mirror of
https://github.com/openmc-dev/openmc.git
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Fix broken links, use https over http, replace redirects
This commit is contained in:
Paul Romano
parent
c4e0881d07
commit
0ef1f95978
33 changed files with 115 additions and 114 deletions
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@ -215,7 +215,7 @@ Documentation
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-------------
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Classes, structs, and functions are to be annotated for the `Doxygen
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<http://www.doxygen.nl/>`_ documentation generation tool. Use the ``\`` form of
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<https://www.doxygen.nl/>`_ documentation generation tool. Use the ``\`` form of
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Doxygen commands, e.g., ``\brief`` instead of ``@brief``.
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------
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@ -14,7 +14,7 @@ functions/classes in the OpenMC Python API.
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Prerequisites
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-------------
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- The test suite relies on the third-party `pytest <https://pytest.org>`_
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- The test suite relies on the third-party `pytest <https://docs.pytest.org>`_
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package. To run either or both the regression and unit test suites, it is
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assumed that you have OpenMC fully installed, i.e., the :ref:`scripts_openmc`
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executable is available on your :envvar:`PATH` and the :mod:`openmc` Python
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@ -46,7 +46,7 @@ To execute the test suite, go to the ``tests/`` directory and run::
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pytest
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If you want to collect information about source line coverage in the Python API,
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you must have the `pytest-cov <https://pypi.python.org/pypi/pytest-cov>`_ plugin
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you must have the `pytest-cov <https://pypi.org/project/pytest-cov>`_ plugin
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installed and run::
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pytest --cov=../openmc --cov-report=html
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@ -65,7 +65,7 @@ developer or send a message to the `developers mailing list`_.
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.. _property attribute: https://docs.python.org/3.6/library/functions.html#property
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.. _XML Schema Part 2: http://www.w3.org/TR/xmlschema-2/
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.. _boolean: http://www.w3.org/TR/xmlschema-2/#boolean
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.. _RELAX NG: http://relaxng.org/
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.. _compact syntax: http://relaxng.org/compact-tutorial-20030326.html
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.. _trang: http://www.thaiopensource.com/relaxng/trang.html
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.. _RELAX NG: https://relaxng.org/
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.. _compact syntax: https://relaxng.org/compact-tutorial-20030326.html
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.. _trang: https://relaxng.org/jclark/trang.html
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.. _developers mailing list: https://groups.google.com/forum/?fromgroups=#!forum/openmc-dev
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@ -122,11 +122,11 @@ can interfere with virtual environments.
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.. _git: http://git-scm.com/
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.. _GitHub: https://github.com/
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.. _git flow: http://nvie.com/git-model
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.. _valgrind: http://valgrind.org/
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.. _git flow: https://nvie.com/git-model
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.. _valgrind: https://www.valgrind.org/
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.. _style guide: https://docs.openmc.org/en/latest/devguide/styleguide.html
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.. _pull request: https://help.github.com/articles/using-pull-requests
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.. _pull request: https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests
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.. _openmc-dev/openmc: https://github.com/openmc-dev/openmc
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.. _paid plan: https://github.com/plans
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.. _paid plan: https://github.com/pricing
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.. _Bitbucket: https://bitbucket.org
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.. _pip: https://pip.pypa.io/en/stable/
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@ -13,8 +13,8 @@ programming model.
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OpenMC was originally developed by members of the `Computational Reactor Physics
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Group <http://crpg.mit.edu>`_ at the `Massachusetts Institute of Technology
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<http://web.mit.edu>`_ starting in 2011. Various universities, laboratories, and
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other organizations now contribute to the development of OpenMC. For more
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<https://web.mit.edu>`_ starting in 2011. Various universities, laboratories,
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and other organizations now contribute to the development of OpenMC. For more
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information on OpenMC, feel free to post a message on the `OpenMC Discourse
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Forum <https://openmc.discourse.group/>`_.
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@ -49,4 +49,4 @@ Windowed Multipole Library Format
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windows[i, 1] are, respectively, the indexes (1-based) of the first and
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last pole in window i.
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.. _h5py: http://docs.h5py.org/en/latest/
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.. _h5py: https://docs.h5py.org/en/latest/
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@ -21,7 +21,7 @@ nuclides or materials.
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The current version of the multi-group library file format is 1.0.
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.. _HDF5: http://www.hdfgroup.org/HDF5/
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.. _HDF5: https://www.hdfgroup.org/solutions/hdf5/
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.. _mgxs_lib_spec:
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@ -269,12 +269,12 @@ or even isotropic scattering.
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.. _logarithmic mapping technique:
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https://laws.lanl.gov/vhosts/mcnp.lanl.gov/pdf_files/la-ur-14-24530.pdf
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.. _Hwang: http://www.ans.org/pubs/journals/nse/a_16381
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.. _Hwang: https://doi.org/10.13182/NSE87-A16381
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.. _Josey: https://doi.org/10.1016/j.jcp.2015.08.013
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.. _WMP Library: https://github.com/mit-crpg/WMP_Library
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.. _MCNP: http://mcnp.lanl.gov
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.. _MCNP: https://mcnp.lanl.gov
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.. _Serpent: http://montecarlo.vtt.fi
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.. _NJOY: http://t2.lanl.gov/codes.shtml
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.. _ENDF/B data: http://www.nndc.bnl.gov/endf
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.. _NJOY: https://www.njoy21.io/NJOY21/
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.. _ENDF/B data: https://www.nndc.bnl.gov/endf/b8.0/
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.. _Leppanen: https://doi.org/10.1016/j.anucene.2009.03.019
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.. _algorithms: http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package
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@ -962,6 +962,6 @@ surface is known as in :ref:`reflection`.
