Stiff Chemistry Combustion Simulation on the Native GPU Solver

- August 4, 2025 at 9:10 pm
  
  abtharpe42
  Subscriber
  
  I'm trying to figure out how to use the native gpu solver in Fluent 2024 R1 on my desktop to simulate a combustor with intricate nozzle geometry. My mesh has almost 4.8 million poly-hexcore cells to get good resolution in the small nozzle passageways. I can probably get the cell count decently lower than this. The chemkin mechanism I'm using is the Stagni 2023 mechanism with roughly 30 species and 200 reactions. I know Fluent 2025 R2 just released with the Chemistry Agglomeration feature for the native GPU solver, but I probably won't have access to that for a while. I'm stuck using 2024 R1, which has no chemistry acceleration and is strictly allows Direct Integration only. I also set my simulations to double percision. My workstation desktop has an Intel i9-14900 CPU with 8 p-cores and 16 e-cores (hyperthreading disabled) and 64 GB of RAM. The GPU is an NVIDIA RTX Ada 2000 with 16 GB of VRAM. My attempts to run a simulation up to this point has yielded slow intialization phases, especially with hybrid-initialization, and seemingly freezing or stalling at the start of the calculation phase with no progress for minutes on end. I need to know if my combustion scenario that I've described above is overkill for this computer, as I have not found a straight answer to this online. I suspect that it is in fact too heavy for my hardware, considering the long wait times. When using 2 cores, my CPU utilization stays at 100% and my GPU oscillates anywhere between 30% to 100%. What recommendations, advice, or rules of thumb do you guys have that could help me make sure that I am utilizing my GPU as optimally as possible?
- August 7, 2025 at 2:11 pm
  
  jcooper
  Ansys Employee
  
  Hi:
  A good rule of thumb is about 150 k nodes per processor, so I would say you are over the limit, as combustion is one of the more data and memory intensive types of simulation you can run.
  To avoid GPU memory overloading, it is recommended that you match the number of CPUs to the number of dedicated GPUs. This approach is the simplest way to optimize GPU resource utilization across hardware. GPU remapping can be used if there is a many-to-one situation for GPU and CPU. For more information on GPU remapping, you can search on this topic in the Fluent help documentation:
  https://ansyshelp.ansys.com/Views/Secured/corp/v252/en/flu_ug/flu_ug_sec_gpu_solver_starting.html#flu_ug_gpgpu_solver_remapping