Comments (6)
This is amazing, @PavelBlend ! Yeah, current meshing methods use regular grids which makes it slow. I want to implement adaptive version which should make performance better, and I do have early code for that. Let me see what I can do in the short term + more documentation you asked earlier.
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Interactive demos (when viz and gpu branch is merged)
Does this imply that realtime rendering / demos (i.e. OpenGL) are being worked on?
I see jet.viz with a bunch of OpenGL files. Exciting!
What help needed did you have in mind for interactive / realtime?
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There is super simple 2D smoke demo ingpu
branch actually. The solver is still based on CPU yet.
For any help on the interactive demos, I think simple game-like 2-D demo could be fun. For example, Figure 4 from the following paper shows some interesting stuff:
http://graphics.snu.ac.kr/publications/journals/2005%20Song%20ACM%20TOG.pdf
You can pour water or add boundaries interactively, and I think that could be a nice demo.
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I got the gpu
branch, compiled, and ran stam_sf
smoke demo on my 2015 MacBookPro (ATI graphics, no CUDA). Worked great.
Fig4
would be a great 2nd demo, agreed. I have lots of experience with OpenGL/games, but just reading the fluidengine book now. I'll keep this idea in mind as i read, and check back here when I understand the fluid side enough to execute on the idea.
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Thanks, @subatomicglue. That would be awesome :)
BTW, stam_sf
demo is from Jos Stam's 2003 GDC presentation and will be excluded before merge. (It's the most minimal fluid solver you can get which is great for perf baseline.) Please try out smoke_sim2d
which uses Jet implementation.
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I would like to add a demos video, but so far I have run into difficulties.
The convert function runs slowly on a large number of particles (3 million).
converter = pyjet.AnisotropicPointsToImplicit3(0.1, 0.5, 0.5, 25, True)
converter.convert(points, grid)
Does this function convert particles to voxels?
I have a baked sequence of 900 frames. Converting particles to mesh is too slow at a resolution of 480 voxels. Is it possible to speed up or optimize this function in any way?
So far I have been able to bake and render a limited number of frames. I adapted the scene taken from here for jet fluids:
https://youtu.be/-IiRzmfs5aw
Here is the result:
LuxCoreRender
Simulation Method: APIC.
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Related Issues (20)
- How do I run the examples and the animations?
- How to run the particles2obj example? HOT 14
- Compile error when compiling with Clang 10 HOT 1
- Promotion of projects/research using Jet Framework HOT 4
- How to implement the xyz to mesh generation to another code for fluid simulation? HOT 1
- How do I set up a fluid particles at rest and a fluid particles with initial velocity HOT 3
- Suggestion to replace Travis CI/Appveyor with GitHub Actions HOT 5
- Some unit tests failure on 32-bit system
- Fix Visual Studio 2017 build failure on GitHub Action
- Timer test code failure on MinGW system
- No known features for CXX compiler "Clang" HOT 5
- running the Python example show error HOT 8
- Replace Clara
- makeTranslationMatrix should be transposed HOT 8
- About the local method in the surface_to_implicit HOT 1
- GridSmokeSolver3::computeDiffusion is diffusing smokeDensity instead of temperature HOT 2
- Incorrect formula for collider boundary conditions (Equation 3.18 in the book)
- can't build cmake in win64 HOT 1
- bug in void Matrix<T, M, N>::invert() in "include/detail/matrix-inl.h" HOT 1
- Newer Versions of Flatbuffer is breaking the build in particle_system_data2.cpp HOT 1
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