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Merge pull request #1795 from paulromano/prepare-0.12.1

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Preparation for release of version 0.12.1
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2
docs/source/devguide/styleguide.rst
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@ -215,7 +215,7 @@ Documentation
-------------
Classes, structs, and functions are to be annotated for the `Doxygen
<http://www.doxygen.nl/>`_ documentation generation tool. Use the ``\`` form of
<https://www.doxygen.nl/>`_ documentation generation tool. Use the ``\`` form of
Doxygen commands, e.g., ``\brief`` instead of ``@brief``.
------

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docs/source/devguide/tests.rst
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@ -14,7 +14,7 @@ functions/classes in the OpenMC Python API.
Prerequisites
-------------
- The test suite relies on the third-party `pytest <https://pytest.org>`_
- The test suite relies on the third-party `pytest <https://docs.pytest.org>`_
package. To run either or both the regression and unit test suites, it is
assumed that you have OpenMC fully installed, i.e., the :ref:`scripts_openmc`
executable is available on your :envvar:`PATH` and the :mod:`openmc` Python
@ -46,7 +46,7 @@ To execute the test suite, go to the ``tests/`` directory and run::
pytest
If you want to collect information about source line coverage in the Python API,
you must have the `pytest-cov <https://pypi.python.org/pypi/pytest-cov>`_ plugin
you must have the `pytest-cov <https://pypi.org/project/pytest-cov>`_ plugin
installed and run::
pytest --cov=../openmc --cov-report=html

6
docs/source/devguide/user-input.rst
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@ -65,7 +65,7 @@ developer or send a message to the `developers mailing list`_.
.. _property attribute: https://docs.python.org/3.6/library/functions.html#property
.. _XML Schema Part 2: http://www.w3.org/TR/xmlschema-2/
.. _boolean: http://www.w3.org/TR/xmlschema-2/#boolean
.. _RELAX NG: http://relaxng.org/
.. _compact syntax: http://relaxng.org/compact-tutorial-20030326.html
.. _trang: http://www.thaiopensource.com/relaxng/trang.html
.. _RELAX NG: https://relaxng.org/
.. _compact syntax: https://relaxng.org/compact-tutorial-20030326.html
.. _trang: https://relaxng.org/jclark/trang.html
.. _developers mailing list: https://groups.google.com/forum/?fromgroups=#!forum/openmc-dev

8
docs/source/devguide/workflow.rst
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@ -122,11 +122,11 @@ can interfere with virtual environments.
.. _git: http://git-scm.com/
.. _GitHub: https://github.com/
.. _git flow: http://nvie.com/git-model
.. _valgrind: http://valgrind.org/
.. _git flow: https://nvie.com/git-model
.. _valgrind: https://www.valgrind.org/
.. _style guide: https://docs.openmc.org/en/latest/devguide/styleguide.html
.. _pull request: https://help.github.com/articles/using-pull-requests
.. _pull request: https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests
.. _openmc-dev/openmc: https://github.com/openmc-dev/openmc
.. _paid plan: https://github.com/plans
.. _paid plan: https://github.com/pricing
.. _Bitbucket: https://bitbucket.org
.. _pip: https://pip.pypa.io/en/stable/

1
docs/source/examples/capi.ipynb Symbolic link
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@ -0,0 +1 @@
../../../examples/jupyter/capi.ipynb

2
docs/source/examples/index.rst
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@ -19,7 +19,7 @@ General Usage
post-processing
pandas-dataframes
tally-arithmetic
CAPI
capi
expansion-filters
search
nuclear-data

4
docs/source/index.rst
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@ -13,8 +13,8 @@ programming model.
OpenMC was originally developed by members of the `Computational Reactor Physics
Group <http://crpg.mit.edu>`_ at the `Massachusetts Institute of Technology
<http://web.mit.edu>`_ starting in 2011. Various universities, laboratories, and
other organizations now contribute to the development of OpenMC. For more
<https://web.mit.edu>`_ starting in 2011. Various universities, laboratories,
and other organizations now contribute to the development of OpenMC. For more
information on OpenMC, feel free to post a message on the `OpenMC Discourse
Forum <https://openmc.discourse.group/>`_.

2
docs/source/io_formats/data_wmp.rst
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@ -49,4 +49,4 @@ Windowed Multipole Library Format
windows[i, 1] are, respectively, the indexes (1-based) of the first and
last pole in window i.
.. _h5py: http://docs.h5py.org/en/latest/
.. _h5py: https://docs.h5py.org/en/latest/

2
docs/source/io_formats/mgxs_library.rst
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@ -21,7 +21,7 @@ nuclides or materials.
The current version of the multi-group library file format is 1.0.
.. _HDF5: http://www.hdfgroup.org/HDF5/
.. _HDF5: https://www.hdfgroup.org/solutions/hdf5/
.. _mgxs_lib_spec:

8
docs/source/methods/cross_sections.rst
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@ -269,12 +269,12 @@ or even isotropic scattering.
.. _logarithmic mapping technique:
https://laws.lanl.gov/vhosts/mcnp.lanl.gov/pdf_files/la-ur-14-24530.pdf
.. _Hwang: http://www.ans.org/pubs/journals/nse/a_16381
.. _Hwang: https://doi.org/10.13182/NSE87-A16381
.. _Josey: https://doi.org/10.1016/j.jcp.2015.08.013
.. _WMP Library: https://github.com/mit-crpg/WMP_Library
.. _MCNP: http://mcnp.lanl.gov
.. _MCNP: https://mcnp.lanl.gov
.. _Serpent: http://montecarlo.vtt.fi
.. _NJOY: http://t2.lanl.gov/codes.shtml
.. _ENDF/B data: http://www.nndc.bnl.gov/endf
.. _NJOY: https://www.njoy21.io/NJOY21/
.. _ENDF/B data: https://www.nndc.bnl.gov/endf/b8.0/
.. _Leppanen: https://doi.org/10.1016/j.anucene.2009.03.019
.. _algorithms: http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package

