The learning curve is wild when collaborating with a bunch of AI instances.  
  


I didn’t sleep for too long, but what I’ve learned about simulation approaches in the last few days blows my mind.  
  


I started 2 months ago with simply applying a procedurally generated (Perlin noise) advection field.  
  


Then I figured out how to actually simulate the advection based on a Newtonian fluid.  
  


And now… I’ve started to simulate fire. For now, only a few of its properties, to get familiar with the architecture of a complex simulation pipeline.  
  


I’m simulating a coupled system of flow, heat, and smoke.



the flow moves heat and smoke

the heat creates flow

cooling heat creates smoke

the renderer maps those fields into a flame appearance

  
It works as follows:  
  
This simulation uses a visually driven coupled field model on a fixed 64x64 grid.



What is stored per cell:



u, v: the two components of the velocity field

temperature: how hot that cell is

smoke: how much smoke / density is in that cell



What happens each step:



The source injects new temperature, smoke, and upward momentum near the bottom. (source term / forcing)

Gravity and especially Buoyancy modify the velocity field. Hotter cells get extra upward force. (buoyancy force model)

The velocity field is diffused, projected, and advected. This is the classic incompressible fluid core: smooth it, enforce near zero divergence, then move the flow through itself. (Eulerian incompressible fluid solver / Stable Fluids style)

temperature and smoke are transported by that velocity field. So heat and smoke get carried by the flow. (semi-Lagrangian advection on an Eulerian grid)

temperature cools down over time. (simple exponential decay / cooling model)

Part of that lost heat is converted into new smoke. This is an important feedback loop: hot flame gradually produces smoke as it cools. (heuristic reaction / transfer model)

Vorticity confinement artificially boosts small vortices so the flow does not become too smooth and dead-looking. (vorticity confinement)



The key feedback loop:

more temperature creates more buoyancy

more buoyancy changes the velocity field

the velocity field transports temperature and smoke

temperature cools and generates smoke

smoke changes the visible shape and softness of the flame in rendering

vortices in the velocity field tear and fold the heat/smoke fields into richer structures



Why it looks good:

The solver transports everything coherently, so shapes do not break up randomly, they deform like flowing material.

temperature and smoke play different visual roles:

temperature gives the bright active flame core

smoke gives softer, darker, trailing regions



Fire color is not simulated directly as RGB fuel chemistry. Instead, it is rendered from the fields:

hot = bright / yellow / white

cooler = orange / red

smoke-heavy = darker / grayer



Vorticity confinement brings back the small flickering tongues and curls that numerical damping would otherwise wash out.



The system is only a rough physical approximation, but it emphasizes exactly the cues our eyes read as fire:

upward rise

turbulent breakup

bright core

unstable edges

smoke appearing as things cool







From zero to fire simulation - building my own model