Hydrodynamics (SPH) was one of the modes in Visions of Chaos that really needed a speed up. See here and here for my previous experiments with SPH.

This next screenshot shows all the 12 cores of an i7 being used for calculating the SPH formulas. Note that the actual displaying of the particles cannot be parallel so there is not 100% CPU utilization. As the particle count goes up (and the time it takes to calculate the million particles moving takes longer than the display time) the CPU usage jumps closer to 100% on all cores.

Parallel Multiphase Smoother Particle Hydrodynamics

After conversion to multi-CPU capable code it was time to render some new 4K resolution SPH simulations. Once the particles count and resolution goes up to fill a 4K screen the times start to plummet again, but seeing these in full 4K resolution is really nice. The parts in the following movie used 1,000,000 SPH particles each which is double my previous particle counts.

Other than SPH, the other modes that take advantage of the rewritten parallel processing code are 4D Cellular Automata, Coupled Cellular Automata 2, Ying-Yang Fire Cellular Automata, Large Neighborhood Totalistic Cellular Automata and Liquid Crystal Cellular Automata. The CA modes are the easiest to convert to parallel processing as by nature they update each cell independent of the others.

There is still a lot of code and modes that would benefit from parallel processing. Yet another entry on my ever expanding to do list of features for Visions of Chaos.


More fun with Lattice Boltzman Method (LBM) fluid simulations

Back in September 2010 I was experimenting with Lattice Boltzmann Method (LBM) fluid flows.

At that time I managed to translate some (probably Fortran) LBM source code provided by the now defunct “LB Method” website (here is how LB Method looked around that time). The algorithms worked and did give me some nice results, but there were problems like lack of detail and pulsating colors due to my display routines scaling minimum and maximum velocities to a color palette.

Yesterday I was looking around for some new LBM source code and found Daniel Schroeder‘s LBM page here. Daniel graciously shares the source code for his applet so I was able to convert his main LBM algorithms into something I could use in Visions of Chaos. Many thanks Dan!

Dan’s code was based on code by Alexander Wagner which you can see here including a PDF describing the LBM processes.

Using Dan’s code/algorithms was much faster than my older code. It also allows me to render much more finer detailed fluids without causing the system to blow out. I can push the simulation parameters further. Dan’s method of coloring solved the pulsing colors issue my older code had and includes a really nice way of visualizing the “curl” of the flowing fluid. Tracer particles are also used to follow the velocity of the underlying fluid to give another way of visualizing the fluid flow. Once particles leave the right side of the screen they are buffered up until they fill up and can be reinjected to the left side of the flow. Tracer particles help seeing the vortices easier than shading alone.

With less memory requirements (another plus from Dan’s code) I was able to render some nice 4K resolution LBM flows. This movie must be watched at 4K if possible as the compression of lower resolutions cannot handle displaying the tracer particles.

Years after the above movie was made I was revisiting the code to try and speed it up a bit by adding multi-threading support.  While doing this I found an error with the fluid collision code that caused incorrect results.

Have a look at the movie above and notice the initial part with flow past the three circles.  The curl patterns should initially be symmetric around the circles.  The background flow is an even speed from the left of the screen to the right.  Going around the circle should make the same shaped curls above and below the circles.  With a bit of tweaking I got the right bounce code working.  The next movie shows the corrected code results.

For that movie the fluid is visualized using only tracer particles.  Imagine a bunch of small confetti or glitter particles have been scattered on the fluid surface and are floating.  As the fluid flows it drags the floating tracer particles along with it.  This is a different way of seeing the curls and eddies the fluid forms as it flows past the various obstacles.

The next movie shows the fluid flow by displaying the curl of the fluid at each pixel location.

The new LBM code is now included with Visions of Chaos.

There are still some visual artifacts with this latest code and the maths can spontaneously explode crashing the simulation if the fluid speed is pushed too far or the viscosity is lowered too far.  The downside here is that you cannot get very detailed flows.  I will no doubt be revisiting LBM again in the future to experiment with new code and try to get more detailed (and faster) simulations running.


Using Multiphase Smoothed-Particle Hydrodynamics to show the emergence of Rayleigh-Taylor instability patterns

Rayleigh-Taylor Instability

Rayleigh-Taylor instability (RT) occurs when a less dense fluid is forced into a heavier fluid. If a heavier fluid is resting on a lighter fluid then gravity pulls the heavier down through the lighter fluid resulting in fingering, mushrooming and swirling patterns.