2
docs/source/methods/geometry.rst
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@ -962,6 +962,6 @@ surface is known as in :ref:`reflection`.
.. _constructive solid geometry: https://en.wikipedia.org/wiki/Constructive_solid_geometry
.. _surfaces: https://en.wikipedia.org/wiki/Surface
.. _MCNP: http://mcnp.lanl.gov
.. _MCNP: https://mcnp.lanl.gov
.. _Serpent: http://montecarlo.vtt.fi
.. _Monte Carlo Performance benchmark: https://github.com/mit-crpg/benchmarks/tree/master/mc-performance/openmc

14
docs/source/methods/neutron_physics.rst
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@ -1695,7 +1695,7 @@ another.
.. _SIGMA1 method: https://doi.org/10.13182/NSE76-1
.. _scaled interpolation: http://www.ans.org/pubs/journals/nse/a_26575
.. _scaled interpolation: https://doi.org/10.13182/NSE73-A26575
.. _probability table method: https://doi.org/10.13182/NSE72-3
@ -1703,23 +1703,23 @@ another.
.. _Foderaro: http://hdl.handle.net/1721.1/1716
.. _OECD: http://www.oecd-nea.org/tools/abstract/detail/NEA-1792
.. _OECD: https://www.oecd-nea.org/tools/abstract/detail/NEA-1792
.. _NJOY: https://www.njoy21.io/NJOY2016/
.. _PREPRO: http://www-nds.iaea.org/ndspub/endf/prepro/
.. _PREPRO: https://www-nds.iaea.org/ndspub/endf/prepro/
.. _endf102: https://www.oecd-nea.org/dbdata/data/manual-endf/endf102.pdf
.. _Monte Carlo Sampler: https://laws.lanl.gov/vhosts/mcnp.lanl.gov/pdf_files/la-9721.pdf
.. _Monte Carlo Sampler: https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-09721-MS
.. _LA-UR-14-27694: http://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-14-27694
.. _LA-UR-14-27694: https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-14-27694
.. _MC21: http://www.osti.gov/bridge/servlets/purl/903083-HT5p1o/903083.pdf
.. _MC21: https://www.osti.gov/biblio/903083
.. _Romano: https://doi.org/10.1016/j.cpc.2014.11.001
.. _Sutton and Brown: http://www.osti.gov/bridge/product.biblio.jsp?osti_id=307911
.. _Sutton and Brown: https://www.osti.gov/biblio/307911
.. _lectures: https://laws.lanl.gov/vhosts/mcnp.lanl.gov/pdf_files/la-ur-05-4983.pdf

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docs/source/methods/parallelization.rst
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@ -251,8 +251,8 @@ depending on how many nodes are communicating and the size of the message. Using
multiple algorithms allows one to minimize latency for small messages and
minimize bandwidth for long messages.
We will focus here on the implementation of broadcast in the MPICH2_
implementation. For short messages, MPICH2 uses a `binomial tree`_ algorithm. In
We will focus here on the implementation of broadcast in the MPICH_
implementation. For short messages, MPICH uses a `binomial tree`_ algorithm. In
this algorithm, the root process sends the data to one node in the first step,
and then in the subsequent, both the root and the other node can send the data
to other nodes. Thus, it takes a total of :math:`\lceil \log_2 p \rceil` steps
@ -266,7 +266,7 @@ to complete the communication. The time to complete the communication is
This algorithm works well for short messages since the latency term scales
logarithmically with the number of nodes. However, for long messages, an
algorithm that has lower bandwidth has been proposed by Barnett_ and implemented
in MPICH2. Rather than using a binomial tree, the broadcast is divided into a
in MPICH. Rather than using a binomial tree, the broadcast is divided into a
scatter and an allgather. The time to complete the scatter is :math:` \log_2 p
\: \alpha + \frac{p-1}{p} N\beta` using a binomial tree algorithm. The allgather
is performed using a ring algorithm that completes in :math:`p-1) \alpha +
@ -613,7 +613,7 @@ is actually independent of the number of nodes:
.. _Brissenden and Garlick: https://doi.org/10.1016/0306-4549(86)90095-2
.. _MPICH2: http://www.mcs.anl.gov/mpi/mpich
.. _MPICH: http://www.mpich.org
.. _binomial tree: https://www.mcs.anl.gov/~thakur/papers/ijhpca-coll.pdf
@ -629,19 +629,19 @@ is actually independent of the number of nodes:
.. _message-passing interface: https://en.wikipedia.org/wiki/Message_Passing_Interface
.. _PVM: http://www.csm.ornl.gov/pvm/pvm_home.html
.. _PVM: https://www.csm.ornl.gov/pvm/pvm_home.html
.. _MPI: http://www.mcs.anl.gov/research/projects/mpi/
.. _MPI: https://www.mcs.anl.gov/research/projects/mpi/
.. _embarrassingly parallel: https://en.wikipedia.org/wiki/Embarrassingly_parallel
.. _sends: http://www.mcs.anl.gov/research/projects/mpi/www/www3/MPI_Send.html
.. _sends: https://www.mpich.org//static/docs/latest/www3/MPI_Send.html
.. _broadcasts: http://www.mcs.anl.gov/research/projects/mpi/www/www3/MPI_Bcast.html
.. _broadcasts: https://www.mpich.org//static/docs/latest/www3/MPI_Bcast.html
.. _scatter: http://www.mcs.anl.gov/research/projects/mpi/www/www3/MPI_Scatter.html
.. _scatter: https://www.mpich.org//static/docs/latest/www3/MPI_Scatter.html
.. _allgather: http://www.mcs.anl.gov/research/projects/mpi/www/www3/MPI_Allgather.html
.. _allgather: https://www.mpich.org//static/docs/latest/www3/MPI_Allgather.html
.. _Cauchy distribution: https://en.wikipedia.org/wiki/Cauchy_distribution