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.. _constructive solid geometry: https://en.wikipedia.org/wiki/Constructive_solid_geometry
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.. _surfaces: https://en.wikipedia.org/wiki/Surface
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.. _MCNP: http://mcnp.lanl.gov
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.. _MCNP: https://mcnp.lanl.gov
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.. _Serpent: http://montecarlo.vtt.fi
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.. _Monte Carlo Performance benchmark: https://github.com/mit-crpg/benchmarks/tree/master/mc-performance/openmc
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@ -1695,7 +1695,7 @@ another.
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.. _SIGMA1 method: https://doi.org/10.13182/NSE76-1
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.. _scaled interpolation: http://www.ans.org/pubs/journals/nse/a_26575
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.. _scaled interpolation: https://doi.org/10.13182/NSE73-A26575
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.. _probability table method: https://doi.org/10.13182/NSE72-3
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@ -1703,23 +1703,23 @@ another.
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.. _Foderaro: http://hdl.handle.net/1721.1/1716
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.. _OECD: http://www.oecd-nea.org/tools/abstract/detail/NEA-1792
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.. _OECD: https://www.oecd-nea.org/tools/abstract/detail/NEA-1792
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.. _NJOY: https://www.njoy21.io/NJOY2016/
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.. _PREPRO: http://www-nds.iaea.org/ndspub/endf/prepro/
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.. _PREPRO: https://www-nds.iaea.org/ndspub/endf/prepro/
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.. _endf102: https://www.oecd-nea.org/dbdata/data/manual-endf/endf102.pdf
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.. _Monte Carlo Sampler: https://laws.lanl.gov/vhosts/mcnp.lanl.gov/pdf_files/la-9721.pdf
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.. _Monte Carlo Sampler: https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-09721-MS
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.. _LA-UR-14-27694: http://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-14-27694
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.. _LA-UR-14-27694: https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-14-27694
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.. _MC21: http://www.osti.gov/bridge/servlets/purl/903083-HT5p1o/903083.pdf
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.. _MC21: https://www.osti.gov/biblio/903083
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.. _Romano: https://doi.org/10.1016/j.cpc.2014.11.001
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.. _Sutton and Brown: http://www.osti.gov/bridge/product.biblio.jsp?osti_id=307911
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.. _Sutton and Brown: https://www.osti.gov/biblio/307911
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.. _lectures: https://laws.lanl.gov/vhosts/mcnp.lanl.gov/pdf_files/la-ur-05-4983.pdf
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@ -251,8 +251,8 @@ depending on how many nodes are communicating and the size of the message. Using
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multiple algorithms allows one to minimize latency for small messages and
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minimize bandwidth for long messages.
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We will focus here on the implementation of broadcast in the MPICH2_
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implementation. For short messages, MPICH2 uses a `binomial tree`_ algorithm. In
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We will focus here on the implementation of broadcast in the MPICH_
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implementation. For short messages, MPICH uses a `binomial tree`_ algorithm. In
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this algorithm, the root process sends the data to one node in the first step,
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and then in the subsequent, both the root and the other node can send the data
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to other nodes. Thus, it takes a total of :math:`\lceil \log_2 p \rceil` steps
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@ -266,7 +266,7 @@ to complete the communication. The time to complete the communication is
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This algorithm works well for short messages since the latency term scales
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logarithmically with the number of nodes. However, for long messages, an
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algorithm that has lower bandwidth has been proposed by Barnett_ and implemented
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in MPICH2. Rather than using a binomial tree, the broadcast is divided into a
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in MPICH. Rather than using a binomial tree, the broadcast is divided into a
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scatter and an allgather. The time to complete the scatter is :math:` \log_2 p
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\: \alpha + \frac{p-1}{p} N\beta` using a binomial tree algorithm. The allgather
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is performed using a ring algorithm that completes in :math:`p-1) \alpha +
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@ -613,7 +613,7 @@ is actually independent of the number of nodes:
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.. _Brissenden and Garlick: https://doi.org/10.1016/0306-4549(86)90095-2
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.. _MPICH2: http://www.mcs.anl.gov/mpi/mpich
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.. _MPICH: http://www.mpich.org
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.. _binomial tree: https://www.mcs.anl.gov/~thakur/papers/ijhpca-coll.pdf
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@ -629,19 +629,19 @@ is actually independent of the number of nodes:
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.. _message-passing interface: https://en.wikipedia.org/wiki/Message_Passing_Interface
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.. _PVM: http://www.csm.ornl.gov/pvm/pvm_home.html
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.. _PVM: https://www.csm.ornl.gov/pvm/pvm_home.html
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.. _MPI: http://www.mcs.anl.gov/research/projects/mpi/
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.. _MPI: https://www.mcs.anl.gov/research/projects/mpi/
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.. _embarrassingly parallel: https://en.wikipedia.org/wiki/Embarrassingly_parallel
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.. _sends: http://www.mcs.anl.gov/research/projects/mpi/www/www3/MPI_Send.html
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.. _sends: https://www.mpich.org//static/docs/latest/www3/MPI_Send.html
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.. _broadcasts: http://www.mcs.anl.gov/research/projects/mpi/www/www3/MPI_Bcast.html
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.. _broadcasts: https://www.mpich.org//static/docs/latest/www3/MPI_Bcast.html
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.. _scatter: http://www.mcs.anl.gov/research/projects/mpi/www/www3/MPI_Scatter.html
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.. _scatter: https://www.mpich.org//static/docs/latest/www3/MPI_Scatter.html
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.. _allgather: http://www.mcs.anl.gov/research/projects/mpi/www/www3/MPI_Allgather.html
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.. _allgather: https://www.mpich.org//static/docs/latest/www3/MPI_Allgather.html
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.. _Cauchy distribution: https://en.wikipedia.org/wiki/Cauchy_distribution
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@ -1069,6 +1069,6 @@ emitted photon.
