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!

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.

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

Jason.

Evolving Genetic Virtual Creatures

Inspiration

Back in 1994 Karl Sims developed his Evolved Virtual Creatures. More info here and here.

I have always found these sort of simulations fascinating, using the principals of genetics to evolve better solutions to a problem. For years I have wanted to try writing my own evolved creatures, but coding a physics engine to handle the movements and joints was beyond me so it was yet another entry on my to do list (until now).

2D Physics

For my virtual creatures I decided to start with 2D. I needed a general physics engine that takes care of all the individual world parts interacting and moving. Erin Catto’s Box2D has all the physics simulation features that I need to finally start experimenting with a 2D environment. Box2D is great. You only need some relatively simple coding to get the physics setup and then Box2D handles all the collisions etc for you.

Random Creatures

The creatures I am using are the simplest structures I could come up with that hopefully lead to some interesting movements. Creatures consist of three segments (rectangles) joined together by rotating (revolute joints in Box2D) joints. The size of the rectangle segments and joint rotation speeds are picked at random. Once the random creature is created it is set free into a virtual world to see what it does.

Virtual Creature

Many of them crunch up and go nowhere,

Virtual Creature

but some setups result in jumping and crawling creatures.

In only a few minutes thousands of random creatures can be setup and simulated. From these thousands the creatures that perform well are saved.

Performance Criteria

Once thousands of random virtual creatures have been created you need a way to pick the best ones. For these creatures I used three different criteria;

1. Distance traveled. The maximum X distance the creature travels in 5,000 time steps.

Virtual Creature

2. Distance crawled. The maximum X distance the creature travels in 5,000 time steps, but with a height restriction to weed out creatures that jump too high.

Virtual Creature

3. Height reached. The maximum Y height the creature reaches in 5,000 time steps.

Virtual Creature

The best picks become a set of creatures in the saved “gene pool”. If you have a large enough random set of creatures (say 10,000) and only take the top 10 performers then you do tend to get a set of creatures that perform the task well.

Mutations

Mutation takes a current “good creature” that passed criteria searching and scales segment length, segment width, joint rotation torque and joint rotation speed by a random percentage. The mutated creature is then run through 5,000 time steps and checked if it performs better than the original. If so, it is saved over the original and mutations continue. This process can be left to churn away for hours hands free and when the mutations are stopped you have a new set of best creatures.

For the creatures described here the features I randomly change are the segment widths and heights, the joint motor torques and the joint motor speeds (for 10 total attributes that can be tweaked by mutation). The user specifies a max mutation percentage and then each of the creature values are changed by


changepercentage:=maxmutationpercentage/100*random;
amount:=(MaxSegmentWidth-MinSegmentWidth)*changepercentage;
if random(2)=0 then amount:=-amount;
segmentwidth:=segmentwidth+amount;

The new attribute is clamped to a min and max value so as not to suddenly grow extremely long segments or super fast motors. You can also randomly mutate only 1 of the attributes rather than all 10 each mutation.

Choosing the right mutation amount can be tricky. Too high a random percentage and you may as well be randomly picking creatures. Too low a percentage and you will get very few mutations that beat the current creature. After some experimenting I am now using a mutation rate of 15% and mutating 3 of the attributes (ie a segment length, a motor’s torque, etc) each mutation.

Running on an i7-6800K CPU my current code can churn through up to 21 mutation tests per second. This screen shot shows 9 copies of Visions of Chaos running, each looking for mutations of different creature types, ie 3 segment distance, 4 segment height reached, etc etc.

Virtual Creatures

A mutation test requires the new mutated creature to be run for 5000 time steps and then comparing against its “parent” to see if it is better in the required fitness criteria (distance traveled, distance crawled or height reached).

Mutation Results

After mutating the best randomly found creatures for a while, this movie shows the best creature for distance traveled, distance crawled and height reached.

I will have to run the mutation searches overnight or for a few days to see if even better results are evolved.

4 Segment Creatures

Here are some results from evolving (mutating) 4 segment creatures. Same criteria of distance, crawl distance and height for best creatures. Note how only the white “arms” collide with each other. The grey “body” segments are set to pass through each other.

5 Segment Creatures

And finally, using 5 segments per creature. Only the 2 end arms collide with each other (otherwise the creatures always bunched up in a virtual knot and moved nowhere).