2
docs/source/methods/photon_physics.rst
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@ -1069,6 +1069,6 @@ emitted photon.
.. _Kaltiaisenaho: https://aaltodoc.aalto.fi/bitstream/handle/123456789/21004/master_Kaltiaisenaho_Toni_2016.pdf
.. _Salvat: http://www.oecd-nea.org/globalsearch/download.php?doc=77434
.. _Salvat: https://www.oecd-nea.org/globalsearch/download.php?doc=77434
.. _Sternheimer: https://doi.org/10.1103/PhysRevB.26.6067

6
docs/source/pythonapi/index.rst
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@ -7,8 +7,8 @@ Python API
OpenMC includes a rich Python API that enables programmatic pre- and
post-processing. The easiest way to begin using the API is to take a look at the
:ref:`examples`. This assumes that you are already familiar with Python and
common third-party packages such as `NumPy <http://www.numpy.org/>`_. If you
have never used Python before, the prospect of learning a new code *and* a
common third-party packages such as `NumPy <https://numpy.org/>`_. If you have
never used Python before, the prospect of learning a new code *and* a
programming language might sound daunting. However, you should keep in mind that
there are many substantial benefits to using the Python API, including:
@ -28,7 +28,7 @@ there are many substantial benefits to using the Python API, including:
For those new to Python, there are many good tutorials available online. We
recommend going through the modules from `Codecademy
<https://www.codecademy.com/learn/learn-python-3>`_ and/or the `Scipy lectures
<https://scipy-lectures.github.io/>`_.
<https://scipy-lectures.org/>`_.
The full API documentation serves to provide more information on a given module
or class.

75
docs/source/quickinstall.rst
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@ -12,7 +12,7 @@ OpenMC, see :ref:`usersguide_install` in the User's Manual.
Installing on Linux/Mac with conda-forge
----------------------------------------
`Conda <http://conda.pydata.org/docs/>`_ is an open source package management
`Conda <https://conda.io/en/latest/>`_ is an open source package management
system and environment management system for installing multiple versions of
software packages and their dependencies and switching easily between them. If
you have `conda` installed on your system, OpenMC can be installed via the
@ -25,16 +25,16 @@ you have `conda` installed on your system, OpenMC can be installed via the
To list the versions of OpenMC that are available on the `conda-forge` channel,
in your terminal window or an Anaconda Prompt run:
.. code-block:: sh
.. code-block:: sh
conda search openmc
OpenMC can then be installed with:
.. code-block:: sh
conda create -n openmc-env openmc
This will install OpenMC in a conda environment called `openmc-env`. To activate
the environment, run:
@ -42,53 +42,26 @@ the environment, run:
conda activate openmc-env
--------------------------------
Installing on Ubuntu through PPA
--------------------------------
For users with Ubuntu 15.04 or later, a binary package for OpenMC is available
through a `Personal Package Archive`_ (PPA) and can be installed through the
`APT package manager`_. First, add the following PPA to the repository sources:
.. code-block:: sh
sudo apt-add-repository ppa:paulromano/staging
Next, resynchronize the package index files:
.. code-block:: sh
sudo apt update
Now OpenMC should be recognized within the repository and can be installed:
.. code-block:: sh
sudo apt install openmc
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
.. _APT package manager: https://help.ubuntu.com/community/AptGet/Howto
-------------------------------------------
Installing on Linux/Mac/Windows with Docker
-------------------------------------------
OpenMC can be easily deployed using `Docker <https://www.docker.com/>`_ on any
Windows, Mac or Linux system. With Docker running, execute the following
command in the shell to download and run a `Docker image`_ with the most recent release of OpenMC from `DockerHub <https://hub.docker.com/>`_ called ``openmc/openmc:v0.10.0``:
Windows, Mac, or Linux system. With Docker running, execute the following command
in the shell to download and run a `Docker image`_ with the most recent release
of OpenMC from `DockerHub <https://hub.docker.com/>`_:
.. code-block:: sh
docker run openmc/openmc:v0.10.0
docker run openmc/openmc:latest
This will take several minutes to run depending on your internet download speed. The command will place you in an interactive shell running in a `Docker container`_ with OpenMC installed.
This will take several minutes to run depending on your internet download speed.
The command will place you in an interactive shell running in a `Docker
container`_ with OpenMC installed.
.. note:: The ``docker run`` command supports many `options`_ for spawning
containers -- including `mounting volumes`_ from the host
filesystem -- which many users will find useful.
containers including `mounting volumes`_ from the host filesystem,
which many users will find useful.
.. _Docker image: https://docs.docker.com/engine/reference/commandline/images/
.. _Docker container: https://www.docker.com/resources/what-container
@ -112,18 +85,18 @@ To install the latest OpenMC with the Python API, use the following command:
For more information about customizations including MPI, see the
:ref:`detailed installation instructions using Spack <install-spack>`.
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.
.. _Spack: https://spack.readthedocs.io/en/latest/
.. _setup guide: https://spack.readthedocs.io/en/latest/getting_started.html
---------------------------------------
Installing from Source on Ubuntu 15.04+
---------------------------------------
--------------------------------
Installing from Source on Ubuntu
--------------------------------
To build OpenMC from source, several :ref:`prerequisites <prerequisites>` are
needed. If you are using Ubuntu 15.04 or higher, all prerequisites can be
installed directly from the package manager.
needed. If you are using Ubuntu or higher, all prerequisites can be installed
directly from the package manager:
.. code-block:: sh
@ -139,9 +112,9 @@ Installing from Source on Linux or Mac OS X
All OpenMC source code is hosted on `GitHub
<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:
.. code-block:: sh
@ -162,8 +135,8 @@ should specify an installation directory where you have write access, e.g.
The :mod:`openmc` Python package must be installed separately. The easiest way
to install it is using `pip <https://pip.pypa.io/en/stable/>`_, which is
included by default in Python 2.7 and Python 3.4+. From the root directory of
the OpenMC distribution/repository, run:
included by default in Python 3.4+. From the root directory of the OpenMC
distribution/repository, run:
.. code-block:: sh