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.. _Kaltiaisenaho: https://aaltodoc.aalto.fi/bitstream/handle/123456789/21004/master_Kaltiaisenaho_Toni_2016.pdf
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.. _Salvat: http://www.oecd-nea.org/globalsearch/download.php?doc=77434
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.. _Salvat: https://www.oecd-nea.org/globalsearch/download.php?doc=77434
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.. _Sternheimer: https://doi.org/10.1103/PhysRevB.26.6067
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@ -7,8 +7,8 @@ Python API
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OpenMC includes a rich Python API that enables programmatic pre- and
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post-processing. The easiest way to begin using the API is to take a look at the
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:ref:`examples`. This assumes that you are already familiar with Python and
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common third-party packages such as `NumPy <http://www.numpy.org/>`_. If you
|
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have never used Python before, the prospect of learning a new code *and* a
|
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common third-party packages such as `NumPy <https://numpy.org/>`_. If you have
|
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never used Python before, the prospect of learning a new code *and* a
|
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programming language might sound daunting. However, you should keep in mind that
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there are many substantial benefits to using the Python API, including:
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@ -28,7 +28,7 @@ there are many substantial benefits to using the Python API, including:
|
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For those new to Python, there are many good tutorials available online. We
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recommend going through the modules from `Codecademy
|
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<https://www.codecademy.com/learn/learn-python-3>`_ and/or the `Scipy lectures
|
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<https://scipy-lectures.github.io/>`_.
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<https://scipy-lectures.org/>`_.
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The full API documentation serves to provide more information on a given module
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or class.
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@ -12,7 +12,7 @@ OpenMC, see :ref:`usersguide_install` in the User's Manual.
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Installing on Linux/Mac with conda-forge
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----------------------------------------
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`Conda <http://conda.pydata.org/docs/>`_ is an open source package management
|
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`Conda <https://conda.io/en/latest/>`_ is an open source package management
|
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system and environment management system for installing multiple versions of
|
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software packages and their dependencies and switching easily between them. If
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you have `conda` installed on your system, OpenMC can be installed via the
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@ -25,16 +25,16 @@ you have `conda` installed on your system, OpenMC can be installed via the
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To list the versions of OpenMC that are available on the `conda-forge` channel,
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in your terminal window or an Anaconda Prompt run:
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.. code-block:: sh
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.. code-block:: sh
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conda search openmc
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OpenMC can then be installed with:
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.. code-block:: sh
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conda create -n openmc-env openmc
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This will install OpenMC in a conda environment called `openmc-env`. To activate
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the environment, run:
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@ -69,7 +69,7 @@ Now OpenMC should be recognized within the repository and can be installed:
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Binary packages from this PPA may exist for earlier versions of Ubuntu, but they
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are no longer supported.
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.. _Personal Package Archive: https://launchpad.net/~paulromano/+archive/staging
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.. _Personal Package Archive: https://launchpad.net/~paulromano/+archive/ubuntu/staging
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.. _APT package manager: https://help.ubuntu.com/community/AptGet/Howto
|
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-------------------------------------------
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@ -112,7 +112,7 @@ To install the latest OpenMC with the Python API, use the following command:
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For more information about customizations including MPI, see the
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:ref:`detailed installation instructions using Spack <install-spack>`.
|
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Once installed, environment/lmod modules can be generated or Spack's `load` feature
|
||||
can be used to access the installed packages.
|
||||
can be used to access the installed packages.
|
||||
|
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.. _Spack: https://spack.readthedocs.io/en/latest/
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.. _setup guide: https://spack.readthedocs.io/en/latest/getting_started.html
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@ -139,9 +139,9 @@ Installing from Source on Linux or Mac OS X
|
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All OpenMC source code is hosted on `GitHub
|
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<https://github.com/openmc-dev/openmc>`_. If you have `git
|
||||
<https://git-scm.com>`_, the `gcc <https://gcc.gnu.org/>`_ compiler suite,
|
||||
`CMake <http://www.cmake.org>`_, and `HDF5 <https://www.hdfgroup.org/HDF5/>`_
|
||||
installed, you can download and install OpenMC by entering the following
|
||||
commands in a terminal:
|
||||
`CMake <https://cmake.org>`_, and `HDF5
|
||||
<https://www.hdfgroup.org/solutions/hdf5/>`_ installed, you can download and
|
||||
install OpenMC be entering the following commands in a terminal:
|
||||
|
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.. code-block:: sh
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|
||||
|
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|
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@ -113,7 +113,7 @@ or Mac OS X (also Unix-derived), `this tutorial
|
|||
commonly-used commands.
|
||||
|
||||
To reap the full benefits of OpenMC, you should also have basic proficiency in
|
||||
the use of `Python <http://www.python.org/>`_, as OpenMC includes a rich Python
|
||||
the use of `Python <https://www.python.org/>`_, as OpenMC includes a rich Python
|
||||
API that offers many usability improvements over dealing with raw XML input
|
||||
files.
|
||||
|
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|
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@ -126,8 +126,9 @@ at the git documentation website. The `OpenMC source code`_ and documentation
|
|||
are hosted at `GitHub`_. In order to receive updates to the code directly,
|
||||
submit `bug reports`_, and perform other development tasks, you may want to sign
|
||||
up for a free account on GitHub. Once you have an account, you can follow `these
|
||||
instructions <https://help.github.com/articles/set-up-git/>`_ on how to set up
|
||||
your computer for using GitHub.
|
||||
instructions
|
||||
<https://docs.github.com/en/github/getting-started-with-github/set-up-git>`_ on
|
||||
how to set up your computer for using GitHub.