Availability

These Virtual Creatures are now included in the latest version of Visions of Chaos. I have also included the Box2D test bed to show some of the extra potential that I can use Box2D for in future creatures.

To Do

This is only the beginning. I have plenty of ideas for future improvements and expansions;

1. Using more than just mutations when evolving creatures. With more complex creatures crossover breeding could be experimented with.

2. Use more of the features of Box2D to create more complex creature setups. Arms and legs that “wave” back and forth like a fish tail rather than just spinning around.

3. 3D creatures and environments. I will need to find another physics engine supporting 3D hopefully as easily as Box2D supports 2D.

Jason.

2D Strange Attractors

Strange Attractors are plots of relatively simple formulas. They are created by repeating (or iterating) a formula over and over again and using the results at each iteration to plot a point. The result of each iteration is fed back into the equation. After millions of points have been plotted fractal structures appear. The repeated points fall within a basin of attraction (they are attracted to the points that make up these shapes).

I recently revisited my old strange attractor code in Visions of Chaos to add some new variations. This post will show many of the strange attractor formulas and some 4K resolution sample images they create. The images were created using over 1 billion points each. They have also been oversampled at least 3×3 pixels to reduce aliasing artifacts.

Bedhead Attractor

Discovered by Ivan Emrich.


x and y both start at 1.0

xnew=sin(x*y/b)*y+cos(a*x-y)
ynew=x+sin(y)/b

Variables a and b are floating point values bewteen -1 and +1

Bedhead Attractor

A=0.65343 B=0.7345345

Bedhead Attractor

A=-0.81 B=-0.92

Bedhead Attractor

A=-0.64 B=0.76

Bedhead Attractor

A=0.06 B=0.98

Bedhead Attractor

A=-0.67 B=0.83

Clifford Attractor

Discovered by Clifford A Pickover. I found them explained on Paul Bourke‘s page here.


x and y both start at 0.1

xnew=sin(a*y)+c*cos(a*x)
ynew=sin(b*x)+d*cos(b*y)

Variables a,b,c and d are floating point values bewteen -3 and +3

Clifford Attractor

A=-1.7 B=1.3 C=-0.1 D=-1.21

Clifford Attractor

A=-1.7 B=1.8 C=-0.9 D=-0.4

Clifford Attractor

A=1.5 B=-1.8 C=1.6 D=2

Clifford Attractor

A=-2.239 B=-2.956 C=1.272 D=1.419

Clifford Attractor

A=-1.7 B=1.8 C=-1.9 D=-0.4

Fractal Dream Attractor

Discovered by Clifford A Pickover and discussed in his book “Chaos In Wonderland”.


x and y both start at 0.1

xnew=sin(y*b)+c*sin(x*b)
ynew=sin(x*a)+d*sin(y*a)

Variables a and b are floating point values bewteen -3 and +3
Variables c and d are floating point values between -0.5 and +1.5

Fractal Dream Attractor

A=-0.966918 B=2.879879 C=0.765145 D=0.744728

Fractal Dream Attractor

A=-2.9585 B=-2.2965 C=-2.8829 D=-0.1622

Fractal Dream Attractor

A=-2.8276 B=1.2813 C=1.9655 D=0.597

Fractal Dream Attractor

A=-1.1554 B=-2.3419 C=-1.9799 D=2.1828

Fractal Dream Attractor

A=-1.9956 B=-1.4528 C=-2.6206 D=0.8517

Gumowski-Mira Attractor

The Gumowski-Mira equation was developed in 1980 at CERN by I. Gumowski and C. Mira to calculate the trajectories of sub-atomic particles. It can also be used to create attractor images.


x and y both start at any floating point value between -20 and +20

t=x
xnew=b*y+w
w=a*x+(1-a)*2*x*x/(1+x*x)
ynew=w-t

The a and b parameters can be any floating point value between -1 and +1.

