FDS

Available Versions:

version 6.9.1 / version 6.8

Fire Dynamics Simulator (FDS) is a computational fluid dynamics (CFD) model designed to simulate fire-driven fluid flows. FDS numerically solves the Navier-Stokes equations to model low-speed, thermally-driven flows with a focus on smoke and heat transport from fires. It is commonly used in simulations for designing smoke management systems, evaluating sprinkler and detector activation, and fire reconstructions.

FDS contains a hydrodynamics model, a combustion model and a radiation transport model. All are configured by a single input file input_file.fds. Since FDS requires separate mesh setups assigned to individual processors, you will need to specify the number of cores for your simulation. Unlike other simulators in the Inductiva API, FDS does not automatically assign cores, so it’s important to configure this step manually.

Give it a Go: Try FDS on the Cloud with Inductiva!

Here’s a quick example code to get you started. In this example, we simulate fire-driven fluid flow using FDS on a c3d-standard-180 Google Cloud machine through the Inductiva API. When running FDS simulations, remember to configure the number of cores manually in the .fds input file to match the simulation’s mesh setup for optimal performance! Here, we configured it with 1 core. To try it out, simply paste the code into your Python environment.

For more details on the simulator’s features and configurations, visit the official FDS site.

                            """FDS example."""
import inductiva

# Allocate Google cloud machine
cloud_machine = inductiva.resources.MachineGroup( \
    provider="GCP",
    machine_type="c3d-standard-180")

# Initialize the Simulator
fds = inductiva.simulators.FDS()

# Run simulation with config files in the input directory
task = fds.run( \
    input_dir="path/to/my/fds/files",
    sim_config_filename="my_config_file.fds",
    on=cloud_machine)

# Wait for the simulation to finish and download the results
task.wait()
cloud_machine.terminate()

task.download_outputs()

                        

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