|
||||
|
||||
If you are new to nuclear engineering, you may want to review the NRC's `Reactor
|
||||
Concepts Manual`_. This manual describes the basics of nuclear power for
|
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@ -149,7 +150,7 @@ and `Volume II`_. You may also find it helpful to review the following terms:
|
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.. _discretization: https://en.wikipedia.org/wiki/Discretization
|
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.. _constructive solid geometry: https://en.wikipedia.org/wiki/Constructive_solid_geometry
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.. _git: http://git-scm.com/
|
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.. _git tutorials: http://git-scm.com/documentation
|
||||
.. _git tutorials: https://git-scm.com/doc
|
||||
.. _Reactor Concepts Manual: http://www.tayloredge.com/periodic/trivia/ReactorConcepts.pdf
|
||||
.. _Volume I: https://www.standards.doe.gov/standards-documents/1000/1019-bhdbk-1993-v1
|
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.. _Volume II: https://www.standards.doe.gov/standards-documents/1000/1019-bhdbk-1993-v2
|
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|
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|
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@ -260,7 +260,7 @@ For an example of how to create a multi-group library, see the `example notebook
|
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.. _MCNP: https://mcnp.lanl.gov
|
||||
.. _Serpent: http://montecarlo.vtt.fi
|
||||
.. _ENDF/B: https://www.nndc.bnl.gov/endf/b7.1/acefiles.html
|
||||
.. _JEFF: http://www.oecd-nea.org/dbdata/jeff/jeff33/
|
||||
.. _JEFF: https://www.oecd-nea.org/dbdata/jeff/jeff33/
|
||||
.. _TENDL: https://tendl.web.psi.ch/tendl_2017/tendl2017.html
|
||||
.. _Seltzer and Berger: https://doi.org/10.1016/0092-640X(86)90014-8
|
||||
.. _NIST ESTAR database: https://physics.nist.gov/PhysRefData/Star/Text/ESTAR.html
|
||||
|
|
|
|||
|
|
@ -261,7 +261,7 @@ lowest-level cell at that location::
|
|||
|
||||
As you are building a geometry, it is also possible to display a plot of single
|
||||
universe using the :meth:`Universe.plot` method. This method requires that you
|
||||
have `matplotlib <http://matplotlib.org/>`_ installed.
|
||||
have `matplotlib <https://matplotlib.org/>`_ installed.
|
||||
|
||||
.. _usersguide_lattices:
|
||||
|
||||
|
|
|
|||
|
|
@ -71,7 +71,7 @@ Now OpenMC should be recognized within the repository and can be installed:
|
|||
Binary packages from this PPA may exist for earlier versions of Ubuntu, but they
|
||||
are no longer supported.
|
||||
|
||||
.. _Personal Package Archive: https://launchpad.net/~paulromano/+archive/staging
|
||||
.. _Personal Package Archive: https://launchpad.net/~paulromano/+archive/ubuntu/staging
|
||||
.. _APT package manager: https://help.ubuntu.com/community/AptGet/Howto
|
||||
|
||||
.. _install-spack:
|
||||
|
|
@ -242,9 +242,9 @@ Prerequisites
|
|||
|
||||
|
||||
.. _gcc: https://gcc.gnu.org/
|
||||
.. _CMake: http://www.cmake.org
|
||||
.. _OpenMPI: http://www.open-mpi.org
|
||||
.. _MPICH: http://www.mpich.org
|
||||
.. _CMake: https://cmake.org
|
||||
.. _OpenMPI: https://www.open-mpi.org
|
||||
.. _MPICH: https://www.mpich.org
|
||||
.. _HDF5: https://www.hdfgroup.org/solutions/hdf5/
|
||||
.. _DAGMC: https://svalinn.github.io/DAGMC/index.html
|
||||
|
||||
|
|
@ -256,10 +256,10 @@ directly from GitHub or, if you have the git_ version control software installed
|
|||
on your computer, you can use git to obtain the source code. The latter method
|
||||
has the benefit that it is easy to receive updates directly from the GitHub
|
||||
repository. GitHub has a good set of `instructions
|
||||
<http://help.github.com/set-up-git-redirect>`_ for how to set up git to work
|
||||
with GitHub since this involves setting up ssh_ keys. With git installed and
|
||||
setup, the following command will download the full source code from the GitHub
|
||||
repository::
|
||||
<https://docs.github.com/en/github/getting-started-with-github/set-up-git>`_ for
|
||||
how to set up git to work with GitHub since this involves setting up ssh_ keys.
|
||||
With git installed and setup, the following command will download the full
|
||||
source code from the GitHub repository::
|
||||
|
||||
git clone --recurse-submodules https://github.com/openmc-dev/openmc.git
|
||||
|
||||
|
|
@ -408,8 +408,8 @@ Compiling on Windows 10
|
|||
|
||||
Recent versions of Windows 10 include a subsystem for Linux that allows one to
|
||||
run Bash within Ubuntu running in Windows. First, follow the installation guide
|
||||
`here <https://msdn.microsoft.com/en-us/commandline/wsl/install_guide>`_ to get
|
||||
Bash on Ubuntu on Windows setup. Once you are within bash, obtain the necessary
|
||||
`here <https://docs.microsoft.com/en-us/windows/wsl/install-win10>`_ to get Bash
|
||||
on Ubuntu on Windows setup. Once you are within bash, obtain the necessary
|
||||
:ref:`prerequisites <prerequisites>` via ``apt``. Finally, follow the
|
||||
:ref:`instructions for compiling on linux <compile_linux>`.
|
||||
|
||||
|
|
@ -494,7 +494,7 @@ distributions.
|
|||
.. admonition:: Required
|
||||
:class: error
|
||||
|
||||
`NumPy <http://www.numpy.org/>`_
|
||||
`NumPy <https://numpy.org/>`_
|
||||
NumPy is used extensively within the Python API for its powerful
|
||||
N-dimensional array.
|
||||
|
||||
|
|
@ -502,7 +502,7 @@ distributions.
|
|||
SciPy's special functions, sparse matrices, and spatial data structures
|
||||
are used for several optional features in the API.
|
||||
|
||||
`pandas <http://pandas.pydata.org/>`_
|
||||
`pandas <https://pandas.pydata.org/>`_
|
||||
Pandas is used to generate tally DataFrames as demonstrated in
|
||||
an `example notebook <../examples/pandas-dataframes.ipynb>`_.