Gumowski Mira Attractor

Initial X=0 Initial Y=0.5 A=0.008 B=-0.7

Gumowski Mira Attractor

Initial X=-0.723135391715914 Initial Y=-0.327585775405169 A=0.79253300698474 B=0.345703079365194

Gumowski Mira Attractor

Initial X=-0.312847771216184 Initial Y=-0.710899183526635 A=0.579161538276821 B=-0.820410779677331

Gumowski Mira Attractor

Initial X=-0.325819793157279 Initial Y=0.48573582014069 A=0.062683217227459 B=-0.436713613104075

Gumowski Mira Attractor

Initial X=0.78662442881614 Initial Y=0.919355855789036 A=0.900278024375439 B=0.661233567167073

Hopalong Attractor

The Hopalong attractor was discovered by Barry Martin.


x and y both start at 0

xnew=y-1-sqrt(abs(b*x-1-c))*sign(x-1)
ynew=a-x-1

The parameters a, b and c can be any floating point value between 0 and +10.

Hopalong Attractor

A=7.16878197155893 B=8.43659746693447 C=2.55983412731439

Hopalong Attractor

A=7.7867514709942 B=0.132189802825451 C=8.14610984409228

Hopalong Attractor

A=9.74546888144687 B=1.56320227775723 C=7.86818214459345

Hopalong Attractor

A=9.8724800767377 B=8.66862616268918 C=8.66950439289212

Hopalong Attractor

A=9.7671244922094 B=4.10973468795419 C=3.78332691499963

Jason Rampe 1

A variation I discovered while trying random formula changes.


x and y both start at 0.1

xnew=cos(y*b)+c*sin(x*b)
ynew=cos(x*a)+d*sin(y*a)

Variables a, b, c and d are floating point values between -3 and +3

Jason Rampe 1 Attractor

A=2.6 B=-2.5995 C=-2.9007 D=0.3565

Jason Rampe 1 Attractor

A=1.8285 B=-1.8539 C=0.3816 D=1.9765

Jason Rampe 1 Attractor

A=2.5425 B=2.8358 C=-0.8721 D=2.7044

Jason Rampe 1 Attractor

A=-1.8669 B=1.2768 C=-2.9296 D=-0.4121

Jason Rampe 1 Attractor

A=-2.7918 B=2.1196 C=1.0284 D=0.1384

Jason Rampe 2

Another variation I discovered while trying random formula changes.


x and y both start at 0.1

xnew=cos(y*b)+c*cos(x*b)
ynew=cos(x*a)+d*cos(y*a)

Variables a, b, c and d are floating point values between -3 and +3

Jason Rampe 2 Attractor

A=1.546 B=1.929 C=1.09 D=1.41

Jason Rampe 2 Attractor

A=2.907 B=-1.9472 C=1.2833 D=1.3206

Jason Rampe 2 Attractor

A=0.8875 B=0.7821 C=-2.3262 D=1.5379

Jason Rampe 2 Attractor

A=-2.4121 B=-1.0028 C=-2.2386 D=0.274

Jason Rampe 2 Attractor

A=-2.9581 B=0.927 C=2.7842 D=0.6267

Jason Rampe 3

Yet another variation I discovered while trying random formula changes.


x and y both start at 0.1

xnew=sin(y*b)+c*cos(x*b)
ynew=cos(x*a)+d*sin(y*a)

Variables a, b, c and d are floating point values between -3 and +3

Jason Rampe 3 Attractor

A=2.0246 B=-1.323 C=-2.8151 D=0.2277

Jason Rampe 3 Attractor

A=1.4662 B=-2.3632 C=-0.4167 D=2.4162

Jason Rampe 3 Attractor

A=-2.7564 B=-1.8234 C=2.8514 D=-0.8745

Jason Rampe 3 Attractor

A=-2.218 B=1.4318 C=-0.3346 D=2.4993

Jason Rampe 3 Attractor

A=1.2418 B=-2.4174 C=-0.7112 D=-1.9802

Johnny Svensson Attractor

See here.


x and y both start at 0.1

xnew=d*sin(x*a)-sin(y*b)
ynew=c*cos(x*a)+cos(y*b)

Variables a, b, c and d are floating point values between -3 and +3

Johnny Svensson Attractor

A=1.40 B=1.56 C=1.40 D=-6.56

Johnny Svensson Attractor

A=-2.538 B=1.362 C=1.315 D=0.513

Johnny Svensson Attractor

A=1.913 B=2.796 C=1.468 D=1.01

Johnny Svensson Attractor

A=-2.337 B=-2.337 C=0.533 D=1.378

Johnny Svensson Attractor

A=-2.722 B=2.574 C=1.284 D=1.043

Peter DeJong Attractor

See here.


