Reproducing a complex simulation like the one you described requires access to the specific source code used for the simulation, as well as the necessary hardware and software environment. Unfortunately, I don’t have direct access to external repositories, and I can’t help you directly execute code or run simulations. However, I can provide you with a general outline of the steps you might need to take if you have access to the FluidX3D source code and appropriate hardware:
- Access the Source Code:
Make sure you have access to the FluidX3D source code from the GitHub repository you mentioned: https://github.com/ProjectPhysX/FluidX3D.
- Set Up Hardware:
Ensure you have access to a high-performance GPU server with sufficient VRAM (similar to the hardware you mentioned: 8x AMD Instinct MI200, 512GB VRAM).
- Compile the Code:
Follow the instructions provided in the repository’s documentation to compile the FluidX3D source code for your specific hardware and software environment.
- Configure Simulation Parameters:
Modify the simulation parameters in the code to match the settings you described, such as fan diameter, RPM, grid size, simulation steps, revoxelization interval, and rendering interval.
- Run the Simulation:
Execute the simulation code with the updated parameters. The simulation will run and generate the necessary data files, including the velocity field data.
- Data Storage:
Ensure you have sufficient storage space available to store the generated data files. Given the large volume of data, you may need a high-capacity storage solution.
If you wish to visualize the results, you can follow the rendering process described in the original simulation. Keep in mind that handling and rendering large datasets can be challenging due to their size.
- Energy Consumption:
Be aware of the energy consumption of running such a demanding simulation, especially if you’re using powerful hardware for an extended period.
- Troubleshooting and Optimization:
Simulations of this complexity may encounter issues or require optimization for efficient execution. Be prepared to troubleshoot and fine-tune parameters as needed.
Remember that replicating a simulation of this scale requires a solid understanding of fluid dynamics, numerical methods, programming, and access to substantial computational resources. If you’re not familiar with these areas, you might consider collaborating with experts in the field or seeking guidance from the FluidX3D community if available.