|
||||
|
||||
|
|
@ -511,14 +511,14 @@ distributions.
|
|||
various HDF5 files, h5py is needed to provide access to data within these
|
||||
files from Python.
|
||||
|
||||
`Matplotlib <http://matplotlib.org/>`_
|
||||
`Matplotlib <https://matplotlib.org/>`_
|
||||
Matplotlib is used to providing plotting functionality in the API like the
|
||||
:meth:`Universe.plot` method and the :func:`openmc.plot_xs` function.
|
||||
|
||||
`uncertainties <https://pythonhosted.org/uncertainties/>`_
|
||||
Uncertainties are used for decay data in the :mod:`openmc.data` module.
|
||||
|
||||
`lxml <http://lxml.de/>`_
|
||||
`lxml <https://lxml.de/>`_
|
||||
lxml is used for the :ref:`scripts_validate` script and various other
|
||||
parts of the Python API.
|
||||
|
||||
|
|
@ -530,11 +530,11 @@ distributions.
|
|||
parallel runs. This package is needed if you plan on running depletion
|
||||
simulations in parallel using MPI.
|
||||
|
||||
`Cython <http://cython.org/>`_
|
||||
`Cython <https://cython.org/>`_
|
||||
Cython is used for resonance reconstruction for ENDF data converted to
|
||||
:class:`openmc.data.IncidentNeutron`.
|
||||
|
||||
`vtk <http://www.vtk.org/>`_
|
||||
`vtk <https://vtk.org/>`_
|
||||
The Python VTK bindings are needed to convert voxel and track files to VTK
|
||||
format.
|
||||
|
||||
|
|
@ -588,7 +588,7 @@ schemas.xml file in your own OpenMC source directory.
|
|||
|
||||
.. _GNU Emacs: http://www.gnu.org/software/emacs/
|
||||
.. _validation: https://en.wikipedia.org/wiki/XML_validation
|
||||
.. _RELAX NG: http://relaxng.org/
|
||||
.. _RELAX NG: https://relaxng.org/
|
||||
.. _ctest: https://cmake.org/cmake/help/latest/manual/ctest.1.html
|
||||
.. _Conda: https://conda.io/en/latest/
|
||||
.. _pip: https://pip.pypa.io/en/stable/
|
||||
|
|
|
|||
|
|
@ -45,7 +45,7 @@ This method can also accept case-insensitive element names such as
|
|||
::
|
||||
|
||||
mat.add_element('aluminium', 1.0)
|
||||
|
||||
|
||||
Internally, OpenMC stores data on the atomic masses and natural abundances of
|
||||
all known isotopes and then uses this data to determine what isotopes should be
|
||||
added to the material. When the material is later exported to XML for use by the
|
||||
|
|
@ -105,7 +105,7 @@ you would need to add hydrogen and oxygen to a material and then assign the
|
|||
Naming Conventions
|
||||
------------------
|
||||
|
||||
OpenMC uses the GND_ naming convention for nuclides, metastable states, and
|
||||
OpenMC uses the GNDS_ naming convention for nuclides, metastable states, and
|
||||
compounds:
|
||||
|
||||
:Nuclides: ``SymA`` where "A" is the mass number (e.g., ``Fe56``)
|
||||
|
|
@ -122,7 +122,7 @@ compounds:
|
|||
ENDF/B-VII.1! If you are adding an element via
|
||||
:meth:`Material.add_element`, just use ``Sym``.
|
||||
|
||||
.. _GND: https://www.oecd-nea.org/science/wpec/sg38/Meetings/2016_May/tlh4gnd-main.pdf
|
||||
.. _GNDS: https://www.oecd-nea.org/jcms/pl_39689/specifications-for-the-generalised-nuclear-database-structure-gnds
|
||||
|
||||
-----------
|
||||
Temperature
|
||||
|
|
@ -160,26 +160,26 @@ Material Mixtures
|
|||
-----------------
|
||||
|
||||
In OpenMC it is possible to mix any number of materials to create a new material
|
||||
with the correct nuclide composition and density. The
|
||||
with the correct nuclide composition and density. The
|
||||
:meth:`Material.mix_materials` method takes a list of materials and
|
||||
a list of their mixing fractions. Mixing fractions can be provided as atomic
|
||||
a list of their mixing fractions. Mixing fractions can be provided as atomic
|
||||
fractions, weight fractions, or volume fractions. The fraction type
|
||||
can be specified by passing 'ao', 'wo', or 'vo' as the third argument, respectively.
|
||||
For example, assuming the required materials have already been defined, a MOX
|
||||
can be specified by passing 'ao', 'wo', or 'vo' as the third argument, respectively.
|
||||
For example, assuming the required materials have already been defined, a MOX
|
||||
material with 3% plutonium oxide by weight could be created using the following:
|
||||
|
||||
::
|
||||
|
||||
mox = openmc.Material.mix_materials([uo2, puo2], [0.97, 0.03], 'wo')
|
||||
|
||||
It should be noted that, if mixing fractions are specifed as atomic or weight
|
||||
It should be noted that, if mixing fractions are specifed as atomic or weight
|
||||
fractions, the supplied fractions should sum to one. If the fractions are specified
|
||||
as volume fractions, and the sum of the fractions is less than one, then the remaining
|
||||
fraction is set as void material.
|
||||
as volume fractions, and the sum of the fractions is less than one, then the remaining
|
||||
fraction is set as void material.
|
||||
|
||||
.. warning:: Materials with :math:`S(\alpha,\beta)` thermal scattering data
|
||||
cannot be used in :meth:`Material.mix_materials`. However, thermal
|
||||
scattering data can be added to a material created by
|
||||
scattering data can be added to a material created by
|
||||
:meth:`Material.mix_materials`.
