Yes! A friend and I had the same thought a while back and then remembered the curl operator from vector calculus is divergence free, so you can take the curl of any 3d field to get a divergence free result, and then use a 2d slice of that if you want. It does look like fluid flow when you do that. I’ve even used this trick in CG films back when I worked at DreamWorks. We wrote a bit about this divergence free noise field fake fluid flow in a Siggraph course. A few years later, another researcher came up with some techniques for putting boundaries and obstacles into the noise field. He may have come up with the same idea for using the curl of noise independently, but he referenced the article my friend and I wrote. https://www.cs.ubc.ca/~rbridson/docs/bridson-siggraph2007-cu...
But! I’m not sure divergence free noise would necessarily solve any problems for what this artist is trying to achieve. The art he’s generating is currently making use of the field’s divergence and convergence properties; in the images it’s important that lines move further apart and become less dense sometimes, and then converge and become more dense in other places. The divergence can be ugly if you see too much of it or identify the pattern, and that’s a big reason the article uses collision detection to terminate converging lines. But the finished images he shows are also setting the noise scale and framing the image manually in order to maximize the benefits of the divergent noise, and minimize the downsides.
When I googled for the paper I found a few interesting links about curl noise:
I get that a perfectly divergence-free field might not be what the artist is after, but if you go the projection route where you "subtract out the divergence" so to speak, I was thinking you could just do a scaled subtraction to not take all of it out. Alternatively perhaps it could be artistically interesting to do a non-linear scaling.
But! I’m not sure divergence free noise would necessarily solve any problems for what this artist is trying to achieve. The art he’s generating is currently making use of the field’s divergence and convergence properties; in the images it’s important that lines move further apart and become less dense sometimes, and then converge and become more dense in other places. The divergence can be ugly if you see too much of it or identify the pattern, and that’s a big reason the article uses collision detection to terminate converging lines. But the finished images he shows are also setting the noise scale and framing the image manually in order to maximize the benefits of the divergent noise, and minimize the downsides.
When I googled for the paper I found a few interesting links about curl noise:
2d example: https://al-ro.github.io/projects/curl/
3d example: https://al-ro.github.io/projects/embers/
Houdini offers curl noise. Houdini is amazing for generative art, btw, and has a free personal learning edition.
Docs: https://www.sidefx.com/docs/houdini/nodes/vop/curlnoise.html
Example usage: https://entagma.com/houdini-curl-noise-flow/