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docs/source/releasenotes/0.12.1.rst Normal file
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@ -0,0 +1,129 @@
====================
What's New in 0.12.1
====================
.. currentmodule:: openmc
-------
Summary
-------
This release of OpenMC includes an assortment of new features and many bug fixes.
In particular, the :mod:`openmc.deplete` module has been heavily tested which
has resulted in a number of usability improvements, bug fixes, and other
enhancements. Energy deposition calculations, particularly for coupled
neutron-photon simulations, have been improved as well.
Improvements in modeling capabilities continue to be added to the code,
including the ability to rotate surfaces in the Python API, several new
"composite" surfaces, a variety of new methods on :class:`openmc.Material`,
unstructured mesh tallies that leverage the existing DAGMC infrastructure,
effective dose coefficients from ICRP-116, and a new cell instance tally
filter.
------------
New Features
------------
- Boundary conditions have been refactored and generalized. Rotational periodic
boundary conditions can now be applied to any N-fold symmetric geometry.
- External source distributions have been refactored and extended. Users writing
their own C++ custom sources need to write a class that derives from
``openmc::Source``. These changes have enabled new functionality, such as:
- Mixing more than one custom source library together
- Mixing a normal source with a custom source
- Using a file-based source for fixed source simulations
- Using a file-based source for eigenvalue simulations even when the number of
particles doesn't match
- New capability to read and write a source file based on particles that cross a
surface (known as a "surface source").
- Various improvements related to depletion:
- Reactions used in a depletion chain can now be configured through the
``reactions`` argument to :meth:`openmc.deplete.Chain.from_endf`.
- Specifying a power of zero during a depletion simulation no longer results
in an unnecessary transport solve.
- Reaction rates can be computed either directly or using multigroup flux
tallies that are used to collapse reaction rates afterward. This is enabled
through the ``reaction_rate_mode`` and ``reaction_rate_opts`` to
:class:`openmc.deplete.Operator`.
- Depletion results can be used to create a new :class:`openmc.Materials`
object using the :meth:`openmc.deplete.ResultsList.export_to_materials`
method.
- Multigroup current and diffusion cross sections can be generated through the
:class:`openmc.mgxs.Current` and :class:`openmc.mgxs.DiffusionCoefficient`
classes.
- Added :func:`openmc.data.isotopes` function that returns a list of naturally
occurring isotopes for a given element.
- Windowed multipole libraries can now be generated directly from the Python API
using :meth:`openmc.data.WindowedMultipole.from_endf`.
- The new :func:`openmc.write_source_file` function allows source files to be
generated programmatically.
---------
Bug Fixes
---------
- `Proper detection of MPI wrappers <https://github.com/openmc-dev/openmc/pull/1619>`_
- `Fix related to declaration order of maps/vectors <https://github.com/openmc-dev/openmc/pull/1622>`_
- `Check for existence of decay rate attribute <https://github.com/openmc-dev/openmc/pull/1629>`_
- `Small updates to deal with JEFF 3.3 data <https://github.com/openmc-dev/openmc/pull/1638>`_
- `Fix for depletion chain generation <https://github.com/openmc-dev/openmc/pull/1642>`_
- `Fix call to superclass constructor in MeshPlotter <https://github.com/openmc-dev/openmc/pull/1644>`_
- `Fix for data crossover in VTK files <https://github.com/openmc-dev/openmc/pull/1645>`_
- `Make sure reaction names are recognized as valid tally scores <https://github.com/openmc-dev/openmc/pull/1647>`_
- `Fix bug related to logging of particle restarts <https://github.com/openmc-dev/openmc/pull/1649>`_
- `Examine if region exists before removing redundant surfaces <https://github.com/openmc-dev/openmc/pull/1650>`_
- `Fix plotting of individual universe levels <https://github.com/openmc-dev/openmc/pull/1651>`_
- `Mixed materials should inherit depletable attribute <https://github.com/openmc-dev/openmc/pull/1657>`_
- `Fix typo in energy units in dose coefficients <https://github.com/openmc-dev/openmc/pull/1659/files>`_
- `Fixes for large tally cases <https://github.com/openmc-dev/openmc/pull/1666>`_
- `Fix verification of volume calculation results <https://github.com/openmc-dev/openmc/pull/1677>`_
- `Fix calculation of decay energy for depletion chains <https://github.com/openmc-dev/openmc/pull/1679>`_
- `Fix pointers in CartesianIndependent <https://github.com/openmc-dev/openmc/pull/1681>`_
- `Ensure correct initialization of members for RegularMesh <https://github.com/openmc-dev/openmc/pull/1683>`_
- `Add missing import in depletion module <https://github.com/openmc-dev/openmc/pull/1715>`_
- `Fixed several bugs related to decay-rate <https://github.com/openmc-dev/openmc/pull/1718>`_
- `Fix how depletion operator distributes burnable materials <https://github.com/openmc-dev/openmc/pull/1719>`_
- `Fix assignment of elemental carbon in JEFF 3.3 <https://github.com/openmc-dev/openmc/pull/1722>`_
- `Fix typo in RectangularParallelepiped.__pos__ <https://github.com/openmc-dev/openmc/pull/1724>`_
- `Fix temperature tolerance with S(a,b) data <https://github.com/openmc-dev/openmc/pull/1733>`_
- `Fix sampling or normal distribution <https://github.com/openmc-dev/openmc/pull/1739>`_
- `Fix for SharedArray relaxed memory ordering <https://github.com/openmc-dev/openmc/pull/1764>`_
- `Check for proper format of source files <https://github.com/openmc-dev/openmc/pull/1771>`_
- `Ensure (n,gamma) reaction rate tally uses sampled cross section <https://github.com/openmc-dev/openmc/pull/1776>`_
- `Fix for temperature range behavior <https://github.com/openmc-dev/openmc/pull/1777>`_
------------
Contributors
------------
This release contains new contributions from the following people:
- `Andrew Davis <https://github.com/makeclean>`_
- `Guillaume Giudicelli <https://github.com/GiudGiud>`_
- `Sterling Harper <https://github.com/smharper>`_
- `Bryan Herman <https://github.com/bryanherman>`_
- `Yue Jin <https://github.com/kingyue737>`_
- `Andrew Johnson <https://github.com/drewejohnson>`_
- `Miriam Kreher <https://github.com/mkreher13>`_
- `Shikhar Kumar <https://github.com/shikhar413>`_
- `Jingang Liang <https://github.com/liangjg>`_
- `Amanda Lund <https://github.com/amandalund>`_
- `Adam Nelson <https://github.com/nelsonag>`_
- `April Novak <https://github.com/aprilnovak>`_
- `YoungHui Park <https://github.com/ypark234>`_
- `Ariful Islam Pranto <https://github.com/AI-Pranto>`_
- `Ron Rahaman <https://github.com/RonRahaman>`_
- `Gavin Ridley <https://github.com/gridley>`_
- `Paul Romano <https://github.com/paulromano>`_
- `Jonathan Shimwell <https://github.com/Shimwell>`_
- `Dan Short <https://github.com/DanShort12>`_
- `Patrick Shriwise <https://github.com/pshriwise>`_
- `Roy Stogner <https://github.com/roystgnr>`_
- `John Tramm <https://github.com/jtramm>`_
- `Cyrus Wyett <https://github.com/cjwyett>`_
- `Jiankai Yu <https://github.com/rockfool>`_

