FluidX3D on GigaIO SuperNODE - Concorde 40 Billion Cell CFD Simulation

Uploaded August 2023 | Updated September 2026, 2 weeks ago

This is one of the largest CFD simulations ever done, on the world's largest GPU server, the GigaIO SuperNODE, equipped with 32x AMD Instinct MI210 64GB GPUs, for a total 2TB VRAM.
gigaio.com/supernode

The simulation shows the 62m long Concorde before landing at 300km/h airspeed and 10° angle of attack, for 1 second in flight. The Reynolds number based on wingspan is 146 Million.

The simulation resolution is 2976×8936×1489 = 40 Billion cells, with a tiny cell size of (12.4mm)³. 67268 time steps were computed in 29 hours, plus 4 hours for rendering 5×600 4K frames, for a total runtime of 33 hours. The video shows velocity-magnitude colored Q-criterion isosurfaces. A single frame of the velocity field is 475GB, so the 600 frames visualize a total of 285TB data.
This is a test of the newly implemented free-slip boundaries, which are a more accurate model for the turbulent boundary layer than no-slip boundaries.

On the same hardware, commercial CFD software like Ansys or Star-CCM+ would need several years of compute time for such a simulation. FluidX3D does it over the weekend.

The FluidX3D source code is on GitHub, and the software is free for non-commercial use: github.com/ProjectPhysX/FluidX3D

Concorde model: thingiverse.com/thing:1176931/files

Timestamps
0:00 front view
0:10 follow view
0:20 wing view
0:30 top view
0:40 side view

#FluidX3D #Concorde #CFD #GPU #AMDInstinct

FluidX3D on GigaIO SuperNODE - Concorde 40 Billion Cell CFD Simulation - 33h on 32x MI210 64GB GPUs

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$Liquid metal on a speaker (Slow Mo Guys), but as simulation The Slow Mo Guys did a video about liquid gallium on a vibrating speaker membrane, wondering what it would look like in simulation: https://youtu.be/A9J1gkw9BI0?t=135 Well, I can now simulate liquid metal with my FluidX3D software, and it even runs in real time. Note that I couldnt do the simulation with 1:1 the physical parameters of liquid gallium, as it just wouldnt be stable. The parameters that I used are (in lattice units): box size 128x128x96, density rho=1, kinematic shear viscosity nu=0.01, force per volume f=0.0005, surface tension sigma=0.005, peak velocity of speaker membrane u=0.09, frequency of speaker freq=0.01. The raytracer here does reflections only at a depth of 2, so 1-3 rays per pixel. The simulation software used is FluidX3D, an OpenCL implementation of the lattice Boltzmann method. FluidX3D runs at peak hardware efficiency on the worlds fastest data-center GPUs, on gaming GPUs and CPUs and it even runs my smartphone. The fluid simulation is done with the lattice Boltzmann method (LBM) with the Volume-of-Fluid (VoF) extension and piecewise linear interface construction (PLIC) for surface tension. VoF-LBM provides a 3D grid with fluid fill levels, used as isovalues for marching-cubes, which generates a contiguous triangle mesh representing the water surface. Marching-cubes: http://paulbourke.net/geometry/polygonise/ Raindrop simulations: https://doi.org/10.1186/s43591-021-00018-8 Volume-of-Fluid: https://doi.org/10.3390/computation10020021 Esoteric-Pull: https://doi.org/10.3390/computation10060092 FP32/FP16 mixed precision: https://www.researchgate.net/publication/362275548_Accuracy_and_performance_of_the_lattice_Boltzmann_method_with_64-bit_32-bit_and_customized_16-bit_number_formats FluidX3D: https://doi.org/10.15495/EPub_UBT_00005400 With raytracing I shoot light rays from the camera through the 3D isogrid. For each grid cell that a ray traverses, I run marching-cubes to generate the triangles on-the-fly, then check for ray-triangle intersections with any of these triangles. If the ray intersects, I dont use the flat-face-surface nromal of the triangle, as this would lead to visual artifacts on the reflected/refracted rays. Instead, I interpolate the surface normal at the intersection point in order to have a smoothly appearing surface. How does surface normal interpolation work with marching-cubes? I get the normals directly at the lattice points with gradient (central derivative) on the isogrid, then do trilinear interpolation to the intersection point. This is only computed once, after the intersection point has been found. The 3D lattice that the LBM provides already is one of the ideal acceleration structures for raytracing. Any ray only has to check for possible intersections with isosurface triangles contained within the traversed grid cells. This makes it - when the lattice is not too large - even run in real time on any OpenCL 1.2 capable GPU with enough memory. No RTX or professional GPU required. Also see the most realistic raindrop simulation ever done: https://youtu.be/9Rfu9ZqW894 Timestamps: 0:00 intro 0:03 simulation start 0:29 almost melts the GPU 1:15 pressure waves from speaker membrane 1:32 isosurface mesh generation 2:15 outro #FluidX3D #OpenCL #Raytracing #GPU #LBM$

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