HEC-RAS Available Versions:
version 6.6
Available Versions:
version 6.6
HEC-RAS, developed by the Hydrologic Engineering Center (CEIWR-HEC), is a powerful river analysis system designed for simulating and analyzing hydraulic conditions in natural and engineered waterways. The software provides a wide range of capabilities, including one-dimensional steady flow simulations, one- and two-dimensional unsteady flow calculations, sediment transport and mobile bed modeling, as well as water temperature and water quality analysis. These features make HEC-RAS a comprehensive tool for engineers and researchers working on river hydraulics, floodplain management, sediment dynamics, and environmental studies, supporting both practical decision-making and scientific research.
With Inductiva, you can speed up your HEC-RAS simulations by sending them to Cloud machines with hundreds of cores and terabytes of disk space.
How to Run HEC-RAS on the Cloud
Running your HEC-RAS simulations on the Cloud is easy. All you need is to create a short Python script that points Inductiva to the simulation artifacts you have on your computer, and we will take it from there.
On the right, we show how to use the Inductiva API to send a HEC-RAS simulation to a 180 vCPU machine (c3d-highcpu-180) hosted on Google Cloud (GCP).
You can copy paste this Python script and adapt it to your own case. Your simulation will start right away, without waiting in a queue.
"""HEC-RAS example."""
import inductiva
# Allocate Google cloud machine
cloud_machine = inductiva.resources.MachineGroup( \
provider="GCP",
machine_type="c3d-highcpu-180")
# Initialize the Simulator
hec_ras = inductiva.simulators.HecRas( \
version="6.6")
# Specify the HEC-RAS commands you want to run, separated by commas
hec_ras_commands = [ \
"RasGeomPreprocess Muncie.p04.tmp.hdf x04"]
# Run simulation
task = hec_ras.run( \
input_dir="/path/to/my/hec-ras/files",
commands=hec_ras_commands,
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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