1
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@ -7,6 +7,7 @@ Release Notes
.. toctree::
:maxdepth: 1
0.12.1
0.12.0
0.11.0
0.10.0

9
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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.
@ -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
@ -149,7 +150,7 @@ and `Volume II`_. You may also find it helpful to review the following terms:
.. _discretization: https://en.wikipedia.org/wiki/Discretization
.. _constructive solid geometry: https://en.wikipedia.org/wiki/Constructive_solid_geometry
.. _git: http://git-scm.com/
.. _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
.. _Volume II: https://www.standards.doe.gov/standards-documents/1000/1019-bhdbk-1993-v2

2
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@ -260,7 +260,7 @@ For an example of how to create a multi-group library, see the `example notebook
.. _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

6
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@ -30,6 +30,12 @@ operator class requires a :class:`openmc.Geometry` instance and a
Any material that contains a fissionable nuclide is depleted by default, but
this can behavior can be changed with the :attr:`Material.depletable` attribute.
.. important:: The volume must be specified for each material that is depleted by
setting the :attr:`Material.volume` attribute. This is necessary
in order to calculate the proper normalization of tally results
based on the source rate.
:mod:`openmc.deplete` supports multiple time-integration methods for determining
material compositions over time. Each method appears as a different class.
For example, :class:`openmc.deplete.CECMIntegrator` runs a depletion calculation

2
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@ -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:

164
docs/source/usersguide/install.rst
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@ -42,37 +42,31 @@ the environment, run:
conda activate openmc-env
.. _install_ppa:
-------------------------------------------
Installing on Linux/Mac/Windows with Docker
-------------------------------------------
-----------------------------
Installing on Ubuntu with PPA
-----------------------------
For users with Ubuntu 15.04 or later, a binary package for OpenMC is available
through a `Personal Package Archive`_ (PPA) and can be installed through the
`APT package manager`_. First, add the following PPA to the repository sources:
OpenMC can be easily deployed using `Docker <https://www.docker.com/>`_ on any
Windows, Mac, or Linux system. With Docker running, execute the following
command in the shell to download and run a `Docker image`_ with the most recent
release of OpenMC from `DockerHub <https://hub.docker.com/>`_:
.. code-block:: sh
sudo apt-add-repository ppa:paulromano/staging
docker run openmc/openmc:latest
Next, resynchronize the package index files:
This will take several minutes to run depending on your internet download speed.
The command will place you in an interactive shell running in a `Docker
container`_ with OpenMC installed.
.. code-block:: sh
.. note:: The ``docker run`` command supports many `options`_ for spawning
containers including `mounting volumes`_ from the host filesystem,
which many users will find useful.
sudo apt update
Now OpenMC should be recognized within the repository and can be installed:
.. code-block:: sh
sudo apt install openmc
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
.. _APT package manager: https://help.ubuntu.com/community/AptGet/Howto
.. _Docker image: https://docs.docker.com/engine/reference/commandline/images/
.. _Docker container: https://www.docker.com/resources/what-container
.. _options: https://docs.docker.com/engine/reference/commandline/run/
.. _mounting volumes: https://docs.docker.com/storage/volumes/
.. _install-spack:
@ -84,9 +78,9 @@ Spack_ is a package management tool designed to support multiple versions and
configurations of software on a wide variety of platforms and environments.
Please follow Spack's `setup guide`_ to configure the Spack system.
The OpenMC Spack recipe has been configured with variants that match most options
provided in the CMakeLists.txt file. To see a list of these variants and other
information use:
The OpenMC Spack recipe has been configured with variants that match most
options provided in the CMakeLists.txt file. To see a list of these variants and
other information use:
.. code-block:: sh
@ -109,7 +103,8 @@ Spack recipe can be found with the following command:
The only variant for the Python API is ``mpi``.
The most basic installation of OpenMC can be accomplished by entering the following command:
The most basic installation of OpenMC can be accomplished by entering the
following command:
.. code-block::
@ -117,14 +112,17 @@ The most basic installation of OpenMC can be accomplished by entering the follow
.. caution::
When installing any Spack package, dependencies are assumed to be at configured defaults unless otherwise specfied in the
specification on the command line. In the above example, assuming the default options weren't changed in Spack's package
configuration, py-openmc will link against a non-optimized non-MPI openmc. Even if an optimized openmc was built separately,
it will rebuild openmc with optimization OFF. Thus, if you are trying to link against dependencies that were configured
different than defaults, ``^openmc[variants]`` will have to be present in the command.
When installing any Spack package, dependencies are assumed to be at
configured defaults unless otherwise specfied in the specification on the
command line. In the above example, assuming the default options weren't
changed in Spack's package configuration, py-openmc will link against a
non-optimized non-MPI openmc. Even if an optimized openmc was built
separately, it will rebuild openmc with optimization OFF. Thus, if you are
trying to link against dependencies that were configured different than
defaults, ``^openmc[variants]`` will have to be present in the command.
For a more performant build of OpenMC with optimization turned ON and MPI provided by OpenMPI, the following command can be
used:
For a more performant build of OpenMC with optimization turned ON and MPI
provided by OpenMPI, the following command can be used:
.. code-block:: sh
@ -136,24 +134,27 @@ used:
.. tip::
When installing py-openmc, it will use Spack's preferred Python. For example, assuming Spack's preferred Python
is 3.8.7, to build py-openmc against the latest Python 3.7 instead, ``^python@3.7.0:3.7.99`` should be added to the
specification on the command line. Additionally, a compiler type and version can be specified at the end of the
command using ``%gcc@<version>``, ``%intel@<version>``, etc.
When installing py-openmc, it will use Spack's preferred Python. For
example, assuming Spack's preferred Python is 3.8.7, to build py-openmc
against the latest Python 3.7 instead, ``^python@3.7.0:3.7.99`` should be
added to the specification on the command line. Additionally, a compiler
type and version can be specified at the end of the command using
``%gcc@<version>``, ``%intel@<version>``, etc.
A useful tool in Spack is to look at the dependency tree before installation. This can be observed using
Spack's `spec` tool:
A useful tool in Spack is to look at the dependency tree before installation.
This can be observed using Spack's ``spec`` tool:
.. code-block::
spack spec py-openmc+mpi ^openmc+optimize
Once installed, environment/lmod modules can be generated or Spack's `load` feature
can be used to access the installed packages.
Once installed, environment/lmod modules can be generated or Spack's ``load``
feature can be used to access the installed packages.
.. _Spack: https://spack.readthedocs.io/en/latest/
.. _setup guide: https://spack.readthedocs.io/en/latest/getting_started.html
.. _install_source:
----------------------
@ -242,9 +243,9 @@ 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 +257,10 @@ 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
@ -408,36 +409,11 @@ 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>`.
Compiling for the Intel Xeon Phi
--------------------------------
For the second generation Knights Landing architecture, nothing special is
required to compile OpenMC. You may wish to experiment with compiler flags that
control generation of vector instructions to see what configuration gives
optimal performance for your target problem.
For the first generation Knights Corner architecture, it is necessary to
cross-compile OpenMC. If you are using the Intel compiler, it is necessary to
specify that all objects be compiled with the ``-mmic`` flag as follows:
.. code-block:: sh
mkdir build && cd build
CXX=icpc CXXFLAGS=-mmic cmake -Dopenmp=on ..
make
Note that unless an HDF5 build for the Intel Xeon Phi (Knights Corner) is
already on your target machine, you will need to cross-compile HDF5 for the Xeon
Phi. An `example script`_ to build zlib and HDF5 provides several necessary
workarounds.
.. _example script: https://github.com/paulromano/install-scripts/blob/master/install-hdf5-mic
Testing Build
-------------
@ -449,16 +425,16 @@ section library along with windowed multipole data. Please refer to our
Installing Python API
---------------------
If you installed OpenMC using :ref:`Conda <install_conda>` or :ref:`PPA
<install_ppa>`, no further steps are necessary in order to use OpenMC's
:ref:`Python API <pythonapi>`. However, if you are :ref:`installing from source
<install_source>`, the Python API is not installed by default when ``make
install`` is run because in many situations it doesn't make sense to install a
Python package in the same location as the ``openmc`` executable (for example,
if you are installing the package into a `virtual environment
<https://docs.python.org/3/tutorial/venv.html>`_). The easiest way to install
the :mod:`openmc` Python package is to use pip_, which is included by default in
Python 3.4+. From the root directory of the OpenMC distribution/repository, run:
If you installed OpenMC using :ref:`Conda <install_conda>`, no further steps are
necessary in order to use OpenMC's :ref:`Python API <pythonapi>`. However, if
you are :ref:`installing from source <install_source>`, the Python API is not
installed by default when ``make install`` is run because in many situations it
doesn't make sense to install a Python package in the same location as the
``openmc`` executable (for example, if you are installing the package into a
`virtual environment <https://docs.python.org/3/tutorial/venv.html>`_). The
easiest way to install the :mod:`openmc` Python package is to use pip_, which is
included by default in Python 3.4+. From the root directory of the OpenMC
distribution/repository, run:
.. code-block:: sh
@ -494,7 +470,7 @@ 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 +478,7 @@ 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 +487,14 @@ 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 +506,11 @@ 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 +564,7 @@ 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/

22
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@ -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`.
--------------------

6
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@ -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

4
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@ -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

2
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@ -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.