x and y both start at 0.1

xnew=sin(y*a)-cos(x*b)
ynew=sin(x*c)-cos(y*d)

Variables a, b, c and d are floating point values between -3 and +3

Peter DeJong Attractor

A=0.970 B=-1.899 C=1.381 D=-1.506

Peter DeJong Attractor

A=1.4 B=-2.3 C=2.4 D=-2.1

Peter DeJong Attractor

A=2.01 B=-2.53 C=1.61 D=-0.33

Peter DeJong Attractor

A=-2.7 B=-0.09 C=-0.86 D=-2.2

Peter DeJong Attractor

A=-0.827 B=-1.637 C=1.659 D=-0.943

Peter DeJong Attractor

A=-2 B=-2 C=-1.2 D=2

Peter DeJong Attractor

A=-0.709 B=1.638 C=0.452 D=1.740

Symmetric Icon Attractor

These attractors came from the book “Symmetry in Chaos” by Michael Field and Martin Golubitsky. They give symmetric results to the attractors formed.


x and y both start at 0.01

zzbar=sqr(x)+sqr(y)
p=alpha*zzbar+lambda
zreal=x
zimag=y
for i=1 to degree-2 do
begin
     za=zreal*x-zimag*y
     zb=zimag*x+zreal*y
     zreal=za
     zimag=zb
end
zn=x*zreal-y*zimag
p=p+beta*zn
xnew=p*x+gamma*zreal-omega*y
ynew=p*y-gamma*zimag+omega*x
x=xnew
y=ynew

The Lambda, Alpha, Beta, Gamma, Omega and Degree parameters can be changed to create new plot shapes.

These sample images all come from paramters in the “Symmetry in Chaos” book.

Symmetric Icon - Chaotic Flower

L=-2.5 A=5 B=-1.9 G=1 O=0.188 D=5

Symmetric Icon - Clam Triple

L=1.56 A=-1 B=0.1 G=-0.82 O=0.12 D=3

Symmetric Icon - Emporer's Cloak

L=-1.806 A=1.806 B=0 G=1 O=0 D=5

Symmetric Icon - Fish and Eye

L=-2.195 A=10 B=-12 G=1 O=0 D=3

Symmetric Icon - Flintstone

L=2.5 A=-2.5 B=0 G=0.9 O=0 D=3

Symmetric Icon - French Glass

L=-2.05 A=3 B=-16.79 G=1 O=0 D=9

Symmetric Icon - Halloween

L=-2.7 A=5 B=1.5 G=1.0 O=0 D=6

Symmetric Icon - Kachina Dolls

L=2.409 A=-2.5 B=0 G=0.9 O=0 D=23

Symmetric Icon - Mayan Bracelet

L=-2.08 A=1 B=-0.1 G=0.167 O=0 D=7

Symmetric Icon - Pentacle

L=-2.32 A=2.32 B=0 G=0.75 O=0 D=5

Symmetric Icon - Pentagon

L=2.6 A=-2 B=0 G=-0.5 O=0 D=5

Symmetric Icon - Sanddollar

L=-2.34 A=2 B=0.2 G=0.1 O=0 D=5

Symmetric Icon - Swirling Streamers

L=-1.86 A=2 B=0 G=1 O=0.1 D=4

Symmetric Icon - Trampoline

L=1.56 A=-1 B=0.1 G=-0.82 O=0 D=3

Symmetric Icon - Trinity

L=1.5 A=-1 B=0.1 G=-0.805 O=0 D=3

Symmetric Icon - Untitled 01

L=1.455 A=-1 B=0.03 G=-0.8 O=0 D=3

Symmetric Icon - Unititled 02

L=2.39 A=-2.5 B=-0.1 G=0.9 O=-0.15 D=16

3D Alternatives

Strange Attractors can also be extended into three dimensions. See here and here for my previous experiments with 3D Strange Attractors.

All of the images in this post were created using Visions of Chaos.

Jason.

Fractal Spirographs

Inspiration

Daniel Shiffman has been making YouTube movies for some time now. His videos focus on programming and include coding challenges in which he writes code for a target idea from scratch. If you are a coder I recommend Dan’s videos for entertainment and inspiration.

