Run an UnSWAN Simulation

This tutorial will guide you through running simulations with UnSWAN, the unstructured-grid version of the SWAN model, using the Inductiva API. Unlike traditional structured-grid versions, UnSWAN employs an unstructured mesh, offering greater flexibility for modeling wave dynamics in complex coastal areas, tidal inlets, and regions with varying resolutions.

We will cover the Haringvliet field case from the official SWAN documentation.

Prerequisites

Download the required files here and place them in a folder called f32harin. Then, you’ll be ready to send your simulation to the Cloud.

Running the Simulation

Here is the code required to run a UnSWAN simulation using the Inductiva API:

"""UnSWAN example"""
import inductiva

# Allocate cloud machine on Google Cloud Platform
cloud_machine = inductiva.resources.MachineGroup( \
    provider="GCP",
    machine_type="c2d-highcpu-4",
    spot=True)

# Initialize the Simulator
swan = inductiva.simulators.SWAN(\
    version="41.51")

# Run simulation
task = swan.run(input_dir="/Path/to/f32harin",
    sim_config_filename="f32har01.swn",
    # run unswan
    command="unswan",
    on=cloud_machine)

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

task.download_outputs()

task.print_summary()

In this basic example, we're using a cloud machine (c2d-highcpu-4) equipped with 4 virtual CPUs. For larger or more compute-intensive simulations, consider adjusting the machine_type parameter to select a machine with more virtual CPUs and increased memory capacity. You can explore the full range of available machines here.

Note: Setting spot=True enables the use of spot machines, which are available at substantial discounts. However, your simulation may be interrupted if the cloud provider reclaims the machine.

To adapt this script for other UnSWAN simulations, replace input_dir with the path to your UnSWAN input files and set the sim_config_filename accordingly.

When the simulation is complete, we terminate the machine, download the results and print a summary of the simulation as shown below.

Task status: Success

Timeline:
    Waiting for Input         at 17/09, 14:36:59      0.945 s
    In Queue                  at 17/09, 14:37:00      43.861 s
    Preparing to Compute      at 17/09, 14:37:44      4.474 s
    In Progress               at 17/09, 14:37:48      28.266 s
        └> 28.093 s        unswanrun -input f32har01.swn -omp 4
    Finalizing                at 17/09, 14:38:16      0.504 s
    Success                   at 17/09, 14:38:17

Data:
    Size of zipped output:    345.55 KB
    Size of unzipped output:  536.86 KB
    Number of output files:   10

Total estimated cost (US$): 0.01024 US$
    Estimated computation cost (US$): 0.00024 US$
    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 28.3 seconds.