57
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@ -102,61 +102,6 @@ otherwise.
--fission_energy_release FISSION_ENERGY_RELEASE
HDF5 file containing fission energy release data
.. _scripts_compton:
-----------------------
``openmc-make-compton``
-----------------------
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.
.. _scripts_depletion_chain:
-------------------------------
``openmc-make-depletion-chain``
-------------------------------
This script generates a depletion chain file called ``chain_endfb71.xml``
using ENDF/B-VII.1 nuclear data. If the :envvar:`OPENMC_ENDF_DATA` variable
is not set, and ``"neutron"``, ``"decay"``, ``"nfy"`` directories
do not exist, then ENDF/B-VII.1 data will be downloaded.
.. _scripts_depletion_chain_casl:
------------------------------------
``openmc-make-depletion-chain-casl``
------------------------------------
This script generates a depletion chain called ``chain_casl.xml``
using ENDF/B-VII.1 nuclear data for a simplified chain.
The nuclides were chosen by CASL-ORIGEN, which can be found in
Appendix A of Kang Seog Kim, `"Specification for the VERA Depletion
Benchmark Suite" <https://doi.org/10.2172/1256820>`_,
CASL-U-2015-1014-000, Rev. 0, ORNL/TM-2016/53, 2016.
``Te129`` has been added into this chain due to its link to
``I129`` production.
If the :envvar:`OPENMC_ENDF_DATA` variable is not set,
and ``"neutron"``, ``"decay"``, ``"nfy"`` directories
to not exist, then ENDF/B-VII.1 data will be downloaded.
.. _scripts_stopping:
-------------------------------
``openmc-make-stopping-powers``
-------------------------------
This script generates an HDF5 file called ``stopping_power.h5`` that contains
radiative and collision stopping powers and mean excitation energy pulled from
the `NIST ESTAR database
<https://physics.nist.gov/PhysRefData/Star/Text/ESTAR.html>`_. Note that OpenMC
includes this data file by default so it should not be necessary in practice to
generate it yourself.
.. _scripts_plot:
--------------------------
@ -250,7 +195,7 @@ 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:

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@ -54,9 +54,9 @@ If you don't specify a run mode, the default run mode is 'eigenvalue'.
.. _usersguide_particles:
-------------------
Number of Particles
-------------------
------------
Run Strategy
------------
For a fixed source simulation, the total number of source particle histories
simulated is broken up into a number of *batches*, each corresponding to a
@ -88,6 +88,79 @@ for accumulating tallies.
settings.batches = 150
settings.inactive = 5
.. _usersguide_batches:
Number of Batches
-----------------
In general, the stochastic uncertainty in your simulation results is directly
related to how many total active particles are simulated (the product of the
number of active batches, number of generations per batch, and number of
particles). At a minimum, you should use enough active batches so that the
central limit theorem is satisfied (about 30). Otherwise, reducing the overall
uncertainty in your simulation by a factor of 2 will require using 4 times as
many batches (since the standard deviation decreases as :math:`1/\sqrt{N}`).
Number of Inactive Batches
--------------------------
For :math:`k` eigenvalue simulations, the source distribution is not known a
priori. Thus, a "guess" of the source distribution is made and then iterated on,
with the source evolving closer to the true distribution at each iteration. Once
the source distribution has converged, it is then safe to start accumulating
tallies. Consequently, a preset number of inactive batches are run before the
active batches (where tallies are turned on) begin. The number of inactive
batches necessary to reach a converged source depends on the spatial extent of
the problem, its dominance ratio, what boundary conditions are used, and many
other factors. For small problems, using 50--100 inactive batches is likely
sufficient. For larger models, many hundreds of inactive batches may be
necessary. Users are recommended to use the :ref:`Shannon entropy
<usersguide_entropy>` diagnostic as a way of determining how many inactive
batches are necessary.
Specifying the initial source used for the very first batch is described in
:ref:`below <usersguide_source>`. Although the initial source is arbitrary in
the sense that any source will eventually converge to the correct distribution,
using a source guess that is closer to the actual converged source distribution
will translate into needing fewer inactive batches (and hence less simulation
time).
For fixed source simulations, the source distribution is known exactly, so no
inactive batches are needed. In this case the :attr:`Settings.inactive`
attribute can be omitted since it defaults to zero.
Number of Generations per Batch
-------------------------------
The standard deviation of tally results is calculated assuming that all
realizations (batches) are independent. However, in a :math:`k` eigenvalue
calculation, the source sites for each batch are produced from fissions in the
preceding batch, resulting in a correlation between successive batches. This
correlation can result in an underprediction of the variance. That is, the
variance reported is actually less than the true variance. To mitigate this
effect, OpenMC allows you to group together multiple fission generations into a
single batch for statistical purposes, rather than having each fission
generation be a separate batch, which is the default behavior.
Number of Particles per Generation
----------------------------------
There are several considerations for choosing the number of particles per
generation. As discussed in :ref:`usersguide_batches`, the total number of
active particles will determine the level of stochastic uncertainty in
simulation results, so using a higher number of particles will result in less
uncertainty. For parallel simulations that use OpenMP and/or MPI, the number of
particles per generation should be large enough to ensure good load balancing
between threads. For example, if you are running on a single processor with 32
cores, each core should have at least 100 particles or so (i.e., at least 3,200
particles per generation should be used). Using a larger number of particles per
generation can also help reduce the cost of synchronization and communication
between batches. For :math:`k` eigenvalue calculations, experts recommend_ at
least 10,000 particles per generation to avoid any bias in the estimate of
:math:`k` eigenvalue or tallies.
.. _recommend: https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-09-03136
.. _usersguide_source:
-----------------------------
@ -175,6 +248,32 @@ following would generate a photon source::
For a full list of all classes related to statistical distributions, see
:ref:`pythonapi_stats`.
File-based Sources
------------------
OpenMC can use a pregenerated HDF5 source file by specifying the ``filename``
argument to :class:`openmc.Source`::
settings.source = openmc.Source(filename='source.h5')
Statepoint and source files are generated automatically when a simulation is run
and can be used as the starting source in a new simulation. Alternatively, a
source file can be manually generated with the :func:`openmc.write_source_file`
function. This is particularly useful for coupling OpenMC with another program
that generates a source to be used in OpenMC.
A source file based on particles that cross one or more surfaces can be
generated during a simulation using the :attr:`Settings.surf_source_write`
attribute::
settings.surf_source_write = {
'surfaces_ids': [1, 2, 3],
'max_particles': 10000
}
In this example, at most 10,000 source particles are stored when particles cross
surfaces with IDs of 1, 2, or 3.
.. _custom_source:
Custom Sources
@ -301,6 +400,8 @@ the source class when it is created:
As with the basic custom source functionality, the custom source library
location must be provided in the :attr:`openmc.Source.library` attribute.
.. _usersguide_entropy:
---------------
Shannon Entropy
---------------