Nicole Sharp from FYFD has this into video to RT.

Simulating Rayleigh-Taylor Instability

Here is an exmaple image courtesy of Wikipedia showing some steps from simulating RT.

Rayleigh-Taylor Instability

This is a much more complex example from a supercomputer run at the Laboratory for Computational Science and Engineering, University of Minnesota. Also check out their movie gallery for more incredible fluid simulation examples.

Rayleigh-Taylor Instability

RT patterns also emerge in supernova simulations like the following two images.

Rayleigh-Taylor Instability

Rayleigh-Taylor Instability

Mark J Stock uses his own fluid simulation code to create incredibly detailed RT examples like this

thunabrain has this example of using the GPU to simulate fluids showing RT

Real Life Rayleigh-Taylor Instabilities

Pouring milk into coffee leads to RT patterns. I took these with my phone so they are not as crisp as I would have liked.

Milk and coffee

Milk and coffee

Dropping ink into water also leads to RT patterns as in these photos by Alberto Seveso

Rayleigh-Taylor Instability

Rayleigh-Taylor Instability

Using SPH to simulate RT

I had some previous success with implementing Multiphase Smoothed-Particle Hydrodynamics so I was curious to see what sorts of RT like results the SPH code could create. I have now added the options to generate RT setups in the SPH mode of Visions of Chaos.

The following SPH RT simulations use approximately 500,000 discreet individual particles to make up the fluids. They are all full HD 1080p 1920×1080 60fps videos. It was very tedious to try various settings and wait for them to render. I spent the last few weeks tweaking code and (99.99% of that time) rendering test movies to see the changes before I was happy with the following three example movies.

The code is single threaded CPU only at this stage, so much patience was required for these movies.

For this first example the top half of the screen was filled with heavier purple particles and the lower half with lighter yellow particles. A very small random movement was added to each of the particles (just enough to stop a perfect grid of particles) and then the simulation was started. 73 hours (!!) later the calculations were completed for the 3000 frames making up the movie.

The next example took around 105 hours for the 4000 frames. This time three fluids are used. Heaviest on top, medium in the middle and lightest on the bottom.

And a final three fluid example that took 74 hours for the 3000 frames.

If you click the title text of the movies they will open in a new tab allowing them to be viewed in full screen HD resolution.


Multiphase Smoothed-Particle Hydrodynamics

Smoothed-Particle Hydrodynamics (SPH) is a method of simulating fluid flow. It is based on representing a fluid by a large number of discreet (individual) particles. Each particle has properties like position, pressure, density, mass, etc. The particles are fed through a bunch of equations that make them move in a fluid like flow.

If you are a maths nerd then one of the better and recommended papers for SPH fluids is Particle-based Viscoelastic Fluid Simulation.

Multiphase SPH is when you have 2 or more fluids with different densities etc (think oil and water) that flow around each other and clump together.

I have always had a fascination with simulating fluids. Over the years I have tried to understand SPH and have had many failed attempts at writing programs to do it (the basic formulas seem relatively simple, but getting the code to run stable without explosions and crashes is far from simple). Then I found this blog post by Tom Madams that had sample source code. With that I was finally able to get SPH working. It can still be fiddly to find a nice set of parameters to make a nice looking movie, but Tom’s code seems to be a great start. Thanks Tom!!

Multiphase SPH is now available in Visions Of Chaos.

A future release will include 3D support.


Lattice Boltzmann Method fluid simulation

The new version of Visions Of Chaos now supports simulating fluid flows using the Lattice Boltzmann method. Thanks to finding some sample code at LB Method I was able to finally get my LBM code working.

The best thing about being able to implement LBM is that you do get results that can simulate real world phenomena. A classic example is the Karman Vortex Street effect that creates turbulent spiral vortexes behind an obstacle in fluid flow. Here are some examples of real world large scale Von Karman patterns in clouds. Both of them show the air/cloud flowing upwards from the bottom of the pictures hitting an island. This small obstacle creates the vortices behind the obstacles.

So after some work implementing LBM in code it does show similar shapes behind obstacles. This is an example of what Visions Of Chaos can now output. The colors show velocity (both the X and Y velocities combined) shaded between the minimum and maximum velocities. Each part also has a contour plot that is like a contour map showing the areas that have the same velocity amount.

Double-click the above movie and set the resolution to 720 to watch in full HD resolution. The smaller size blurs out a lot of the finer details.