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Let’s begin by running a single OpenFOAM simulation using the occDrivAerStaticMesh case.
All the required input files are already prepared, as outlined in the previous section.
The Python code required to run a OpenFOAM simulation using the Inductiva API follows a consistent structure. We adapted it for this specific use case, as shown below.
import inductiva
# Allocate cloud machine on Google Cloud Platform
cloud_machine = inductiva.resources.MachineGroup( \
provider="GCP",
machine_type="c3d-highcpu-180",
data_disk_gb=100,
spot=True)
# Initialize OpenFOAM stack
openfoam = inductiva.simulators.OpenFOAM(
version="2412",
distribution="esi"
)
task = openfoam.run( \
input_dir="/Path/to/openfoam-occDrivAerStaticMesh",
shell_script="./Allrun",
on=cloud_machine)
# Wait for the simulation to finish and download the results
task.wait()
cloud_machine.terminate()
task.download_outputs()
task.print_summary()
When the simulation is complete, we terminate the machine, download the results and print a summary of the simulation as shown.
inductiva tasks info 6j356wymfn7t614fncoff2dnf
Task status: Success
Timeline:
Waiting for Input at 09/07, 09:25:15 1933.174 s
In Queue at 09/07, 09:57:28 4.302 s
Preparing to Compute at 09/07, 09:57:32 53.658 s
In Progress at 09/07, 09:58:26 51074.226 s
└> 51074.226 s bash Allrun
Finalizing at 10/07, 00:09:40 23.903 s
Success at 10/07, 00:10:04
Data:
Size of zipped output: 5.51 GB
Size of unzipped output: 10.72 GB
Number of output files: 3276
Total estimated cost (US$): 24.32 US$
Estimated computation cost (US$): 24.31 US$
Task orchestration fee (US$): 0.010 US$
Note: A per-run orchestration fee (0.010 US$) applies to tasks run from 01 Dec 2025, in addition to the computation costs.
Learn more about costs at: https://inductiva.ai/guides/basics/how-much-does-it-cost
As you can see in the “In Progress” line, the part of the timeline that represents the actual execution of the simulation, the core computation time of this simulation was approximately 14 hours and 13 minutes.
Next, we’ll show you how to scale this same simulation across multiple machines and significantly reduce simulation time - by up to 1.8x!
Stay tuned!