His latest live stream focused on Fractal Spirographs.

If you prefer to watch a shorter edited version, here it is.

He was inspired by the following image from the Benice Equation blog.

Fractal Spirograph

Fractal Spirographs (aka Fractal Routlette) are generated by tracking a series (or chain) of circles rotating around each other as shown in the above gif animation. You track the chain of 10 or so circles and plot the path the final smallest circle takes. Changing the number of circles, the size ratio between circles, the speed of angle change, and the constant “k” changes the resulting plots and images.

How I Coded It

As I watched Daniel’s video I coded my own version. For my code (Delphi/pascal) I used a dynamic array of records to hold the details of each circle/orbit. This seemed the simplest approach to me for keeping track of a list of the linked circles.

  
type orbit=record
     x,y:double;
     radius:double;
     angle:double;
     speed:double;
end;

Before the main loop you fill the array;

     
//parent orbit
orbits[0].x:=destimage.width/2;
orbits[0].y:=min(destimage.width,destimage.height)/2;
orbits[0].radius:=orbits[0].y/2.5;
orbits[0].angle:=0;
orbits[0].speed:=0;
rsum:=orbits[0].radius;
//children orbits
for loop:=1 to numorbits-1 do
begin
     newr:=orbits[loop-1].radius/orbitsizeratio;
     newx:=orbits[loop-1].x+orbits[loop-1].radius+newr;
     newy:=orbits[loop-1].y;
     orbits[loop].x:=newx;
     orbits[loop].y:=newy;
     orbits[loop].radius:=newr;
     orbits[loop].angle:=orbits[loop-1].angle;
     orbits[loop].speed:=power(k,loop-1)/sqr(k*k);
end;

Then inside the main loop, you update the orbits;


//update orbits
for loop:=1 to numorbits-1 do
begin
     orbits[loop].angle:=orbits[loop].angle+orbits[loop].speed;
     rsum:=orbits[loop-1].radius+orbits[loop].radius;
     orbits[loop].x:=orbits[loop-1].x+rsum*cos(orbits[loop].angle*pi/180);
     orbits[loop].y:=orbits[loop-1].y+rsum*sin(orbits[loop].angle*pi/180);
end;

and then you use the last orbit positions to plot the line, ie


canvas.lineto(round(orbits[numorbits-1].x),round(orbits[numorbits-1].y));

Results

Once the code was working I rendered the following images and movie. They are all 4K resolution to see the details. Click the images to see them full size.

Fractal Spirograph

Fractal Spirograph

Fractal Spirograph

Fractal Spirograph

Fractal Spirograph

Here is a 4K movie showing how these curves are built up.

Fractal Spirographs are now included with the latest version of Visions of Chaos.

Finally, here is an 8K Fulldome 8192×8192 pixel resolution image. Must be seen full size to see the fine detailed plot line.

Fractal Spirograph

To Do

Experiment with more changes in the circle sizes. The original blog post links to another 4 posts here, here, here and here and even this sumo wrestler

Fractal Spirograph

Plenty of inspiration for future enhancements.

I have already experimented with 3D Spirographs in the past, but they are using spheres rotating within other spheres. Plotting the sqheres rotating around the outside of other spheres should give more new unique results.

Jason.

The Burning Ship Fractal

The Burning Ship Fractal is a slight variant on the Mandelbrot Set Fractal.

The basic Mandelbrot Fractal formula is z=z^2+c. The Burning Ship Fractal formula is z=abs(z)^2+c.

The following image is the standard power 2 Burning Ship Fractal rendered using CPM smooth coloring.

Burning Ship Fractal

Zooming in to the right antenna part of the fractal shows why it was named the Burning Ship.

Burning Ship Fractal

The next 3 images change the exponent 2 in z=abs(z)^2+c to 3, 4 and 5.

Burning Ship Fractal

Burning Ship Fractal

Burning Ship Fractal

The same power 2 through power 5 Burning Ships but this time using Triangle Inequality Average (TIA) coloring

Burning Ship Fractal

Burning Ship Fractal

Burning Ship Fractal

Burning Ship Fractal

The next 4K resolution movie shows a series of zooms into Burning Ship Fractals between power 2 and power 5 colored using CPM coloring

and finally another 4K movie showing more Burning Ship zooms colored using TIA coloring

All of the above images and movies were created with Visions of Chaos.