|
||||
|
||||
--------------------
|
||||
|
|
|
|||
|
|
@ -8,8 +8,8 @@ If you are running a simulation on a computer with multiple cores, multiple
|
|||
sockets, or multiple nodes (i.e., a cluster), you can benefit from the fact that
|
||||
OpenMC is able to use all available hardware resources if configured
|
||||
correctly. OpenMC is capable of using both distributed-memory (`MPI
|
||||
<http://mpi-forum.org/>`_) and shared-memory (`OpenMP
|
||||
<http://www.openmp.org/>`_) parallelism. If you are on a single-socket
|
||||
<https://www.mpi-forum.org/>`_) and shared-memory (`OpenMP
|
||||
<https://www.openmp.org/>`_) parallelism. If you are on a single-socket
|
||||
workstation or a laptop, using shared-memory parallelism is likely
|
||||
sufficient. On a multi-socket node, cluster, or supercomputer, chances are you
|
||||
will need to use both distributed-memory (across nodes) and shared-memory
|
||||
|
|
@ -49,7 +49,7 @@ Distributed-Memory Parallelism (MPI)
|
|||
|
||||
MPI defines a library specification for message-passing between processes. There
|
||||
are two major implementations of MPI, `OpenMPI <https://www.open-mpi.org/>`_ and
|
||||
`MPICH <http://www.mpich.org/>`_. Both implementations are known to work with
|
||||
`MPICH <https://www.mpich.org/>`_. Both implementations are known to work with
|
||||
OpenMC; there is no obvious reason to prefer one over the other. Building OpenMC
|
||||
with support for MPI requires that you have one of these implementations
|
||||
installed on your system. For instructions on obtaining MPI, see
|
||||
|
|
|
|||
|
|
@ -97,7 +97,7 @@ derivatives: ``sudo apt install imagemagick``). Images are then converted like:
|
|||
|
||||
convert myplot.ppm myplot.png
|
||||
|
||||
Alternatively, if you're working within a `Jupyter <http://jupyter.org/>`_
|
||||
Alternatively, if you're working within a `Jupyter <https://jupyter.org/>`_
|
||||
Notebook or QtConsole, you can use the :func:`openmc.plot_inline` to run OpenMC
|
||||
in plotting mode and display the resulting plot within the notebook.
|
||||
|
||||
|
|
@ -122,7 +122,7 @@ should be three items long, e.g.::
|
|||
|
||||
The voxel plot data is written to an :ref:`HDF5 file <io_voxel>`. The voxel file
|
||||
can subsequently be converted into a standard mesh format that can be viewed in
|
||||
`ParaView <http://www.paraview.org/>`_, `VisIt
|
||||
`ParaView <https://www.paraview.org/>`_, `VisIt
|
||||
<https://wci.llnl.gov/simulation/computer-codes/visit>`_, etc. This typically
|
||||
will compress the size of the file significantly. The provided
|
||||
:ref:`scripts_voxel` script can convert the HDF5 voxel file to VTK formats. Once
|
||||
|
|
|
|||
|
|
@ -54,7 +54,7 @@ Getting Data into MATLAB
|
|||
There is currently no front-end utility to dump tally data to MATLAB files, but
|
||||
the process is straightforward. First extract the data using the Python API via
|
||||
``openmc.statepoint`` and then use the `Scipy MATLAB IO routines
|
||||
<http://docs.scipy.org/doc/scipy/reference/tutorial/io.html>`_ to save to a MAT
|
||||
<https://docs.scipy.org/doc/scipy/reference/tutorial/io.html>`_ to save to a MAT
|
||||
file. Note that all arrays that are accessible in a statepoint are already in
|
||||
NumPy arrays that can be reshaped and dumped to MATLAB in one step.
|
||||
|
||||
|
|
|
|||
|
|
@ -110,8 +110,8 @@ otherwise.
|
|||
|
||||
This script generates an HDF5 file called ``compton_profiles.h5`` that contains
|
||||
Compton profile data using an existing data library from `Geant4
|
||||
<http://geant4.cern.ch/>`_. Note that OpenMC includes this data file by default
|
||||
so it should not be necessary in practice to generate it yourself.
|
||||
<https://geant4.web.cern.ch/>`_. Note that OpenMC includes this data file by
|
||||
default so it should not be necessary in practice to generate it yourself.
|
||||
|
||||
|
||||
.. _scripts_depletion_chain:
|
||||
|
|
@ -250,7 +250,7 @@ Message Description
|
|||
When OpenMC generates :ref:`voxel plots <usersguide_voxel>`, they are in an
|
||||
:ref:`HDF5 format <io_voxel>` that is not terribly useful by itself. The
|
||||
``openmc-voxel-to-vtk`` script converts a voxel HDF5 file to a `VTK
|
||||
<http://www.vtk.org/>`_ file. To run this script, you will need to have the VTK
|
||||
<https://vtk.org/>`_ file. To run this script, you will need to have the VTK
|
||||
Python bindings installed. To convert a voxel file, simply provide the path to
|
||||
the file:
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@
|
|||
"metadata": {},
|
||||
"source": [
|
||||
"# Using CAD-Based Geometries\n",
|
||||
"In this notebook we'll be exploring how to use CAD-based geometries in OpenMC via the [DagMC](https://svalinn.github.io/DAGMC/index.html) toolkit. The models we'll be using in this notebook have already been created using [Trelis](https://www.csimsoft.com/trelis) and faceted into a surface mesh represented as `.h5m` files in the [Mesh Oriented DatABase](https://press3.mcs.anl.gov/sigma/moab-library/) format. We'll be retrieving these files using the function below.\n",
|
||||
"In this notebook we'll be exploring how to use CAD-based geometries in OpenMC via the [DagMC](https://svalinn.github.io/DAGMC/index.html) toolkit. The models we'll be using in this notebook have already been created using [Trelis](https://coreform.com/products/trelisnew/) and faceted into a surface mesh represented as `.h5m` files in the [Mesh Oriented DatABase](https://sigma.mcs.anl.gov/moab-library/) format. We'll be retrieving these files using the function below.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
|
|
@ -93,7 +93,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let's download the DAGMC model. These models come in the form of triangle surface meshes stored using the the Mesh Oriented datABase ([MOAB](https://press3.mcs.anl.gov/sigma/moab-library/)) in an HDF5 file with the extension `.h5m`. An example of a coarse triangle mesh looks like:"
|
||||
"Let's download the DAGMC model. These models come in the form of triangle surface meshes stored using the the Mesh Oriented datABase ([MOAB](https://sigma.mcs.anl.gov/moab-library/)) in an HDF5 file with the extension `.h5m`. An example of a coarse triangle mesh looks like:"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -234,7 +234,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Note:** Applying tally filters in DagMC models requires prior knowledge of the model. Here, we know that the fuel cell's volume ID in the CAD sofware is 1. To identify cells without use of CAD software, load them into the [OpenMC plotter](https://github.com/openmc/plotter) where cell, material, and volume IDs can be identified for native both OpenMC and DagMC geometries."
