Skip to main content

# Aurora: a modern toolbox for impurity transport, neutrals and radiation modeling

Project description

Aurora is an expanding package to simulate heavy-ion transportm neutrals and radiation in magnetically-confined plasmas. It includes a 1.5D impurity transport forward model which inherits many of the methods from the historical STRAHL code and has been thoroughly benchmarked with it. It also offers routines to analyze neutral states of hydrogen isotopes, both from the edge of fusion plasmas and from neutral beam injection. Aurora’s code is mostly written in Python 3 and Fortran 90. A Julia interface has also recently been added. The package enables radiation calculations using ADAS atomic rates, which can easily be applied to the output of Aurora’s own forward model, or coupled with other 1D, 2D or 3D transport codes.

Documentation is available at https://aurora-fusion.readthedocs.io.

# Development

The code is developed and maintained by F. Sciortino (MIT-PSFC) in collaboration with T. Odstrcil (GA) and A. Cavallaro (MIT), with support from O. Linder (MPI-IPP), C. Johnson (U. Auburn), D. Stanczak (IPPLM) and S. Smith (GA). The STRAHL documentation provided by R.Dux (MPI-IPP) was extremely helpful to guide the initial development of Aurora.

New contributors are more than welcome! Please get in touch at sciortino-at-psfc.mit.edu or open a pull-request via Github.

Generally, we would appreciate if you could work with us to merge your features back into the main Aurora distribution if there is any chance that the changes that you made could be useful to others.

# Installation

We recommend installing from source, by git-cloning [this repo](https://github.com/fsciortino/aurora) from Github. This will ensure that you can access the latest version of the tools. Make sure to use the master branch to use a stable version. Make use of the Makefile in the package directory to build the Fortran or Julia code using ` make clean; make aurora ` Note that the Julia version of the code is not built by default. If you have Julia installed on your system, you can do ` make julia ` from the main package directory. See the documentation to read about interfacing Python3 and Julia.

In the near future, the latest release of the package will also available on the Anaconda Cloud:

[![Anaconda-Server Badge](https://anaconda.org/sciortino/aurorafusion/badges/latest_release_date.svg)](https://anaconda.org/sciortino/aurorafusion)

and from PyPI using ` pip install aurorafusion `

# License

The package is made open-source with the hope that this will speed up research on fusion energy and make further code development easier. However, we kindly ask that all users communicate to us their purposes, difficulties and successes with Aurora, so that we may support users as much as possible and grow the code further.

# Citing Aurora

Please see the [User Agreement](https://github.com/fsciortino/Aurora/blob/master/USER_AGREEMENT.txt).

Home-page: https://github.com/fsciortino/Aurora Author: F. Sciortino Author-email: sciortino@psfc.mit.edu License: UNKNOWN Description: UNKNOWN Platform: UNKNOWN Classifier: Programming Language :: Python :: 3 Classifier: Operating System :: OS Independent Description-Content-Type: text/markdown

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aurorafusion-0.1.7.tar.gz (149.4 kB view details)

Uploaded Source

File details

Details for the file aurorafusion-0.1.7.tar.gz.

File metadata

  • Download URL: aurorafusion-0.1.7.tar.gz
  • Upload date:
  • Size: 149.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.0 setuptools/51.0.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.6

File hashes

Hashes for aurorafusion-0.1.7.tar.gz
Algorithm Hash digest
SHA256 9e9df7311c8aa60adb211a8f8e0ef850a90ce8c0315ae55be4591b5349ce6658
MD5 9b13600720c2b01a356f0aad810da3d9
BLAKE2b-256 40403d987bff6fef709bee8c8b97eba65e89016812aea4f90e3f7d835dc89e09

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page