Jason.

Meta-Mandelbrots

Ian McDonald came up with a new novel way to render Mandelbrot (actually Julia) Set fractals.

The usual Mandelbrot fomula is
z=z*z+c

Taking the z*z+c part, replace the z’s with (z*z+c) and replace the c’s with (c*c+z)

After one level of replacement you get
((z*z+c)*(z*z+c)+(c*c+z))

Level 2 is
(((z*z+c)*(z*z+c)+(c*c+z)) * ((z*z+c)*(z*z+c)+(c*c+z)) + ((c*c+z)*(c*c+z)+(z*z+c)))

and Level 3 is
((((z*z+c)*(z*z+c)+(c*c+z))*((z*z+c)*(z*z+c)+(c*c+z))+((c*c+z)*(c*c+z)+(z*z+c)))*(((z*z+c)*(z*z+c)+(c*c+z))*((z*z+c)*(z*z+c)+(c*c+z))+((c*c+z)*(c*c+z)+(z*z+c)))+(((c*c+z)*(c*c+z)+(z*z+c))*((c*c+z)*(c*c+z)+(z*z+c))+((z*z+c)*(z*z+c)+(c*c+z))))

Then you use the level 3 formula and render it as a Julia Set.

Complex C (-0.2,0.0)

Meta-Mandelbrot

Complex C (-0.14 0.0)

Meta-Mandelbrot

Complex C (-0.141 0.0)

Meta-Mandelbrot

The following movie shows the complex C changing slowly from 0 to -0.2 and three zooms into Meta-Mandelbrots. Unfortunately because these are Julia sets the shapes deeper in are virtually identical to the original fractal. You don’t get those totally different looking areas as you do with Mandelbrot fractals.

For more information see the original Fractal Forums post here.

The GLSL shader to generate these fractals is now included with Visions of Chaos.

The Belousov-Zhabotinsky Reaction and The Hodgepodge Machine

Inspiration for this post

The other day I saw this YouTube video of a Belousov-Zhabotinsky Cellular Automaton (BZ CA) by John BitSavage

After a while of running in an oscillating state it begins to grow cell like structures. I had never seen this in BZ CAs before. I have seen similar cell like growths in Digital Inkblot CAs and in the Yin Yang Fire CA. Seeing John’s results different to the usual BZ CA was what got me back into researching BZ in more depth.

The Belousov-Zhabotinsky Reaction

Belousov-Zhabotinsky Reactions (see here for more info) are examples of a chemical reactions that can oscillate between two different states and form intetesting patterns when performed in shallow petri dishes.

Here are some sample high res images of real BZ reaction by Stephen Morris. Click for full size.

Belousov-Zhabotinsky Reaction

Belousov-Zhabotinsky Reaction

Belousov-Zhabotinsky Reaction

and some other images from around the Internet

Belousov-Zhabotinsky Reaction

Belousov-Zhabotinsky Reaction

Belousov-Zhabotinsky Reaction

Belousov-Zhabotinsky Reaction

and some sample movies I found on YouTube

The Hodgepodge Machine Cellular Automaton

Back in August 1988, Scientific American‘s Computer Recreations section had an article by A. K. Dewdney named “The hodgepodge machine makes waves”. After a fair bit of hunting around I could not find any copies of the article online so I ended up paying $8 USD to get the issue in PDF format. The PDF is a high quality version of the issue, but $8 is still a rip off.

In the article Dewdney describes the “hodgepodge machine” cellular automaton designed by Martin Gerhardt and Heike Schuster of the University of Bielefeld in West Germany. A copy of their original paper can be seen here.

How the Hodgepodge Machine works

The inidividual cells/pixels in the hodgepodge automaton have n+1 states (between 0 and n). Cells at state 0 are considered “healthy” and cells at the maximum state n are said to be “ill”. All cells with states inbetween 0 and n are “infected” with the larger the state representing the greater level of infection.

Each cycle of the cellular automaton a series of rules are applied to each cell depending on its state.