|
||||
"**Note:** Applying tally filters in DagMC models requires prior knowledge of the model. Here, we know that the fuel cell's volume ID in the CAD sofware is 1. To identify cells without use of CAD software, load them into the [OpenMC plotter](https://github.com/openmc-dev/plotter) where cell, material, and volume IDs can be identified for native both OpenMC and DagMC geometries."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -89,7 +89,7 @@
|
|||
"\n",
|
||||
"$$\\nu_d \\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\nu_d \\sigma_{f,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n",
|
||||
"\n",
|
||||
"This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for only the delayed-nu-fission and delayed neutron fraction reaction type at the moment. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](https://docs.openmc.org/en/stable/usersguide/tallies.html#filters) on the energy range and spatial zone (material, cell, universe, or mesh) define the bounds of integration for both numerator and denominator."
|
||||
"This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for only the delayed-nu-fission and delayed neutron fraction reaction type at the moment. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](../usersguide/tallies.rst#filters) on the energy range and spatial zone (material, cell, universe, or mesh) define the bounds of integration for both numerator and denominator."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -702,7 +702,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Since the `openmc.mgxs` module uses [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](http://pandas.pydata.org/) `DataFrame` of the multi-group cross section data."
|
||||
"Since the `openmc.mgxs` module uses [tally arithmetic](tally-arithmetic.ipynb) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](https://pandas.pydata.org/) `DataFrame` of the multi-group cross section data."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -1077,7 +1077,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta`, `DelayedNuFissionXS`, and `DecayRate` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
|
||||
"Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](tally-arithmetic.ipynb) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta`, `DelayedNuFissionXS`, and `DecayRate` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
|
||||
"\n",
|
||||
"$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -652,7 +652,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
|
||||
"Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](tally-arithmetic.ipynb) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
|
||||
"\n",
|
||||
"$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@
|
|||
"metadata": {},
|
||||
"source": [
|
||||
"# Multigroup Mode Part I: Introduction\n",
|
||||
"This Notebook illustrates the usage of OpenMC's multi-group calculational mode with the Python API. This example notebook creates and executes the 2-D [C5G7](https://www.oecd-nea.org/science/docs/2003/nsc-doc2003-16.pdf) benchmark model using the `openmc.MGXSLibrary` class to create the supporting data library on the fly."
|
||||
"This Notebook illustrates the usage of OpenMC's multi-group calculational mode with the Python API. This example notebook creates and executes the 2-D [C5G7](https://www.oecd-nea.org/jcms/pl_17882) benchmark model using the `openmc.MGXSLibrary` class to create the supporting data library on the fly."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -38,7 +38,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We will now create the multi-group library using data directly from Appendix A of the [C5G7](https://www.oecd-nea.org/science/docs/2003/nsc-doc2003-16.pdf) benchmark documentation. All of the data below will be created at 294K, consistent with the benchmark.\n",
|
||||
"We will now create the multi-group library using data directly from Appendix A of the [C5G7](https://www.oecd-nea.org/jcms/pl_17882) benchmark documentation. All of the data below will be created at 294K, consistent with the benchmark.\n",
|
||||
"\n",
|
||||
"This notebook will first begin by setting the group structure and building the groupwise data for UO2. As you can see, the cross sections are input in the order of increasing groups (or decreasing energy).\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -88,7 +88,7 @@
|
|||
"\n",
|
||||
"$$\\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n",
|
||||
"\n",
|
||||
"This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for most multi-group cross sections, including total, absorption, and fission reaction types. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](http://openmc.readthedocs.io/en/latest/usersguide/tallies.html#filters) on the energy range and spatial zone (material, cell or universe) define the bounds of integration for both numerator and denominator."
|
||||
"This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for most multi-group cross sections, including total, absorption, and fission reaction types. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](../usersguide/tallies.rst#filters) on the energy range and spatial zone (material, cell or universe) define the bounds of integration for both numerator and denominator."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -712,7 +712,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Since the `openmc.mgxs` module uses [tally arithmetic](http://openmc.readthedocs.io/en/latest/examples/tally-arithmetic.html) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](http://pandas.pydata.org/) `DataFrame` of the multi-group cross section data."
|
||||
"Since the `openmc.mgxs` module uses [tally arithmetic](../examples/tally-arithmetic.rst) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](https://pandas.pydata.org/) `DataFrame` of the multi-group cross section data."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -817,7 +817,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](http://openmc.readthedocs.io/en/latest/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to confirm that the `TotalXS` is equal to the sum of the `AbsorptionXS` and `ScatterXS` objects."
