simvue-fds 1.0.0a0

Connector to allow you to easily add Simvue tracking and monitoring to FDS (Fire Dynamics Simulator) simulations.

Simvue Integrations - FDS

Allow easy connection between Simvue and FDS (Fire Dynamics Simulator), allowing for easy tracking and monitoring of fire simulations in real time.

Website • Documentation

Implementation

A customised FDSRun class has been created which automatically does the following:

• Uploads your FDS input file as an input artifact
• Uploads information from the FDS input file as metadata
• Uploads other important metadata, such as the FDS and compiler versions used
• Tracks the FDS simulation itself, alerting the user via the web UI if the simulation crashes unexpectedly
• Tracks the log file produced by the simulation, uploading data produced as metadata and events
• Tracks variable values output in the DEVC and HRR CSV files after each step, logging them as metrics
• Tracks the DEVC and CTRL log, recording activations as metadata and events
• Uploads results files as Output artifacts

The FDSRun class also inherits from the Run() class of the Simvue Python API, allowing for further detailed control over how your simulation is tracked.

Installation

To install and use this connector, first create a virtual environment:

python -m venv venv

Then activate it:

source venv/bin/activate

And then use pip to install this module:

pip install simvue-integrations-fds

Configuration

The service URL and token can be defined as environment variables:

export SIMVUE_URL=...
export SIMVUE_TOKEN=...

or a file simvue.toml can be created containing:

[server]
url = "..."
token = "..."

The exact contents of both of the above options can be obtained directly by clicking the Create new run button on the web UI. Note that the environment variables have preference over the config file.

Usage example

from simvue_fds.connector import FDSRun

...

if __name__ == "__main__":

    ...

    # Using a context manager means that the status will be set to completed automatically,
    # and also means that if the code exits with an exception this will be reported to Simvue
    with FDSRun() as run:

        # Specify a run name, along with any other optional parameters:
        run.init(
          name = 'my-fds-simulation',                                   # Run name
          metadata = {'number_fires': 3},                               # Metadata
          tags = ['fds', 'multiple-fires'],                             # Tags
          description = 'FDS simulation of fires in a parking garage.', # Description
          folder = '/fds/parking-garage/trial_1'                        # Folder path
        )

        # Set folder details if necessary
        run.set_folder_details(
          metadata = {'simulation_type': 'parking_garage'},             # Metadata
          tags = ['fds'],                                               # Tags
          description = 'FDS simulations with fires in different areas' # Description
        )

        # Can use the base Simvue Run() methods to upload extra information, eg:
        run.save_file(os.path.abspath(__file__), "code")

        # Can add alerts specific to your simulation, eg:
        run.create_metric_threshold_alert(
          name="visibility_below_five_metres",    # Name of Alert
          metric="eye_level_visibility",          # Metric to monitor
          frequency=1,                            # Frequency to evaluate rule at (mins)
          rule="is below",                        # Rule to alert on
          threshold=5,                            # Threshold to alert on
          notification='email',                   # Notification type
          trigger_abort=True                      # Abort simulation if triggered
        )

        # Launch the FDS simulation
        run.launch(
            fds_input_path='path/to/my/input_file.i',   # Path to your FDS input file
            workdir_path='path/to/my/results_dir',      # Path where results should be created
            run_in_parallel=True,                       # Whether to run in parallel using MPI
            num_processors=2                            # Number of cores to use if in parallel

            )

License

Released under the terms of the Apache 2 license.