(a) If the cell is healthy (i.e., in state 0) then its new state is [a/k1] + [b/k2], where a is the number of infected cells among its eight neighbors, b is the number of ill cells among its neighbors, and k1 and k2 are constants. Here “[]” means the integer part of the number enclosed, so that, for example, [7/3] = [2+1/3] = 2.

(b) If the cell is ill (i.e., in state n) then it miraculously becomes healthy (i.e., its state becomes 0).

(c) If the cell is infected (i.e., in a state other than 0 and n) then its new state is [s/(a+b+1)] + g, where a and b are as above, s is the sum of the states of the cell and of its neighbors and g is a constant.

The parameters given for these CA are usual q (for max states), k1 and k2 (the above constants) and g (which is a constant for how quickly the infection tends to spread).

My previous limited history experimenting with BZ

Years ago I implemented BZ CA in Visions of Chaos (I have now correctly renamed the mode Hodgepodge Machine) and got the following result. This resolution used to be considered the norm for YouTube and looked OK on lower resolution screens. How times have changed.

The above run used these parameters
q=200
k1=3
k2=3
g=28

Replicating Gerhardt and Miner’s results

Gerhadt and Miner used fixed values of k1=2 and k2=3. The majority of their experiments used a grid size of q=20 (ie only 20×20 cells) without a wraparound toroidal world. This leaves the single infection spreading g variable to play with. Their paper states they used values of g between 1 and 10, but I get no spirals with g in that range.

Here are a few samples which are 512×512 sized grids with wraparound edges and many thousands of generations to be sure they had finally settled down. Each cell is 2×2 pixels in size so they are 1024×1024 images.

Hodgepodge Machine

q=100, k1=2, k2=3, g=5

Hodgepodge Machine

q=100, k1=2, k2=3, g=20

Hodgepodge Machine

q=100, k1=2, k2=3, g=25

Hodgepodge Machine

q=100, k1=2, k2=3, g=30

Results from other parameters

Hodgepodge Machine

q=100, k1=3, k2=3, g=10

Hodgepodge Machine

q=100, k1=3, k2=3, g=15

Hodgepodge Machine

q=100, k1=3, k2=3, g=20

Extending into 3D

The next logical step was extending it into three dimensions. This blog post from Rudy Rucker shows a 3D BZ CA from Harry Fu back in 2004 for his Master’s degree writing project. I must be a nerd as I whipped up my 3D version over two afternoons. Surprisingly there are no other references to experiments with 3D Hodgepodge that I can find.

The algorithms are almost identical to their 2D counterparts. The Moore neighborhood is extended into three dimensions (so 26 neighbors rather than 8 in the 2D version). It is difficult to see the internal structures as they are hidden from view. Methods I have used to try and see more of the internals are to slice out 1/8th of the cubes and to render only some of the states.

Clicking these sample images will show them in 4K resolution.

3D Hodgepodge Machine

q=100, k1=1, k2=18, g=43 (150x150x150 grid)

3D Hodgepodge Machine

q=100, k1=1, k2=18, g=43 (150x150x150 grid – same as previous with a 1/8th slice out to see the same patterns are extending through the 3D structure)

3D Hodgepodge Machine

q=100, k1=1, k2=18, g=43 (150x150x150 grid – same rules again, but this time with only state 0 to state 50 cells being shown)

3D Hodgepodge Machine

q=100, k1=2, k2=3, g=15 (150x150x150 grid)

3D Hodgepodge Machine

q=100, k1=3, k2=6, g=31 (150x150x150 grid)

3D Hodgepodge Machine

q=100, k1=4, k2=6, g=10 (150x150x150 grid)

3D Hodgepodge Machine

q=100, k1=4, k2=6, g=10 (150x150x150 grid – same rules as the previous image – without the 1/8th slice – with only states 70 to 100 visible)

3D Hodgepodge Machine

q=100, k1=3, k2=31, g=43 (250x250x250 sized grid – 15,625,000 total cells)

3D Hodgepodge Machine

q=100, k1=4, k2=12, g=34 (350x350x350 sized grid – 42,875,000 total cells)

3D Hodgepodge Machine

q=100, k1=1, k2=9, g=36 (400x400x400 sized grid – 64,000,000 total cells)

Download Visions of Chaos if you would like to experiment with both 2D and 3D Hodgepodge Machine cellular automata. If you find any interesting rules please let know in the comments or via email.