6
examples/jupyter/cad-based-geometry.ipynb
View 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."
]
},
{

3
docs/source/examples/CAPI.ipynb → examples/jupyter/capi.ipynb
View file

@ -4,6 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using the C/C++ API\n",
"This notebook shows how to use the OpenMC C/C++ API through the openmc.lib module. This module is particularly useful for multiphysics coupling because it allows you to update the density of materials and the temperatures of cells in memory, without stopping the simulation.\n",
"\n",
"Warning: these bindings are still somewhat experimental and may be subject to change in future versions of OpenMC."
@ -466,7 +467,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.0"
"version": "3.8.3"
}
},
"nbformat": 4,

6
examples/jupyter/mdgxs-part-i.ipynb
View file

@ -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",

2
examples/jupyter/mdgxs-part-ii.ipynb
View file

@ -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",

4
examples/jupyter/mg-mode-part-i.ipynb
View file

@ -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",

6
examples/jupyter/mgxs-part-i.ipynb
View file

@ -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."
]
},
{

8
examples/jupyter/mgxs-part-ii.ipynb
View file

@ -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` ."
]
},
{

6
examples/jupyter/mgxs-part-iii.ipynb
View file

@ -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."
]
},
{

2
examples/jupyter/nuclear-data.ipynb
View file

@ -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 you 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."
]
},
{

2
examples/jupyter/post-processing.ipynb
View file

@ -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:"
]
},
{

2
examples/jupyter/search.ipynb
View file

@ -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."
]
},
{

2
include/openmc/error.h
View file

@ -10,7 +10,7 @@
#include "openmc/capi.h"
#include "openmc/settings.h"
#ifdef __GNUC__
#if defined(__GNUC__) || defined(__clang__)
#define UNREACHABLE() __builtin_unreachable()
#else
#define UNREACHABLE() (void)0

4
openmc/data/data.py
View file

@ -402,7 +402,9 @@ def gnd_name(Z, A, m=0):
def isotopes(element):
"""Return naturally-occurring isotopes and their abundances
"""Return naturally occurring isotopes and their abundances
.. versionadded:: 0.12.1
Parameters
----------

2
openmc/data/multipole.py
View file

@ -1018,6 +1018,8 @@ class WindowedMultipole(EqualityMixin):
def from_endf(cls, endf_file, log=False, vf_options=None, wmp_options=None):
"""Generate windowed multipole neutron data from an ENDF evaluation.
.. versionadded:: 0.12.1
Parameters
----------
endf_file : str

2
openmc/deplete/chain.py
View file

@ -326,6 +326,8 @@ class Chain:
`["(n,2n)", "(n,gamma)"]`. Note that fission is always included if
it is present. A complete listing of transmutation reactions can be
found in :data:`openmc.deplete.chain.REACTIONS`.
.. versionadded:: 0.12.1
progress : bool, optional
Flag to print status messages during processing. Does not
effect warning messages

2
openmc/deplete/results_list.py
View file

@ -302,6 +302,8 @@ class ResultsList(list):
def export_to_materials(self, burnup_index, nuc_with_data=None) -> Materials:
"""Return openmc.Materials object based on results at a given step
.. versionadded:: 0.12.1
Parameters
----------
burn_index : int

6
openmc/mgxs/mgxs.py
View file

@ -3020,6 +3020,8 @@ class DiffusionCoefficient(TransportXS):
To incorporate the effect of scattering multiplication in the above
relation, the `nu` parameter can be set to `True`.
.. versionadded:: 0.12.1
Parameters
----------
domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.RegularMesh
@ -6610,6 +6612,8 @@ class MeshSurfaceMGXS(MGXS):
.. note:: Users should instantiate the subclasses of this abstract class.
.. versionadded:: 0.12.1
Parameters
----------
domain : openmc.RegularMesh
@ -6992,6 +6996,8 @@ class Current(MeshSurfaceMGXS):
\frac{\int_{r \in S} dS \int_{E_g}^{E_{g-1}} dE \;
J(r, E)}{\int_{r \in S} dS \int_{E_g}^{E_{g-1}} dE}.
.. versionadded:: 0.12.1
Parameters
----------
domain : openmc.RegularMesh

4
openmc/source.py
View file

@ -298,6 +298,10 @@ def write_source_file(source_particles, filename, **kwargs):
**kwargs
Keyword arguments to pass to :class:`h5py.File`
See Also
--------
openmc.SourceParticle
"""
# Create compound datatype for source particles
pos_dtype = np.dtype([('x', '<f8'), ('y', '<f8'), ('z', '<f8')])

2
src/tallies/trigger.cpp
View file

@ -87,6 +87,8 @@ check_tally_triggers(double& ratio, int& tally_id, int& score)
case TriggerMetric::relative_error:
uncertainty = rel_err;
break;
case TriggerMetric::not_active:
UNREACHABLE();
}
// Compute the uncertainty / threshold ratio.