|
||||
"Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](../examples/tally-arithmetic.rst) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to confirm that the `TotalXS` is equal to the sum of the `AbsorptionXS` and `ScatterXS` objects."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -9,12 +9,12 @@
|
|||
"\n",
|
||||
"* Creation of multi-group cross sections on a **heterogeneous geometry**\n",
|
||||
"* Calculation of cross sections on a **nuclide-by-nuclide basis**\n",
|
||||
"* The use of **[tally precision triggers](http://docs.openmc.org/en/latest/io_formats/settings.html#trigger-element)** with multi-group cross sections\n",
|
||||
"* The use of **[tally precision triggers](../io_formats/settings.rst#trigger-element)** with multi-group cross sections\n",
|
||||
"* Built-in features for **energy condensation** in downstream data processing\n",
|
||||
"* The use of the **`openmc.data`** module to plot continuous-energy vs. multi-group cross sections\n",
|
||||
"* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n",
|
||||
"\n",
|
||||
"**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. You must install [OpenMOC](https://mit-crpg.github.io/OpenMOC/) on your system in order to run this Notebook in its entirety. In addition, this Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data."
|
||||
"**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. You must install [OpenMOC](https://mit-crpg.github.io/OpenMOC/) on your system in order to run this Notebook in its entirety. In addition, this Notebook illustrates the use of [Pandas](https://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -264,7 +264,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Next, we showcase the use of OpenMC's [tally precision trigger](http://openmc.readthedocs.io/en/latest/io_formats/settings.html#trigger-element) feature in conjunction with the `openmc.mgxs` module. In particular, we will assign a tally trigger of 1E-2 on the standard deviation for each of the tallies used to compute multi-group cross sections."
|
||||
"Next, we showcase the use of OpenMC's [tally precision trigger](../io_formats/settings.rst#trigger-element) feature in conjunction with the `openmc.mgxs` module. In particular, we will assign a tally trigger of 1E-2 on the standard deviation for each of the tallies used to compute multi-group cross sections."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -745,7 +745,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Although a printed report is nice, it is not scalable or flexible. Let's extract the microscopic cross section data for the moderator as a [Pandas](http://pandas.pydata.org/) `DataFrame` ."
|
||||
"Although a printed report is nice, it is not scalable or flexible. Let's extract the microscopic cross section data for the moderator as a [Pandas](https://pandas.pydata.org/) `DataFrame` ."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@
|
|||
"* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n",
|
||||
"* Steady-state pin-by-pin **fission rates comparison** between OpenMC and [OpenMOC](https://mit-crpg.github.io/OpenMOC/)\n",
|
||||
"\n",
|
||||
"**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. You must install [OpenMOC](https://mit-crpg.github.io/OpenMOC/) on your system to run this Notebook in its entirety. In addition, this Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data."
|
||||
"**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. You must install [OpenMOC](https://mit-crpg.github.io/OpenMOC/) on your system to run this Notebook in its entirety. In addition, this Notebook illustrates the use of [Pandas](https://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -811,7 +811,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The `NuFissionXS` object supports all of the methods described previously in the `openmc.mgxs` tutorials, such as [Pandas](http://pandas.pydata.org/) `DataFrames`:\n",
|
||||
"The `NuFissionXS` object supports all of the methods described previously in the `openmc.mgxs` tutorials, such as [Pandas](https://pandas.pydata.org/) `DataFrames`:\n",
|
||||
"Note that since so few histories were simulated, we should expect a few division-by-error errors as some tallies have not yet scored any results."
|
||||
]
|
||||
},
|
||||
|
|
@ -994,7 +994,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The HDF5 store will contain the numerical multi-group cross section data indexed by domain, nuclide and cross section type. Some data workflows may be optimized by storing and retrieving binary representations of the `MGXS` objects in the `Library`. This feature is supported through the `Library.dump_to_file(...)` and `Library.load_from_file(...)` routines which use Python's [`pickle`](https://docs.python.org/2/library/pickle.html) module. This is illustrated as follows."
|
||||
"The HDF5 store will contain the numerical multi-group cross section data indexed by domain, nuclide and cross section type. Some data workflows may be optimized by storing and retrieving binary representations of the `MGXS` objects in the `Library`. This feature is supported through the `Library.dump_to_file(...)` and `Library.load_from_file(...)` routines which use Python's [`pickle`](https://docs.python.org/3/library/pickle.html) module. This is illustrated as follows."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -900,7 +900,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If had [Cython](http://cython.org/) installed when you built/installed OpenMC, you should be able to evaluate resonant cross sections from ENDF data directly, i.e., OpenMC will reconstruct resonances behind the scenes for you."
|
||||
"If had [Cython](https://cython.org/) installed when you built/installed OpenMC, you should be able to evaluate resonant cross sections from ENDF data directly, i.e., OpenMC will reconstruct resonances behind the scenes for you."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -567,7 +567,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The statepoint file actually stores the sum and sum-of-squares for each tally bin from which the mean and variance can be calculated as described [here](http://openmc.readthedocs.io/en/latest/methods/tallies.html#variance). The sum and sum-of-squares can be accessed using the ``sum`` and ``sum_sq`` properties:"
|
||||
"The statepoint file actually stores the sum and sum-of-squares for each tally bin from which the mean and variance can be calculated as described [here](../methods/tallies.rst#variance). The sum and sum-of-squares can be accessed using the ``sum`` and ``sum_sq`` properties:"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -36,7 +36,7 @@
|
|||
"\n",
|
||||
"To perform the search we will use the `openmc.search_for_keff` function. This function requires a different function be defined which creates an parametrized model to analyze. This model is required to be stored in an `openmc.model.Model` object. The first parameter of this function will be modified during the search process for our critical eigenvalue.\n",
|
||||
"\n",
|
||||
"Our model will be a pin-cell from the [Multi-Group Mode Part II](http://docs.openmc.org/en/latest/examples/mg-mode-part-ii.html) assembly, except this time the entire model building process will be contained within a function, and the Boron concentration will be parametrized."
|
||||
"Our model will be a pin-cell from the [Multi-Group Mode Part II](mg-mode-part-ii.ipynb) assembly, except this time the entire model building process will be contained within a function, and the Boron concentration will be parametrized."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue