diff --git a/.travis.yml b/.travis.yml index 096459b00..859fb5d04 100644 --- a/.travis.yml +++ b/.travis.yml @@ -28,6 +28,7 @@ env: - LD_LIBRARY_PATH=$HOME/MOAB/lib:$HOME/DAGMC/lib - PATH=$PATH:$HOME/NJOY2016/build - COVERALLS_PARALLEL=true + - NUMPY_EXPERIMENTAL_ARRAY_FUNCTION=0 matrix: include: - python: "3.5" diff --git a/docs/source/devguide/tests.rst b/docs/source/devguide/tests.rst index 475d0f69b..b26f5c762 100644 --- a/docs/source/devguide/tests.rst +++ b/docs/source/devguide/tests.rst @@ -4,9 +4,6 @@ Test Suite ========== -Running Tests -------------- - The OpenMC test suite consists of two parts, a regression test suite and a unit test suite. The regression test suite is based on regression or integrated testing where different types of input files are configured and the full OpenMC @@ -14,27 +11,35 @@ code is executed. Results from simulations are compared with expected results. The unit tests are primarily intended to test individual functions/classes in the OpenMC Python API. -The test suite relies on the third-party `pytest `_ -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 -module is importable. In development where it would be onerous to continually -install OpenMC every time a small change is made, it is recommended to install -OpenMC in development/editable mode. With setuptools, this is accomplished by -running:: +Prerequisites +------------- - python setup.py develop +- The test suite relies on the third-party `pytest `_ + 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 + module is importable. In development where it would be onerous to continually + install OpenMC every time a small change is made, it is recommended to install + OpenMC in development/editable mode. With setuptools, this is accomplished by + running:: -or using pip (recommended):: + python setup.py develop - pip install -e .[test] + or using pip (recommended):: -It is also assumed that you have cross section data available that is pointed to -by the :envvar:`OPENMC_CROSS_SECTIONS` environment variables. Furthermore, to -run unit tests for the :mod:`openmc.data` module, it is necessary to have -ENDF/B-VII.1 data available and pointed to by the :envvar:`OPENMC_ENDF_DATA` -environment variable. All data sources can be obtained using the -``tools/ci/travis-before-script.sh`` script. + pip install -e .[test] + +- The test suite requires a specific set of cross section data in order for + tests to pass. A download URL for the data that OpenMC expects can be found + within ``tools/ci/download-xs.sh``. +- In addition to the HDF5 data, some tests rely on ENDF files. A download URL + for those can also be found in ``tools/ci/download-xs.sh``. +- Some tests require `NJOY `_ to preprocess + cross section data. The test suite assumes that you have an ``njoy`` + executable available on your :envvar:`PATH`. + +Running Tests +------------- To execute the test suite, go to the ``tests/`` directory and run:: @@ -46,6 +51,17 @@ installed and run:: pytest --cov=../openmc --cov-report=html +Generating XML Inputs +--------------------- + +Many of the regression tests rely on the Python API to build an appropriate +model. However, it can sometimes be desirable to work directly with the XML +input files rather than having to run a script in order to run the problem/test. +To build the input files for a test without actually running the test, you can +run:: + + pytest --build-inputs + Adding Tests to the Regression Suite ------------------------------------ diff --git a/docs/source/io_formats/cross_sections.rst b/docs/source/io_formats/cross_sections.rst index 60f1d4f50..9f0759a3a 100644 --- a/docs/source/io_formats/cross_sections.rst +++ b/docs/source/io_formats/cross_sections.rst @@ -51,3 +51,20 @@ attributes: :type: The type of data contained in the file. Accepted values are 'neutron', 'thermal', 'photon', and 'wmp'. + +.. _depletion_element: + +----------------------------- +```` Element +----------------------------- + +The ```` element indicates the location of the depletion chain file. +This file contains information describing how nuclides decay and transmute to other +nuclides through the depletion process. This element has a single attribute, ``path``, +pointing to the location of the chain file. + +.. code-block:: xml + + + +The structure of the depletion chain file is explained in :ref:`io_depletion_chain`. diff --git a/docs/source/io_formats/depletion_results.rst b/docs/source/io_formats/depletion_results.rst index 3c782b1d9..de28b0477 100644 --- a/docs/source/io_formats/depletion_results.rst +++ b/docs/source/io_formats/depletion_results.rst @@ -44,3 +44,10 @@ The current version of the depletion results file format is 1.0. **/reactions//** :Attributes: - **index** (*int*) -- Index user in results for this reaction + +.. note:: + + The reaction rates for some isotopes not originally present may + be non-zero, but should be negligible compared to other atoms. + This can be controlled by changing the + :class:`openmc.deplete.Operator` ``dilute_initial`` attribute. diff --git a/docs/source/io_formats/materials.rst b/docs/source/io_formats/materials.rst index cd198d006..ac2cfe271 100644 --- a/docs/source/io_formats/materials.rst +++ b/docs/source/io_formats/materials.rst @@ -42,8 +42,7 @@ Each ``material`` element can have the following attributes or sub-elements: Volume of the material in cm^3. :temperature: - An element with no attributes which is used to set the default temperature - of the material in Kelvin. + Temperature of the material in Kelvin. *Default*: If a material default temperature is not given and a cell temperature is not specified, the :ref:`global default temperature diff --git a/docs/source/io_formats/source.rst b/docs/source/io_formats/source.rst index cff77d2fa..6058241e1 100644 --- a/docs/source/io_formats/source.rst +++ b/docs/source/io_formats/source.rst @@ -13,8 +13,9 @@ is that documented here. :Attributes: - **filetype** (*char[]*) -- String indicating the type of file. :Datasets: + - **source_bank** (Compound type) -- Source bank information for each particle. The compound type has fields ``wgt``, ``xyz``, ``uvw``, - ``E``, and ``delayed_group``, which represent the weight, position, - direction, energy, energy group, and delayed_group of the source - particle, respectively. + ``E``, ``delayed_group``, and ``particle``, which represent the + weight, position, direction, energy, energy group, delayed group, + and type of the source particle, respectively. diff --git a/docs/source/io_formats/summary.rst b/docs/source/io_formats/summary.rst index ee0d85fe3..cf0eca4aa 100644 --- a/docs/source/io_formats/summary.rst +++ b/docs/source/io_formats/summary.rst @@ -116,6 +116,13 @@ The current version of the summary file format is 6.0. - **sab_names** (*char[][]*) -- Names of S(:math:`\alpha,\beta`) tables assigned to the material. +:Attributes: - **volume** (*double[]*) -- Volume of this material [cm^3]. Only + present if ``volume`` supplied + - **temperature** (*double[]*) -- Temperature of this material [K]. + Only present in ``temperature`` supplied + - **depletable** (*int[]*) -- ``1`` if the material can be depleted, + ``0`` otherwise. Always present + **/nuclides/** :Attributes: - **n_nuclides** (*int*) -- Number of nuclides in the problem. diff --git a/docs/source/pythonapi/deplete.rst b/docs/source/pythonapi/deplete.rst index aa1288411..6e1afc542 100644 --- a/docs/source/pythonapi/deplete.rst +++ b/docs/source/pythonapi/deplete.rst @@ -75,10 +75,24 @@ data, such as number densities and reaction rates for each material. :template: myclass.rst AtomNumber + ChainFissionHelper + DirectReactionRateHelper OperatorResult ReactionRates Results ResultsList + + +The following classes are abstract classes that can be used to extend the +:mod:`openmc.deplete` capabilities: + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: myclass.rst + + ReactionRateHelper + EnergyHelper TransportOperator Each of the integrator functions also relies on a number of "helper" functions diff --git a/docs/source/pythonapi/model.rst b/docs/source/pythonapi/model.rst index ee038987b..1091d7cae 100644 --- a/docs/source/pythonapi/model.rst +++ b/docs/source/pythonapi/model.rst @@ -15,6 +15,7 @@ Convenience Functions openmc.model.hexagonal_prism openmc.model.rectangular_prism openmc.model.subdivide + openmc.model.pin TRISO Fuel Modeling ------------------- diff --git a/examples/jupyter/cad-based-geometry.ipynb b/examples/jupyter/cad-based-geometry.ipynb index 6ff79d27c..b13c010cb 100644 --- a/examples/jupyter/cad-based-geometry.ipynb +++ b/examples/jupyter/cad-based-geometry.ipynb @@ -236,6 +236,13 @@ "tallies.export_to_xml()" ] }, + { + "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." + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/examples/jupyter/candu.ipynb b/examples/jupyter/candu.ipynb index 8f10b13b3..672d56f89 100644 --- a/examples/jupyter/candu.ipynb +++ b/examples/jupyter/candu.ipynb @@ -10,9 +10,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -31,9 +29,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "fuel = openmc.Material(name='fuel')\n", @@ -56,15 +52,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "With out materials created, we'll now define key dimensions in our model. These dimensions are taken from the example in section 11.1.3 of the [Serpent manual](http://montecarlo.vtt.fi/download/Serpent_manual.pdf)." + "With our materials created, we'll now define key dimensions in our model. These dimensions are taken from the example in section 11.1.3 of the [Serpent manual](http://montecarlo.vtt.fi/download/Serpent_manual.pdf)." ] }, { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Outer radius of fuel and clad\n", @@ -91,13 +85,11 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# These are the surfaces that will divide each of the rings\n", - "radial_surf = [openmc.ZCylinder(R=r) for r in\n", + "radial_surf = [openmc.ZCylinder(r=r) for r in\n", " (ring_radii[:-1] + ring_radii[1:])/2]\n", "\n", "water_cells = []\n", @@ -123,18 +115,28 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, + "execution_count": 5, "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ksYj4dNudRsQZ25+T9IikGUn3RcSzbfe7xJWSPiHpGdtPjZ+7MyIezjyOEuyWtG8cyMck3ZKiUa4YBCpX+nIAQMsIAaByhABQOUIAqBwhAFSOEAAqRwgAlSMEgMr9P9Ske2C/wpRUAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -154,12 +156,10 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "surf_fuel = openmc.ZCylinder(R=r_fuel)\n", + "surf_fuel = openmc.ZCylinder(r=r_fuel)\n", "\n", "fuel_cell = openmc.Cell(fill=fuel, region=-surf_fuel)\n", "clad_cell = openmc.Cell(fill=clad, region=+surf_fuel)\n", @@ -170,18 +170,28 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, + "execution_count": 7, "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -199,9 +209,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "num_pins = [1, 6, 12, 18]\n", @@ -214,7 +222,7 @@ " x = r*cos(theta)\n", " y = r*sin(theta)\n", " \n", - " pin_boundary = openmc.ZCylinder(x0=x, y0=y, R=r_clad)\n", + " pin_boundary = openmc.ZCylinder(x0=x, y0=y, r=r_clad)\n", " water_cells[i].region &= +pin_boundary\n", " \n", " # Create each fuel pin -- note that we explicitly assign an ID so \n", @@ -228,18 +236,28 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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Ah7i4FnpfVF3Ii6mP9yhihkxElCBmyJHwcSGvTN48ZdeZclXtNkTNOjuesjpy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\n", "text/plain": [ - "" + "" ] }, + "execution_count": 9, "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -257,15 +275,13 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ - "pt_inner = openmc.ZCylinder(R=pressure_tube_ir)\n", - "pt_outer = openmc.ZCylinder(R=pressure_tube_or)\n", - "calendria_inner = openmc.ZCylinder(R=calendria_ir)\n", - "calendria_outer = openmc.ZCylinder(R=calendria_or, boundary_type='vacuum')\n", + "pt_inner = openmc.ZCylinder(r=pressure_tube_ir)\n", + "pt_outer = openmc.ZCylinder(r=pressure_tube_or)\n", + "calendria_inner = openmc.ZCylinder(r=calendria_ir)\n", + "calendria_outer = openmc.ZCylinder(r=calendria_or, boundary_type='vacuum')\n", "\n", "bundle = openmc.Cell(fill=bundle_universe, region=-pt_inner)\n", "pressure_tube = openmc.Cell(fill=clad, region=+pt_inner & -pt_outer)\n", @@ -285,9 +301,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "geom = openmc.Geometry(root_universe)\n", @@ -300,19 +314,18 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ "" ] }, + "execution_count": 12, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -323,7 +336,7 @@ " clad: 'silver',\n", " heavy_water: 'blue'\n", "}\n", - "openmc.plot_inline(p)" + "p.to_ipython_image()" ] }, { @@ -338,9 +351,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "settings = openmc.Settings()\n", @@ -354,9 +365,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "fuel_tally = openmc.Tally()\n", @@ -370,21 +379,8 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], "source": [ "openmc.run(output=False)" ] @@ -399,9 +395,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "sp = openmc.StatePoint('statepoint.{}.h5'.format(settings.batches))" @@ -410,14 +404,25 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -463,12 +468,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -477,12 +482,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -491,12 +496,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -505,12 +510,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -519,12 +524,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -533,12 +538,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -547,12 +552,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -561,12 +566,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -575,12 +580,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -589,12 +594,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -603,12 +608,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -617,12 +622,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -631,12 +636,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -645,12 +650,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -659,12 +664,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -673,12 +678,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -687,12 +692,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -701,12 +706,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -715,12 +720,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -729,12 +734,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -743,12 +748,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -757,12 +762,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -771,12 +776,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -785,12 +790,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -799,12 +804,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -813,12 +818,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -827,12 +832,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -841,12 +846,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -855,12 +860,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -869,12 +874,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -883,12 +888,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -897,12 +902,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -911,12 +916,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -925,12 +930,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -939,12 +944,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -953,12 +958,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -967,12 +972,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -984,87 +989,87 @@ "" ], "text/plain": [ - " level 1 level 2 level 3 distribcell nuclide score \\\n", - " univ cell univ cell univ cell \n", - " id id id id id id \n", - "0 10002 10043 10000 100 10001 10004 0 total flux \n", - "1 10002 10043 10000 200 10001 10004 1 total flux \n", - "2 10002 10043 10000 201 10001 10004 2 total flux \n", - "3 10002 10043 10000 202 10001 10004 3 total flux \n", - "4 10002 10043 10000 203 10001 10004 4 total flux \n", - "5 10002 10043 10000 204 10001 10004 5 total flux \n", - "6 10002 10043 10000 205 10001 10004 6 total flux \n", - "7 10002 10043 10000 300 10001 10004 7 total flux \n", - "8 10002 10043 10000 301 10001 10004 8 total flux \n", - "9 10002 10043 10000 302 10001 10004 9 total flux \n", - "10 10002 10043 10000 303 10001 10004 10 total flux \n", - "11 10002 10043 10000 304 10001 10004 11 total flux \n", - "12 10002 10043 10000 305 10001 10004 12 total flux \n", - "13 10002 10043 10000 306 10001 10004 13 total flux \n", - "14 10002 10043 10000 307 10001 10004 14 total flux \n", - "15 10002 10043 10000 308 10001 10004 15 total flux \n", - "16 10002 10043 10000 309 10001 10004 16 total flux \n", - "17 10002 10043 10000 310 10001 10004 17 total flux \n", - "18 10002 10043 10000 311 10001 10004 18 total flux \n", - "19 10002 10043 10000 400 10001 10004 19 total flux \n", - "20 10002 10043 10000 401 10001 10004 20 total flux \n", - "21 10002 10043 10000 402 10001 10004 21 total flux \n", - "22 10002 10043 10000 403 10001 10004 22 total flux \n", - "23 10002 10043 10000 404 10001 10004 23 total flux \n", - "24 10002 10043 10000 405 10001 10004 24 total flux \n", - "25 10002 10043 10000 406 10001 10004 25 total flux \n", - "26 10002 10043 10000 407 10001 10004 26 total flux \n", - "27 10002 10043 10000 408 10001 10004 27 total flux \n", - "28 10002 10043 10000 409 10001 10004 28 total flux \n", - "29 10002 10043 10000 410 10001 10004 29 total flux \n", - "30 10002 10043 10000 411 10001 10004 30 total flux \n", - "31 10002 10043 10000 412 10001 10004 31 total flux \n", - "32 10002 10043 10000 413 10001 10004 32 total flux \n", - "33 10002 10043 10000 414 10001 10004 33 total flux \n", - "34 10002 10043 10000 415 10001 10004 34 total flux \n", - "35 10002 10043 10000 416 10001 10004 35 total flux \n", - "36 10002 10043 10000 417 10001 10004 36 total flux \n", + " level 1 level 2 level 3 distribcell nuclide score mean \\\n", + " univ cell univ cell univ cell \n", + " id id id id id id \n", + "0 3 44 1 100 2 5 0 total flux 2.08e-01 \n", + "1 3 44 1 200 2 5 1 total flux 1.97e-01 \n", + "2 3 44 1 201 2 5 2 total flux 1.90e-01 \n", + "3 3 44 1 202 2 5 3 total flux 1.95e-01 \n", + "4 3 44 1 203 2 5 4 total flux 1.91e-01 \n", + "5 3 44 1 204 2 5 5 total flux 1.90e-01 \n", + "6 3 44 1 205 2 5 6 total flux 1.82e-01 \n", + "7 3 44 1 300 2 5 7 total flux 1.66e-01 \n", + "8 3 44 1 301 2 5 8 total flux 1.69e-01 \n", + "9 3 44 1 302 2 5 9 total flux 1.60e-01 \n", + "10 3 44 1 303 2 5 10 total flux 1.59e-01 \n", + "11 3 44 1 304 2 5 11 total flux 1.49e-01 \n", + "12 3 44 1 305 2 5 12 total flux 1.51e-01 \n", + "13 3 44 1 306 2 5 13 total flux 1.54e-01 \n", + "14 3 44 1 307 2 5 14 total flux 1.66e-01 \n", + "15 3 44 1 308 2 5 15 total flux 1.57e-01 \n", + "16 3 44 1 309 2 5 16 total flux 1.65e-01 \n", + "17 3 44 1 310 2 5 17 total flux 1.57e-01 \n", + "18 3 44 1 311 2 5 18 total flux 1.60e-01 \n", + "19 3 44 1 400 2 5 19 total flux 9.66e-02 \n", + "20 3 44 1 401 2 5 20 total flux 1.18e-01 \n", + "21 3 44 1 402 2 5 21 total flux 1.06e-01 \n", + "22 3 44 1 403 2 5 22 total flux 1.11e-01 \n", + "23 3 44 1 404 2 5 23 total flux 1.12e-01 \n", + "24 3 44 1 405 2 5 24 total flux 1.10e-01 \n", + "25 3 44 1 406 2 5 25 total flux 1.00e-01 \n", + "26 3 44 1 407 2 5 26 total flux 9.54e-02 \n", + "27 3 44 1 408 2 5 27 total flux 9.26e-02 \n", + "28 3 44 1 409 2 5 28 total flux 9.55e-02 \n", + "29 3 44 1 410 2 5 29 total flux 1.14e-01 \n", + "30 3 44 1 411 2 5 30 total flux 1.08e-01 \n", + "31 3 44 1 412 2 5 31 total flux 1.07e-01 \n", + "32 3 44 1 413 2 5 32 total flux 1.12e-01 \n", + "33 3 44 1 414 2 5 33 total flux 1.15e-01 \n", + "34 3 44 1 415 2 5 34 total flux 1.14e-01 \n", + "35 3 44 1 416 2 5 35 total flux 1.14e-01 \n", + "36 3 44 1 417 2 5 36 total flux 1.11e-01 \n", "\n", - " mean std. dev. \n", - " \n", - " \n", - "0 2.08e-01 7.04e-03 \n", - "1 1.97e-01 5.27e-03 \n", - "2 1.90e-01 7.82e-03 \n", - "3 1.95e-01 6.47e-03 \n", - "4 1.91e-01 6.43e-03 \n", - "5 1.90e-01 4.89e-03 \n", - "6 1.82e-01 3.85e-03 \n", - "7 1.66e-01 5.82e-03 \n", - "8 1.69e-01 8.30e-03 \n", - "9 1.60e-01 3.09e-03 \n", - "10 1.59e-01 5.91e-03 \n", - "11 1.49e-01 5.31e-03 \n", - "12 1.51e-01 6.65e-03 \n", - "13 1.54e-01 3.67e-03 \n", - "14 1.66e-01 4.73e-03 \n", - "15 1.57e-01 6.54e-03 \n", - "16 1.65e-01 5.94e-03 \n", - "17 1.57e-01 5.73e-03 \n", - "18 1.60e-01 4.58e-03 \n", - "19 9.66e-02 4.47e-03 \n", - "20 1.18e-01 5.45e-03 \n", - "21 1.06e-01 4.72e-03 \n", - "22 1.11e-01 4.21e-03 \n", - "23 1.12e-01 5.08e-03 \n", - "24 1.10e-01 4.15e-03 \n", - "25 1.00e-01 5.08e-03 \n", - "26 9.54e-02 3.62e-03 \n", - "27 9.26e-02 4.00e-03 \n", - "28 9.55e-02 4.02e-03 \n", - "29 1.14e-01 9.53e-03 \n", - "30 1.08e-01 7.24e-03 \n", - "31 1.07e-01 5.72e-03 \n", - "32 1.12e-01 5.00e-03 \n", - "33 1.15e-01 6.24e-03 \n", - "34 1.14e-01 4.92e-03 \n", - "35 1.14e-01 5.32e-03 \n", - "36 1.11e-01 5.05e-03 " + " std. dev. \n", + " \n", + " \n", + "0 7.04e-03 \n", + "1 5.27e-03 \n", + "2 7.82e-03 \n", + "3 6.47e-03 \n", + "4 6.43e-03 \n", + "5 4.89e-03 \n", + "6 3.85e-03 \n", + "7 5.82e-03 \n", + "8 8.30e-03 \n", + "9 3.09e-03 \n", + "10 5.91e-03 \n", + "11 5.31e-03 \n", + "12 6.65e-03 \n", + "13 3.67e-03 \n", + "14 4.73e-03 \n", + "15 6.54e-03 \n", + "16 5.94e-03 \n", + "17 5.73e-03 \n", + "18 4.58e-03 \n", + "19 4.47e-03 \n", + "20 5.45e-03 \n", + "21 4.72e-03 \n", + "22 4.21e-03 \n", + "23 5.08e-03 \n", + "24 4.15e-03 \n", + "25 5.08e-03 \n", + "26 3.62e-03 \n", + "27 4.00e-03 \n", + "28 4.02e-03 \n", + "29 9.53e-03 \n", + "30 7.24e-03 \n", + "31 5.72e-03 \n", + "32 5.00e-03 \n", + "33 6.24e-03 \n", + "34 4.92e-03 \n", + "35 5.32e-03 \n", + "36 5.05e-03 " ] }, "execution_count": 17, @@ -1102,7 +1107,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/examples/jupyter/mdgxs-part-i.ipynb b/examples/jupyter/mdgxs-part-i.ipynb index 4ca079b31..239b0f2df 100644 --- a/examples/jupyter/mdgxs-part-i.ipynb +++ b/examples/jupyter/mdgxs-part-i.ipynb @@ -27,13 +27,11 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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wsGA3b95U+7jKepkRQkh1GqUEJpfLERERgYMHD+LSpUuIjY1VeMYEKG1gjY6Oxrvvvqv0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\nCJ0VSenq6sKmTZuwZ88eobMiKQUFBTh8+LDQ2ZCEQ4cOISo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\n", "text/plain": [ "" ] @@ -90,7 +88,7 @@ "\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://mit-crpg.github.io/openmc/pythonapi/filter.html) 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](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." ] }, { @@ -121,9 +119,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -138,50 +134,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material." + "First we need to define materials that will be used in the problem. Let's create a material for the homogeneous medium." ] }, { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H1')\n", - "o16 = openmc.Nuclide('O16')\n", - "u235 = openmc.Nuclide('U235')\n", - "u238 = openmc.Nuclide('U238')\n", - "pu239 = openmc.Nuclide('Pu239')\n", - "zr90 = openmc.Nuclide('Zr90')" - ] - }, - { - "cell_type": "markdown", "metadata": {}, - "source": [ - "With the nuclides we defined, we will now create a material for the homogeneous medium." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, "outputs": [], "source": [ "# Instantiate a Material and register the Nuclides\n", "inf_medium = openmc.Material(name='moderator')\n", "inf_medium.set_density('g/cc', 5.)\n", - "inf_medium.add_nuclide(h1, 0.03)\n", - "inf_medium.add_nuclide(o16, 0.015)\n", - "inf_medium.add_nuclide(u235 , 0.0001)\n", - "inf_medium.add_nuclide(u238 , 0.007)\n", - "inf_medium.add_nuclide(pu239, 0.00003)\n", - "inf_medium.add_nuclide(zr90, 0.002)" + "inf_medium.add_nuclide('H1', 0.03)\n", + "inf_medium.add_nuclide('O16', 0.015)\n", + "inf_medium.add_nuclide('U235', 0.0001)\n", + "inf_medium.add_nuclide('U238', 0.007)\n", + "inf_medium.add_nuclide('Pu239', 0.00003)\n", + "inf_medium.add_nuclide('Zr90', 0.002)" ] }, { @@ -193,15 +163,12 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection and export to XML\n", "materials_file = openmc.Materials([inf_medium])\n", - "materials_file.default_xs = '71c'\n", "materials_file.export_to_xml()" ] }, @@ -214,10 +181,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "# Instantiate boundary Planes\n", @@ -236,10 +201,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Cell\n", @@ -256,40 +219,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "OpenMC requires that there is a \"root\" universe. Let us create a root universe and add our square cell to it." + "We now must create a geometry and export it to XML." ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Instantiate Universe\n", - "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", - "root_universe.add_cell(cell)" - ] - }, - { - "cell_type": "markdown", + "execution_count": 7, "metadata": {}, - "source": [ - "We now must create a geometry that is assigned a root universe and export it to XML." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", - "openmc_geometry = openmc.Geometry()\n", - "openmc_geometry.root_universe = root_universe\n", + "openmc_geometry = openmc.Geometry([cell])\n", "\n", "# Export to \"geometry.xml\"\n", "openmc_geometry.export_to_xml()" @@ -304,10 +244,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "# OpenMC simulation parameters\n", @@ -340,10 +278,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a 100-group EnergyGroups object\n", @@ -391,10 +327,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a few different sections\n", @@ -422,22 +356,20 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('delayed-nu-fission', Tally\n", - " \tID =\t10000\n", + " \tID =\t1\n", " \tName =\t\n", " \tFilters =\tCellFilter, DelayedGroupFilter, EnergyFilter\n", " \tNuclides =\tU235 Pu239 \n", " \tScores =\t['delayed-nu-fission']\n", " \tEstimator =\ttracklength), ('decay-rate', Tally\n", - " \tID =\t10001\n", + " \tID =\t2\n", " \tName =\t\n", " \tFilters =\tCellFilter, DelayedGroupFilter, EnergyFilter\n", " \tNuclides =\tU235 Pu239 \n", @@ -445,7 +377,7 @@ " \tEstimator =\ttracklength)])" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -463,11 +395,26 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=6.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=5.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=8.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=14.\n", + " warn(msg, IDWarning)\n" + ] + } + ], "source": [ "# Instantiate an empty Tallies object\n", "tallies_file = openmc.Tallies()\n", @@ -503,84 +450,75 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | 5e313cf5f1d601074ad95c17ae589bf564972adb\n", - " Date/Time | 2017-02-26 06:05:10\n", - " OpenMP Threads | 8\n", - "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", + " Date/Time | 2019-07-19 06:56:34\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", " Reading cross sections XML file...\n", - " Reading H1 from /opt/xsdata/nndc/H1.h5\n", - " Reading O16 from /opt/xsdata/nndc/O16.h5\n", - " Reading U235 from /opt/xsdata/nndc/U235.h5\n", - " Reading U238 from /opt/xsdata/nndc/U238.h5\n", - " Reading Pu239 from /opt/xsdata/nndc/Pu239.h5\n", - " Reading Zr90 from /opt/xsdata/nndc/Zr90.h5\n", - " Maximum neutron transport energy: 2.00000E+07 eV for H1\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", + " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", + " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", + " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", + " Reading Pu239 from /opt/data/hdf5/nndc_hdf5_v15/Pu239.h5\n", + " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for H1\n", " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", - " ===========================================================================\n", " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.21670 \n", - " 2/1 1.24155 \n", - " 3/1 1.21924 \n", - " 4/1 1.22486 \n", - " 5/1 1.21719 \n", - " 6/1 1.24330 \n", - " 7/1 1.22322 \n", - " 8/1 1.24133 \n", - " 9/1 1.21840 \n", - " 10/1 1.25141 \n", - " 11/1 1.21217 \n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.21670\n", + " 2/1 1.24155\n", + " 3/1 1.21924\n", + " 4/1 1.22486\n", + " 5/1 1.21719\n", + " 6/1 1.24330\n", + " 7/1 1.22322\n", + " 8/1 1.24133\n", + " 9/1 1.21840\n", + " 10/1 1.25141\n", + " 11/1 1.21217\n", " 12/1 1.25625 1.23421 +/- 0.02204\n", " 13/1 1.22056 1.22966 +/- 0.01351\n", " 14/1 1.21757 1.22664 +/- 0.01002\n", @@ -622,47 +560,32 @@ " 50/1 1.24724 1.23260 +/- 0.00345\n", " Creating state point statepoint.50.h5...\n", "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.8846E-01 seconds\n", - " Reading cross sections = 3.0221E-01 seconds\n", - " Total time in simulation = 1.2666E+01 seconds\n", - " Time in transport only = 1.2196E+01 seconds\n", - " Time in inactive batches = 5.1652E-01 seconds\n", - " Time in active batches = 1.2150E+01 seconds\n", - " Time synchronizing fission bank = 5.1914E-03 seconds\n", - " Sampling source sites = 3.6297E-03 seconds\n", - " SEND/RECV source sites = 1.5222E-03 seconds\n", - " Time accumulating tallies = 5.2027E-04 seconds\n", - " Total time for finalization = 4.8293E-02 seconds\n", - " Total time elapsed = 1.3117E+01 seconds\n", - " Calculation Rate (inactive) = 96801.2 neutrons/second\n", - " Calculation Rate (active) = 16461.5 neutrons/second\n", + " Total time for initialization = 4.7388e-01 seconds\n", + " Reading cross sections = 4.4709e-01 seconds\n", + " Total time in simulation = 3.9290e+01 seconds\n", + " Time in transport only = 3.9005e+01 seconds\n", + " Time in inactive batches = 1.4079e+00 seconds\n", + " Time in active batches = 3.7882e+01 seconds\n", + " Time synchronizing fission bank = 1.8814e-02 seconds\n", + " Sampling source sites = 1.6376e-02 seconds\n", + " SEND/RECV source sites = 2.3626e-03 seconds\n", + " Time accumulating tallies = 8.3299e-04 seconds\n", + " Total time for finalization = 1.1533e-02 seconds\n", + " Total time elapsed = 3.9783e+01 seconds\n", + " Calculation Rate (inactive) = 35514.2 particles/second\n", + " Calculation Rate (active) = 5279.54 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.23256 +/- 0.00308\n", - " k-effective (Track-length) = 1.23260 +/- 0.00345\n", - " k-effective (Absorption) = 1.23111 +/- 0.00186\n", - " Combined k-effective = 1.23135 +/- 0.00184\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.23256 +/- 0.00308\n", + " k-effective (Track-length) = 1.23260 +/- 0.00345\n", + " k-effective (Absorption) = 1.23111 +/- 0.00186\n", + " Combined k-effective = 1.23135 +/- 0.00184\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -686,10 +609,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "# Load the last statepoint file\n", @@ -712,10 +633,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "# Load the tallies from the statepoint into each MGXS object\n", @@ -750,28 +669,26 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[[ 5.14223507e-06, 1.16426087e-06]],\n", + "array([[[5.14223507e-06, 1.16426087e-06]],\n", "\n", - " [[ 2.65426350e-05, 7.58220468e-06]],\n", + " [[2.65426350e-05, 7.58220468e-06]],\n", "\n", - " [[ 2.53399053e-05, 5.73796202e-06]],\n", + " [[2.53399053e-05, 5.73796202e-06]],\n", "\n", - " [[ 5.68141581e-05, 1.04757933e-05]],\n", + " [[5.68141581e-05, 1.04757933e-05]],\n", "\n", - " [[ 2.32930026e-05, 5.45658817e-06]],\n", + " [[2.32930026e-05, 5.45658817e-06]],\n", "\n", - " [[ 9.75735783e-06, 1.65150949e-06]]])" + " [[9.75735783e-06, 1.65150949e-06]]])" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -789,9 +706,8 @@ }, { "cell_type": "code", - 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\n", + "\n", "\n", " \n", " \n", @@ -1082,7 +1022,7 @@ "11 1 6 1 Pu239 2.729700 0.010858" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1101,10 +1041,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "beta.export_xs_data(filename='beta', format='excel')" @@ -1119,20 +1057,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" - ] - } - ], + "execution_count": 20, + "metadata": {}, + "outputs": [], "source": [ "chi_prompt.build_hdf5_store(filename='mdgxs', append=True)\n", "chi_delayed.build_hdf5_store(filename='mdgxs', append=True)" @@ -1160,29 +1087,29 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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trXiWGahyBYsB/AvArgObHrJDe3EqNB+AipNeuJY8TvBBMFZCSg8o94AyTwjIA7MrFPhB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\ncig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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1222,15 +1149,26 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -1386,7 +1324,7 @@ "11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.48e-13 " ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1410,10 +1348,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1429,18 +1365,20 @@ "(0, 7)" ] }, - "execution_count": 25, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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07IoyzcxsTe0mmIi4FLhU0ikRcVkHy24BhpU8HgrMa2OfFkm9gYFkzV95ji33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\nGLP3OMGYta115u3PAMekqW0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+ "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1481,10 +1419,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { @@ -1492,18 +1428,20 @@ "(1000.0, 20000000.0)" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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q3njjDfr06RNZUBeE9oQtv/766ykuLua1117j/vvvjyiX9mBhy41086v9lka2\ndFCxoiKyGR0jSLiPv4vIkWmXpBtwzDHHsG7dulZJkebOnYsT5xWxPWHL33zzTSZOnAjAwQcfTF1d\nHR988EGrti1suZENXDO1LLIZ2U0QZXEc8KKI/EtEXhOR10UkUA7ubMOf1GVMVbThq6iyKFJWtaol\nRnn129VRiWD8rGoIHrRm586dPProo4waNapdsgcNW3744Yfzxz/+EXDjRa1fv576+vpW7VnY8txg\nzoQ5kS0eRUUtWzyyPTZUuun1UUlkMzpGEAP3Se1tXEROBG4FegJ3q+qNMeXHArcAo4EpqrrEV/Z9\n4Ffe4bWqurC9cmSSbdu2RWI9HXPMMVxwwQVtnn5JFrb8uuuu44YbbmDevHlUVFQwe/ZsLrnkEoqL\nixk1ahRHHHEEvXq1/ppjw4n7o9zGhi0PK5+pU6dyxRVXRMrihS0HImHLi4uLk97HSE2qtQ0bk4eG\nyvrYUOfd0vJi9sClM5LUbB87b/e90M3r9Oa7FUGUxbWqOtV/QkQWAVMT1A/X6QncDhwP1AMrRWSp\nqr7pq/YfYBpwecy1X8P1ih4LKLDKuzY63ncOELZZ+OnVqxfNzc2R4y+++AJwDdplXtb5mTNnMnPm\nzMBhy0855RQqKirYddddIwEAVZXhw4czfPjwlHJa2HIjEzz4yUWR/QfofGVhdB5BlMWh/gNPCQRx\nXj4KNxXru951i4FTgYiyUNU6r6w55tpvA0+q6mav/EngROChAPdNiFPqJHxTS/QGVXZQWUL/77hR\nMAOw99578+GHH7Jp0yZCoRDLli3jxBNPjIQtD5MqbPmIESOA6LDlW7duZZdddqFPnz7cfffdHHvs\nsVGjkTDhcOJTpkwJFLZ86tSpHQpbHnsfC1tudAXz52dagvwhobIQkV8AVwL9ReRTXLdZcKPPViW6\nzsdgYIPvuB4Yl6BukGsHJ6ibc/Tu3ZurrrqKcePGMXz48MiDPpb2hC1fu3Yt3/ve9+jZsyeHHHII\n99xzT9y2LWx5blBU2WKMaM+UULbHhurxWXptIU5jS//NwLzvOkKQEOU3qGqbF+GJyJnAt1X1Qu94\nKnCUql4cp+4CYFnYZiEiPwP6quq13vGvgc9VtTLmuhngjl2HDh06Zv369VHtWmjrxORS2PLu/D2m\nWuHsn9WL91NOVd7R+2c7uS5/V9BpIcqBR0Xk2NgtwHX1wBDf8T4QWLUHulZVq1R1rKqO3XPPPQM2\nbRhGVxGZ/HolAAAgAElEQVTOhOfPduc4LVnwUmWbNLKHIDaLn/n2++HaIlYB30xx3UpghIgMB94D\npgDnBJTrceB6EdndOz4BCzHSqVjYcqMrcBxoaoK6ugwphmqf0cICCXaIlMpCVaNWy4jIEOCmANft\nFJFZuA/+nsC9qrpGRK4GalV1qbfY70/A7kCZiFSo6qGqullErsFVOABXh43dhmG0MKcND0ARd72F\n353Wcdy3f8eB8hSJ5OI5ssW2F0vTqEoodTjjjSZ4A2qn1xLxjyl1eLm0gkE3fY1huw1j1Yw0JNte\nZR5WnUXQqLN+6oFAwYa8vN3LY85d5dtfiTvFFO/ae4F72yFfbDvmrpnDpLKpdXdSGa1DIffNPhF1\ndW55EGXRLkqdSNrUMI7jbTVQsQI2b9vM9q+2p+HmRmcSJPnRb3HXOoBr4ygGXk2nUJ1Fv3792LRp\nE4MGDTKFkYOoKps2baJfv36ZFiUraM+bffjBnEhhLPSWuqZrtnDVD95mQ1MdZy4rTZiIKFna1I6y\n9C1/Jj4LKdIRgnhDfd93uBOoU9Xn0ypVOxg7dqzW1tZGnduxYwf19fWRRW9G7tGvXz/22Wcfevfu\nnWlRMkJUiBmn9W81lbJI2X4qb6oc9ybKdfm7gqDeUEFsFgtFpD8wVFXf7hTpuojevXsHWr1sGNnK\nBJ3D1q3waobG8qliQnV0HYeROwQZWZQBc4E+qjpcRIpxDc6Tu0LAoMQbWRiGkZwOr8NIcf3IK1oM\nzGtvCrKWt3OJHVmUPVTGsneWATC9ZDpVZV0vU7bRaSML3IwhRwE1AKq6WkSGdUA2wzByBH802/ZM\nd7014C7fUQYezP6pO3Od7RBBlMVOVf3EDMSG0f1IFdU212lobNGARQWWtTEZQZTFGyJyDtBTREYA\nPwFeSK9YhmEA9Lyi5QH21U2dH9soNs+F44AvtYnr+tpS2ub299o6qe1CdSLTp0cfV59dHXVsBvDg\nBFEWFwO/BL7Ejfr6OHBNOoUyDMOleUB6X+1TTi2V+jWH0+b2P7i5OnWlNFJlJolOI2VsKFX9XFV/\nqapHenGYfqmq5otqGEZO0NDQEotKJDpOlRGclMpCRA4UkSoReUJEnglvXSGcYRhdi+O4Xk3hLd+Z\n9F4tzK+l/yLzpExFkGmoPwB3AncDX6VXHMMwcolUub2z3Saw7C43TtW2DMuRCwT1hroj7ZIYhpFz\ndDTHd1dQVJR4lFRS0rWy5DJBlEW1iPwINzrsl+GTFgXWMIxcp6zS8R05CWoZEExZhGND+fNaKLBf\n54tjGEY+8av9lmZahKRUrGjx9kpH2th8IkhsKAuuZBgZYoJmdtlxR2NDXTPVIr3mCyljQ+UKFhvK\nyEUqX6jEWeHQtL11jPDCUCEN5dltFOhobKlM03tWS/q+HfO6p09tZ8aGMgwjTSRSFPnCebe0rIp7\n4NLsy1q383afgpiXOTlyAVMWhpFB8klRiLjeRf5Fbw9+clFk/wGyT1kYwUmoLEQkqVOZqr7c+eIY\nRvdizgTXJlFTAysqnKiyjYBc7u53NMlRe3FqnJb9PDQAz5+faQlyh2Qji0rvbz9gLG4qVQFGA/8A\nxqdXNMPIf8IPYKcGViSp19jYFdK0pqPeQj0+K0QVCgo6UahOxGlsCdQ4g+y2D2WahMpCVY8DEJHF\nwAxVfd07Pgy4PEjjInIicCvQE7hbVW+MKe8L3A+MATYBZ6lqnYj0xl0xXuLJeL+q3tDGz2YYeUEo\nlBtZ6OIZuNMRKbcz2diU5zHYO5GUsaGAg8OKAkBV3wCKU10kIj2B24GTgEOAs0XkkJhqFwBbVPUA\n4GbgN975M4G+qjoKV5FcZAmXjHwmNiaTf2tshPLyTEtodHeCGLjXisjdwAO4i/HOA9YGuO4oYJ2q\nvguREcqpwJu+OqfSsmxyCTBP3CxLCgwQkV5Af2A78GmAexpGTpHufBVGCqp9RgvLpJeUIMriB8AP\ngUu842eBILGiBgMbfMf1wLhEdVR1p4h8AgzCVRyn4tr4dgEus/AiRj6S7nwVmWZMVcs6hlUzsnAd\nwyrz0ApKkBXcX4jIncByVX27DW3Hy8MaO6uZqM5RuBFui4Ddgb+JyFPhUUrkYpEZ4PrjDR06tA2i\nGYbRaRQ0QPlgxLOF106vZUyRqyRe3mhOk/lCkHwWk4HVwGPecbGIBAn4Ug8M8R3vA63cDSJ1vCmn\ngcBm4BzgMVXdoaofAs/jemRFoapVXkKmsXvuuWcAkQzD6Ezeey95MqHvH+6Glgv1CXWRRG1j6VvV\nkc1ITpBpqDm4b/o1AKq6OqCxeSUwQkSGA+8BU3CVgJ+luIEKXwTOAJ5RVRWR/wDfFJEHcKehvg7c\nEuCehmF0IqliQ1VVQSPEnyMAhu02jFCfEM4Ep7NF6xQmL54c2c/GfBvZRNB8Fp+IJPhvSIBng5iF\nm7O7J3Cvqq4RkauBWlVdCtwDLBKRdbgjiine5bcD9wFv4P4b3qeqr7VJAMMwOkyq2FQVFeDOFmtc\n11mn1MnLxXzdkSDK4g0ROQfoKSIjgJ8ALwRpXFWXA8tjzl3l2/8C10029rqmeOcNw8gyyvwG4qqE\n1YzcJ4iyuBj4JW7io9/hjhSuSadQhmHkCGPu8h3koLJwfMMhc51NShBlcYqq/hJXYQAgImfi5uY2\nDKMDZDpfRSryPTaUEZyU+SxE5GVVLUl1LtNYPgvD6HykosVWGc8ALOe0JDfS3+WeR9EM3yxaVQ4O\njDqDDuezEJGTgJOBwSJym69oV2Bnx0U0DCPnecinIH6XOTHay8bj/Jn8ck/ZdSXJpqEagFpgMuD3\npG4ELkunUIZhGF3BsneWZVqEnCFZ1NlXgVdFZG9VXegvE5FLcKPJGobRAXI9NlRh8mUYRh4RxMA9\nBbgp5tw0TFkYRofJpdhQRZVFUesunBqHjRdV+Grk4KK2VdMzLUHOkMxmcTbuiuvhMeE9CnBzTxiG\nkeeE+oTyKvVrK6q7qVW7HSQbWbyAG/V1D1qy5oFrs7DV1IbRDXAmODgrnPxWGEYgUrrO5grmOmvk\nIqlcU7MdmX5UZF/veimDkrSPx55voOHzOi56sZSduiMqYq5T41CxoiIS26r86PzMQNUZrrPPqep4\nEWkkejJSAFXVXTtBTsMwcpl9VmZagg5x5rMHpRw1NW1vwlmRv8oiKAlDlKvqeO9vgaru6tsKTFEY\nhpEPOBOclOHTv9b/axw46MAukih7CeINhYjsjpt3IlJfVS2riWHkOZWVbn7wpiYoKYnOXVFUBBTN\nhwOWQ//c9HlpfKKcctwRg+NEl1nE3GhSKgsRuQbXVfZdoNk7rcA30yeWYXQPsj42lOMqioSUXdRV\noqSFCp/nb6yyMKIJMrL4LrC/qm5PtzCG0d2oyfInVHk51NXBwoUpqxp5TqB8FsBuwIdplsUwjCwj\nrMsWLGhd1tAAcnluL+EuSREOtaiyZYV9qkRQ+U4QZXED8IqIvIGb0wIAVZ2c+BLDMLoFlb4H6NzM\nidFekuUPB9jYlDsr7NNNQm8oHwuB3wA34i7OC2+G0e2prISCAhCJ3oqKous5Tus6IiCXFyGXF0XF\niMomLnu4kgHXFvDA0y1PVafGQSoEqRB6/KyI0CSH0CQnc0J2AkVFLd9Jdw1VnoogI4uPVfW21NUM\no/uR0gCcigL3zbU5RbVMcctqB/o2MfXiOs57c0yr8uYBG2kaG7YSO10pWpdw1i7z+f3voW9fun0m\nvSDKYpWI3AAsJXoaylxnjW5PhxRFLtDX+4BnnUGqQIEi8c+HQq5SLc/BNW2/v8LNjvRlinrdgSCZ\n8v4a57SqakrXWRE5ETc6bU/gblW9Maa8L3A/MAY3OOFZqlrnlY0G5uMmW2oGjlTVLxLdy8J9GJnA\n/4BsT+ScbA/3kTJTnq88Kp91DKEQNDZ2qmhdQke/31ygw+E+wqjqce0UoCdwO3A8UA+sFJGlqvqm\nr9oFwBZVPUBEpuDaRs4SkV7AA8BUVX1VRAYBO9ojh2Gkkzl5PjWx6pz3OqWdgoJOaabLWfqWP3te\nWcJ63YEgi/L2Bq4HilT1JBE5BPiGqt6T4tKjgHWq+q7XzmLgVMCvLE6lZaJzCTBPRAQ4AXjNS8CE\nqubm8lAj78nyZRIdpmREcMN7Pr55T17c4vSZjSO/riSIN9QC4HEg/F/zDnBpgOsGAxt8x/Xeubh1\nVHUn8AkwCDgQUBF5XEReFpErAtzPMAzDSBNBDNx7qOrDIvILcB/qIvJVgOvimbtiVXOiOr2A8cCR\nwOfA09682tNRF4vMAGYADB06NIBIhmG0hZGegRdg7U3mU9qdCaIsPvNsBgogIl/HHQGkoh43+GCY\nfYDYJZDhOvWenWIgsNk7v0JVP/buuRwoAaKUhapWAVXgGrgDyGQYnYp/PUVDOxb4zpmQ3UaPtwbc\n5TtqrSwKQ8lXcPun6XJyys5vtM/uryrtBPGGKgF+CxyGG/pjT+AMVU2aLc97+L8DTATeA1YC56jq\nGl+dHwOjVHWmZ+D+jqp+14ty+zTu6GI78Bhws6r+JdH9zBvKyAT57i3TUW+tXO+fXJc/CJ3pDfWy\niEwADsKdNnpbVVN6JnnTVbNw7R09gXtVdY2IXA3UqupS4B5gkYiswx1RTPGu3SIi/w9XwSiwPJmi\nMIxspfKFyqRpSQtDhVkdc2ivrZMyLUJGmT490xJkD5ZW1TA6QKo3z4IbCpJmYst2ZdFRcv3NvOyh\nFnfZ6rOrk9TMXTptZGEYRvsp/0Y5dVvrWPhqfsb4dmqclv08TBS07J1lmRYha7CRhWF0gFx/c+4o\nKVd453j/ZPsK+86gU0cWIjIY2JfotKrPtl88wzByge7wsEzKKjNahAmygvs3wFm4K6/D6ysUMGVh\nGHlCURFs9FI3zJ8PM2Ykr99tqLa1JWGCjCxOAw5SVQu8aBgxpIoN1d0zrRXmdiI9w0cQZfEu0BuL\n0msYrUi10CzXM639ar+lzJ0LZ5/Tvuvbs1Axm3j0Of8HyM4EVV1FEGXxObBaRJ4mOp/FT9ImlWEY\nXUqih/o1U8u4ZmrXypJNnPRUSzg7/T/d0GbjI4iyWOpthmHkGft8t5KNBzn0bTqQz29uSZ1aVFkU\nGRXNnzSfGWPMiNHdCbKCe6GI9MGNBAsBV3AbRi7h1DhUrKhIWB7SQpoqfK/fpQ6UevW/DFHwssOn\nj+VeKrj3DnCgVxPb+tS16/q8jw1lRAjiDVUKLATqcMN9DBGR75vrrGF49G2icYwD5J6yiKRN3WVz\nuy5PZbSv8OnfnFQW831rt7p5IMEg01CVwAmq+jaAiBwIPISbCtUwDIA+uZmMe4LGfwJ2R8+tuGy0\nx1yYIFFnX1PV0anOZRpbwW1kgrbkqO6Oi9pyfQX3GJ+uWLUqcb1cpjNXcNeKyD3AIu/4XCBPu83o\nbqRaB5Hv+So6Sr7HhiqrdHxHToJa3YMgI4u+wI9xc0sI7srt/8m2RXo2sjDaQ0djG+X6yOGBp1ve\n+86b2PYpF4sNlft0Zj6LL4H/522GYfhI5Q2U7Ux9ruUZcd7E/HwYGp2DhSg3jA5ghuD8ptdHJZkW\nIWswZWEYacRiQ2Vago6x83afeXZe5uTIBkxZGEYayfbYUKvOeS+t7ed6bCijhSCL8g4EfkbrfBbf\nTKNchpET5Lo3UMmI7h0cLxXz52daguwhyMjiD8CdwF205LMwDAOiQoTkorIwkuM0tijTGXTvYVIQ\nZbFTVe9IuySGkQFSrYNIla8i1xl5RUuAwLU3tT3RT77Hhoo3jVj9djWTF0+OHOerS20sQZRFtYj8\nCPgT0SHKUwaTEZETgVuBnsDdqnpjTHlf4H7c0CGbgLNUtc5XPhQ3Q5+jqnMDyGoYbSLVaCAXH3Bt\n4a0Bd/mO2q4s8j02VP8eIbY1N3HG3r/ItCgZp0eAOt/HtVm8gLtyexWQcvWbiPQEbgdOAg4BzhaR\nQ2KqXQBsUdUDgJuB38SU3ww8GkBGwzCMTmfbow58GWLJ3cMyLUrGCbIob3g72z4KWKeq7wKIyGLg\nVNyRQphTaVlDvwSYJyKiqioip+Fm6fusnfc3DMNHdTVMbpk9QRX22jopcwLlAi+Wu5uPsoPKus3U\nk58g3lC9gR8Cx3qnaoD5AXJaDAY2+I7rgXGJ6qjqThH5BBgkItuAnwPHA5cnkW0GMANg6NChqT6K\nYbQivA6isZHofBUxFBbmZ2yoD26u7tD1ue4N1lHKHiqL7Fef3bG+zHaC2CzuwM3B/T/e8VTv3IUp\nrpM452LVcaI6FcDNqtokEq+KV1G1Cm+idezYsd1P1RsdJmLATPxvBrjKpD1k+gFa5TNDpGOBXL57\ng6WKZ7XsnWVdI0gWEERZHKmqh/uOnxGRVwNcVw8M8R3vA618z8J16kWkFzAQ2Iw7AjlDRG4CdgOa\nReQLVe3mayiNTBAKJTbOZntsqIsuatlXzc1gfkZ2EERZfCUi+6vqvwBEZD+CrbdYCYwQkeHAe8AU\n4JyYOktxDegvAmcAz6gbBveYcAURcYAmUxRGumnPgzTrQ3h8o9JNAdu3CamIzqfdXV1A20pdHZSW\nwvr1MH16y2itoQFYNZ1evaLzXuQrQZTFz4C/isi7uIP1fYEfpLrIs0HMAh7HdZ29V1XXiMjVQK2q\nLgXuARaJyDrcEcWUdn4Ow8hKMh0bque4KnRnAc19k2fyC/UJpeX+uR4b6pxz3CnIgoIEFaqr2Ams\neRL4ZRcKlgGCeEM9LSIjgINwlcVbQXNZqOpyYHnMuat8+18AZ6ZowwlyL8PIRjIdG2rnzW9Tt7WO\n0gWlrP9kfdw6oT4hnAlOh+9VVFkUpRCdGoeNF1X42s+9HOVjxrhTkE0psuamKs8HEioLEfmmqj4j\nIt+JKdpfRFDVP6ZZNsPIenLBG2jYbsOou7Su1fnOcAEN9QnRtD35k7JpexPOCofyo3NPWZSXu1s8\niorg0ef8o8X8jrOVbGQxAXgGKItTpoApC6Pbk+/eQKlwJjg4K5xACiMfOempwZF9/T/5bfNJqCxU\nNewgfrWq/ttf5hmtDSP3qfGtg8juJRHtYpfLWiyvn9+8KknN9lF+dHnCEYNT6kQpUyO3CWLgfgSI\nTRe1BDeek2HkNr5ppHxk224vZ1oEI09IZrM4GDgUGBhjt9gV6JduwQzDMLKe+b4weXk4MvWTbGRx\nEDAJd1Gc327RCExPp1CGYXQOEzTPn2CZZmP3mWBJZrP4M/BnEfmGqr7YhTIZRpcRmuP3YOn8dRCZ\njg1Vk+m44I05vtAiBSWxE/R5TBCbxUwRWauqWwFEZHegUlXPT69ohpF+miS96yDS7SFVWemuAzj7\n7OiVxYNbnHSorc3gCuNKnwLOw4w0ZZWO78hJUCs/CKIsRocVBYCqbhGRI9Iok2HkDJmODRVeMFZX\nl6BC4SrWbgUaYExR95ky6Sq6k+t0EGXRQ0R2V9UtACLytYDXGUbek+nYUOGVw08+maDCRWOZ+hzw\nnMV+MjpGkId+JfCCiCzxjs8ErkufSIaRezg1ydcUFIYK06JYJnm5i146oAypcMNlTy+Zjhu9H/pc\n05sdzalSz6SP0CSHrwrq2HbQQiRO96SrX7qKXh91H6NFyrSqqno/bkTYD4APge+o6qJ0C2YY+UTj\n9nYmxEhBdbW7HXVU/PKaaTVA+gIFpuToSrYdtDBh8caNbpC+ysoulKkT2Xn7qsiW7wTJwY2qrgEe\nBv4MNImIpaUzjIB0VqC+9jBst2EZvb8zwUmpqJqaEucLMbIH0RRB/EVkMu5UVBHuyGJfYK2qHpp+\n8YIzduxYra2tTV3RMHxIRUuKPJvTTy+OAxVJon/kYmImfybCGTMyJ0dHEJFVqjo2Vb0gNotrgK8D\nT6nqESJyHHB2RwU0jKwgx2ND5ZKyc5zWI4iCMv+JmMIcwGlsWaczIw3rdLKJIMpih6puEpEeItJD\nVf8qIr9Ju2SG0RXkeWyobKdprH+o4WRKjHaT6XwlXUkQZbFVRELAs8CDIvIhsDO9YhmGYRjZRBBl\ncSqwDbgMOBcYCFydTqEMwwjGr/ZbmmkRujfV81v2c3Aasy0kVRYi0hP4s6p+C2gGEvvAGUYOku7Y\nUOnmmqnxcpMZXcaqHLVqt4OkrrOq+hXwuYgM7CJ5DKNLaZKNkS0bqax01yHExnYqKgIRd/N75BhG\nugiyzuIL4HURuUdEbgtvQRoXkRNF5G0RWScis+OU9xWR33vl/xCRYd7540VklYi87v39Zls+lGHk\nC1cuq6Tp4gJeniyMqYrRGOVF4Ah/+vRKqlbll8ZwahykQpAKoeCGAipfyM5Ve0vfqo5s+U4Qm8Vf\nvK1NeFNYtwPHA/XAShFZqqpv+qpdAGxR1QNEZArwG+As4GOgTFUbROQw4HFgMIbRzdj+DQf6Js9f\n/dhnN/DcEyFmjMnBKZEvQyk/X9P2JpwVTsL0rZlk8uLJkX2/63LZQ2Use8cNvxJeFJmN8reFZJny\nhqrqf1S1vXaKo4B1qvqu195iXGO5X1mcSou/3BJgnoiIqr7iq7MG6CcifVX1y3bKYhg5yYQ+5WzV\nOl6V6J9hQwMUVcLGpsyuEO8wNQ6UOrC9IGm1pu3JFUqmCPUJJZXt+P2OZ9huw2hozD17WCzJRhb/\ni5d7W0QeUdXT29j2YGCD77geGJeojqruFJFPgEG4I4swpwOvxFMUIjIDmAEwdKhFIDHyj5bkRQta\nleVyAL4IL5a7G0Tlu3BKHZxSJ2rRYTYy5F8O7xQ5fNUzvsJ4Z9M7VJVVMWy3YV0rWBpIpiz839J+\n7Wg73rccu8Q0aR0RORR3auqEeDdQN7RmFbjhPtoho2EYRrtZe0854Ck7n+ts9dn5Z8NIZuDWBPtB\nqQeG+I73obVvYqSOiPTCXcOx2TveB/gT8D1V/Vc77m8YVFbCQQdFn3OcFk+ibOeBp1dFtnxm331b\n9nPp+0lFQ2NDZMt1ko0sDheRT3Hf/vt7+3jHqqq7pmh7JTBCRIYD7wFTgHNi6iwFvg+8iBsG/RlV\nVRHZDdeo/gtVfb5Nn8gwfDiO63paVwfDhsWp4MWG6t2nC4VqA1Ofa4nvdt7E/Bs8h0Lu91NTk6CC\nl8M7WxXH9Okt+/HS2Y5d1nIi22N3pSKhslDVnh1p2LNBzML1ZOoJ3Kuqa0TkaqBWVZcC9wCLRGQd\n7ohiinf5LOAA4Nci8mvv3Amq+mFHZDK6H01N7lZamiD1aI1DKGQhsjOF47jrROIqcojk8FaA/+4a\nmdqCf41LQ+4PHpKSMkR5rmAhyo14+N9Is/1f3e9uOb1kOlVlVTkVVTYd5NL3F39kkf3fX2eGKDcM\no4u56y6gGlb97L1Mi2IEpKgojkIr873A5njsKFMW3ZzKFypxVjitfMW7Kjdy7P1j7+vUOFS+WNnx\nRU2lDpIk80625oIuGVGUulIek/M5vDeOSV0nRzBl0c2Jpyiy6f51W+uSruCtrHTnvZuaoLAwet64\nLXaIdOXITkXsNFNZGSxb5p2YHv+absXRlWzL0gV5QSgpybQEnYcpi25OKkXh+JIDOaVOwnrpuv/C\nVxcmrRdWFKno3Qd2JCjLphXQ1fnnnt8hnAlOxl9oOkJZpeM7chLUyg1MWRgR4hngKla0jP3ToSxS\n3T8eqXI5xxIKgXOCQ3m50y65jMxRfnR5TsdU6srfT7oxZdHNmTMhx61uHqFQa9dFx4GqAnfOvxIo\nz8J8FZa8KDn+qcR404rpHvkaLZiy6Obkww8s2TqJTOdIrqx0p5b8i86KimCjJ9b8+WXMyMFgsV2F\nfwQZ7zvO9jf3Xh/lj9HClIWRUdozsnGc3FlEd+WySnYe4zDqtwfy+sW+kB3lRVCwkYs2Aqvm52Z4\ncSMlO2/3fefzMidHZ2DKwsgo2fg22Jls/4YDvZp4s6Eu06IYRocwZdHNKaps8ePPSn91LzZQzuIl\n9mnutzlyyp+Lwshv5s/PtASdhymLbk6m5/RTUulTYHMTV8sGXv5nA2N+1xLvYdH4WiZo/Gm2rFTM\nWU5RUZx1NFkaYDCM09jyMjYjCx0s2oIpCyMphaH0vtln/cimg9TkinElSwmFgq2jyVbivYxVv12d\nMB1rNmPKIsupfKGSqpereHvW25FzTo0T5QWSzhy/saE3/PeNJV6ojooVFbA9BH91WjKi+XGyfGST\ngpFXtBimH5zuZE6QPCXszJCrCqN/jxDbmps4Y+9fZFqUDmPKIstxVjgU9CmgbmtdwtSM2ZzQHoA+\nTfQ63mFnPGWRgglzHN+Rk6BWYtK9juStAXdF9ktGVOXMW2KuUF7ubvFwHKi4PLttWtsedaDUYcnd\nw2BmpqXpGKYsspym7U00bW+idEEpdZfWJa0XD3/spFgKC4GLWo4TrYyOrGNIERNt48bokNKhSYAX\n+Hhnj/a9Gq6Iih7ntPn6zvK2+vWiaq59t/XUQc/GfenVXMCXA9/olPsYbSTbbVpejnF/JsCqy8tg\nmfv/Mz2H4n+ZssgR1n+yPrIfTmYPpExo3xlD+KYmt53GRqfVw7egIHH7Bascmsa2POzj5SOIF0k0\nl6iZVsPp91zMh9vrMi2KkYWEbS6JMgE+/rUyyh5y97M9b7cpizyns+Z6E7XTmXPJcVNnOp3XfjzK\nbqhkWZND/88P5PObWxZQ9byiiOYBrj3l3IHzGT4o/nTH+MOG0b9HAZNCaRbUyEnCNpdEmQD/038Z\n/3mnCwXqAKYscpxU3kpzfFP28WPrtFRwSlvXKUqRTiFV+5keOaTytlrW5ECfJr74qi5pO9dMLeMa\n4tsj6ip/1xERjQ4QmuT4jpwEtTJHPJuLP7Jwpn8fbcGURYZpi00hHqncTVN5bqaa00+VVzjbPUMj\nrouNe0VN2S0aX8t5E8dAH7fjtf/meJcbWY5/mtNxnKQ2t0SG8oyyKneMFqYsMkwuuwUGIeXIx+et\n5LTDphFWto2+3EVRCYRK5sPkxBo30aK5r27KvzUf3ZWwzS0rlUV1lfu3oCHqZaZ2ei1jirIry15a\nlYcup9EAAAsmSURBVIWInAjcCvQE7lbVG2PK+wL34/rZbALOUtU6r+wXwAXAV8BPVPXxdMqaKXJB\nUdTVQWkprF/fumzffV3jXaI52ZQjn1TeSnXHQuHL0LcJqRDOHTifBy511zb8elE11zZNhstdpRLl\ntnp2GRy0DD4flLR5WzTXPciF35mfsWOhpDA6eVKm46ilTVmISE/gduB4oB5YKSJLVfVNX7ULgC2q\neoCITAF+A5wlIocAU4BDgSLgKRE5UFW/Spe8mSLVnH+pk3ydQKp4/n6bQ7wppVRz+pWV7lt7aSks\nXNj6+vVFlQyvcqBvU9TDeu/LyvhwN/f1/uDPprP2JvcNKl5IjPMmum9QpY4TcZXtv7XENTgP3AA7\nBkRiLLWZpr1gl01Q8KGtgchz4kUjTmVzyzS1te7fj76Ak55qXZ5NIdjTObI4Clinqu8CiMhi4FTA\nryxOpcUqtQSYJyLinV+sql8C/xaRdV57Lya62aqGVe4w7r0jYfBK9+S/S2HhX939ggYob3lI8dHB\nsOdbXr0JMHyFu99QAlWuV0xokkPTwXdC6AO3rG48DHvO3a8/EvZZ2dKeo279sRXRMviv+eBQ2HtN\n5JK+//gFX467ISJDRcWKKNkKQ4VsrGiIhLOWigqong+r3DfrASXVfDa55Z8panV1WIaLcOdFq6vc\nFdXL7oCCD+P2Q9yHNa7CaLqoqGW1tU8Gvn15nG8jmu1f7WDYLcNc99/GvaCgpezSxy5l/JhFCRcc\n6i3v8twbdZQuKOWrgvU8+OoiHqzwppXqj4R9WuoWVRbRUN5AdTWUPQTL3gH2WptSPiM/CH//YZwa\nh40XtfwmWk1pNhZGrdOI/H4T0cX1V3/u7Tf3gMZC11tw0gwY27IQ1P9b7Hfu2XwxYnFLWXhet6Qq\neirWex4AMHNUYnli6BG4ZtsZDGzwHdd75+LWUdWdwCfAoIDXIiIzRKRWRGo7Ue6sIhTqmnZCfeJX\nKC93RxY9fP8p557nrplQhdN3uS3lvT/54hNKh5XGLdu07WOOvudoDpp3UMLrxx82jJ1z61z7Rljx\nQrSyjqH67Oooe0iiz2fkNvn8ve5yTBVs3wU+PBTuey71BWkmncointd87DxAojpBrkVVq1R1rKqO\nbYd8OUFnTKmLJG8nHFuqPYz8r2GwPcSkPtHLZz+4uRqdo8w9fi5f7vMkC1/15rD8oxqAPd+icXsj\nM0pmUOM46BxF52jUmoeO0pHPZ2Q3zgQnrxUGDWPhy4GwdVimJUE03rLazmhY5BuAo6rf9o5/AaCq\nN/jqPO7VeVFEegHvA3sCs/11/fUS3W/s2LFaW5u3AwzDMIy0ICKrgrxwp3NksRIYISLDRaQPrsE6\nNjv9UuD73v4ZwDPqaq+lwBQR6Ssiw4ERwEtplNUwDMNIQtoM3Kq6U0RmAY/jus7eq6prRORqoFZV\nlwL3AIs8A/ZmXIWCV+9hXGP4TuDH+egJZRiGkSukbRqqq7FpKMMwjLaTDdNQhmEYRp5gysIwDMNI\niSkLwzAMIyWmLAzDMIyU5I2BW0QagbfTeIuBuCvM03FdqjqJyuOdD3LOf7wH8HEK+TpCe/ot6DXW\nb+27Jp39luo4nf2Wzt9oqnptLcumfhuhqgNT1lLVvNhw3XHT2X5Vuq5LVSdRebzzQc75j7Ox34Je\nY/2Wff0W4Dht/ZbO32iqem0ty8V+s2mo4LQ3QW6Q61LVSVQe73yQc12Z7Lc99wp6jfVb+65JZ7/l\nWp+15bpk9dpalnP9lk/TULWaxzGi0oX1W/uwfmsf1m/tIxv6LZ9GFlWZFiBHsX5rH9Zv7cP6rX1k\nvN/yZmRhGIZhpI98GlkYhmEYacKUhWEYhpESUxaGYRhGSvJWWYjISBG5U0SWiMgPMy1PLiEiA0Rk\nlYhMyrQsuYKIlIrI37z/udJMy5MriEgPEblORH4rIt9PfYUhIsd4/2d3i8gLXXXfnFIWInKviHwo\nIm/EnD9RRN4WkXUiEs6yt1ZVZwLfBbq1q15b+s3j58DDXStl9tHGflOgCeiHmzO+29LGfjsVGAzs\noBv3WxufbX/znm3LgIVdJmS6VgWmaaXhsUAJ8IbvXE/gX8B+QB/gVeAQr2wy8AJwTqZlz5V+A76F\nm4RqGjAp07LnUL/18Mr3Bh7MtOw51G+zgYu8OksyLXsu9Jmv/GFg166SMadGFqr6LG5GPT9HAetU\n9V1V3Q4sxn1bQVWXqurRwLldK2l20cZ+Ow74OnAOMF1Ecup/pDNpS7+parNXvgXo24ViZh1t/H+r\nx+0zgG6bDbOtzzYRGQp8oqqfdpWMaUur2oUMBjb4juuBcd688Xdwf7jLMyBXthO331R1FoCITAM+\n9j0EDZdE/2/fAb4N7AbMy4RgWU7cfgNuBX4rIscAz2ZCsCwmUZ8BXADc15XC5IOykDjnVFVrgJqu\nFSWniNtvkR3VBV0nSk6R6P/tj8Afu1qYHCJRv32O++AzWpPwN6qqc7pYltyahkpAPTDEd7wP0JAh\nWXIJ67f2Yf3WPqzf2k5W9Vk+KIuVwAgRGS4ifXCNs0szLFMuYP3WPqzf2of1W9vJqj7LKWUhIg8B\nLwIHiUi9iFygqjuBWcDjwFrgYVVdk0k5sw3rt/Zh/dY+rN/aTi70mQUSNAzDMFKSUyMLwzAMIzOY\nsjAMwzBSYsrCMAzDSIkpC8MwDCMlpiwMwzCMlJiyMAzDMFJiysLodojIVyKy2rfNTn1V1+DlX9kv\nSbkjIjfEnCsWkbXe/lMisnu65TS6H6YsjO7INlUt9m03drRBEelwnDURORToqarvJqn2EHBWzLkp\nwO+8/UXAjzoqi2HEYsrCMDxEpE5EKkTkZRF5XUQO9s4P8JLTrBSRV0QkHCZ6moj8QUSqgSe8rG//\nIyJrRGSZiCwXkTNEZKKI/Ml3n+NFJF7QwXOBP///9u4mxKYwjuP497eQGSwU8laymERSXpLRKJJm\naTOllLyUrY2xM2UxSwspJQuNkkZNNl4WmsxsMMikNJGkJiNNxoSioUx/i+e55hr3OM1tbPh9Nvec\n87ycc27d+z/P85yepypfq6SBfD09khZExAvgo6RtVeX2kaavhjQdxP7Z/WbMHCzs/9Q4rRuq+kn9\nfURsBs4DJ/Kxk0BfRGwlrfdxWtL8nLYdOBQRu0lT4q8GNgBHcxpAH7BO0pK8f4Ta00u3AIMAkhYD\nHcCefD2PgeM5XzepNYGkZmA8Il4CRMQHYK6kRXV8L2aF/oUpys1maiIiNhakVZ74B0l//gCtwF5J\nleDRAKzK270RUVm0ZgfQk9cAGZXUD2kebkmXgQOSukhB5GCNcy8HxvJ2M2kluXuSIK2UNpDTrgL3\nJbWTgkb3tHreASuA8YJ7NJsxBwuzX33Ln5NM/T4EtOUuoJ9yV9CX6kN/qLcLuAF8JQWU7zXyTJAC\nUaWu3oj4rUspIkYkDQM7gTamWjAVDbkus1njbiizcreBY8qP+JI2FeS7C7TlsYulwK5KQkS8Ja1F\n0AFcKij/HGjK2w+AFklN+ZzzJK2pytsNnAFeRcSbysF8jcuA4Rncn1kpBwv7H00fsyh7G6oTmAM8\nlTSU92u5RlqwZgi4ADwEPlWlXwFGIuJZQflb5AATEWPAYaBb0lNS8FhblbcHWM/UwHbFFuBBQcvF\nrG6eotxsFuU3lj7nAeZHQEtEjOa0c8CTiLhYULYR6M9lJus8/1ngekTcqe8OzGrzmIXZ7LopaSFp\nQLqzKlAMksY32osKRsSEpFPASuB1necfcqCwv8EtCzMzK+UxCzMzK+VgYWZmpRwszMyslIOFmZmV\ncrAwM7NSDhZmZlbqBy72En+5o4CmAAAAAElFTkSuQmCC\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1548,9 +1486,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.7.0" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/examples/jupyter/mdgxs-part-ii.ipynb b/examples/jupyter/mdgxs-part-ii.ipynb index 23ee4d251..77b5b5525 100644 --- a/examples/jupyter/mdgxs-part-ii.ipynb +++ b/examples/jupyter/mdgxs-part-ii.ipynb @@ -45,9 +45,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# 1.6 enriched fuel\n", @@ -102,8 +100,8 @@ "outputs": [], "source": [ "# Create cylinders for the fuel and clad\n", - "fuel_outer_radius = openmc.ZCylinder(R=0.39218)\n", - "clad_outer_radius = openmc.ZCylinder(R=0.45720)\n", + "fuel_outer_radius = openmc.ZCylinder(r=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(r=0.45720)\n", "\n", "# Create boundary planes to surround the geometry\n", "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n", @@ -124,9 +122,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create a Universe to encapsulate a fuel pin\n", @@ -322,7 +318,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nMDB/tUH+AAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -386,7 +382,26 @@ "cell_type": "code", "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=1.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=5.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=6.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=17.\n", + " warn(msg, IDWarning)\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=23.\n", + " warn(msg, IDWarning)\n" + ] + } + ], "source": [ "# Instantiate a tally mesh \n", "mesh = openmc.RegularMesh(mesh_id=1)\n", @@ -417,7 +432,7 @@ "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)\n", "\n", "# Instantiate a current tally\n", - "mesh_filter = openmc.MeshFilter(mesh)\n", + "mesh_filter = openmc.MeshSurfaceFilter(mesh)\n", "current_tally = openmc.Tally(name='current tally')\n", "current_tally.scores = ['current']\n", "current_tally.filters = [mesh_filter]\n", @@ -445,145 +460,135 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | f7edad68f0654d775ed363bfdcbe4aa5d3cfba23\n", - " Date/Time | 2017-04-03 21:15:56\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", + " Date/Time | 2019-07-18 22:07:58\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", " Reading cross sections XML file...\n", - " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", - " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/romano/openmc/scripts/nndc_hdf5/B10.h5\n", - " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", - " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", + " Reading materials XML file...\n", + " Reading geometry XML file...\n", + " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", + " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", + " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", + " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", + " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", + " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.03852 \n", - " 2/1 0.99743 \n", - " 3/1 1.02987 \n", - " 4/1 1.04397 \n", - " 5/1 1.06262 \n", - " 6/1 1.06657 \n", - " 7/1 0.98574 \n", - " 8/1 1.04364 \n", - " 9/1 1.01253 \n", - " 10/1 1.02094 \n", - " 11/1 0.99586 \n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.03852\n", + " 2/1 0.99743\n", + " 3/1 1.02987\n", + " 4/1 1.04397\n", + " 5/1 1.06262\n", + " 6/1 1.06657\n", + " 7/1 0.98574\n", + " 8/1 1.04364\n", + " 9/1 1.01253\n", + " 10/1 1.02094\n", + " 11/1 0.99586\n", " 12/1 1.00508 1.00047 +/- 0.00461\n", " 13/1 1.05292 1.01795 +/- 0.01769\n", " 14/1 1.04732 1.02530 +/- 0.01450\n", " 15/1 1.04886 1.03001 +/- 0.01218\n", " 16/1 1.00948 1.02659 +/- 0.01052\n", - " 17/1 1.02684 1.02662 +/- 0.00889\n", - " 18/1 0.97234 1.01984 +/- 0.01026\n", - " 19/1 0.99754 1.01736 +/- 0.00938\n", - " 20/1 0.98964 1.01459 +/- 0.00884\n", - " 21/1 1.04140 1.01703 +/- 0.00836\n", - " 22/1 1.03854 1.01882 +/- 0.00784\n", - " 23/1 1.05917 1.02192 +/- 0.00785\n", - " 24/1 1.02413 1.02208 +/- 0.00727\n", - " 25/1 1.03113 1.02268 +/- 0.00679\n", - " 26/1 1.05113 1.02446 +/- 0.00660\n", - " 27/1 1.03252 1.02494 +/- 0.00622\n", - " 28/1 1.05196 1.02644 +/- 0.00605\n", - " 29/1 0.99663 1.02487 +/- 0.00593\n", - " 30/1 1.01820 1.02454 +/- 0.00564\n", - " 31/1 1.02753 1.02468 +/- 0.00537\n", - " 32/1 1.02162 1.02454 +/- 0.00512\n", - " 33/1 1.04083 1.02525 +/- 0.00494\n", - " 34/1 1.03335 1.02558 +/- 0.00474\n", - " 35/1 1.01304 1.02508 +/- 0.00458\n", - " 36/1 0.99299 1.02385 +/- 0.00457\n", - " 37/1 1.04936 1.02479 +/- 0.00450\n", - " 38/1 1.02856 1.02493 +/- 0.00433\n", - " 39/1 1.03706 1.02535 +/- 0.00420\n", - " 40/1 1.08118 1.02721 +/- 0.00447\n", - " 41/1 1.00149 1.02638 +/- 0.00440\n", - " 42/1 1.00233 1.02563 +/- 0.00433\n", - " 43/1 1.03023 1.02577 +/- 0.00419\n", - " 44/1 1.03230 1.02596 +/- 0.00407\n", - " 45/1 0.98123 1.02468 +/- 0.00416\n", - " 46/1 1.02126 1.02458 +/- 0.00404\n", - " 47/1 0.99772 1.02386 +/- 0.00400\n", - " 48/1 1.02773 1.02396 +/- 0.00389\n", - " 49/1 1.01690 1.02378 +/- 0.00379\n", - " 50/1 1.02890 1.02391 +/- 0.00370\n", + " 17/1 1.02644 1.02657 +/- 0.00889\n", + " 18/1 1.03080 1.02710 +/- 0.00772\n", + " 19/1 1.00018 1.02411 +/- 0.00743\n", + " 20/1 1.05668 1.02736 +/- 0.00740\n", + " 21/1 1.01160 1.02593 +/- 0.00685\n", + " 22/1 1.04334 1.02738 +/- 0.00642\n", + " 23/1 1.03105 1.02766 +/- 0.00591\n", + " 24/1 1.01174 1.02653 +/- 0.00559\n", + " 25/1 0.99844 1.02465 +/- 0.00553\n", + " 26/1 1.02241 1.02451 +/- 0.00517\n", + " 27/1 1.02904 1.02478 +/- 0.00487\n", + " 28/1 1.02132 1.02459 +/- 0.00459\n", + " 29/1 1.01384 1.02402 +/- 0.00438\n", + " 30/1 1.03891 1.02477 +/- 0.00422\n", + " 31/1 1.04092 1.02553 +/- 0.00409\n", + " 32/1 1.00058 1.02440 +/- 0.00406\n", + " 33/1 0.99940 1.02331 +/- 0.00403\n", + " 34/1 0.98362 1.02166 +/- 0.00420\n", + " 35/1 1.05358 1.02294 +/- 0.00422\n", + " 36/1 0.99923 1.02202 +/- 0.00416\n", + " 37/1 1.08491 1.02435 +/- 0.00463\n", + " 38/1 1.01838 1.02414 +/- 0.00447\n", + " 39/1 0.98567 1.02281 +/- 0.00451\n", + " 40/1 1.05047 1.02374 +/- 0.00445\n", + " 41/1 1.01993 1.02361 +/- 0.00431\n", + " 42/1 1.01223 1.02326 +/- 0.00419\n", + " 43/1 1.06259 1.02445 +/- 0.00423\n", + " 44/1 1.01993 1.02432 +/- 0.00411\n", + " 45/1 0.99233 1.02340 +/- 0.00409\n", + " 46/1 0.98532 1.02234 +/- 0.00411\n", + " 47/1 1.02513 1.02242 +/- 0.00400\n", + " 48/1 1.01637 1.02226 +/- 0.00390\n", + " 49/1 1.03215 1.02251 +/- 0.00381\n", + " 50/1 1.01826 1.02241 +/- 0.00371\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.7616E-01 seconds\n", - " Reading cross sections = 3.2363E-01 seconds\n", - " Total time in simulation = 5.8159E+01 seconds\n", - " Time in transport only = 5.7959E+01 seconds\n", - " Time in inactive batches = 3.9349E+00 seconds\n", - " Time in active batches = 5.4224E+01 seconds\n", - " Time synchronizing fission bank = 3.6903E-03 seconds\n", - " Sampling source sites = 2.4990E-03 seconds\n", - " SEND/RECV source sites = 1.1328E-03 seconds\n", - " Time accumulating tallies = 1.7598E-01 seconds\n", - " Total time for finalization = 3.5126E-03 seconds\n", - " Total time elapsed = 5.8551E+01 seconds\n", - " Calculation Rate (inactive) = 6353.42 neutrons/second\n", - " Calculation Rate (active) = 1844.20 neutrons/second\n", + " Total time for initialization = 4.2397e-01 seconds\n", + " Reading cross sections = 4.0321e-01 seconds\n", + " Total time in simulation = 2.0407e+01 seconds\n", + " Time in transport only = 2.0154e+01 seconds\n", + " Time in inactive batches = 1.0937e+00 seconds\n", + " Time in active batches = 1.9314e+01 seconds\n", + " Time synchronizing fission bank = 7.8056e-03 seconds\n", + " Sampling source sites = 6.7223e-03 seconds\n", + " SEND/RECV source sites = 9.5783e-04 seconds\n", + " Time accumulating tallies = 9.2006e-02 seconds\n", + " Total time for finalization = 1.0890e-02 seconds\n", + " Total time elapsed = 2.0869e+01 seconds\n", + " Calculation Rate (inactive) = 22858.4 particles/second\n", + " Calculation Rate (active) = 5177.70 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02621 +/- 0.00393\n", - " k-effective (Track-length) = 1.02391 +/- 0.00370\n", - " k-effective (Absorption) = 1.02077 +/- 0.00423\n", - " Combined k-effective = 1.02331 +/- 0.00353\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.02207 +/- 0.00343\n", + " k-effective (Track-length) = 1.02241 +/- 0.00371\n", + " k-effective (Absorption) = 1.02408 +/- 0.00356\n", + " Combined k-effective = 1.02306 +/- 0.00307\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -658,26 +663,23 @@ "execution_count": 18, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/tallies.py:1875: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1876: RuntimeWarning: invalid value encountered in true_divide\n", - " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1877: RuntimeWarning: invalid value encountered in true_divide\n", - " new_tally._mean = data['self']['mean'] / data['other']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1869: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1870: RuntimeWarning: invalid value encountered in true_divide\n", - " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n" - ] - }, { "data": { "text/html": [ "
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0.00000 0.000000\n", + "6 1 1 1 y-max out total current 0.03072 0.000677\n", + "7 1 1 1 y-max in total current 0.03104 0.000652\n", + "8 1 1 1 z-min out total current 0.00000 0.000000\n", + "9 1 1 1 z-min in total current 0.00000 0.000000" ] }, "execution_count": 19, @@ -1077,22 +1091,10 @@ "execution_count": 20, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/tallies.py:1875: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1876: RuntimeWarning: invalid value encountered in true_divide\n", - " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1877: RuntimeWarning: invalid value encountered in true_divide\n", - " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" - ] - }, { "data": { "text/plain": [ - "" + "Text(0.5, 1.0, 'Beta - delayed group 6')" ] }, "execution_count": 20, @@ -1101,12 +1103,14 @@ }, { "data": { - "image/png": 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0m4y0Cddhsw7na+K6WtHv8imydeU3bMFrmVk7ce/s2sS12KxTuRavLVyHzTqV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lAAAg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\nRU5ErkJggg==\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1166,7 +1170,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/examples/jupyter/mgxs-part-i.ipynb b/examples/jupyter/mgxs-part-i.ipynb index 90beeaae2..264f8967c 100644 --- a/examples/jupyter/mgxs-part-i.ipynb +++ b/examples/jupyter/mgxs-part-i.ipynb @@ -28,9 +28,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -134,9 +132,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -224,9 +220,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Cell\n", @@ -268,9 +262,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", @@ -326,9 +318,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a 2-group EnergyGroups object\n", @@ -365,9 +355,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a few different sections\n", @@ -390,9 +378,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -431,9 +417,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -473,9 +457,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -645,9 +627,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Load the last statepoint file\n", @@ -671,9 +651,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Load the tallies from the statepoint into each MGXS object\n", @@ -706,9 +684,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -741,9 +717,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -807,9 +781,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "absorption.export_xs_data(filename='absorption-xs', format='excel')" @@ -825,9 +797,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "total.build_hdf5_store(filename='mgxs', append=True)\n", @@ -852,9 +822,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -931,9 +899,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1003,9 +969,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1082,9 +1046,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1168,9 +1130,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.7.0" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/examples/jupyter/mgxs-part-ii.ipynb b/examples/jupyter/mgxs-part-ii.ipynb index 797a357f0..9e5cde42d 100644 --- a/examples/jupyter/mgxs-part-ii.ipynb +++ b/examples/jupyter/mgxs-part-ii.ipynb @@ -8,7 +8,7 @@ "\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://openmc.readthedocs.io/en/latest/io_formats/settings.html#trigger-element)** with multi-group cross sections\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", "* 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", @@ -26,23 +26,8 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/miniconda3/envs/python3/lib/python3.6/site-packages/matplotlib/__init__.py:1401: UserWarning: This call to matplotlib.use() has no effect\n", - "because the backend has already been chosen;\n", - "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", - "or matplotlib.backends is imported for the first time.\n", - "\n", - " warnings.warn(_use_error_msg)\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -68,9 +53,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# 1.6% enriched fuel\n", @@ -102,9 +85,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection\n", @@ -124,22 +105,15 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create cylinders for the fuel and clad\n", - "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n", - "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n", "\n", - "# Create boundary planes to surround the geometry\n", - "min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n", - "max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n", - "min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n", - "max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n", - "min_z = openmc.ZPlane(z0=-0.63, boundary_type='reflective')\n", - "max_z = openmc.ZPlane(z0=+0.63, boundary_type='reflective')" + "# Create box to surround the geometry\n", + "box = openmc.model.rectangular_prism(1.26, 1.26, boundary_type='reflective')" ] }, { @@ -152,9 +126,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Create a Universe to encapsulate a fuel pin\n", @@ -175,7 +147,7 @@ "# Create a moderator Cell\n", "moderator_cell = openmc.Cell(name='1.6% Moderator')\n", "moderator_cell.fill = water\n", - "moderator_cell.region = +clad_outer_radius\n", + "moderator_cell.region = +clad_outer_radius & box\n", "pin_cell_universe.add_cell(moderator_cell)" ] }, @@ -183,44 +155,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe." + "We now must create a geometry with the pin cell universe and export it to XML." ] }, { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create root Cell\n", - "root_cell = openmc.Cell(name='root cell')\n", - "root_cell.region = +min_x & -max_x & +min_y & -max_y\n", - "root_cell.fill = pin_cell_universe\n", - "\n", - "# Create root Universe\n", - "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", - "root_universe.add_cell(root_cell)" - ] - }, - { - "cell_type": "markdown", "metadata": {}, - "source": [ - "We now must create a geometry that is assigned a root universe and export it to XML." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", - "openmc_geometry = openmc.Geometry(root_universe)\n", + "openmc_geometry = openmc.Geometry(pin_cell_universe)\n", "\n", "# Export to \"geometry.xml\"\n", "openmc_geometry.export_to_xml()" @@ -235,10 +180,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "# OpenMC simulation parameters\n", @@ -275,10 +218,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a \"coarse\" 2-group EnergyGroups object\n", @@ -298,10 +239,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "# Extract all Cells filled by Materials\n", @@ -329,14 +268,12 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "# Create a tally trigger for +/- 0.01 on each tally used to compute the multi-group cross sections\n", - "tally_trigger = openmc.Trigger('std_dev', 1E-2)\n", + "tally_trigger = openmc.Trigger('std_dev', 1e-2)\n", "\n", "# Add the tally trigger to each of the multi-group cross section tallies\n", "for cell in openmc_cells:\n", @@ -353,28 +290,26 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=48.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=53.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=18.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=2.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyoutFilter instance already exists with id=3.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=40.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another CellFilter instance already exists with id=43.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=41.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another EnergyFilter instance already exists with id=13.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=15.\n", " warn(msg, IDWarning)\n" ] } @@ -410,78 +345,74 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.9.0\n", - " Git SHA1 | da61fb4a55e1feaa127799ad9293a766161fbb3e\n", - " Date/Time | 2017-12-11 16:37:11\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", + " Date/Time | 2019-07-19 07:08:16\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", - " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", - " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", - " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", + " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", + " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", + " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", + " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", + " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Reading tallies XML file...\n", " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.20332 \n", - " 2/1 1.22209 \n", - " 3/1 1.24322 \n", - " 4/1 1.21622 \n", - " 5/1 1.25850 \n", - " 6/1 1.22581 \n", - " 7/1 1.21118 \n", - " 8/1 1.23377 \n", - " 9/1 1.24254 \n", - " 10/1 1.21241 \n", - " 11/1 1.21042 \n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.20332\n", + " 2/1 1.22209\n", + " 3/1 1.24322\n", + " 4/1 1.21622\n", + " 5/1 1.25850\n", + " 6/1 1.22581\n", + " 7/1 1.21118\n", + " 8/1 1.23377\n", + " 9/1 1.24254\n", + " 10/1 1.21241\n", + " 11/1 1.21042\n", " 12/1 1.23539 1.22290 +/- 0.01249\n", " 13/1 1.22436 1.22339 +/- 0.00723\n", " 14/1 1.22888 1.22476 +/- 0.00529\n", @@ -521,82 +452,138 @@ " 48/1 1.22204 1.22140 +/- 0.00239\n", " 49/1 1.22077 1.22139 +/- 0.00232\n", " 50/1 1.23166 1.22164 +/- 0.00228\n", - " Triggers unsatisfied, max unc./thresh. is 1.17623 for flux in tally 58\n", + " Triggers unsatisfied, max unc./thresh. is 1.17623 for flux in tally 53\n", " The estimated number of batches is 66\n", " Creating state point statepoint.050.h5...\n", " 51/1 1.20071 1.22113 +/- 0.00228\n", + " Triggers unsatisfied, max unc./thresh. is 1.26577 for flux in tally 53\n", + " The estimated number of batches is 76\n", " 52/1 1.21423 1.22097 +/- 0.00223\n", + " Triggers unsatisfied, max unc./thresh. is 1.24 for flux in tally 53\n", + " The estimated number of batches is 75\n", " 53/1 1.25595 1.22178 +/- 0.00233\n", + " Triggers unsatisfied, max unc./thresh. is 1.2112 for flux in tally 53\n", + " The estimated number of batches is 74\n", " 54/1 1.21806 1.22170 +/- 0.00227\n", + " Triggers unsatisfied, max unc./thresh. is 1.18484 for flux in tally 53\n", + " The estimated number of batches is 72\n", " 55/1 1.22911 1.22186 +/- 0.00223\n", + " Triggers unsatisfied, max unc./thresh. is 1.1596 for flux in tally 53\n", + " The estimated number of batches is 71\n", " 56/1 1.23054 1.22205 +/- 0.00219\n", + " Triggers unsatisfied, max unc./thresh. is 1.13453 for flux in tally 53\n", + " The estimated number of batches is 70\n", " 57/1 1.19384 1.22145 +/- 0.00222\n", + " Triggers unsatisfied, max unc./thresh. is 1.11914 for flux in tally 53\n", + " The estimated number of batches is 69\n", " 58/1 1.20625 1.22114 +/- 0.00220\n", + " Triggers unsatisfied, max unc./thresh. is 1.11471 for flux in tally 53\n", + " The estimated number of batches is 70\n", " 59/1 1.21977 1.22111 +/- 0.00216\n", + " Triggers unsatisfied, max unc./thresh. is 1.10334 for flux in tally 53\n", + " The estimated number of batches is 70\n", " 60/1 1.20813 1.22085 +/- 0.00213\n", + " Triggers unsatisfied, max unc./thresh. is 1.09813 for flux in tally 53\n", + " The estimated number of batches is 71\n", " 61/1 1.22077 1.22085 +/- 0.00209\n", + " Triggers unsatisfied, max unc./thresh. is 1.10221 for flux in tally 53\n", + " The estimated number of batches is 72\n", " 62/1 1.21956 1.22082 +/- 0.00205\n", + " Triggers unsatisfied, max unc./thresh. is 1.11395 for flux in tally 53\n", + " The estimated number of batches is 75\n", " 63/1 1.22360 1.22087 +/- 0.00201\n", + " Triggers unsatisfied, max unc./thresh. is 1.09283 for flux in tally 53\n", + " The estimated number of batches is 74\n", " 64/1 1.23955 1.22122 +/- 0.00200\n", + " Triggers unsatisfied, max unc./thresh. is 1.07416 for flux in tally 53\n", + " The estimated number of batches is 73\n", " 65/1 1.21143 1.22104 +/- 0.00197\n", + " Triggers unsatisfied, max unc./thresh. is 1.06461 for flux in tally 53\n", + " The estimated number of batches is 73\n", " 66/1 1.21791 1.22099 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 1.13207 for flux in tally 58\n", + " Triggers unsatisfied, max unc./thresh. is 1.13207 for flux in tally 53\n", " The estimated number of batches is 82\n", " 67/1 1.24897 1.22148 +/- 0.00196\n", + " Triggers unsatisfied, max unc./thresh. is 1.11277 for flux in tally 53\n", + " The estimated number of batches is 81\n", " 68/1 1.22221 1.22149 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 1.09514 for flux in tally 53\n", + " The estimated number of batches is 80\n", " 69/1 1.25627 1.22208 +/- 0.00199\n", + " Triggers unsatisfied, max unc./thresh. is 1.07653 for flux in tally 53\n", + " The estimated number of batches is 79\n", " 70/1 1.21493 1.22196 +/- 0.00196\n", + " Triggers unsatisfied, max unc./thresh. is 1.12831 for flux in tally 53\n", + " The estimated number of batches is 87\n", " 71/1 1.23406 1.22216 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 1.11005 for flux in tally 53\n", + " The estimated number of batches is 86\n", " 72/1 1.23842 1.22242 +/- 0.00192\n", + " Triggers unsatisfied, max unc./thresh. is 1.09352 for flux in tally 53\n", + " The estimated number of batches is 85\n", " 73/1 1.24542 1.22279 +/- 0.00193\n", - " 74/1 1.21314 1.22263 +/- 0.00190\n", + " Triggers unsatisfied, max unc./thresh. is 1.08766 for flux in tally 53\n", + " The estimated number of batches is 85\n", + " 74/1 1.21314 1.22263 +/- 0.00190\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Triggers unsatisfied, max unc./thresh. is 1.07419 for flux in tally 53\n", + " The estimated number of batches is 84\n", " 75/1 1.26484 1.22328 +/- 0.00198\n", + " Triggers unsatisfied, max unc./thresh. is 1.06788 for flux in tally 53\n", + " The estimated number of batches is 85\n", " 76/1 1.22243 1.22327 +/- 0.00195\n", + " Triggers unsatisfied, max unc./thresh. is 1.05164 for flux in tally 53\n", + " The estimated number of batches is 83\n", " 77/1 1.21865 1.22320 +/- 0.00192\n", + " Triggers unsatisfied, max unc./thresh. is 1.04022 for flux in tally 53\n", + " The estimated number of batches is 83\n", " 78/1 1.23500 1.22338 +/- 0.00190\n", + " Triggers unsatisfied, max unc./thresh. is 1.0275 for flux in tally 53\n", + " The estimated number of batches is 82\n", " 79/1 1.22125 1.22334 +/- 0.00187\n", + " Triggers unsatisfied, max unc./thresh. is 1.0283 for flux in tally 53\n", + " The estimated number of batches is 83\n", " 80/1 1.23793 1.22355 +/- 0.00186\n", + " Triggers unsatisfied, max unc./thresh. is 1.01363 for flux in tally 53\n", + " The estimated number of batches is 82\n", " 81/1 1.24238 1.22382 +/- 0.00185\n", + " Triggers unsatisfied, max unc./thresh. is 1.01172 for flux in tally 53\n", + " The estimated number of batches is 83\n", " 82/1 1.23493 1.22397 +/- 0.00183\n", " Triggers satisfied for batch 82\n", " Creating state point statepoint.082.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.1610E-01 seconds\n", - " Reading cross sections = 3.7942E-01 seconds\n", - " Total time in simulation = 1.1100E+02 seconds\n", - " Time in transport only = 1.1076E+02 seconds\n", - " Time in inactive batches = 5.8101E+00 seconds\n", - " Time in active batches = 1.0519E+02 seconds\n", - " Time synchronizing fission bank = 3.8707E-02 seconds\n", - " Sampling source sites = 2.7232E-02 seconds\n", - " SEND/RECV source sites = 1.1284E-02 seconds\n", - " Time accumulating tallies = 1.0514E-03 seconds\n", - " Total time for finalization = 1.4526E-02 seconds\n", - " Total time elapsed = 1.1150E+02 seconds\n", - " Calculation Rate (inactive) = 17211.3 neutrons/second\n", - " Calculation Rate (active) = 6844.60 neutrons/second\n", + " Total time for initialization = 9.5644e-01 seconds\n", + " Reading cross sections = 9.0579e-01 seconds\n", + " Total time in simulation = 9.9887e+01 seconds\n", + " Time in transport only = 9.9333e+01 seconds\n", + " Time in inactive batches = 5.4841e+00 seconds\n", + " Time in active batches = 9.4403e+01 seconds\n", + " Time synchronizing fission bank = 7.3998e-02 seconds\n", + " Sampling source sites = 5.9021e-02 seconds\n", + " SEND/RECV source sites = 1.4787e-02 seconds\n", + " Time accumulating tallies = 1.2234e-03 seconds\n", + " Total time for finalization = 2.8416e-02 seconds\n", + " Total time elapsed = 1.0094e+02 seconds\n", + " Calculation Rate (inactive) = 18234.5 particles/second\n", + " Calculation Rate (active) = 7626.89 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.22348 +/- 0.00169\n", - " k-effective (Track-length) = 1.22397 +/- 0.00183\n", - " k-effective (Absorption) = 1.22467 +/- 0.00117\n", - " Combined k-effective = 1.22448 +/- 0.00108\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.22348 +/- 0.00169\n", + " k-effective (Track-length) = 1.22397 +/- 0.00183\n", + " k-effective (Absorption) = 1.22467 +/- 0.00117\n", + " Combined k-effective = 1.22448 +/- 0.00108\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -620,10 +607,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "# Load the last statepoint file\n", @@ -639,10 +624,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "# Iterate over all cells and cross section types\n", @@ -674,10 +657,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -712,14 +693,6 @@ "\n", "\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" - ] } ], "source": [ @@ -736,10 +709,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -778,23 +749,26 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" - ] - }, { "data": { "text/html": [ "
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\n", " \n", " \n", @@ -1080,7 +1061,7 @@ "2 1 2 O16 3.788383 0.007676" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1106,10 +1087,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [], "source": [ "# Create an OpenMOC Geometry from the OpenMC Geometry\n", @@ -1125,24 +1104,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1799: RuntimeWarning: invalid value encountered in true_divide\n", - " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1800: RuntimeWarning: invalid value encountered in true_divide\n", - " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" - ] - } - ], + "execution_count": 22, + "metadata": {}, + "outputs": [], "source": [ "# Get all OpenMOC cells in the gometry\n", "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", @@ -1183,250 +1147,530 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ NORMAL ] Importing ray tracing data from file...\n", + "[ NORMAL ] Initializing a default angular quadrature...\n", + "[ NORMAL ] Initializing 2D tracks...\n", + "[ NORMAL ] Initializing 2D tracks reflections...\n", + "[ NORMAL ] Initializing 2D tracks array...\n", + "[ NORMAL ] Ray tracing for 2D track segmentation...\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 0.09 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 10.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 19.94 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 29.87 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 39.80 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 49.72 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 59.65 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 69.58 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 79.50 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 89.43 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n", + "[ NORMAL ] Initializing FSR lookup vectors\n", + "[ NORMAL ] Total number of FSRs 3\n", + "[ RESULT ] Total Track Generation & Segmentation Time...........2.5566E-02 sec\n", + "[ NORMAL ] Initializing MOC eigenvalue solver...\n", + "[ NORMAL ] Initializing solver arrays...\n", + "[ NORMAL ] Centering segments around FSR centroid...\n", + "[ NORMAL ] Max boundary angular flux storage per domain = 0.42 MB\n", + "[ NORMAL ] Max scalar flux storage per domain = 0.00 MB\n", + "[ NORMAL ] Max source storage per domain = 0.00 MB\n", + "[ NORMAL ] Number of azimuthal angles = 128\n", + "[ NORMAL ] Azimuthal ray spacing = 0.100000\n", + "[ NORMAL ] Number of polar angles = 6\n", + "[ NORMAL ] Source type = Flat\n", + "[ NORMAL ] MOC transport undamped\n", + "[ NORMAL ] CMFD acceleration: OFF\n", + "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.423134\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.475951\tres = 5.769E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.491466\tres = 1.248E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.487444\tres = 3.260E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.483929\tres = 8.184E-03\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.477278\tres = 7.213E-03\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.468936\tres = 1.374E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.460317\tres = 1.748E-02\n", - "[ NORMAL ] Iteration 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2.561E-05\n", - "[ NORMAL ] Iteration 215:\tk_eff = 1.220360\tres = 2.450E-05\n", - "[ NORMAL ] Iteration 216:\tk_eff = 1.220387\tres = 2.299E-05\n", - "[ NORMAL ] Iteration 217:\tk_eff = 1.220413\tres = 2.190E-05\n", - "[ NORMAL ] Iteration 218:\tk_eff = 1.220437\tres = 2.109E-05\n", - "[ NORMAL ] Iteration 219:\tk_eff = 1.220460\tres = 1.982E-05\n", - "[ NORMAL ] Iteration 220:\tk_eff = 1.220482\tres = 1.916E-05\n", - "[ NORMAL ] Iteration 221:\tk_eff = 1.220503\tres = 1.792E-05\n", - "[ NORMAL ] Iteration 222:\tk_eff = 1.220523\tres = 1.701E-05\n", - "[ NORMAL ] Iteration 223:\tk_eff = 1.220541\tres = 1.615E-05\n", - "[ NORMAL ] Iteration 224:\tk_eff = 1.220559\tres = 1.526E-05\n", - "[ NORMAL ] Iteration 225:\tk_eff = 1.220576\tres = 1.439E-05\n", - "[ NORMAL ] Iteration 226:\tk_eff = 1.220592\tres = 1.418E-05\n", - "[ NORMAL ] Iteration 227:\tk_eff = 1.220608\tres = 1.350E-05\n", - "[ NORMAL ] Iteration 228:\tk_eff = 1.220623\tres = 1.269E-05\n", - "[ NORMAL ] Iteration 229:\tk_eff = 1.220637\tres = 1.193E-05\n", - "[ NORMAL ] Iteration 230:\tk_eff = 1.220650\tres = 1.161E-05\n", - "[ NORMAL ] Iteration 231:\tk_eff = 1.220663\tres = 1.090E-05\n", - "[ NORMAL ] Iteration 232:\tk_eff = 1.220675\tres = 1.033E-05\n" + "[ NORMAL ] Iteration 0: k_eff = 0.423133 res = 5.671E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -57686 D.R. = 0.00\n", + "[ NORMAL ] Iteration 1: k_eff = 0.475953 res = 2.442E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 5282 D.R. = 4.31\n", + "[ NORMAL ] Iteration 2: k_eff = 0.491468 res = 4.764E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1551 D.R. = 1.95\n", + "[ NORMAL ] Iteration 3: k_eff = 0.487446 res = 2.253E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -402 D.R. = 0.47\n", + "[ NORMAL ] Iteration 4: k_eff = 0.483930 res = 6.957E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -351 D.R. = 0.31\n", + "[ NORMAL ] Iteration 5: k_eff = 0.477280 res = 3.902E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -665 D.R. = 5.61\n", + "[ NORMAL ] Iteration 6: k_eff = 0.468938 res = 3.161E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -834 D.R. = 0.81\n", + "[ NORMAL ] Iteration 7: k_eff = 0.460319 res = 2.480E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -861 D.R. = 0.78\n", + "[ NORMAL ] Iteration 8: k_eff = 0.450591 res = 9.377E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -972 D.R. = 0.38\n", + "[ NORMAL ] Iteration 9: k_eff = 0.441377 res = 3.085E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -921 D.R. = 3.29\n", + "[ NORMAL ] Iteration 10: k_eff = 0.431990 res = 1.028E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -938 D.R. = 0.33\n", + "[ NORMAL ] Iteration 11: k_eff = 0.422932 res = 1.180E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -905 D.R. = 1.15\n", + "[ NORMAL ] Iteration 12: k_eff = 0.414487 res = 1.633E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -844 D.R. = 1.38\n", + "[ NORMAL ] Iteration 13: k_eff = 0.406708 res = 1.754E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -777 D.R. = 1.07\n", + "[ NORMAL ] Iteration 14: k_eff = 0.399378 res = 5.021E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -732 D.R. = 2.86\n", + "[ NORMAL ] Iteration 15: k_eff = 0.393067 res = 9.074E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -631 D.R. = 0.18\n", + "[ NORMAL ] Iteration 16: k_eff = 0.387427 res = 4.840E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... -564 D.R. = 0.53\n", + "[ NORMAL ] Iteration 17: k_eff = 0.382668 res = 2.299E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -475 D.R. = 4.75\n", + "[ NORMAL ] Iteration 18: k_eff = 0.378741 res = 1.573E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -392 D.R. = 0.68\n", + "[ NORMAL ] Iteration 19: k_eff = 0.375642 res = 7.017E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -309 D.R. = 4.46\n", + "[ NORMAL ] Iteration 20: k_eff = 0.373489 res = 4.053E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -215 D.R. = 0.58\n", + "[ NORMAL ] Iteration 21: k_eff = 0.372357 res = 4.235E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -113 D.R. = 1.04\n", + "[ NORMAL ] Iteration 22: k_eff = 0.371974 res = 6.352E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -38 D.R. = 1.50\n", + "[ NORMAL ] Iteration 23: k_eff = 0.372581 res = 3.267E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 60 D.R. = 0.51\n", + "[ NORMAL ] Iteration 24: k_eff = 0.374056 res = 1.573E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 147 D.R. = 0.48\n", + "[ NORMAL ] Iteration 25: k_eff = 0.376384 res = 3.630E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 232 D.R. = 2.31\n", + "[ NORMAL ] Iteration 26: k_eff = 0.379563 res = 2.420E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 317 D.R. = 0.07\n", + "[ NORMAL ] Iteration 27: k_eff = 0.383583 res = 3.146E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 401 D.R. = 13.00\n", + "[ NORMAL ] Iteration 28: k_eff = 0.388380 res = 1.089E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 479 D.R. = 0.35\n", + "[ NORMAL ] Iteration 29: k_eff = 0.393938 res = 6.049E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 5.56\n", + "[ NORMAL ] Iteration 30: k_eff = 0.400234 res = 3.267E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 629 D.R. = 0.54\n", + "[ NORMAL ] Iteration 31: k_eff = 0.407235 res = 2.420E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 700 D.R. = 0.74\n", + "[ NORMAL ] Iteration 32: k_eff = 0.414884 res = 1.815E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 764 D.R. = 0.75\n", + "[ NORMAL ] Iteration 33: k_eff = 0.423172 res = 7.259E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 828 D.R. = 0.40\n", + "[ NORMAL ] Iteration 34: k_eff = 0.432051 res = 6.049E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 887 D.R. = 8.33\n", + "[ NORMAL ] Iteration 35: k_eff = 0.441471 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 942 D.R. = 0.96\n", + "[ NORMAL ] Iteration 36: k_eff = 0.451430 res = 2.662E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 995 D.R. = 0.46\n", + "[ NORMAL ] Iteration 37: k_eff = 0.461853 res = 8.227E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1042 D.R. = 3.09\n", + "[ NORMAL ] Iteration 38: k_eff = 0.472730 res = 5.928E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1087 D.R. = 0.72\n", + "[ NORMAL ] Iteration 39: k_eff = 0.484006 res = 2.299E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1127 D.R. = 0.39\n", + "[ NORMAL ] Iteration 40: k_eff = 0.495653 res = 2.299E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1164 D.R. = 1.00\n", + "[ NORMAL ] Iteration 41: k_eff = 0.507634 res = 7.017E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1198 D.R. = 3.05\n", + "[ NORMAL ] Iteration 42: k_eff = 0.519914 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1227 D.R. = 0.28\n", + "[ NORMAL ] Iteration 43: k_eff = 0.532458 res = 1.089E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1254 D.R. = 0.56\n", + "[ NORMAL ] Iteration 44: k_eff = 0.545234 res = 6.291E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1277 D.R. = 5.78\n", + "[ NORMAL ] Iteration 45: k_eff = 0.558210 res = 3.509E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1297 D.R. = 0.56\n", + "[ NORMAL ] Iteration 46: k_eff = 0.571353 res = 3.025E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1314 D.R. = 0.86\n", + "[ NORMAL ] Iteration 47: k_eff = 0.584635 res = 7.259E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1328 D.R. = 0.24\n", + "[ NORMAL ] Iteration 48: k_eff = 0.598027 res = 4.961E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1339 D.R. = 6.83\n", + "[ NORMAL ] Iteration 49: k_eff = 0.611500 res = 9.014E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1347 D.R. = 1.82\n", + "[ NORMAL ] Iteration 50: k_eff = 0.625029 res = 5.203E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1352 D.R. = 0.58\n", + "[ NORMAL ] Iteration 51: k_eff = 0.638590 res = 1.512E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1356 D.R. = 0.29\n", + "[ NORMAL ] Iteration 52: k_eff = 0.652158 res = 2.359E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1356 D.R. = 1.56\n", + "[ NORMAL ] Iteration 53: k_eff = 0.665710 res = 4.598E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1355 D.R. = 1.95\n", + "[ NORMAL ] Iteration 54: k_eff = 0.679228 res = 2.783E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1351 D.R. = 0.61\n", + "[ NORMAL ] Iteration 55: k_eff = 0.692689 res = 2.117E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1346 D.R. = 0.76\n", + "[ NORMAL ] Iteration 56: k_eff = 0.706075 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1338 D.R. = 0.91\n", + "[ NORMAL ] Iteration 57: k_eff = 0.719370 res = 5.505E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1329 D.R. = 2.84\n", + "[ NORMAL ] Iteration 58: k_eff = 0.732556 res = 2.238E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1318 D.R. = 0.41\n", + "[ NORMAL ] Iteration 59: k_eff = 0.745619 res = 7.864E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1306 D.R. = 0.35\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Iteration 60: k_eff = 0.758546 res = 4.477E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1292 D.R. = 5.69\n", + "[ NORMAL ] Iteration 61: k_eff = 0.771323 res = 4.658E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1277 D.R. = 1.04\n", + "[ NORMAL ] Iteration 62: k_eff = 0.783939 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1261 D.R. = 0.21\n", + "[ NORMAL ] Iteration 63: k_eff = 0.796383 res = 4.235E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1244 D.R. = 0.44\n", + "[ NORMAL ] Iteration 64: k_eff = 0.808646 res = 9.074E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1226 D.R. = 2.14\n", + "[ NORMAL ] Iteration 65: k_eff = 0.820719 res = 6.412E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1207 D.R. = 7.07\n", + "[ NORMAL ] Iteration 66: k_eff = 0.832594 res = 2.420E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 1187 D.R. = 0.04\n", + "[ NORMAL ] Iteration 67: k_eff = 0.844264 res = 2.299E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1167 D.R. = 9.50\n", + "[ NORMAL ] Iteration 68: k_eff = 0.855724 res = 5.928E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1146 D.R. = 2.58\n", + "[ NORMAL ] Iteration 69: k_eff = 0.866968 res = 6.049E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1124 D.R. = 1.02\n", + "[ NORMAL ] Iteration 70: k_eff = 0.877992 res = 2.722E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1102 D.R. = 0.45\n", + "[ NORMAL ] Iteration 71: k_eff = 0.888792 res = 1.633E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1079 D.R. = 0.60\n", + "[ NORMAL ] Iteration 72: k_eff = 0.899364 res = 2.117E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1057 D.R. = 1.30\n", + "[ NORMAL ] Iteration 73: k_eff = 0.909708 res = 3.327E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1034 D.R. = 1.57\n", + "[ NORMAL ] Iteration 74: k_eff = 0.919819 res = 4.477E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1011 D.R. = 1.35\n", + "[ NORMAL ] Iteration 75: k_eff = 0.929699 res = 3.569E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 987 D.R. = 0.80\n", + "[ NORMAL ] Iteration 76: k_eff = 0.939346 res = 4.840E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 964 D.R. = 0.14\n", + "[ NORMAL ] Iteration 77: k_eff = 0.948758 res = 4.840E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 941 D.R. = 1.00\n", + "[ NORMAL ] Iteration 78: k_eff = 0.957938 res = 6.049E-10 delta-k (pcm) =\n", + "[ NORMAL ] ... 917 D.R. = 0.12\n", + "[ NORMAL ] Iteration 79: k_eff = 0.966885 res = 2.057E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 894 D.R. = 34.00\n", + "[ NORMAL ] Iteration 80: k_eff = 0.975601 res = 4.235E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 871 D.R. = 2.06\n", + "[ NORMAL ] Iteration 81: k_eff = 0.984087 res = 5.868E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 848 D.R. = 1.39\n", + "[ NORMAL ] Iteration 82: k_eff = 0.992344 res = 7.259E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 825 D.R. = 0.12\n", + "[ NORMAL ] Iteration 83: k_eff = 1.000375 res = 1.512E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 803 D.R. = 2.08\n", + "[ NORMAL ] Iteration 84: k_eff = 1.008182 res = 7.864E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 780 D.R. = 0.52\n", + "[ NORMAL ] Iteration 85: k_eff = 1.015768 res = 7.259E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 758 D.R. = 0.92\n", + "[ NORMAL ] Iteration 86: k_eff = 1.023136 res = 3.025E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 736 D.R. = 0.42\n", + "[ NORMAL ] Iteration 87: k_eff = 1.030288 res = 1.210E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 715 D.R. = 4.00\n", + "[ NORMAL ] Iteration 88: k_eff = 1.037228 res = 3.690E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 693 D.R. = 3.05\n", + "[ NORMAL ] Iteration 89: k_eff = 1.043960 res = 5.203E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 673 D.R. = 1.41\n", + "[ NORMAL ] Iteration 90: k_eff = 1.050486 res = 6.231E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 652 D.R. = 1.20\n", + "[ NORMAL ] Iteration 91: k_eff = 1.056812 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 632 D.R. = 1.09\n", + "[ NORMAL ] Iteration 92: k_eff = 1.062939 res = 2.964E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 612 D.R. = 0.44\n", + "[ NORMAL ] Iteration 93: k_eff = 1.068872 res = 5.505E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 593 D.R. = 1.86\n", + "[ NORMAL ] Iteration 94: k_eff = 1.074616 res = 4.235E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 574 D.R. = 0.08\n", + "[ NORMAL ] Iteration 95: k_eff = 1.080173 res = 2.541E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 6.00\n", + "[ NORMAL ] Iteration 96: k_eff = 1.085550 res = 1.996E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 537 D.R. = 0.79\n", + "[ NORMAL ] Iteration 97: k_eff = 1.090748 res = 3.388E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 519 D.R. = 1.70\n", + "[ NORMAL ] Iteration 98: k_eff = 1.095774 res = 3.085E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 502 D.R. = 0.91\n", + "[ NORMAL ] Iteration 99: k_eff = 1.100629 res = 3.267E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 485 D.R. = 1.06\n", + "[ NORMAL ] Iteration 100: k_eff = 1.105320 res = 3.025E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 469 D.R. = 0.09\n", + "[ NORMAL ] Iteration 101: k_eff = 1.109851 res = 6.654E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 453 D.R. = 2.20\n", + "[ NORMAL ] Iteration 102: k_eff = 1.114224 res = 3.569E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 437 D.R. = 5.36\n", + "[ NORMAL ] Iteration 103: k_eff = 1.118444 res = 5.203E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 421 D.R. = 1.46\n", + "[ NORMAL ] Iteration 104: k_eff = 1.122516 res = 1.452E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 407 D.R. = 0.28\n", + "[ NORMAL ] Iteration 105: k_eff = 1.126445 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 392 D.R. = 0.38\n", + "[ NORMAL ] Iteration 106: k_eff = 1.130232 res = 2.783E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 378 D.R. = 5.11\n", + "[ NORMAL ] Iteration 107: k_eff = 1.133884 res = 1.996E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 365 D.R. = 0.72\n", + "[ NORMAL ] Iteration 108: k_eff = 1.137403 res = 4.235E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 351 D.R. = 2.12\n", + "[ NORMAL ] Iteration 109: k_eff = 1.140793 res = 3.751E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 339 D.R. = 0.89\n", + "[ NORMAL ] Iteration 110: k_eff = 1.144059 res = 4.174E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 326 D.R. = 1.11\n", + "[ NORMAL ] Iteration 111: k_eff = 1.147203 res = 4.416E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 314 D.R. = 1.06\n", + "[ NORMAL ] Iteration 112: k_eff = 1.150231 res = 3.025E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 302 D.R. = 0.68\n", + "[ NORMAL ] Iteration 113: k_eff = 1.153146 res = 5.384E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 291 D.R. = 1.78\n", + "[ NORMAL ] Iteration 114: k_eff = 1.155950 res = 3.569E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 280 D.R. = 0.66\n", + "[ NORMAL ] Iteration 115: k_eff = 1.158649 res = 5.142E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 269 D.R. = 1.44\n", + "[ NORMAL ] Iteration 116: k_eff = 1.161244 res = 2.843E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 259 D.R. = 0.55\n", + "[ NORMAL ] Iteration 117: k_eff = 1.163739 res = 3.267E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 249 D.R. = 1.15\n", + "[ NORMAL ] Iteration 118: k_eff = 1.166139 res = 5.505E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 239 D.R. = 1.69\n", + "[ NORMAL ] Iteration 119: k_eff = 1.168445 res = 4.719E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 230 D.R. = 0.86\n", + "[ NORMAL ] Iteration 120: k_eff = 1.170662 res = 6.170E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 221 D.R. = 1.31\n", + "[ NORMAL ] Iteration 121: k_eff = 1.172791 res = 5.384E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 212 D.R. = 0.87\n", + "[ NORMAL ] Iteration 122: k_eff = 1.174837 res = 1.331E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 204 D.R. = 0.25\n", + "[ NORMAL ] Iteration 123: k_eff = 1.176801 res = 1.391E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 196 D.R. = 1.05\n", + "[ NORMAL ] Iteration 124: k_eff = 1.178688 res = 1.089E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 188 D.R. = 0.78\n", + "[ NORMAL ] Iteration 125: k_eff = 1.180500 res = 3.448E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 181 D.R. = 3.17\n", + "[ NORMAL ] Iteration 126: k_eff = 1.182238 res = 1.041E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 173 D.R. = 3.02\n", + "[ NORMAL ] Iteration 127: k_eff = 1.183907 res = 3.690E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 166 D.R. = 0.35\n", + "[ NORMAL ] Iteration 128: k_eff = 1.185508 res = 2.722E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 160 D.R. = 0.74\n", + "[ NORMAL ] Iteration 129: k_eff = 1.187044 res = 9.074E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 153 D.R. = 0.33\n", + "[ NORMAL ] Iteration 130: k_eff = 1.188518 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 147 D.R. = 3.20\n", + "[ NORMAL ] Iteration 131: k_eff = 1.189930 res = 2.783E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 141 D.R. = 0.96\n", + "[ NORMAL ] Iteration 132: k_eff = 1.191285 res = 2.541E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 135 D.R. = 0.91\n", + "[ NORMAL ] Iteration 133: k_eff = 1.192584 res = 4.537E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 129 D.R. = 1.79\n", + "[ NORMAL ] Iteration 134: k_eff = 1.193829 res = 5.505E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 124 D.R. = 1.21\n", + "[ NORMAL ] Iteration 135: k_eff = 1.195022 res = 6.412E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 119 D.R. = 1.16\n", + "[ NORMAL ] Iteration 136: k_eff = 1.196166 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 114 D.R. = 0.01\n", + "[ NORMAL ] Iteration 137: k_eff = 1.197262 res = 4.416E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 109 D.R. = 73.00\n", + "[ NORMAL ] Iteration 138: k_eff = 1.198311 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 104 D.R. = 0.88\n", + "[ NORMAL ] Iteration 139: k_eff = 1.199316 res = 2.420E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 100 D.R. = 0.06\n", + "[ NORMAL ] Iteration 140: k_eff = 1.200279 res = 4.053E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 96 D.R. = 16.75\n", + "[ NORMAL ] Iteration 141: k_eff = 1.201201 res = 1.028E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 92 D.R. = 0.25\n", + "[ NORMAL ] Iteration 142: k_eff = 1.202083 res = 3.509E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 88 D.R. = 3.41\n", + "[ NORMAL ] Iteration 143: k_eff = 1.202927 res = 1.815E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 84 D.R. = 0.52\n", + "[ NORMAL ] Iteration 144: k_eff = 1.203736 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 80 D.R. = 1.07\n", + "[ NORMAL ] Iteration 145: k_eff = 1.204510 res = 6.836E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 77 D.R. = 3.53\n", + "[ NORMAL ] Iteration 146: k_eff = 1.205251 res = 5.324E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 74 D.R. = 0.78\n", + "[ NORMAL ] Iteration 147: k_eff = 1.205959 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 70 D.R. = 0.43\n", + "[ NORMAL ] Iteration 148: k_eff = 1.206637 res = 1.815E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 67 D.R. = 0.79\n", + "[ NORMAL ] Iteration 149: k_eff = 1.207285 res = 8.469E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 64 D.R. = 0.47\n", + "[ NORMAL ] Iteration 150: k_eff = 1.207905 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 61 D.R. = 2.29\n", + "[ NORMAL ] Iteration 151: k_eff = 1.208498 res = 9.074E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 59 D.R. = 0.47\n", + "[ NORMAL ] Iteration 152: k_eff = 1.209065 res = 2.178E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 56 D.R. = 2.40\n", + "[ NORMAL ] Iteration 153: k_eff = 1.209607 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 54 D.R. = 3.11\n", + "[ NORMAL ] Iteration 154: k_eff = 1.210125 res = 9.074E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 51 D.R. = 1.34\n", + "[ NORMAL ] Iteration 155: k_eff = 1.210621 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 49 D.R. = 0.06\n", + "[ NORMAL ] Iteration 156: k_eff = 1.211094 res = 6.049E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 47 D.R. = 1.11\n", + "[ NORMAL ] Iteration 157: k_eff = 1.211546 res = 6.049E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 45 D.R. = 1.00\n", + "[ NORMAL ] Iteration 158: k_eff = 1.211978 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 43 D.R. = 7.70\n", + "[ NORMAL ] Iteration 159: k_eff = 1.212391 res = 1.815E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 41 D.R. = 0.04\n", + "[ NORMAL ] Iteration 160: k_eff = 1.212786 res = 1.210E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 39 D.R. = 6.67\n", + "[ NORMAL ] Iteration 161: k_eff = 1.213162 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 37 D.R. = 0.05\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Iteration 162: k_eff = 1.213522 res = 1.996E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 35 D.R. = 33.00\n", + "[ NORMAL ] Iteration 163: k_eff = 1.213866 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 34 D.R. = 2.33\n", + "[ NORMAL ] Iteration 164: k_eff = 1.214194 res = 1.996E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 32 D.R. = 0.43\n", + "[ NORMAL ] Iteration 165: k_eff = 1.214507 res = 1.210E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 31 D.R. = 0.06\n", + "[ NORMAL ] Iteration 166: k_eff = 1.214806 res = 1.875E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 29 D.R. = 15.50\n", + "[ NORMAL ] Iteration 167: k_eff = 1.215092 res = 4.961E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 28 D.R. = 2.65\n", + "[ NORMAL ] Iteration 168: k_eff = 1.215365 res = 6.049E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 27 D.R. = 1.22\n", + "[ NORMAL ] Iteration 169: k_eff = 1.215625 res = 2.964E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 26 D.R. = 0.49\n", + "[ NORMAL ] Iteration 170: k_eff = 1.215874 res = 5.505E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 24 D.R. = 1.86\n", + "[ NORMAL ] Iteration 171: k_eff = 1.216110 res = 2.117E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 23 D.R. = 0.38\n", + "[ NORMAL ] Iteration 172: k_eff = 1.216337 res = 4.174E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 1.97\n", + "[ NORMAL ] Iteration 173: k_eff = 1.216552 res = 3.509E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 0.84\n", + "[ NORMAL ] Iteration 174: k_eff = 1.216759 res = 2.722E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 0.78\n", + "[ NORMAL ] Iteration 175: k_eff = 1.216954 res = 2.601E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 0.96\n", + "[ NORMAL ] Iteration 176: k_eff = 1.217142 res = 4.295E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 1.65\n", + "[ NORMAL ] Iteration 177: k_eff = 1.217320 res = 4.598E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 1.07\n", + "[ NORMAL ] Iteration 178: k_eff = 1.217491 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 1.26\n", + "[ NORMAL ] Iteration 179: k_eff = 1.217654 res = 2.480E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 0.43\n", + "[ NORMAL ] Iteration 180: k_eff = 1.217809 res = 6.049E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 0.24\n", + "[ NORMAL ] Iteration 181: k_eff = 1.217956 res = 6.412E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 10.60\n", + "[ NORMAL ] Iteration 182: k_eff = 1.218098 res = 7.138E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 14 D.R. = 1.11\n", + "[ NORMAL ] Iteration 183: k_eff = 1.218232 res = 1.633E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 13 D.R. = 0.23\n", + "[ NORMAL ] Iteration 184: k_eff = 1.218360 res = 2.541E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 1.56\n", + "[ NORMAL ] Iteration 185: k_eff = 1.218482 res = 1.028E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 12 D.R. = 0.40\n", + "[ NORMAL ] Iteration 186: k_eff = 1.218599 res = 7.864E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 0.76\n", + "[ NORMAL ] Iteration 187: k_eff = 1.218709 res = 4.053E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 11 D.R. = 5.15\n", + "[ NORMAL ] Iteration 188: k_eff = 1.218815 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 0.57\n", + "[ NORMAL ] Iteration 189: k_eff = 1.218916 res = 5.445E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 10 D.R. = 2.37\n", + "[ NORMAL ] Iteration 190: k_eff = 1.219011 res = 3.327E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 0.61\n", + "[ NORMAL ] Iteration 191: k_eff = 1.219103 res = 4.840E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 9 D.R. = 0.15\n", + "[ NORMAL ] Iteration 192: k_eff = 1.219190 res = 1.210E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 2.50\n", + "[ NORMAL ] Iteration 193: k_eff = 1.219273 res = 1.573E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 8 D.R. = 1.30\n", + "[ NORMAL ] Iteration 194: k_eff = 1.219352 res = 1.331E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.85\n", + "[ NORMAL ] Iteration 195: k_eff = 1.219428 res = 2.783E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 2.09\n", + "[ NORMAL ] Iteration 196: k_eff = 1.219499 res = 2.722E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.98\n", + "[ NORMAL ] Iteration 197: k_eff = 1.219567 res = 2.057E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.76\n", + "[ NORMAL ] Iteration 198: k_eff = 1.219633 res = 1.331E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.65\n", + "[ NORMAL ] Iteration 199: k_eff = 1.219695 res = 3.932E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 2.95\n", + "[ NORMAL ] Iteration 200: k_eff = 1.219753 res = 1.996E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.51\n", + "[ NORMAL ] Iteration 201: k_eff = 1.219810 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 2.42\n", + "[ NORMAL ] Iteration 202: k_eff = 1.219863 res = 1.270E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.26\n", + "[ NORMAL ] Iteration 203: k_eff = 1.219914 res = 2.662E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 2.10\n", + "[ NORMAL ] Iteration 204: k_eff = 1.219962 res = 5.445E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.20\n", + "[ NORMAL ] Iteration 205: k_eff = 1.220009 res = 3.327E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 6.11\n", + "[ NORMAL ] Iteration 206: k_eff = 1.220052 res = 4.658E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.40\n", + "[ NORMAL ] Iteration 207: k_eff = 1.220094 res = 3.025E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.65\n", + "[ NORMAL ] Iteration 208: k_eff = 1.220134 res = 2.117E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.70\n", + "[ NORMAL ] Iteration 209: k_eff = 1.220172 res = 1.875E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.89\n", + "[ NORMAL ] Iteration 210: k_eff = 1.220208 res = 1.028E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.55\n", + "[ NORMAL ] Iteration 211: k_eff = 1.220243 res = 5.263E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 5.12\n", + "[ NORMAL ] Iteration 212: k_eff = 1.220275 res = 2.480E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.47\n", + "[ NORMAL ] Iteration 213: k_eff = 1.220306 res = 4.598E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.85\n", + "[ NORMAL ] Iteration 214: k_eff = 1.220336 res = 3.630E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.08\n", + "[ NORMAL ] Iteration 215: k_eff = 1.220364 res = 1.391E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 3.83\n", + "[ NORMAL ] Iteration 216: k_eff = 1.220391 res = 6.049E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 4.35\n", + "[ NORMAL ] Iteration 217: k_eff = 1.220416 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.32\n", + "[ NORMAL ] Iteration 218: k_eff = 1.220441 res = 6.049E-10 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.03\n", + "[ NORMAL ] Iteration 219: k_eff = 1.220464 res = 1.270E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 21.00\n", + "[ NORMAL ] Iteration 220: k_eff = 1.220486 res = 1.452E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.14\n", + "[ NORMAL ] Iteration 221: k_eff = 1.220507 res = 2.299E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.58\n", + "[ NORMAL ] Iteration 222: k_eff = 1.220527 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.42\n", + "[ NORMAL ] Iteration 223: k_eff = 1.220545 res = 7.078E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 7.31\n", + "[ NORMAL ] Iteration 224: k_eff = 1.220563 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.14\n", + "[ NORMAL ] Iteration 225: k_eff = 1.220580 res = 3.146E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 3.25\n", + "[ NORMAL ] Iteration 226: k_eff = 1.220596 res = 1.633E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.52\n", + "[ NORMAL ] Iteration 227: k_eff = 1.220612 res = 3.569E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.19\n", + "[ NORMAL ] Iteration 228: k_eff = 1.220627 res = 2.722E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.76\n", + "[ NORMAL ] Iteration 229: k_eff = 1.220641 res = 1.875E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.69\n", + "[ NORMAL ] Iteration 230: k_eff = 1.220655 res = 3.448E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.84\n", + "[ NORMAL ] Iteration 231: k_eff = 1.220667 res = 8.046E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.33\n", + "[ NORMAL ] Iteration 232: k_eff = 1.220679 res = 3.751E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.47\n", + "[ NORMAL ] Iteration 233: k_eff = 1.220690 res = 5.686E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.52\n", + "[ NORMAL ] Iteration 234: k_eff = 1.220701 res = 1.270E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.22\n", + "[ NORMAL ] Iteration 235: k_eff = 1.220711 res = 6.715E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 5.29\n" ] } ], @@ -1449,25 +1693,23 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "openmc keff = 1.224484\n", - "openmoc keff = 1.220675\n", - "bias [pcm]: -380.9\n" + "openmoc keff = 1.220711\n", + "bias [pcm]: -377.3\n" ] } ], "source": [ "# Print report of keff and bias with OpenMC\n", "openmoc_keff = solver.getKeff()\n", - "openmc_keff = sp.k_combined[0]\n", + "openmc_keff = sp.k_combined.n\n", "bias = (openmoc_keff - openmc_keff) * 1e5\n", "\n", "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", @@ -1484,24 +1726,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1799: RuntimeWarning: invalid value encountered in true_divide\n", - " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1800: RuntimeWarning: invalid value encountered in true_divide\n", - " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" - ] - } - ], + "execution_count": 25, + "metadata": {}, + "outputs": [], "source": [ "openmoc_geometry = get_openmoc_geometry(sp.summary.geometry)\n", "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", @@ -1537,357 +1764,760 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ NORMAL ] Importing ray tracing data from file...\n", + "[ NORMAL ] Initializing a default angular quadrature...\n", + "[ NORMAL ] Initializing 2D tracks...\n", + "[ NORMAL ] Initializing 2D tracks reflections...\n", + "[ NORMAL ] Initializing 2D tracks array...\n", + "[ NORMAL ] Ray tracing for 2D track segmentation...\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 0.09 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 10.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 19.94 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 29.87 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 39.80 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 49.72 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 59.65 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 69.58 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 79.50 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 89.43 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n", + "[ NORMAL ] Initializing FSR lookup vectors\n", + "[ NORMAL ] Total number of FSRs 3\n", + "[ RESULT ] Total Track Generation & Segmentation Time...........3.9517E-02 sec\n", + "[ NORMAL ] Initializing MOC eigenvalue solver...\n", + "[ NORMAL ] Initializing solver arrays...\n", + "[ NORMAL ] Centering segments around FSR centroid...\n", + "[ NORMAL ] Max boundary angular flux storage per domain = 0.10 MB\n", + "[ NORMAL ] Max scalar flux storage per domain = 0.00 MB\n", + "[ NORMAL ] Max source storage per domain = 0.00 MB\n", + "[ NORMAL ] Number of azimuthal angles = 128\n", + "[ NORMAL ] Azimuthal ray spacing = 0.100000\n", + "[ NORMAL ] Number of polar angles = 6\n", + "[ NORMAL ] Source type = Flat\n", + "[ NORMAL ] MOC transport undamped\n", + "[ NORMAL ] CMFD acceleration: OFF\n", + "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.366885\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.391184\tres = 6.331E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.392990\tres = 6.623E-02\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.381099\tres = 4.617E-03\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.375018\tres = 3.026E-02\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.369593\tres = 1.596E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.365543\tres = 1.446E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.363055\tres = 1.096E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.361474\tres = 6.809E-03\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.361280\tres = 4.354E-03\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.362004\tres = 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Iteration 5: k_eff = 0.369593 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -542 D.R. = inf\n", + "[ NORMAL ] Iteration 6: k_eff = 0.365543 res = 1.065E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... -405 D.R. = 5.50\n", + "[ NORMAL ] Iteration 7: k_eff = 0.363054 res = 1.065E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... -248 D.R. = 1.00\n", + "[ NORMAL ] Iteration 8: k_eff = 0.361473 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -158 D.R. = 0.18\n", + "[ NORMAL ] Iteration 9: k_eff = 0.361280 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... -19 D.R. = 3.00\n", + "[ NORMAL ] Iteration 10: k_eff = 0.362003 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 72 D.R. = 0.33\n", + "[ NORMAL ] Iteration 11: k_eff = 0.363718 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 171 D.R. = 2.50\n", + "[ NORMAL ] Iteration 12: k_eff = 0.366338 res = 1.258E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 262 D.R. = 2.60\n", + "[ NORMAL ] Iteration 13: k_eff = 0.369804 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 346 D.R. = 0.77\n", + "[ NORMAL ] Iteration 14: k_eff = 0.373989 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 418 D.R. = 0.40\n", + "[ NORMAL ] Iteration 15: k_eff = 0.378923 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 493 D.R. = 0.00\n", + "[ NORMAL ] Iteration 16: k_eff = 0.384479 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = inf\n", + "[ NORMAL ] Iteration 17: k_eff = 0.390637 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 615 D.R. = 1.00\n", + "[ NORMAL ] Iteration 18: k_eff = 0.397338 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 670 D.R. = 0.00\n", + "[ NORMAL ] Iteration 19: k_eff = 0.404533 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 719 D.R. = inf\n", + "[ NORMAL ] Iteration 20: k_eff = 0.412184 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 765 D.R. = 1.67\n", + "[ NORMAL ] Iteration 21: k_eff = 0.420253 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 806 D.R. = 0.80\n", + "[ NORMAL ] Iteration 22: k_eff = 0.428686 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 843 D.R. = 0.75\n", + "[ NORMAL ] Iteration 23: k_eff = 0.437462 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 877 D.R. = 1.67\n", + "[ NORMAL ] Iteration 24: k_eff = 0.446538 res = 1.161E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 907 D.R. = 1.20\n", + "[ NORMAL ] Iteration 25: k_eff = 0.455883 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 934 D.R. = 0.17\n", + "[ NORMAL ] Iteration 26: k_eff = 0.465469 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 958 D.R. = 1.00\n", + "[ NORMAL ] Iteration 27: k_eff = 0.475265 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 979 D.R. = 2.00\n", + "[ NORMAL ] Iteration 28: k_eff = 0.485246 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 998 D.R. = 1.00\n", + "[ NORMAL ] Iteration 29: k_eff = 0.495385 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1013 D.R. = 0.50\n", + "[ NORMAL ] Iteration 30: k_eff = 0.505661 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1027 D.R. = 4.00\n", + "[ NORMAL ] Iteration 31: k_eff = 0.516051 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1038 D.R. = 1.25\n", + "[ NORMAL ] Iteration 32: k_eff = 0.526534 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1048 D.R. = 1.00\n", + "[ NORMAL ] Iteration 33: k_eff = 0.537092 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1055 D.R. = 1.00\n", + "[ NORMAL ] Iteration 34: k_eff = 0.547706 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1061 D.R. = 0.60\n", + "[ NORMAL ] Iteration 35: k_eff = 0.558361 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1065 D.R. = 0.33\n", + "[ NORMAL ] Iteration 36: k_eff = 0.569040 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1067 D.R. = 3.00\n", + "[ NORMAL ] Iteration 37: k_eff = 0.579730 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1068 D.R. = 1.67\n", + "[ NORMAL ] Iteration 38: k_eff = 0.590416 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 1068 D.R. = 0.00\n", + "[ NORMAL ] Iteration 39: k_eff = 0.601087 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1067 D.R. = inf\n", + "[ NORMAL ] Iteration 40: k_eff = 0.611731 res = 1.549E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 1064 D.R. = 1.60\n", + "[ NORMAL ] Iteration 41: k_eff = 0.622338 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1060 D.R. = 0.50\n", + "[ NORMAL ] Iteration 42: k_eff = 0.632897 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1055 D.R. = 1.00\n", + "[ NORMAL ] Iteration 43: k_eff = 0.643400 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1050 D.R. = 0.75\n", + "[ NORMAL ] Iteration 44: k_eff = 0.653837 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 1043 D.R. = 0.00\n", + "[ NORMAL ] Iteration 45: k_eff = 0.664203 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1036 D.R. = inf\n", + "[ NORMAL ] Iteration 46: k_eff = 0.674488 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1028 D.R. = 1.25\n", + "[ NORMAL ] Iteration 47: k_eff = 0.684688 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1020 D.R. = 1.00\n", + "[ NORMAL ] Iteration 48: k_eff = 0.694796 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1010 D.R. = 0.40\n", + "[ NORMAL ] Iteration 49: k_eff = 0.704807 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 1001 D.R. = 1.00\n", + "[ NORMAL ] Iteration 50: k_eff = 0.714715 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 990 D.R. = 0.50\n", + "[ NORMAL ] Iteration 51: k_eff = 0.724517 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 980 D.R. = 1.00\n", + "[ NORMAL ] Iteration 52: k_eff = 0.734209 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 969 D.R. = 1.00\n", + "[ NORMAL ] Iteration 53: k_eff = 0.743787 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 957 D.R. = 0.00\n", + "[ NORMAL ] Iteration 54: k_eff = 0.753247 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 946 D.R. = inf\n", + "[ NORMAL ] Iteration 55: k_eff = 0.762588 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 934 D.R. = 1.50\n", + "[ NORMAL ] Iteration 56: k_eff = 0.771806 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 921 D.R. = 0.33\n", + "[ NORMAL ] Iteration 57: k_eff = 0.780901 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 909 D.R. = 2.00\n", + "[ NORMAL ] Iteration 58: k_eff = 0.789868 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 896 D.R. = 0.50\n", + "[ NORMAL ] Iteration 59: k_eff = 0.798708 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 884 D.R. = 1.00\n", + "[ NORMAL ] Iteration 60: k_eff = 0.807419 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 871 D.R. = 4.00\n", + "[ NORMAL ] Iteration 61: k_eff = 0.816000 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 858 D.R. = 0.50\n", + "[ NORMAL ] Iteration 62: k_eff = 0.824450 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 844 D.R. = 1.00\n", + "[ NORMAL ] Iteration 63: k_eff = 0.832768 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 831 D.R. = 2.00\n", + "[ NORMAL ] Iteration 64: k_eff = 0.840954 res = 1.742E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 818 D.R. = 2.25\n", + "[ NORMAL ] Iteration 65: k_eff = 0.849008 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 805 D.R. = 0.44\n", + "[ NORMAL ] Iteration 66: k_eff = 0.856930 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 792 D.R. = 0.50\n", + "[ NORMAL ] Iteration 67: k_eff = 0.864720 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 778 D.R. = 1.00\n", + "[ NORMAL ] Iteration 68: k_eff = 0.872378 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 765 D.R. = 1.00\n", + "[ NORMAL ] Iteration 69: k_eff = 0.879905 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 752 D.R. = 0.50\n", + "[ NORMAL ] Iteration 70: k_eff = 0.887301 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 739 D.R. = 2.00\n", + "[ NORMAL ] Iteration 71: k_eff = 0.894566 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 726 D.R. = 1.50\n", + "[ NORMAL ] Iteration 72: k_eff = 0.901702 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 713 D.R. = 0.33\n", + "[ NORMAL ] Iteration 73: k_eff = 0.908710 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 700 D.R. = 2.00\n", + "[ NORMAL ] Iteration 74: k_eff = 0.915590 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 687 D.R. = 1.50\n", + "[ NORMAL ] Iteration 75: k_eff = 0.922342 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 675 D.R. = 0.00\n", + "[ NORMAL ] Iteration 76: k_eff = 0.928971 res = 1.161E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 662 D.R. = inf\n", + "[ NORMAL ] Iteration 77: k_eff = 0.935474 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 650 D.R. = 0.17\n", + "[ NORMAL ] Iteration 78: k_eff = 0.941853 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 637 D.R. = 1.00\n", + "[ NORMAL ] Iteration 79: k_eff = 0.948112 res = 9.679E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 625 D.R. = 5.00\n", + "[ NORMAL ] Iteration 80: k_eff = 0.954249 res = 1.065E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 613 D.R. = 1.10\n", + "[ NORMAL ] Iteration 81: k_eff = 0.960267 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 601 D.R. = 0.09\n", + "[ NORMAL ] Iteration 82: k_eff = 0.966168 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 590 D.R. = 8.00\n", + "[ NORMAL ] Iteration 83: k_eff = 0.971953 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 578 D.R. = 0.75\n", + "[ NORMAL ] Iteration 84: k_eff = 0.977623 res = 1.936E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 566 D.R. = 0.33\n", + "[ NORMAL ] Iteration 85: k_eff = 0.983179 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 555 D.R. = 1.50\n", + "[ NORMAL ] Iteration 86: k_eff = 0.988624 res = 0.000E+00 delta-k (pcm) =\n", + "[ NORMAL ] ... 544 D.R. = 0.00\n", + "[ NORMAL ] Iteration 87: k_eff = 0.993958 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 533 D.R. = inf\n", + "[ NORMAL ] Iteration 88: k_eff = 0.999184 res = 9.679E-09 delta-k (pcm) =\n", + "[ NORMAL ] ... 522 D.R. = 0.14\n", + "[ NORMAL ] Iteration 89: k_eff = 1.004304 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 511 D.R. = 5.00\n", + "[ NORMAL ] Iteration 90: k_eff = 1.009318 res = 3.872E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 501 D.R. = 0.80\n", + "[ NORMAL ] Iteration 91: k_eff = 1.014228 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 491 D.R. = 1.25\n", + "[ NORMAL ] Iteration 92: k_eff = 1.019037 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 480 D.R. = 1.20\n", + "[ NORMAL ] Iteration 93: k_eff = 1.023744 res = 2.904E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 470 D.R. = 0.50\n", + "[ NORMAL ] Iteration 94: k_eff = 1.028353 res = 6.775E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 460 D.R. = 2.33\n", + "[ NORMAL ] Iteration 95: k_eff = 1.032865 res = 7.743E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 451 D.R. = 1.14\n", + "[ NORMAL ] Iteration 96: k_eff = 1.037281 res = 1.355E-07 delta-k (pcm) =\n", + "[ NORMAL ] ... 441 D.R. = 1.75\n", + "[ NORMAL ] Iteration 97: k_eff = 1.041604 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 432 D.R. = 0.43\n", + "[ NORMAL ] Iteration 98: k_eff = 1.045834 res = 4.840E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 422 D.R. = 0.83\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Iteration 99: k_eff = 1.049974 res = 5.807E-08 delta-k (pcm) =\n", + "[ NORMAL ] ... 413 D.R. = 1.20\n", + "[ NORMAL ] Iteration 100: k_eff = 1.054024 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 405 D.R. = 1.00\n", + "[ NORMAL ] Iteration 101: k_eff = 1.057987 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 396 D.R. = 0.83\n", + "[ NORMAL ] Iteration 102: k_eff = 1.061864 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 387 D.R. = 0.20\n", + "[ NORMAL ] Iteration 103: k_eff = 1.065657 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 379 D.R. = 3.00\n", + "[ NORMAL ] Iteration 104: k_eff = 1.069367 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 370 D.R. = 1.33\n", + "[ NORMAL ] Iteration 105: k_eff = 1.072996 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 362 D.R. = 1.00\n", + "[ NORMAL ] Iteration 106: k_eff = 1.076545 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 354 D.R. = 0.50\n", + "[ NORMAL ] Iteration 107: k_eff = 1.080016 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 347 D.R. = 3.50\n", + "[ NORMAL ] Iteration 108: k_eff = 1.083411 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 339 D.R. = 1.71\n", + "[ NORMAL ] Iteration 109: k_eff = 1.086731 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 331 D.R. = 0.50\n", + "[ NORMAL ] Iteration 110: k_eff = 1.089976 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 324 D.R. = 0.33\n", + "[ NORMAL ] Iteration 111: k_eff = 1.093150 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 317 D.R. = 4.00\n", + "[ NORMAL ] Iteration 112: k_eff = 1.096253 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 310 D.R. = 1.00\n", + "[ NORMAL ] Iteration 113: k_eff = 1.099286 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 303 D.R. = 0.50\n", + "[ NORMAL ] Iteration 114: k_eff = 1.102251 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 296 D.R. = 1.25\n", + "[ NORMAL ] Iteration 115: k_eff = 1.105150 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 289 D.R. = 0.20\n", + "[ NORMAL ] Iteration 116: k_eff = 1.107983 res = 1.452E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 283 D.R. = 15.00\n", + "[ NORMAL ] Iteration 117: k_eff = 1.110753 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 276 D.R. = 0.33\n", + "[ NORMAL ] Iteration 118: k_eff = 1.113459 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 270 D.R. = 1.80\n", + "[ NORMAL ] Iteration 119: k_eff = 1.116105 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 264 D.R. = 1.22\n", + "[ NORMAL ] Iteration 120: k_eff = 1.118690 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 258 D.R. = 0.36\n", + "[ NORMAL ] Iteration 121: k_eff = 1.121216 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 252 D.R. = 0.25\n", + "[ NORMAL ] Iteration 122: k_eff = 1.123685 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 246 D.R. = 1.00\n", + "[ NORMAL ] Iteration 123: k_eff = 1.126097 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 241 D.R. = 4.00\n", + "[ NORMAL ] Iteration 124: k_eff = 1.128454 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 235 D.R. = 0.00\n", + "[ NORMAL ] Iteration 125: k_eff = 1.130758 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 230 D.R. = inf\n", + "[ NORMAL ] Iteration 126: k_eff = 1.133008 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 224 D.R. = 5.50\n", + "[ NORMAL ] Iteration 127: k_eff = 1.135207 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 219 D.R. = 0.45\n", + "[ NORMAL ] Iteration 128: k_eff = 1.137354 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 214 D.R. = 0.40\n", + "[ NORMAL ] Iteration 129: k_eff = 1.139453 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 209 D.R. = 0.00\n", + "[ NORMAL ] Iteration 130: k_eff = 1.141503 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 204 D.R. = inf\n", + "[ NORMAL ] Iteration 131: k_eff = 1.143505 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 200 D.R. = 1.50\n", + "[ NORMAL ] Iteration 132: k_eff = 1.145461 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 195 D.R. = 2.00\n", + "[ NORMAL ] Iteration 133: k_eff = 1.147372 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 191 D.R. = 1.50\n", + "[ NORMAL ] Iteration 134: k_eff = 1.149238 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 186 D.R. = 1.22\n", + "[ NORMAL ] Iteration 135: k_eff = 1.151061 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 182 D.R. = 0.18\n", + "[ NORMAL ] Iteration 136: k_eff = 1.152842 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 178 D.R. = 1.00\n", + "[ NORMAL ] Iteration 137: k_eff = 1.154581 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 173 D.R. = 2.00\n", + "[ NORMAL ] Iteration 138: k_eff = 1.156279 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 169 D.R. = 2.50\n", + "[ NORMAL ] Iteration 139: k_eff = 1.157938 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 165 D.R. = 0.80\n", + "[ NORMAL ] Iteration 140: k_eff = 1.159557 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 161 D.R. = 1.50\n", + "[ NORMAL ] Iteration 141: k_eff = 1.161139 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 158 D.R. = 0.33\n", + "[ NORMAL ] Iteration 142: k_eff = 1.162684 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 154 D.R. = 1.00\n", + "[ NORMAL ] Iteration 143: k_eff = 1.164193 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 150 D.R. = 2.75\n", + "[ NORMAL ] Iteration 144: k_eff = 1.165666 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 147 D.R. = 0.09\n", + "[ NORMAL ] Iteration 145: k_eff = 1.167105 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 143 D.R. = 2.00\n", + "[ NORMAL ] Iteration 146: k_eff = 1.168509 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 140 D.R. = 4.00\n", + "[ NORMAL ] Iteration 147: k_eff = 1.169881 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 137 D.R. = 1.25\n", + "[ NORMAL ] Iteration 148: k_eff = 1.171220 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 133 D.R. = 0.10\n", + "[ NORMAL ] Iteration 149: k_eff = 1.172528 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 130 D.R. = 6.00\n", + "[ NORMAL ] Iteration 150: k_eff = 1.173804 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 127 D.R. = 1.67\n", + "[ NORMAL ] Iteration 151: k_eff = 1.175051 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 124 D.R. = 0.80\n", + "[ NORMAL ] Iteration 152: k_eff = 1.176268 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 121 D.R. = 1.00\n", + "[ NORMAL ] Iteration 153: k_eff = 1.177456 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 118 D.R. = 1.00\n", + "[ NORMAL ] Iteration 154: k_eff = 1.178616 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 115 D.R. = 0.63\n", + "[ NORMAL ] Iteration 155: k_eff = 1.179749 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 113 D.R. = 1.20\n", + "[ NORMAL ] Iteration 156: k_eff = 1.180855 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 110 D.R. = 0.83\n", + "[ NORMAL ] Iteration 157: k_eff = 1.181935 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 107 D.R. = 0.20\n", + "[ NORMAL ] Iteration 158: k_eff = 1.182988 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 105 D.R. = 3.00\n", + "[ NORMAL ] Iteration 159: k_eff = 1.184017 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 102 D.R. = 0.67\n", + "[ NORMAL ] Iteration 160: k_eff = 1.185021 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 100 D.R. = 1.50\n", + "[ NORMAL ] Iteration 161: k_eff = 1.186002 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 98 D.R. = 0.67\n", + "[ NORMAL ] Iteration 162: k_eff = 1.186959 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 95 D.R. = 2.50\n", + "[ NORMAL ] Iteration 163: k_eff = 1.187893 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 93 D.R. = 1.40\n", + "[ NORMAL ] Iteration 164: k_eff = 1.188805 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 91 D.R. = 1.29\n", + "[ NORMAL ] Iteration 165: k_eff = 1.189695 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 89 D.R. = 0.22\n", + "[ NORMAL ] Iteration 166: k_eff = 1.190564 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 86 D.R. = 2.00\n", + "[ NORMAL ] Iteration 167: k_eff = 1.191413 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 84 D.R. = 2.00\n", + "[ NORMAL ] Iteration 168: k_eff = 1.192241 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 82 D.R. = 0.00\n", + "[ NORMAL ] Iteration 169: k_eff = 1.193049 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 80 D.R. = inf\n", + "[ NORMAL ] Iteration 170: k_eff = 1.193838 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 78 D.R. = 4.50\n", + "[ NORMAL ] Iteration 171: k_eff = 1.194607 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] 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(pcm)\n", + "[ NORMAL ] ... = 50 D.R. = 0.57\n", + "[ NORMAL ] Iteration 189: k_eff = 1.205698 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 49 D.R. = 0.50\n", + "[ NORMAL ] Iteration 190: k_eff = 1.206183 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 48 D.R. = 0.50\n", + "[ NORMAL ] Iteration 191: k_eff = 1.206656 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 47 D.R. = 6.00\n", + "[ NORMAL ] Iteration 192: k_eff = 1.207118 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 46 D.R. = 0.00\n", + "[ NORMAL ] Iteration 193: k_eff = 1.207570 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 45 D.R. = inf\n", + "[ NORMAL ] Iteration 194: k_eff = 1.208010 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 44 D.R. = 0.33\n", + "[ NORMAL ] Iteration 195: k_eff = 1.208439 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 42 D.R. = 1.33\n", + "[ NORMAL ] Iteration 196: k_eff = 1.208858 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 41 D.R. = 0.50\n", + "[ NORMAL ] Iteration 197: k_eff = 1.209267 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 40 D.R. = 1.50\n", + "[ NORMAL ] Iteration 198: k_eff = 1.209665 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 39 D.R. = 1.33\n", + "[ NORMAL ] Iteration 199: k_eff = 1.210055 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 38 D.R. = 1.50\n", + "[ NORMAL ] Iteration 200: k_eff = 1.210435 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 37 D.R. = 0.17\n", + "[ NORMAL ] Iteration 201: k_eff = 1.210805 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 37 D.R. = 9.00\n", + "[ NORMAL ] Iteration 202: k_eff = 1.211167 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 36 D.R. = 0.11\n", + "[ NORMAL ] Iteration 203: k_eff = 1.211520 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 35 D.R. = 5.00\n", + "[ NORMAL ] Iteration 204: k_eff = 1.211864 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 34 D.R. = 0.60\n", + "[ NORMAL ] Iteration 205: k_eff = 1.212200 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 33 D.R. = 0.67\n", + "[ NORMAL ] Iteration 206: k_eff = 1.212528 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 32 D.R. = 2.50\n", + "[ NORMAL ] Iteration 207: k_eff = 1.212848 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 32 D.R. = 1.60\n", + "[ NORMAL ] Iteration 208: k_eff = 1.213160 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 31 D.R. = 0.25\n", + "[ NORMAL ] Iteration 209: k_eff = 1.213466 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 30 D.R. = 5.50\n", + "[ NORMAL ] Iteration 210: k_eff = 1.213763 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 29 D.R. = 0.55\n", + "[ NORMAL ] Iteration 211: k_eff = 1.214053 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 29 D.R. = 1.67\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Iteration 212: k_eff = 1.214337 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 28 D.R. = 0.30\n", + "[ NORMAL ] Iteration 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(pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 0.25\n", + "[ NORMAL ] Iteration 222: k_eff = 1.216817 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 22 D.R. = 3.00\n", + "[ NORMAL ] Iteration 223: k_eff = 1.217033 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 0.33\n", + "[ NORMAL ] Iteration 224: k_eff = 1.217244 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 21 D.R. = 4.00\n", + "[ NORMAL ] Iteration 225: k_eff = 1.217450 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 0.75\n", + "[ NORMAL ] Iteration 226: k_eff = 1.217651 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 20 D.R. = 0.17\n", + "[ NORMAL ] Iteration 227: k_eff = 1.217847 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 4.00\n", + "[ NORMAL ] Iteration 228: k_eff = 1.218039 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 19 D.R. = 2.50\n", + "[ NORMAL ] Iteration 229: k_eff = 1.218225 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 0.20\n", + "[ NORMAL ] Iteration 230: k_eff = 1.218407 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 18 D.R. = 2.50\n", + "[ NORMAL ] Iteration 231: k_eff = 1.218585 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 1.00\n", + "[ NORMAL ] Iteration 232: k_eff = 1.218758 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 17 D.R. = 1.40\n", + "[ NORMAL ] Iteration 233: k_eff = 1.218927 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 0.71\n", + "[ NORMAL ] Iteration 234: k_eff = 1.219092 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 1.00\n", + "[ NORMAL ] Iteration 235: k_eff = 1.219253 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 16 D.R. = 1.40\n", + "[ NORMAL ] Iteration 236: k_eff = 1.219410 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 0.57\n", + "[ NORMAL ] Iteration 237: k_eff = 1.219563 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 15 D.R. = 0.50\n", + "[ NORMAL ] Iteration 238: k_eff = 1.219712 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"[ NORMAL ] ... = 8 D.R. = 2.71\n", + "[ NORMAL ] Iteration 264: k_eff = 1.222547 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.16\n", + "[ NORMAL ] Iteration 265: k_eff = 1.222624 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 1.00\n", + "[ NORMAL ] Iteration 266: k_eff = 1.222699 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 0.33\n", + "[ NORMAL ] Iteration 267: k_eff = 1.222772 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 6.00\n", + "[ NORMAL ] Iteration 268: k_eff = 1.222844 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 7 D.R. = 1.17\n", + "[ NORMAL ] Iteration 269: k_eff = 1.222913 res = 1.452E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 2.14\n", + "[ NORMAL ] Iteration 270: k_eff = 1.222981 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.40\n", + "[ NORMAL ] Iteration 271: k_eff = 1.223048 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.17\n", + "[ NORMAL ] 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(pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.20\n", + "[ NORMAL ] Iteration 281: k_eff = 1.223629 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 5.00\n", + "[ NORMAL ] Iteration 282: k_eff = 1.223680 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 2.60\n", + "[ NORMAL ] Iteration 283: k_eff = 1.223729 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.77\n", + "[ NORMAL ] Iteration 284: k_eff = 1.223777 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.70\n", + "[ NORMAL ] Iteration 285: k_eff = 1.223824 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.00\n", + "[ NORMAL ] Iteration 286: k_eff = 1.223870 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 1.00\n", + "[ NORMAL ] Iteration 287: k_eff = 1.223915 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.29\n", + "[ NORMAL ] Iteration 288: k_eff = 1.223959 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.00\n", + "[ NORMAL ] Iteration 289: k_eff = 1.224001 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = inf\n", + "[ NORMAL ] Iteration 290: k_eff = 1.224043 res = 1.355E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 2.80\n", + "[ NORMAL ] Iteration 291: k_eff = 1.224083 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.57\n", + "[ NORMAL ] Iteration 292: k_eff = 1.224123 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.37\n", + "[ NORMAL ] Iteration 293: k_eff = 1.224161 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.33\n", + "[ NORMAL ] Iteration 294: k_eff = 1.224199 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 2.00\n", + "[ NORMAL ] Iteration 295: k_eff = 1.224235 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.50\n", + "[ NORMAL ] Iteration 296: k_eff = 1.224271 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.75\n", + "[ NORMAL ] Iteration 297: k_eff = 1.224306 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 1.71\n", + "[ NORMAL ] Iteration 298: k_eff = 1.224340 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.08\n", + "[ NORMAL ] Iteration 299: k_eff = 1.224373 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 2.00\n", + "[ NORMAL ] Iteration 300: k_eff = 1.224406 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 5.50\n", + "[ NORMAL ] Iteration 301: k_eff = 1.224438 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.82\n", + "[ NORMAL ] Iteration 302: k_eff = 1.224468 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.78\n", + "[ NORMAL ] Iteration 303: k_eff = 1.224498 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.71\n", + "[ NORMAL ] Iteration 304: k_eff = 1.224528 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.40\n", + "[ NORMAL ] Iteration 305: k_eff = 1.224557 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 3.00\n", + "[ NORMAL ] Iteration 306: k_eff = 1.224585 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.83\n", + "[ NORMAL ] Iteration 307: k_eff = 1.224612 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.20\n", + "[ NORMAL ] Iteration 308: k_eff = 1.224639 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 3.00\n", + "[ NORMAL ] Iteration 309: k_eff = 1.224665 res = 9.679E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 3.33\n", + "[ NORMAL ] Iteration 310: k_eff = 1.224691 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.80\n", + "[ NORMAL ] Iteration 311: k_eff = 1.224716 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.50\n", + "[ NORMAL ] Iteration 312: k_eff = 1.224740 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.75\n", + "[ NORMAL ] Iteration 313: k_eff = 1.224764 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 4.00\n", + "[ NORMAL ] Iteration 314: k_eff = 1.224786 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.17\n", + "[ NORMAL ] Iteration 315: k_eff = 1.224809 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.50\n", + "[ NORMAL ] Iteration 316: k_eff = 1.224831 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 3.67\n", + "[ NORMAL ] Iteration 317: k_eff = 1.224852 res = 1.161E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 1.09\n", + "[ NORMAL ] Iteration 318: k_eff = 1.224873 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.00\n", + "[ NORMAL ] Iteration 319: k_eff = 1.224893 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = inf\n", + "[ NORMAL ] Iteration 320: k_eff = 1.224913 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.63\n", + "[ NORMAL ] Iteration 321: k_eff = 1.224932 res = 6.775E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.40\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Iteration 322: k_eff = 1.224951 res = 8.711E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.29\n", + "[ NORMAL ] Iteration 323: k_eff = 1.224969 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.11\n", + "[ NORMAL ] Iteration 324: k_eff = 1.224987 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 8.00\n", + "[ NORMAL ] Iteration 325: k_eff = 1.225005 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.25\n", + "[ NORMAL ] Iteration 326: k_eff = 1.225022 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.50\n", + "[ NORMAL ] Iteration 327: k_eff = 1.225039 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.00\n", + "[ NORMAL ] Iteration 328: k_eff = 1.225055 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 3.00\n", + "[ NORMAL ] Iteration 329: k_eff = 1.225071 res = 1.065E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.83\n", + "[ NORMAL ] Iteration 330: k_eff = 1.225086 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.27\n", + "[ NORMAL ] Iteration 331: k_eff = 1.225102 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.00\n", + "[ NORMAL ] Iteration 332: k_eff = 1.225116 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.00\n", + "[ NORMAL ] Iteration 333: k_eff = 1.225131 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.00\n", + "[ NORMAL ] Iteration 334: k_eff = 1.225145 res = 4.840E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.83\n", + "[ NORMAL ] Iteration 335: k_eff = 1.225159 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.20\n", + "[ NORMAL ] Iteration 336: k_eff = 1.225172 res = 5.807E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 6.00\n", + "[ NORMAL ] Iteration 337: k_eff = 1.225185 res = 1.355E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 2.33\n", + "[ NORMAL ] Iteration 338: k_eff = 1.225198 res = 1.258E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.93\n", + "[ NORMAL ] Iteration 339: k_eff = 1.225210 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.00\n", + "[ NORMAL ] Iteration 340: k_eff = 1.225222 res = 9.679E-09 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 341: k_eff = 1.225233 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 4.00\n", + "[ NORMAL ] Iteration 342: k_eff = 1.225245 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.50\n", + "[ NORMAL ] Iteration 343: k_eff = 1.225256 res = 0.000E+00 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.00\n", + "[ NORMAL ] Iteration 344: k_eff = 1.225267 res = 7.743E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = inf\n", + "[ NORMAL ] Iteration 345: k_eff = 1.225278 res = 2.904E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.38\n", + "[ NORMAL ] Iteration 346: k_eff = 1.225288 res = 3.872E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 1.33\n", + "[ NORMAL ] Iteration 347: k_eff = 1.225298 res = 1.936E-08 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 0.50\n" ] } ], @@ -1903,25 +2533,23 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "openmc keff = 1.224484\n", - "openmoc keff = 1.225211\n", - "bias [pcm]: 72.7\n" + "openmoc keff = 1.225298\n", + "bias [pcm]: 81.4\n" ] } ], "source": [ "# Print report of keff and bias with OpenMC\n", "openmoc_keff = solver.getKeff()\n", - "openmc_keff = sp.k_combined[0]\n", + "openmc_keff = sp.k_combined.n\n", "bias = (openmoc_keff - openmc_keff) * 1e5\n", "\n", "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", @@ -1962,37 +2590,29 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 28, + "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/romano/openmc/openmc/tallies.py:1798: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" - ] - }, { "data": { "text/plain": [ - "(1.0000000000000001e-05, 20000000.0)" + "(1e-05, 20000000.0)" ] }, - "execution_count": 29, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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qukFE6gO/AQ8HOc4kCJfLxbUDO5TbVlRcEt+rnnmqhtxP/v0DzEJrD7CSgIlF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\nxhiT4P4fD4z+wrudGZ0AAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2033,10 +2653,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ "# Construct a Pandas DataFrame for the microscopic nu-scattering matrix\n", @@ -2065,19 +2683,19 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 30, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2118,9 +2736,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.7.0" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/examples/jupyter/mgxs-part-iii.ipynb b/examples/jupyter/mgxs-part-iii.ipynb index 5ffb66d3a..a4c440b3a 100644 --- a/examples/jupyter/mgxs-part-iii.ipynb +++ b/examples/jupyter/mgxs-part-iii.ipynb @@ -25,20 +25,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nelsonag/python/openmc/lib/python3.6/site-packages/matplotlib/__init__.py:1405: UserWarning: \n", - "This call to matplotlib.use() has no effect because the backend has already\n", - "been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", - "or matplotlib.backends is imported for the first time.\n", - "\n", - " warnings.warn(_use_error_msg)\n" - ] - } - ], + "outputs": [], "source": [ "import math\n", "import pickle\n", @@ -124,8 +111,8 @@ "outputs": [], "source": [ "# Create cylinders for the fuel and clad\n", - "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n", - "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n", "\n", "# Create boundary planes to surround the geometry\n", "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n", @@ -345,68 +332,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let us also create a `Plots` file that we can use to verify that our fuel assembly geometry was created successfully." + "Let us also create a plot to verify that our fuel assembly geometry was created successfully." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate a Plot\n", - "plot = openmc.Plot(plot_id=1)\n", - "plot.filename = 'materials-xy'\n", - "plot.origin = [0, 0, 0]\n", - "plot.pixels = [250, 250]\n", - "plot.width = [-10.71*2, -10.71*2]\n", - "plot.color_by = 'material'\n", - "\n", - "# Instantiate a Plots object, add Plot, and export to \"plots.xml\"\n", - "plot_file = openmc.Plots([plot])\n", - "plot_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# Run openmc in plotting mode\n", - "openmc.plot_geometry(output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEX///9yEhLpgJFNv8TqQYT7AAAAAWJLR0QAiAUdSAAAAAd0SU1FB+IEGA8UMPReaV0AAAWFSURBVGje7Zs7cttADIZ9CSvXcrP0iCxUqbBc8Ag6xR6BhV2EvYvwFD4CCx1ABT1jMdgndpegRQnOrCbjpPlGESISC4A/gd27e8H583CX3b4+iKJrRHkS4vkghMPBonRYWGwtfgD2YN+dRDUOoh6lACw0Noi9w2fESuEoAR/uVuMolX039oXGT7F3eFL2iEfhUX1f4cPdL/ishs+68ai+udE4xPhexbjX2FfjGNoPj/DPNX4Tsd+EODr8FvsVdf1Hd9P2VvCi4+s/aXvrf+upAD+1/9GV1mkOH5X9vV6THtfvACslcaUCbESL61drBPtdI8SrFMWrELsXCkuFDYW75gbiP7d9Cf7bAYI/aCwUShrBvh30+lWQkzVgZ/HD4OixNCgcQpJ3BxU/Ln91elKoM5VEE38QtJ+Yv6cQ9xjKNYayyl8TypP8DfJnQ2H/b/N3ye9P83cT33SQv/sQh9gV7zZ/0dNj5HQaC5vVzv9+/WFN2w8KVaZ2BwL1+pv4g0x1QRfjq0dB4Q3kT277oP6VNL6gKxNU9a8zK+WLbi/WwpdihbboKqyxFOulHMj6v4W/AXbmUeAxrv9J/CqEBXaRKsXaodD4nsYvkT/G6H1D4SR/iPy1Roj9JsQ5e18/7EUHv1+Fvx/Xj5V9Ugb5K8TW4TZEEdcvoz/up0VTe9qsVIppKVX6a7D6y9ZvwEKjrtQxPtv6fXII9vCxKOGaIeAIfEF8IvAG8ie3vRK9rRQl+PPpSctbhfpTUCpviH+kxsZgpT91+snoX1l49KK3iUQvICRy5aUw6l8leoVwoo3Uv1rKreF/UFLY6d9QP4L9Wf2r7EP9GOSfcsjZ56f60kz+XmVPXv+RuP49ff0T/53Rv6n/7m2lvXT9Wqd/VUz8hvh5M/ED6ILmt4mfHYZSaePnTWpsf/SvqV9O6dLYYClLEetnoH/LBLFoBvrX189uTv8++kot5vTvQD4/9jP690g9P/4z/bvo/XVG/xYoZZx+8fr3MxAtsf7tUOkG2JqsTtCIpgCt/qX1226KqZS7gfzJbe+c9jLrtIZ8lXD+s4umlW6AKIVrlML2/cXjgPFjlJqIRC+Fj0bVJe+vSh56pSdR6YkQ1ygF10Wqf0FeLta/iKn9Mv1L24ti2e+7W4n1b3T/W+L+t9H9T/SvVboUmqJJon1/hZq8LnzRDlDrX1u0xRT1+6vEpomMmyYkqi95vIH8yW1PN+122KkLcNLKi/WTF01z/cNASrWE/l3ev6T17zX909z9X27/euK/Rf3zWP+Waf9eEv37KkWJ+rfDl6ZglNDa+cEBhwYDvkoNP/rX69814NaI3imq0l7OYDy/qSdDGwr7r+Y3VbzoKZr6XX2lfxfOb87qXzr+b1j/Xlp/nP6dn98McdH7cn7zjPObKsYWS3Eb9w8n85smHtqQuPuZ30T2dlIT6F9xFl+n8xslegL9a4c2KRr9W4rp/GYqumiM9Nec/j2v/yj9u1h//hv9e93vc++f63/u+rPjL3f+5Lbn1j9m/eXWf+7zh/v8+2b9e/Hzn6s/uPqHrb8g71n6L3f+5Lbnvn8w33+4718/+5d47//c/gO7/5E7/nPbc/tv3P4fs//I7X9y+6/fqH+v6j9z+9/c/ju3/8+eP+TOn9z23PkXc/7Gnf9x5483q38Xzn+582fu/Js9fy8kb/6fO39y23P3n3S8/S/c/Tfc/T83uX/pgv1XE/9duP+Lu/+Mvf8td/znti8kb/8ld/9nx9t/Sjw/Ltr/yt1/+337f6/bf0zoB3nJ/ucVc/81d/83e/957vzJbc89/8A8f8E9/5HE78XnT/4H/cs5f8Q9/8Q9f8U+/5U7f3Lbc88fdrzzjyvm+cuf/Uu887/c88fs88954/8vO4SjPC+2QRIAAAAldEVYdGRhdGU6Y3JlYXRlADIwMTgtMDQtMjRUMTk6MjA6NDgtMDQ6MDCGN87lAAAAJXRFWHRkYXRlOm1vZGlmeQAyMDE4LTA0LTI0VDE5OjIwOjQ4LTA0OjAw92p2WQAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Convert OpenMC's funky ppm to png\n", - "!convert materials-xy.ppm materials-xy.png\n", - "\n", - "# Display the materials plot inline\n", - "Image(filename='materials-xy.png')" + "# Instantiate a Plot\n", + "plot = openmc.Plot.from_geometry(geometry)\n", + "plot.pixels = (250, 250)\n", + "plot.color_by = 'material'\n", + "plot.to_ipython_image()" ] }, { @@ -432,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -450,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -488,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -507,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -527,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -544,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -563,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -581,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -605,28 +556,28 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=126.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=126.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=96.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=96.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=15.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=15.\n", " warn(msg, IDWarning)\n", - "/home/nelsonag/git/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=114.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=114.\n", " warn(msg, IDWarning)\n" ] } @@ -638,142 +589,140 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 6c2d82a4d7dfe10312329d5969568fc03a698416\n", - " Date/Time | 2018-04-24 19:20:48\n", - " OpenMP Threads | 8\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", + " Date/Time | 2019-07-19 07:12:55\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Reading U235 from /opt/xsdata/nndc/U235.h5\n", - " Reading U238 from /opt/xsdata/nndc/U238.h5\n", - " Reading O16 from /opt/xsdata/nndc/O16.h5\n", - " Reading H1 from /opt/xsdata/nndc/H1.h5\n", - " Reading B10 from /opt/xsdata/nndc/B10.h5\n", - " Reading Zr90 from /opt/xsdata/nndc/Zr90.h5\n", - " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", + " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", + " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", + " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", + " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", + " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", + " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Reading tallies XML file...\n", " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.03784 \n", - " 2/1 1.02297 \n", - " 3/1 1.02244 \n", - " 4/1 1.02344 \n", - " 5/1 1.02057 \n", - " 6/1 1.04077 \n", - " 7/1 1.00795 \n", - " 8/1 1.02418 \n", - " 9/1 1.02241 \n", - " 10/1 1.03731 \n", - " 11/1 1.01477 \n", - " 12/1 1.05315 1.03396 +/- 0.01919\n", - " 13/1 1.02824 1.03205 +/- 0.01124\n", - " 14/1 1.02858 1.03118 +/- 0.00800\n", - " 15/1 1.02176 1.02930 +/- 0.00647\n", - " 16/1 1.06046 1.03449 +/- 0.00741\n", - " 17/1 1.02066 1.03252 +/- 0.00657\n", - " 18/1 1.03088 1.03231 +/- 0.00569\n", - " 19/1 1.02021 1.03097 +/- 0.00520\n", - " 20/1 1.02717 1.03059 +/- 0.00466\n", - " 21/1 1.03455 1.03095 +/- 0.00423\n", - " 22/1 1.02917 1.03080 +/- 0.00387\n", - " 23/1 1.02800 1.03058 +/- 0.00356\n", - " 24/1 1.02935 1.03050 +/- 0.00330\n", - " 25/1 1.01612 1.02954 +/- 0.00322\n", - " 26/1 1.00549 1.02803 +/- 0.00336\n", - " 27/1 1.02824 1.02805 +/- 0.00316\n", - " 28/1 1.01487 1.02731 +/- 0.00307\n", - " 29/1 1.05544 1.02879 +/- 0.00326\n", - " 30/1 1.00467 1.02759 +/- 0.00332\n", - " 31/1 1.03942 1.02815 +/- 0.00321\n", - " 32/1 1.02587 1.02805 +/- 0.00306\n", - " 33/1 1.02938 1.02811 +/- 0.00292\n", - " 34/1 1.02838 1.02812 +/- 0.00280\n", - " 35/1 1.00052 1.02701 +/- 0.00290\n", - " 36/1 1.01722 1.02664 +/- 0.00281\n", - " 37/1 1.01881 1.02635 +/- 0.00272\n", - " 38/1 1.03928 1.02681 +/- 0.00266\n", - " 39/1 1.03802 1.02720 +/- 0.00260\n", - " 40/1 1.00710 1.02653 +/- 0.00260\n", - " 41/1 1.02558 1.02650 +/- 0.00251\n", - " 42/1 1.03499 1.02676 +/- 0.00245\n", - " 43/1 1.01128 1.02629 +/- 0.00242\n", - " 44/1 1.00442 1.02565 +/- 0.00243\n", - " 45/1 1.03444 1.02590 +/- 0.00238\n", - " 46/1 1.01799 1.02568 +/- 0.00232\n", - " 47/1 1.00814 1.02521 +/- 0.00231\n", - " 48/1 1.00500 1.02467 +/- 0.00231\n", - " 49/1 1.01960 1.02454 +/- 0.00225\n", - " 50/1 1.02431 1.02454 +/- 0.00219\n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.03784\n", + " 2/1 1.02297\n", + " 3/1 1.02244\n", + " 4/1 1.02344\n", + " 5/1 1.02057\n", + " 6/1 1.04077\n", + " 7/1 1.00775\n", + " 8/1 1.03892\n", + " 9/1 1.01606\n", + " 10/1 1.02209\n", + " 11/1 1.03259\n", + " 12/1 1.03331 1.03295 +/- 0.00036\n", + " 13/1 1.02027 1.02872 +/- 0.00423\n", + " 14/1 1.03901 1.03130 +/- 0.00395\n", + " 15/1 1.02000 1.02904 +/- 0.00380\n", + " 16/1 1.04469 1.03164 +/- 0.00405\n", + " 17/1 1.01862 1.02978 +/- 0.00390\n", + " 18/1 1.03265 1.03014 +/- 0.00340\n", + " 19/1 1.00489 1.02734 +/- 0.00410\n", + " 20/1 1.04533 1.02914 +/- 0.00409\n", + " 21/1 1.01534 1.02788 +/- 0.00390\n", + " 22/1 1.02204 1.02739 +/- 0.00360\n", + " 23/1 1.02181 1.02696 +/- 0.00334\n", + " 24/1 0.99207 1.02447 +/- 0.00397\n", + " 25/1 1.03041 1.02487 +/- 0.00372\n", + " 26/1 1.03652 1.02560 +/- 0.00355\n", + " 27/1 1.03793 1.02632 +/- 0.00341\n", + " 28/1 1.02099 1.02603 +/- 0.00323\n", + " 29/1 1.01953 1.02568 +/- 0.00308\n", + " 30/1 1.01690 1.02525 +/- 0.00295\n", + " 31/1 1.01938 1.02497 +/- 0.00282\n", + " 32/1 1.01800 1.02465 +/- 0.00271\n", + " 33/1 1.01598 1.02427 +/- 0.00262\n", + " 34/1 1.01735 1.02398 +/- 0.00252\n", + " 35/1 1.01080 1.02346 +/- 0.00247\n", + " 36/1 1.01267 1.02304 +/- 0.00241\n", + " 37/1 1.01907 1.02289 +/- 0.00233\n", + " 38/1 1.02333 1.02291 +/- 0.00224\n", + " 39/1 1.01516 1.02264 +/- 0.00218\n", + " 40/1 1.02797 1.02282 +/- 0.00211\n", + " 41/1 1.03949 1.02336 +/- 0.00211\n", + " 42/1 1.01456 1.02308 +/- 0.00207\n", + " 43/1 1.02376 1.02310 +/- 0.00200\n", + " 44/1 1.01917 1.02299 +/- 0.00195\n", + " 45/1 1.01631 1.02280 +/- 0.00190\n", + " 46/1 1.02381 1.02282 +/- 0.00185\n", + " 47/1 1.04002 1.02329 +/- 0.00185\n", + " 48/1 1.01059 1.02296 +/- 0.00184\n", + " 49/1 1.02647 1.02305 +/- 0.00179\n", + " 50/1 1.02451 1.02308 +/- 0.00175\n", " Creating state point statepoint.50.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.8179E-01 seconds\n", - " Reading cross sections = 2.5741E-01 seconds\n", - " Total time in simulation = 2.5787E+01 seconds\n", - " Time in transport only = 2.5724E+01 seconds\n", - " Time in inactive batches = 1.7591E+00 seconds\n", - " Time in active batches = 2.4028E+01 seconds\n", - " Time synchronizing fission bank = 1.3217E-02 seconds\n", - " Sampling source sites = 1.0464E-02 seconds\n", - " SEND/RECV source sites = 2.6486E-03 seconds\n", - " Time accumulating tallies = 2.7351E-04 seconds\n", - " Total time for finalization = 5.5454E-05 seconds\n", - " Total time elapsed = 2.6109E+01 seconds\n", - " Calculation Rate (inactive) = 56847.1 neutrons/second\n", - " Calculation Rate (active) = 16647.3 neutrons/second\n", + " Total time for initialization = 5.7635e-01 seconds\n", + " Reading cross sections = 5.4002e-01 seconds\n", + " Total time in simulation = 7.0174e+01 seconds\n", + " Time in transport only = 6.9687e+01 seconds\n", + " Time in inactive batches = 7.1832e+00 seconds\n", + " Time in active batches = 6.2991e+01 seconds\n", + " Time synchronizing fission bank = 3.9991e-02 seconds\n", + " Sampling source sites = 3.4633e-02 seconds\n", + " SEND/RECV source sites = 5.2616e-03 seconds\n", + " Time accumulating tallies = 4.9801e-04 seconds\n", + " Total time for finalization = 1.3501e-05 seconds\n", + " Total time elapsed = 7.0791e+01 seconds\n", + " Calculation Rate (inactive) = 13921.3 particles/second\n", + " Calculation Rate (active) = 6350.11 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02204 +/- 0.00176\n", - " k-effective (Track-length) = 1.02454 +/- 0.00219\n", - " k-effective (Absorption) = 1.02370 +/- 0.00186\n", - " Combined k-effective = 1.02329 +/- 0.00157\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.02434 +/- 0.00173\n", + " k-effective (Track-length) = 1.02308 +/- 0.00175\n", + " k-effective (Absorption) = 1.02494 +/- 0.00175\n", + " Combined k-effective = 1.02408 +/- 0.00144\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] } @@ -799,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -816,7 +765,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -849,7 +798,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -867,25 +816,25 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "
\n", " \n", @@ -904,16 +853,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -928,16 +877,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -953,15 +902,15 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "3 1 1 U235 8.093482e-03 1.597406e-05\n", - "4 1 1 U238 7.347745e-03 2.082526e-05\n", + "3 1 1 U235 8.089079e-03 1.461462e-05\n", + "4 1 1 U238 7.358661e-03 2.302063e-05\n", "5 1 1 O16 0.000000e+00 0.000000e+00\n", - "0 1 2 U235 3.615911e-01 1.206052e-03\n", - "1 1 2 U238 6.743056e-07 2.229534e-09\n", + "0 1 2 U235 3.617174e-01 9.467633e-04\n", + "1 1 2 U238 6.744743e-07 1.750450e-09\n", "2 1 2 O16 0.000000e+00 0.000000e+00" ] }, - "execution_count": 29, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -980,7 +929,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -993,13 +942,13 @@ "\tDomain ID =\t1\n", "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t8.09e-03 +/- 1.97e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t3.62e-01 +/- 3.34e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t8.09e-03 +/- 1.81e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t3.62e-01 +/- 2.62e-01%\n", "\n", "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [0.625 - 20000000.0eV]:\t7.35e-03 +/- 2.83e-01%\n", - " Group 2 [0.0 - 0.625 eV]:\t6.74e-07 +/- 3.31e-01%\n", + " Group 1 [0.625 - 20000000.0eV]:\t7.36e-03 +/- 3.13e-01%\n", + " Group 2 [0.0 - 0.625 eV]:\t6.74e-07 +/- 2.60e-01%\n", "\n", "\tNuclide =\tO16\n", "\tCross Sections [cm^-1]:\n", @@ -1014,7 +963,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/nelsonag/git/openmc/openmc/tallies.py:1269: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/romano/openmc/openmc/tallies.py:1269: RuntimeWarning: invalid value encountered in true_divide\n", " data = self.std_dev[indices] / self.mean[indices]\n" ] } @@ -1032,7 +981,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1049,7 +998,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1059,7 +1008,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1076,7 +1025,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1089,25 +1038,25 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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11U2358.093482e-031.597406e-058.089079e-031.461462e-05
411U2387.347745e-032.082526e-057.358661e-032.302063e-05
512U2353.615911e-011.206052e-033.617174e-019.467633e-04
112U2386.743056e-072.229534e-096.744743e-071.750450e-09
2
\n", " \n", @@ -1126,16 +1075,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1151,12 +1100,12 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "0 1 1 U235 0.074672 0.000179\n", - "1 1 1 U238 0.005964 0.000017\n", + "0 1 1 U235 0.074556 0.000144\n", + "1 1 1 U238 0.005976 0.000019\n", "2 1 1 O16 0.000000 0.000000" ] }, - "execution_count": 35, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1185,7 +1134,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1202,7 +1151,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1219,7 +1168,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 36, "metadata": { "scrolled": true }, @@ -1228,133 +1177,301 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ NORMAL ] Importing ray tracing data from file...\n", + "[ NORMAL ] Initializing a default angular quadrature...\n", + "[ NORMAL ] Initializing 2D tracks...\n", + "[ NORMAL ] Initializing 2D tracks reflections...\n", + "[ NORMAL ] Initializing 2D tracks array...\n", + "[ NORMAL ] Ray tracing for 2D track segmentation...\n", + "[ WARNING ] The Geometry was set with non-infinite z-boundaries and supplied\n", + "[ WARNING ] ... to a 2D TrackGenerator. The min-z boundary was set to -10.00 \n", + "[ WARNING ] ... and the max-z boundary was set to 10.00. Z-boundaries are \n", + "[ WARNING ] ... assumed to be infinite in 2D TrackGenerators.\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 0.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 10.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 20.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 30.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 40.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 50.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 60.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 70.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 80.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 90.02 %\n", + "[ NORMAL ] Progress Segmenting 2D tracks: 100.00 %\n", + "[ NORMAL ] Initializing FSR lookup vectors\n", + "[ NORMAL ] Total number of FSRs 867\n", + "[ RESULT ] Total Track Generation & Segmentation Time...........4.2139E-01 sec\n", + "[ NORMAL ] Initializing MOC eigenvalue solver...\n", + "[ NORMAL ] Initializing solver arrays...\n", + "[ NORMAL ] Centering segments around FSR centroid...\n", + "[ NORMAL ] Max boundary angular flux storage per domain = 0.42 MB\n", + "[ NORMAL ] Max scalar flux storage per domain = 0.01 MB\n", + "[ NORMAL ] Max source storage per domain = 0.01 MB\n", + "[ NORMAL ] Number of azimuthal angles = 32\n", + "[ NORMAL ] Azimuthal ray spacing = 0.100000\n", + "[ NORMAL ] Number of polar angles = 6\n", + "[ NORMAL ] Source type = Flat\n", + "[ NORMAL ] MOC transport undamped\n", + "[ NORMAL ] CMFD acceleration: OFF\n", + "[ NORMAL ] Using 1 threads\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.823436\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.780042\tres = 1.941E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.739063\tres = 6.559E-02\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.710328\tres = 5.301E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.689038\tres = 3.942E-02\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.674339\tres = 3.021E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.665075\tres = 2.149E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.660373\tres = 1.387E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.659460\tres = 7.242E-03\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.661679\tres = 1.995E-03\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.666463\tres = 3.649E-03\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.673324\tres = 7.367E-03\n", - "[ NORMAL ] Iteration 12:\tk_eff = 0.681842\tres = 1.039E-02\n", - "[ NORMAL ] Iteration 13:\tk_eff = 0.691660\tres = 1.273E-02\n", - "[ NORMAL ] Iteration 14:\tk_eff = 0.702469\tres = 1.447E-02\n", - "[ NORMAL ] Iteration 15:\tk_eff = 0.714008\tres = 1.569E-02\n", - "[ NORMAL ] Iteration 16:\tk_eff = 0.726057\tres = 1.649E-02\n", - "[ NORMAL ] Iteration 17:\tk_eff = 0.738428\tres = 1.693E-02\n", - "[ NORMAL ] Iteration 18:\tk_eff = 0.750965\tres = 1.709E-02\n", - "[ NORMAL ] Iteration 19:\tk_eff = 0.763536\tres = 1.703E-02\n", - "[ NORMAL ] Iteration 20:\tk_eff = 0.776034\tres = 1.679E-02\n", - "[ NORMAL ] Iteration 21:\tk_eff = 0.788369\tres = 1.641E-02\n", - "[ NORMAL ] Iteration 22:\tk_eff = 0.800470\tres = 1.594E-02\n", - "[ NORMAL ] Iteration 23:\tk_eff = 0.812280\tres = 1.539E-02\n", - "[ NORMAL ] Iteration 24:\tk_eff = 0.823753\tres = 1.479E-02\n", - "[ NORMAL ] Iteration 25:\tk_eff = 0.834854\tres = 1.416E-02\n", - "[ NORMAL ] Iteration 26:\tk_eff = 0.845560\tres = 1.351E-02\n", - "[ NORMAL ] Iteration 27:\tk_eff = 0.855851\tres = 1.286E-02\n", - "[ NORMAL ] Iteration 28:\tk_eff = 0.865717\tres = 1.220E-02\n", - "[ NORMAL ] Iteration 29:\tk_eff = 0.875152\tres = 1.156E-02\n", - "[ NORMAL ] Iteration 30:\tk_eff = 0.884153\tres = 1.093E-02\n", - "[ NORMAL ] Iteration 31:\tk_eff = 0.892725\tres = 1.031E-02\n", - "[ NORMAL ] Iteration 32:\tk_eff = 0.900872\tres = 9.722E-03\n", - "[ NORMAL ] Iteration 33:\tk_eff = 0.908602\tres = 9.152E-03\n", - "[ NORMAL ] Iteration 34:\tk_eff = 0.915926\tres = 8.605E-03\n", - "[ NORMAL ] Iteration 35:\tk_eff = 0.922853\tres = 8.083E-03\n", - "[ NORMAL ] Iteration 36:\tk_eff = 0.929399\tres = 7.586E-03\n", - "[ NORMAL ] Iteration 37:\tk_eff = 0.935576\tres = 7.114E-03\n", - "[ NORMAL ] Iteration 38:\tk_eff = 0.941398\tres = 6.666E-03\n", - "[ NORMAL ] Iteration 39:\tk_eff = 0.946880\tres = 6.242E-03\n", - "[ NORMAL ] Iteration 40:\tk_eff = 0.952037\tres = 5.841E-03\n", - "[ NORMAL ] Iteration 41:\tk_eff = 0.956883\tres = 5.463E-03\n", - "[ NORMAL ] Iteration 42:\tk_eff = 0.961434\tres = 5.107E-03\n", - "[ NORMAL ] Iteration 43:\tk_eff = 0.965705\tres = 4.771E-03\n", - "[ NORMAL ] Iteration 44:\tk_eff = 0.969708\tres = 4.456E-03\n", - "[ NORMAL ] Iteration 45:\tk_eff = 0.973460\tres = 4.159E-03\n", - "[ NORMAL ] Iteration 46:\tk_eff = 0.976972\tres = 3.881E-03\n", - "[ NORMAL ] Iteration 47:\tk_eff = 0.980259\tres = 3.620E-03\n", - "[ NORMAL ] Iteration 48:\tk_eff = 0.983333\tres = 3.375E-03\n", - "[ NORMAL ] Iteration 49:\tk_eff = 0.986206\tres = 3.146E-03\n", - "[ NORMAL ] Iteration 50:\tk_eff = 0.988890\tres = 2.932E-03\n", - "[ NORMAL ] Iteration 51:\tk_eff = 0.991396\tres = 2.731E-03\n", - "[ NORMAL ] Iteration 52:\tk_eff = 0.993735\tres = 2.543E-03\n", - "[ NORMAL ] Iteration 53:\tk_eff = 0.995917\tres = 2.368E-03\n", - "[ NORMAL ] Iteration 54:\tk_eff = 0.997952\tres = 2.204E-03\n", - "[ NORMAL ] Iteration 55:\tk_eff = 0.999848\tres = 2.050E-03\n", - "[ NORMAL ] Iteration 56:\tk_eff = 1.001616\tres = 1.907E-03\n", - "[ NORMAL ] Iteration 57:\tk_eff = 1.003262\tres = 1.774E-03\n", - "[ NORMAL ] Iteration 58:\tk_eff = 1.004795\tres = 1.650E-03\n", - "[ NORMAL ] Iteration 59:\tk_eff = 1.006222\tres = 1.534E-03\n", - "[ NORMAL ] Iteration 60:\tk_eff = 1.007550\tres = 1.425E-03\n", - "[ NORMAL ] Iteration 61:\tk_eff = 1.008785\tres = 1.325E-03\n", - "[ NORMAL ] Iteration 62:\tk_eff = 1.009934\tres = 1.231E-03\n", - "[ NORMAL ] Iteration 63:\tk_eff = 1.011002\tres = 1.143E-03\n", - "[ NORMAL ] Iteration 64:\tk_eff = 1.011995\tres = 1.062E-03\n", - "[ NORMAL ] Iteration 65:\tk_eff = 1.012918\tres = 9.859E-04\n", - "[ NORMAL ] Iteration 66:\tk_eff = 1.013775\tres = 9.153E-04\n", - "[ NORMAL ] Iteration 67:\tk_eff = 1.014571\tres = 8.497E-04\n", - "[ NORMAL ] Iteration 68:\tk_eff = 1.015311\tres = 7.886E-04\n", - "[ NORMAL ] Iteration 69:\tk_eff = 1.015997\tres = 7.318E-04\n", - "[ NORMAL ] Iteration 70:\tk_eff = 1.016635\tres = 6.790E-04\n", - "[ NORMAL ] Iteration 71:\tk_eff = 1.017226\tres = 6.300E-04\n", - "[ NORMAL ] Iteration 72:\tk_eff = 1.017775\tres = 5.844E-04\n", - "[ NORMAL ] Iteration 73:\tk_eff = 1.018285\tres = 5.420E-04\n", - "[ NORMAL ] Iteration 74:\tk_eff = 1.018757\tres = 5.026E-04\n", - "[ NORMAL ] Iteration 75:\tk_eff = 1.019195\tres = 4.660E-04\n", - "[ NORMAL ] Iteration 76:\tk_eff = 1.019602\tres = 4.321E-04\n", - "[ NORMAL ] Iteration 77:\tk_eff = 1.019979\tres = 4.005E-04\n", - "[ NORMAL ] Iteration 78:\tk_eff = 1.020328\tres = 3.713E-04\n", - "[ NORMAL ] Iteration 79:\tk_eff = 1.020652\tres = 3.441E-04\n", - "[ NORMAL ] Iteration 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- "[ NORMAL ] Iteration 110:\tk_eff = 1.024341\tres = 3.137E-05\n", - "[ NORMAL ] Iteration 111:\tk_eff = 1.024368\tres = 2.904E-05\n", - "[ NORMAL ] Iteration 112:\tk_eff = 1.024393\tres = 2.686E-05\n", - "[ NORMAL ] Iteration 113:\tk_eff = 1.024417\tres = 2.481E-05\n", - "[ NORMAL ] Iteration 114:\tk_eff = 1.024438\tres = 2.296E-05\n", - "[ NORMAL ] Iteration 115:\tk_eff = 1.024458\tres = 2.121E-05\n", - "[ NORMAL ] Iteration 116:\tk_eff = 1.024477\tres = 1.961E-05\n", - "[ NORMAL ] Iteration 117:\tk_eff = 1.024494\tres = 1.815E-05\n", - "[ NORMAL ] Iteration 118:\tk_eff = 1.024510\tres = 1.679E-05\n", - "[ NORMAL ] Iteration 119:\tk_eff = 1.024524\tres = 1.551E-05\n", - "[ NORMAL ] Iteration 120:\tk_eff = 1.024538\tres = 1.435E-05\n", - "[ NORMAL ] Iteration 121:\tk_eff = 1.024550\tres = 1.326E-05\n", - "[ NORMAL ] Iteration 122:\tk_eff = 1.024561\tres = 1.226E-05\n", - "[ NORMAL ] Iteration 123:\tk_eff = 1.024572\tres = 1.133E-05\n", - "[ NORMAL ] Iteration 124:\tk_eff = 1.024582\tres = 1.047E-05\n" + "[ NORMAL ] Iteration 0: k_eff = 0.823216 res = 9.828E-02 delta-k (pcm) =\n", + "[ NORMAL ] ... -17678 D.R. = 0.10\n", + "[ NORMAL ] Iteration 1: k_eff = 0.779788 res = 4.642E-02 delta-k (pcm) =\n", + "[ NORMAL ] ... -4342 D.R. = 0.47\n", + "[ NORMAL ] Iteration 2: k_eff = 0.738779 res = 9.633E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -4100 D.R. = 0.21\n", + "[ NORMAL ] Iteration 3: k_eff = 0.710046 res = 8.556E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -2873 D.R. = 0.89\n", + "[ NORMAL ] Iteration 4: k_eff = 0.688781 res = 5.190E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -2126 D.R. = 0.61\n", + "[ NORMAL ] Iteration 5: k_eff = 0.674128 res = 3.585E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -1465 D.R. = 0.69\n", + "[ NORMAL ] Iteration 6: k_eff = 0.664928 res = 2.516E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -919 D.R. = 0.70\n", + "[ NORMAL ] Iteration 7: k_eff = 0.660304 res = 1.866E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -462 D.R. = 0.74\n", + "[ NORMAL ] Iteration 8: k_eff = 0.659481 res = 1.471E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... -82 D.R. = 0.79\n", + "[ NORMAL ] Iteration 9: k_eff = 0.661799 res = 1.248E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 231 D.R. = 0.85\n", + "[ NORMAL ] Iteration 10: k_eff = 0.666690 res = 1.123E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 489 D.R. = 0.90\n", + "[ NORMAL ] Iteration 11: k_eff = 0.673664 res = 1.049E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 697 D.R. = 0.93\n", + "[ NORMAL ] Iteration 12: k_eff = 0.682301 res = 1.001E-03 delta-k (pcm) =\n", + "[ NORMAL ] ... 863 D.R. = 0.95\n", + "[ NORMAL ] Iteration 13: k_eff = 0.692239 res = 9.638E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 993 D.R. = 0.96\n", + "[ NORMAL ] Iteration 14: k_eff = 0.703171 res = 9.329E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1093 D.R. = 0.97\n", + "[ NORMAL ] Iteration 15: k_eff = 0.714835 res = 9.055E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1166 D.R. = 0.97\n", + "[ NORMAL ] Iteration 16: k_eff = 0.727008 res = 8.803E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1217 D.R. = 0.97\n", + "[ NORMAL ] Iteration 17: k_eff = 0.739503 res = 8.566E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1249 D.R. = 0.97\n", + "[ NORMAL ] Iteration 18: k_eff = 0.752162 res = 8.335E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1265 D.R. = 0.97\n", + "[ NORMAL ] Iteration 19: k_eff = 0.764855 res = 8.108E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1269 D.R. = 0.97\n", + "[ NORMAL ] Iteration 20: k_eff = 0.777472 res = 7.879E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1261 D.R. = 0.97\n", + "[ NORMAL ] Iteration 21: k_eff = 0.789924 res = 7.647E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1245 D.R. = 0.97\n", + "[ NORMAL ] Iteration 22: k_eff = 0.802140 res = 7.410E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1221 D.R. = 0.97\n", + "[ NORMAL ] Iteration 23: k_eff = 0.814061 res = 7.168E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1192 D.R. = 0.97\n", + "[ NORMAL ] Iteration 24: k_eff = 0.825643 res = 6.922E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1158 D.R. = 0.97\n", + "[ NORMAL ] Iteration 25: k_eff = 0.836850 res = 6.672E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1120 D.R. = 0.96\n", + "[ NORMAL ] Iteration 26: k_eff = 0.847658 res = 6.419E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1080 D.R. = 0.96\n", + "[ NORMAL ] Iteration 27: k_eff = 0.858047 res = 6.165E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 1038 D.R. = 0.96\n", + "[ NORMAL ] Iteration 28: k_eff = 0.868008 res = 5.911E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 996 D.R. = 0.96\n", + "[ NORMAL ] Iteration 29: k_eff = 0.877535 res = 5.658E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 952 D.R. = 0.96\n", + "[ NORMAL ] Iteration 30: k_eff = 0.886625 res = 5.409E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 909 D.R. = 0.96\n", + "[ NORMAL ] Iteration 31: k_eff = 0.895281 res = 5.163E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 865 D.R. = 0.95\n", + "[ NORMAL ] Iteration 32: k_eff = 0.903509 res = 4.921E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 822 D.R. = 0.95\n", + "[ NORMAL ] Iteration 33: k_eff = 0.911317 res = 4.685E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 780 D.R. = 0.95\n", + "[ NORMAL ] Iteration 34: k_eff = 0.918715 res = 4.456E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 739 D.R. = 0.95\n", + "[ NORMAL ] Iteration 35: k_eff = 0.925715 res = 4.232E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 699 D.R. = 0.95\n", + "[ NORMAL ] Iteration 36: k_eff = 0.932329 res = 4.016E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 661 D.R. = 0.95\n", + "[ NORMAL ] Iteration 37: k_eff = 0.938571 res = 3.807E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 624 D.R. = 0.95\n", + "[ NORMAL ] Iteration 38: k_eff = 0.944455 res = 3.606E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 588 D.R. = 0.95\n", + "[ NORMAL ] Iteration 39: k_eff = 0.949996 res = 3.413E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 554 D.R. = 0.95\n", + "[ NORMAL ] Iteration 40: k_eff = 0.955210 res = 3.227E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 521 D.R. = 0.95\n", + "[ NORMAL ] Iteration 41: k_eff = 0.960110 res = 3.049E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 490 D.R. = 0.94\n", + "[ NORMAL ] Iteration 42: k_eff = 0.964713 res = 2.880E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 460 D.R. = 0.94\n", + "[ NORMAL ] Iteration 43: k_eff = 0.969032 res = 2.717E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 431 D.R. = 0.94\n", + "[ NORMAL ] Iteration 44: k_eff = 0.973082 res = 2.562E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 405 D.R. = 0.94\n", + "[ NORMAL ] Iteration 45: k_eff = 0.976877 res = 2.414E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 379 D.R. = 0.94\n", + "[ NORMAL ] Iteration 46: k_eff = 0.980431 res = 2.274E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 355 D.R. = 0.94\n", + "[ NORMAL ] Iteration 47: k_eff = 0.983757 res = 2.140E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 332 D.R. = 0.94\n", + "[ NORMAL ] Iteration 48: k_eff = 0.986869 res = 2.014E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 311 D.R. = 0.94\n", + "[ NORMAL ] Iteration 49: k_eff = 0.989777 res = 1.894E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 290 D.R. = 0.94\n", + "[ NORMAL ] Iteration 50: k_eff = 0.992495 res = 1.780E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 271 D.R. = 0.94\n", + "[ NORMAL ] Iteration 51: k_eff = 0.995033 res = 1.672E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 253 D.R. = 0.94\n", + "[ NORMAL ] Iteration 52: k_eff = 0.997402 res = 1.569E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 236 D.R. = 0.94\n", + "[ NORMAL ] Iteration 53: k_eff = 0.999613 res = 1.473E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 221 D.R. = 0.94\n", + "[ NORMAL ] Iteration 54: k_eff = 1.001675 res = 1.382E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 206 D.R. = 0.94\n", + "[ NORMAL ] Iteration 55: k_eff = 1.003597 res = 1.296E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 192 D.R. = 0.94\n", + "[ NORMAL ] Iteration 56: k_eff = 1.005388 res = 1.215E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 179 D.R. = 0.94\n", + "[ NORMAL ] Iteration 57: k_eff = 1.007057 res = 1.138E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 166 D.R. = 0.94\n", + "[ NORMAL ] Iteration 58: k_eff = 1.008612 res = 1.066E-04 delta-k (pcm) =\n", + "[ NORMAL ] ... 155 D.R. = 0.94\n", + "[ NORMAL ] Iteration 59: k_eff = 1.010059 res = 9.980E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 144 D.R. = 0.94\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Iteration 60: k_eff = 1.011406 res = 9.342E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 134 D.R. = 0.94\n", + "[ NORMAL ] Iteration 61: k_eff = 1.012659 res = 8.740E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 125 D.R. = 0.94\n", + "[ NORMAL ] Iteration 62: k_eff = 1.013825 res = 8.175E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 116 D.R. = 0.94\n", + "[ NORMAL ] Iteration 63: k_eff = 1.014909 res = 7.642E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 108 D.R. = 0.93\n", + "[ NORMAL ] Iteration 64: k_eff = 1.015917 res = 7.142E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 100 D.R. = 0.93\n", + "[ NORMAL ] Iteration 65: k_eff = 1.016853 res = 6.675E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 93 D.R. = 0.93\n", + "[ NORMAL ] Iteration 66: k_eff = 1.017724 res = 6.235E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 87 D.R. = 0.93\n", + "[ NORMAL ] Iteration 67: k_eff = 1.018533 res = 5.822E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 80 D.R. = 0.93\n", + "[ NORMAL ] Iteration 68: k_eff = 1.019284 res = 5.436E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 75 D.R. = 0.93\n", + "[ NORMAL ] Iteration 69: k_eff = 1.019982 res = 5.074E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 69 D.R. = 0.93\n", + "[ NORMAL ] Iteration 70: k_eff = 1.020630 res = 4.734E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 64 D.R. = 0.93\n", + "[ NORMAL ] Iteration 71: k_eff = 1.021232 res = 4.417E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 60 D.R. = 0.93\n", + "[ NORMAL ] Iteration 72: k_eff = 1.021790 res = 4.119E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 55 D.R. = 0.93\n", + "[ NORMAL ] Iteration 73: k_eff = 1.022308 res = 3.841E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 51 D.R. = 0.93\n", + "[ NORMAL ] Iteration 74: k_eff = 1.022789 res = 3.579E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 48 D.R. = 0.93\n", + "[ NORMAL ] Iteration 75: k_eff = 1.023235 res = 3.336E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 44 D.R. = 0.93\n", + "[ NORMAL ] Iteration 76: k_eff = 1.023648 res = 3.107E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 41 D.R. = 0.93\n", + "[ NORMAL ] Iteration 77: k_eff = 1.024032 res = 2.897E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 38 D.R. = 0.93\n", + "[ NORMAL ] Iteration 78: k_eff = 1.024388 res = 2.696E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 35 D.R. = 0.93\n", + "[ NORMAL ] Iteration 79: k_eff = 1.024718 res = 2.510E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 32 D.R. = 0.93\n", + "[ NORMAL ] Iteration 80: k_eff = 1.025024 res = 2.338E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 30 D.R. = 0.93\n", + "[ NORMAL ] Iteration 81: k_eff = 1.025307 res = 2.175E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 28 D.R. = 0.93\n", + "[ NORMAL ] Iteration 82: k_eff = 1.025570 res = 2.025E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 26 D.R. = 0.93\n", + "[ NORMAL ] Iteration 83: k_eff = 1.025814 res = 1.886E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 24 D.R. = 0.93\n", + "[ NORMAL ] Iteration 84: k_eff = 1.026039 res = 1.752E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 22 D.R. = 0.93\n", + "[ NORMAL ] Iteration 85: k_eff = 1.026249 res = 1.628E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 20 D.R. = 0.93\n", + "[ NORMAL ] Iteration 86: k_eff = 1.026442 res = 1.517E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 19 D.R. = 0.93\n", + "[ NORMAL ] Iteration 87: k_eff = 1.026622 res = 1.408E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 17 D.R. = 0.93\n", + "[ NORMAL ] Iteration 88: k_eff = 1.026788 res = 1.308E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 16 D.R. = 0.93\n", + "[ NORMAL ] Iteration 89: k_eff = 1.026942 res = 1.218E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 15 D.R. = 0.93\n", + "[ NORMAL ] Iteration 90: k_eff = 1.027085 res = 1.132E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 14 D.R. = 0.93\n", + "[ NORMAL ] Iteration 91: k_eff = 1.027217 res = 1.049E-05 delta-k (pcm) =\n", + "[ NORMAL ] ... 13 D.R. = 0.93\n", + "[ NORMAL ] Iteration 92: k_eff = 1.027339 res = 9.760E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 12 D.R. = 0.93\n", + "[ NORMAL ] Iteration 93: k_eff = 1.027453 res = 9.076E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 11 D.R. = 0.93\n", + "[ NORMAL ] Iteration 94: k_eff = 1.027557 res = 8.434E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 10 D.R. = 0.93\n", + "[ NORMAL ] Iteration 95: k_eff = 1.027655 res = 7.827E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 9 D.R. = 0.93\n", + "[ NORMAL ] Iteration 96: k_eff = 1.027744 res = 7.266E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 8 D.R. = 0.93\n", + "[ NORMAL ] Iteration 97: k_eff = 1.027828 res = 6.737E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 8 D.R. = 0.93\n", + "[ NORMAL ] Iteration 98: k_eff = 1.027905 res = 6.255E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 7 D.R. = 0.93\n", + "[ NORMAL ] Iteration 99: k_eff = 1.027976 res = 5.803E-06 delta-k (pcm) =\n", + "[ NORMAL ] ... 7 D.R. = 0.93\n", + "[ NORMAL ] Iteration 100: k_eff = 1.028042 res = 5.383E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.93\n", + "[ NORMAL ] Iteration 101: k_eff = 1.028103 res = 5.017E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 6 D.R. = 0.93\n", + "[ NORMAL ] Iteration 102: k_eff = 1.028160 res = 4.618E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.92\n", + "[ NORMAL ] Iteration 103: k_eff = 1.028212 res = 4.306E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 5 D.R. = 0.93\n", + "[ NORMAL ] Iteration 104: k_eff = 1.028260 res = 3.999E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.93\n", + "[ NORMAL ] Iteration 105: k_eff = 1.028305 res = 3.706E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.93\n", + "[ NORMAL ] Iteration 106: k_eff = 1.028347 res = 3.429E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 4 D.R. = 0.93\n", + "[ NORMAL ] Iteration 107: k_eff = 1.028385 res = 3.213E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.94\n", + "[ NORMAL ] Iteration 108: k_eff = 1.028420 res = 2.943E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.92\n", + "[ NORMAL ] Iteration 109: k_eff = 1.028453 res = 2.740E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.93\n", + "[ NORMAL ] Iteration 110: k_eff = 1.028484 res = 2.531E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 3 D.R. = 0.92\n", + "[ NORMAL ] Iteration 111: k_eff = 1.028512 res = 2.369E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.94\n", + "[ NORMAL ] Iteration 112: k_eff = 1.028538 res = 2.186E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.92\n", + "[ NORMAL ] Iteration 113: k_eff = 1.028562 res = 2.026E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.93\n", + "[ NORMAL ] Iteration 114: k_eff = 1.028584 res = 1.858E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.92\n", + "[ NORMAL ] Iteration 115: k_eff = 1.028604 res = 1.760E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 2 D.R. = 0.95\n", + "[ NORMAL ] Iteration 116: k_eff = 1.028623 res = 1.612E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.92\n", + "[ NORMAL ] Iteration 117: k_eff = 1.028641 res = 1.496E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.93\n", + "[ NORMAL ] Iteration 118: k_eff = 1.028657 res = 1.382E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.92\n", + "[ NORMAL ] Iteration 119: k_eff = 1.028672 res = 1.293E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.94\n", + "[ NORMAL ] Iteration 120: k_eff = 1.028686 res = 1.191E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.92\n", + "[ NORMAL ] Iteration 121: k_eff = 1.028699 res = 1.112E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.93\n", + "[ NORMAL ] Iteration 122: k_eff = 1.028711 res = 1.005E-06 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.90\n", + "[ NORMAL ] Iteration 123: k_eff = 1.028722 res = 9.443E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.94\n", + "[ NORMAL ] Iteration 124: k_eff = 1.028732 res = 8.583E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 1 D.R. = 0.91\n", + "[ NORMAL ] Iteration 125: k_eff = 1.028742 res = 8.028E-07 delta-k (pcm)\n", + "[ NORMAL ] ... = 0 D.R. = 0.94\n" ] } ], @@ -1377,16 +1494,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.023293\n", - "openmoc keff = 1.024582\n", - "bias [pcm]: 128.8\n" + "openmc keff = 1.024078\n", + "openmoc keff = 1.028742\n", + "bias [pcm]: 466.4\n" ] } ], @@ -1428,7 +1545,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1452,7 +1569,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1482,27 +1599,29 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0.5, 1.0, 'OpenMOC Fission Rates')" ] }, - "execution_count": 42, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1540,7 +1659,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/examples/jupyter/nuclear-data-resonance-covariance.ipynb b/examples/jupyter/nuclear-data-resonance-covariance.ipynb index 2c44b6f7f..d8bca235d 100644 --- a/examples/jupyter/nuclear-data-resonance-covariance.ipynb +++ b/examples/jupyter/nuclear-data-resonance-covariance.ipynb @@ -208,7 +208,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -217,17 +217,19 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "plt.imshow(covariance,cmap='seismic',vmin=-0.08, vmax=0.08)\n", + "plt.imshow(covariance, cmap='seismic',vmin=-0.008, vmax=0.008)\n", "plt.colorbar()" ] }, @@ -246,7 +248,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -255,12 +257,14 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -296,7 +300,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/icmeyer/openmc/openmc/data/resonance_covariance.py:233: UserWarning: Sampling routine does not guarantee positive values for parameters. This can lead to undefined behavior in the reconstruction routine.\n", + "/home/romano/openmc/openmc/data/resonance_covariance.py:233: UserWarning: Sampling routine does not guarantee positive values for parameters. This can lead to undefined behavior in the reconstruction routine.\n", " warnings.warn(warn_str)\n" ] }, @@ -370,51 +374,51 @@ "
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0.084476 0.0 0.0\n", - "4 20.561690 0 2.0 0.011163 0.086802 0.0 0.0" + "0 0.032858 0 2.0 0.000479 0.105208 0.0 0.0\n", + "1 2.823859 0 2.0 0.000361 0.093748 0.0 0.0\n", + "2 16.203069 0 1.0 0.000264 0.015233 0.0 0.0\n", + "3 16.765055 0 2.0 0.013648 0.076119 0.0 0.0\n", + "4 20.557679 0 2.0 0.011140 0.097548 0.0 0.0" ] }, "execution_count": 9, @@ -572,8 +576,8 @@ { "data": { "text/plain": [ - "[,\n", - " ]" + "[,\n", + " ]" ] }, "execution_count": 10, @@ -595,7 +599,7 @@ { "data": { "text/plain": [ - "Text(0,0.5,'Cross section (b)')" + "Text(0, 0.5, 'Cross section (b)')" ] }, "execution_count": 11, @@ -604,12 +608,14 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -338,15 +338,15 @@ "output_type": "stream", "text": [ "[,\n", - " ,\n", + " ,\n", + " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", - " ,\n", - " ]\n" + " ]\n" ] } ], @@ -394,7 +394,7 @@ { "data": { "text/plain": [ - "{'294K': }" + "{'294K': }" ] }, "execution_count": 15, @@ -423,7 +423,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -478,7 +478,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 18, @@ -506,36 +506,36 @@ { "data": { "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, "execution_count": 19, @@ -562,7 +562,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -576,7 +576,7 @@ "source": [ "for e_in, e_out_dist in zip(dist.energy[::5], dist.energy_out[::5]):\n", " plt.semilogy(e_out_dist.x, e_out_dist.p, label='E={:.2f} MeV'.format(e_in/1e6))\n", - "plt.ylim(ymax=1e-6)\n", + "plt.ylim(top=1e-6)\n", "plt.legend()\n", "plt.xlabel('Outgoing energy (eV)')\n", "plt.ylabel('Probability/eV')\n", @@ -600,7 +600,7 @@ { "data": { "text/plain": [ - "Text(0,0.5,'Cross section(b)')" + "Text(0, 0.5, 'Cross section(b)')" ] }, "execution_count": 21, @@ -609,7 +609,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -814,7 +814,7 @@ } ], "source": [ - "n2n_group['294K/xs'].value" + "n2n_group['294K/xs'][()]" ] }, { @@ -883,7 +883,7 @@ { "data": { "text/plain": [ - "{'0K': }" + "{'0K': }" ] }, "execution_count": 29, @@ -937,8 +937,8 @@ { "data": { "text/plain": [ - "[,\n", - " ]" + "[,\n", + " ]" ] }, "execution_count": 31, @@ -992,7 +992,7 @@ { "data": { "text/plain": [ - "Text(0,0.5,'Cross section (b)')" + "Text(0, 0.5, 'Cross section (b)')" ] }, "execution_count": 33, @@ -1001,7 +1001,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1230,8 +1230,8 @@ { "data": { "text/plain": [ - "{'294K': ,\n", - " '0K': }" + "{'294K': ,\n", + " '0K': }" ] }, "execution_count": 36, @@ -1249,7 +1249,7 @@ "source": [ "## Generating data from NJOY\n", "\n", - "To run OpenMC in continuous-energy mode, you generally need to have ACE files already available that can be converted to OpenMC's native HDF5 format. If you don't already have suitable ACE files or need to generate new data, both the `IncidentNeutron` and `ThermalScattering` classes include `from_njoy()` methods that will run [NJOY](https://njoy.github.io/NJOY2016/) to generate ACE files and then read those files to create OpenMC class instances. The `from_njoy()` methods take as input the name of an ENDF file on disk. By default, it is assumed that you have an executable named `njoy` available on your path. This can be configured with the optional `njoy_exec` argument. Additionally, if you want to show the progress of NJOY as it is running, you can pass `stdout=True`.\n", + "To run OpenMC in continuous-energy mode, you generally need to have ACE files already available that can be converted to OpenMC's native HDF5 format. If you don't already have suitable ACE files or need to generate new data, both the `IncidentNeutron` and `ThermalScattering` classes include `from_njoy()` methods that will run [NJOY](https://www.njoy21.io/) to generate ACE files and then read those files to create OpenMC class instances. The `from_njoy()` methods take as input the name of an ENDF file on disk. By default, it is assumed that you have an executable named `njoy` available on your path. This can be configured with the optional `njoy_exec` argument. Additionally, if you want to show the progress of NJOY as it is running, you can pass `stdout=True`.\n", "\n", "Let's use `IncidentNeutron.from_njoy()` to run NJOY to create data for $^2$H using an ENDF file. We'll specify that we want data specifically at 300, 400, and 500 K." ] @@ -1264,7 +1264,7 @@ "output_type": "stream", "text": [ "\n", - " njoy 2016.44 11Oct18 11/09/18 20:25:51\n", + " njoy 2016.49 25Jan19 07/19/19 06:12:49\n", " *****************************************************************************\n", "\n", " reconr... 0.0s\n", @@ -1274,21 +1274,23 @@ " 400.0 deg 0.2s\n", " 500.0 deg 0.3s\n", "\n", - " heatr... 0.4s\n", + " heatr... 0.3s\n", "\n", - " purr... 0.9s\n", + " gaspr... 0.6s\n", "\n", - " mat = 128 0.9s\n", + " purr... 0.7s\n", + "\n", + " mat = 128 0.7s\n", "\n", " ---message from purr---mat 128 has no resonance parameters\n", " copy as is to nout\n", "\n", - " acer... 0.9s\n", + " acer... 0.7s\n", + "\n", + " acer... 1.0s\n", "\n", " acer... 1.1s\n", - "\n", - " acer... 1.3s\n", - " 1.4s\n", + " 1.2s\n", " *****************************************************************************\n" ] } @@ -1317,10 +1319,10 @@ { "data": { "text/plain": [ - "{'300K': ,\n", - " '400K': ,\n", - " '500K': ,\n", - " '0K': }" + "{'300K': ,\n", + " '400K': ,\n", + " '500K': ,\n", + " '0K': }" ] }, "execution_count": 38, @@ -1409,7 +1411,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 42, @@ -1418,7 +1420,7 @@ }, { "data": { - "image/png": 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cDopcdg53aUVvlbR79MaYdUm2PwI8Mpo3N8asB9Y3NjZeP5rnZ0uq1k1VkYsDx/rHOiSlJrz2Xj/hiGHKMK0bEWFqmYcjmugto0sgDKGzP4hNhr5LfWWRm2O9ejGHUiMVT9zThqnoAaaVFWhFbyG9OfgQjvX5qSxyYbO99ebFVcUu+gJhvXesUiMUT9zDDcbG99GK3jp6c/AhtPX4qSkZ+mCsLo62c3QtDqVGpqUzOrg6LY1EP63MQ2uPj5De+8ES2roZQmuPn9rYXN7B4hdzaPtGqZE51NFPsduR8qrYuOnlBUQMHO3RvzMraOtmCK3dyRN9lVb0So3KgWN9NFQWIvLWluhgMysLo89p78t2WJOCtm4GiUQM7b3+gavzBqseqOg10Ss1Egc6+plZVZjWvjNjV8Tu1xlultDWzSDH+wOEIiZ56yZe0WvrRqm0RSKG5g4vDZXpJfpppR5cDhv7j2lFbwVt3QzSGusJJhuMLXLZcTts2rpRagSOdPsIhCM0pFnR22zCzMpC9mvrxhLauhkknuhrS4eu6EWE2lI3R3UdDqXSFr/IMN2KHmBWdZFW9BbR1s0gbfFEn6R1AzCtVC/mUGokmtp6AZhbU5z2c2ZVFXLgWD+RyLha3HZC0kQ/SLxSTzYYC3oxh1IjtedoD8VuR1pz6ONmVRfhD0V0FUsLaKIfpKXTS0WhM+WNEabFEr3e6kyp9Ow52su82uK0plbGzaqKzbzRPn3GdDB2kJbjXuoqClLuM7XMQyAcGbjRsVIqtT2tPSyYkn7bBk60efa09mYjpElFB2MHaen0Ul+eesAofvqpfXqlhtfRF6C9N8CCKSUjet6UUjdlBU5eP9KTpcgmD23dJDDGpFnRR3+ufXqlhrf7aDRRz6sdWUUvIiyaWsKuI93ZCGtS0USfoKMvgDcYpq48daI/UdHrHXCUGk689TLSih6IJfoenXmTIU30CeKr69UPU9FXF7tx2W00d2qiV2o4r73ZTYlnZDNu4hZOLaUvEB7421Sjo4OxCVqORw+m4Vo3dptQX1HAQV2HQ6lhbW/pYnl92Yhm3MQtmhY9C9A+fWZ0MDZBfAGlGWlcvTezqlAXXFJqGP5QmNePdLO0bnR/4/F2z87D2qfPhLZuEuxr66WmxE2pxznsvjOrijh4rE/n0iuVwu4jvQTDhuV15aN6frHbwZzqIrY2j4+z/olKE32Cfe19zIktjzqchspC+gJhXdxMqRS2tnQCsLx+9Gftp84oZ8uhTi2qMqCJPsEb7X3MSXMtjlnVsRsj6KJLSiW1vaWLsgLnsBMcUlkxo5z2Xj9v6nTmUdNEH9PZH6CjL8DcmnQr+uh+B7RPr1RSmw90jnogNu7UGdG2z5aDnVaFNelooo/Z2xatzGen2bqZUVmAiCZ6pZLp7A+w62gPa2ZVZvQ6p0wrxeWwseXQcYsim3w00cfsiy2jmm7rxu2wM72sQFs3SiWxaX80MZ82O7NE73LYWDK9lC2HtKIfLZ1HH7OvvQ+nXZgxgl7i7OqigTMBpdTJNu7vwGmXgdZLJk6dUc62li6C4YgFkU0+Oo8+ZveRHmZXF+Gwp/+RLJhSwp5WvTxbqaG8tL+D5fXleJz2jF9r9cwKfMEI21pyXxRORNq6idl5uJvF00pH9JwFU4rxBSMcOq59eqUSeQNhtjV3cVqG/fm4M+ZUAbBh7zFLXm+y0URPdNDozS4fp4w00U+NXrW3+6iul61Uos0HjxOKGNbMrrDk9aqL3SycUsIL+zTRj4YmeuC12OXVi6ePLNHPjy27Gl+GVSkV9czuNpx24fTZVZa95plzq9i4vwN/KGzZa04WmuiJrq4HjLiiL/E4qSsv0ESv1CBP727jtFmVFLmT35JzpM6cW4UvGOHVQ9qnHylN9EQr+toSN9XFyW8InsyCKcXs0pX1lBpwpMvH60d6+IcFNZa+7hmzqxCB5/e2W/q6k4EmeqIV/Uir+biFU0vZ29arp5NKxTyzuw2Af1hobaIvK3SyvL6cp2Ovr9I36RN9rz/E7qM9o57ru6yujGDYaFWvVMxTu1uZUhodPLXa+Ytq2XKok/Zev+Wvnc8mfaLf2txJxMDKhtEl+viqfDq/VynwBcM8vauNty+szWh9m2TesagWY+CpXVrVj4TliV5E5ojInSLyO6tfOxteiS2UtHLG6KaB1VcUUF7oZJuul60Uz+1ppy8QZu3SqVl5/SXTS5lS6uZvrx/Nyuvnq7QSvYjcJSKtIrJ90Pa1IrJLRJpE5GYAY8w+Y8zHsxFsNrxy8Dhza4ooKxz+ZiNDERGW1ZXpjRGUAh7dfoQSj4Oz5lZn5fVFhHcsquWZ3e0EQrocQrrSrejvBtYmbhARO/Bj4GJgMbBORBZbGl2WGWN45WAnKxsyu6hjWV0Zu4/24AvqgKyavILhCE/sPMo7T5mCy5G9rvAFp0yh1x/i7006+yZdaf02jDHPAB2DNq8BmmIVfAC4D7jM4viyam9bH8f6AqyemVmiXzGjnFDEsONNrerV5PX83mN0eYNZa9vEnTO/mlKPg/Vb38zq++STTP63WwccSnjcDNSJSJWI3AasFJEvJ3uyiNwgIptEZFNbW24GVuLzcc+am9nVe42x/1G89Iaul60mrwc2N1PqcfA2i+fPD+Z22LloyVT+suOonkWnyfLzK2PMMWPMp4wxc40x30mx3x3GmEZjTGNNTXYPjGSebzpGXXkBDZWFGb1OVbGbuTVFbNw/+KRHqcmhxxfksR1HuGTFdEtWqxzOe1ZMp9cf0jn1acok0bcAMxIe18e2pS2X69FHIoYN+45x1twqS6aBrZldyab9HbpksZqUHt12BF8wwhWr68fk/c6aW0VFoZM/bj08Ju830WWS6DcC80Vktoi4gKuBh0fyArlcj/61w910eYOcNc+aRZcaZ1bS7QuxS9e9UZPQ7zY3M6e6iJUW3GQkHU67jYuXTeOJ147S4wuOyXtOZOlOr7wX2AAsFJFmEfm4MSYE3AQ8BuwE7jfG7BjJm+eyoj/Rn7dmGtia2O3StH2jJpv97X289EYHl6+qy8pFUslctboebzDM+le1qh9OurNu1hljphljnMaYemPMnbHtjxhjFsT68f9npG+ey4r+ydfbWDClmCmlHkter76igGllHl7cp4leTS6/fuEADptwVeOM4Xe20Kkzylk4pYTfbDo0/M6T3KS8Z2xXf5CX9ndwwSlTLHtNEeGsudU819ROWPv0apLwBsLcv+kQFy2dalnRlC4R4f2nzeDVQ53sjN1TQg1tUt4z9qndrYQjhvMtTPQQXa2vyxtka7PerV5NDn/Y0kK3L8RHzpyVk/d/38o6XHYbv9moVX0qk3JRs7/ubKW62GXJ3ekTnTuvGhF4Zrdesafyky8Y5g9bWujsDxCOGO54dh+nTCvltFnW3DJwpCqLXKxdOpXfv9xMrz+UkxgmgknXugmGIzy5q5W3L6zFbrN24KiiyMXyujKe2aNze1V++v6fd/G5+7Zw/S838dArLexr6+Mz75g3poOwg113zmx6/CF+q736pCZd6+a5Pe30+EJcuCQ7l2m/bUENrxw8Tle/TvlS+cUYwx+2RC+V2bj/OP//b19laV0pa7P0t5SuU2eUs3pmBXf9/Q0dH0ti0rVuHn71zdhl2tlZXe+8hbVETHQcYLzbc7SHlw/oLCGVnv3H+jnWF+A7ly/js+fPZ83sSm5dtwqbxWfGo/Hxc2ZzqMPL46/p8sVDse7OvROANxDmL7HLtN2O7FymvXJGObUlbh7bcYTLTq3LyntY5Z0/fAaA/d99d44jURPB5gPRtZxWNpSzburobr2ZLRcunkJ9RQF3Prcv64uqTUSTqkf/19eP0hcIc+mK6Vl7D5tNuHDJFJ58vW1cL7jUHzgxcKXreqt0vNrcSZHLzvxa628RmCmH3cYnzpnNxv3H2bD3WK7DGXcmVY/+wc0t1Ja4OX2ONcseJHPRkql4g+GBmySPR+09gYHv3+z05jASNVFsbe5iSV2Z5ZMYrHL1mgZqS9z89xO7cx3KuDNpevRvdnp5clcrVzXWZ/1APWNOFaUeB3/efiSr75OJ1h7fwPddXh04VqkFwxF2Hu5med3YX8WeLo/TzqfPm8uLb3RoVT/IpEn09286hAGuPq0h6+/ltNu4eOk0/rzjCH3jdG5vZ8KsIE30ajh7jvbiD0VYVj9+Ez2cqOp/+MRujNEZOHGTokcfjhh+s/EQ586vYUaGa8+n66rGevoDYf60bXwuuJR4cUm3rv6nhhG/2nvpOK7oIVrV/+Pb5/HSGx08tWv8tk7H2qTo0T+1q5XDXT4+uGbsFl1aPbOCOdVF/G5T85i950j0JCR6rejVcJ7Z00ZtiZvZVUW5DmVY69Y0MLu6iG/96TWCYZ1oAJOkdfOzZ99gaqnH8rVtUhGJrub30v4OmlrH3xr1vb4Tib7HNz7bS2p88AXDPLO7nfNPqR0Xc+aH43LY+PLFi9jb1sd9Lx3MdTjjQt4n+m3NXWzYd4zrzpmF0z62/7nvb6zHZbfxqw0HxvR909HrDxK/an08TwNVufeHLS30+kNcsjx705Kt9s7FUzhjTiU/fGKPnrEyCRL9Hc/uo8TtYN2a7A/CDlZV7Obdy6fx+80t425QttcXosTtwO2w4Q1ooldD8wXD3PLXJpZML+XMudmdlmwlEeGr717M8f4AP3xcp1vmdaI/1NHPI9sO88HTGyjxOHMSwzWnN9DrD427QVlvMEyhy0GBy45XK3qVxE+ebKKl08tX3704pwuXjcbSujKuPX0mv9iwny2HJvfS4Xk96+aWv+7BbhM+dvbsrLx+OlbPrGBOTRH3j7P1sr3BCAUuOwVOu1b0akjbW7r4yVN7ee+p0ydUNZ/on9YupLbEzZcf2DapB2bzdtbNvrZefr+5mQ+dMZOpZWN755tEIsL7G2ew6cBxmlp7cxbHYN5AGI/TToHLTr9W9GoQfyjM/3f/FiqLXHzj0qW5DmfUSj1OvnHpUnYe7ubO597IdTg5k7etmx8+sQe3w86N583NdShcvqoOu024fxytl+0Lhilw2ihw2vFpRa8G+cHju9l9tJfvXbGcssLctD2tsnbpVC5cPIUfPr6bPUfH3wy4sZCXiX5bcxd/3PomHzt7FtXF7lyHQ22Jh7VLpnLvSwfpGScXJ/mCsYreqT16dbInX2/l9qf3sW5NA29fVJvrcCzxf963jGK3g8/dt2VSLuKXd4neGMPXHt5OVZGLT42Daj7uU/8wlx5fiF+/MD7m9XqDYQpirRtN9CqupdPLF+7fwinTSvnaJYtzHY5lakrcfPeK5bx2uJsfTMJZOHmX6B/a0sLmg518ae0iSnM002Yoy+rLOHd+NXc+98a4mLfuDYbx6GCsShAIRfjM/91MKGz4yTWr8Dizc8+GXHnn4imsW9PA7c/s5fmmyXVf57xK9L3+EN955HVW1Jdx5ar6XIfzFjeeN5f2Xv+4uLelL6AVvTrBGMNXHtzG5oOdfO+K5cyuHv9LHYzGv77nFObWFPOZe1/hcNfkWZ47rxL9dx/dSVuvn69fumRcXqp95pwqTptVwS1/a8p5Fe0LRfDEBmNzHYvKvZ8+u4/fvtzMZ8+fz7uXT8t1OFlT6HJw27Wr8QXDfPqezZOmX5838+if39vOr184yHVnz2ZlQ4UF0VlPRPjS2kW09fj5+fO5nerljVX0Hh2MnfQef+0o33n0dd69bBqfP39+rsPJunm1xfzHVSt45WAn3/zja7kOZ0zkxTz6/kCIm3+/jVlVhXzxwoUWRZcdp82q5PxFtdz21F66+nMzA8cYMzAYW+jSin4ye/lAB5+99xWW1ZXxn1etGJdnwtnwrmXTuP7c2fzqhQP8+oXxtxaV1fKidfONh1/j0PF+vnfFcgpc438A6YsXLaTHH+LHTzXl5P39sdPV+GBsKGIm9VWDk9XOw9187OcbmVLq5s6PnDYh/nas9M9rF/GORbX82x+28+Su1lyHk1UTPtE/9EoLv9l0iE+fNzfr94K1yinTSrlyVT13PfdGTpYwjs/68TjsA3/c/VrVTyr72/v40J0vUehy8OtPnE5NSe6vNxlrDruNW9et5JRppdx0z2Zee7M71yFlzYRO9G+2ERL3AAAUnUlEQVS09/GVB7fROLOCL1ywINfhjMg/X7yIApedrz28Y8xveRbvyRe47ANT6Pzap580DnX0c83PXiQcifDrT6yhvmJs7ro2HhW5Hdz10dMoLXDy0Z+/xP72vlyHlBUTOtE/+EoLToeNW9atxDHGa81nqrrYzRcvXMjfm47xyLaxvYl4vCdfELsyFtAB2UnijfY+3n/7Bnr9IX553enMqy3JdUg5N6XUwy+vW0MwHOGan71I8/H+XIdkuYmVHQf5wgXz+dNnz2V6eUGuQxmVa05vYPG0Ur6xfseYDszGk7rHaRto3Wiiz39NrT28//YN+EMR7r3+jHF/o++xNH9KCb/6+Ol0+4Jc+7MXae325TokS03oRC8i1E3QJA/RHuH3r1zOsb4A31i/Y8zed6CidzlOVPSj6NF39AV4cd8xS2NT2bHlUCcfuP0FAH5zwxksnl6a44jGn6V1ZfziujW09fhZ99MXONKVP8l+Qif6fLC0rox/fPs8Hnilhb/sGJsWTnzgtTChRz+aiv6//rKLD9zxAq8cPG5pfMpaj792lKvv2ECR28H9nzyT+VO0XZPMqoYKfv6xNRzt9nPV7c9z8Fh+tHE00Y8DN719HqdMK+VfHtzOsV5/1t+vP7FHH2vdjGb9nQ17o9X8a4fzd7bCRPerDfv55K82sXBKCQ98+qy8XdrASmtmV/J/rz+dHl+IK297nt15sLSx5YleRIpE5Bci8lMRucbq189HLoeNH7x/Bd2+IF+4/1UikezOwvEGo/evLXSdGIz1BUc+jz4cmy2UL1VPPgmGI3z94R386x928I5Ftdx7wxnjYsnuiWJ5fTn3f/JMAK66bcNAUTNRpZXoReQuEWkVke2Dtq8VkV0i0iQiN8c2Xw78zhhzPXCpxfHmrfiysM/sbuMnWb6QKl7RF7kz69F3e6MDyG1jcBai0tfa4+Oan77I3c/v5+PnzOa2a1dT6HLkOqwJZ8GUEn5/41nUlLj58F0vjqsbB41UuhX93cDaxA0iYgd+DFwMLAbWichioB6IfyI6lWMEPrimgctOnc4PHt/N83uzt4zqicFYOx5n9BAYaY/eGEO3L3pmEE/4Kvc2HzzOJbc+x9aWTn509an863sWT7ipx+PJjMpCfn/jWZwxp4ov/W4r33309ayfcWdDWkeAMeYZoGPQ5jVAkzFmnzEmANwHXAY0E032KV9fRG4QkU0isqmtrW3kkechEeHb71vG7Ooi/vGezRw4lp2LNwYGY512PKPs0ff6Q4RjB3yXJvqci0QMtz+9lw/cvgGXw8YDN57NZafW5TqsvFBW4OSuj57GNac3cNvTe/no3RvHZCzNSpn8r76OE5U7RBN8HfAAcIWI/C+wPtmTjTF3GGMajTGNNTU1GYSRX4rcDu78yGkY4Lq7N2YlifYHwrjsNhx226hbN4lxdeZocTYV1drt48N3vcR3Hn2d8xdNYf1N5+j0SYs57Ta+9d6lfPt9y3hh3zHefctzbNo/uPYdvyw/pzPG9BljPmaMudEYc0+qfa1cpjifzKou4rZrV3Owo59P3/Oy5WtmewOhgdk2TrsNh01G3LqJJ/qyAqdW9Dn02I4jrP3Rs2w60MF3Ll/G/167ivJCV67DyksiwgdPb+DBT5+F22njA3e8wC1/3UMgFMEYw2M7jvCzZ/fRHwjlOtS3yGSEpgWYkfC4PrYtbcaY9cD6xsbG6zOIIy+dMaeKb79vGf/0u618/jevcMvV1i3z0B8IU5iwUuFobhAeT+4NlYV5Mf1somnr8fP19Tv409bDLJ5Wyi3rVjKvtjjXYU0KS6aXsf4z5/CVB7fzg8d38/CrbzK11MNzsdsTbm3u4pZ1K3Mc5ckySfQbgfkiMptogr8a+KAlUSkArmqcQZc3yLf+tJMC5zb+48rllqwX3h8Mn7QkrcdlH3GPvjsh0W9r6cIXDOfdPUbHI2MMD21p4RvrX6PfH+aLFy7gk/8wF6cOuI6pUo+TW9et5LIV0/nhE7vZ19bLzRcvot8f4pa/NbFuTQNnzh0/q+mmlehF5F7gPKBaRJqBrxlj7hSRm4DHADtwlzFmRNfxi8glwCXz5s0bWdSTyCfOnUOvP8R/P7EHj9PGNy9bmnGy9w5V0Y+yRz+jMrryYbc3qIk+y5pae/jG+td4dk87KxvK+f4Vy/Uq1xy7YPEULlg8ZeCxLxjm/k3NfP+x13ngxrMQGR83ckkr0Rtj1iXZ/gjwyGjfXFs36fnc+fPxBsPc/vQ+vIEw379yeUZtnP5AiELniV99octOr3/0rZv449pSz6hjUsn1+IL86Ik93P38fgpcdr5+yWI+dOYs7JPkblATicdp5/MXzOfmB7bx25ebeX/jjOGfNAZyehWFVvTpERFuXruIYpeD/3p8Nz3+ELeuWznqCtobCJ80YFfqcdLjG9mAapc3iN0mTC/3DDxW1gqFIzywuYXvP7aLY31+PtA4gy9etFCvcB3nrlxdz0NbWvjyA9vo7A9w3dmzc34tQ17cM3YyEBE+c/58vnHpEh5/7SjX/uxF2kc5l/d4f5CKQufA4xKPgx7fyGYKdHmDlHoclBVEX2ekz1fJRSKGR7Yd5qL/foYv/X4rMyoLeOjTZ/PdK5Zrkp8AHHYbP/1wI+9YVMu3H3mdd93yLE/tah3zGwwl0hGcCeYjZ83i1nUr2dbSxWX/83d2jmJBseN9ASqKEir6Aic9/pFW9CHKCpyUxhJ99wjPCNRbGWN4alcrl/74OT59z2ZEhNuuXcUDN57FihnluQ5PjUCJx8kdH1rN7R9ajS8Y4aM/38gV//s8z+5py0nC19bNBHTJiunMrCrk+l9u4vKfPM8337uUK1fXD/9EwB8K0+MPUZnQuinxOOj2jryiLytwUuqJJXpt3YyaMYandrfxkyeb2Lj/OPUVBfzXVSt478o67cNPYCLCRUum8vaFtfz25UP8+G9NfOjOlzhtVgU3njeX8xbUWjKLLh05TfQ6GDt6y+vLWX/TOXz2vlf44m9f5fmmdr753qUUuVP/So/1BgCoLD6R6MsLnHT7goQjJu3E0uUNUlrgpMQTfb9ubd2MWCgc4Y9bD3Pb03t5/UgP08o8/PtlS7j6tAZcDj3Zzhcuh41rTp/JlavruX/jIX7y1F6uu3sTc2uK+MS5c3jfyrqsz1jTJe0msNpSD/d84gxu/dsefvTXPWw6cJxvv28Z58yvTvqcgx3RJYXjs2UAakrcGAPHev1pz5zp9gaZUVGAx2nH7bBpRT8Cvf4Qv3+5mZ8+u4/m417m1Rbzn1et4NIV0zXB5zG3w86HzpzF1Wsa+NPWw/z02X18+YFt+INhPnr27Ky+tyb6Cc5uEz5/wQLOnFPFzQ9s49o7X+TK1fX8y7tOobLorZfC723rBWBW1YkbUNSURJN7a0/6iT7euoFoj1979MPbdaSHX79wgAdfaaHXH2JVQzlfu2QJ5y8au1N4lXtOu433rqzjslOn88K+DpbWZX9dIu3R54nT51Tx6OfO5da/7eH2p/fx5+1HuOFtc7junNkUJ7Rznm86Rk2Jm/qKE/fanVYWTe4tnV6W1g0/A8oYc3KiH0WPf7IIhCL8eccRfv3CAV56owOXw8Z7lk3jmjNmsqqhfNxcUKPGnoiM2dWz2qPPIx6nnX+6aBHvW1nHfzy2ix88vpvbn97LpadO59z5NXR7gzy24wjXnjHzpAQzN7ZGSlNrLxctGf594ksUa0U/NGMMmw8e58FXWvjj1sN09gdpqCzkX961iCtXzxjyTEupbNLWTR6aV1vC7R9qZGtzJ7/cEG0V3PtSdEXpJdNL+fwF80/av9jtYHZ1ERvTXHY1PqAbn9Nd4nHS1R+w8L9gYtrb1ssfXmnhoS1vcrCjH4/TxoWLp3L5qjreNr9G2zMqZzTR57Hl9eX851XlfOu9S9lztBeDYen0siETzjsW1fKrDQfo8QUp8TiHeLUT4hdqVcVm7lQVudgX6/1PJsYYdh3t4S87jvLYjiPseLMbm8DZ86r53PnzuWjp1JPaZkrlivboJwGP086y+tS990tXTOfO597gZ8++wRfeuSDlvvFEH6/oa0vctPb4Mcbkfc85HIm2Zf6y4wh/ee0oB471IwKrGir46rtP4dIV03XNHzXuaI9eAbBiRjmXrJjO/zzZxCnTSlm7dGrSfeNTNGdURKdo1pS4CYQidHtDlBWmPhuYiA4c6+PZPe08t6ed5/e20+0L4bLbOGteFZ9821wuWFxLbYkmdzV+6XmlGvDdy5dxsKOfG+95mQ+fMZPPnj+fqiHWVtnb2kdlkWsgqdeVR2fwHDreT1nhxF+3qKXTy6b9Hbyw7xjPNbVzqMMLwPQyD2uXTuXc+TWct7Bm2BaXUuOFJno1oMjt4L7rz+A7j+7kVy8c4N6Nh1i7ZCqXrJjO6XMqKfU4CYUj/H1vOysT1l6J39lo99GetKZnjieBUITXj3Tz8oHjbDpwnM0HjnO4ywdAidvBGXOruP7cOZw9r5o51UV535pS+UkTvTpJgcvOv1+2lA+fOYtfbdjPg6+08PCrb2ITqK8oJBSO8GaXj6+865SB58yuLqLU4+DZPe1cviq9NXdyoas/SFNbDzsP97DjzS62t3Sz60gPgXD0nrzTyzw0zqpkdUM5jbMqWTS1JOfLyyplBcnl0plxjY2NZtOmTbkOQw3BHwqz+UAnG/Yd4432PgDOW1DDFYMWUfv6wzv4xYb9fCu2wJrbkZu7TQVCEQ53eTnU4eWN9l72tPbS1Br92tZzYlnn8kInS6eXsWR6KUvrylg9s4Lp5QUpXlmp8UdEXjbGNA67Xy4TfcKsm+v37NmTszhU5nr9Ia77+UZe2t9BocvOqoYK5k8pZlZVETMqC6gqclNZ5KKiyEWRy552C8QYgz8Uoc8fotcfotsbor3XT1uvP/q1x097b4AjXV6aj3s50u0j8ZAudjuYV1vMvNpi5se+LphSQn1FgbZh1IQ3IRJ9nFb0+SESMTzX1M4TO4/y8oHjvNHeR/8Q96J12AS3w4Y7tiCa22FDRAhHDOGIIWKiX+MJPhRJfowWuexUl7iZUuKhvrKAGRWF1FcUUF9RyKzqQqaWejShq7yVbqLXHr2yjM0mvG1BDW9bUANEq/G2Xj+HOrwc7wvQ0R/geF+ALm8QfyiCPxTGH4zgD0UwgF2ir2EXwW4TXA4bxW4HRW7HwNcSj4OaEjc1xW6qi90UuPSG5EoNRxO9yhoRobbEo3PMlcoxnVKglFJ5ThO9UkrlOU30SimV5zTRK6VUnstpoheRS0Tkjq6urlyGoZRSeS2nid4Ys94Yc0NZ2cRaH0UppSYSbd0opVSe00SvlFJ5blwsgSAibcCBUT69Gmi3MByraFwjo3GNzHiNC8ZvbPkY10xjTM1wO42LRJ8JEdmUzloPY03jGhmNa2TGa1wwfmObzHFp60YppfKcJnqllMpz+ZDo78h1AEloXCOjcY3MeI0Lxm9skzauCd+jV0oplVo+VPRKKaVSmDCJXkT2i8g2EdkiIm+5HZVE3SIiTSKyVURWjUFMC2PxxP91i8jnB+1znoh0Jezzb1mK5S4RaRWR7QnbKkXkcRHZE/takeS5H4nts0dEPjIGcf2HiLwe+z09KCLlSZ6b8neehbi+LiItCb+rdyV57loR2RU71m4eg7h+kxDTfhHZkuS52fy8ZojIkyLymojsEJHPxbbn9BhLEVdOj7EUceXmGDPGTIh/wH6gOsXP3wU8CghwBvDiGMdnB44QndeauP084I9j8P5vA1YB2xO2fR+4Ofb9zcD3hnheJbAv9rUi9n1FluO6EHDEvv/eUHGl8zvPQlxfB76Yxu95LzAHcAGvAouzGdegn/8X8G85+LymAati35cAu4HFuT7GUsSV02MsRVw5OcYmTEWfhsuAX5qoF4ByEZk2hu9/PrDXGDPaC78yYox5BugYtPky4Bex738BvHeIp14EPG6M6TDGHAceB9ZmMy5jzF+MMaHYwxeAeqveL5O40rQGaDLG7DPGBID7iH7OWY9LRAR4P3CvVe+XLmPMYWPM5tj3PcBOoI4cH2PJ4sr1MZbi80qH5cfYREr0BviLiLwsIjcM8fM64FDC42bS/2CtcDXJ/wDPFJFXReRREVkyhjFNMcYcjn1/BJgyxD65/tyuI3omNpThfufZcFPsdP+uJG2IXH5e5wJHjTF7kvx8TD4vEZkFrAReZBwdY4PiSpTTY2yIuMb8GJtIif4cY8wq4GLgH0XkbbkOKE5EXMClwG+H+PFmou2cFcCtwENjGVuciZ4TjqspViLyFSAE3JNkl7H+nf8vMBc4FThMtE0ynqwjdTWf9c9LRIqB3wOfN8Z0J/4sl8dYsrhyfYwNEVdOjrEJk+iNMS2xr63Ag0RPbxK1ADMSHtfHto2Fi4HNxpijg39gjOk2xvTGvn8EcIpI9RjFdTTevop9bR1in5x8biLyUeA9wDWxBPEWafzOLWWMOWqMCRtjIsBPk7xfrj4vB3A58Jtk+2T78xIRJ9GkdY8x5oHY5pwfY0niyvkxNlRcuTrGJkSiF5EiESmJf090oGX7oN0eBj4sUWcAXQmnlNmWtNISkamx3ioisoboZ35sjOJ6GIjPcPgI8Ich9nkMuFBEKmKnkRfGtmWNiKwFvgRcaozpT7JPOr9zq+NKHNN5X5L32wjMF5HZsTO5q4l+ztl2AfC6MaZ5qB9m+/OKHcN3AjuNMT9I+FFOj7FkceX6GEsRV26OMatHm7Pxj+jo86uxfzuAr8S2fwr4VOx7AX5MdLR6G9A4RrEVEU3cZQnbEuO6KRbzq0QHhc7KUhz3Ej0VDBLt6X0cqAL+CuwBngAqY/s2Aj9LeO51QFPs38fGIK4moj3ILbF/t8X2nQ48kup3nuW4fhU7drYS/cOaNjiu2ON3EZ1FsXcs4optvzt+TCXsO5af1zlE2zJbE35v78r1MZYirpweYyniyskxplfGKqVUnpsQrRullFKjp4leKaXynCZ6pZTKc5rolVIqz2miV0qpPKeJXiml8pwmeqWUynOa6JVSKs/9P5C1CbudEwAzAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] diff --git a/examples/jupyter/pandas-dataframes.ipynb b/examples/jupyter/pandas-dataframes.ipynb index 80a0b880f..7cc2d92e9 100644 --- a/examples/jupyter/pandas-dataframes.ipynb +++ b/examples/jupyter/pandas-dataframes.ipynb @@ -99,8 +99,8 @@ "outputs": [], "source": [ "# Create cylinders for the fuel and clad\n", - "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n", - "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n", "\n", "# Create boundary planes to surround the geometry\n", "# Use both reflective and vacuum boundaries to make life interesting\n", @@ -253,7 +253,18 @@ "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Instantiate a Plot\n", "plot = openmc.Plot(plot_id=1)\n", @@ -263,51 +274,8 @@ "plot.pixels = [250, 250]\n", "plot.color_by = 'material'\n", "\n", - "# Instantiate a Plots collection and export to \"plots.xml\"\n", - "plot_file = openmc.Plots([plot])\n", - "plot_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Run openmc in plotting mode\n", - "openmc.plot_geometry(output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Convert OpenMC's funky ppm to png\n", - "!convert materials-xy.ppm materials-xy.png\n", - "\n", - "# Display the materials plot inline\n", - "Image(filename='materials-xy.png')" + "# Show plot\n", + "openmc.plot_inline(plot)" ] }, { @@ -319,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -336,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -370,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -419,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -436,122 +404,677 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 MIT and OpenMC contributors\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 199126b2fcc5cb094f2cc820ae13e1a972cacddd\n", - " Date/Time | 2018-10-11 16:41:25\n", - " OpenMP Threads | 8\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", + " Date/Time | 2019-07-18 22:46:04\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Reading U235 from /home/jan/openmc/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/jan/openmc/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/jan/openmc/nndc_hdf5/O16.h5\n", - " Reading H1 from /home/jan/openmc/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/jan/openmc/nndc_hdf5/B10.h5\n", - " Reading Zr90 from /home/jan/openmc/nndc_hdf5/Zr90.h5\n", - " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", + " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", + " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", + " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", + " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", + " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", + " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Reading tallies XML file...\n", " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 0.55921 \n", - " 2/1 0.63816 \n", - " 3/1 0.68834 \n", - " 4/1 0.71192 \n", - " 5/1 0.67935 \n", - " 6/1 0.68254 \n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.55921\n", + " 2/1 0.63816\n", + " 3/1 0.68834\n", + " 4/1 0.71192\n", + " 5/1 0.67935\n", + " 6/1 0.68254\n", " 7/1 0.65804 0.67029 +/- 0.01225\n", " 8/1 0.66225 0.66761 +/- 0.00756\n", " 9/1 0.66336 0.66655 +/- 0.00545\n", - " 10/1 0.68037 0.66931 +/- 0.00505\n", - " 11/1 0.71728 0.67731 +/- 0.00899\n", - " 12/1 0.66098 0.67498 +/- 0.00795\n", - " 13/1 0.69969 0.67806 +/- 0.00755\n", - " 14/1 0.70998 0.68161 +/- 0.00754\n", - " 15/1 0.70092 0.68354 +/- 0.00702\n", - " 16/1 0.71586 0.68648 +/- 0.00699\n", - " 17/1 0.65949 0.68423 +/- 0.00677\n", - " 18/1 0.67696 0.68367 +/- 0.00625\n", - " 19/1 0.65444 0.68158 +/- 0.00615\n", - " 20/1 0.69766 0.68266 +/- 0.00583\n", - " Triggers unsatisfied, max unc./thresh. is 1.17617 for absorption in tally 3\n", - " The estimated number of batches is 26\n", + " 10/1 0.70686 0.67461 +/- 0.00910\n", + " 11/1 0.71753 0.68176 +/- 0.01031\n", + " 12/1 0.66967 0.68004 +/- 0.00889\n", + " 13/1 0.67800 0.67978 +/- 0.00770\n", + " 14/1 0.65634 0.67718 +/- 0.00727\n", + " 15/1 0.66891 0.67635 +/- 0.00656\n", + " 16/1 0.66281 0.67512 +/- 0.00606\n", + " 17/1 0.68160 0.67566 +/- 0.00556\n", + " 18/1 0.63835 0.67279 +/- 0.00586\n", + " 19/1 0.66200 0.67202 +/- 0.00548\n", + " 20/1 0.67156 0.67199 +/- 0.00510\n", + " Triggers unsatisfied, max unc./thresh. is 68.3537 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 70089 --- greater than max batches\n", " Creating state point statepoint.020.h5...\n", - " 21/1 0.64126 0.68007 +/- 0.00603\n", - " 22/1 0.69287 0.68082 +/- 0.00572\n", - " 23/1 0.70254 0.68203 +/- 0.00552\n", - " 24/1 0.68198 0.68203 +/- 0.00523\n", - " 25/1 0.67214 0.68153 +/- 0.00498\n", - " 26/1 0.68171 0.68154 +/- 0.00474\n", - " Triggers satisfied for batch 26\n", - " Creating state point statepoint.026.h5...\n", + " 21/1 0.67469 0.67216 +/- 0.00478\n", + " Triggers unsatisfied, max unc./thresh. is 63.9814 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 65503 --- greater than max batches\n", + " 22/1 0.69218 0.67334 +/- 0.00464\n", + " Triggers unsatisfied, max unc./thresh. is 64.4829 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 70692 --- greater than max batches\n", + " 23/1 0.72838 0.67639 +/- 0.00534\n", + " Triggers unsatisfied, max unc./thresh. is 65.1347 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 76371 --- greater than max batches\n", + " 24/1 0.68472 0.67683 +/- 0.00507\n", + " Triggers unsatisfied, max unc./thresh. is 61.6163 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 72140 --- greater than max batches\n", + " 25/1 0.66664 0.67632 +/- 0.00483\n", + " Triggers unsatisfied, max unc./thresh. is 59.0208 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 69675 --- greater than max batches\n", + " 26/1 0.65315 0.67522 +/- 0.00473\n", + " Triggers unsatisfied, max unc./thresh. is 56.5216 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 67094 --- greater than max batches\n", + " 27/1 0.63865 0.67356 +/- 0.00480\n", + " Triggers unsatisfied, max unc./thresh. is 53.8991 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 63918 --- greater than max batches\n", + " 28/1 0.68053 0.67386 +/- 0.00460\n", + " Triggers unsatisfied, max unc./thresh. is 51.504 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61017 --- greater than max batches\n", + " 29/1 0.71585 0.67561 +/- 0.00474\n", + " Triggers unsatisfied, max unc./thresh. is 49.3115 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58364 --- greater than max batches\n", + " 30/1 0.67268 0.67549 +/- 0.00455\n", + " Triggers unsatisfied, max unc./thresh. is 47.3457 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 56046 --- greater than max batches\n", + " 31/1 0.67027 0.67529 +/- 0.00437\n", + " Triggers unsatisfied, max unc./thresh. is 48.2456 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60524 --- greater than max batches\n", + " 32/1 0.67324 0.67522 +/- 0.00421\n", + " Triggers unsatisfied, max unc./thresh. is 47.1077 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59922 --- greater than max batches\n", + " 33/1 0.66398 0.67481 +/- 0.00408\n", + " Triggers unsatisfied, max unc./thresh. is 45.4352 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57807 --- greater than max batches\n", + " 34/1 0.66373 0.67443 +/- 0.00395\n", + " Triggers unsatisfied, max unc./thresh. is 44.8243 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58273 --- greater than max batches\n", + " 35/1 0.68412 0.67476 +/- 0.00383\n", + " Triggers unsatisfied, max unc./thresh. is 43.7412 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57404 --- greater than max batches\n", + " 36/1 0.66026 0.67429 +/- 0.00374\n", + " Triggers unsatisfied, max unc./thresh. is 43.0549 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57471 --- greater than max batches\n", + " 37/1 0.67283 0.67424 +/- 0.00362\n", + " Triggers unsatisfied, max unc./thresh. is 42.9634 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59073 --- greater than max batches\n", + " 38/1 0.69507 0.67487 +/- 0.00356\n", + " Triggers unsatisfied, max unc./thresh. is 41.6527 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57259 --- greater than max batches\n", + " 39/1 0.68681 0.67522 +/- 0.00347\n", + " Triggers unsatisfied, max unc./thresh. is 40.4174 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 55547 --- greater than max batches\n", + " 40/1 0.65886 0.67476 +/- 0.00340\n", + " Triggers unsatisfied, max unc./thresh. is 39.424 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 54404 --- greater than max batches\n", + " 41/1 0.63736 0.67372 +/- 0.00347\n", + " Triggers unsatisfied, max unc./thresh. is 40.094 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57877 --- greater than max batches\n", + " 42/1 0.71800 0.67491 +/- 0.00358\n", + " Triggers unsatisfied, max unc./thresh. is 39.0603 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 56457 --- greater than max batches\n", + " 43/1 0.67193 0.67484 +/- 0.00348\n", + " Triggers unsatisfied, max unc./thresh. is 38.8448 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57344 --- greater than max batches\n", + " 44/1 0.66680 0.67463 +/- 0.00340\n", + " Triggers unsatisfied, max unc./thresh. is 38.227 for absorption in tally 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " WARNING: The estimated number of batches is 56996 --- greater than max batches\n", + " 45/1 0.65956 0.67425 +/- 0.00334\n", + " Triggers unsatisfied, max unc./thresh. is 37.2591 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 55535 --- greater than max batches\n", + " 46/1 0.64705 0.67359 +/- 0.00332\n", + " Triggers unsatisfied, max unc./thresh. is 37.802 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58594 --- greater than max batches\n", + " 47/1 0.67729 0.67368 +/- 0.00324\n", + " Triggers unsatisfied, max unc./thresh. is 36.9727 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57419 --- greater than max batches\n", + " 48/1 0.68259 0.67389 +/- 0.00317\n", + " Triggers unsatisfied, max unc./thresh. is 36.3752 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 56901 --- greater than max batches\n", + " 49/1 0.64395 0.67320 +/- 0.00317\n", + " Triggers unsatisfied, max unc./thresh. is 35.7676 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 56296 --- greater than max batches\n", + " 50/1 0.68839 0.67354 +/- 0.00312\n", + " Triggers unsatisfied, max unc./thresh. is 34.977 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 55058 --- greater than max batches\n", + " 51/1 0.71108 0.67436 +/- 0.00316\n", + " Triggers unsatisfied, max unc./thresh. is 34.453 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 54608 --- greater than max batches\n", + " 52/1 0.66286 0.67411 +/- 0.00310\n", + " Triggers unsatisfied, max unc./thresh. is 33.9781 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 54268 --- greater than max batches\n", + " 53/1 0.62666 0.67313 +/- 0.00319\n", + " Triggers unsatisfied, max unc./thresh. is 33.4946 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 53856 --- greater than max batches\n", + " 54/1 0.67124 0.67309 +/- 0.00313\n", + " Triggers unsatisfied, max unc./thresh. is 32.8639 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 52927 --- greater than max batches\n", + " 55/1 0.67741 0.67317 +/- 0.00306\n", + " Triggers unsatisfied, max unc./thresh. is 32.2922 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 52145 --- greater than max batches\n", + " 56/1 0.67182 0.67315 +/- 0.00300\n", + " Triggers unsatisfied, max unc./thresh. is 31.9136 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 51948 --- greater than max batches\n", + " 57/1 0.68764 0.67343 +/- 0.00296\n", + " Triggers unsatisfied, max unc./thresh. is 31.3059 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 50969 --- greater than max batches\n", + " 58/1 0.72310 0.67436 +/- 0.00305\n", + " Triggers unsatisfied, max unc./thresh. is 30.8841 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 50558 --- greater than max batches\n", + " 59/1 0.67689 0.67441 +/- 0.00299\n", + " Triggers unsatisfied, max unc./thresh. is 30.5895 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 50534 --- greater than max batches\n", + " 60/1 0.65890 0.67413 +/- 0.00295\n", + " Triggers unsatisfied, max unc./thresh. is 30.0567 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 49693 --- greater than max batches\n", + " 61/1 0.69128 0.67443 +/- 0.00291\n", + " Triggers unsatisfied, max unc./thresh. is 29.8144 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 49784 --- greater than max batches\n", + " 62/1 0.65469 0.67409 +/- 0.00288\n", + " Triggers unsatisfied, max unc./thresh. is 29.3138 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 48986 --- greater than max batches\n", + " 63/1 0.71839 0.67485 +/- 0.00293\n", + " Triggers unsatisfied, max unc./thresh. is 28.9465 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 48604 --- greater than max batches\n", + " 64/1 0.69556 0.67520 +/- 0.00291\n", + " Triggers unsatisfied, max unc./thresh. is 29.1602 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 50174 --- greater than max batches\n", + " 65/1 0.70067 0.67563 +/- 0.00289\n", + " Triggers unsatisfied, max unc./thresh. is 28.9248 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 50204 --- greater than max batches\n", + " 66/1 0.67994 0.67570 +/- 0.00284\n", + " Triggers unsatisfied, max unc./thresh. is 28.7841 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 50545 --- greater than max batches\n", + " 67/1 0.74539 0.67682 +/- 0.00301\n", + " Triggers unsatisfied, max unc./thresh. is 28.4946 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 50346 --- greater than max batches\n", + " 68/1 0.67753 0.67683 +/- 0.00296\n", + " Triggers unsatisfied, max unc./thresh. is 28.1166 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 49810 --- greater than max batches\n", + " 69/1 0.69595 0.67713 +/- 0.00293\n", + " Triggers unsatisfied, max unc./thresh. is 28.0441 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 50340 --- greater than max batches\n", + " 70/1 0.70621 0.67758 +/- 0.00292\n", + " Triggers unsatisfied, max unc./thresh. is 27.708 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 49908 --- greater than max batches\n", + " 71/1 0.71027 0.67807 +/- 0.00292\n", + " Triggers unsatisfied, max unc./thresh. is 27.2979 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 49187 --- greater than max batches\n", + " 72/1 0.63710 0.67746 +/- 0.00294\n", + " Triggers unsatisfied, max unc./thresh. is 27.3359 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 50071 --- greater than max batches\n", + " 73/1 0.70979 0.67794 +/- 0.00294\n", + " Triggers unsatisfied, max unc./thresh. is 29.5308 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59306 --- greater than max batches\n", + " 74/1 0.65957 0.67767 +/- 0.00291\n", + " Triggers unsatisfied, max unc./thresh. is 29.2344 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58976 --- greater than max batches\n", + " 75/1 0.66611 0.67751 +/- 0.00287\n", + " Triggers unsatisfied, max unc./thresh. is 28.8289 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58183 --- greater than max batches\n", + " 76/1 0.66033 0.67726 +/- 0.00284\n", + " Triggers unsatisfied, max unc./thresh. is 28.4986 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57670 --- greater than max batches\n", + " 77/1 0.68535 0.67738 +/- 0.00280\n", + " Triggers unsatisfied, max unc./thresh. is 28.2548 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57486 --- greater than max batches\n", + " 78/1 0.71920 0.67795 +/- 0.00282\n", + " Triggers unsatisfied, max unc./thresh. is 28.2853 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58410 --- greater than max batches\n", + " 79/1 0.67645 0.67793 +/- 0.00278\n", + " Triggers unsatisfied, max unc./thresh. is 27.9534 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57829 --- greater than max batches\n", + " 80/1 0.68300 0.67800 +/- 0.00275\n", + " Triggers unsatisfied, max unc./thresh. is 27.5813 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57060 --- greater than max batches\n", + " 81/1 0.69810 0.67826 +/- 0.00272\n", + " Triggers unsatisfied, max unc./thresh. is 27.2164 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 56301 --- greater than max batches\n", + " 82/1 0.68213 0.67831 +/- 0.00269\n", + " Triggers unsatisfied, max unc./thresh. is 26.8628 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 55570 --- greater than max batches\n", + " 83/1 0.68745 0.67843 +/- 0.00265\n", + " Triggers unsatisfied, max unc./thresh. is 26.5172 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 54852 --- greater than max batches\n", + " 84/1 0.65239 0.67810 +/- 0.00264\n", + " Triggers unsatisfied, max unc./thresh. is 26.2016 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 54241 --- greater than max batches\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 85/1 0.64990 0.67775 +/- 0.00263\n", + " Triggers unsatisfied, max unc./thresh. is 25.9705 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 53963 --- greater than max batches\n", + " 86/1 0.68586 0.67785 +/- 0.00260\n", + " Triggers unsatisfied, max unc./thresh. is 25.7908 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 53884 --- greater than max batches\n", + " 87/1 0.63453 0.67732 +/- 0.00262\n", + " Triggers unsatisfied, max unc./thresh. is 25.5271 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 53439 --- greater than max batches\n", + " 88/1 0.65402 0.67704 +/- 0.00261\n", + " Triggers unsatisfied, max unc./thresh. is 25.321 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 53221 --- greater than max batches\n", + " 89/1 0.69063 0.67720 +/- 0.00258\n", + " Triggers unsatisfied, max unc./thresh. is 25.8769 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 56253 --- greater than max batches\n", + " 90/1 0.65729 0.67697 +/- 0.00256\n", + " Triggers unsatisfied, max unc./thresh. is 25.7648 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 56431 --- greater than max batches\n", + " 91/1 0.72355 0.67751 +/- 0.00259\n", + " Triggers unsatisfied, max unc./thresh. is 25.5034 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 55942 --- greater than max batches\n", + " 92/1 0.63010 0.67696 +/- 0.00262\n", + " Triggers unsatisfied, max unc./thresh. is 25.2708 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 55565 --- greater than max batches\n", + " 93/1 0.68610 0.67707 +/- 0.00259\n", + " Triggers unsatisfied, max unc./thresh. is 24.9941 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 54980 --- greater than max batches\n", + " 94/1 0.67618 0.67706 +/- 0.00256\n", + " Triggers unsatisfied, max unc./thresh. is 24.7139 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 54365 --- greater than max batches\n", + " 95/1 0.68946 0.67719 +/- 0.00253\n", + " Triggers unsatisfied, max unc./thresh. is 25.4371 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58240 --- greater than max batches\n", + " 96/1 0.70557 0.67751 +/- 0.00252\n", + " Triggers unsatisfied, max unc./thresh. is 25.5082 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59216 --- greater than max batches\n", + " 97/1 0.64689 0.67717 +/- 0.00252\n", + " Triggers unsatisfied, max unc./thresh. is 25.2374 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58603 --- greater than max batches\n", + " 98/1 0.70194 0.67744 +/- 0.00251\n", + " Triggers unsatisfied, max unc./thresh. is 25.393 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59972 --- greater than max batches\n", + " 99/1 0.68278 0.67750 +/- 0.00248\n", + " Triggers unsatisfied, max unc./thresh. is 25.5651 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61441 --- greater than max batches\n", + " 100/1 0.67066 0.67742 +/- 0.00246\n", + " Triggers unsatisfied, max unc./thresh. is 25.3552 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61079 --- greater than max batches\n", + " 101/1 0.64907 0.67713 +/- 0.00245\n", + " Triggers unsatisfied, max unc./thresh. is 25.3463 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61679 --- greater than max batches\n", + " 102/1 0.69810 0.67735 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 25.1877 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61544 --- greater than max batches\n", + " 103/1 0.70659 0.67764 +/- 0.00242\n", + " Triggers unsatisfied, max unc./thresh. is 24.9371 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60948 --- greater than max batches\n", + " 104/1 0.64152 0.67728 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 24.6848 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60330 --- greater than max batches\n", + " 105/1 0.68117 0.67732 +/- 0.00240\n", + " Triggers unsatisfied, max unc./thresh. is 24.4368 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59721 --- greater than max batches\n", + " 106/1 0.71963 0.67774 +/- 0.00242\n", + " Triggers unsatisfied, max unc./thresh. is 24.2091 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59200 --- greater than max batches\n", + " 107/1 0.69488 0.67790 +/- 0.00240\n", + " Triggers unsatisfied, max unc./thresh. is 23.9711 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58616 --- greater than max batches\n", + " 108/1 0.65697 0.67770 +/- 0.00238\n", + " Triggers unsatisfied, max unc./thresh. is 23.8071 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58384 --- greater than max batches\n", + " 109/1 0.70032 0.67792 +/- 0.00237\n", + " Triggers unsatisfied, max unc./thresh. is 23.5788 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57825 --- greater than max batches\n", + " 110/1 0.66571 0.67780 +/- 0.00235\n", + " Triggers unsatisfied, max unc./thresh. is 23.5035 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58009 --- greater than max batches\n", + " 111/1 0.69676 0.67798 +/- 0.00234\n", + " Triggers unsatisfied, max unc./thresh. is 23.3157 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57629 --- greater than max batches\n", + " 112/1 0.68219 0.67802 +/- 0.00231\n", + " Triggers unsatisfied, max unc./thresh. is 23.1525 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57361 --- greater than max batches\n", + " 113/1 0.69025 0.67813 +/- 0.00230\n", + " Triggers unsatisfied, max unc./thresh. is 23.0036 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57156 --- greater than max batches\n", + " 114/1 0.69241 0.67826 +/- 0.00228\n", + " Triggers unsatisfied, max unc./thresh. is 22.792 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 56628 --- greater than max batches\n", + " 115/1 0.68646 0.67834 +/- 0.00226\n", + " Triggers unsatisfied, max unc./thresh. is 22.6864 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 56620 --- greater than max batches\n", + " 116/1 0.69601 0.67850 +/- 0.00224\n", + " Triggers unsatisfied, max unc./thresh. is 22.5007 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 56203 --- greater than max batches\n", + " 117/1 0.68761 0.67858 +/- 0.00222\n", + " Triggers unsatisfied, max unc./thresh. is 22.3093 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 55749 --- greater than max batches\n", + " 118/1 0.71356 0.67889 +/- 0.00223\n", + " Triggers unsatisfied, max unc./thresh. is 22.6651 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58054 --- greater than max batches\n", + " 119/1 0.69850 0.67906 +/- 0.00221\n", + " Triggers unsatisfied, max unc./thresh. is 22.4712 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57570 --- greater than max batches\n", + " 120/1 0.70957 0.67933 +/- 0.00221\n", + " Triggers unsatisfied, max unc./thresh. is 22.3266 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57331 --- greater than max batches\n", + " 121/1 0.69643 0.67947 +/- 0.00220\n", + " Triggers unsatisfied, max unc./thresh. is 22.6029 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59269 --- greater than max batches\n", + " 122/1 0.67717 0.67945 +/- 0.00218\n", + " Triggers unsatisfied, max unc./thresh. is 22.4667 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59062 --- greater than max batches\n", + " 123/1 0.68419 0.67949 +/- 0.00216\n", + " Triggers unsatisfied, max unc./thresh. is 22.3764 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59089 --- greater than max batches\n", + " 124/1 0.69221 0.67960 +/- 0.00214\n", + " Triggers unsatisfied, max unc./thresh. is 22.3341 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59364 --- greater than max batches\n", + " 125/1 0.73940 0.68010 +/- 0.00218\n", + " Triggers unsatisfied, max unc./thresh. is 22.1478 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58868 --- greater than max batches\n", + " 126/1 0.66908 0.68001 +/- 0.00217\n", + " Triggers unsatisfied, max unc./thresh. is 22.0085 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58615 --- greater than max batches\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 127/1 0.66041 0.67985 +/- 0.00216\n", + " Triggers unsatisfied, max unc./thresh. is 21.8274 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58131 --- greater than max batches\n", + " 128/1 0.69395 0.67996 +/- 0.00214\n", + " Triggers unsatisfied, max unc./thresh. is 21.6537 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57678 --- greater than max batches\n", + " 129/1 0.68665 0.68002 +/- 0.00212\n", + " Triggers unsatisfied, max unc./thresh. is 21.7739 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58794 --- greater than max batches\n", + " 130/1 0.64849 0.67976 +/- 0.00212\n", + " Triggers unsatisfied, max unc./thresh. is 21.7492 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59134 --- greater than max batches\n", + " 131/1 0.69734 0.67990 +/- 0.00211\n", + " Triggers unsatisfied, max unc./thresh. is 21.59 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58738 --- greater than max batches\n", + " 132/1 0.69482 0.68002 +/- 0.00210\n", + " Triggers unsatisfied, max unc./thresh. is 21.4249 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58302 --- greater than max batches\n", + " 133/1 0.68884 0.68009 +/- 0.00208\n", + " Triggers unsatisfied, max unc./thresh. is 21.2587 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57853 --- greater than max batches\n", + " 134/1 0.63042 0.67971 +/- 0.00210\n", + " Triggers unsatisfied, max unc./thresh. is 21.1851 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57902 --- greater than max batches\n", + " 135/1 0.69209 0.67980 +/- 0.00209\n", + " Triggers unsatisfied, max unc./thresh. is 21.0525 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57623 --- greater than max batches\n", + " 136/1 0.69873 0.67995 +/- 0.00208\n", + " Triggers unsatisfied, max unc./thresh. is 20.9996 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57774 --- greater than max batches\n", + " 137/1 0.70270 0.68012 +/- 0.00207\n", + " Triggers unsatisfied, max unc./thresh. is 20.8455 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 57364 --- greater than max batches\n", + " 138/1 0.67295 0.68006 +/- 0.00205\n", + " Triggers unsatisfied, max unc./thresh. is 21.3716 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60752 --- greater than max batches\n", + " 139/1 0.63853 0.67975 +/- 0.00206\n", + " Triggers unsatisfied, max unc./thresh. is 21.2124 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60301 --- greater than max batches\n", + " 140/1 0.66645 0.67966 +/- 0.00205\n", + " Triggers unsatisfied, max unc./thresh. is 21.1279 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60268 --- greater than max batches\n", + " 141/1 0.70730 0.67986 +/- 0.00204\n", + " Triggers unsatisfied, max unc./thresh. is 20.9845 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59893 --- greater than max batches\n", + " 142/1 0.68838 0.67992 +/- 0.00203\n", + " Triggers unsatisfied, max unc./thresh. is 20.8774 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59719 --- greater than max batches\n", + " 143/1 0.64900 0.67970 +/- 0.00203\n", + " Triggers unsatisfied, max unc./thresh. is 21.3772 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 63069 --- greater than max batches\n", + " 144/1 0.64490 0.67945 +/- 0.00203\n", + " Triggers unsatisfied, max unc./thresh. is 21.2531 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 62791 --- greater than max batches\n", + " 145/1 0.69221 0.67954 +/- 0.00201\n", + " Triggers unsatisfied, max unc./thresh. is 21.2049 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 62956 --- greater than max batches\n", + " 146/1 0.69481 0.67965 +/- 0.00200\n", + " Triggers unsatisfied, max unc./thresh. is 21.0645 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 62569 --- greater than max batches\n", + " 147/1 0.70394 0.67982 +/- 0.00200\n", + " Triggers unsatisfied, max unc./thresh. is 20.9156 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 62125 --- greater than max batches\n", + " 148/1 0.69482 0.67992 +/- 0.00198\n", + " Triggers unsatisfied, max unc./thresh. is 20.7699 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61694 --- greater than max batches\n", + " 149/1 0.63886 0.67964 +/- 0.00199\n", + " Triggers unsatisfied, max unc./thresh. is 20.6366 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61331 --- greater than max batches\n", + " 150/1 0.69377 0.67973 +/- 0.00198\n", + " Triggers unsatisfied, max unc./thresh. is 20.5819 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61430 --- greater than max batches\n", + " 151/1 0.71045 0.67994 +/- 0.00198\n", + " Triggers unsatisfied, max unc./thresh. is 20.5417 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61612 --- greater than max batches\n", + " 152/1 0.66093 0.67982 +/- 0.00197\n", + " Triggers unsatisfied, max unc./thresh. is 20.4124 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61256 --- greater than max batches\n", + " 153/1 0.68564 0.67985 +/- 0.00196\n", + " Triggers unsatisfied, max unc./thresh. is 20.3025 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61010 --- greater than max batches\n", + " 154/1 0.66961 0.67979 +/- 0.00194\n", + " Triggers unsatisfied, max unc./thresh. is 20.2239 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60948 --- greater than max batches\n", + " 155/1 0.67099 0.67973 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 20.0962 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60584 --- greater than max batches\n", + " 156/1 0.72742 0.68004 +/- 0.00194\n", + " Triggers unsatisfied, max unc./thresh. is 19.9753 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60256 --- greater than max batches\n", + " 157/1 0.66458 0.67994 +/- 0.00193\n", + " Triggers unsatisfied, max unc./thresh. is 19.8852 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60109 --- greater than max batches\n", + " 158/1 0.69052 0.68001 +/- 0.00192\n", + " Triggers unsatisfied, max unc./thresh. is 19.7963 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59965 --- greater than max batches\n", + " 159/1 0.70643 0.68018 +/- 0.00192\n", + " Triggers unsatisfied, max unc./thresh. is 19.6991 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59766 --- greater than max batches\n", + " 160/1 0.68576 0.68022 +/- 0.00191\n", + " Triggers unsatisfied, max unc./thresh. is 19.6197 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59670 --- greater than max batches\n", + " 161/1 0.69854 0.68034 +/- 0.00190\n", + " Triggers unsatisfied, max unc./thresh. is 19.8287 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61341 --- greater than max batches\n", + " 162/1 0.65983 0.68020 +/- 0.00189\n", + " Triggers unsatisfied, max unc./thresh. is 20.0243 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 62958 --- greater than max batches\n", + " 163/1 0.66316 0.68010 +/- 0.00188\n", + " Triggers unsatisfied, max unc./thresh. is 19.8975 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 62560 --- greater than max batches\n", + " 164/1 0.66179 0.67998 +/- 0.00187\n", + " Triggers unsatisfied, max unc./thresh. is 19.895 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 62940 --- greater than max batches\n", + " 165/1 0.70881 0.68016 +/- 0.00187\n", + " Triggers unsatisfied, max unc./thresh. is 19.8013 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 62740 --- greater than max batches\n", + " 166/1 0.70729 0.68033 +/- 0.00187\n", + " Triggers unsatisfied, max unc./thresh. is 19.6876 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 62410 --- greater than max batches\n", + " 167/1 0.71073 0.68052 +/- 0.00186\n", + " Triggers unsatisfied, max unc./thresh. is 19.5695 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 62046 --- greater than max batches\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 168/1 0.69610 0.68061 +/- 0.00185\n", + " Triggers unsatisfied, max unc./thresh. is 19.4797 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61857 --- greater than max batches\n", + " 169/1 0.67141 0.68056 +/- 0.00184\n", + " Triggers unsatisfied, max unc./thresh. is 19.438 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61970 --- greater than max batches\n", + " 170/1 0.67727 0.68054 +/- 0.00183\n", + " Triggers unsatisfied, max unc./thresh. is 19.3208 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61599 --- greater than max batches\n", + " 171/1 0.64150 0.68030 +/- 0.00184\n", + " Triggers unsatisfied, max unc./thresh. is 19.2066 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61242 --- greater than max batches\n", + " 172/1 0.68758 0.68035 +/- 0.00183\n", + " Triggers unsatisfied, max unc./thresh. is 19.114 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61018 --- greater than max batches\n", + " 173/1 0.67126 0.68029 +/- 0.00182\n", + " Triggers unsatisfied, max unc./thresh. is 19.1545 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61644 --- greater than max batches\n", + " 174/1 0.65933 0.68017 +/- 0.00181\n", + " Triggers unsatisfied, max unc./thresh. is 19.0415 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 61281 --- greater than max batches\n", + " 175/1 0.70572 0.68032 +/- 0.00181\n", + " Triggers unsatisfied, max unc./thresh. is 18.9347 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60954 --- greater than max batches\n", + " 176/1 0.66175 0.68021 +/- 0.00180\n", + " Triggers unsatisfied, max unc./thresh. is 18.8337 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60660 --- greater than max batches\n", + " 177/1 0.68714 0.68025 +/- 0.00179\n", + " Triggers unsatisfied, max unc./thresh. is 18.7329 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60364 --- greater than max batches\n", + " 178/1 0.70181 0.68037 +/- 0.00178\n", + " Triggers unsatisfied, max unc./thresh. is 18.6297 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 60048 --- greater than max batches\n", + " 179/1 0.66700 0.68030 +/- 0.00177\n", + " Triggers unsatisfied, max unc./thresh. is 18.5239 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59711 --- greater than max batches\n", + " 180/1 0.68980 0.68035 +/- 0.00176\n", + " Triggers unsatisfied, max unc./thresh. is 18.4186 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59374 --- greater than max batches\n", + " 181/1 0.69586 0.68044 +/- 0.00176\n", + " Triggers unsatisfied, max unc./thresh. is 18.3816 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59473 --- greater than max batches\n", + " 182/1 0.68689 0.68048 +/- 0.00175\n", + " Triggers unsatisfied, max unc./thresh. is 18.2781 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59139 --- greater than max batches\n", + " 183/1 0.69257 0.68054 +/- 0.00174\n", + " Triggers unsatisfied, max unc./thresh. is 18.1773 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58819 --- greater than max batches\n", + " 184/1 0.69926 0.68065 +/- 0.00173\n", + " Triggers unsatisfied, max unc./thresh. is 18.2191 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59422 --- greater than max batches\n", + " 185/1 0.67801 0.68063 +/- 0.00172\n", + " Triggers unsatisfied, max unc./thresh. is 18.1184 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59096 --- greater than max batches\n", + " 186/1 0.67049 0.68058 +/- 0.00171\n", + " Triggers unsatisfied, max unc./thresh. is 18.0484 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58965 --- greater than max batches\n", + " 187/1 0.68164 0.68058 +/- 0.00170\n", + " Triggers unsatisfied, max unc./thresh. is 17.9808 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58848 --- greater than max batches\n", + " 188/1 0.66856 0.68052 +/- 0.00170\n", + " Triggers unsatisfied, max unc./thresh. is 17.9146 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58736 --- greater than max batches\n", + " 189/1 0.71850 0.68073 +/- 0.00170\n", + " Triggers unsatisfied, max unc./thresh. is 17.8551 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58665 --- greater than max batches\n", + " 190/1 0.67095 0.68067 +/- 0.00169\n", + " Triggers unsatisfied, max unc./thresh. is 17.8953 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59250 --- greater than max batches\n", + " 191/1 0.70857 0.68082 +/- 0.00169\n", + " Triggers unsatisfied, max unc./thresh. is 17.8197 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59068 --- greater than max batches\n", + " 192/1 0.65322 0.68067 +/- 0.00169\n", + " Triggers unsatisfied, max unc./thresh. is 17.8199 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59387 --- greater than max batches\n", + " 193/1 0.67888 0.68066 +/- 0.00168\n", + " Triggers unsatisfied, max unc./thresh. is 17.8072 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59620 --- greater than max batches\n", + " 194/1 0.72890 0.68092 +/- 0.00169\n", + " Triggers unsatisfied, max unc./thresh. is 17.7152 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59319 --- greater than max batches\n", + " 195/1 0.64688 0.68074 +/- 0.00169\n", + " Triggers unsatisfied, max unc./thresh. is 17.6252 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59029 --- greater than max batches\n", + " 196/1 0.68906 0.68078 +/- 0.00168\n", + " Triggers unsatisfied, max unc./thresh. is 17.5465 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58810 --- greater than max batches\n", + " 197/1 0.69381 0.68085 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 17.4939 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58764 --- greater than max batches\n", + " 198/1 0.70057 0.68095 +/- 0.00167\n", + " Triggers unsatisfied, max unc./thresh. is 17.4414 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58717 --- greater than max batches\n", + " 199/1 0.67868 0.68094 +/- 0.00166\n", + " Triggers unsatisfied, max unc./thresh. is 17.4394 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 59008 --- greater than max batches\n", + " 200/1 0.69190 0.68100 +/- 0.00165\n", + " Triggers unsatisfied, max unc./thresh. is 17.3511 for absorption in tally 3\n", + " WARNING: The estimated number of batches is 58712 --- greater than max batches\n", + " Creating state point statepoint.200.h5...\n", "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 6.5303E-01 seconds\n", - " Reading cross sections = 5.8105E-01 seconds\n", - " Total time in simulation = 4.6015E+00 seconds\n", - " Time in transport only = 3.5767E+00 seconds\n", - " Time in inactive batches = 4.5008E-01 seconds\n", - " Time in active batches = 4.1514E+00 seconds\n", - " Time synchronizing fission bank = 2.3493E-03 seconds\n", - " Sampling source sites = 1.7160E-03 seconds\n", - " SEND/RECV source sites = 4.7010E-04 seconds\n", - " Time accumulating tallies = 2.3040E-04 seconds\n", - " Total time for finalization = 2.6451E-02 seconds\n", - " Total time elapsed = 5.3123E+00 seconds\n", - " Calculation Rate (inactive) = 27772.9 particles/second\n", - " Calculation Rate (active) = 12646.3 particles/second\n", + " Total time for initialization = 9.3777e-01 seconds\n", + " Reading cross sections = 8.7757e-01 seconds\n", + " Total time in simulation = 4.0652e+01 seconds\n", + " Time in transport only = 3.9022e+01 seconds\n", + " Time in inactive batches = 9.1120e-01 seconds\n", + " Time in active batches = 3.9741e+01 seconds\n", + " Time synchronizing fission bank = 4.0496e-02 seconds\n", + " Sampling source sites = 3.3700e-02 seconds\n", + " SEND/RECV source sites = 6.4404e-03 seconds\n", + " Time accumulating tallies = 2.0272e-03 seconds\n", + " Total time for finalization = 4.0896e-03 seconds\n", + " Total time elapsed = 4.1621e+01 seconds\n", + " Calculation Rate (inactive) = 13718.1 particles/second\n", + " Calculation Rate (active) = 12267.1 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 0.67976 +/- 0.00436\n", - " k-effective (Track-length) = 0.68154 +/- 0.00474\n", - " k-effective (Absorption) = 0.68320 +/- 0.00518\n", - " Combined k-effective = 0.68122 +/- 0.00432\n", - " Leakage Fraction = 0.34011 +/- 0.00283\n", + " k-effective (Collision) = 0.68122 +/- 0.00150\n", + " k-effective (Track-length) = 0.68100 +/- 0.00165\n", + " k-effective (Absorption) = 0.68224 +/- 0.00159\n", + " Combined k-effective = 0.68162 +/- 0.00134\n", + " Leakage Fraction = 0.34047 +/- 0.00082\n", "\n" ] } @@ -573,7 +1096,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -594,7 +1117,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -629,20 +1152,20 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[[0.17581417]]\n", + "[[[0.16617932]]\n", "\n", - " [[0.06842901]]\n", + " [[0.06455926]]\n", "\n", - " [[0.30578219]]\n", + " [[0.32266365]]\n", "\n", - " [[0.12436752]]]\n" + " [[0.13355528]]]\n" ] } ], @@ -658,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -710,8 +1233,8 @@ "
\n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -721,8 +1244,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -732,8 +1255,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -743,8 +1266,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -754,8 +1277,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -765,8 +1288,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -776,8 +1299,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -787,8 +1310,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -798,8 +1321,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -809,8 +1332,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -820,8 +1343,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -831,8 +1354,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -842,8 +1365,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -853,8 +1376,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -864,8 +1387,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -875,8 +1398,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -886,8 +1409,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -897,8 +1420,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -908,8 +1431,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -919,8 +1442,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
11U2350.0746720.0001790.0745560.000144
111U2380.0059640.0000170.0059760.000019
2
00.0311510.03067902.00.0004710.1070450.0004730.1085760.00.0
12.8209212.82384302.00.0003340.0988850.0003510.0864180.00.0
216.21740816.28114701.00.0004530.0683850.0004580.1068250.00.0
316.77102116.77176002.00.0136700.0712780.0135980.0728370.00.0
420.55968520.56154502.00.0106090.0975460.0111640.0866160.00.0
00.0338380.03285802.00.0004800.1033250.0004790.1052080.00.0
12.8223702.82385902.00.0003670.0915990.0003610.0937480.00.0
216.24396816.20306901.00.0003110.0896550.0002640.0152330.00.0
316.77599316.76505502.00.0130500.0844760.0136480.0761190.00.0
420.56169020.55767902.00.0111630.0868020.0111400.0975480.00.0
00.0310650.03048802.00.0004740.1079540.0004730.1089460.00.0
12.8238092.82594402.00.0003340.0982180.0003280.0983280.00.0
216.76918616.77388602.00.0139870.0729100.0129840.0767790.00.0
320.55664920.56573702.00.0108140.0987800.0116280.0889580.00.0
421.65459121.64646902.00.0003660.1176790.0003890.1278330.00.0
0.00e+006.25e-01fission2.24e-043.94e-051.76e-042.92e-05
10.00e+006.25e-01nu-fission5.46e-049.59e-054.28e-047.12e-05
26.25e-012.00e+07fission8.42e-056.79e-066.67e-056.94e-06
36.25e-012.00e+07nu-fission2.22e-041.65e-051.75e-041.71e-05
40.00e+006.25e-01fission1.85e-042.70e-052.04e-043.80e-05
50.00e+006.25e-01nu-fission4.52e-046.58e-054.96e-049.27e-05
66.25e-012.00e+07fission6.82e-055.29e-065.76e-056.97e-06
76.25e-012.00e+07nu-fission1.81e-041.35e-051.52e-041.91e-05
80.00e+006.25e-01fission2.05e-042.25e-051.80e-043.15e-05
90.00e+006.25e-01nu-fission5.00e-045.49e-054.38e-047.68e-05
106.25e-012.00e+07fission7.53e-057.06e-067.19e-059.68e-06
116.25e-012.00e+07nu-fission1.99e-041.81e-051.89e-042.49e-05
120.00e+006.25e-01fission2.06e-042.79e-051.91e-043.67e-05
130.00e+006.25e-01nu-fission5.03e-046.80e-054.66e-048.93e-05
146.25e-012.00e+07fission6.65e-053.91e-066.78e-059.81e-06
156.25e-012.00e+07nu-fission1.75e-041.04e-051.76e-042.44e-05
160.00e+006.25e-01fission2.03e-042.78e-051.56e-042.32e-05
170.00e+006.25e-01nu-fission4.94e-046.78e-053.81e-045.65e-05
186.25e-012.00e+07fission6.26e-055.71e-066.28e-058.06e-06
196.25e-012.00e+07nu-fission1.64e-041.53e-051.62e-042.05e-05
\n", @@ -929,52 +1452,52 @@ "text/plain": [ " mesh 1 energy low [eV] energy high [eV] score mean \\\n", " x y z \n", - "0 1 1 1 0.00e+00 6.25e-01 fission 2.24e-04 \n", - "1 1 1 1 0.00e+00 6.25e-01 nu-fission 5.46e-04 \n", - "2 1 1 1 6.25e-01 2.00e+07 fission 8.42e-05 \n", - "3 1 1 1 6.25e-01 2.00e+07 nu-fission 2.22e-04 \n", - "4 2 1 1 0.00e+00 6.25e-01 fission 1.85e-04 \n", - "5 2 1 1 0.00e+00 6.25e-01 nu-fission 4.52e-04 \n", - "6 2 1 1 6.25e-01 2.00e+07 fission 6.82e-05 \n", - "7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.81e-04 \n", - "8 3 1 1 0.00e+00 6.25e-01 fission 2.05e-04 \n", - "9 3 1 1 0.00e+00 6.25e-01 nu-fission 5.00e-04 \n", - "10 3 1 1 6.25e-01 2.00e+07 fission 7.53e-05 \n", - "11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.99e-04 \n", - "12 4 1 1 0.00e+00 6.25e-01 fission 2.06e-04 \n", - "13 4 1 1 0.00e+00 6.25e-01 nu-fission 5.03e-04 \n", - "14 4 1 1 6.25e-01 2.00e+07 fission 6.65e-05 \n", - "15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.75e-04 \n", - "16 5 1 1 0.00e+00 6.25e-01 fission 2.03e-04 \n", - "17 5 1 1 0.00e+00 6.25e-01 nu-fission 4.94e-04 \n", - "18 5 1 1 6.25e-01 2.00e+07 fission 6.26e-05 \n", - "19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.64e-04 \n", + "0 1 1 1 0.00e+00 6.25e-01 fission 1.76e-04 \n", + "1 1 1 1 0.00e+00 6.25e-01 nu-fission 4.28e-04 \n", + "2 1 1 1 6.25e-01 2.00e+07 fission 6.67e-05 \n", + "3 1 1 1 6.25e-01 2.00e+07 nu-fission 1.75e-04 \n", + "4 2 1 1 0.00e+00 6.25e-01 fission 2.04e-04 \n", + "5 2 1 1 0.00e+00 6.25e-01 nu-fission 4.96e-04 \n", + "6 2 1 1 6.25e-01 2.00e+07 fission 5.76e-05 \n", + "7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.52e-04 \n", + "8 3 1 1 0.00e+00 6.25e-01 fission 1.80e-04 \n", + "9 3 1 1 0.00e+00 6.25e-01 nu-fission 4.38e-04 \n", + "10 3 1 1 6.25e-01 2.00e+07 fission 7.19e-05 \n", + "11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.89e-04 \n", + "12 4 1 1 0.00e+00 6.25e-01 fission 1.91e-04 \n", + "13 4 1 1 0.00e+00 6.25e-01 nu-fission 4.66e-04 \n", + "14 4 1 1 6.25e-01 2.00e+07 fission 6.78e-05 \n", + "15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.76e-04 \n", + "16 5 1 1 0.00e+00 6.25e-01 fission 1.56e-04 \n", + "17 5 1 1 0.00e+00 6.25e-01 nu-fission 3.81e-04 \n", + "18 5 1 1 6.25e-01 2.00e+07 fission 6.28e-05 \n", + "19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.62e-04 \n", "\n", " std. dev. \n", " \n", - "0 3.94e-05 \n", - "1 9.59e-05 \n", - "2 6.79e-06 \n", - "3 1.65e-05 \n", - "4 2.70e-05 \n", - "5 6.58e-05 \n", - "6 5.29e-06 \n", - "7 1.35e-05 \n", - "8 2.25e-05 \n", - "9 5.49e-05 \n", - "10 7.06e-06 \n", - "11 1.81e-05 \n", - "12 2.79e-05 \n", - "13 6.80e-05 \n", - "14 3.91e-06 \n", - "15 1.04e-05 \n", - "16 2.78e-05 \n", - "17 6.78e-05 \n", - "18 5.71e-06 \n", - "19 1.53e-05 " + "0 2.92e-05 \n", + "1 7.12e-05 \n", + "2 6.94e-06 \n", + "3 1.71e-05 \n", + "4 3.80e-05 \n", + "5 9.27e-05 \n", + "6 6.97e-06 \n", + "7 1.91e-05 \n", + "8 3.15e-05 \n", + "9 7.68e-05 \n", + "10 9.68e-06 \n", + "11 2.49e-05 \n", + "12 3.67e-05 \n", + "13 8.93e-05 \n", + "14 9.81e-06 \n", + "15 2.44e-05 \n", + "16 2.32e-05 \n", + "17 5.65e-05 \n", + "18 8.06e-06 \n", + "19 2.05e-05 " ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -992,12 +1515,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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FpAu7a4XMzKxndRkckuqAecDRQAMwTVJDRbXpwPqIGAtcBlyc2jYAU4EJwGRgfprf68ChEbE/cAAwWdKHu2eVzMysJ1VzxHEwsDoiHouIvwA3AlMq6kwBrk3PbwYOk6RUfmNEvB4RjwOrgYOj0Jrq75oesZ3rYmZmO0A1wTECeLL0em0qa7dORGwGNgBDO2srqU7SA8BzwM8j4p5tWQEzM9uxBtRqwRGxBThA0p7ATyX9dUQ8XFlP0gxgBsDw4cNpaWnZsR3tB1pbW/2+Wq/jbbZ2qgmOp4CRpdf7prL26qyVNAAYArxQTduIeFHSEopzIG8Jjoi4HLgcoLGxMZqamqrosuVoaWnB76v1Krff6m22hqoZqroPGCdpjKTdKE52L6yosxA4JT0/HlgcEZHKp6arrsYA44B7Jb0zHWkgaXfgCODR7V8dMzPraV0ecUTEZklnAXcAdcBVEbFC0kXA0ohYCFwJ/FDSamAdRbiQ6i0AVgKbgTMjYoukvYFr0xVWuwALIuJnPbGCZmbWvao6xxERi4BFFWXnl56/BpzQQdtmoLmi7CHgwNzOmplZ7fmb42ZmlsXBYWZmWRwcZmaWxcFhZmZZHBxmZpbFwWFmZlkcHGZmlsXBYWZmWRwcZmaWxcFhZmZZHBxmZpbFwWFmZlkcHGZmlsXBYWZmWRwcZmaWxcFhZmZZHBxmZpbFwWFmZlkcHGZmlsXBYWZmWRwcZmaWxcFhZmZZHBxmZpbFwWFmZlkcHGZmlsXBYWZmWRwcZmaWxcFhZmZZHBxmZpbFwWFmZlkcHGZmlsXBYWZmWRwcZmaWxcFhZmZZHBxmZpbFwWFmZlkcHGZmlsXBYWZmWaoKDkmTJa2StFrS7HamD5R0U5p+j6TRpWlzUvkqSUelspGSlkhaKWmFpC901wqZmVnP6jI4JNUB84CjgQZgmqSGimrTgfURMRa4DLg4tW0ApgITgMnA/DS/zcA5EdEAfBg4s515mpnZTqiaI46DgdUR8VhE/AW4EZhSUWcKcG16fjNwmCSl8hsj4vWIeBxYDRwcEc9ExO8BIuJl4BFgxPavjpmZ9bRqgmME8GTp9VreupN/o05EbAY2AEOraZuGtQ4E7qm+22ZmVisDarlwSYOBHwNfjIiXOqgzA5gBMHz4cFpaWnZcB/uJ1tZWv6/W63ibrZ1qguMpYGTp9b6prL06ayUNAIYAL3TWVtKuFKHxzxHxk44WHhGXA5cDNDY2RlNTUxVdthwtLS34fbVe5fZbvc3WUDVDVfcB4ySNkbQbxcnuhRV1FgKnpOfHA4sjIlL51HTV1RhgHHBvOv9xJfBIRFzaHStiZmY7RpdHHBGxWdJZwB1AHXBVRKyQdBGwNCIWUoTADyWtBtZRhAup3gJgJcWVVGdGxBZJHwVOBpZLeiAt6qsRsai7V9DMzLpXVec40g59UUXZ+aXnrwEndNC2GWiuKLsbUG5nzcys9vzNcTMzy+LgMDOzLA4OMzPL4uAwM7MsDg4zM8tS02+OW23tt99+LF++/I3XEydO5KGHHqphj6w/2v/CO9nw6qbsdqNn35pVf8juu/LgBUdmL8feysHRT7WFxrHHHstpp53G1VdfzcKFC9lvv/0cHrZDbXh1E2u+9fGsNttyt4PcoLGOeaiqn2oLjVtuuYU999yTW265hWOPPfZNRyBmZu1xcPRjw4YNo76+nkmTJlFfX8+wYcNq3SUz6wU8VNWPXX311XznO9+hoaGBlStXcu6559a6S2bWCzg4+ilJRAQXXHABra2tDB48mIiguP+kmVnHPFTVT0UEdXV1tLa2AsVvctTV1VHc1NjMrGMOjn5KEjNmzCAiWLJkCRHBjBkzfMRhZl3yUFU/FRFcccUVjB07loaGBi699FKuuOIKH3GYWZccHP1Ee0cSmzdv5pxzzum0roPEzCp5qKqfiIg3Pa6//nrGjBnD4sWLefe5/8rixYsZM2YM119//ZvqmZlV8hFHPzVt2jQAZs6cyR9XPsLM28bT3Nz8RrmZWUccHP3YtGnTmDZtGqNn38rDmbd8MLP+y0NVZmaWxcFhZmZZHBxmZpbFwWFmZlkcHGZmlsXBYWZmWRwcZmaWxcFhZmZZHBxmZpbFwWFmZlkcHGZmlsXBYWZmWRwcZmaWxcFhZmZZHBxmZpbFwWFmZlkcHGZmlsXBYWZmWRwcZmaWxcFhZmZZqgoOSZMlrZK0WtLsdqYPlHRTmn6PpNGlaXNS+SpJR5XKr5L0nKSHu2NFzMxsx+gyOCTVAfOAo4EGYJqkhopq04H1ETEWuAy4OLVtAKYCE4DJwPw0P4BrUpmZmfUi1RxxHAysjojHIuIvwI3AlIo6U4Br0/ObgcMkKZXfGBGvR8TjwOo0PyLiLmBdN6yDmZntQNUExwjgydLrtams3ToRsRnYAAytsq2ZmfUiA2rdga5ImgHMABg+fDgtLS217VAf5ffVail3+2ttbd2mbdbbefeoJjieAkaWXu+bytqrs1bSAGAI8EKVbTsVEZcDlwM0NjZGU1NTTnOrxu234vfVamYbtr+Wlpb8bdbbebepZqjqPmCcpDGSdqM42b2wos5C4JT0/HhgcUREKp+arroaA4wD7u2erpuZWS10GRzpnMVZwB3AI8CCiFgh6SJJx6ZqVwJDJa0GzgZmp7YrgAXASuB24MyI2AIg6Qbgt8D7Ja2VNL17V83MzHpCVec4ImIRsKii7PzS89eAEzpo2ww0t1M+LaunZma2U/A3x83MLIuDw8zMsjg4zMwsi4PDzMyyODjMzCyLg8PMzLI4OMzMLIuKL3j3Do2NjbF06dJad2Ontv+Fd7Lh1U09vpwhu+/Kgxcc2ePLsb5v4rUTd9iylp+yfIctqzeSdH9ENHZVb6e/yaHl2fDqJtZ86+NZbbblvj+jZ9+aVd+sIy8/8i1vs72Mh6rMzCyLg8PMzLI4OMzMLIuDw8zMsjg4zMwsi4PDzMyyODjMzCyLg8PMzLI4OMzMLIuDw8zMsjg4zMwsi4PDzMyyODjMzCyLg8PMzLI4OMzMLIt/j8PMam6bfivj9rw2Q3bfNX8Z1i4Hh5nVVO6POEERNNvSzrqHh6rMzCyLg8PMzLJ4qKqP2WP8bCZeOzu/4bW5ywHwUIFZf+Tg6GNefuRb2WO/LS0tNDU1ZbXZppOZZtYneKjKzMyyODjMzCyLg8PMzLI4OMzMLIuDw8zMsviqqj7It28ws57k4OhjfPsGM+tpHqoyM7MsVQWHpMmSVklaLektX0uWNFDSTWn6PZJGl6bNSeWrJB1V7TzNzGzn1GVwSKoD5gFHAw3ANEkNFdWmA+sjYixwGXBxatsATAUmAJOB+ZLqqpynmZnthKo5x3EwsDoiHgOQdCMwBVhZqjMF+Fp6fjPwXUlK5TdGxOvA45JWp/lRxTzNrB8rdiGdTL+4/fKI6IHeWFk1Q1UjgCdLr9emsnbrRMRmYAMwtJO21czTupGkDh9PXPyJDqeZ1UpEdPhYsmRJh9Os5+30V1VJmgHMABg+fDgtLS217VAvtWTJkg6ntba2Mnjw4Han+f22nVFra6u3zRqqJjieAkaWXu+bytqrs1bSAGAI8EIXbbuaJwARcTlwOUBjY2Pk3sXVurYtd8c1qyVvs7VVzVDVfcA4SWMk7UZxsnthRZ2FwCnp+fHA4iiOGRcCU9NVV2OAccC9Vc7TzMx2Ql0ecUTEZklnAXcAdcBVEbFC0kXA0ohYCFwJ/DCd/F5HEQSkegsoTnpvBs6MiC0A7c2z+1fPzMy6W1XnOCJiEbCoouz80vPXgBM6aNsMNFczTzMz2/n5m+NmZpbFwWFmZlkcHGZmlsXBYWZmWdSbvmkp6c/AE7XuRx80DHi+1p0wy+BttmeMioh3dlWpVwWH9QxJSyOisdb9MKuWt9na8lCVmZllcXCYmVkWB4dBuheYWS/ibbaGfI7DzMyy+IjDzMyyODj6CEmfl/SIpPXb8hvukn7TE/0y2xaSPiDpAUnLJL13W7ZPSRdJOrwn+tffeaiqj5D0KHB4RKytdV/Mtlf68DMgIr5e677YW/mIow+Q9H3gPcBtkr4k6bup/ARJD0t6UNJdqWyCpHvTp7mHJI1L5a3pX0n6dmq3XNKJqbxJUoukmyU9Kumf5d+WtQ5IGp2OgK+QtELSnZJ2T9tQY6ozTNKadtoeA3wROEPSklTWtn3uLemutP0+LOkQSXWSrilts19Kda+RdHx6flg6elku6SpJA1P5GkkXSvp9mvaBHfIG9XIOjj4gIk4HngYmAetLk84HjoqI/YFjU9npwP+KiAOARorfey87DjgA2B84HPi2pL3TtAMp/kM3UATVR7p/bawPGQfMi4gJwIvAp6pplH5y4fvAZRExqWLyScAdafvdH3iAYnsdERF/HRETgavLDSTVA9cAJ6bpA4AzSlWej4iDgO8B5+atYv/k4Ojbfg1cI+mzFD+YBfBb4KuSvkJxe4FXK9p8FLghIrZExLPAL4EPpmn3RsTaiNhK8R92dI+vgfVmj0fEA+n5/XTP9nIfcJqkrwETI+Jl4DHgPZLmSpoMvFTR5v2pL39Ir68FPlaa/pNu7mOf5+Dow9KRyD9R/L77/ZKGRsT1FEcfrwKLJB2aMcvXS8+3UOUPgVm/1d72spn/v9+pb5so6eo0/NTpj7tFxF0UO/2nKD4UfSYi1lMcfbRQHFH/YBv76W26Sg6OPkzSeyPinvRrjX8GRkp6D/BYRPxv4BZgv4pmvwJOTOPG76T4T3rvDu249WVrgL9Jz49vK4yI0yLigIg4prPGkkYBz0bEFRQBcZCkYcAuEfFjig9KB1U0WwWMljQ2vT6Z4kjatpHTtW/7djr5LeAXwIPAV4CTJW0C/gR8o6LNT4G/TXUDmBURf/JJQ+sm3wEWSJoB3LoN7ZuAL6fttxX4DDACuFpS2wfhOeUGEfGapNOAf5E0gGK46/vb2H/Dl+OamVkmD1WZmVkWB4eZmWVxcJiZWRYHh5mZZXFwmJlZFgeHmZllcXCY7WDpuwRmvZaDw6wKkgZJujXdafhhSSdK+qCk36SyeyXtIak+3T5jebob66TU/lRJCyUtpvgyJpK+LOm+dJfiC2u6gmYZ/MnHrDqTgacj4uMAkoYAyyjuuHqfpL+iuP/XF4CIiInp2/Z3SnpfmsdBwH4RsU7SkRR3jz2Y4pv9CyV9LN2LyWyn5iMOs+osB46QdLGkQ4B3A89ExH0AEfFSRGymuLvwj1LZo8ATQFtw/Dwi1qXnR6bHMuD3wAcogsRsp+cjDrMqRMQfJB0EHAN8HVi8DbPZWHou4JsR8X+6o39mO5KPOMyqIGkf4JWI+BHwbeBDwN6SPpim75FOev8K+HQqex/FkcmqdmZ5B/BfJA1OdUdI2qvn18Rs+/mIw6w6EynuNrwV2ETxC3IC5kraneL8xuHAfOB7kpZT/PbEqRHxeuWv7EbEnZLGA79N01qBfwCe20HrY7bNfHdcMzPL4qEqMzPL4uAwM7MsDg4zM8vi4DAzsywODjMzy+LgMDOzLA4OMzPL4uAwM7Ms/wESgYbc2a/wowAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -1016,22 +1539,22 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1066,7 +1589,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1094,7 +1617,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1131,8 +1654,8 @@ " 1\n", " U235\n", " scatter\n", - " 3.81e-02\n", - " 1.65e-04\n", + " 3.80e-02\n", + " 1.33e-04\n", " \n", " \n", " 1\n", @@ -1140,7 +1663,7 @@ " U238\n", " scatter\n", " 2.33e+00\n", - " 9.59e-03\n", + " 8.12e-03\n", " \n", " \n", "\n", @@ -1148,11 +1671,11 @@ ], "text/plain": [ " cell nuclide score mean std. dev.\n", - "0 1 U235 scatter 3.81e-02 1.65e-04\n", - "1 1 U238 scatter 2.33e+00 9.59e-03" + "0 1 U235 scatter 3.80e-02 1.33e-04\n", + "1 1 U238 scatter 2.33e+00 8.12e-03" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1174,15 +1697,15 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[[0.00958717]\n", - " [0.00016469]]]\n" + "[[[0.00811746]\n", + " [0.00013266]]]\n" ] } ], @@ -1202,7 +1725,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1237,32 +1760,32 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[[0.03500496]]\n", + "[[[0.04347272]]\n", "\n", - " [[0.02745568]]\n", + " [[0.04671736]]\n", "\n", - " [[0.02988488]]\n", + " [[0.04878286]]\n", "\n", - " [[0.04474905]]\n", + " [[0.03059582]]\n", "\n", - " [[0.03697764]]\n", + " [[0.04548096]]\n", "\n", - " [[0.0409214 ]]\n", + " [[0.04288085]]\n", "\n", - " [[0.03366461]]\n", + " [[0.02557663]]\n", "\n", - " [[0.03210393]]\n", + " [[0.0419826 ]]\n", "\n", - " [[0.03216398]]\n", + " [[0.05878954]]\n", "\n", - " [[0.04003553]]]\n" + " [[0.04217666]]]\n" ] } ], @@ -1283,7 +1806,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1354,8 +1877,8 @@ " 3\n", " 279\n", " absorption\n", - " 6.81e-04\n", - " 2.84e-05\n", + " 6.26e-04\n", + " 4.62e-05\n", " \n", " \n", " 559\n", @@ -1368,8 +1891,8 @@ " 3\n", " 279\n", " scatter\n", - " 8.82e-02\n", - " 1.86e-03\n", + " 8.73e-02\n", + " 2.14e-03\n", " \n", " \n", " 560\n", @@ -1382,8 +1905,8 @@ " 3\n", " 280\n", " absorption\n", - " 6.65e-04\n", - " 3.46e-05\n", + " 6.15e-04\n", + " 3.12e-05\n", " \n", " \n", " 561\n", @@ -1396,8 +1919,8 @@ " 3\n", " 280\n", " scatter\n", - " 8.37e-02\n", - " 2.02e-03\n", + " 8.06e-02\n", + " 1.85e-03\n", " \n", " \n", " 562\n", @@ -1410,8 +1933,8 @@ " 3\n", " 281\n", " absorption\n", - " 5.61e-04\n", - " 2.91e-05\n", + " 6.36e-04\n", + " 4.24e-05\n", " \n", " \n", " 563\n", @@ -1424,8 +1947,8 @@ " 3\n", " 281\n", " scatter\n", - " 7.52e-02\n", - " 1.79e-03\n", + " 7.59e-02\n", + " 1.93e-03\n", " \n", " \n", " 564\n", @@ -1438,8 +1961,8 @@ " 3\n", " 282\n", " absorption\n", - " 4.77e-04\n", - " 2.33e-05\n", + " 5.30e-04\n", + " 2.75e-05\n", " \n", " \n", " 565\n", @@ -1452,8 +1975,8 @@ " 3\n", " 282\n", " scatter\n", - " 6.68e-02\n", - " 1.14e-03\n", + " 6.82e-02\n", + " 1.02e-03\n", " \n", " \n", " 566\n", @@ -1466,8 +1989,8 @@ " 3\n", " 283\n", " absorption\n", - " 4.64e-04\n", - " 2.05e-05\n", + " 4.67e-04\n", + " 2.84e-05\n", " \n", " \n", " 567\n", @@ -1480,8 +2003,8 @@ " 3\n", " 283\n", " scatter\n", - " 6.20e-02\n", - " 1.61e-03\n", + " 6.42e-02\n", + " 1.81e-03\n", " \n", " \n", " 568\n", @@ -1494,8 +2017,8 @@ " 3\n", " 284\n", " absorption\n", - " 4.44e-04\n", - " 2.93e-05\n", + " 4.52e-04\n", + " 2.13e-05\n", " \n", " \n", " 569\n", @@ -1508,8 +2031,8 @@ " 3\n", " 284\n", " scatter\n", - " 5.47e-02\n", - " 1.53e-03\n", + " 5.64e-02\n", + " 1.20e-03\n", " \n", " \n", " 570\n", @@ -1522,8 +2045,8 @@ " 3\n", " 285\n", " absorption\n", - " 3.67e-04\n", - " 2.63e-05\n", + " 3.85e-04\n", + " 1.99e-05\n", " \n", " \n", " 571\n", @@ -1536,8 +2059,8 @@ " 3\n", " 285\n", " scatter\n", - " 4.68e-02\n", - " 1.52e-03\n", + " 4.86e-02\n", + " 1.58e-03\n", " \n", " \n", " 572\n", @@ -1550,8 +2073,8 @@ " 3\n", " 286\n", " absorption\n", - " 2.76e-04\n", - " 1.75e-05\n", + " 2.84e-04\n", + " 2.16e-05\n", " \n", " \n", " 573\n", @@ -1564,8 +2087,8 @@ " 3\n", " 286\n", " scatter\n", - " 3.81e-02\n", - " 1.28e-03\n", + " 3.91e-02\n", + " 1.66e-03\n", " \n", " \n", " 574\n", @@ -1578,8 +2101,8 @@ " 3\n", " 287\n", " absorption\n", - " 2.08e-04\n", - " 1.69e-05\n", + " 2.17e-04\n", + " 2.15e-05\n", " \n", " \n", " 575\n", @@ -1592,8 +2115,8 @@ " 3\n", " 287\n", " scatter\n", - " 2.85e-02\n", - " 1.13e-03\n", + " 3.02e-02\n", + " 1.71e-03\n", " \n", " \n", " 576\n", @@ -1606,8 +2129,8 @@ " 3\n", " 288\n", " absorption\n", - " 1.32e-04\n", - " 1.30e-05\n", + " 1.50e-04\n", + " 1.42e-05\n", " \n", " \n", " 577\n", @@ -1620,8 +2143,8 @@ " 3\n", " 288\n", " scatter\n", - " 1.86e-02\n", - " 7.12e-04\n", + " 1.89e-02\n", + " 9.31e-04\n", " \n", " \n", "\n", @@ -1655,29 +2178,29 @@ " mean std. dev. \n", " \n", " \n", - "558 6.81e-04 2.84e-05 \n", - "559 8.82e-02 1.86e-03 \n", - "560 6.65e-04 3.46e-05 \n", - "561 8.37e-02 2.02e-03 \n", - "562 5.61e-04 2.91e-05 \n", - "563 7.52e-02 1.79e-03 \n", - "564 4.77e-04 2.33e-05 \n", - "565 6.68e-02 1.14e-03 \n", - "566 4.64e-04 2.05e-05 \n", - "567 6.20e-02 1.61e-03 \n", - "568 4.44e-04 2.93e-05 \n", - "569 5.47e-02 1.53e-03 \n", - "570 3.67e-04 2.63e-05 \n", - "571 4.68e-02 1.52e-03 \n", - "572 2.76e-04 1.75e-05 \n", - "573 3.81e-02 1.28e-03 \n", - "574 2.08e-04 1.69e-05 \n", - "575 2.85e-02 1.13e-03 \n", - "576 1.32e-04 1.30e-05 \n", - "577 1.86e-02 7.12e-04 " + "558 6.26e-04 4.62e-05 \n", + "559 8.73e-02 2.14e-03 \n", + "560 6.15e-04 3.12e-05 \n", + "561 8.06e-02 1.85e-03 \n", + "562 6.36e-04 4.24e-05 \n", + "563 7.59e-02 1.93e-03 \n", + "564 5.30e-04 2.75e-05 \n", + "565 6.82e-02 1.02e-03 \n", + "566 4.67e-04 2.84e-05 \n", + "567 6.42e-02 1.81e-03 \n", + "568 4.52e-04 2.13e-05 \n", + "569 5.64e-02 1.20e-03 \n", + "570 3.85e-04 1.99e-05 \n", + "571 4.86e-02 1.58e-03 \n", + "572 2.84e-04 2.16e-05 \n", + "573 3.91e-02 1.66e-03 \n", + "574 2.17e-04 2.15e-05 \n", + "575 3.02e-02 1.71e-03 \n", + "576 1.50e-04 1.42e-05 \n", + "577 1.89e-02 9.31e-04 " ] }, - "execution_count": 30, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1692,7 +2215,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1738,38 +2261,38 @@ " \n", " \n", " mean\n", - " 4.17e-04\n", - " 2.05e-05\n", + " 4.15e-04\n", + " 2.29e-05\n", " \n", " \n", " std\n", - " 2.42e-04\n", - " 8.32e-06\n", + " 2.33e-04\n", + " 9.14e-06\n", " \n", " \n", " min\n", - " 2.27e-05\n", - " 4.04e-06\n", + " 1.84e-05\n", + " 3.31e-06\n", " \n", " \n", " 25%\n", - " 2.01e-04\n", - " 1.40e-05\n", + " 2.08e-04\n", + " 1.58e-05\n", " \n", " \n", " 50%\n", - " 4.00e-04\n", - " 2.05e-05\n", + " 4.10e-04\n", + " 2.24e-05\n", " \n", " \n", " 75%\n", - " 6.08e-04\n", - " 2.60e-05\n", + " 6.25e-04\n", + " 2.93e-05\n", " \n", " \n", " max\n", - " 9.38e-04\n", - " 4.27e-05\n", + " 8.87e-04\n", + " 5.06e-05\n", " \n", " \n", "\n", @@ -1780,16 +2303,16 @@ " \n", " \n", "count 2.89e+02 2.89e+02\n", - "mean 4.17e-04 2.05e-05\n", - "std 2.42e-04 8.32e-06\n", - "min 2.27e-05 4.04e-06\n", - "25% 2.01e-04 1.40e-05\n", - "50% 4.00e-04 2.05e-05\n", - "75% 6.08e-04 2.60e-05\n", - "max 9.38e-04 4.27e-05" + "mean 4.15e-04 2.29e-05\n", + "std 2.33e-04 9.14e-06\n", + "min 1.84e-05 3.31e-06\n", + "25% 2.08e-04 1.58e-05\n", + "50% 4.10e-04 2.24e-05\n", + "75% 6.25e-04 2.93e-05\n", + "max 8.87e-04 5.06e-05" ] }, - "execution_count": 31, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1812,14 +2335,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 0.3933685843661936\n" + "Mann-Whitney Test p-value: 0.3531165056829588\n" ] } ], @@ -1848,14 +2371,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mann-Whitney Test p-value: 7.927841393301949e-42\n" + "Mann-Whitney Test p-value: 2.835784441937541e-42\n" ] } ], @@ -1882,14 +2405,14 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/jan/.local/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", + "/home/romano/.pyenv/versions/3.7.0/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -1900,16 +2423,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1932,22 +2455,22 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 35, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1985,7 +2508,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/examples/jupyter/pincell.ipynb b/examples/jupyter/pincell.ipynb index f8641db13..e50a4a322 100644 --- a/examples/jupyter/pincell.ipynb +++ b/examples/jupyter/pincell.ipynb @@ -4,15 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook is intended to demonstrate the basic features of the Python API for constructing input files and running OpenMC. In it, we will show how to create a basic reflective pin-cell model that is equivalent to modeling an infinite array of fuel pins. If you have never used OpenMC, this can serve as a good starting point to learn the Python API. We highly recommend having a copy of the [Python API reference documentation](http://openmc.readthedocs.org/en/latest/pythonapi/index.html) open in another browser tab that you can refer to." + "This notebook is intended to demonstrate the basic features of the Python API for constructing input files and running OpenMC. In it, we will show how to create a basic reflective pin-cell model that is equivalent to modeling an infinite array of fuel pins. If you have never used OpenMC, this can serve as a good starting point to learn the Python API. We highly recommend having a copy of the [Python API reference documentation](https://docs.openmc.org/en/stable/pythonapi/index.html) open in another browser tab that you can refer to." ] }, { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -31,9 +29,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -43,7 +39,7 @@ "\tID =\t1\n", "\tName =\tuo2\n", "\tTemperature =\tNone\n", - "\tDensity =\tNone []\n", + "\tDensity =\tNone [sum]\n", "\tS(a,b) Tables \n", "\tNuclides \n", "\n" @@ -65,9 +61,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -77,7 +71,7 @@ "\tID =\t2\n", "\tName =\t\n", "\tTemperature =\tNone\n", - "\tDensity =\tNone []\n", + "\tDensity =\tNone [sum]\n", "\tS(a,b) Tables \n", "\tNuclides \n", "\n" @@ -99,9 +93,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -115,7 +107,7 @@ " Parameters\n", " ----------\n", " nuclide : str\n", - " Nuclide to add\n", + " Nuclide to add, e.g., 'Mo95'\n", " percent : float\n", " Atom or weight percent\n", " percent_type : {'ao', 'wo'}\n", @@ -138,9 +130,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Add nuclides to uo2\n", @@ -159,9 +149,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "uo2.set_density('g/cm3', 10.0)" @@ -179,15 +167,13 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another Material instance already exists with id=2.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Material instance already exists with id=2.\n", " warn(msg, IDWarning)\n" ] } @@ -213,9 +199,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "water.add_s_alpha_beta('c_H_in_H2O')" @@ -231,9 +215,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "mats = openmc.Materials([uo2, zirconium, water])" @@ -249,9 +231,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -281,9 +261,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -291,7 +269,7 @@ "text": [ "\r\n", "\r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -338,9 +316,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -348,7 +324,7 @@ "text": [ "\r\n", "\r\n", - " \r\n", + " \r\n", " \r\n", " \r\n", " \r\n", @@ -402,9 +378,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -412,24 +386,24 @@ "text": [ "\n", "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " ...\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", "\n" ] } @@ -452,9 +426,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "uo2_three = openmc.Material()\n", @@ -485,12 +457,10 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "sph = openmc.Sphere(R=1.0)" + "sph = openmc.Sphere(r=1.0)" ] }, { @@ -505,9 +475,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "inside_sphere = -sph\n", @@ -524,9 +492,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -552,9 +518,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "z_plane = openmc.ZPlane(z0=0)\n", @@ -571,14 +535,12 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([-1., -1., 0.]), array([ 1., 1., 1.]))" + "(array([-1., -1., 0.]), array([1., 1., 1.]))" ] }, "execution_count": 19, @@ -600,9 +562,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "cell = openmc.Cell()\n", @@ -622,9 +582,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "cell.fill = water" @@ -647,9 +605,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "universe = openmc.Universe()\n", @@ -669,18 +625,28 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, + "execution_count": 23, "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -765,14 +751,12 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "fuel_or = openmc.ZCylinder(R=0.39)\n", - "clad_ir = openmc.ZCylinder(R=0.40)\n", - "clad_or = openmc.ZCylinder(R=0.46)" + "fuel_or = openmc.ZCylinder(r=0.39)\n", + "clad_ir = openmc.ZCylinder(r=0.40)\n", + "clad_or = openmc.ZCylinder(r=0.46)" ] }, { @@ -785,9 +769,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "fuel_region = -fuel_or\n", @@ -805,17 +787,15 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another Cell instance already exists with id=1.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Cell instance already exists with id=1.\n", " warn(msg, IDWarning)\n", - "/home/romano/openmc/openmc/mixin.py:61: IDWarning: Another Cell instance already exists with id=2.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Cell instance already exists with id=2.\n", " warn(msg, IDWarning)\n" ] } @@ -843,9 +823,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "pitch = 1.26\n", @@ -865,9 +843,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "water_region = +left & -right & +bottom & -top & +clad_or\n", @@ -887,9 +863,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -918,9 +892,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "water_region = box & +clad_or" @@ -936,9 +908,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -985,9 +955,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "point = openmc.stats.Point((0, 0, 0))\n", @@ -1004,9 +972,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "settings = openmc.Settings()\n", @@ -1019,9 +985,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1063,9 +1027,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "cell_filter = openmc.CellFilter(fuel)\n", @@ -1084,9 +1046,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "t.nuclides = ['U235']\n", @@ -1103,9 +1063,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1144,7 +1102,6 @@ "cell_type": "code", "execution_count": 40, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1152,74 +1109,73 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.9.0\n", - " Git SHA1 | 168f202e3ecf48cdd15b541dc396b24465832986\n", - " Date/Time | 2017-12-12 15:10:36\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", + " Date/Time | 2019-07-19 06:20:10\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", - " Reading materials XML file...\n", " Reading cross sections XML file...\n", + " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", - " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", - " Reading Zr91 from /home/romano/openmc/scripts/nndc_hdf5/Zr91.h5\n", - " Reading Zr92 from /home/romano/openmc/scripts/nndc_hdf5/Zr92.h5\n", - " Reading Zr94 from /home/romano/openmc/scripts/nndc_hdf5/Zr94.h5\n", - " Reading Zr96 from /home/romano/openmc/scripts/nndc_hdf5/Zr96.h5\n", - " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", - " Reading O17 from /home/romano/openmc/scripts/nndc_hdf5/O17.h5\n", - " Reading c_H_in_H2O from /home/romano/openmc/scripts/nndc_hdf5/c_H_in_H2O.h5\n", - " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", + " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", + " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", + " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", + " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", + " Reading Zr91 from /opt/data/hdf5/nndc_hdf5_v15/Zr91.h5\n", + " Reading Zr92 from /opt/data/hdf5/nndc_hdf5_v15/Zr92.h5\n", + " Reading Zr94 from /opt/data/hdf5/nndc_hdf5_v15/Zr94.h5\n", + " Reading Zr96 from /opt/data/hdf5/nndc_hdf5_v15/Zr96.h5\n", + " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", + " Reading O17 from /opt/data/hdf5/nndc_hdf5_v15/O17.h5\n", + " Reading c_H_in_H2O from /opt/data/hdf5/nndc_hdf5_v15/c_H_in_H2O.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Reading tallies XML file...\n", + " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.32572 \n", - " 2/1 1.46138 \n", - " 3/1 1.46068 \n", - " 4/1 1.39592 \n", - " 5/1 1.37519 \n", - " 6/1 1.38777 \n", - " 7/1 1.50242 \n", - " 8/1 1.42042 \n", - " 9/1 1.47458 \n", - " 10/1 1.49148 \n", - " 11/1 1.39339 \n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 1.32572\n", + " 2/1 1.46138\n", + " 3/1 1.46068\n", + " 4/1 1.39592\n", + " 5/1 1.37519\n", + " 6/1 1.38777\n", + " 7/1 1.50242\n", + " 8/1 1.42042\n", + " 9/1 1.47458\n", + " 10/1 1.49148\n", + " 11/1 1.39339\n", " 12/1 1.40637 1.39988 +/- 0.00649\n", " 13/1 1.42972 1.40983 +/- 0.01063\n", " 14/1 1.46319 1.42317 +/- 0.01531\n", @@ -1313,40 +1269,30 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 1.5898E+00 seconds\n", - " Reading cross sections = 1.4448E+00 seconds\n", - " Total time in simulation = 8.3458E+00 seconds\n", - " Time in transport only = 7.7626E+00 seconds\n", - " Time in inactive batches = 6.6138E-01 seconds\n", - " Time in active batches = 7.6845E+00 seconds\n", - " Time synchronizing fission bank = 7.1921E-03 seconds\n", - " Sampling source sites = 4.8562E-03 seconds\n", - " SEND/RECV source sites = 2.0519E-03 seconds\n", - " Time accumulating tallies = 2.3785E-04 seconds\n", - " Total time for finalization = 3.0973E-03 seconds\n", - " Total time elapsed = 9.9670E+00 seconds\n", - " Calculation Rate (inactive) = 15120.0 neutrons/second\n", - " Calculation Rate (active) = 11711.9 neutrons/second\n", + " Total time for initialization = 7.5853e-01 seconds\n", + " Reading cross sections = 7.3383e-01 seconds\n", + " Total time in simulation = 6.5719e+00 seconds\n", + " Time in transport only = 5.9772e+00 seconds\n", + " Time in inactive batches = 4.8850e-01 seconds\n", + " Time in active batches = 6.0834e+00 seconds\n", + " Time synchronizing fission bank = 5.9939e-03 seconds\n", + " Sampling source sites = 5.1295e-03 seconds\n", + " SEND/RECV source sites = 7.3640e-04 seconds\n", + " Time accumulating tallies = 9.9301e-05 seconds\n", + " Total time for finalization = 1.1585e-04 seconds\n", + " Total time elapsed = 7.3346e+00 seconds\n", + " Calculation Rate (inactive) = 20471.0 particles/second\n", + " Calculation Rate (active) = 14794.4 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.39737 +/- 0.00470\n", - " k-effective (Track-length) = 1.40141 +/- 0.00513\n", - " k-effective (Absorption) = 1.39596 +/- 0.00308\n", - " Combined k-effective = 1.39719 +/- 0.00286\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", + " k-effective (Collision) = 1.39737 +/- 0.00470\n", + " k-effective (Track-length) = 1.40141 +/- 0.00513\n", + " k-effective (Absorption) = 1.39596 +/- 0.00308\n", + " Combined k-effective = 1.39719 +/- 0.00286\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -1363,23 +1309,20 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\r\n", " ============================> TALLY 1 <============================\r\n", "\r\n", " Cell 1\r\n", " U235\r\n", - " Total Reaction Rate 0.731003 +/- 2.53759E-03\r\n", - " Fission Rate 0.547587 +/- 2.10114E-03\r\n", - " Absorption Rate 0.657406 +/- 2.45390E-03\r\n", - " (n,gamma) 0.109821 +/- 3.68054E-04\r\n" + " Total Reaction Rate 0.731003 +/- 0.00253759\r\n", + " Fission Rate 0.547587 +/- 0.00210114\r\n", + " Absorption Rate 0.657406 +/- 0.0024539\r\n", + " (n,gamma) 0.109821 +/- 0.000368054\r\n" ] } ], @@ -1399,9 +1342,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "p = openmc.Plot()\n", @@ -1422,9 +1363,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1459,79 +1398,65 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.9.0\n", - " Git SHA1 | 168f202e3ecf48cdd15b541dc396b24465832986\n", - " Date/Time | 2017-12-12 15:10:46\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", + " Date/Time | 2019-07-19 06:20:18\n", " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", - " Reading materials XML file...\n", " Reading cross sections XML file...\n", + " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Building neighboring cells lists for each surface...\n", " Reading tallies XML file...\n", " Reading plot XML file...\n", "\n", " =======================> PLOTTING SUMMARY <========================\n", "\n", - " Plot ID: 1\n", - " Plot file: pinplot.ppm\n", - " Universe depth: -1\n", - " Plot Type: Slice\n", - " Origin: 0.0 0.0 0.0\n", - " Width: 1.26000 1.26000\n", - " Coloring: Materials\n", - " Basis: xy\n", - " Pixels: 200 200\n", + "Plot ID: 1\n", + "Plot file: pinplot.ppm\n", + "Universe depth: -1\n", + "Plot Type: Slice\n", + "Origin: 0 0 0\n", + "Width: 1.26 1.26\n", + "Coloring: Materials\n", + "Basis: XY\n", + "Pixels: 200 200 \n", "\n", - " Processing plot 1: pinplot.ppm ...\n" + " Processing plot 1: pinplot.ppm...\n" ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -1548,9 +1473,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "!convert pinplot.ppm pinplot.png" @@ -1567,13 +1490,12 @@ "cell_type": "code", "execution_count": 46, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMgAAADIAgMAAADQNkYNAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "" ] @@ -1592,29 +1514,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "That was a little bit cumbersome. Thankfully, OpenMC provides us with a function that does all that \"boilerplate\" work." + "That was a little bit cumbersome. Thankfully, OpenMC provides us with a method on the `Plot` class that does all that \"boilerplate\" work." ] }, { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMgAAADIAgMAAADQNkYNAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "" ] }, + "execution_count": 47, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "openmc.plot_inline(p)" + "p.to_ipython_image()" ] } ], @@ -1635,9 +1556,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.7.0" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/examples/jupyter/post-processing.ipynb b/examples/jupyter/post-processing.ipynb index a6d134b54..22ccb2b5b 100644 --- a/examples/jupyter/post-processing.ipynb +++ b/examples/jupyter/post-processing.ipynb @@ -203,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -236,63 +236,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate a Plot\n", - "plot = openmc.Plot(plot_id=1)\n", - "plot.filename = 'materials-xy'\n", - "plot.origin = [0, 0, 0]\n", - "plot.width = [1.26, 1.26]\n", - "plot.pixels = [250, 250]\n", - "plot.color_by = 'material'\n", - "\n", - "# Instantiate a Plots collection and export to \"plots.xml\"\n", - "plot_file = openmc.Plots([plot])\n", - "plot_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Run openmc in plotting mode\n", - "openmc.plot_geometry(output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Convert OpenMC's funky ppm to png\n", - "!convert materials-xy.ppm materials-xy.png\n", - "\n", - "# Display the materials plot inline\n", - "Image(filename='materials-xy.png')" + "plot = openmc.Plot.from_geometry(geometry)\n", + "plot.pixels = (250, 250)\n", + "plot.to_ipython_image()" ] }, { @@ -304,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -314,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -336,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -353,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "scrolled": true }, @@ -389,21 +351,21 @@ " | The OpenMC Monte Carlo Code\n", " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | cc09ba4a5bf2edd95624e386b72803d83ddc9f4f\n", - " Date/Time | 2019-04-03 14:50:23\n", - " OpenMP Threads | 2\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", + " Date/Time | 2019-07-19 06:22:24\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n", - " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n", - " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n", + " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", + " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", + " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", + " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", + " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", + " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Reading tallies XML file...\n", " Writing summary.h5 file...\n", @@ -427,67 +389,119 @@ " 12/1 1.04395 1.04191 +/- 0.00204\n", " 13/1 1.04971 1.04451 +/- 0.00285\n", " 14/1 1.03880 1.04308 +/- 0.00247\n", - " 15/1 1.03092 1.04065 +/- 0.00310\n", - " 16/1 1.04653 1.04163 +/- 0.00271\n", - " 17/1 1.04114 1.04156 +/- 0.00229\n", - " 18/1 1.06033 1.04391 +/- 0.00307\n", - " 19/1 1.04163 1.04365 +/- 0.00272\n", - " 20/1 1.04992 1.04428 +/- 0.00251\n", - " 21/1 1.01577 1.04169 +/- 0.00345\n", - " 22/1 1.03611 1.04122 +/- 0.00318\n", - " 23/1 1.03251 1.04055 +/- 0.00300\n", - " 24/1 1.03996 1.04051 +/- 0.00278\n", - " 25/1 1.06132 1.04190 +/- 0.00294\n", - " 26/1 1.03581 1.04152 +/- 0.00277\n", - " 27/1 1.05195 1.04213 +/- 0.00268\n", - " 28/1 1.03721 1.04186 +/- 0.00254\n", - " 29/1 1.03475 1.04148 +/- 0.00243\n", - " 30/1 1.03057 1.04094 +/- 0.00237\n", - " 31/1 1.04407 1.04109 +/- 0.00226\n", - " 32/1 1.04661 1.04134 +/- 0.00217\n", - " 33/1 1.03286 1.04097 +/- 0.00210\n", - " 34/1 1.01253 1.03978 +/- 0.00234\n", - " 35/1 1.03488 1.03959 +/- 0.00225\n", - " 36/1 1.02509 1.03903 +/- 0.00223\n", - " 37/1 1.03647 1.03894 +/- 0.00215\n", - " 38/1 1.06387 1.03983 +/- 0.00226\n", - " 39/1 1.02418 1.03929 +/- 0.00224\n", - " 40/1 1.05815 1.03992 +/- 0.00226\n", - " 41/1 1.04433 1.04006 +/- 0.00219\n", - " 42/1 1.04627 1.04025 +/- 0.00213\n", - " 43/1 1.05089 1.04057 +/- 0.00209\n", - " 44/1 1.02985 1.04026 +/- 0.00205\n", - " 45/1 1.06236 1.04089 +/- 0.00209\n", - " 46/1 1.04283 1.04094 +/- 0.00203\n", - " 47/1 1.04404 1.04103 +/- 0.00197\n", - " 48/1 1.05946 1.04151 +/- 0.00198\n", - " 49/1 1.03286 1.04129 +/- 0.00194\n", - " 50/1 1.07609 1.04216 +/- 0.00208\n", - " 51/1 1.02097 1.04164 +/- 0.00210\n", - " 52/1 1.08390 1.04265 +/- 0.00228\n", - " 53/1 1.01654 1.04204 +/- 0.00231\n", - " 54/1 1.02701 1.04170 +/- 0.00228\n", - " 55/1 1.04845 1.04185 +/- 0.00223\n", - " 56/1 1.05401 1.04212 +/- 0.00220\n", - " 57/1 1.04673 1.04221 +/- 0.00216\n", - " 58/1 1.04093 1.04219 +/- 0.00211\n", - " 59/1 1.03205 1.04198 +/- 0.00208\n", - " 60/1 1.05368 1.04221 +/- 0.00205\n", - " 61/1 1.02273 1.04183 +/- 0.00204\n", - " 62/1 1.03259 1.04165 +/- 0.00201\n", - " 63/1 1.06216 1.04204 +/- 0.00201\n", - " 64/1 1.03658 1.04194 +/- 0.00198\n", - " 65/1 1.02072 1.04155 +/- 0.00198\n", - " 66/1 1.03019 1.04135 +/- 0.00195\n", - " 67/1 1.05241 1.04155 +/- 0.00193\n", - " 68/1 1.05906 1.04185 +/- 0.00192\n", - " 69/1 1.05263 1.04203 +/- 0.00190\n", - " 70/1 1.02176 1.04169 +/- 0.00189\n", - " 71/1 1.03390 1.04157 +/- 0.00187\n", - " 72/1 1.05470 1.04178 +/- 0.00185\n", - " 73/1 1.03892 1.04173 +/- 0.00182\n", - " 74/1 0.98570 1.04086 +/- 0.00199\n", - " 75/1 1.02591 1.04063 +/- 0.00198\n" + " 15/1 1.03091 1.04065 +/- 0.00310\n", + " 16/1 1.03618 1.03990 +/- 0.00264\n", + " 17/1 1.04109 1.04007 +/- 0.00223\n", + " 18/1 1.02978 1.03879 +/- 0.00232\n", + " 19/1 1.06363 1.04155 +/- 0.00344\n", + " 20/1 1.06549 1.04394 +/- 0.00390\n", + " 21/1 1.03469 1.04310 +/- 0.00362\n", + " 22/1 1.01925 1.04111 +/- 0.00386\n", + " 23/1 1.03268 1.04046 +/- 0.00361\n", + " 24/1 1.03906 1.04036 +/- 0.00334\n", + " 25/1 1.02632 1.03943 +/- 0.00325\n", + " 26/1 1.03906 1.03940 +/- 0.00304\n", + " 27/1 1.05058 1.04006 +/- 0.00293\n", + " 28/1 1.03248 1.03964 +/- 0.00279\n", + " 29/1 1.04076 1.03970 +/- 0.00264\n", + " 30/1 1.00994 1.03821 +/- 0.00292\n", + " 31/1 1.04785 1.03867 +/- 0.00281\n", + " 32/1 1.03080 1.03831 +/- 0.00270\n", + " 33/1 1.01862 1.03746 +/- 0.00272\n", + " 34/1 1.05370 1.03813 +/- 0.00269\n", + " 35/1 1.02226 1.03750 +/- 0.00266\n", + " 36/1 1.02862 1.03716 +/- 0.00258\n", + " 37/1 1.04790 1.03755 +/- 0.00251\n", + " 38/1 1.03762 1.03756 +/- 0.00242\n", + " 39/1 1.02255 1.03704 +/- 0.00239\n", + " 40/1 1.06094 1.03784 +/- 0.00245\n", + " 41/1 1.03842 1.03786 +/- 0.00237\n", + " 42/1 1.00628 1.03687 +/- 0.00249\n", + " 43/1 1.04916 1.03724 +/- 0.00245\n", + " 44/1 1.06237 1.03798 +/- 0.00248\n", + " 45/1 1.08153 1.03922 +/- 0.00271\n", + " 46/1 1.05649 1.03970 +/- 0.00268\n", + " 47/1 1.06265 1.04032 +/- 0.00268\n", + " 48/1 1.05728 1.04077 +/- 0.00265\n", + " 49/1 1.07343 1.04161 +/- 0.00271\n", + " 50/1 1.04640 1.04173 +/- 0.00265\n", + " 51/1 1.05143 1.04196 +/- 0.00259\n", + " 52/1 1.03639 1.04183 +/- 0.00253\n", + " 53/1 1.04846 1.04199 +/- 0.00248\n", + " 54/1 1.02435 1.04158 +/- 0.00245\n", + " 55/1 1.04806 1.04173 +/- 0.00240\n", + " 56/1 1.04798 1.04186 +/- 0.00235\n", + " 57/1 1.06621 1.04238 +/- 0.00236\n", + " 58/1 1.05734 1.04269 +/- 0.00233\n", + " 59/1 1.04581 1.04276 +/- 0.00228\n", + " 60/1 1.02682 1.04244 +/- 0.00226\n", + " 61/1 1.05971 1.04278 +/- 0.00224\n", + " 62/1 1.02357 1.04241 +/- 0.00223\n", + " 63/1 1.02645 1.04211 +/- 0.00221\n", + " 64/1 1.00711 1.04146 +/- 0.00226\n", + " 65/1 1.06171 1.04183 +/- 0.00225\n", + " 66/1 1.03444 1.04170 +/- 0.00221\n", + " 67/1 1.05875 1.04199 +/- 0.00219\n", + " 68/1 1.04640 1.04207 +/- 0.00216\n", + " 69/1 1.04376 1.04210 +/- 0.00212\n", + " 70/1 1.07078 1.04258 +/- 0.00214\n", + " 71/1 1.03916 1.04252 +/- 0.00210\n", + " 72/1 1.01843 1.04213 +/- 0.00211\n", + " 73/1 1.03666 1.04205 +/- 0.00207\n", + " 74/1 1.04625 1.04211 +/- 0.00204\n", + " 75/1 1.05277 1.04228 +/- 0.00202\n", + " 76/1 1.04944 1.04238 +/- 0.00199\n", + " 77/1 1.01898 1.04203 +/- 0.00199\n", + " 78/1 1.03283 1.04190 +/- 0.00197\n", + " 79/1 1.02304 1.04163 +/- 0.00196\n", + " 80/1 1.01539 1.04125 +/- 0.00196\n", + " 81/1 1.03988 1.04123 +/- 0.00194\n", + " 82/1 1.02138 1.04096 +/- 0.00193\n", + " 83/1 1.02473 1.04073 +/- 0.00192\n", + " 84/1 1.03810 1.04070 +/- 0.00189\n", + " 85/1 1.07438 1.04115 +/- 0.00192\n", + " 86/1 1.03048 1.04101 +/- 0.00190\n", + " 87/1 1.06778 1.04135 +/- 0.00191\n", + " 88/1 1.07341 1.04177 +/- 0.00192\n", + " 89/1 1.06729 1.04209 +/- 0.00193\n", + " 90/1 1.05069 1.04220 +/- 0.00191\n", + " 91/1 1.07675 1.04262 +/- 0.00193\n", + " 92/1 1.06470 1.04289 +/- 0.00193\n", + " 93/1 1.02609 1.04269 +/- 0.00191\n", + " 94/1 1.04761 1.04275 +/- 0.00189\n", + " 95/1 1.08802 1.04328 +/- 0.00194\n", + " 96/1 1.04162 1.04326 +/- 0.00192\n", + " 97/1 1.04573 1.04329 +/- 0.00190\n", + " 98/1 1.03232 1.04317 +/- 0.00188\n", + " 99/1 1.03473 1.04307 +/- 0.00186\n", + " 100/1 1.04505 1.04309 +/- 0.00184\n", + " Creating state point statepoint.100.h5...\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 6.4445e-01 seconds\n", + " Reading cross sections = 6.1129e-01 seconds\n", + " Total time in simulation = 2.0000e+02 seconds\n", + " Time in transport only = 1.9970e+02 seconds\n", + " Time in inactive batches = 2.9966e+00 seconds\n", + " Time in active batches = 1.9701e+02 seconds\n", + " Time synchronizing fission bank = 4.0040e-02 seconds\n", + " Sampling source sites = 3.1522e-02 seconds\n", + " SEND/RECV source sites = 8.3459e-03 seconds\n", + " Time accumulating tallies = 9.3582e-03 seconds\n", + " Total time for finalization = 4.6582e-02 seconds\n", + " Total time elapsed = 2.0072e+02 seconds\n", + " Calculation Rate (inactive) = 16685.4 particles/second\n", + " Calculation Rate (active) = 2284.19 particles/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.04342 +/- 0.00159\n", + " k-effective (Track-length) = 1.04309 +/- 0.00184\n", + " k-effective (Absorption) = 1.04107 +/- 0.00140\n", + " Combined k-effective = 1.04195 +/- 0.00117\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" ] } ], @@ -512,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "scrolled": true }, @@ -531,9 +545,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t1\n", + "\tName =\tflux\n", + "\tFilters =\tMeshFilter\n", + "\tNuclides =\ttotal \n", + "\tScores =\t['flux', 'fission']\n", + "\tEstimator =\ttracklength\n", + "\n" + ] + } + ], "source": [ "tally = sp.get_tally(scores=['flux'])\n", "print(tally)" @@ -548,9 +577,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[0.40767451, 0. ]],\n", + "\n", + " [[0.40933814, 0. ]],\n", + "\n", + " [[0.4119165 , 0. ]],\n", + "\n", + " ...,\n", + "\n", + " [[0.40854327, 0. ]],\n", + "\n", + " [[0.40970805, 0. ]],\n", + "\n", + " [[0.40948065, 0. ]]])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tally.sum" ] @@ -564,9 +616,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 1, 2)\n" + ] + }, + { + "data": { + "text/plain": [ + "(array([[[0.00452972, 0. ]],\n", + " \n", + " [[0.0045482 , 0. ]],\n", + " \n", + " [[0.00457685, 0. ]],\n", + " \n", + " ...,\n", + " \n", + " [[0.00453937, 0. ]],\n", + " \n", + " [[0.00455231, 0. ]],\n", + " \n", + " [[0.00454978, 0. ]]]),\n", + " array([[[2.03553236e-05, 0.00000000e+00]],\n", + " \n", + " [[1.83847389e-05, 0.00000000e+00]],\n", + " \n", + " [[1.68647098e-05, 0.00000000e+00]],\n", + " \n", + " ...,\n", + " \n", + " [[1.71606078e-05, 0.00000000e+00]],\n", + " \n", + " [[1.87645811e-05, 0.00000000e+00]],\n", + " \n", + " [[1.94447454e-05, 0.00000000e+00]]]))" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(tally.mean.shape)\n", "(tally.mean, tally.std_dev)" @@ -581,9 +676,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t2\n", + "\tName =\tflux\n", + "\tFilters =\tMeshFilter\n", + "\tNuclides =\ttotal \n", + "\tScores =\t['flux']\n", + "\tEstimator =\ttracklength\n", + "\n" + ] + } + ], "source": [ "flux = tally.get_slice(scores=['flux'])\n", "fission = tally.get_slice(scores=['fission'])\n", @@ -599,7 +709,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -611,9 +721,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig = plt.subplot(121)\n", "fig.imshow(flux.mean)\n", @@ -630,9 +763,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Determine relative error\n", "relative_error = np.zeros_like(flux.std_dev)\n", @@ -659,9 +805,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([((-0.28690552, -0.23731283, 0.51447853), ( 0.02705364, -0.14292142, 0.98936422), 1780128.70101981, 1., 0, 0),\n", + " ((-0.28690552, -0.23731283, 0.51447853), (-0.16786951, 0.86432444, -0.47409186), 1553436.10501094, 1., 0, 0),\n", + " (( 0.17162994, 0.134092 , 0.42932363), ( 0.25199134, -0.11168216, 0.96126347), 829530.02360943, 1., 0, 0),\n", + " ...,\n", + " ((-0.24444068, -0.01351615, -0.41772172), ( 0.10437178, -0.86754673, 0.486281 ), 807617.55637656, 1., 0, 0),\n", + " ((-0.2146841 , 0.14307096, 0.07419328), ( 0.89645066, -0.35557279, -0.26446968), 6036005.44157462, 1., 0, 0),\n", + " ((-0.2146841 , 0.14307096, 0.07419328), (-0.95287644, -0.25857878, 0.15863005), 4923751.04163063, 1., 0, 0)],\n", + " dtype=[('r', [('x', '" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Create log-spaced energy bins from 1 keV to 10 MeV\n", "energy_bins = np.logspace(3,7)\n", @@ -719,9 +925,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5, 0.5)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.quiver(sp.source['r']['x'], sp.source['r']['y'],\n", " sp.source['u']['x'], sp.source['u']['y'],\n", @@ -748,7 +977,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/examples/jupyter/search.ipynb b/examples/jupyter/search.ipynb index 6031c4c4d..bfdc20695 100644 --- a/examples/jupyter/search.ipynb +++ b/examples/jupyter/search.ipynb @@ -35,7 +35,7 @@ "\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://openmc.readthedocs.io/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](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." ] }, { @@ -64,7 +64,7 @@ "\n", " # Include the amount of boron in the water based on the ppm,\n", " # neglecting the other constituents of boric acid\n", - " water.add_element('B', ppm_Boron * 1E-6)\n", + " water.add_element('B', ppm_Boron * 1e-6)\n", " \n", " # Instantiate a Materials object\n", " materials = openmc.Materials([fuel, zircaloy, water])\n", @@ -141,7 +141,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.6.7/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] }, @@ -156,7 +156,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.6.7/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] }, @@ -171,7 +171,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.6.7/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] }, @@ -186,7 +186,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.6.7/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] }, @@ -201,7 +201,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.6.7/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] }, @@ -216,7 +216,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.6.7/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] }, @@ -231,7 +231,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.6.7/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] }, @@ -246,7 +246,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.6.7/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] }, @@ -261,7 +261,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/shriwise/.pyenv/versions/3.6.7/lib/python3.6/site-packages/openmc-0.10.0-py3.6-linux-x86_64.egg/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Universe instance already exists with id=0.\n", " warn(msg, IDWarning)\n" ] }, @@ -277,7 +277,7 @@ "source": [ "# Perform the search\n", "crit_ppm, guesses, keffs = openmc.search_for_keff(build_model, bracket=[1000., 2500.],\n", - " tol=1.E-2, bracketed_method='bisect',\n", + " tol=1e-2, bracketed_method='bisect',\n", " print_iterations=True)\n", "\n", "print('Critical Boron Concentration: {:4.0f} ppm'.format(crit_ppm))" @@ -344,7 +344,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/examples/jupyter/tally-arithmetic.ipynb b/examples/jupyter/tally-arithmetic.ipynb index 35c096ba4..40eb2b05f 100644 --- a/examples/jupyter/tally-arithmetic.ipynb +++ b/examples/jupyter/tally-arithmetic.ipynb @@ -10,9 +10,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import glob\n", @@ -39,9 +37,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# 1.6 enriched fuel\n", @@ -74,9 +70,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Instantiate a Materials collection\n", @@ -96,14 +90,12 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create cylinders for the fuel and clad\n", - "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n", - "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n", + "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n", + "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n", "\n", "# Create boundary planes to surround the geometry\n", "# Use both reflective and vacuum boundaries to make life interesting\n", @@ -125,9 +117,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create a Universe to encapsulate a fuel pin\n", @@ -162,9 +152,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create root Cell\n", @@ -189,9 +177,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create Geometry and set root Universe\n", @@ -201,9 +187,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Export to \"geometry.xml\"\n", @@ -220,9 +204,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# OpenMC simulation parameters\n", @@ -256,10 +238,19 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Instantiate a Plot\n", "plot = openmc.Plot(plot_id=1)\n", @@ -269,51 +260,8 @@ "plot.pixels = [250, 250]\n", "plot.color_by = 'material'\n", "\n", - "# Instantiate a Plots collection and export to \"plots.xml\"\n", - "plot_file = openmc.Plots([plot])\n", - "plot_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Run openmc in plotting mode\n", - "openmc.plot_geometry(output=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC\nAK7OHOkAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAAAAxQTFRF\n////chIS6YCRTb/E6kGE+wAAAAFiS0dEAIgFHUgAAAAJcEhZcwAAAEgAAABIAEbJaz4AAALKSURB\nVGje7dpLcqQwDAbgHHE2YeEj+D4cwQucBUfo+3CEXoSp8OhuhF70T4qpKXmdr21LogK2Pj7A8QmN\nP+HDhw8fPnz48Kf6VH9G+66vy+je8k19jnf8C5dXIPv86ms56lPdjvaYbyodx3ze+XLE76cXFiD4\nzPji99z0/AJ4n1lfvJ6fnl0A6x+578efMSg1wPr172/jPO5yFXM+Ef78gdblM+WPHyguP//t1/g6\npA0wfln+ho/fwgYYn19C/xwDvwHGc9OvC+hs37DTrwuwfWanXxdQTC9Mvyygs3wjTL8uwPJpn/tN\nDbSGz7T0SBEWw4vLXzbQ6b6RoveIoO6TvPxlA63qs7z8ZQPF9F+SH22vbX8OQKf5Rtv+EgDNJ3X5\n8wZaxWd1+fMGiuFvir8bvjp8J/tGy/6jAmRvhW8fwL3vVT+o3grfPoB7r/IpALI3tz8FoJN84/NV\n873hB8UnM3xzANtf8nb4dwmg3grfFEDJO8JPE0i9Ff4pAYL3pI8mkHor/HMCeO9JH00g9SafEsh7\nT/ppARBvp48UwJnelT5SACd7O31TAlnvKx9SQCd7B58KgPO+8iMFuPWe9E8F8BveWX7bAjzX9y4/\n/Jve+fhsH6Ctv7n8PTzjvY/v9gEOHz58+PBX+6v/f/wPvnd54f3j6venE/yl769Xv7+j3x/o98/V\n32/o9+fl389Xnx+g5x/o+Qt6/oOeP6HnX+j5G3z+h54/ouefV5/foufP6Pk3ev4On/+j9w/o/Qd6\n/4Le/6D3T/D9V67Y/ZsVQBq+s+8f0ftP+P41axXguP9NWgDuu/Cdfv+N3r/D9/9TAID+A7T/Ae2/\ngPs/0P4TtP8F7r9J3AIO9P+g/Udw/9Oygbf7r9D+L7j/DO1/Q/vv4P4/tP8Q7n9E+y/h/k+0/xTu\nf4X7b+H+X7T/+BPuf3aM8OHDhw8fPnz4w/4vzcvgeY10sY0AAAAldEVYdGRhdGU6Y3JlYXRlADIw\nMTgtMDQtMDNUMjE6MTE6MzgtMDQ6MDD1dVTHAAAAJXRFWHRkYXRlOm1vZGlmeQAyMDE4LTA0LTAz\nVDIxOjExOjM4LTA0OjAwhCjsewAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Convert OpenMC's funky ppm to png\n", - "!convert materials-xy.ppm materials-xy.png\n", - "\n", - "# Display the materials plot inline\n", - "Image(filename='materials-xy.png')" + "# Show plot\n", + "openmc.plot_inline(plot)" ] }, { @@ -325,10 +273,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "# Instantiate an empty Tallies object\n", @@ -337,10 +283,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "# Create Tallies to compute microscopic multi-group cross-sections\n", @@ -397,10 +341,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "# K-Eigenvalue (infinity) tallies\n", @@ -413,10 +355,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "# Resonance Escape Probability tallies\n", @@ -428,10 +368,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "# Thermal Flux Utilization tallies\n", @@ -444,10 +382,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "# Fast Fission Factor tallies\n", @@ -459,10 +395,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [], "source": [ "# Instantiate energy filter to illustrate Tally slicing\n", @@ -479,18 +413,18 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/liangjg/.local/lib/python3.5/site-packages/openmc-0.10.0-py3.5.egg/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=6.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=6.\n", " warn(msg, IDWarning)\n", - "/home/liangjg/.local/lib/python3.5/site-packages/openmc-0.10.0-py3.5.egg/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n", " warn(msg, IDWarning)\n", - "/home/liangjg/.local/lib/python3.5/site-packages/openmc-0.10.0-py3.5.egg/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", + "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n", " warn(msg, IDWarning)\n" ] } @@ -509,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": { "scrolled": true }, @@ -518,66 +452,63 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", + " %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", + " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", + " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%%%%%%\n", + " ##################### %%%%%%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%%\n", + " ####################### %%%%%%%%%%%%%%%%%\n", + " ###################### %%%%%%%%%%%%%%%%%\n", + " #################### %%%%%%%%%%%%%%%%%\n", + " ################# %%%%%%%%%%%%%%%%%\n", + " ############### %%%%%%%%%%%%%%%%\n", + " ############ %%%%%%%%%%%%%%%\n", + " ######## %%%%%%%%%%%%%%\n", + " %%%%%%%%%%%\n", "\n", " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2018 Massachusetts Institute of Technology\n", + " Copyright | 2011-2019 MIT and OpenMC contributors\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.10.0\n", - " Git SHA1 | 47fbf8282ea94c138f75219bd10fdb31501d3fb7\n", - " Date/Time | 2018-04-03 21:12:27\n", - " MPI Processes | 1\n", - " OpenMP Threads | 20\n", + " Version | 0.11.0-dev\n", + " Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n", + " Date/Time | 2019-07-18 22:51:02\n", + " OpenMP Threads | 4\n", "\n", " Reading settings XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", " Reading geometry XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Reading U235 from /home/liangjg/nucdata/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/liangjg/nucdata/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/liangjg/nucdata/nndc_hdf5/O16.h5\n", - " Reading H1 from /home/liangjg/nucdata/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/liangjg/nucdata/nndc_hdf5/B10.h5\n", - " Reading Zr90 from /home/liangjg/nucdata/nndc_hdf5/Zr90.h5\n", - " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", + " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n", + " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n", + " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n", + " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n", + " Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n", + " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n", + " Maximum neutron transport energy: 20000000.000000 eV for U235\n", " Reading tallies XML file...\n", " Writing summary.h5 file...\n", " Initializing source particles...\n", "\n", " ====================> K EIGENVALUE SIMULATION <====================\n", "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 0.96168 \n", - " 2/1 0.96651 \n", - " 3/1 1.00678 \n", - " 4/1 0.98773 \n", - " 5/1 1.01883 \n", - " 6/1 1.02959 \n", + " Bat./Gen. k Average k\n", + " ========= ======== ====================\n", + " 1/1 0.96168\n", + " 2/1 0.96651\n", + " 3/1 1.00678\n", + " 4/1 0.98773\n", + " 5/1 1.01883\n", + " 6/1 1.02959\n", " 7/1 0.99859 1.01409 +/- 0.01550\n", " 8/1 1.03441 1.02086 +/- 0.01123\n", " 9/1 1.06097 1.03089 +/- 0.01279\n", @@ -596,28 +527,28 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.9090E-01 seconds\n", - " Reading cross sections = 4.2387E-01 seconds\n", - " Total time in simulation = 1.4928E+00 seconds\n", - " Time in transport only = 1.3545E+00 seconds\n", - " Time in inactive batches = 1.3625E-01 seconds\n", - " Time in active batches = 1.3565E+00 seconds\n", - " Time synchronizing fission bank = 2.4053E-03 seconds\n", - " Sampling source sites = 1.6466E-03 seconds\n", - " SEND/RECV source sites = 5.6159E-04 seconds\n", - " Time accumulating tallies = 3.3647E-04 seconds\n", - " Total time for finalization = 1.6066E-02 seconds\n", - " Total time elapsed = 2.0336E+00 seconds\n", - " Calculation Rate (inactive) = 91743.2 neutrons/second\n", - " Calculation Rate (active) = 27644.5 neutrons/second\n", + " Total time for initialization = 3.4427e-01 seconds\n", + " Reading cross sections = 3.1628e-01 seconds\n", + " Total time in simulation = 3.7319e+00 seconds\n", + " Time in transport only = 3.6302e+00 seconds\n", + " Time in inactive batches = 4.9601e-01 seconds\n", + " Time in active batches = 3.2359e+00 seconds\n", + " Time synchronizing fission bank = 2.8100e-03 seconds\n", + " Sampling source sites = 2.4682e-03 seconds\n", + " SEND/RECV source sites = 3.2484e-04 seconds\n", + " Time accumulating tallies = 4.4538e-05 seconds\n", + " Total time for finalization = 9.3656e-04 seconds\n", + " Total time elapsed = 4.0859e+00 seconds\n", + " Calculation Rate (inactive) = 25201.2 particles/second\n", + " Calculation Rate (active) = 11588.7 particles/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02889 +/- 0.00492\n", - " k-effective (Track-length) = 1.02842 +/- 0.00527\n", - " k-effective (Absorption) = 1.02637 +/- 0.00349\n", - " Combined k-effective = 1.02700 +/- 0.00291\n", - " Leakage Fraction = 0.01717 +/- 0.00107\n", + " k-effective (Collision) = 1.02889 +/- 0.00492\n", + " k-effective (Track-length) = 1.02842 +/- 0.00527\n", + " k-effective (Absorption) = 1.02637 +/- 0.00349\n", + " Combined k-effective = 1.02700 +/- 0.00291\n", + " Leakage Fraction = 0.01717 +/- 0.00107\n", "\n" ] } @@ -643,9 +574,8 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": { - "collapsed": true, "scrolled": true }, "outputs": [], @@ -665,13 +595,26 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -699,7 +642,7 @@ "0 total (nu-fission / (absorption + current)) 1.02e+00 6.65e-03" ] }, - "execution_count": 23, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -729,13 +672,26 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -770,7 +726,7 @@ "0 ((absorption + current) / (absorption + current)) 6.94e-01 4.61e-03 " ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -794,13 +750,26 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -835,7 +804,7 @@ "0 1.20e+00 9.61e-03 " ] }, - "execution_count": 25, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -858,13 +827,26 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -901,7 +883,7 @@ "0 7.49e-01 6.09e-03 " ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -922,13 +904,26 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -965,7 +960,7 @@ "0 1.66e+00 1.44e-02 " ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -985,13 +980,26 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -1026,7 +1034,7 @@ "0 ((absorption + current) / (absorption + current)) 9.85e-01 5.51e-03 " ] }, - "execution_count": 28, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1045,13 +1053,26 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -1086,7 +1107,7 @@ "0 (absorption / (absorption + current)) 9.97e-01 7.55e-03 " ] }, - "execution_count": 29, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1105,13 +1126,26 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -1148,7 +1182,7 @@ "0 (((((((absorption + current) / (absorption + c... 1.02e+00 1.88e-02 " ] }, - "execution_count": 30, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1169,9 +1203,8 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "metadata": { - "collapsed": true, "scrolled": true }, "outputs": [], @@ -1185,13 +1218,26 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -1312,7 +1358,7 @@ "7 (scatter / flux) 3.36e-03 1.34e-05 " ] }, - "execution_count": 32, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1331,18 +1377,18 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[[ 6.65948580e-07]\n", - " [ 3.56632881e-01]]\n", + "[[[6.65948580e-07]\n", + " [3.56632881e-01]]\n", "\n", - " [[ 7.25130446e-03]\n", - " [ 7.92016892e-03]]]\n" + " [[7.25130446e-03]\n", + " [7.92016892e-03]]]\n" ] } ], @@ -1361,16 +1407,16 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.00555547]]\n", + "[[[0.00555547]]\n", "\n", - " [[ 0.00335828]]]\n" + " [[0.00335828]]]\n" ] } ], @@ -1383,15 +1429,15 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[[ 0.22726611]\n", - " [ 0.00335828]]]\n" + "[[[0.22726611]\n", + " [0.00335828]]]\n" ] } ], @@ -1412,13 +1458,26 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -1491,7 +1550,7 @@ "3 5.98e-04 " ] }, - "execution_count": 36, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1504,13 +1563,26 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -1643,7 +1715,7 @@ "8 2.90e-03 " ] }, - "execution_count": 37, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1673,7 +1745,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/examples/jupyter/triso.ipynb b/examples/jupyter/triso.ipynb index 36e0c1f14..1934433e9 100644 --- a/examples/jupyter/triso.ipynb +++ b/examples/jupyter/triso.ipynb @@ -10,9 +10,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -33,9 +31,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "fuel = openmc.Material(name='Fuel')\n", @@ -81,13 +77,11 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Create TRISO universe\n", - "spheres = [openmc.Sphere(R=r*1e-4)\n", + "spheres = [openmc.Sphere(r=1e-4*r)\n", " for r in [215., 315., 350., 385.]]\n", "cells = [openmc.Cell(fill=fuel, region=-spheres[0]),\n", " openmc.Cell(fill=buff, region=+spheres[0] & -spheres[1]),\n", @@ -107,9 +101,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "min_x = openmc.XPlane(x0=-0.5, boundary_type='reflective')\n", @@ -148,9 +140,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "trisos = [openmc.model.TRISO(outer_radius, triso_univ, c) for c in centers]" @@ -166,9 +156,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -199,9 +187,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -228,9 +214,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -278,9 +262,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "box.fill = lattice" @@ -296,19 +278,18 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "" ] }, + "execution_count": 12, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ @@ -325,7 +306,7 @@ "settings.export_to_xml()\n", "\n", "p = openmc.Plot.from_geometry(geom)\n", - "openmc.plot_inline(p)" + "p.to_ipython_image()" ] }, { @@ -338,25 +319,24 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ "" ] }, + "execution_count": 13, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ "p.color_by = 'material'\n", "p.colors = {graphite: 'gray'}\n", - "openmc.plot_inline(p)" + "p.to_ipython_image()" ] } ], @@ -377,7 +357,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/include/openmc/capi.h b/include/openmc/capi.h index 9f05a56dc..3d2e8f57b 100644 --- a/include/openmc/capi.h +++ b/include/openmc/capi.h @@ -10,15 +10,21 @@ extern "C" { #endif int openmc_calculate_volumes(); - int openmc_cell_filter_get_bins(int32_t index, int32_t** cells, int32_t* n); + int openmc_cell_filter_get_bins(int32_t index, const int32_t** cells, int32_t* n); int openmc_cell_get_fill(int32_t index, int* type, int32_t** indices, int32_t* n); int openmc_cell_get_id(int32_t index, int32_t* id); int openmc_cell_get_temperature(int32_t index, const int32_t* instance, double* T); + int openmc_cell_get_name(int32_t index, const char** name); + int openmc_cell_set_name(int32_t index, const char* name); int openmc_cell_set_fill(int32_t index, int type, int32_t n, const int32_t* indices); int openmc_cell_set_id(int32_t index, int32_t id); int openmc_cell_set_temperature(int32_t index, double T, const int32_t* instance); - int openmc_energy_filter_get_bins(int32_t index, double** energies, int32_t* n); - int openmc_energy_filter_set_bins(int32_t index, int32_t n, const double* energies); + int openmc_energy_filter_get_bins(int32_t index, const double** energies, size_t* n); + int openmc_energy_filter_set_bins(int32_t index, size_t n, const double* energies); + int openmc_energyfunc_filter_get_energy(int32_t index, size_t* n, const double** energy); + int openmc_energyfunc_filter_get_y(int32_t index, size_t* n, const double** y); + int openmc_energyfunc_filter_set_data(int32_t index, size_t n, + const double* energies, const double* y); int openmc_extend_cells(int32_t n, int32_t* index_start, int32_t* index_end); int openmc_extend_filters(int32_t n, int32_t* index_start, int32_t* index_end); int openmc_extend_materials(int32_t n, int32_t* index_start, int32_t* index_end); @@ -29,6 +35,8 @@ extern "C" { int openmc_filter_set_id(int32_t index, int32_t id); int openmc_finalize(); int openmc_find_cell(const double* xyz, int32_t* index, int32_t* instance); + int openmc_cell_bounding_box(const int32_t index, double* llc, double* urc); + int openmc_global_bounding_box(double* llc, double* urc); int openmc_fission_bank(void** ptr, int64_t* n); int openmc_get_cell_index(int32_t id, int32_t* index); int openmc_get_filter_index(int32_t id, int32_t* index); @@ -48,7 +56,7 @@ extern "C" { int openmc_legendre_filter_set_order(int32_t index, int order); int openmc_load_nuclide(const char* name); int openmc_material_add_nuclide(int32_t index, const char name[], double density); - int openmc_material_get_densities(int32_t index, int** nuclides, double** densities, int* n); + int openmc_material_get_densities(int32_t index, const int** nuclides, const double** densities, int* n); int openmc_material_get_id(int32_t index, int32_t* id); int openmc_material_get_fissionable(int32_t index, bool* fissionable); int openmc_material_get_density(int32_t index, double* density); @@ -56,9 +64,11 @@ extern "C" { int openmc_material_set_density(int32_t index, double density, const char* units); int openmc_material_set_densities(int32_t index, int n, const char** name, const double* density); int openmc_material_set_id(int32_t index, int32_t id); + int openmc_material_get_name(int32_t index, const char** name); + int openmc_material_set_name(int32_t index, const char* name); int openmc_material_set_volume(int32_t index, double volume); - int openmc_material_filter_get_bins(int32_t index, int32_t** bins, int32_t* n); - int openmc_material_filter_set_bins(int32_t index, int32_t n, const int32_t* bins); + int openmc_material_filter_get_bins(int32_t index, const int32_t** bins, size_t* n); + int openmc_material_filter_set_bins(int32_t index, size_t n, const int32_t* bins); int openmc_mesh_filter_get_mesh(int32_t index, int32_t* index_mesh); int openmc_mesh_filter_set_mesh(int32_t index, int32_t index_mesh); int openmc_mesh_get_id(int32_t index, int32_t* id); @@ -95,7 +105,7 @@ extern "C" { int openmc_tally_get_active(int32_t index, bool* active); int openmc_tally_get_estimator(int32_t index, int* estimator); int openmc_tally_get_id(int32_t index, int32_t* id); - int openmc_tally_get_filters(int32_t index, const int32_t** indices, int* n); + int openmc_tally_get_filters(int32_t index, const int32_t** indices, size_t* n); int openmc_tally_get_n_realizations(int32_t index, int32_t* n); int openmc_tally_get_nuclides(int32_t index, int** nuclides, int* n); int openmc_tally_get_scores(int32_t index, int** scores, int* n); @@ -104,7 +114,7 @@ extern "C" { int openmc_tally_results(int32_t index, double** ptr, size_t shape_[3]); int openmc_tally_set_active(int32_t index, bool active); int openmc_tally_set_estimator(int32_t index, const char* estimator); - int openmc_tally_set_filters(int32_t index, int n, const int32_t* indices); + int openmc_tally_set_filters(int32_t index, size_t n, const int32_t* indices); int openmc_tally_set_id(int32_t index, int32_t id); int openmc_tally_set_nuclides(int32_t index, int n, const char** nuclides); int openmc_tally_set_scores(int32_t index, int n, const char** scores); diff --git a/include/openmc/cell.h b/include/openmc/cell.h index ee1230595..db8d60ed4 100644 --- a/include/openmc/cell.h +++ b/include/openmc/cell.h @@ -65,6 +65,8 @@ public: //! \param group_id An HDF5 group id. void to_hdf5(hid_t group_id) const; + BoundingBox bounding_box() const; + std::unique_ptr partitioner_; }; @@ -72,9 +74,77 @@ public: //! A geometry primitive that links surfaces, universes, and materials //============================================================================== -class Cell -{ +class Cell { public: + //---------------------------------------------------------------------------- + // Constructors, destructors, factory functions + + explicit Cell(pugi::xml_node cell_node); + Cell() {}; + virtual ~Cell() = default; + + //---------------------------------------------------------------------------- + // Methods + + //! \brief Determine if a cell contains the particle at a given location. + //! + //! The bounds of the cell are detemined by a logical expression involving + //! surface half-spaces. At initialization, the expression was converted + //! to RPN notation. + //! + //! The function is split into two cases, one for simple cells (those + //! involving only the intersection of half-spaces) and one for complex cells. + //! Simple cells can be evaluated with short circuit evaluation, i.e., as soon + //! as we know that one half-space is not satisfied, we can exit. This + //! provides a performance benefit for the common case. In + //! contains_complex, we evaluate the RPN expression using a stack, similar to + //! how a RPN calculator would work. + //! \param r The 3D Cartesian coordinate to check. + //! \param u A direction used to "break ties" the coordinates are very + //! close to a surface. + //! \param on_surface The signed index of a surface that the coordinate is + //! known to be on. This index takes precedence over surface sense + //! calculations. + virtual bool + contains(Position r, Direction u, int32_t on_surface) const = 0; + + //! Find the oncoming boundary of this cell. + virtual std::pair + distance(Position r, Direction u, int32_t on_surface) const = 0; + + //! Write all information needed to reconstruct the cell to an HDF5 group. + //! \param group_id An HDF5 group id. + virtual void to_hdf5(hid_t group_id) const = 0; + + //! Get the BoundingBox for this cell. + virtual BoundingBox bounding_box() const = 0; + + //---------------------------------------------------------------------------- + // Accessors + + //! Get the temperature of a cell instance + //! \param[in] instance Instance index. If -1 is given, the temperature for + //! the first instance is returned. + //! \return Temperature in [K] + double temperature(int32_t instance = -1) const; + + //! Set the temperature of a cell instance + //! \param[in] T Temperature in [K] + //! \param[in] instance Instance index. If -1 is given, the temperature for + //! all instances is set. + void set_temperature(double T, int32_t instance = -1); + + //! Get the name of a cell + //! \return Cell name + const std::string& name() const { return name_; }; + + //! Set the temperature of a cell instance + //! \param[in] name Cell name + void set_name(const std::string& name) { name_ = name; }; + + //---------------------------------------------------------------------------- + // Data members + int32_t id_; //!< Unique ID std::string name_; //!< User-defined name int type_; //!< Material, universe, or lattice @@ -116,41 +186,6 @@ public: std::vector rotation_; std::vector offset_; //!< Distribcell offset table - - explicit Cell(pugi::xml_node cell_node); - Cell() {}; - - //! \brief Determine if a cell contains the particle at a given location. - //! - //! The bounds of the cell are detemined by a logical expression involving - //! surface half-spaces. At initialization, the expression was converted - //! to RPN notation. - //! - //! The function is split into two cases, one for simple cells (those - //! involving only the intersection of half-spaces) and one for complex cells. - //! Simple cells can be evaluated with short circuit evaluation, i.e., as soon - //! as we know that one half-space is not satisfied, we can exit. This - //! provides a performance benefit for the common case. In - //! contains_complex, we evaluate the RPN expression using a stack, similar to - //! how a RPN calculator would work. - //! \param r The 3D Cartesian coordinate to check. - //! \param u A direction used to "break ties" the coordinates are very - //! close to a surface. - //! \param on_surface The signed index of a surface that the coordinate is - //! known to be on. This index takes precedence over surface sense - //! calculations. - virtual bool - contains(Position r, Direction u, int32_t on_surface) const = 0; - - //! Find the oncoming boundary of this cell. - virtual std::pair - distance(Position r, Direction u, int32_t on_surface) const = 0; - - //! Write all information needed to reconstruct the cell to an HDF5 group. - //! @param group_id An HDF5 group id. - virtual void to_hdf5(hid_t group_id) const = 0; - - virtual ~Cell() {} }; //============================================================================== @@ -170,9 +205,14 @@ public: void to_hdf5(hid_t group_id) const; + BoundingBox bounding_box() const; + protected: bool contains_simple(Position r, Direction u, int32_t on_surface) const; bool contains_complex(Position r, Direction u, int32_t on_surface) const; + BoundingBox bounding_box_simple() const; + static BoundingBox bounding_box_complex(std::vector rpn); + static void apply_demorgan(std::vector& rpn); }; //============================================================================== @@ -181,16 +221,19 @@ protected: class DAGCell : public Cell { public: - moab::DagMC* dagmc_ptr_; DAGCell(); - int32_t dag_index_; bool contains(Position r, Direction u, int32_t on_surface) const; std::pair distance(Position r, Direction u, int32_t on_surface) const; + BoundingBox bounding_box() const; + void to_hdf5(hid_t group_id) const; + + moab::DagMC* dagmc_ptr_; //!< Pointer to DagMC instance + int32_t dag_index_; //!< DagMC index of cell }; #endif diff --git a/include/openmc/material.h b/include/openmc/material.h index 3109f6d78..652f3db8e 100644 --- a/include/openmc/material.h +++ b/include/openmc/material.h @@ -6,6 +6,7 @@ #include #include +#include #include #include "pugixml.hpp" #include "xtensor/xtensor.hpp" @@ -35,6 +36,7 @@ extern std::unordered_map material_map; class Material { public: + //---------------------------------------------------------------------------- // Types struct ThermalTable { int index_table; //!< Index of table in data::thermal_scatt @@ -42,11 +44,15 @@ public: double fraction; //!< How often to use table }; - // Constructors + //---------------------------------------------------------------------------- + // Constructors, destructors, factory functions Material() {}; explicit Material(pugi::xml_node material_node); + ~Material(); + //---------------------------------------------------------------------------- // Methods + void calculate_xs(Particle& p) const; //! Assign thermal scattering tables to specific nuclides within the material @@ -59,12 +65,72 @@ public: //! Finalize the material, assigning tables, normalize density, etc. void finalize(); - //! Set total density of the material - int set_density(double density, std::string units); - //! Write material data to HDF5 void to_hdf5(hid_t group) const; + //! Add nuclide to the material + // + //! \param[in] nuclide Name of the nuclide + //! \param[in] density Density of the nuclide in [atom/b-cm] + void add_nuclide(const std::string& nuclide, double density); + + //! Set atom densities for the material + // + //! \param[in] name Name of each nuclide + //! \param[in] density Density of each nuclide in [atom/b-cm] + void set_densities(const std::vector& name, + const std::vector& density); + + //---------------------------------------------------------------------------- + // Accessors + + //! Get density in [atom/b-cm] + //! \return Density in [atom/b-cm] + double density() const { return density_; } + + //! Get density in [g/cm^3] + //! \return Density in [g/cm^3] + double density_gpcc() const { return density_gpcc_; } + + //! Get name + //! \return Material name + const std::string& name() const { return name_; } + + //! Set name + void set_name(const std::string& name) { name_ = name; } + + //! Set total density of the material + // + //! \param[in] density Density value + //! \param[in] units Units of density + void set_density(double density, gsl::cstring_span units); + + //! Get nuclides in material + //! \return Indices into the global nuclides vector + gsl::span nuclides() const { return {nuclide_.data(), nuclide_.size()}; } + + //! Get densities of each nuclide in material + //! \return Densities in [atom/b-cm] + gsl::span densities() const { return {atom_density_.data(), atom_density_.size()}; } + + //! Get ID of material + //! \return ID of material + int32_t id() const { return id_; } + + //! Assign a unique ID to the material + //! \param[in] Unique ID to assign. A value of -1 indicates that an ID + //! should be automatically assigned. + void set_id(int32_t id); + + //! Get whether material is fissionable + //! \return Whether material is fissionable + bool fissionable() const { return fissionable_; } + + //! Get volume of material + //! \return Volume in [cm^3] + double volume() const; + + //---------------------------------------------------------------------------- // Data int32_t id_; //!< Unique ID std::string name_; //!< Name of material @@ -95,6 +161,9 @@ public: std::unique_ptr ttb_; private: + //---------------------------------------------------------------------------- + // Private methods + //! Calculate the collision stopping power void collision_stopping_power(double* s_col, bool positron); @@ -106,6 +175,10 @@ private: void calculate_neutron_xs(Particle& p) const; void calculate_photon_xs(Particle& p) const; + + //---------------------------------------------------------------------------- + // Private data members + gsl::index index_; }; //============================================================================== diff --git a/include/openmc/surface.h b/include/openmc/surface.h index 7b0ccefd1..d61ae07ce 100644 --- a/include/openmc/surface.h +++ b/include/openmc/surface.h @@ -43,12 +43,46 @@ namespace model { struct BoundingBox { - double xmin; - double xmax; - double ymin; - double ymax; - double zmin; - double zmax; + double xmin = -INFTY; + double xmax = INFTY; + double ymin = -INFTY; + double ymax = INFTY; + double zmin = -INFTY; + double zmax = INFTY; + + + inline BoundingBox operator &(const BoundingBox& other) { + BoundingBox result = *this; + return result &= other; + } + + inline BoundingBox operator |(const BoundingBox& other) { + BoundingBox result = *this; + return result |= other; + } + + // intersect operator + inline BoundingBox& operator &=(const BoundingBox& other) { + xmin = std::max(xmin, other.xmin); + xmax = std::min(xmax, other.xmax); + ymin = std::max(ymin, other.ymin); + ymax = std::min(ymax, other.ymax); + zmin = std::max(zmin, other.zmin); + zmax = std::min(zmax, other.zmax); + return *this; + } + + // union operator + inline BoundingBox& operator |=(const BoundingBox& other) { + xmin = std::min(xmin, other.xmin); + xmax = std::max(xmax, other.xmax); + ymin = std::min(ymin, other.ymin); + ymax = std::max(ymax, other.ymax); + zmin = std::min(zmin, other.zmin); + zmax = std::max(zmax, other.zmax); + return *this; + } + }; //============================================================================== @@ -105,6 +139,8 @@ public: //TODO: this probably needs to include i_periodic for PeriodicSurface virtual void to_hdf5(hid_t group_id) const = 0; + //! Get the BoundingBox for this surface. + virtual BoundingBox bounding_box(bool pos_side) const { return {}; } }; class CSGSurface : public Surface @@ -126,18 +162,17 @@ protected: class DAGSurface : public Surface { public: - moab::DagMC* dagmc_ptr_; DAGSurface(); - int32_t dag_index_; double evaluate(Position r) const; double distance(Position r, Direction u, bool coincident) const; Direction normal(Position r) const; Direction reflect(Position r, Direction u) const; - //! Get the bounding box of this surface. - BoundingBox bounding_box() const; void to_hdf5(hid_t group_id) const; + + moab::DagMC* dagmc_ptr_; //!< Pointer to DagMC instance + int32_t dag_index_; //!< DagMC index of surface }; #endif //============================================================================== @@ -166,8 +201,6 @@ public: virtual bool periodic_translate(const PeriodicSurface* other, Position& r, Direction& u) const = 0; - //! Get the bounding box for this surface. - virtual BoundingBox bounding_box() const = 0; }; //============================================================================== @@ -186,7 +219,7 @@ public: void to_hdf5_inner(hid_t group_id) const; bool periodic_translate(const PeriodicSurface* other, Position& r, Direction& u) const; - BoundingBox bounding_box() const; + BoundingBox bounding_box(bool pos_side) const; double x0_; }; @@ -207,7 +240,7 @@ public: void to_hdf5_inner(hid_t group_id) const; bool periodic_translate(const PeriodicSurface* other, Position& r, Direction& u) const; - BoundingBox bounding_box() const; + BoundingBox bounding_box(bool pos_side) const; double y0_; }; @@ -228,7 +261,7 @@ public: void to_hdf5_inner(hid_t group_id) const; bool periodic_translate(const PeriodicSurface* other, Position& r, Direction& u) const; - BoundingBox bounding_box() const; + BoundingBox bounding_box(bool pos_side) const; double z0_; }; @@ -249,7 +282,6 @@ public: void to_hdf5_inner(hid_t group_id) const; bool periodic_translate(const PeriodicSurface* other, Position& r, Direction& u) const; - BoundingBox bounding_box() const; double A_, B_, C_, D_; }; @@ -269,6 +301,7 @@ public: double distance(Position r, Direction u, bool coincident) const; Direction normal(Position r) const; void to_hdf5_inner(hid_t group_id) const; + BoundingBox bounding_box(bool pos_side) const; double y0_, z0_, radius_; }; @@ -288,6 +321,7 @@ public: double distance(Position r, Direction u, bool coincident) const; Direction normal(Position r) const; void to_hdf5_inner(hid_t group_id) const; + BoundingBox bounding_box(bool pos_side) const; double x0_, z0_, radius_; }; @@ -307,6 +341,7 @@ public: double distance(Position r, Direction u, bool coincident) const; Direction normal(Position r) const; void to_hdf5_inner(hid_t group_id) const; + BoundingBox bounding_box(bool pos_side) const; double x0_, y0_, radius_; }; @@ -326,6 +361,7 @@ public: double distance(Position r, Direction u, bool coincident) const; Direction normal(Position r) const; void to_hdf5_inner(hid_t group_id) const; + BoundingBox bounding_box(bool pos_side) const; double x0_, y0_, z0_, radius_; }; diff --git a/include/openmc/tallies/filter.h b/include/openmc/tallies/filter.h index c2fd4dfb3..ba6ca0883 100644 --- a/include/openmc/tallies/filter.h +++ b/include/openmc/tallies/filter.h @@ -7,6 +7,8 @@ #include #include +#include + #include "openmc/hdf5_interface.h" #include "openmc/particle.h" #include "pugixml.hpp" @@ -43,13 +45,34 @@ namespace openmc { class Filter { public: - virtual ~Filter() = default; + //---------------------------------------------------------------------------- + // Constructors, destructors, factory functions - virtual std::string type() const = 0; + Filter(); + virtual ~Filter(); + + //! Create a new tally filter + // + //! \param[in] type Type of the filter + //! \param[in] id Unique ID for the filter. If none is passed, an ID is + //! automatically assigned + //! \return Pointer to the new filter object + static Filter* create(const std::string& type, int32_t id = -1); + + //! Create a new tally filter from an XML node + // + //! \param[in] node XML node + //! \return Pointer to the new filter object + static Filter* create(pugi::xml_node node); //! Uses an XML input to fill the filter's data fields. virtual void from_xml(pugi::xml_node node) = 0; + //---------------------------------------------------------------------------- + // Methods + + virtual std::string type() const = 0; + //! Matches a tally event to a set of filter bins and weights. //! //! \param[out] match will contain the matching bins and corresponding @@ -71,11 +94,32 @@ public: //! "Incoming Energy [0.625E-6, 20.0)". virtual std::string text_label(int bin) const = 0; - virtual void initialize() {} + //---------------------------------------------------------------------------- + // Accessors - int32_t id_; + //! Get unique ID of filter + //! \return Unique ID + int32_t id() const { return id_; } + //! Assign a unique ID to the filter + //! \param[in] Unique ID to assign. A value of -1 indicates that an ID should + //! be automatically assigned + void set_id(int32_t id); + + //! Get number of bins + //! \return Number of bins + int n_bins() const { return n_bins_; } + + gsl::index index() const { return index_; } + + //---------------------------------------------------------------------------- + // Data members + +protected: int n_bins_; +private: + int32_t id_ {-1}; + gsl::index index_; }; //============================================================================== @@ -99,8 +143,6 @@ namespace model { // Non-member functions //============================================================================== -Filter* allocate_filter(const std::string& type); - //! Make sure index corresponds to a valid filter int verify_filter(int32_t index); diff --git a/include/openmc/tallies/filter_azimuthal.h b/include/openmc/tallies/filter_azimuthal.h index b89640031..84c2a2d8c 100644 --- a/include/openmc/tallies/filter_azimuthal.h +++ b/include/openmc/tallies/filter_azimuthal.h @@ -4,6 +4,8 @@ #include #include +#include + #include "openmc/tallies/filter.h" namespace openmc { @@ -15,8 +17,14 @@ namespace openmc { class AzimuthalFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~AzimuthalFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "azimuthal";} void from_xml(pugi::xml_node node) override; @@ -28,6 +36,15 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + void set_bins(gsl::span bins); + +private: + //---------------------------------------------------------------------------- + // Data members + std::vector bins_; }; diff --git a/include/openmc/tallies/filter_cell.h b/include/openmc/tallies/filter_cell.h index 9c654e82e..b570ee027 100644 --- a/include/openmc/tallies/filter_cell.h +++ b/include/openmc/tallies/filter_cell.h @@ -5,6 +5,8 @@ #include #include +#include + #include "openmc/tallies/filter.h" namespace openmc { @@ -16,14 +18,18 @@ namespace openmc { class CellFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~CellFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "cell";} void from_xml(pugi::xml_node node) override; - void initialize() override; - void get_all_bins(const Particle* p, int estimator, FilterMatch& match) const override; @@ -31,6 +37,17 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + const std::vector& cells() const { return cells_; } + + void set_cells(gsl::span cells); + +protected: + //---------------------------------------------------------------------------- + // Data members + //! The indices of the cells binned by this filter. std::vector cells_; diff --git a/include/openmc/tallies/filter_cellborn.h b/include/openmc/tallies/filter_cellborn.h index 400e82c28..706f6c47a 100644 --- a/include/openmc/tallies/filter_cellborn.h +++ b/include/openmc/tallies/filter_cellborn.h @@ -14,6 +14,9 @@ namespace openmc { class CellbornFilter : public CellFilter { public: + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "cellborn";} void get_all_bins(const Particle* p, int estimator, FilterMatch& match) diff --git a/include/openmc/tallies/filter_cellfrom.h b/include/openmc/tallies/filter_cellfrom.h index bd3e08dcb..e86e34854 100644 --- a/include/openmc/tallies/filter_cellfrom.h +++ b/include/openmc/tallies/filter_cellfrom.h @@ -14,6 +14,9 @@ namespace openmc { class CellFromFilter : public CellFilter { public: + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "cellfrom";} void get_all_bins(const Particle* p, int estimator, FilterMatch& match) diff --git a/include/openmc/tallies/filter_delayedgroup.h b/include/openmc/tallies/filter_delayedgroup.h index 2f65a5059..8a2bbeeaa 100644 --- a/include/openmc/tallies/filter_delayedgroup.h +++ b/include/openmc/tallies/filter_delayedgroup.h @@ -3,6 +3,8 @@ #include +#include + #include "openmc/tallies/filter.h" namespace openmc { @@ -17,8 +19,14 @@ namespace openmc { class DelayedGroupFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~DelayedGroupFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "delayedgroup";} void from_xml(pugi::xml_node node) override; @@ -30,6 +38,17 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + const std::vector& groups() const { return groups_; } + + void set_groups(gsl::span groups); + +private: + //---------------------------------------------------------------------------- + // Data members + std::vector groups_; }; diff --git a/include/openmc/tallies/filter_distribcell.h b/include/openmc/tallies/filter_distribcell.h index 73857e7cb..9430a0906 100644 --- a/include/openmc/tallies/filter_distribcell.h +++ b/include/openmc/tallies/filter_distribcell.h @@ -14,14 +14,18 @@ namespace openmc { class DistribcellFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~DistribcellFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "distribcell";} void from_xml(pugi::xml_node node) override; - void initialize() override; - void get_all_bins(const Particle* p, int estimator, FilterMatch& match) const override; @@ -29,6 +33,17 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + int32_t cell() const { return cell_; } + + void set_cell(int32_t cell); + +private: + //---------------------------------------------------------------------------- + // Data members + int32_t cell_; }; diff --git a/include/openmc/tallies/filter_energy.h b/include/openmc/tallies/filter_energy.h index 025a77c62..af7601721 100644 --- a/include/openmc/tallies/filter_energy.h +++ b/include/openmc/tallies/filter_energy.h @@ -3,6 +3,8 @@ #include +#include + #include "openmc/tallies/filter.h" namespace openmc { @@ -14,8 +16,14 @@ namespace openmc { class EnergyFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~EnergyFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "energy";} void from_xml(pugi::xml_node node) override; @@ -27,6 +35,18 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + const std::vector& bins() const { return bins_; } + void set_bins(gsl::span bins); + + bool matches_transport_groups() const { return matches_transport_groups_; } + +protected: + //---------------------------------------------------------------------------- + // Data members + std::vector bins_; //! True if transport group number can be used directly to get bin number @@ -43,6 +63,9 @@ public: class EnergyoutFilter : public EnergyFilter { public: + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "energyout";} void get_all_bins(const Particle* p, int estimator, FilterMatch& match) diff --git a/include/openmc/tallies/filter_energyfunc.h b/include/openmc/tallies/filter_energyfunc.h index 6f5182626..df82e659e 100644 --- a/include/openmc/tallies/filter_energyfunc.h +++ b/include/openmc/tallies/filter_energyfunc.h @@ -15,6 +15,9 @@ namespace openmc { class EnergyFunctionFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + EnergyFunctionFilter() : Filter {} { @@ -23,6 +26,9 @@ public: ~EnergyFunctionFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "energyfunction";} void from_xml(pugi::xml_node node) override; @@ -34,6 +40,17 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + const std::vector& energy() const { return energy_; } + const std::vector& y() const { return y_; } + void set_data(gsl::span energy, gsl::span y); + +private: + //---------------------------------------------------------------------------- + // Data members + //! Incident neutron energy interpolation grid. std::vector energy_; diff --git a/include/openmc/tallies/filter_legendre.h b/include/openmc/tallies/filter_legendre.h index 054ba14e7..3a14ec3cf 100644 --- a/include/openmc/tallies/filter_legendre.h +++ b/include/openmc/tallies/filter_legendre.h @@ -14,8 +14,14 @@ namespace openmc { class LegendreFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~LegendreFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "legendre";} void from_xml(pugi::xml_node node) override; @@ -27,6 +33,17 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + int order() const { return order_; } + + void set_order(int order); + +private: + //---------------------------------------------------------------------------- + // Data members + int order_; }; diff --git a/include/openmc/tallies/filter_material.h b/include/openmc/tallies/filter_material.h index d272451db..65cf832a3 100644 --- a/include/openmc/tallies/filter_material.h +++ b/include/openmc/tallies/filter_material.h @@ -5,6 +5,8 @@ #include #include +#include + #include "openmc/tallies/filter.h" namespace openmc { @@ -16,14 +18,18 @@ namespace openmc { class MaterialFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~MaterialFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "material";} void from_xml(pugi::xml_node node) override; - void initialize() override; - void get_all_bins(const Particle* p, int estimator, FilterMatch& match) const override; @@ -31,6 +37,19 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + std::vector& materials() { return materials_; } + + const std::vector& materials() const { return materials_; } + + void set_materials(gsl::span materials); + +private: + //---------------------------------------------------------------------------- + // Data members + //! The indices of the materials binned by this filter. std::vector materials_; diff --git a/include/openmc/tallies/filter_mesh.h b/include/openmc/tallies/filter_mesh.h index a6caa8f56..98dec5d50 100644 --- a/include/openmc/tallies/filter_mesh.h +++ b/include/openmc/tallies/filter_mesh.h @@ -16,8 +16,14 @@ namespace openmc { class MeshFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~MeshFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "mesh";} void from_xml(pugi::xml_node node) override; @@ -29,11 +35,17 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + virtual int32_t mesh() const {return mesh_;} virtual void set_mesh(int32_t mesh); protected: + //---------------------------------------------------------------------------- + // Data members + int32_t mesh_; }; diff --git a/include/openmc/tallies/filter_meshsurface.h b/include/openmc/tallies/filter_meshsurface.h index 32393cfac..19d178c7f 100644 --- a/include/openmc/tallies/filter_meshsurface.h +++ b/include/openmc/tallies/filter_meshsurface.h @@ -8,6 +8,9 @@ namespace openmc { class MeshSurfaceFilter : public MeshFilter { public: + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "meshsurface";} void get_all_bins(const Particle* p, int estimator, FilterMatch& match) @@ -15,6 +18,9 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + void set_mesh(int32_t mesh) override; }; diff --git a/include/openmc/tallies/filter_mu.h b/include/openmc/tallies/filter_mu.h index 68b2f7b02..ae9c3e06a 100644 --- a/include/openmc/tallies/filter_mu.h +++ b/include/openmc/tallies/filter_mu.h @@ -3,6 +3,8 @@ #include +#include + #include "openmc/tallies/filter.h" namespace openmc { @@ -15,8 +17,14 @@ namespace openmc { class MuFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~MuFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "mu";} void from_xml(pugi::xml_node node) override; @@ -28,6 +36,15 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + void set_bins(gsl::span bins); + +private: + //---------------------------------------------------------------------------- + // Data members + std::vector bins_; }; diff --git a/include/openmc/tallies/filter_particle.h b/include/openmc/tallies/filter_particle.h index 268c19c6d..61aa3106b 100644 --- a/include/openmc/tallies/filter_particle.h +++ b/include/openmc/tallies/filter_particle.h @@ -15,8 +15,14 @@ namespace openmc { class ParticleFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~ParticleFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "particle";} void from_xml(pugi::xml_node node) override; @@ -28,6 +34,17 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + const std::vector& particles() const { return particles_; } + + void set_particles(gsl::span particles); + +private: + //---------------------------------------------------------------------------- + // Data members + std::vector particles_; }; diff --git a/include/openmc/tallies/filter_polar.h b/include/openmc/tallies/filter_polar.h index 2bcdcd07f..965950456 100644 --- a/include/openmc/tallies/filter_polar.h +++ b/include/openmc/tallies/filter_polar.h @@ -4,6 +4,8 @@ #include #include +#include + #include "openmc/tallies/filter.h" namespace openmc { @@ -15,8 +17,14 @@ namespace openmc { class PolarFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~PolarFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "polar";} void from_xml(pugi::xml_node node) override; @@ -28,6 +36,15 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + void set_bins(gsl::span bins); + +private: + //---------------------------------------------------------------------------- + // Data members + std::vector bins_; }; diff --git a/include/openmc/tallies/filter_sph_harm.h b/include/openmc/tallies/filter_sph_harm.h index b53b62441..80f998b2f 100644 --- a/include/openmc/tallies/filter_sph_harm.h +++ b/include/openmc/tallies/filter_sph_harm.h @@ -3,6 +3,8 @@ #include +#include + #include "openmc/tallies/filter.h" namespace openmc { @@ -18,8 +20,14 @@ enum class SphericalHarmonicsCosine { class SphericalHarmonicsFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~SphericalHarmonicsFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "sphericalharmonics";} void from_xml(pugi::xml_node node) override; @@ -31,6 +39,21 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + int order() const { return order_; } + + void set_order(int order); + + SphericalHarmonicsCosine cosine() const { return cosine_; } + + void set_cosine(gsl::cstring_span cosine); + +private: + //---------------------------------------------------------------------------- + // Data members + int order_; //! The type of angle that this filter measures when binning events. diff --git a/include/openmc/tallies/filter_sptl_legendre.h b/include/openmc/tallies/filter_sptl_legendre.h index 995bc7360..fea727515 100644 --- a/include/openmc/tallies/filter_sptl_legendre.h +++ b/include/openmc/tallies/filter_sptl_legendre.h @@ -18,8 +18,14 @@ enum class LegendreAxis { class SpatialLegendreFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~SpatialLegendreFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "spatiallegendre";} void from_xml(pugi::xml_node node) override; @@ -31,6 +37,23 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + int order() const { return order_; } + void set_order(int order); + + LegendreAxis axis() const { return axis_; } + void set_axis(LegendreAxis axis); + + double min() const { return min_; } + double max() const { return max_; } + void set_minmax(double min, double max); + +private: + //---------------------------------------------------------------------------- + // Data members + int order_; //! The Cartesian coordinate axis that the Legendre expansion is applied to. diff --git a/include/openmc/tallies/filter_surface.h b/include/openmc/tallies/filter_surface.h index ea4dc5b99..23b9be33d 100644 --- a/include/openmc/tallies/filter_surface.h +++ b/include/openmc/tallies/filter_surface.h @@ -5,6 +5,8 @@ #include #include +#include + #include "openmc/tallies/filter.h" namespace openmc { @@ -16,14 +18,18 @@ namespace openmc { class SurfaceFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~SurfaceFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "surface";} void from_xml(pugi::xml_node node) override; - void initialize() override; - void get_all_bins(const Particle* p, int estimator, FilterMatch& match) const override; @@ -31,6 +37,15 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + void set_surfaces(gsl::span surfaces); + +private: + //---------------------------------------------------------------------------- + // Data members + //! The indices of the surfaces binned by this filter. std::vector surfaces_; diff --git a/include/openmc/tallies/filter_universe.h b/include/openmc/tallies/filter_universe.h index 3ae0093cb..fc2ace18f 100644 --- a/include/openmc/tallies/filter_universe.h +++ b/include/openmc/tallies/filter_universe.h @@ -5,6 +5,8 @@ #include #include +#include + #include "openmc/tallies/filter.h" namespace openmc { @@ -16,14 +18,18 @@ namespace openmc { class UniverseFilter : public Filter { public: + //---------------------------------------------------------------------------- + // Constructors, destructors + ~UniverseFilter() = default; + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "universe";} void from_xml(pugi::xml_node node) override; - void initialize() override; - void get_all_bins(const Particle* p, int estimator, FilterMatch& match) const override; @@ -31,6 +37,15 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + + void set_universes(gsl::span universes); + +private: + //---------------------------------------------------------------------------- + // Data members + //! The indices of the universes binned by this filter. std::vector universes_; diff --git a/include/openmc/tallies/filter_zernike.h b/include/openmc/tallies/filter_zernike.h index 19fff1336..e3ae89dec 100644 --- a/include/openmc/tallies/filter_zernike.h +++ b/include/openmc/tallies/filter_zernike.h @@ -14,10 +14,16 @@ namespace openmc { class ZernikeFilter : public Filter { public: - std::string type() const override {return "zernike";} + //---------------------------------------------------------------------------- + // Constructors, destructors ~ZernikeFilter() = default; + //---------------------------------------------------------------------------- + // Methods + + std::string type() const override {return "zernike";} + void from_xml(pugi::xml_node node) override; void get_all_bins(const Particle* p, int estimator, FilterMatch& match) @@ -27,10 +33,25 @@ public: std::string text_label(int bin) const override; - int order() const {return order_;} + //---------------------------------------------------------------------------- + // Accessors + int order() const { return order_; } virtual void set_order(int order); + double x() const { return x_; } + void set_x(double x) { x_ = x; } + + double y() const { return y_; } + void set_y(double y) { y_ = y; } + + double r() const { return r_; } + void set_r(double r) { r_ = r; } + + //---------------------------------------------------------------------------- + // Data members + +protected: //! Cartesian x coordinate for the origin of this expansion. double x_; @@ -40,7 +61,6 @@ public: //! Maximum radius from the origin covered by this expansion. double r_; -protected: int order_; }; @@ -51,6 +71,9 @@ protected: class ZernikeRadialFilter : public ZernikeFilter { public: + //---------------------------------------------------------------------------- + // Methods + std::string type() const override {return "zernikeradial";} void get_all_bins(const Particle* p, int estimator, FilterMatch& match) @@ -58,6 +81,9 @@ public: std::string text_label(int bin) const override; + //---------------------------------------------------------------------------- + // Accessors + void set_order(int order) override; }; diff --git a/include/openmc/tallies/tally.h b/include/openmc/tallies/tally.h index 969c064e7..2a166738d 100644 --- a/include/openmc/tallies/tally.h +++ b/include/openmc/tallies/tally.h @@ -2,8 +2,10 @@ #define OPENMC_TALLIES_TALLY_H #include "openmc/constants.h" +#include "openmc/tallies/filter.h" #include "openmc/tallies/trigger.h" +#include #include "pugixml.hpp" #include "xtensor/xfixed.hpp" #include "xtensor/xtensor.hpp" @@ -21,24 +23,33 @@ namespace openmc { class Tally { public: - Tally(); + //---------------------------------------------------------------------------- + // Constructors, destructors, factory functions + explicit Tally(int32_t id); + explicit Tally(pugi::xml_node node); + ~Tally(); + static Tally* create(int32_t id = -1); - void init_from_xml(pugi::xml_node node); + //---------------------------------------------------------------------------- + // Accessors + + void set_id(int32_t id); + + void set_active(bool active) { active_ = active; } void set_scores(pugi::xml_node node); - void set_scores(std::vector scores); + void set_scores(const std::vector& scores); void set_nuclides(pugi::xml_node node); - //---------------------------------------------------------------------------- - // Methods for getting and setting filter/stride data. + void set_nuclides(const std::vector& nuclides); const std::vector& filters() const {return filters_;} int32_t filters(int i) const {return filters_[i];} - void set_filters(const int32_t filter_indices[], int n); + void set_filters(gsl::span filters); int32_t strides(int i) const {return strides_[i];} @@ -112,6 +123,8 @@ private: std::vector strides_; int32_t n_filter_bins_ {0}; + + gsl::index index_; }; //============================================================================== diff --git a/openmc/capi/cell.py b/openmc/capi/cell.py index 959ab08fc..784ebd087 100644 --- a/openmc/capi/cell.py +++ b/openmc/capi/cell.py @@ -1,3 +1,5 @@ +import sys + from collections.abc import Mapping, Iterable from ctypes import c_int, c_int32, c_double, c_char_p, POINTER from weakref import WeakValueDictionary @@ -28,6 +30,12 @@ _dll.openmc_cell_get_temperature.argtypes = [ c_int32, POINTER(c_int32), POINTER(c_double)] _dll.openmc_cell_get_temperature.restype = c_int _dll.openmc_cell_get_temperature.errcheck = _error_handler +_dll.openmc_cell_get_name.argtypes = [c_int32, POINTER(c_char_p)] +_dll.openmc_cell_get_name.restype = c_int +_dll.openmc_cell_get_name.errcheck = _error_handler +_dll.openmc_cell_set_name.argtypes = [c_int32, c_char_p] +_dll.openmc_cell_set_name.restype = c_int +_dll.openmc_cell_set_name.errcheck = _error_handler _dll.openmc_cell_set_fill.argtypes = [ c_int32, c_int, c_int32, POINTER(c_int32)] _dll.openmc_cell_set_fill.restype = c_int @@ -43,6 +51,11 @@ _dll.openmc_get_cell_index.argtypes = [c_int32, POINTER(c_int32)] _dll.openmc_get_cell_index.restype = c_int _dll.openmc_get_cell_index.errcheck = _error_handler _dll.cells_size.restype = c_int +_dll.openmc_cell_bounding_box.argtypes = [c_int, + POINTER(c_double), + POINTER(c_double)] +_dll.openmc_cell_bounding_box.restype = c_int +_dll.openmc_cell_bounding_box.errcheck = _error_handler class Cell(_FortranObjectWithID): @@ -102,6 +115,17 @@ class Cell(_FortranObjectWithID): def id(self, cell_id): _dll.openmc_cell_set_id(self._index, cell_id) + @property + def name(self): + name = c_char_p() + _dll.openmc_cell_get_name(self._index, name) + return name.value.decode() + + @name.setter + def name(self, name): + name_ptr = c_char_p(name.encode()) + _dll.openmc_cell_set_name(self._index, name_ptr) + @property def fill(self): fill_type = c_int() @@ -166,6 +190,20 @@ class Cell(_FortranObjectWithID): _dll.openmc_cell_set_temperature(self._index, T, instance) + @property + def bounding_box(self): + inf = sys.float_info.max + llc = np.zeros(3) + urc = np.zeros(3) + _dll.openmc_cell_bounding_box(self._index, + llc.ctypes.data_as(POINTER(c_double)), + urc.ctypes.data_as(POINTER(c_double))) + llc[llc == inf] = np.inf + urc[urc == inf] = np.inf + llc[llc == -inf] = -np.inf + urc[urc == -inf] = -np.inf + + return llc, urc class _CellMapping(Mapping): def __getitem__(self, key): diff --git a/openmc/capi/core.py b/openmc/capi/core.py index aae17e06a..a470f0665 100644 --- a/openmc/capi/core.py +++ b/openmc/capi/core.py @@ -1,3 +1,5 @@ +import sys + from contextlib import contextmanager from ctypes import (CDLL, c_bool, c_int, c_int32, c_int64, c_double, c_char_p, c_char, POINTER, Structure, c_void_p, create_string_buffer) @@ -73,8 +75,26 @@ _dll.openmc_simulation_finalize.errcheck = _error_handler _dll.openmc_statepoint_write.argtypes = [c_char_p, POINTER(c_bool)] _dll.openmc_statepoint_write.restype = c_int _dll.openmc_statepoint_write.errcheck = _error_handler +_dll.openmc_global_bounding_box.argtypes = [POINTER(c_double), + POINTER(c_double)] +_dll.openmc_global_bounding_box.restype = c_int +_dll.openmc_global_bounding_box.errcheck = _error_handler +def global_bounding_box(): + """Calculate a global bounding box for the model""" + inf = sys.float_info.max + llc = np.zeros(3) + urc = np.zeros(3) + _dll.openmc_global_bounding_box(llc.ctypes.data_as(POINTER(c_double)), + urc.ctypes.data_as(POINTER(c_double))) + llc[llc == inf] = np.inf + urc[urc == inf] = np.inf + llc[llc == -inf] = -np.inf + urc[urc == -inf] = -np.inf + + return llc, urc + def calculate_volumes(): """Run stochastic volume calculation""" _dll.openmc_calculate_volumes() diff --git a/openmc/capi/error.py b/openmc/capi/error.py index b35de4e60..89e7b6e39 100644 --- a/openmc/capi/error.py +++ b/openmc/capi/error.py @@ -36,4 +36,6 @@ def _error_handler(err, func, args): elif err == errcode('OPENMC_E_WARNING'): warn(msg) elif err < 0: - raise exc.OpenMCError("Unknown error encountered (code {}).".format(err)) + if not msg: + msg = "Unknown error encountered (code {}).".format(err) + raise exc.OpenMCError(msg) diff --git a/openmc/capi/filter.py b/openmc/capi/filter.py index c0716bcef..92818a2aa 100644 --- a/openmc/capi/filter.py +++ b/openmc/capi/filter.py @@ -28,12 +28,24 @@ _dll.openmc_cell_filter_get_bins.argtypes = [ _dll.openmc_cell_filter_get_bins.restype = c_int _dll.openmc_cell_filter_get_bins.errcheck = _error_handler _dll.openmc_energy_filter_get_bins.argtypes = [ - c_int32, POINTER(POINTER(c_double)), POINTER(c_int32)] + c_int32, POINTER(POINTER(c_double)), POINTER(c_size_t)] _dll.openmc_energy_filter_get_bins.restype = c_int _dll.openmc_energy_filter_get_bins.errcheck = _error_handler -_dll.openmc_energy_filter_set_bins.argtypes = [c_int32, c_int32, POINTER(c_double)] +_dll.openmc_energy_filter_set_bins.argtypes = [c_int32, c_size_t, POINTER(c_double)] _dll.openmc_energy_filter_set_bins.restype = c_int _dll.openmc_energy_filter_set_bins.errcheck = _error_handler +_dll.openmc_energyfunc_filter_set_data.restype = c_int +_dll.openmc_energyfunc_filter_set_data.errcheck = _error_handler +_dll.openmc_energyfunc_filter_set_data.argtypes = [ + c_int32, c_size_t, POINTER(c_double), POINTER(c_double)] +_dll.openmc_energyfunc_filter_get_energy.resttpe = c_int +_dll.openmc_energyfunc_filter_get_energy.errcheck = _error_handler +_dll.openmc_energyfunc_filter_get_energy.argtypes = [ + c_int32, POINTER(c_size_t), POINTER(POINTER(c_double))] +_dll.openmc_energyfunc_filter_get_y.resttpe = c_int +_dll.openmc_energyfunc_filter_get_y.errcheck = _error_handler +_dll.openmc_energyfunc_filter_get_y.argtypes = [ + c_int32, POINTER(c_size_t), POINTER(POINTER(c_double))] _dll.openmc_filter_get_id.argtypes = [c_int32, POINTER(c_int32)] _dll.openmc_filter_get_id.restype = c_int _dll.openmc_filter_get_id.errcheck = _error_handler @@ -53,10 +65,10 @@ _dll.openmc_legendre_filter_set_order.argtypes = [c_int32, c_int] _dll.openmc_legendre_filter_set_order.restype = c_int _dll.openmc_legendre_filter_set_order.errcheck = _error_handler _dll.openmc_material_filter_get_bins.argtypes = [ - c_int32, POINTER(POINTER(c_int32)), POINTER(c_int32)] + c_int32, POINTER(POINTER(c_int32)), POINTER(c_size_t)] _dll.openmc_material_filter_get_bins.restype = c_int _dll.openmc_material_filter_get_bins.errcheck = _error_handler -_dll.openmc_material_filter_set_bins.argtypes = [c_int32, c_int32, POINTER(c_int32)] +_dll.openmc_material_filter_set_bins.argtypes = [c_int32, c_size_t, POINTER(c_int32)] _dll.openmc_material_filter_set_bins.restype = c_int _dll.openmc_material_filter_set_bins.errcheck = _error_handler _dll.openmc_mesh_filter_get_mesh.argtypes = [c_int32, POINTER(c_int32)] @@ -94,6 +106,7 @@ _dll.openmc_zernike_filter_set_order.restype = c_int _dll.openmc_zernike_filter_set_order.errcheck = _error_handler _dll.tally_filters_size.restype = c_size_t + class Filter(_FortranObjectWithID): __instances = WeakValueDictionary() @@ -148,7 +161,7 @@ class EnergyFilter(Filter): @property def bins(self): energies = POINTER(c_double)() - n = c_int32() + n = c_size_t() _dll.openmc_energy_filter_get_bins(self._index, energies, n) return as_array(energies, (n.value,)) @@ -200,6 +213,48 @@ class DistribcellFilter(Filter): class EnergyFunctionFilter(Filter): filter_type = 'energyfunction' + def __new__(cls, energy=None, y=None, uid=None, new=True, index=None): + return super().__new__(cls, uid=uid, new=new, index=index) + + def __init__(self, energy=None, y=None, uid=None, new=True, index=None): + if (energy is None) != (y is None): + raise AttributeError("Need both energy and y or neither") + super().__init__(uid, new, index) + if energy is not None: + self.set_data(energy, y) + + def set_data(self, energy, y): + """Set the interpolation information for the filter + + Parameters + ---------- + energy : numpy.ndarray + Independent variable for the interpolation + y : numpy.ndarray + Dependent variable for the interpolation + """ + energy_array = np.asarray(energy) + y_array = np.asarray(y) + energy_p = energy_array.ctypes.data_as(POINTER(c_double)) + y_p = y_array.ctypes.data_as(POINTER(c_double)) + + _dll.openmc_energyfunc_filter_set_data( + self._index, len(energy_array), energy_p, y_p) + + @property + def energy(self): + return self._get_attr(_dll.openmc_energyfunc_filter_get_energy) + + @property + def y(self): + return self._get_attr(_dll.openmc_energyfunc_filter_get_y) + + def _get_attr(self, cfunc): + array_p = POINTER(c_double)() + n = c_size_t() + cfunc(self._index, n, array_p) + return as_array(array_p, (n.value, )) + class LegendreFilter(Filter): filter_type = 'legendre' @@ -231,7 +286,7 @@ class MaterialFilter(Filter): @property def bins(self): materials = POINTER(c_int32)() - n = c_int32() + n = c_size_t() _dll.openmc_material_filter_get_bins(self._index, materials, n) return [Material(index=materials[i]) for i in range(n.value)] diff --git a/openmc/capi/material.py b/openmc/capi/material.py index 43959e244..f0ecac761 100644 --- a/openmc/capi/material.py +++ b/openmc/capi/material.py @@ -48,6 +48,12 @@ _dll.openmc_material_set_densities.errcheck = _error_handler _dll.openmc_material_set_id.argtypes = [c_int32, c_int32] _dll.openmc_material_set_id.restype = c_int _dll.openmc_material_set_id.errcheck = _error_handler +_dll.openmc_material_get_name.argtypes = [c_int32, POINTER(c_char_p)] +_dll.openmc_material_get_name.restype = c_int +_dll.openmc_material_get_name.errcheck = _error_handler +_dll.openmc_material_set_name.argtypes = [c_int32, c_char_p] +_dll.openmc_material_set_name.restype = c_int +_dll.openmc_material_set_name.errcheck = _error_handler _dll.openmc_material_set_volume.argtypes = [c_int32, c_double] _dll.openmc_material_set_volume.restype = c_int _dll.openmc_material_set_volume.errcheck = _error_handler @@ -124,6 +130,17 @@ class Material(_FortranObjectWithID): def id(self, mat_id): _dll.openmc_material_set_id(self._index, mat_id) + @property + def name(self): + name = c_char_p() + _dll.openmc_material_get_name(self._index, name) + return name.value.decode() + + @name.setter + def name(self, name): + name_ptr = c_char_p(name.encode()) + _dll.openmc_material_set_name(self._index, name_ptr) + @property def volume(self): volume = c_double() diff --git a/openmc/capi/tally.py b/openmc/capi/tally.py index 9529a31f2..88c14e449 100644 --- a/openmc/capi/tally.py +++ b/openmc/capi/tally.py @@ -36,7 +36,7 @@ _dll.openmc_tally_get_id.argtypes = [c_int32, POINTER(c_int32)] _dll.openmc_tally_get_id.restype = c_int _dll.openmc_tally_get_id.errcheck = _error_handler _dll.openmc_tally_get_filters.argtypes = [ - c_int32, POINTER(POINTER(c_int32)), POINTER(c_int)] + c_int32, POINTER(POINTER(c_int32)), POINTER(c_size_t)] _dll.openmc_tally_get_filters.restype = c_int _dll.openmc_tally_get_filters.errcheck = _error_handler _dll.openmc_tally_get_n_realizations.argtypes = [c_int32, POINTER(c_int32)] @@ -63,7 +63,7 @@ _dll.openmc_tally_results.errcheck = _error_handler _dll.openmc_tally_set_active.argtypes = [c_int32, c_bool] _dll.openmc_tally_set_active.restype = c_int _dll.openmc_tally_set_active.errcheck = _error_handler -_dll.openmc_tally_set_filters.argtypes = [c_int32, c_int, POINTER(c_int32)] +_dll.openmc_tally_set_filters.argtypes = [c_int32, c_size_t, POINTER(c_int32)] _dll.openmc_tally_set_filters.restype = c_int _dll.openmc_tally_set_filters.errcheck = _error_handler _dll.openmc_tally_set_estimator.argtypes = [c_int32, c_char_p] @@ -249,7 +249,7 @@ class Tally(_FortranObjectWithID): @property def filters(self): filt_idx = POINTER(c_int32)() - n = c_int() + n = c_size_t() _dll.openmc_tally_get_filters(self._index, filt_idx, n) return [_get_filter(filt_idx[i]) for i in range(n.value)] diff --git a/openmc/data/ace.py b/openmc/data/ace.py index b93de0d24..ee194560b 100644 --- a/openmc/data/ace.py +++ b/openmc/data/ace.py @@ -411,7 +411,7 @@ class Library(EqualityMixin): # after it). If it's too short, then we apply the ENDF float regular # expression. We don't do this by default because it's expensive! if xss.size != nxs[1] + 1: - datastr = ENDF_FLOAT_RE.sub(r'\1e\2', datastr) + datastr = ENDF_FLOAT_RE.sub(r'\1e\2\3', datastr) xss = np.fromstring(datastr, sep=' ') assert xss.size == nxs[1] + 1 diff --git a/openmc/data/neutron.py b/openmc/data/neutron.py index 7b340f0b0..9c73152d4 100644 --- a/openmc/data/neutron.py +++ b/openmc/data/neutron.py @@ -801,15 +801,14 @@ class IncidentNeutron(EqualityMixin): """ with tempfile.TemporaryDirectory() as tmpdir: # Run NJOY to create an ACE library - ace_file = os.path.join(tmpdir, 'ace') - xsdir_file = os.path.join(tmpdir, 'xsdir') - pendf_file = os.path.join(tmpdir, 'pendf') + kwargs.setdefault('ace', os.path.join(tmpdir, 'ace')) + kwargs.setdefault('xsdir', os.path.join(tmpdir, 'xsdir')) + kwargs.setdefault('pendf', os.path.join(tmpdir, 'pendf')) kwargs['evaluation'] = evaluation - make_ace(filename, temperatures, ace_file, xsdir_file, - pendf_file, **kwargs) + make_ace(filename, temperatures, **kwargs) # Create instance from ACE tables within library - lib = Library(ace_file) + lib = Library(kwargs['ace']) data = cls.from_ace(lib.tables[0]) for table in lib.tables[1:]: data.add_temperature_from_ace(table) @@ -821,7 +820,7 @@ class IncidentNeutron(EqualityMixin): # Add 0K elastic scattering cross section if '0K' not in data.energy: - pendf = Evaluation(pendf_file) + pendf = Evaluation(kwargs['pendf']) file_obj = StringIO(pendf.section[3, 2]) get_head_record(file_obj) params, xs = get_tab1_record(file_obj) diff --git a/openmc/data/thermal.py b/openmc/data/thermal.py index 79cad3282..06e50c661 100644 --- a/openmc/data/thermal.py +++ b/openmc/data/thermal.py @@ -761,15 +761,14 @@ class ThermalScattering(EqualityMixin): """ with tempfile.TemporaryDirectory() as tmpdir: # Run NJOY to create an ACE library - ace_file = os.path.join(tmpdir, 'ace') - xsdir_file = os.path.join(tmpdir, 'xsdir') + kwargs.setdefault('ace', os.path.join(tmpdir, 'ace')) + kwargs.setdefault('xsdir', os.path.join(tmpdir, 'xsdir')) kwargs['evaluation'] = evaluation kwargs['evaluation_thermal'] = evaluation_thermal - make_ace_thermal(filename, filename_thermal, temperatures, - ace_file, xsdir_file, **kwargs) + make_ace_thermal(filename, filename_thermal, temperatures, **kwargs) # Create instance from ACE tables within library - lib = Library(ace_file) + lib = Library(kwargs['ace']) data = cls.from_ace(lib.tables[0]) for table in lib.tables[1:]: data.add_temperature_from_ace(table) diff --git a/openmc/deplete/abc.py b/openmc/deplete/abc.py index f1321379d..770a482ab 100644 --- a/openmc/deplete/abc.py +++ b/openmc/deplete/abc.py @@ -7,11 +7,15 @@ to run a full depletion simulation. from collections import namedtuple import os from pathlib import Path -from abc import ABCMeta, abstractmethod +from abc import ABC, abstractmethod from xml.etree import ElementTree as ET from warnings import warn +from numbers import Real -from openmc.data import DataLibrary +from numpy import nonzero, empty + +from openmc.data import DataLibrary, JOULE_PER_EV +from openmc.checkvalue import check_type, check_greater_than from .chain import Chain OperatorResult = namedtuple('OperatorResult', ['k', 'rates']) @@ -34,7 +38,7 @@ except AttributeError: pass -class TransportOperator(metaclass=ABCMeta): +class TransportOperator(ABC): """Abstract class defining a transport operator Each depletion integrator is written to work with a generic transport @@ -52,17 +56,21 @@ class TransportOperator(metaclass=ABCMeta): fission_q : dict, optional Dictionary of nuclides and their fission Q values [eV]. If not given, values will be pulled from the ``chain_file``. + dilute_initial : float, optional + Initial atom density [atoms/cm^3] to add for nuclides that are zero + in initial condition to ensure they exist in the decay chain. + Only done for nuclides with reaction rates. + Defaults to 1.0e3. Attributes ---------- dilute_initial : float - Initial atom density to add for nuclides that are zero in initial - condition to ensure they exist in the decay chain. Only done for - nuclides with reaction rates. Defaults to 1.0e3. - + Initial atom density [atoms/cm^3] to add for nuclides that are zero + in initial condition to ensure they exist in the decay chain. + Only done for nuclides with reaction rates. """ - def __init__(self, chain_file=None, fission_q=None): - self.dilute_initial = 1.0e3 + def __init__(self, chain_file=None, fission_q=None, dilute_initial=1.0e3): + self.dilute_initial = dilute_initial self.output_dir = '.' # Read depletion chain @@ -86,6 +94,17 @@ class TransportOperator(metaclass=ABCMeta): FutureWarning) self.chain = Chain.from_xml(chain_file, fission_q) + @property + def dilute_initial(self): + """Initial atom density for nuclides with zero initial concentration""" + return self._dilute_initial + + @dilute_initial.setter + def dilute_initial(self, value): + check_type("dilute_initial", value, Real) + check_greater_than("dilute_initial", value, 0.0, equality=True) + self._dilute_initial = value + @abstractmethod def __call__(self, vec): """Runs a simulation. @@ -158,3 +177,161 @@ class TransportOperator(metaclass=ABCMeta): def finalize(self): pass + + +class ReactionRateHelper(ABC): + """Abstract class for generating reaction rates for operators + + Responsible for generating reaction rate tallies for burnable + materials, given nuclides and scores from the operator. + + Reaction rates are passed back to the operator for be used in + an :class:`openmc.deplete.OperatorResult` instance + + Parameters + ---------- + n_nucs : int + Number of burnable nuclides tracked by :class:`openmc.deplete.Operator` + n_react : int + Number of reactions tracked by :class:`openmc.deplete.Operator` + + Attributes + ---------- + nuclides : list of str + All nuclides with desired reaction rates. + """ + + def __init__(self, n_nucs, n_react): + self._nuclides = None + self._rate_tally = None + self._results_cache = empty((n_nucs, n_react)) + + @abstractmethod + def generate_tallies(self, materials, scores): + """Use the C API to build tallies needed for reaction rates""" + + @property + def nuclides(self): + """List of nuclides with requested reaction rates""" + return self._nuclides + + @nuclides.setter + def nuclides(self, nuclides): + check_type("nuclides", nuclides, list, str) + self._nuclides = nuclides + self._rate_tally.nuclides = nuclides + + @abstractmethod + def get_material_rates(self, mat_id, nuc_index, react_index): + """Return 2D array of [nuclide, reaction] reaction rates + + Parameters + ---------- + mat_id : int + Unique ID for the requested material + nuc_index : list of str + Ordering of desired nuclides + react_index : list of str + Ordering of reactions + """ + + def divide_by_adens(self, number): + """Normalize reaction rates by number of nuclides + + Acts on the current material examined by + :meth:`get_material_rates` + + Parameters + ---------- + number : iterable of float + Number density [atoms/b-cm] of each nuclide tracked in the calculation. + + Returns + ------- + results : numpy.ndarray + Array of reactions rates of shape ``(n_nuclides, n_rxns)`` + normalized by the number of nuclides + """ + + mask = nonzero(number) + results = self._results_cache + for col in range(results.shape[1]): + results[mask, col] /= number[mask] + return results + + +class EnergyHelper(ABC): + """Abstract class for obtaining energy produced + + The ultimate goal of this helper is to provide instances of + :class:`openmc.deplete.Operator` with the total energy produced + in a transport simulation. This information, provided with the + power requested by the user and reaction rates from a + :class:`ReactionRateHelper` will scale reaction rates to the + correct values. + + Attributes + ---------- + nuclides : list of str + All nuclides with desired reaction rates. Ordered to be + consistent with :class:`openmc.deplete.Operator` + energy : float + Total energy [J/s/source neutron] produced in a transport simulation. + Updated in the material iteration with :meth:`update`. + """ + + def __init__(self): + self._nuclides = None + self._energy = 0.0 + + @property + def energy(self): + return self._energy * JOULE_PER_EV + + def reset(self): + """Reset energy produced prior to unpacking tallies""" + self._energy = 0.0 + + @abstractmethod + def prepare(self, chain_nucs, rate_index, materials): + """Perform work needed to obtain energy produced + + This method is called prior to the transport simulations + in :meth:`openmc.deplete.Operator.initial_condition`. + + Parameters + ---------- + chain_nucs : list of str + All nuclides to be tracked in this problem + rate_index : dict of str to int + Mapping from nuclide name to index in the + `fission_rates` for :meth:`update`. + materials : list of str + All materials tracked on the operator helped by this + object. Should correspond to + :attr:`openmc.deplete.Operator.burnable_materials` + """ + + def update(self, fission_rates, mat_index): + """Update the energy produced + + Parameters + ---------- + fission_rates : numpy.ndarray + fission reaction rate for each isotope in the specified + material. Should be ordered corresponding to initial + ``rate_index`` used in :meth:`prepare` + mat_index : int + Index for the specific material in the list of all burnable + materials. + """ + + @property + def nuclides(self): + """List of nuclides with requested reaction rates""" + return self._nuclides + + @nuclides.setter + def nuclides(self, nuclides): + check_type("nuclides", nuclides, list, str) + self._nuclides = nuclides diff --git a/openmc/deplete/chain.py b/openmc/deplete/chain.py index 643235d9f..1f16d9caf 100644 --- a/openmc/deplete/chain.py +++ b/openmc/deplete/chain.py @@ -10,8 +10,10 @@ import math import re from collections import OrderedDict, defaultdict from collections.abc import Mapping +from warnings import warn -from openmc.checkvalue import check_type +from openmc.checkvalue import check_type, check_less_than +from openmc.data import gnd_name, zam # Try to use lxml if it is available. It preserves the order of attributes and # provides a pretty-printer by default. If not available, @@ -451,3 +453,163 @@ class Chain(object): matrix_dok = sp.dok_matrix((n, n)) dict.update(matrix_dok, matrix) return matrix_dok.tocsr() + + def get_capture_branches(self): + """Return a dictionary with capture branching ratios + + Returns + ------- + capt : + nested dict of parent nuclide keys with capture targets and + branching ratios:: + + {"Am241": {"Am242": 0.91, "Am242_m1": 0.09}} + + See Also + -------- + :meth:`set_capture_branches` + + """ + + capt = {} + for nuclide in self.nuclides: + nuc_capt = {} + for rx in nuclide.reactions: + if rx.type == "(n,gamma)" and rx.branching_ratio != 1.0: + nuc_capt[rx.target] = rx.branching_ratio + if len(nuc_capt) > 0: + capt[nuclide.name] = nuc_capt + return capt + + def set_capture_branches(self, branch_ratios, strict=True): + """Set the capture branching ratios + + To provide a buffer around floating point precisions, + the sum of all branching ratios from a single parent + cannot be greater than 1.00001. + + Parameters + ---------- + branch_ratios : dict of {str: {str: float}} + Capture branching ratios to be inserted. + First layer keys are names of parent nuclides, e.g. + ``"Am241"``. The capture branching ratios for these + parents will be modified. Corresponding values are + dictionaries of ``{target: branching_ratio}`` + strict : bool + If this evalutes to ``True``, then all parents and + products must exist in the :class:`Chain`. A + :class:`KeyError` will be raised at the first + nuclide that does not exist. Otherwise, print + a warning message for missing parents and/or + products. + + See Also + -------- + :meth:`get_capture_branches` + """ + + # Store some useful information through the validation stage + + sums = {} + capt_ix_map = {} + grounds = {} + + missing_parents = set() + missing_products = {} + no_capture = set() + + # Check for validity before manipulation + + for parent, sub in branch_ratios.items(): + if parent not in self: + if strict: + raise KeyError(parent) + missing_parents.add(parent) + continue + + # Make sure all products are present in the chain + + prod_flag = False + + for product in sub: + if product not in self: + if strict: + raise KeyError(product) + missing_products[parent] = product + prod_flag = True + break + + if prod_flag: + continue + + # Make sure this nuclide has capture reactions + + indexes = [] + for ix, rx in enumerate(self[parent].reactions): + if rx.type == "(n,gamma)": + indexes.append(ix) + if "_m" not in rx.target: + grounds[parent] = rx.target + + if len(indexes) == 0: + if strict: + raise AttributeError( + "Nuclide {} does not have capture reactions in " + "this {}".format(parent, self.__class__.__name__)) + no_capture.add(parent) + continue + + capt_ix_map[parent] = indexes + + this_sum = sum(sub.values()) + check_less_than(parent + " ratios", this_sum, 1.00001) + sums[parent] = this_sum + + if len(missing_parents) > 0: + warn("The following nuclides were not found in {}: {}".format( + self.__class__.__name__, ", ".join(sorted(missing_parents)))) + + if len(no_capture) > 0: + warn("The following nuclides did not have capture reactions: " + "{}".format(", ".join(sorted(no_capture)))) + + if len(missing_products) > 0: + tail = ("{} -> {}".format(k, v) + for k, v in sorted(missing_products.items())) + warn("The following products were not found in the {} and " + "parents were unmodified: \n{}".format( + self.__class__.__name__, ", ".join(tail))) + + # Insert new ReactionTuples with updated branch ratios + + for parent_name, capt_index in capt_ix_map.items(): + + parent = self[parent_name] + new_ratios = branch_ratios[parent_name] + capt_index = capt_ix_map[parent_name] + + # Assume Q value is independent of target state + capt_Q = parent.reactions[capt_index[0]].Q + + # Remove existing capture reactions + + for ix in reversed(capt_index): + parent.reactions.pop(ix) + + all_meta = True + + for tgt, br in new_ratios.items(): + all_meta = all_meta and ("_m" in tgt) + parent.reactions.append(ReactionTuple( + "(n,gamma)", tgt, capt_Q, br)) + + if all_meta and sums[parent_name] != 1.0: + ground_br = 1.0 - sums[parent_name] + ground_tgt = grounds.get(parent_name) + if ground_tgt is None: + pz, pa, pm = zam(parent_name) + ground_tgt = gnd_name(pz, pa + 1, 0) + new_ratios[ground_tgt] = ground_br + parent.reactions.append(ReactionTuple( + "(n,gamma)", ground_tgt, capt_Q, ground_br)) diff --git a/openmc/deplete/helpers.py b/openmc/deplete/helpers.py new file mode 100644 index 000000000..66722c00a --- /dev/null +++ b/openmc/deplete/helpers.py @@ -0,0 +1,144 @@ +""" +Class for normalizing fission energy deposition +""" +from itertools import product + +from numpy import dot, zeros + +from openmc.capi import Tally, MaterialFilter +from .abc import ReactionRateHelper, EnergyHelper + +# ------------------------------------- +# Helpers for generating reaction rates +# ------------------------------------- + + +class DirectReactionRateHelper(ReactionRateHelper): + """Class that generates tallies for one-group rates + + Parameters + ---------- + n_nucs : int + Number of burnable nuclides tracked by :class:`openmc.deplete.Operator` + n_react : int + Number of reactions tracked by :class:`openmc.deplete.Operator` + + Attributes + ---------- + nuclides : list of str + All nuclides with desired reaction rates. + """ + + def generate_tallies(self, materials, scores): + """Produce one-group reaction rate tally + + Uses the :mod:`openmc.capi` to generate a tally + of relevant reactions across all burnable materials. + + Parameters + ---------- + materials : iterable of :class:`openmc.Material` + Burnable materials in the problem. Used to + construct a :class:`openmc.MaterialFilter` + scores : iterable of str + Reaction identifiers, e.g. ``"(n, fission)"``, + ``"(n, gamma)"``, needed for the reaction rate tally. + """ + self._rate_tally = Tally() + self._rate_tally.scores = scores + self._rate_tally.filters = [MaterialFilter(materials)] + + def get_material_rates(self, mat_id, nuc_index, react_index): + """Return an array of reaction rates for a material + + Parameters + ---------- + mat_id : int + Unique ID for the requested material + nuc_index : iterable of int + Index for each nuclide in :attr:`nuclides` in the + desired reaction rate matrix + react_index : iterable of int + Index for each reaction scored in the tally + + Returns + ------- + rates : numpy.ndarray + Array with shape ``(n_nuclides, n_rxns)`` with the + reaction rates in this material + """ + self._results_cache.fill(0.0) + full_tally_res = self._rate_tally.results[mat_id, :, 1] + for i_tally, (i_nuc, i_react) in enumerate( + product(nuc_index, react_index)): + self._results_cache[i_nuc, i_react] = full_tally_res[i_tally] + + return self._results_cache + + +# ---------------------------- +# Helpers for obtaining energy +# ---------------------------- + + +class ChainFissionHelper(EnergyHelper): + """Computes energy using fission Q values from depletion chain + + Attributes + ---------- + nuclides : list of str + All nuclides with desired reaction rates. Ordered to be + consistent with :class:`openmc.deplete.Operator` + energy : float + Total energy [J/s/source neutron] produced in a transport simulation. + Updated in the material iteration with :meth:`update`. + """ + + def __init__(self): + super().__init__() + self._fission_q_vector = None + + def prepare(self, chain_nucs, rate_index, _materials): + """Populate the fission Q value vector from a chain. + + Parameters + ---------- + chain_nucs : iterable of :class:`openmc.deplete.Nuclide` + Nuclides used in this depletion chain. Do not need + to be ordered + rate_index : dict of str to int + Dictionary mapping names of nuclides, e.g. ``"U235"``, + to a corresponding index in the desired fission Q + vector. + _materials : list of str + Unused. Materials to be tracked for this helper. + """ + if (self._fission_q_vector is not None + and self._fission_q_vector.shape == (len(rate_index),)): + return + + fission_qs = zeros(len(rate_index)) + + for nuclide in chain_nucs: + if nuclide.name in rate_index: + for rx in nuclide.reactions: + if rx.type == "fission": + fission_qs[rate_index[nuclide.name]] = rx.Q + break + + self._fission_q_vector = fission_qs + + def update(self, fission_rates, _mat_index): + """Update energy produced with fission rates in a material + + Parameters + ---------- + fission_rates : numpy.ndarray + fission reaction rate for each isotope in the specified + material. Should be ordered corresponding to initial + ``rate_index`` used in :meth:`prepare` + _mat_index : int + index for the material requested. Unused, as identical + isotopes in all materials have the same Q value. + """ + self._energy += dot(fission_rates, self._fission_q_vector) diff --git a/openmc/deplete/operator.py b/openmc/deplete/operator.py index 6ee992c4f..f60a4a2d4 100644 --- a/openmc/deplete/operator.py +++ b/openmc/deplete/operator.py @@ -20,11 +20,11 @@ from uncertainties import ufloat import openmc import openmc.capi -from openmc.data import JOULE_PER_EV from . import comm from .abc import TransportOperator, OperatorResult from .atom_number import AtomNumber from .reaction_rates import ReactionRates +from .helpers import DirectReactionRateHelper, ChainFissionHelper def _distribute(items): @@ -77,6 +77,11 @@ class Operator(TransportOperator): fission_q : dict, optional Dictionary of nuclides and their fission Q values [eV]. If not given, values will be pulled from the ``chain_file``. + dilute_initial : float, optional + Initial atom density [atoms/cm^3] to add for nuclides that are zero + in initial condition to ensure they exist in the decay chain. + Only done for nuclides with reaction rates. + Defaults to 1.0e3. Attributes ---------- @@ -85,9 +90,9 @@ class Operator(TransportOperator): settings : openmc.Settings OpenMC settings object dilute_initial : float - Initial atom density to add for nuclides that are zero in initial - condition to ensure they exist in the decay chain. Only done for - nuclides with reaction rates. Defaults to 1.0e3. + Initial atom density [atoms/cm^3] to add for nuclides that + are zero in initial condition to ensure they exist in the decay + chain. Only done for nuclides with reaction rates. output_dir : pathlib.Path Path to output directory to save results. round_number : bool @@ -111,11 +116,11 @@ class Operator(TransportOperator): Results from a previous depletion calculation diff_burnable_mats : bool Whether to differentiate burnable materials with multiple instances - """ def __init__(self, geometry, settings, chain_file=None, prev_results=None, - diff_burnable_mats=False, fission_q=None): - super().__init__(chain_file, fission_q) + diff_burnable_mats=False, fission_q=None, + dilute_initial=1.0e3): + super().__init__(chain_file, fission_q, dilute_initial) self.round_number = False self.settings = settings self.geometry = geometry @@ -139,6 +144,11 @@ class Operator(TransportOperator): self.burnable_mats, volume, nuclides = self._get_burnable_mats() self.local_mats = _distribute(self.burnable_mats) + # Generate map from local materials => material index + self._mat_index_map = { + lm: self.burnable_mats.index(lm) for lm in self.local_mats} + + # Determine which nuclides have incident neutron data self.nuclides_with_data = self._get_nuclides_with_data() @@ -153,6 +163,11 @@ class Operator(TransportOperator): self.reaction_rates = ReactionRates( self.local_mats, self._burnable_nucs, self.chain.reactions) + # Get classes to assist working with tallies + self._rate_helper = DirectReactionRateHelper( + self.reaction_rates.n_nuc, self.reaction_rates.n_react) + self._energy_helper = ChainFissionHelper() + def __call__(self, vec, power): """Runs a simulation. @@ -179,7 +194,8 @@ class Operator(TransportOperator): # Update material compositions and tally nuclides self._update_materials() - self._tally.nuclides = self._get_tally_nuclides() + self._rate_helper.nuclides = self._get_tally_nuclides() + self._energy_helper.nuclides = self._rate_helper.nuclides # Run OpenMC openmc.capi.reset() @@ -365,7 +381,11 @@ class Operator(TransportOperator): openmc.capi.init(intracomm=comm) # Generate tallies in memory - self._generate_tallies() + materials = [openmc.capi.materials[int(i)] + for i in self.burnable_mats] + self._rate_helper.generate_tallies(materials, self.chain.reactions) + self._energy_helper.prepare( + self.chain.nuclides, self.reaction_rates.index_nuc, materials) # Return number density vector return list(self.number.get_mat_slice(np.s_[:])) @@ -474,27 +494,6 @@ class Operator(TransportOperator): nuc_list = comm.bcast(nuc_list) return [nuc for nuc in nuc_list if nuc in self.chain] - def _generate_tallies(self): - """Generates depletion tallies. - - Using information from the depletion chain as well as the nuclides - currently in the problem, this function automatically generates a - tally.xml for the simulation. - - """ - # Create tallies for depleting regions - materials = [openmc.capi.materials[int(i)] - for i in self.burnable_mats] - mat_filter = openmc.capi.MaterialFilter(materials) - - # Set up a tally that has a material filter covering each depletable - # material and scores corresponding to all reactions that cause - # transmutation. The nuclides for the tally are set later when eval() is - # called. - self._tally = openmc.capi.Tally() - self._tally.scores = self.chain.reactions - self._tally.filters = [mat_filter] - def _unpack_tallies_and_normalize(self, power): """Unpack tallies from OpenMC and return an operator result @@ -515,78 +514,54 @@ class Operator(TransportOperator): """ rates = self.reaction_rates - rates[:, :, :] = 0.0 + rates.fill(0.0) # Get k and uncertainty k_combined = ufloat(*openmc.capi.keff()) # Extract tally bins - materials = self.burnable_mats - nuclides = self._tally.nuclides + nuclides = self._rate_helper.nuclides # Form fast map nuc_ind = [rates.index_nuc[nuc] for nuc in nuclides] react_ind = [rates.index_rx[react] for react in self.chain.reactions] # Compute fission power - # TODO : improve this calculation # Keep track of energy produced from all reactions in eV per source # particle - energy = 0.0 + self._energy_helper.reset() # Create arrays to store fission Q values, reaction rates, and nuclide - # numbers - fission_Q = np.zeros(rates.n_nuc) - rates_expanded = np.zeros((rates.n_nuc, rates.n_react)) - number = np.zeros(rates.n_nuc) + # numbers, zeroed out in material iteration + number = np.empty(rates.n_nuc) fission_ind = rates.index_rx["fission"] - for nuclide in self.chain.nuclides: - if nuclide.name in rates.index_nuc: - for rx in nuclide.reactions: - if rx.type == 'fission': - ind = rates.index_nuc[nuclide.name] - fission_Q[ind] = rx.Q - break - # Extract results for i, mat in enumerate(self.local_mats): # Get tally index - slab = materials.index(mat) - - # Get material results hyperslab - results = self._tally.results[slab, :, 1] + mat_index = self._mat_index_map[mat] # Zero out reaction rates and nuclide numbers - rates_expanded[:] = 0.0 - number[:] = 0.0 + number.fill(0.0) - # Expand into our memory layout - j = 0 + # Get new number densities for nuc, i_nuc_results in zip(nuclides, nuc_ind): number[i_nuc_results] = self.number[mat, nuc] - for react in react_ind: - rates_expanded[i_nuc_results, react] = results[j] - j += 1 + + tally_rates = self._rate_helper.get_material_rates( + mat_index, nuc_ind, react_ind) # Accumulate energy from fission - energy += np.dot(rates_expanded[:, fission_ind], fission_Q) + self._energy_helper.update(tally_rates[:, fission_ind], mat_index) # Divide by total number and store - for i_nuc_results in nuc_ind: - if number[i_nuc_results] != 0.0: - for react in react_ind: - rates_expanded[i_nuc_results, react] /= number[i_nuc_results] - - rates[i, :, :] = rates_expanded + rates[i] = self._rate_helper.divide_by_adens(number) # Reduce energy produced from all processes - energy = comm.allreduce(energy) - - # Determine power in eV/s - power /= JOULE_PER_EV + # J / s / source neutron + energy = comm.allreduce(self._energy_helper.energy) # Scale reaction rates to obtain units of reactions/sec rates *= power / energy diff --git a/openmc/deplete/results_list.py b/openmc/deplete/results_list.py index 79203a631..0ce5b0158 100644 --- a/openmc/deplete/results_list.py +++ b/openmc/deplete/results_list.py @@ -26,7 +26,15 @@ class ResultsList(list): self.append(Results.from_hdf5(fh, i)) def get_atoms(self, mat, nuc): - """Get nuclide concentration over time from a single material + """Get number of nuclides over time from a single material + + .. note:: + + Initial values for some isotopes that do not appear in + initial concentrations may be non-zero, depending on the + value of :class:`openmc.deplete.Operator` ``dilute_initial``. + The :class:`openmc.deplete.Operator` adds isotopes according + to this setting, which can be set to zero. Parameters ---------- @@ -56,6 +64,14 @@ class ResultsList(list): def get_reaction_rate(self, mat, nuc, rx): """Get reaction rate in a single material/nuclide over time + .. note:: + + Initial values for some isotopes that do not appear in + initial concentrations may be non-zero, depending on the + value of :class:`openmc.deplete.Operator` ``dilute_initial`` + The :class:`openmc.deplete.Operator` adds isotopes according + to this setting, which can be set to zero. + Parameters ---------- mat : str diff --git a/openmc/filter.py b/openmc/filter.py index dbdaf2e19..8527ca9b9 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -32,7 +32,8 @@ _CURRENT_NAMES = ( 'z-min out', 'z-min in', 'z-max out', 'z-max in' ) -_PARTICLE_IDS = {'neutron': 1, 'photon': 2, 'electron': 3, 'positron': 4} +_PARTICLES = {'neutron', 'photon', 'electron', 'positron'} + class FilterMeta(ABCMeta): def __new__(cls, name, bases, namespace, **kwargs): @@ -547,10 +548,9 @@ class ParticleFilter(Filter): Parameters ---------- - bins : str, int, or iterable of Integral - The Particles to tally. Either str with particle type or their - ID numbers can be used ('neutron' = 1, 'photon' = 2, 'electron' = 3, - 'positron' = 4). + bins : str, or iterable of str + The particles to tally represented as strings ('neutron', 'photon', + 'electron', 'positron'). filter_id : int Unique identifier for the filter @@ -571,16 +571,22 @@ class ParticleFilter(Filter): @bins.setter def bins(self, bins): bins = np.atleast_1d(bins) - cv.check_iterable_type('filter bins', bins, (Integral, str)) + cv.check_iterable_type('filter bins', bins, str) for edge in bins: - if isinstance(edge, Integral): - cv.check_value('filter bin', edge, _PARTICLE_IDS.values()) - else: - cv.check_value('filter bin', edge, _PARTICLE_IDS.keys()) - bins = np.atleast_1d([b if isinstance(b, Integral) else _PARTICLE_IDS[b] - for b in bins]) + cv.check_value('filter bin', edge, _PARTICLES) self._bins = bins + @classmethod + def from_hdf5(cls, group, **kwargs): + if group['type'][()].decode() != cls.short_name.lower(): + raise ValueError("Expected HDF5 data for filter type '" + + cls.short_name.lower() + "' but got '" + + group['type'][()].decode() + " instead") + + particles = [b.decode() for b in group['bins'][()]] + filter_id = int(group.name.split('/')[-1].lstrip('filter ')) + return cls(particles, filter_id=filter_id) + class MeshFilter(Filter): """Bins tally event locations onto a regular, rectangular mesh. diff --git a/openmc/material.py b/openmc/material.py index 55aef743c..375e1dca1 100644 --- a/openmc/material.py +++ b/openmc/material.py @@ -287,6 +287,8 @@ class Material(IDManagerMixin): material.depletable = bool(group.attrs['depletable']) if 'volume' in group.attrs: material.volume = group.attrs['volume'] + if "temperature" in group.attrs: + material.temperature = group.attrs["temperature"] # Read the names of the S(a,b) tables for this Material and add them if 'sab_names' in group: @@ -841,8 +843,7 @@ class Material(IDManagerMixin): # Create temperature XML subelement if self.temperature is not None: - subelement = ET.SubElement(element, "temperature") - subelement.text = str(self.temperature) + element.set("temperature", str(self.temperature)) # Create density XML subelement if self._density is not None or self._density_units == 'sum': @@ -931,8 +932,10 @@ class Material(IDManagerMixin): mat_id = int(elem.get('id')) mat = cls(mat_id) mat.name = elem.get('name') - if 'temperature' in elem.attrib: - mat.temperature = float(elem.get('temperature')) + + if "temperature" in elem.attrib: + mat.temperature = float(elem.get("temperature")) + if 'volume' in elem.attrib: mat.volume = float(elem.get('volume')) mat.depletable = bool(elem.get('depletable')) diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 1c8f00f6e..46b004838 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -2751,7 +2751,7 @@ class TransportXS(MGXS): p1_tally = p1_tally.get_slice(filters=[openmc.LegendreFilter], filter_bins=[('P1',)], squeeze=True) - p1_tally.scores = ['scatter-1'] + p1_tally._scores = ['scatter-1'] self._rxn_rate_tally = self.tallies['total'] - p1_tally self._rxn_rate_tally.sparse = self.sparse diff --git a/openmc/model/funcs.py b/openmc/model/funcs.py index 143033eca..bb5d39a2d 100644 --- a/openmc/model/funcs.py +++ b/openmc/model/funcs.py @@ -1,12 +1,16 @@ -from collections import OrderedDict from collections.abc import Iterable from math import sqrt from numbers import Real from functools import partial from warnings import warn +from operator import attrgetter -from openmc import XPlane, YPlane, Plane, ZCylinder, Quadric -from openmc.checkvalue import check_type, check_value +from openmc import ( + XPlane, YPlane, Plane, ZCylinder, Quadric, Cylinder, XCylinder, + YCylinder, Material, Universe, Cell) +from openmc.checkvalue import ( + check_type, check_value, check_length, check_less_than, + check_iterable_type) import openmc.data @@ -395,9 +399,9 @@ def cylinder_from_points(p1, p2, r, **kwargs): dx = x2 - x1 dy = y2 - y1 dz = z2 - z1 - cx = y1*z2 + y2*z1 - cy = -(x1*z2 + x2*z1) - cz = x1*y2 + x2*y1 + cx = y1*z2 - y2*z1 + cy = x2*z1 - x1*z2 + cz = x1*y2 - x2*y1 # Given p=(x,y,z), p1=(x1, y1, z1), p2=(x2, y2, z2), the equation for the # cylinder can be derived as r = |(p - p1) ⨯ (p - p2)| / |p2 - p1|. @@ -409,12 +413,12 @@ def cylinder_from_points(p1, p2, r, **kwargs): kwargs['d'] = -2*dx*dy kwargs['e'] = -2*dy*dz kwargs['f'] = -2*dx*dz - kwargs['g'] = cy*dz - cz*dy - kwargs['h'] = cz*dx - cx*dz - kwargs['j'] = cx*dy - cy*dx - kwargs['k'] = -(dx*dx + dy*dy + dz*dz)*r*r + kwargs['g'] = 2*(cy*dz - cz*dy) + kwargs['h'] = 2*(cz*dx - cx*dz) + kwargs['j'] = 2*(cx*dy - cy*dx) + kwargs['k'] = cx*cx + cy*cy + cz*cz - (dx*dx + dy*dy + dz*dz)*r*r - return openmc.Quadric(**kwargs) + return Quadric(**kwargs) def subdivide(surfaces): @@ -442,3 +446,133 @@ def subdivide(surfaces): regions.append(+s0 & -s1) regions.append(+surfaces[-1]) return regions + + +def pin(surfaces, items, subdivisions=None, divide_vols=True, + **kwargs): + """Convenience function for building a fuel pin + + Parameters + ---------- + surfaces : iterable of :class:`openmc.Cylinder` + Cylinders used to define boundaries + between items. All cylinders must be + concentric and of the same orientation, e.g. + all :class:`openmc.ZCylinder` + items : iterable + Objects to go between ``surfaces``. These can be anything + that can fill a :class:`openmc.Cell`, including + :class:`openmc.Material`, or other :class:`openmc.Universe` + objects. There must be one more item than surfaces, + which will span all space outside the final ring. + subdivisions : None or dict of int to int + Dictionary describing which rings to subdivide and how + many times. Keys are indexes of the annular rings + to be divided. Will construct equal area rings + divide_vols : bool + If this evaluates to ``True``, then volumes of subdivided + :class:`openmc.Material` instances will also be divided by the + number of divisions. Otherwise the volume of the + original material will not be modified before subdivision + kwargs: + Additional key-word arguments to be passed to + :class:`openmc.Universe`, like ``name="Fuel pin"`` + + Returns + ------- + :class:`openmc.Universe` + Universe of concentric cylinders filled with the desired + items + """ + if "cells" in kwargs: + raise SyntaxError( + "Cells will be set by this function, not from input arguments.") + check_type("items", items, Iterable) + check_length("surfaces", surfaces, len(items) - 1, len(items) - 1) + # Check that all surfaces are of similar orientation + check_type("surface", surfaces[0], Cylinder) + surf_type = type(surfaces[0]) + check_iterable_type("surfaces", surfaces[1:], surf_type) + + # Check for increasing radii and equal centers + if surf_type is ZCylinder: + center_getter = attrgetter("x0", "y0") + elif surf_type is YCylinder: + center_getter = attrgetter("x0", "z0") + elif surf_type is XCylinder: + center_getter = attrgetter("z0", "y0") + else: + raise TypeError( + "Not configured to interpret {} surfaces".format( + surf_type.__name__)) + + centers = set() + prev_rad = 0 + for ix, surf in enumerate(surfaces): + cur_rad = surf.r + if cur_rad <= prev_rad: + raise ValueError( + "Surfaces do not appear to be increasing in radius. " + "Surface {} at index {} has radius {:7.3e} compared to " + "previous radius of {:7.5e}".format( + surf.id, ix, cur_rad, prev_rad)) + prev_rad = cur_rad + centers.add(center_getter(surf)) + + if len(centers) > 1: + raise ValueError( + "Surfaces do not appear to be concentric. The following " + "centers were found: {}".format(centers)) + + if subdivisions is not None: + check_length("subdivisions", subdivisions, 1, len(surfaces)) + orig_indexes = list(subdivisions.keys()) + check_iterable_type("ring indexes", orig_indexes, int) + check_iterable_type( + "number of divisions", list(subdivisions.values()), int) + for ix in orig_indexes: + if ix < 0: + subdivisions[len(surfaces) + ix] = subdivisions.pop(ix) + # Dissallow subdivision on outer most, infinite region + check_less_than( + "outer ring", max(subdivisions), len(surfaces), equality=True) + + # ensure ability to concatenate + if not isinstance(items, list): + items = list(items) + if not isinstance(surfaces, list): + surfaces = list(surfaces) + + # generate equal area divisions + # Adding N - 1 new regions + # N - 2 surfaces are made + # Original cell is not removed, but not occupies last ring + for ring_index in reversed(sorted(subdivisions.keys())): + nr = subdivisions[ring_index] + new_surfs = [] + + lower_rad = 0.0 if ring_index == 0 else surfaces[ring_index - 1].r + + upper_rad = surfaces[ring_index].r + + area_term = (upper_rad ** 2 - lower_rad ** 2) / nr + + for new_index in range(nr - 1): + lower_rad = sqrt(area_term + lower_rad ** 2) + new_surfs.append(surf_type(r=lower_rad)) + + surfaces = ( + surfaces[:ring_index] + new_surfs + surfaces[ring_index:]) + + filler = items[ring_index] + if (divide_vols and hasattr(filler, "volume") + and filler.volume is not None): + filler.volume /= nr + + items[ring_index:ring_index] = [ + filler.clone() for _i in range(nr - 1)] + + # Build the universe + regions = subdivide(surfaces) + cells = [Cell(fill=f, region=r) for r, f in zip(regions, items)] + return Universe(cells=cells, **kwargs) diff --git a/src/cell.cpp b/src/cell.cpp index 87b67ef85..3c079f7ae 100644 --- a/src/cell.cpp +++ b/src/cell.cpp @@ -1,11 +1,12 @@ #include "openmc/cell.h" +#include #include #include #include #include -#include +#include #include "openmc/capi.h" #include "openmc/constants.h" @@ -118,7 +119,6 @@ generate_rpn(int32_t cell_id, std::vector infix) if (token < OP_UNION) { // If token is not an operator, add it to output rpn.push_back(token); - } else if (token < OP_RIGHT_PAREN) { // Regular operators union, intersection, complement while (stack.size() > 0) { @@ -158,7 +158,6 @@ generate_rpn(int32_t cell_id, std::vector infix) << cell_id; fatal_error(err_msg); } - rpn.push_back(stack.back()); stack.pop_back(); } @@ -208,10 +207,56 @@ Universe::to_hdf5(hid_t universes_group) const close_group(group); } +BoundingBox Universe::bounding_box() const { + BoundingBox bbox = {INFTY, -INFTY, INFTY, -INFTY, INFTY, -INFTY}; + if (cells_.size() == 0) { + return {}; + } else { + for (const auto& cell : cells_) { + auto& c = model::cells[cell]; + bbox |= c->bounding_box(); + } + } + return bbox; +} + //============================================================================== // Cell implementation //============================================================================== +double +Cell::temperature(int32_t instance) const +{ + if (sqrtkT_.size() < 1) { + throw std::runtime_error{"Cell temperature has not yet been set."}; + } + + if (instance >= 0) { + double sqrtkT = sqrtkT_.size() == 1 ? + sqrtkT_.at(0) : + sqrtkT_.at(instance); + return sqrtkT * sqrtkT / K_BOLTZMANN; + } else { + return sqrtkT_[0] * sqrtkT_[0] / K_BOLTZMANN; + } +} + +void +Cell::set_temperature(double T, int32_t instance) +{ + if (instance >= 0) { + sqrtkT_.at(instance) = std::sqrt(K_BOLTZMANN * T); + } else { + for (auto& T_ : sqrtkT_) { + T_ = std::sqrt(K_BOLTZMANN * T); + } + } +} + +//============================================================================== +// CSGCell implementation +//============================================================================== + CSGCell::CSGCell() {} // empty constructor CSGCell::CSGCell(pugi::xml_node cell_node) @@ -534,6 +579,95 @@ CSGCell::to_hdf5(hid_t cell_group) const close_group(group); } +BoundingBox CSGCell::bounding_box_simple() const { + BoundingBox bbox; + for (int32_t token : rpn_) { + bbox &= model::surfaces[abs(token)-1]->bounding_box(token > 0); + } + return bbox; +} + + +void CSGCell::apply_demorgan(std::vector& rpn) { + for (auto& token : rpn) { + if (token < OP_UNION) { token *= -1; } + else if (token == OP_UNION) { token = OP_INTERSECTION; } + else if (token == OP_INTERSECTION) { token = OP_UNION; } + } +} + +BoundingBox CSGCell::bounding_box_complex(std::vector rpn) { + + // if the last operator is a complement op, there is no + // sub-region that the complement connects to. This indicates + // that the entire region is a complement and we can apply + // De Morgan's laws immediately + if (rpn.back() == OP_COMPLEMENT) { + rpn.pop_back(); + apply_demorgan(rpn); + } + + // reverse the rpn to make popping easier + std::reverse(rpn.begin(), rpn.end()); + + BoundingBox current = model::surfaces[abs(rpn.back()) - 1]->bounding_box(rpn.back() > 0); + rpn.pop_back(); + + while (rpn.size()) { + // move through the rpn in twos + int32_t one = rpn.back(); rpn.pop_back(); + int32_t two = rpn.back(); rpn.pop_back(); + + // the first token should always be a surface + Expects(one < OP_UNION); + + if (two >= OP_UNION) { + if (two == OP_UNION) { + current |= model::surfaces[abs(one)-1]->bounding_box(one > 0); + } else if (two == OP_INTERSECTION) { + current &= model::surfaces[abs(one)-1]->bounding_box(one > 0); + } + } else { + // two surfaces in a row (left parenthesis), + // create sub-rpn for region in parenthesis + std::vector subrpn; + subrpn.push_back(one); + subrpn.push_back(two); + // add until last two tokens in the sub-rpn are operators + // (indicates a right parenthesis) + while (!((subrpn.back() >= OP_UNION) && (*(subrpn.rbegin() + 1) >= OP_UNION))) { + subrpn.push_back(rpn.back()); + rpn.pop_back(); + } + + // handle complement case using De Morgan's laws + if (subrpn.back() == OP_COMPLEMENT) { + subrpn.pop_back(); + apply_demorgan(subrpn); + subrpn.push_back(rpn.back()); + rpn.pop_back(); + } + // save the last operator, tells us how to combine this region + // with our current bounding box + int32_t op = subrpn.back(); subrpn.pop_back(); + // get bounding box for the subrpn + BoundingBox sub_box = bounding_box_complex(subrpn); + // combine the sub-rpn bounding box with our current cell box + if (op == OP_UNION) { + current |= sub_box; + } else if (op == OP_INTERSECTION) { + current &= sub_box; + } + } + } + + return current; +} + +BoundingBox CSGCell::bounding_box() const { + return simple_ ? bounding_box_simple() : bounding_box_complex(rpn_); +} + //============================================================================== bool @@ -658,6 +792,16 @@ bool DAGCell::contains(Position r, Direction u, int32_t on_surface) const void DAGCell::to_hdf5(hid_t group_id) const { return; } +BoundingBox DAGCell::bounding_box() const +{ + moab::ErrorCode rval; + moab::EntityHandle vol = dagmc_ptr_->entity_by_index(3, dag_index_); + double min[3], max[3]; + rval = dagmc_ptr_->getobb(vol, min, max); + MB_CHK_ERR_CONT(rval); + return {min[0], max[0], min[1], max[1], min[2], max[2]}; +} + #endif //============================================================================== @@ -917,27 +1061,18 @@ openmc_cell_set_fill(int32_t index, int type, int32_t n, extern "C" int openmc_cell_set_temperature(int32_t index, double T, const int32_t* instance) { - if (index >= 0 && index < model::cells.size()) { - Cell& c {*model::cells[index]}; - - if (instance) { - if (*instance >= 0 && *instance < c.sqrtkT_.size()) { - c.sqrtkT_[*instance] = std::sqrt(K_BOLTZMANN * T); - } else { - strcpy(openmc_err_msg, "Distribcell instance is out of bounds."); - return OPENMC_E_OUT_OF_BOUNDS; - } - } else { - for (auto& T_ : c.sqrtkT_) { - T_ = std::sqrt(K_BOLTZMANN * T); - } - } - - } else { + if (index < 0 || index >= model::cells.size()) { strcpy(openmc_err_msg, "Index in cells array is out of bounds."); return OPENMC_E_OUT_OF_BOUNDS; } + int32_t instance_index = instance ? *instance : -1; + try { + model::cells[index]->set_temperature(T, instance_index); + } catch (const std::exception& e) { + set_errmsg(e.what()); + return OPENMC_E_UNASSIGNED; + } return 0; } @@ -949,28 +1084,65 @@ openmc_cell_get_temperature(int32_t index, const int32_t* instance, double* T) return OPENMC_E_OUT_OF_BOUNDS; } - Cell& c {*model::cells[index]}; - - if (c.sqrtkT_.size() < 1) { - strcpy(openmc_err_msg, "Cell temperature has not yet been set."); + int32_t instance_index = instance ? *instance : -1; + try { + *T = model::cells[index]->temperature(instance_index); + } catch (const std::exception& e) { + set_errmsg(e.what()); return OPENMC_E_UNASSIGNED; } + return 0; +} - if (instance) { - if (*instance >= 0 && *instance < c.n_instances_) { - double sqrtkT = c.sqrtkT_.size() == 1 ? c.sqrtkT_[0] : c.sqrtkT_[*instance]; - *T = sqrtkT * sqrtkT / K_BOLTZMANN; - } else { - strcpy(openmc_err_msg, "Distribcell instance is out of bounds."); - return OPENMC_E_OUT_OF_BOUNDS; - } - } else { - *T = c.sqrtkT_[0] * c.sqrtkT_[0] / K_BOLTZMANN; - } +//! Get the bounding box of a cell +extern "C" int +openmc_cell_bounding_box(const int32_t index, double* llc, double* urc) { + + BoundingBox bbox; + + const auto& c = model::cells[index]; + bbox = c->bounding_box(); + + // set lower left corner values + llc[0] = bbox.xmin; + llc[1] = bbox.ymin; + llc[2] = bbox.zmin; + + // set upper right corner values + urc[0] = bbox.xmax; + urc[1] = bbox.ymax; + urc[2] = bbox.zmax; return 0; } +//! Get the name of a cell +extern "C" int +openmc_cell_get_name(int32_t index, const char** name) { + if (index < 0 || index >= model::cells.size()) { + set_errmsg("Index in cells array is out of bounds."); + return OPENMC_E_OUT_OF_BOUNDS; + } + + *name = model::cells[index]->name().data(); + + return 0; +} + +//! Set the name of a cell +extern "C" int +openmc_cell_set_name(int32_t index, const char* name) { + if (index < 0 || index >= model::cells.size()) { + set_errmsg("Index in cells array is out of bounds."); + return OPENMC_E_OUT_OF_BOUNDS; + } + + model::cells[index]->set_name(name); + + return 0; +} + + //! Return the index in the cells array of a cell with a given ID extern "C" int openmc_get_cell_index(int32_t id, int32_t* index) diff --git a/src/dagmc.cpp b/src/dagmc.cpp index 78d8bc4fb..3f7c170ce 100644 --- a/src/dagmc.cpp +++ b/src/dagmc.cpp @@ -35,10 +35,9 @@ const bool dagmc_enabled = false; #ifdef DAGMC -const std::string DAGMC_FILENAME = "dagmc.h5m"; - namespace openmc { +const std::string DAGMC_FILENAME = "dagmc.h5m"; namespace simulation { @@ -54,8 +53,16 @@ moab::DagMC* DAG; } // namespace model +void check_dagmc_file() { + std::string filename = settings::path_input + DAGMC_FILENAME; + if (!file_exists(filename)) { + fatal_error("Geometry DAGMC file '" + filename + "' does not exist!"); + } +} + bool get_uwuw_materials_xml(std::string& s) { - UWUW uwuw(DAGMC_FILENAME.c_str()); + check_dagmc_file(); + UWUW uwuw((settings::path_input + DAGMC_FILENAME).c_str()); std::stringstream ss; bool uwuw_mats_present = false; @@ -133,7 +140,7 @@ void legacy_assign_material(const std::string& mat_string, DAGCell* c) } if (settings::verbosity >= 10) { - Material* m = model::materials[model::material_map[c->material_[0]]].get(); + const auto& m = model::materials[model::material_map.at(c->material_[0])]; std::stringstream msg; msg << "DAGMC material " << mat_string << " was assigned"; if (mat_found_by_name) { @@ -147,14 +154,18 @@ void legacy_assign_material(const std::string& mat_string, DAGCell* c) void load_dagmc_geometry() { + check_dagmc_file(); + if (!model::DAG) { model::DAG = new moab::DagMC(); } + + std::string filename = settings::path_input + DAGMC_FILENAME; // --- Materials --- // create uwuw instance - UWUW uwuw(DAGMC_FILENAME.c_str()); + UWUW uwuw(filename.c_str()); // check for uwuw material definitions bool using_uwuw = !uwuw.material_library.empty(); @@ -167,7 +178,7 @@ void load_dagmc_geometry() int32_t dagmc_univ_id = 0; // universe is always 0 for DAGMC runs // load the DAGMC geometry - moab::ErrorCode rval = model::DAG->load_file(DAGMC_FILENAME.c_str()); + moab::ErrorCode rval = model::DAG->load_file(filename.c_str()); MB_CHK_ERR_CONT(rval); // initialize acceleration data structures @@ -215,17 +226,6 @@ void load_dagmc_geometry() model::universes[it->second]->cells_.push_back(i); } - // check for temperature assignment - std::string temp_value; - if (model::DAG->has_prop(vol_handle, "temp")) { - rval = model::DAG->prop_value(vol_handle, "temp", temp_value); - MB_CHK_ERR_CONT(rval); - double temp = std::stod(temp_value); - c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * temp)); - } else { - c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * settings::temperature_default)); - } - // MATERIALS if (model::DAG->is_implicit_complement(vol_handle)) { @@ -295,6 +295,26 @@ void load_dagmc_geometry() legacy_assign_material(mat_value, c); } } + + // check for temperature assignment + std::string temp_value; + + // no temperature if void + if (c->material_[0] == MATERIAL_VOID) continue; + + // assign cell temperature + const auto& mat = model::materials[model::material_map.at(c->material_[0])]; + if (model::DAG->has_prop(vol_handle, "temp")) { + rval = model::DAG->prop_value(vol_handle, "temp", temp_value); + MB_CHK_ERR_CONT(rval); + double temp = std::stod(temp_value); + c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * temp)); + } else if (mat->temperature_ > 0.0) { + c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * mat->temperature_)); + } else { + c->sqrtkT_.push_back(std::sqrt(K_BOLTZMANN * settings::temperature_default)); + } + } // allocate the cell overlap count if necessary @@ -369,11 +389,7 @@ void load_dagmc_geometry() void read_geometry_dagmc() { // Check if dagmc.h5m exists - std::string filename = settings::path_input + "dagmc.h5m"; - if (!file_exists(filename)) { - fatal_error("Geometry DAGMC file '" + filename + "' does not exist!"); - } - + check_dagmc_file(); write_message("Reading DAGMC geometry...", 5); load_dagmc_geometry(); diff --git a/src/geometry.cpp b/src/geometry.cpp index 0f89a0ecd..556d10650 100644 --- a/src/geometry.cpp +++ b/src/geometry.cpp @@ -9,6 +9,7 @@ #include "openmc/lattice.h" #include "openmc/settings.h" #include "openmc/simulation.h" +#include "openmc/string_utils.h" #include "openmc/surface.h" @@ -475,4 +476,21 @@ openmc_find_cell(const double* xyz, int32_t* index, int32_t* instance) return 0; } +extern "C" int openmc_global_bounding_box(double* llc, double* urc) { + auto bbox = model::universes.at(model::root_universe)->bounding_box(); + + // set lower left corner values + llc[0] = bbox.xmin; + llc[1] = bbox.ymin; + llc[2] = bbox.zmin; + + // set upper right corner values + urc[0] = bbox.xmax; + urc[1] = bbox.ymax; + urc[2] = bbox.zmax; + + return 0; +} + + } // namespace openmc diff --git a/src/geometry_aux.cpp b/src/geometry_aux.cpp index f02ee42b1..d195a9de3 100644 --- a/src/geometry_aux.cpp +++ b/src/geometry_aux.cpp @@ -309,7 +309,7 @@ prepare_distribcell() for (auto& filt : model::tally_filters) { auto* distrib_filt = dynamic_cast(filt.get()); if (distrib_filt) { - distribcells.insert(distrib_filt->cell_); + distribcells.insert(distrib_filt->cell()); } } diff --git a/src/material.cpp b/src/material.cpp index ccbbe57a8..685fd1da2 100644 --- a/src/material.cpp +++ b/src/material.cpp @@ -47,9 +47,10 @@ std::unordered_map material_map; //============================================================================== Material::Material(pugi::xml_node node) + : index_{model::materials.size()} { if (check_for_node(node, "id")) { - id_ = std::stoi(get_node_value(node, "id")); + this->set_id(std::stoi(get_node_value(node, "id"))); } else { fatal_error("Must specify id of material in materials XML file."); } @@ -331,6 +332,11 @@ Material::Material(pugi::xml_node node) } } +Material::~Material() +{ + model::material_map.erase(id_); +} + void Material::finalize() { // Set fissionable if any nuclide is fissionable @@ -849,11 +855,41 @@ void Material::calculate_photon_xs(Particle& p) const } } -int Material::set_density(double density, std::string units) +void Material::set_id(int32_t id) { + Expects(id >= -1); + + // Clear entry in material map if an ID was already assigned before + if (id_ != -1) { + model::material_map.erase(id_); + id_ = -1; + } + + // Make sure no other material has same ID + if (model::material_map.find(id) != model::material_map.end()) { + throw std::runtime_error{"Two materials have the same ID: " + std::to_string(id)}; + } + + // If no ID specified, auto-assign next ID in sequence + if (id == -1) { + id = 0; + for (const auto& f : model::materials) { + id = std::max(id, f->id_); + } + ++id; + } + + // Update ID and entry in material map + id_ = id; + model::material_map[id] = index_; +} + +void Material::set_density(double density, gsl::cstring_span units) +{ + Expects(density >= 0.0); + if (nuclide_.empty()) { - set_errmsg("No nuclides exist in material yet."); - return OPENMC_E_ALLOCATE; + throw std::runtime_error{"No nuclides exist in material yet."}; } if (units == "atom/b-cm") { @@ -884,10 +920,51 @@ int Material::set_density(double density, std::string units) density_ *= f; atom_density_ *= f; } else { - set_errmsg("Invalid units '" + units + "' specified."); - return OPENMC_E_INVALID_ARGUMENT; + throw std::invalid_argument{"Invalid units '" + std::string(units.data()) + + "' specified."}; } - return 0; +} + +void Material::set_densities(const std::vector& name, + const std::vector& density) +{ + auto n = name.size(); + Expects(n > 0); + Expects(n == density.size()); + + if (n != nuclide_.size()) { + nuclide_.resize(n); + atom_density_ = xt::zeros({n}); + } + + double sum_density = 0.0; + for (gsl::index i = 0; i < n; ++i) { + const auto& nuc {name[i]}; + if (data::nuclide_map.find(nuc) == data::nuclide_map.end()) { + int err = openmc_load_nuclide(nuc.c_str()); + if (err < 0) throw std::runtime_error{openmc_err_msg}; + } + + nuclide_[i] = data::nuclide_map.at(nuc); + Expects(density[i] > 0.0); + atom_density_(i) = density[i]; + sum_density += density[i]; + } + + // Set total density to the sum of the vector + this->set_density(sum_density, "atom/b-cm"); + + // Assign S(a,b) tables + this->init_thermal(); +} + +double Material::volume() const +{ + if (volume_ < 0.0) { + throw std::runtime_error{"Volume for material with ID=" + + std::to_string(id_) + " not set."}; + } + return volume_; } void Material::to_hdf5(hid_t group) const @@ -898,6 +975,9 @@ void Material::to_hdf5(hid_t group) const if (volume_ > 0.0) { write_attribute(material_group, "volume", volume_); } + if (temperature_ > 0.0) { + write_attribute(material_group, "temperature", temperature_); + } write_dataset(material_group, "name", name_); write_dataset(material_group, "atom_density", density_); @@ -945,6 +1025,42 @@ void Material::to_hdf5(hid_t group) const close_group(material_group); } +void Material::add_nuclide(const std::string& name, double density) +{ + // Check if nuclide is already in material + for (int i = 0; i < nuclide_.size(); ++i) { + int i_nuc = nuclide_[i]; + if (data::nuclides[i_nuc]->name_ == name) { + double awr = data::nuclides[i_nuc]->awr_; + density_ += density - atom_density_(i); + density_gpcc_ += (density - atom_density_(i)) + * awr * MASS_NEUTRON / N_AVOGADRO; + atom_density_(i) = density; + return; + } + } + + // If nuclide wasn't found, extend nuclide/density arrays + int err = openmc_load_nuclide(name.c_str()); + if (err < 0) throw std::runtime_error{openmc_err_msg}; + + // Append new nuclide/density + int i_nuc = data::nuclide_map[name]; + nuclide_.push_back(i_nuc); + + auto n = nuclide_.size(); + + // Create copy of atom_density_ array with one extra entry + xt::xtensor atom_density = xt::zeros({n}); + xt::view(atom_density, xt::range(0, n-1)) = atom_density_; + atom_density(n-1) = density; + atom_density_ = atom_density; + + density_ += density; + density_gpcc_ += density * data::nuclides[i_nuc]->awr_ + * MASS_NEUTRON / N_AVOGADRO; +} + //============================================================================== // Non-method functions //============================================================================== @@ -1098,19 +1214,6 @@ void read_materials_xml() model::materials.push_back(std::make_unique(material_node)); } model::materials.shrink_to_fit(); - - // Populate the material map. - for (int i = 0; i < model::materials.size(); i++) { - int32_t mid = model::materials[i]->id_; - auto search = model::material_map.find(mid); - if (search == model::material_map.end()) { - model::material_map[mid] = i; - } else { - std::stringstream err_msg; - err_msg << "Two or more materials use the same unique ID: " << mid; - fatal_error(err_msg); - } - } } void free_memory_material() @@ -1141,40 +1244,10 @@ openmc_material_add_nuclide(int32_t index, const char* name, double density) { int err = 0; if (index >= 0 && index < model::materials.size()) { - auto& m = model::materials[index]; - - // Check if nuclide is already in material - for (int i = 0; i < m->nuclide_.size(); ++i) { - int i_nuc = m->nuclide_[i]; - if (data::nuclides[i_nuc]->name_ == name) { - double awr = data::nuclides[i_nuc]->awr_; - m->density_ += density - m->atom_density_(i); - m->density_gpcc_ += (density - m->atom_density_(i)) - * awr * MASS_NEUTRON / N_AVOGADRO; - m->atom_density_(i) = density; - return 0; - } - } - - // If nuclide wasn't found, extend nuclide/density arrays - err = openmc_load_nuclide(name); - - if (err == 0) { - // Append new nuclide/density - int i_nuc = data::nuclide_map[name]; - m->nuclide_.push_back(i_nuc); - - auto n = m->nuclide_.size(); - - // Create copy of atom_density_ array with one extra entry - xt::xtensor atom_density = xt::zeros({n}); - xt::view(atom_density, xt::range(0, n-1)) = m->atom_density_; - atom_density(n-1) = density; - m->atom_density_ = atom_density; - - m->density_ += density; - m->density_gpcc_ += density * data::nuclides[i_nuc]->awr_ - * MASS_NEUTRON / N_AVOGADRO; + try { + model::materials[index]->add_nuclide(name, density); + } catch (const std::runtime_error& e) { + return OPENMC_E_DATA; } } else { set_errmsg("Index in materials array is out of bounds."); @@ -1184,14 +1257,14 @@ openmc_material_add_nuclide(int32_t index, const char* name, double density) } extern "C" int -openmc_material_get_densities(int32_t index, int** nuclides, double** densities, int* n) +openmc_material_get_densities(int32_t index, const int** nuclides, const double** densities, int* n) { if (index >= 0 && index < model::materials.size()) { auto& mat = model::materials[index]; - if (!mat->nuclide_.empty()) { - *nuclides = mat->nuclide_.data(); - *densities = mat->atom_density_.data(); - *n = mat->nuclide_.size(); + if (!mat->nuclides().empty()) { + *nuclides = mat->nuclides().data(); + *densities = mat->densities().data(); + *n = mat->nuclides().size(); return 0; } else { set_errmsg("Material atom density array has not been allocated."); @@ -1208,7 +1281,7 @@ openmc_material_get_density(int32_t index, double* density) { if (index >= 0 && index < model::materials.size()) { auto& mat = model::materials[index]; - *density = mat->density_gpcc_; + *density = mat->density_gpcc(); return 0; } else { set_errmsg("Index in materials array is out of bounds."); @@ -1220,7 +1293,7 @@ extern "C" int openmc_material_get_fissionable(int32_t index, bool* fissionable) { if (index >= 0 && index < model::materials.size()) { - *fissionable = model::materials[index]->fissionable_; + *fissionable = model::materials[index]->fissionable(); return 0; } else { set_errmsg("Index in materials array is out of bounds."); @@ -1232,7 +1305,7 @@ extern "C" int openmc_material_get_id(int32_t index, int32_t* id) { if (index >= 0 && index < model::materials.size()) { - *id = model::materials[index]->id_; + *id = model::materials[index]->id(); return 0; } else { set_errmsg("Index in materials array is out of bounds."); @@ -1244,16 +1317,13 @@ extern "C" int openmc_material_get_volume(int32_t index, double* volume) { if (index >= 0 && index < model::materials.size()) { - auto& m = model::materials[index]; - if (m->volume_ >= 0.0) { - *volume = m->volume_; - return 0; - } else { - std::stringstream msg; - msg << "Volume for material with ID=" << m->id_ << " not set."; - set_errmsg(msg); + try { + *volume = model::materials[index]->volume(); + } catch (const std::exception& e) { + set_errmsg(e.what()); return OPENMC_E_UNASSIGNED; } + return 0; } else { set_errmsg("Index in materials array is out of bounds."); return OPENMC_E_OUT_OF_BOUNDS; @@ -1264,59 +1334,75 @@ extern "C" int openmc_material_set_density(int32_t index, double density, const char* units) { if (index >= 0 && index < model::materials.size()) { - return model::materials[index]->set_density(density, units); + try { + model::materials[index]->set_density(density, units); + } catch (const std::exception& e) { + set_errmsg(e.what()); + return OPENMC_E_UNASSIGNED; + } } else { set_errmsg("Index in materials array is out of bounds."); return OPENMC_E_OUT_OF_BOUNDS; } + return 0; } extern "C" int openmc_material_set_densities(int32_t index, int n, const char** name, const double* density) { if (index >= 0 && index < model::materials.size()) { - auto& mat {model::materials[index]}; - if (n != mat->nuclide_.size()) { - mat->nuclide_.resize(n); - mat->atom_density_ = xt::zeros({n}); + try { + model::materials[index]->set_densities({name, name + n}, {density, density + n}); + } catch (const std::exception& e) { + set_errmsg(e.what()); + return OPENMC_E_UNASSIGNED; } - - double sum_density = 0.0; - for (int i = 0; i < n; ++i) { - std::string nuc {name[i]}; - if (data::nuclide_map.find(nuc) == data::nuclide_map.end()) { - int err = openmc_load_nuclide(nuc.c_str()); - if (err < 0) return err; - } - - mat->nuclide_[i] = data::nuclide_map[nuc]; - mat->atom_density_(i) = density[i]; - sum_density += density[i]; - } - - // Set total density to the sum of the vector - int err = mat->set_density(sum_density, "atom/b-cm"); - - // Assign S(a,b) tables - mat->init_thermal(); - return err; } else { set_errmsg("Index in materials array is out of bounds."); return OPENMC_E_OUT_OF_BOUNDS; } + return 0; } extern "C" int openmc_material_set_id(int32_t index, int32_t id) { if (index >= 0 && index < model::materials.size()) { - model::materials[index]->id_ = id; - model::material_map[id] = index; - return 0; + try { + model::materials.at(index)->set_id(id); + } catch (const std::exception& e) { + set_errmsg(e.what()); + return OPENMC_E_UNASSIGNED; + } } else { set_errmsg("Index in materials array is out of bounds."); return OPENMC_E_OUT_OF_BOUNDS; } + return 0; +} + +extern "C" int +openmc_material_get_name(int32_t index, const char** name) { + if (index < 0 || index >= model::materials.size()) { + set_errmsg("Index in materials array is out of bounds."); + return OPENMC_E_OUT_OF_BOUNDS; + } + + *name = model::materials[index]->name().data(); + + return 0; +} + +extern "C" int +openmc_material_set_name(int32_t index, const char* name) { + if (index < 0 || index >= model::materials.size()) { + set_errmsg("Index in materials array is out of bounds."); + return OPENMC_E_OUT_OF_BOUNDS; + } + + model::materials[index]->set_name(name); + + return 0; } extern "C" int diff --git a/src/mgxs.cpp b/src/mgxs.cpp index 3aac41f35..1fca66308 100644 --- a/src/mgxs.cpp +++ b/src/mgxs.cpp @@ -522,7 +522,7 @@ Mgxs::get_xs(int xstype, int gin, const int* gout, const double* mu, break; case MG_GET_XS_DECAY_RATE: if (dg != nullptr) { - val = xs_t->decay_rate(a, *dg + 1); + val = xs_t->decay_rate(a, *dg); } else { val = xs_t->decay_rate(a, 0); } diff --git a/src/mgxs_interface.cpp b/src/mgxs_interface.cpp index c30850e8f..29e5e8b36 100644 --- a/src/mgxs_interface.cpp +++ b/src/mgxs_interface.cpp @@ -263,21 +263,13 @@ get_nuclide_xs(int index, int xstype, int gin, const int* gout, { int gout_c; const int* gout_c_p; - int dg_c; - const int* dg_c_p; if (gout != nullptr) { gout_c = *gout - 1; gout_c_p = &gout_c; } else { gout_c_p = gout; } - if (dg != nullptr) { - dg_c = *dg - 1; - dg_c_p = &dg_c; - } else { - dg_c_p = dg; - } - return data::nuclides_MG[index].get_xs(xstype, gin - 1, gout_c_p, mu, dg_c_p); + return data::nuclides_MG[index].get_xs(xstype, gin - 1, gout_c_p, mu, dg); } //============================================================================== @@ -288,21 +280,13 @@ get_macro_xs(int index, int xstype, int gin, const int* gout, { int gout_c; const int* gout_c_p; - int dg_c; - const int* dg_c_p; if (gout != nullptr) { gout_c = *gout - 1; gout_c_p = &gout_c; } else { gout_c_p = gout; } - if (dg != nullptr) { - dg_c = *dg - 1; - dg_c_p = &dg_c; - } else { - dg_c_p = dg; - } - return data::macro_xs[index].get_xs(xstype, gin - 1, gout_c_p, mu, dg_c_p); + return data::macro_xs[index].get_xs(xstype, gin - 1, gout_c_p, mu, dg); } //============================================================================== diff --git a/src/state_point.cpp b/src/state_point.cpp index 7516b0d48..6ea666fde 100644 --- a/src/state_point.cpp +++ b/src/state_point.cpp @@ -141,13 +141,13 @@ openmc_statepoint_write(const char* filename, bool* write_source) std::vector filter_ids; filter_ids.reserve(model::tally_filters.size()); for (const auto& filt : model::tally_filters) - filter_ids.push_back(filt->id_); + filter_ids.push_back(filt->id()); write_attribute(filters_group, "ids", filter_ids); // Write info for each filter for (const auto& filt : model::tally_filters) { hid_t filter_group = create_group(filters_group, - "filter " + std::to_string(filt->id_)); + "filter " + std::to_string(filt->id())); filt->to_statepoint(filter_group); close_group(filter_group); } @@ -188,7 +188,7 @@ openmc_statepoint_write(const char* filename, bool* write_source) std::vector filter_ids; filter_ids.reserve(tally.filters().size()); for (auto i_filt : tally.filters()) - filter_ids.push_back(model::tally_filters[i_filt]->id_); + filter_ids.push_back(model::tally_filters[i_filt]->id()); write_dataset(tally_group, "filters", filter_ids); } diff --git a/src/summary.cpp b/src/summary.cpp index 500a5c15c..d2721f97e 100644 --- a/src/summary.cpp +++ b/src/summary.cpp @@ -92,6 +92,7 @@ void write_geometry(hid_t file) #ifdef DAGMC if (settings::dagmc) { write_attribute(geom_group, "dagmc", 1); + close_group(geom_group); return; } #endif diff --git a/src/surface.cpp b/src/surface.cpp index f364694a0..14d077cff 100644 --- a/src/surface.cpp +++ b/src/surface.cpp @@ -271,17 +271,8 @@ Direction DAGSurface::reflect(Position r, Direction u) const return simulation::last_dir; } -BoundingBox DAGSurface::bounding_box() const -{ - moab::ErrorCode rval; - moab::EntityHandle surf = dagmc_ptr_->entity_by_index(2, dag_index_); - double min[3], max[3]; - rval = dagmc_ptr_->getobb(surf, min, max); - MB_CHK_ERR_CONT(rval); - return {min[0], max[0], min[1], max[1], min[2], max[2]}; -} - void DAGSurface::to_hdf5(hid_t group_id) const {} + #endif //============================================================================== // PeriodicSurface implementation @@ -366,9 +357,13 @@ bool SurfaceXPlane::periodic_translate(const PeriodicSurface* other, } BoundingBox -SurfaceXPlane::bounding_box() const +SurfaceXPlane::bounding_box(bool pos_side) const { - return {x0_, x0_, -INFTY, INFTY, -INFTY, INFTY}; + if (pos_side) { + return {x0_, INFTY, -INFTY, INFTY, -INFTY, INFTY}; + } else { + return {-INFTY, x0_, -INFTY, INFTY, -INFTY, INFTY}; + } } //============================================================================== @@ -428,9 +423,13 @@ bool SurfaceYPlane::periodic_translate(const PeriodicSurface* other, } BoundingBox -SurfaceYPlane::bounding_box() const +SurfaceYPlane::bounding_box(bool pos_side) const { - return {-INFTY, INFTY, y0_, y0_, -INFTY, INFTY}; + if (pos_side) { + return {-INFTY, INFTY, y0_, INFTY, -INFTY, INFTY}; + } else { + return {-INFTY, INFTY, -INFTY, y0_, -INFTY, INFTY}; + } } //============================================================================== @@ -474,9 +473,13 @@ bool SurfaceZPlane::periodic_translate(const PeriodicSurface* other, } BoundingBox -SurfaceZPlane::bounding_box() const +SurfaceZPlane::bounding_box(bool pos_side) const { - return {-INFTY, INFTY, -INFTY, INFTY, z0_, z0_}; + if (pos_side) { + return {-INFTY, INFTY, -INFTY, INFTY, z0_, INFTY}; + } else { + return {-INFTY, INFTY, -INFTY, INFTY, -INFTY, z0_}; + } } //============================================================================== @@ -538,12 +541,6 @@ bool SurfacePlane::periodic_translate(const PeriodicSurface* other, Position& r, return false; } -BoundingBox -SurfacePlane::bounding_box() const -{ - return {-INFTY, INFTY, -INFTY, INFTY, -INFTY, INFTY}; -} - //============================================================================== // Generic functions for x-, y-, and z-, cylinders //============================================================================== @@ -645,7 +642,6 @@ Direction SurfaceXCylinder::normal(Position r) const return axis_aligned_cylinder_normal<0, 1, 2>(r, y0_, z0_); } - void SurfaceXCylinder::to_hdf5_inner(hid_t group_id) const { write_string(group_id, "type", "x-cylinder", false); @@ -653,6 +649,13 @@ void SurfaceXCylinder::to_hdf5_inner(hid_t group_id) const write_dataset(group_id, "coefficients", coeffs); } +BoundingBox SurfaceXCylinder::bounding_box(bool pos_side) const { + if (!pos_side) { + return {-INFTY, INFTY, y0_ - radius_, y0_ + radius_, z0_ - radius_, z0_ + radius_}; + } else { + return {}; + } +} //============================================================================== // SurfaceYCylinder implementation //============================================================================== @@ -686,6 +689,14 @@ void SurfaceYCylinder::to_hdf5_inner(hid_t group_id) const write_dataset(group_id, "coefficients", coeffs); } +BoundingBox SurfaceYCylinder::bounding_box(bool pos_side) const { + if (!pos_side) { + return {x0_ - radius_, x0_ + radius_, -INFTY, INFTY, z0_ - radius_, z0_ + radius_}; + } else { + return {}; + } +} + //============================================================================== // SurfaceZCylinder implementation //============================================================================== @@ -719,6 +730,15 @@ void SurfaceZCylinder::to_hdf5_inner(hid_t group_id) const write_dataset(group_id, "coefficients", coeffs); } +BoundingBox SurfaceZCylinder::bounding_box(bool pos_side) const { + if (!pos_side) { + return {x0_ - radius_, x0_ + radius_, y0_ - radius_, y0_ + radius_, -INFTY, INFTY}; + } else { + return {}; + } +} + + //============================================================================== // SurfaceSphere implementation //============================================================================== @@ -787,6 +807,16 @@ void SurfaceSphere::to_hdf5_inner(hid_t group_id) const write_dataset(group_id, "coefficients", coeffs); } +BoundingBox SurfaceSphere::bounding_box(bool pos_side) const { + if (!pos_side) { + return {x0_ - radius_, x0_ + radius_, + y0_ - radius_, y0_ + radius_, + z0_ - radius_, z0_ + radius_}; + } else { + return {}; + } +} + //============================================================================== // Generic functions for x-, y-, and z-, cones //============================================================================== @@ -1161,7 +1191,7 @@ void read_surfaces(pugi::xml_node node) } // See if this surface makes part of the global bounding box. - BoundingBox bb = surf->bounding_box(); + auto bb = surf->bounding_box(true) & surf->bounding_box(false); if (bb.xmin > -INFTY && bb.xmin < xmin) { xmin = bb.xmin; i_xmin = i_surf; diff --git a/src/tallies/filter.cpp b/src/tallies/filter.cpp index e9aae53a7..c67eec830 100644 --- a/src/tallies/filter.cpp +++ b/src/tallies/filter.cpp @@ -7,6 +7,7 @@ #include "openmc/capi.h" #include "openmc/constants.h" // for MAX_LINE_LEN; #include "openmc/error.h" +#include "openmc/xml_interface.h" #include "openmc/tallies/filter_azimuthal.h" #include "openmc/tallies/filter_cell.h" #include "openmc/tallies/filter_cellborn.h" @@ -50,8 +51,46 @@ namespace model { // Non-member functions //============================================================================== -Filter* -allocate_filter(const std::string& type) +extern "C" size_t tally_filters_size() +{ + return model::tally_filters.size(); +} + +//============================================================================== +// Filter implementation +//============================================================================== + +Filter::Filter() : index_{model::tally_filters.size()} +{ } + +Filter::~Filter() +{ + model::filter_map.erase(id_); +} + +Filter* Filter::create(pugi::xml_node node) +{ + // Copy filter id + if (!check_for_node(node, "id")) { + fatal_error("Must specify id for filter in tally XML file."); + } + int filter_id = std::stoi(get_node_value(node, "id")); + + // Convert filter type to lower case + std::string s; + if (check_for_node(node, "type")) { + s = get_node_value(node, "type", true); + } + + // Allocate according to the filter type + auto f = Filter::create(s, filter_id); + + // Read filter data from XML + f->from_xml(node); + return f; +} + +Filter* Filter::create(const std::string& type, int32_t id) { if (type == "azimuthal") { model::tally_filters.push_back(std::make_unique()); @@ -100,12 +139,40 @@ allocate_filter(const std::string& type) } else { throw std::runtime_error{"Unknown filter type: " + type}; } + + // Assign ID + model::tally_filters.back()->set_id(id); + return model::tally_filters.back().get(); } -extern "C" size_t tally_filters_size() +void Filter::set_id(int32_t id) { - return model::tally_filters.size(); + Expects(id >= -1); + + // Clear entry in filter map if an ID was already assigned before + if (id_ != -1) { + model::filter_map.erase(id_); + id_ = -1; + } + + // Make sure no other filter has same ID + if (model::filter_map.find(id) != model::filter_map.end()) { + throw std::runtime_error{"Two filters have the same ID: " + std::to_string(id)}; + } + + // If no ID specified, auto-assign next ID in sequence + if (id == -1) { + id = 0; + for (const auto& f : model::tally_filters) { + id = std::max(id, f->id_); + } + ++id; + } + + // Update ID and entry in filter map + id_ = id; + model::filter_map[id] = index_; } //============================================================================== @@ -126,7 +193,7 @@ openmc_filter_get_id(int32_t index, int32_t* id) { if (int err = verify_filter(index)) return err; - *id = model::tally_filters[index]->id_; + *id = model::tally_filters[index]->id(); return 0; } @@ -135,13 +202,7 @@ openmc_filter_set_id(int32_t index, int32_t id) { if (int err = verify_filter(index)) return err; - if (model::filter_map.find(id) != model::filter_map.end()) { - set_errmsg("Two filters have the same ID: " + std::to_string(id)); - return OPENMC_E_INVALID_ID; - } - - model::tally_filters[index]->id_ = id; - model::filter_map[id] = index; + model::tally_filters[index]->set_id(id); return 0; } @@ -172,7 +233,7 @@ openmc_get_filter_next_id(int32_t* id) { int32_t largest_filter_id = 0; for (const auto& t : model::tally_filters) { - largest_filter_id = std::max(largest_filter_id, t->id_); + largest_filter_id = std::max(largest_filter_id, t->id()); } *id = largest_filter_id + 1; } @@ -181,7 +242,7 @@ extern "C" int openmc_new_filter(const char* type, int32_t* index) { *index = model::tally_filters.size(); - allocate_filter(type); + Filter::create(type); return 0; } diff --git a/src/tallies/filter_azimuthal.cpp b/src/tallies/filter_azimuthal.cpp index 1a33a6bfe..eb02319c2 100644 --- a/src/tallies/filter_azimuthal.cpp +++ b/src/tallies/filter_azimuthal.cpp @@ -15,22 +15,35 @@ AzimuthalFilter::from_xml(pugi::xml_node node) { auto bins = get_node_array(node, "bins"); - if (bins.size() > 1) { - bins_ = bins; - - } else { + if (bins.size() == 1) { // Allow a user to input a lone number which will mean that you subdivide // [-pi,pi) evenly with the input being the number of bins int n_angle = bins[0]; - - if (n_angle <= 1) fatal_error("Number of bins for azimuthal filter must " - "be greater than 1."); + if (n_angle <= 1) throw std::runtime_error{ + "Number of bins for azimuthal filter must be greater than 1."}; double d_angle = 2.0 * PI / n_angle; - bins_.resize(n_angle + 1); - for (int i = 0; i < n_angle; i++) bins_[i] = -PI + i * d_angle; - bins_[n_angle] = PI; + bins.resize(n_angle + 1); + for (int i = 0; i < n_angle; i++) bins[i] = -PI + i * d_angle; + bins[n_angle] = PI; + } + + this->set_bins(bins); +} + +void AzimuthalFilter::set_bins(gsl::span bins) +{ + // Clear existing bins + bins_.clear(); + bins_.reserve(bins.size()); + + // Copy bins, ensuring they are valid + for (gsl::index i = 0; i < bins.size(); ++i) { + if (i > 0 && bins[i] <= bins[i-1]) { + throw std::runtime_error{"Azimuthal bins must be monotonically increasing."}; + } + bins_.push_back(bins[i]); } n_bins_ = bins_.size() - 1; diff --git a/src/tallies/filter_cell.cpp b/src/tallies/filter_cell.cpp index 8286a6976..7cc007756 100644 --- a/src/tallies/filter_cell.cpp +++ b/src/tallies/filter_cell.cpp @@ -12,29 +12,39 @@ namespace openmc { void CellFilter::from_xml(pugi::xml_node node) { - cells_ = get_node_array(node, "bins"); - n_bins_ = cells_.size(); + // Get cell IDs and convert into indices into the global cells vector + auto cells = get_node_array(node, "bins"); + for (auto& c : cells) { + auto search = model::cell_map.find(c); + if (search == model::cell_map.end()) { + std::stringstream err_msg; + err_msg << "Could not find cell " << c + << " specified on tally filter."; + throw std::runtime_error{err_msg.str()}; + } + c = search->second; + } + + this->set_cells(cells); } void -CellFilter::initialize() +CellFilter::set_cells(gsl::span cells) { - // Convert cell IDs to indices of the global array. - for (auto& c : cells_) { - auto search = model::cell_map.find(c); - if (search != model::cell_map.end()) { - c = search->second; - } else { - std::stringstream err_msg; - err_msg << "Could not find cell " << c << " specified on tally filter."; - fatal_error(err_msg); - } + // Clear existing cells + cells_.clear(); + cells_.reserve(cells.size()); + map_.clear(); + + // Update cells and mapping + for (auto& index : cells) { + Expects(index >= 0); + Expects(index < model::cells.size()); + cells_.push_back(index); + map_[index] = cells_.size() - 1; } - // Populate the index->bin map. - for (int i = 0; i < cells_.size(); i++) { - map_[cells_[i]] = i; - } + n_bins_ = cells_.size(); } void @@ -70,7 +80,7 @@ CellFilter::text_label(int bin) const //============================================================================== extern "C" int -openmc_cell_filter_get_bins(int32_t index, int32_t** cells, int32_t* n) +openmc_cell_filter_get_bins(int32_t index, const int32_t** cells, int32_t* n) { if (int err = verify_filter(index)) return err; @@ -81,8 +91,8 @@ openmc_cell_filter_get_bins(int32_t index, int32_t** cells, int32_t* n) } auto cell_filt = static_cast(filt); - *cells = cell_filt->cells_.data(); - *n = cell_filt->cells_.size(); + *cells = cell_filt->cells().data(); + *n = cell_filt->cells().size(); return 0; } diff --git a/src/tallies/filter_delayedgroup.cpp b/src/tallies/filter_delayedgroup.cpp index 4f448358e..2b6978308 100644 --- a/src/tallies/filter_delayedgroup.cpp +++ b/src/tallies/filter_delayedgroup.cpp @@ -8,21 +8,32 @@ namespace openmc { void DelayedGroupFilter::from_xml(pugi::xml_node node) { - groups_ = get_node_array(node, "bins"); - n_bins_ = groups_.size(); + auto groups = get_node_array(node, "bins"); + this->set_groups(groups); +} + +void +DelayedGroupFilter::set_groups(gsl::span groups) +{ + // Clear existing groups + groups_.clear(); + groups_.reserve(groups.size()); // Make sure all the group index values are valid. // TODO: do these need to be decremented for zero-based indexing? - for (auto group : groups_) { + for (auto group : groups) { if (group < 1) { - fatal_error("Encountered delayedgroup bin with index " - + std::to_string(group) + " which is less than 1"); + throw std::invalid_argument{"Encountered delayedgroup bin with index " + + std::to_string(group) + " which is less than 1"}; } else if (group > MAX_DELAYED_GROUPS) { - fatal_error("Encountered delayedgroup bin with index " + throw std::invalid_argument{"Encountered delayedgroup bin with index " + std::to_string(group) + " which is greater than MAX_DELATED_GROUPS (" - + std::to_string(MAX_DELAYED_GROUPS) + ")"); + + std::to_string(MAX_DELAYED_GROUPS) + ")"}; } + groups_.push_back(group); } + + n_bins_ = groups_.size(); } void diff --git a/src/tallies/filter_distribcell.cpp b/src/tallies/filter_distribcell.cpp index 94f23621b..f9c7ab4d6 100644 --- a/src/tallies/filter_distribcell.cpp +++ b/src/tallies/filter_distribcell.cpp @@ -15,23 +15,26 @@ DistribcellFilter::from_xml(pugi::xml_node node) if (cells.size() != 1) { fatal_error("Only one cell can be specified per distribcell filter."); } - cell_ = cells[0]; -} -void -DistribcellFilter::initialize() -{ - // Convert the cell ID to an index of the global array. - auto search = model::cell_map.find(cell_); - if (search != model::cell_map.end()) { - cell_ = search->second; - n_bins_ = model::cells[cell_]->n_instances_; - } else { + // Find index in global cells vector corresponding to cell ID + auto search = model::cell_map.find(cells[0]); + if (search == model::cell_map.end()) { std::stringstream err_msg; err_msg << "Could not find cell " << cell_ << " specified on tally filter."; - fatal_error(err_msg); + throw std::runtime_error{err_msg.str()}; } + + this->set_cell(search->second); +} + +void +DistribcellFilter::set_cell(int32_t cell) +{ + Expects(cell >= 0); + Expects(cell < model::cells.size()); + cell_ = cell; + n_bins_ = model::cells[cell]->n_instances_; } void diff --git a/src/tallies/filter_energy.cpp b/src/tallies/filter_energy.cpp index e11ab3080..dde9b692a 100644 --- a/src/tallies/filter_energy.cpp +++ b/src/tallies/filter_energy.cpp @@ -16,7 +16,25 @@ namespace openmc { void EnergyFilter::from_xml(pugi::xml_node node) { - bins_ = get_node_array(node, "bins"); + auto bins = get_node_array(node, "bins"); + this->set_bins(bins); +} + +void +EnergyFilter::set_bins(gsl::span bins) +{ + // Clear existing bins + bins_.clear(); + bins_.reserve(bins.size()); + + // Copy bins, ensuring they are valid + for (gsl::index i = 0; i < bins.size(); ++i) { + if (i > 0 && bins[i] <= bins[i-1]) { + throw std::runtime_error{"Energy bins must be monotonically increasing."}; + } + bins_.push_back(bins[i]); + } + n_bins_ = bins_.size() - 1; // In MG mode, check if the filter bins match the transport bins. @@ -27,7 +45,7 @@ EnergyFilter::from_xml(pugi::xml_node node) if (!settings::run_CE) { if (n_bins_ == data::num_energy_groups) { matches_transport_groups_ = true; - for (auto i = 0; i < n_bins_ + 1; i++) { + for (gsl::index i = 0; i < n_bins_ + 1; ++i) { if (data::rev_energy_bins[i] != bins_[i]) { matches_transport_groups_ = false; break; @@ -111,7 +129,7 @@ EnergyoutFilter::text_label(int bin) const //============================================================================== extern"C" int -openmc_energy_filter_get_bins(int32_t index, double** energies, int32_t* n) +openmc_energy_filter_get_bins(int32_t index, const double** energies, size_t* n) { // Make sure this is a valid index to an allocated filter. if (int err = verify_filter(index)) return err; @@ -127,13 +145,13 @@ openmc_energy_filter_get_bins(int32_t index, double** energies, int32_t* n) } // Output the bins. - *energies = filt->bins_.data(); - *n = filt->bins_.size(); + *energies = filt->bins().data(); + *n = filt->bins().size(); return 0; } extern "C" int -openmc_energy_filter_set_bins(int32_t index, int32_t n, const double* energies) +openmc_energy_filter_set_bins(int32_t index, size_t n, const double* energies) { // Make sure this is a valid index to an allocated filter. if (int err = verify_filter(index)) return err; @@ -149,10 +167,7 @@ openmc_energy_filter_set_bins(int32_t index, int32_t n, const double* energies) } // Update the filter. - filt->bins_.clear(); - filt->bins_.resize(n); - for (int i = 0; i < n; i++) filt->bins_[i] = energies[i]; - filt->n_bins_ = n - 1; + filt->set_bins({energies, n}); return 0; } diff --git a/src/tallies/filter_energyfunc.cpp b/src/tallies/filter_energyfunc.cpp index 9b28e0f76..3e3fbd909 100644 --- a/src/tallies/filter_energyfunc.cpp +++ b/src/tallies/filter_energyfunc.cpp @@ -21,12 +21,37 @@ EnergyFunctionFilter::from_xml(pugi::xml_node node) if (!check_for_node(node, "energy")) fatal_error("Energy grid not specified for EnergyFunction filter."); - energy_ = get_node_array(node, "energy"); + auto energy = get_node_array(node, "energy"); if (!check_for_node(node, "y")) fatal_error("y values not specified for EnergyFunction filter."); - y_ = get_node_array(node, "y"); + auto y = get_node_array(node, "y"); + + this->set_data(energy, y); +} + +void +EnergyFunctionFilter::set_data(gsl::span energy, + gsl::span y) +{ + // Check for consistent sizes with new data + if (energy.size() != y.size()) { + fatal_error("Energy grid and y values are not consistent"); + } + energy_.clear(); + energy_.reserve(energy.size()); + y_.clear(); + y_.reserve(y.size()); + + // Copy over energy values, ensuring they are valid + for (gsl::index i = 0; i < energy.size(); ++i) { + if (i > 0 && energy[i] <= energy[i - 1]) { + throw std::runtime_error{"Energy bins must be monotonically increasing."}; + } + energy_.push_back(energy[i]); + y_.push_back(y[i]); + } } void @@ -65,4 +90,72 @@ EnergyFunctionFilter::text_label(int bin) const return out.str(); } +//============================================================================== +// C-API functions +//============================================================================== + +extern "C" int +openmc_energyfunc_filter_set_data(int32_t index, size_t n, const double* energy, + const double* y) +{ + // Ensure this is a valid index to allocated filter + if (int err = verify_filter(index)) return err; + + // Get a pointer to the filter + const auto& filt_base = model::tally_filters[index].get(); + // Downcast to EnergyFunctionFilter + auto* filt = dynamic_cast(filt_base); + + // Check if a valid filter was produced + if (!filt) { + set_errmsg("Tried to set interpolation data for non-energy function filter."); + return OPENMC_E_INVALID_TYPE; + } + + filt->set_data({energy, n}, {y, n}); + return 0; +} + +extern "C" int +openmc_energyfunc_filter_get_energy(int32_t index, size_t *n, const double** energy) +{ + // ensure this is a valid index to allocated filter + if (int err = verify_filter(index)) return err; + + // get a pointer to the filter + const auto& filt_base = model::tally_filters[index].get(); + // downcast to EnergyFunctionFilter + auto* filt = dynamic_cast(filt_base); + + // check if a valid filter was produced + if (!filt) { + set_errmsg("Tried to set interpolation data for non-energy function filter."); + return OPENMC_E_INVALID_TYPE; + } + *energy = filt->energy().data(); + *n = filt->energy().size(); + return 0; +} + +extern "C" int +openmc_energyfunc_filter_get_y(int32_t index, size_t *n, const double** y) +{ + // ensure this is a valid index to allocated filter + if (int err = verify_filter(index)) return err; + + // get a pointer to the filter + const auto& filt_base = model::tally_filters[index].get(); + // downcast to EnergyFunctionFilter + auto* filt = dynamic_cast(filt_base); + + // check if a valid filter was produced + if (!filt) { + set_errmsg("Tried to set interpolation data for non-energy function filter."); + return OPENMC_E_INVALID_TYPE; + } + *y = filt->y().data(); + *n = filt->y().size(); + return 0; +} + } // namespace openmc diff --git a/src/tallies/filter_legendre.cpp b/src/tallies/filter_legendre.cpp index 6fa041c76..3961e06d4 100644 --- a/src/tallies/filter_legendre.cpp +++ b/src/tallies/filter_legendre.cpp @@ -10,7 +10,16 @@ namespace openmc { void LegendreFilter::from_xml(pugi::xml_node node) { - order_ = std::stoi(get_node_value(node, "order")); + this->set_order(std::stoi(get_node_value(node, "order"))); +} + +void +LegendreFilter::set_order(int order) +{ + if (order < 0) { + throw std::invalid_argument{"Legendre order must be non-negative."}; + } + order_ = order; n_bins_ = order_ + 1; } @@ -60,7 +69,7 @@ openmc_legendre_filter_get_order(int32_t index, int* order) } // Output the order. - *order = filt->order_; + *order = filt->order(); return 0; } @@ -81,8 +90,7 @@ openmc_legendre_filter_set_order(int32_t index, int order) } // Update the filter. - filt->order_ = order; - filt->n_bins_ = order + 1; + filt->set_order(order); return 0; } diff --git a/src/tallies/filter_material.cpp b/src/tallies/filter_material.cpp index fd9e637bd..bfc216244 100644 --- a/src/tallies/filter_material.cpp +++ b/src/tallies/filter_material.cpp @@ -3,7 +3,6 @@ #include #include "openmc/capi.h" -#include "openmc/error.h" #include "openmc/material.h" #include "openmc/xml_interface.h" @@ -12,30 +11,39 @@ namespace openmc { void MaterialFilter::from_xml(pugi::xml_node node) { - materials_ = get_node_array(node, "bins"); - n_bins_ = materials_.size(); -} - -void -MaterialFilter::initialize() -{ - // Convert material IDs to indices of the global array. - for (auto& m : materials_) { + // Get material IDs and convert to indices in the global materials vector + auto mats = get_node_array(node, "bins"); + for (auto& m : mats) { auto search = model::material_map.find(m); - if (search != model::material_map.end()) { - m = search->second; - } else { + if (search == model::material_map.end()) { std::stringstream err_msg; err_msg << "Could not find material " << m << " specified on tally filter."; - fatal_error(err_msg); + throw std::runtime_error{err_msg.str()}; } + m = search->second; } - // Populate the index->bin map. - for (int i = 0; i < materials_.size(); i++) { - map_[materials_[i]] = i; + this->set_materials(mats); +} + +void +MaterialFilter::set_materials(gsl::span materials) +{ + // Clear existing materials + materials_.clear(); + materials_.reserve(materials.size()); + map_.clear(); + + // Update materials and mapping + for (auto& index : materials) { + Expects(index >= 0); + Expects(index < model::materials.size()); + materials_.push_back(index); + map_[index] = materials_.size() - 1; } + + n_bins_ = materials_.size(); } void @@ -69,7 +77,7 @@ MaterialFilter::text_label(int bin) const //============================================================================== extern "C" int -openmc_material_filter_get_bins(int32_t index, int32_t** bins, int32_t* n) +openmc_material_filter_get_bins(int32_t index, const int32_t** bins, size_t* n) { // Make sure this is a valid index to an allocated filter. if (int err = verify_filter(index)) return err; @@ -85,13 +93,13 @@ openmc_material_filter_get_bins(int32_t index, int32_t** bins, int32_t* n) } // Output the bins. - *bins = filt->materials_.data(); - *n = filt->materials_.size(); + *bins = filt->materials().data(); + *n = filt->materials().size(); return 0; } extern "C" int -openmc_material_filter_set_bins(int32_t index, int32_t n, const int32_t* bins) +openmc_material_filter_set_bins(int32_t index, size_t n, const int32_t* bins) { // Make sure this is a valid index to an allocated filter. if (int err = verify_filter(index)) return err; @@ -107,12 +115,7 @@ openmc_material_filter_set_bins(int32_t index, int32_t n, const int32_t* bins) } // Update the filter. - filt->materials_.clear(); - filt->materials_.resize(n); - for (int i = 0; i < n; i++) filt->materials_[i] = bins[i]; - filt->n_bins_ = filt->materials_.size(); - filt->map_.clear(); - for (int i = 0; i < n; i++) filt->map_[filt->materials_[i]] = i; + filt->set_materials({bins, n}); return 0; } diff --git a/src/tallies/filter_mu.cpp b/src/tallies/filter_mu.cpp index aed8371b8..9aaebea8c 100644 --- a/src/tallies/filter_mu.cpp +++ b/src/tallies/filter_mu.cpp @@ -13,22 +13,36 @@ MuFilter::from_xml(pugi::xml_node node) { auto bins = get_node_array(node, "bins"); - if (bins.size() > 1) { - bins_ = bins; - - } else { + if (bins.size() == 1) { // Allow a user to input a lone number which will mean that you subdivide // [-1,1) evenly with the input being the number of bins int n_angle = bins[0]; - - if (n_angle <= 1) fatal_error("Number of bins for mu filter must " - "be greater than 1."); + if (n_angle <= 1) throw std::runtime_error{ + "Number of bins for mu filter must be greater than 1."}; double d_angle = 2.0 / n_angle; - bins_.resize(n_angle + 1); - for (int i = 0; i < n_angle; i++) bins_[i] = -1 + i * d_angle; - bins_[n_angle] = 1; + bins.resize(n_angle + 1); + for (int i = 0; i < n_angle; i++) bins[i] = -1 + i * d_angle; + bins[n_angle] = 1; + } + + this->set_bins(bins); +} + +void +MuFilter::set_bins(gsl::span bins) +{ + // Clear existing bins + bins_.clear(); + bins_.reserve(bins.size()); + + // Copy bins, ensuring they are valid + for (gsl::index i = 0; i < bins.size(); ++i) { + if (i > 0 && bins[i] <= bins[i-1]) { + throw std::runtime_error{"Mu bins must be monotonically increasing."}; + } + bins_.push_back(bins[i]); } n_bins_ = bins_.size() - 1; diff --git a/src/tallies/filter_particle.cpp b/src/tallies/filter_particle.cpp index 23a305b8d..eb419fec9 100644 --- a/src/tallies/filter_particle.cpp +++ b/src/tallies/filter_particle.cpp @@ -7,9 +7,34 @@ namespace openmc { void ParticleFilter::from_xml(pugi::xml_node node) { - auto particles = get_node_array(node, "bins"); + auto particles = get_node_array(node, "bins"); + + // Convert to vector of Particle::Type + std::vector types; for (auto& p : particles) { - particles_.push_back(static_cast(p - 1)); + if (p == "neutron") { + types.push_back(Particle::Type::neutron); + } else if (p == "photon") { + types.push_back(Particle::Type::photon); + } else if (p == "electron") { + types.push_back(Particle::Type::electron); + } else if (p == "positron") { + types.push_back(Particle::Type::positron); + } + } + this->set_particles(types); +} + +void +ParticleFilter::set_particles(gsl::span particles) +{ + // Clear existing particles + particles_.clear(); + particles_.reserve(particles.size()); + + // Set particles and number of bins + for (auto p : particles) { + particles_.push_back(p); } n_bins_ = particles_.size(); } @@ -30,9 +55,22 @@ void ParticleFilter::to_statepoint(hid_t filter_group) const { Filter::to_statepoint(filter_group); - std::vector particles; + std::vector particles; for (auto p : particles_) { - particles.push_back(static_cast(p) + 1); + switch (p) { + case Particle::Type::neutron: + particles.push_back("neutron"); + break; + case Particle::Type::photon: + particles.push_back("photon"); + break; + case Particle::Type::electron: + particles.push_back("electron"); + break; + case Particle::Type::positron: + particles.push_back("positron"); + break; + } } write_dataset(filter_group, "bins", particles); } diff --git a/src/tallies/filter_polar.cpp b/src/tallies/filter_polar.cpp index 9730bbb58..edbbc8922 100644 --- a/src/tallies/filter_polar.cpp +++ b/src/tallies/filter_polar.cpp @@ -14,22 +14,36 @@ PolarFilter::from_xml(pugi::xml_node node) { auto bins = get_node_array(node, "bins"); - if (bins.size() > 1) { - bins_ = bins; - - } else { + if (bins.size() == 1) { // Allow a user to input a lone number which will mean that you subdivide // [0,pi] evenly with the input being the number of bins int n_angle = bins[0]; - - if (n_angle <= 1) fatal_error("Number of bins for polar filter must " - "be greater than 1."); + if (n_angle <= 1) throw std::runtime_error{ + "Number of bins for polar filter must be greater than 1."}; double d_angle = PI / n_angle; - bins_.resize(n_angle + 1); - for (int i = 0; i < n_angle; i++) bins_[i] = i * d_angle; - bins_[n_angle] = PI; + bins.resize(n_angle + 1); + for (int i = 0; i < n_angle; i++) bins[i] = i * d_angle; + bins[n_angle] = PI; + } + + this->set_bins(bins); +} + +void +PolarFilter::set_bins(gsl::span bins) +{ + // Clear existing bins + bins_.clear(); + bins_.reserve(bins.size()); + + // Copy bins, ensuring they are valid + for (gsl::index i = 0; i < bins.size(); ++i) { + if (i > 0 && bins[i] <= bins[i-1]) { + throw std::runtime_error{"Polar bins must be monotonically increasing."}; + } + bins_.push_back(bins[i]); } n_bins_ = bins_.size() - 1; diff --git a/src/tallies/filter_sph_harm.cpp b/src/tallies/filter_sph_harm.cpp index 0b3a37118..80d6878b7 100644 --- a/src/tallies/filter_sph_harm.cpp +++ b/src/tallies/filter_sph_harm.cpp @@ -12,21 +12,34 @@ namespace openmc { void SphericalHarmonicsFilter::from_xml(pugi::xml_node node) { - order_ = std::stoi(get_node_value(node, "order")); - n_bins_ = (order_ + 1) * (order_ + 1); - + this->set_order(std::stoi(get_node_value(node, "order"))); if (check_for_node(node, "cosine")) { - auto cos = get_node_value(node, "cosine", true); - if (cos == "scatter") { - cosine_ = SphericalHarmonicsCosine::scatter; - } else if (cos == "particle") { - cosine_ = SphericalHarmonicsCosine::particle; - } else { - std::stringstream err_msg; - err_msg << "Unrecognized cosine type, \"" << cos - << "\" in spherical harmonics filter"; - fatal_error(err_msg); - } + this->set_cosine(get_node_value(node, "cosine", true)); + } +} + +void +SphericalHarmonicsFilter::set_order(int order) +{ + if (order < 0) { + throw std::invalid_argument{"Spherical harmonics order must be non-negative."}; + } + order_ = order; + n_bins_ = (order_ + 1) * (order_ + 1); +} + +void +SphericalHarmonicsFilter::set_cosine(gsl::cstring_span cosine) +{ + if (cosine == "scatter") { + cosine_ = SphericalHarmonicsCosine::scatter; + } else if (cosine == "particle") { + cosine_ = SphericalHarmonicsCosine::particle; + } else { + std::stringstream err_msg; + err_msg << "Unrecognized cosine type, \"" << cosine + << "\" in spherical harmonics filter"; + throw std::invalid_argument{err_msg.str()}; } } @@ -121,7 +134,7 @@ openmc_sphharm_filter_get_order(int32_t index, int* order) if (err) return err; // Output the order. - *order = filt->order_; + *order = filt->order(); return 0; } @@ -135,7 +148,7 @@ openmc_sphharm_filter_get_cosine(int32_t index, char cosine[]) if (err) return err; // Output the cosine. - if (filt->cosine_ == SphericalHarmonicsCosine::scatter) { + if (filt->cosine() == SphericalHarmonicsCosine::scatter) { strcpy(cosine, "scatter"); } else { strcpy(cosine, "particle"); @@ -153,8 +166,7 @@ openmc_sphharm_filter_set_order(int32_t index, int order) if (err) return err; // Update the filter. - filt->order_ = order; - filt->n_bins_ = (order + 1) * (order + 1); + filt->set_order(order); return 0; } @@ -168,12 +180,10 @@ openmc_sphharm_filter_set_cosine(int32_t index, const char cosine[]) if (err) return err; // Update the filter. - if (strcmp(cosine, "scatter") == 0) { - filt->cosine_ = SphericalHarmonicsCosine::scatter; - } else if (strcmp(cosine, "particle") == 0) { - filt->cosine_ = SphericalHarmonicsCosine::particle; - } else { - set_errmsg("Invalid spherical harmonics cosine."); + try { + filt->set_cosine(cosine); + } catch (const std::invalid_argument& e) { + set_errmsg(e.what()); return OPENMC_E_INVALID_ARGUMENT; } return 0; diff --git a/src/tallies/filter_sptl_legendre.cpp b/src/tallies/filter_sptl_legendre.cpp index 6562ff01c..40d82f4c1 100644 --- a/src/tallies/filter_sptl_legendre.cpp +++ b/src/tallies/filter_sptl_legendre.cpp @@ -12,25 +12,54 @@ namespace openmc { void SpatialLegendreFilter::from_xml(pugi::xml_node node) { - order_ = std::stoi(get_node_value(node, "order")); + this->set_order(std::stoi(get_node_value(node, "order"))); auto axis = get_node_value(node, "axis"); - if (axis == "x") { - axis_ = LegendreAxis::x; - } else if (axis == "y") { - axis_ = LegendreAxis::y; - } else if (axis == "z") { - axis_ = LegendreAxis::z; - } else { - fatal_error("Unrecognized axis on SpatialLegendreFilter"); + switch (axis[0]) { + case 'x': + this->set_axis(LegendreAxis::x); + break; + case 'y': + this->set_axis(LegendreAxis::y); + break; + case 'z': + this->set_axis(LegendreAxis::z); + break; + default: + throw std::runtime_error{"Axis for SpatialLegendreFilter must be 'x', 'y', or 'z'"}; } - min_ = std::stod(get_node_value(node, "min")); - max_ = std::stod(get_node_value(node, "max")); + double min = std::stod(get_node_value(node, "min")); + double max = std::stod(get_node_value(node, "max")); + this->set_minmax(min, max); +} +void +SpatialLegendreFilter::set_order(int order) +{ + if (order < 0) { + throw std::invalid_argument{"Legendre order must be non-negative."}; + } + order_ = order; n_bins_ = order_ + 1; } +void +SpatialLegendreFilter::set_axis(LegendreAxis axis) +{ + axis_ = axis; +} + +void +SpatialLegendreFilter::set_minmax(double min, double max) +{ + if (max < min) { + throw std::invalid_argument{"Maximum value must be greater than minimum value"}; + } + min_ = min; + max_ = max; +} + void SpatialLegendreFilter::get_all_bins(const Particle* p, int estimator, FilterMatch& match) const @@ -126,7 +155,7 @@ openmc_spatial_legendre_filter_get_order(int32_t index, int* order) if (err) return err; // Output the order. - *order = filt->order_; + *order = filt->order(); return 0; } @@ -141,9 +170,9 @@ openmc_spatial_legendre_filter_get_params(int32_t index, int* axis, if (err) return err; // Output the params. - *axis = static_cast(filt->axis_); - *min = filt->min_; - *max = filt->max_; + *axis = static_cast(filt->axis()); + *min = filt->min(); + *max = filt->max(); return 0; } @@ -157,8 +186,7 @@ openmc_spatial_legendre_filter_set_order(int32_t index, int order) if (err) return err; // Update the filter. - filt->order_ = order; - filt->n_bins_ = order + 1; + filt->set_order(order); return 0; } @@ -173,9 +201,8 @@ openmc_spatial_legendre_filter_set_params(int32_t index, const int* axis, if (err) return err; // Update the filter. - if (axis) filt->axis_ = static_cast(*axis); - if (min) filt->min_ = *min; - if (max) filt->max_ = *max; + if (axis) filt->set_axis(static_cast(*axis)); + if (min && max) filt->set_minmax(*min, *max); return 0; } diff --git a/src/tallies/filter_surface.cpp b/src/tallies/filter_surface.cpp index faa8aafbf..72b356f21 100644 --- a/src/tallies/filter_surface.cpp +++ b/src/tallies/filter_surface.cpp @@ -11,30 +11,41 @@ namespace openmc { void SurfaceFilter::from_xml(pugi::xml_node node) { - surfaces_ = get_node_array(node, "bins"); - n_bins_ = surfaces_.size(); -} + auto surfaces = get_node_array(node, "bins"); -void -SurfaceFilter::initialize() -{ - // Convert surface IDs to indices of the global array. - for (auto& s : surfaces_) { + // Convert surface IDs to indices of the global surfaces vector. + for (auto& s : surfaces) { auto search = model::surface_map.find(s); - if (search != model::surface_map.end()) { - s = search->second; - } else { + if (search == model::surface_map.end()) { std::stringstream err_msg; err_msg << "Could not find surface " << s << " specified on tally filter."; - fatal_error(err_msg); + throw std::runtime_error{err_msg.str()}; } + + s = search->second; } - // Populate the index->bin map. - for (int i = 0; i < surfaces_.size(); i++) { - map_[surfaces_[i]] = i; + this->set_surfaces(surfaces); +} + +void +SurfaceFilter::set_surfaces(gsl::span surfaces) +{ + // Clear existing surfaces + surfaces_.clear(); + surfaces_.reserve(surfaces.size()); + map_.clear(); + + // Update surfaces and mapping + for (auto& index : surfaces) { + Expects(index >= 0); + Expects(index < model::surfaces.size()); + surfaces_.push_back(index); + map_[index] = surfaces_.size() - 1; } + + n_bins_ = surfaces_.size(); } void diff --git a/src/tallies/filter_universe.cpp b/src/tallies/filter_universe.cpp index 50c058c05..dffdee621 100644 --- a/src/tallies/filter_universe.cpp +++ b/src/tallies/filter_universe.cpp @@ -11,30 +11,39 @@ namespace openmc { void UniverseFilter::from_xml(pugi::xml_node node) { - universes_ = get_node_array(node, "bins"); - n_bins_ = universes_.size(); -} - -void -UniverseFilter::initialize() -{ - // Convert universe IDs to indices of the global array. - for (auto& u : universes_) { + // Get material IDs and convert to indices in the global materials vector + auto universes = get_node_array(node, "bins"); + for (auto& u : universes) { auto search = model::universe_map.find(u); - if (search != model::universe_map.end()) { - u = search->second; - } else { + if (search == model::universe_map.end()) { std::stringstream err_msg; err_msg << "Could not find universe " << u << " specified on tally filter."; - fatal_error(err_msg); + throw std::runtime_error{err_msg.str()}; } + u = search->second; } - // Populate the index->bin map. - for (int i = 0; i < universes_.size(); i++) { - map_[universes_[i]] = i; + this->set_universes(universes); +} + +void +UniverseFilter::set_universes(gsl::span universes) +{ + // Clear existing universes + universes_.clear(); + universes_.reserve(universes.size()); + map_.clear(); + + // Update universes and mapping + for (auto& index : universes) { + Expects(index >= 0); + Expects(index < model::universes.size()); + universes_.push_back(index); + map_[index] = universes_.size() - 1; } + + n_bins_ = universes_.size(); } void diff --git a/src/tallies/filter_zernike.cpp b/src/tallies/filter_zernike.cpp index 125bfada8..b732ef793 100644 --- a/src/tallies/filter_zernike.cpp +++ b/src/tallies/filter_zernike.cpp @@ -74,6 +74,9 @@ ZernikeFilter::text_label(int bin) const void ZernikeFilter::set_order(int order) { + if (order < 0) { + throw std::invalid_argument{"Zernike order must be non-negative."}; + } order_ = order; n_bins_ = ((order+1) * (order+2)) / 2; } @@ -111,7 +114,7 @@ ZernikeRadialFilter::text_label(int bin) const void ZernikeRadialFilter::set_order(int order) { - order_ = order; + ZernikeFilter::set_order(order); n_bins_ = order / 2 + 1; } @@ -165,9 +168,9 @@ openmc_zernike_filter_get_params(int32_t index, double* x, double* y, if (err) return err; // Output the params. - *x = filt->x_; - *y = filt->y_; - *r = filt->r_; + *x = filt->x(); + *y = filt->y(); + *r = filt->r(); return 0; } @@ -196,9 +199,9 @@ openmc_zernike_filter_set_params(int32_t index, const double* x, if (err) return err; // Update the filter. - if (x) filt->x_ = *x; - if (y) filt->y_ = *y; - if (r) filt->r_ = *r; + if (x) filt->set_x(*x); + if (y) filt->set_y(*y); + if (r) filt->set_r(*r); return 0; } diff --git a/src/tallies/tally.cpp b/src/tallies/tally.cpp index 2cb0945ab..27d6cc58c 100644 --- a/src/tallies/tally.cpp +++ b/src/tallies/tally.cpp @@ -240,37 +240,293 @@ score_str_to_int(std::string score_str) // Tally object implementation //============================================================================== -Tally::Tally() +Tally::Tally(int32_t id) + : index_{model::tallies.size()} { - this->set_filters(nullptr, 0); + this->set_id(id); + this->set_filters({}); } -void -Tally::init_from_xml(pugi::xml_node node) +Tally::Tally(pugi::xml_node node) + : index_{model::tallies.size()} { + // Copy and set tally id + if (!check_for_node(node, "id")) { + throw std::runtime_error{"Must specify id for tally in tally XML file."}; + } + int32_t id = std::stoi(get_node_value(node, "id")); + this->set_id(id); + if (check_for_node(node, "name")) name_ = get_node_value(node, "name"); + + // ======================================================================= + // READ DATA FOR FILTERS + + // Check if user is using old XML format and throw an error if so + if (check_for_node(node, "filter")) { + throw std::runtime_error{"Tally filters must be specified independently of " + "tallies in a element. The element itself should " + "have a list of filters that apply, e.g., 1 2 " + "where 1 and 2 are the IDs of filters specified outside of " + "."}; + } + + // Determine number of filters + std::vector filter_ids; + if (check_for_node(node, "filters")) { + filter_ids = get_node_array(node, "filters"); + } + + // Allocate and store filter user ids + std::vector filters; + for (int filter_id : filter_ids) { + // Determine if filter ID is valid + auto it = model::filter_map.find(filter_id); + if (it == model::filter_map.end()) { + throw std::runtime_error{"Could not find filter " + std::to_string(filter_id) + + " specified on tally " + std::to_string(id_)}; + } + + // Store the index of the filter + filters.push_back(model::tally_filters[it->second].get()); + } + + // Set the filters + this->set_filters(filters); + + // Check for the presence of certain filter types + bool has_energyout = energyout_filter_ >= 0; + int particle_filter_index = C_NONE; + for (gsl::index j = 0; j < filters_.size(); ++j) { + int i_filter = filters_[j]; + const auto& f = model::tally_filters[i_filter].get(); + + auto pf = dynamic_cast(f); + if (pf) particle_filter_index = i_filter; + + // Change the tally estimator if a filter demands it + std::string filt_type = f->type(); + if (filt_type == "energyout" || filt_type == "legendre") { + estimator_ = ESTIMATOR_ANALOG; + } else if (filt_type == "sphericalharmonics") { + auto sf = dynamic_cast(f); + if (sf->cosine() == SphericalHarmonicsCosine::scatter) { + estimator_ = ESTIMATOR_ANALOG; + } + } else if (filt_type == "spatiallegendre" || filt_type == "zernike" + || filt_type == "zernikeradial") { + estimator_ = ESTIMATOR_COLLISION; + } + } + + // ======================================================================= + // READ DATA FOR NUCLIDES + + this->set_nuclides(node); + + // ======================================================================= + // READ DATA FOR SCORES + + this->set_scores(node); + + if (!check_for_node(node, "scores")) { + fatal_error("No scores specified on tally " + std::to_string(id_) + + "."); + } + + // Check if tally is compatible with particle type + if (settings::photon_transport) { + if (particle_filter_index == C_NONE) { + for (int score : scores_) { + switch (score) { + case SCORE_INVERSE_VELOCITY: + fatal_error("Particle filter must be used with photon " + "transport on and inverse velocity score"); + break; + case SCORE_FLUX: + case SCORE_TOTAL: + case SCORE_SCATTER: + case SCORE_NU_SCATTER: + case SCORE_ABSORPTION: + case SCORE_FISSION: + case SCORE_NU_FISSION: + case SCORE_CURRENT: + case SCORE_EVENTS: + case SCORE_DELAYED_NU_FISSION: + case SCORE_PROMPT_NU_FISSION: + case SCORE_DECAY_RATE: + warning("Particle filter is not used with photon transport" + " on and " + reaction_name(score) + " score."); + break; + } + } + } else { + const auto& f = model::tally_filters[particle_filter_index].get(); + auto pf = dynamic_cast(f); + for (auto p : pf->particles()) { + if (p == Particle::Type::electron || + p == Particle::Type::positron) { + estimator_ = ESTIMATOR_ANALOG; + } + } + } + } else { + if (particle_filter_index >= 0) { + const auto& f = model::tally_filters[particle_filter_index].get(); + auto pf = dynamic_cast(f); + for (auto p : pf->particles()) { + if (p != Particle::Type::neutron) { + warning("Particle filter other than NEUTRON used with photon " + "transport turned off. All tallies for particle type " + + std::to_string(static_cast(p)) + " will have no scores"); + } + } + } + } + + // Check for a tally derivative. + if (check_for_node(node, "derivative")) { + int deriv_id = std::stoi(get_node_value(node, "derivative")); + + // Find the derivative with the given id, and store it's index. + auto it = model::tally_deriv_map.find(deriv_id); + if (it == model::tally_deriv_map.end()) { + fatal_error("Could not find derivative " + std::to_string(deriv_id) + + " specified on tally " + std::to_string(id_)); + } + + deriv_ = it->second; + + // Only analog or collision estimators are supported for differential + // tallies. + if (estimator_ == ESTIMATOR_TRACKLENGTH) { + estimator_ = ESTIMATOR_COLLISION; + } + + const auto& deriv = model::tally_derivs[deriv_]; + if (deriv.variable == DIFF_NUCLIDE_DENSITY + || deriv.variable == DIFF_TEMPERATURE) { + for (int i_nuc : nuclides_) { + if (has_energyout && i_nuc == -1) { + fatal_error("Error on tally " + std::to_string(id_) + + ": Cannot use a 'nuclide_density' or 'temperature' " + "derivative on a tally with an outgoing energy filter and " + "'total' nuclide rate. Instead, tally each nuclide in the " + "material individually."); + // Note that diff tallies with these characteristics would work + // correctly if no tally events occur in the perturbed material + // (e.g. pertrubing moderator but only tallying fuel), but this + // case would be hard to check for by only reading inputs. + } + } + } + } + + // If settings.xml trigger is turned on, create tally triggers + if (settings::trigger_on) { + this->init_triggers(node); + } + + // ======================================================================= + // SET TALLY ESTIMATOR + + // Check if user specified estimator + if (check_for_node(node, "estimator")) { + std::string est = get_node_value(node, "estimator"); + if (est == "analog") { + estimator_ = ESTIMATOR_ANALOG; + } else if (est == "tracklength" || est == "track-length" + || est == "pathlength" || est == "path-length") { + // If the estimator was set to an analog estimator, this means the + // tally needs post-collision information + if (estimator_ == ESTIMATOR_ANALOG) { + throw std::runtime_error{"Cannot use track-length estimator for tally " + + std::to_string(id_)}; + } + + // Set estimator to track-length estimator + estimator_ = ESTIMATOR_TRACKLENGTH; + + } else if (est == "collision") { + // If the estimator was set to an analog estimator, this means the + // tally needs post-collision information + if (estimator_ == ESTIMATOR_ANALOG) { + throw std::runtime_error{"Cannot use collision estimator for tally " + + std::to_string(id_)}; + } + + // Set estimator to collision estimator + estimator_ = ESTIMATOR_COLLISION; + + } else { + throw std::runtime_error{"Invalid estimator '" + est + "' on tally " + + std::to_string(id_)}; + } + } +} + +Tally::~Tally() +{ + model::tally_map.erase(id_); +} + +Tally* +Tally::create(int32_t id) +{ + model::tallies.push_back(std::make_unique(id)); + return model::tallies.back().get(); } void -Tally::set_filters(const int32_t filter_indices[], int n) +Tally::set_id(int32_t id) +{ + Expects(id >= -1); + + // Clear entry in tally map if an ID was already assigned before + if (id_ != -1) { + model::tally_map.erase(id_); + id_ = -1; + } + + // Make sure no other tally has the same ID + if (model::tally_map.find(id) != model::tally_map.end()) { + throw std::runtime_error{"Two tallies have the same ID: " + std::to_string(id)}; + } + + // If no ID specified, auto-assign next ID in sequence + if (id == -1) { + id = 0; + for (const auto& t : model::tallies) { + id = std::max(id, t->id_); + } + ++id; + } + + // Update ID and entry in tally map + id_ = id; + model::tally_map[id] = index_; +} + +void +Tally::set_filters(gsl::span filters) { // Clear old data. filters_.clear(); strides_.clear(); // Copy in the given filter indices. - filters_.assign(filter_indices, filter_indices + n); + auto n = filters.size(); + filters_.reserve(n); for (int i = 0; i < n; ++i) { - auto i_filt = filters_[i]; - if (i_filt < 0 || i_filt >= model::tally_filters.size()) - throw std::out_of_range("Index in tally filter array out of bounds."); + // Add index to vector of filters + auto& f {filters[i]}; + filters_.push_back(model::filter_map.at(f->id())); // Keep track of indices for special filters. - const auto* filt = model::tally_filters[i_filt].get(); - if (dynamic_cast(filt)) { + if (dynamic_cast(f)) { energyout_filter_ = i; - } else if (dynamic_cast(filt)) { + } else if (dynamic_cast(f)) { delayedgroup_filter_ = i; } } @@ -282,7 +538,7 @@ Tally::set_filters(const int32_t filter_indices[], int n) int stride = 1; for (int i = n-1; i >= 0; --i) { strides_[i] = stride; - stride *= model::tally_filters[filters_[i]]->n_bins_; + stride *= model::tally_filters[filters_[i]]->n_bins(); } n_filter_bins_ = stride; } @@ -298,7 +554,7 @@ Tally::set_scores(pugi::xml_node node) } void -Tally::set_scores(std::vector scores) +Tally::set_scores(const std::vector& scores) { // Reset state and prepare for the new scores. scores_.clear(); @@ -450,17 +706,25 @@ Tally::set_nuclides(pugi::xml_node node) // The user provided specifics nuclides. Parse it as an array with either // "total" or a nuclide name like "U-235" in each position. auto words = get_node_array(node, "nuclides"); - for (auto word : words) { - if (word == "total") { - nuclides_.push_back(-1); - } else { - auto search = data::nuclide_map.find(word); - if (search == data::nuclide_map.end()) - fatal_error("Could not find the nuclide " + word - + " specified in tally " + std::to_string(id_) - + " in any material"); - nuclides_.push_back(search->second); - } + this->set_nuclides(words); + } +} + +void +Tally::set_nuclides(const std::vector& nuclides) +{ + nuclides_.clear(); + + for (const auto& nuc : nuclides) { + if (nuc == "total") { + nuclides_.push_back(-1); + } else { + auto search = data::nuclide_map.find(nuc); + if (search == data::nuclide_map.end()) + fatal_error("Could not find the nuclide " + nuc + + " specified in tally " + std::to_string(id_) + + " in any material"); + nuclides_.push_back(search->second); } } } @@ -614,36 +878,7 @@ void read_tallies_xml() // Check for user filters and allocate for (auto node_filt : root.children("filter")) { - // Copy filter id - if (!check_for_node(node_filt, "id")) { - fatal_error("Must specify id for filter in tally XML file."); - } - int filter_id = std::stoi(get_node_value(node_filt, "id")); - - // Check to make sure 'id' hasn't been used - if (model::filter_map.find(filter_id) != model::filter_map.end()) { - fatal_error("Two or more filters use the same unique ID: " - + std::to_string(filter_id)); - } - - // Convert filter type to lower case - std::string s; - if (check_for_node(node_filt, "type")) { - s = get_node_value(node_filt, "type", true); - } - - // Allocate according to the filter type - Filter* f = allocate_filter(s); - - // Read filter data from XML - f->from_xml(node_filt); - - // Set filter id - f->id_ = filter_id; - model::filter_map[filter_id] = model::tally_filters.size() - 1; - - // Initialize filter - f->initialize(); + auto f = Filter::create(node_filt); } // ========================================================================== @@ -657,229 +892,7 @@ void read_tallies_xml() } for (auto node_tal : root.children("tally")) { - model::tallies.push_back(std::make_unique()); - - auto& t {model::tallies.back()}; - t->init_from_xml(node_tal); - - // Copy and set tally id - if (!check_for_node(node_tal, "id")) { - fatal_error("Must specify id for tally in tally XML file."); - } - t->id_ = std::stoi(get_node_value(node_tal, "id")); - model::tally_map[t->id_] = model::tallies.size() - 1; - - // Copy tally name - if (check_for_node(node_tal, "name")) { - t->name_ = get_node_value(node_tal, "name"); - } - - // ======================================================================= - // READ DATA FOR FILTERS - - // Check if user is using old XML format and throw an error if so - if (check_for_node(node_tal, "filter")) { - fatal_error("Tally filters must be specified independently of " - "tallies in a element. The element itself should " - "have a list of filters that apply, e.g., 1 2 " - "where 1 and 2 are the IDs of filters specified outside of " - "."); - } - - // Determine number of filters - std::vector filters; - if (check_for_node(node_tal, "filters")) { - filters = get_node_array(node_tal, "filters"); - } - - // Allocate and store filter user ids - if (!filters.empty()) { - std::vector filter_indices; - for (int filter_id : filters) { - // Determine if filter ID is valid - auto it = model::filter_map.find(filter_id); - if (it == model::filter_map.end()) { - fatal_error("Could not find filter " + std::to_string(filter_id) - + " specified on tally " + std::to_string(t->id_)); - } - - // Store the index of the filter - filter_indices.push_back(it->second); - } - - // Set the filters - t->set_filters(filter_indices.data(), filter_indices.size()); - } - - // Check for the presence of certain filter types - bool has_energyout = t->energyout_filter_ >= 0; - int particle_filter_index = C_NONE; - for (int j = 0; j < t->filters().size(); ++j) { - int i_filter = t->filters(j); - const auto& f = model::tally_filters[i_filter].get(); - - auto pf = dynamic_cast(f); - if (pf) particle_filter_index = i_filter; - - // Change the tally estimator if a filter demands it - std::string filt_type = f->type(); - if (filt_type == "energyout" || filt_type == "legendre") { - t->estimator_ = ESTIMATOR_ANALOG; - } else if (filt_type == "sphericalharmonics") { - auto sf = dynamic_cast(f); - if (sf->cosine_ == SphericalHarmonicsCosine::scatter) { - t->estimator_ = ESTIMATOR_ANALOG; - } - } else if (filt_type == "spatiallegendre" || filt_type == "zernike" - || filt_type == "zernikeradial") { - t->estimator_ = ESTIMATOR_COLLISION; - } - } - - // ======================================================================= - // READ DATA FOR NUCLIDES - - t->set_nuclides(node_tal); - - // ======================================================================= - // READ DATA FOR SCORES - - t->set_scores(node_tal); - - if (!check_for_node(node_tal, "scores")) { - fatal_error("No scores specified on tally " + std::to_string(t->id_) - + "."); - } - - // Check if tally is compatible with particle type - if (settings::photon_transport) { - if (particle_filter_index == C_NONE) { - for (int score : t->scores_) { - switch (score) { - case SCORE_INVERSE_VELOCITY: - fatal_error("Particle filter must be used with photon " - "transport on and inverse velocity score"); - break; - case SCORE_FLUX: - case SCORE_TOTAL: - case SCORE_SCATTER: - case SCORE_NU_SCATTER: - case SCORE_ABSORPTION: - case SCORE_FISSION: - case SCORE_NU_FISSION: - case SCORE_CURRENT: - case SCORE_EVENTS: - case SCORE_DELAYED_NU_FISSION: - case SCORE_PROMPT_NU_FISSION: - case SCORE_DECAY_RATE: - warning("Particle filter is not used with photon transport" - " on and " + reaction_name(score) + " score."); - break; - } - } - } else { - const auto& f = model::tally_filters[particle_filter_index].get(); - auto pf = dynamic_cast(f); - for (auto p : pf->particles_) { - if (p == Particle::Type::electron || - p == Particle::Type::positron) { - t->estimator_ = ESTIMATOR_ANALOG; - } - } - } - } else { - if (particle_filter_index >= 0) { - const auto& f = model::tally_filters[particle_filter_index].get(); - auto pf = dynamic_cast(f); - for (auto p : pf->particles_) { - if (p != Particle::Type::neutron) { - warning("Particle filter other than NEUTRON used with photon " - "transport turned off. All tallies for particle type " + - std::to_string(static_cast(p)) + " will have no scores"); - } - } - } - } - - // Check for a tally derivative. - if (check_for_node(node_tal, "derivative")) { - int deriv_id = std::stoi(get_node_value(node_tal, "derivative")); - - // Find the derivative with the given id, and store it's index. - auto it = model::tally_deriv_map.find(deriv_id); - if (it == model::tally_deriv_map.end()) { - fatal_error("Could not find derivative " + std::to_string(deriv_id) - + " specified on tally " + std::to_string(t->id_)); - } - - t->deriv_ = it->second; - - // Only analog or collision estimators are supported for differential - // tallies. - if (t->estimator_ == ESTIMATOR_TRACKLENGTH) { - t->estimator_ = ESTIMATOR_COLLISION; - } - - const auto& deriv = model::tally_derivs[t->deriv_]; - if (deriv.variable == DIFF_NUCLIDE_DENSITY - || deriv.variable == DIFF_TEMPERATURE) { - for (int i_nuc : t->nuclides_) { - if (has_energyout && i_nuc == -1) { - fatal_error("Error on tally " + std::to_string(t->id_) - + ": Cannot use a 'nuclide_density' or 'temperature' " - "derivative on a tally with an outgoing energy filter and " - "'total' nuclide rate. Instead, tally each nuclide in the " - "material individually."); - // Note that diff tallies with these characteristics would work - // correctly if no tally events occur in the perturbed material - // (e.g. pertrubing moderator but only tallying fuel), but this - // case would be hard to check for by only reading inputs. - } - } - } - } - - // If settings.xml trigger is turned on, create tally triggers - if (settings::trigger_on) { - t->init_triggers(node_tal); - } - - // ======================================================================= - // SET TALLY ESTIMATOR - - // Check if user specified estimator - if (check_for_node(node_tal, "estimator")) { - std::string est = get_node_value(node_tal, "estimator"); - if (est == "analog") { - t->estimator_ = ESTIMATOR_ANALOG; - } else if (est == "tracklength" || est == "track-length" - || est == "pathlength" || est == "path-length") { - // If the estimator was set to an analog estimator, this means the - // tally needs post-collision information - if (t->estimator_ == ESTIMATOR_ANALOG) { - fatal_error("Cannot use track-length estimator for tally " - + std::to_string(t->id_)); - } - - // Set estimator to track-length estimator - t->estimator_ = ESTIMATOR_TRACKLENGTH; - - } else if (est == "collision") { - // If the estimator was set to an analog estimator, this means the - // tally needs post-collision information - if (t->estimator_ == ESTIMATOR_ANALOG) { - fatal_error("Cannot use collision estimator for tally " + - std::to_string(t->id_)); - } - - // Set estimator to collision estimator - t->estimator_ = ESTIMATOR_COLLISION; - - } else { - fatal_error("Invalid estimator '" + est + "' on tally " + - std::to_string(t->id_)); - } - } + model::tallies.push_back(std::make_unique(node_tal)); } } @@ -1057,7 +1070,7 @@ openmc_extend_tallies(int32_t n, int32_t* index_start, int32_t* index_end) if (index_start) *index_start = model::tallies.size(); if (index_end) *index_end = model::tallies.size() + n - 1; for (int i = 0; i < n; ++i) { - model::tallies.push_back(std::make_unique()); + model::tallies.push_back(std::make_unique(-1)); } return 0; } @@ -1141,14 +1154,7 @@ openmc_tally_set_id(int32_t index, int32_t id) return OPENMC_E_OUT_OF_BOUNDS; } - if (model::tally_map.find(id) != model::tally_map.end()) { - set_errmsg("Two or more tallies use the same unique ID: " - + std::to_string(id)); - return OPENMC_E_INVALID_ID; - } - - model::tallies[index]->id_ = id; - model::tally_map[id] = index; + model::tallies[index]->set_id(id); return 0; } @@ -1288,7 +1294,7 @@ openmc_tally_set_nuclides(int32_t index, int n, const char** nuclides) } extern "C" int -openmc_tally_get_filters(int32_t index, const int32_t** indices, int* n) +openmc_tally_get_filters(int32_t index, const int32_t** indices, size_t* n) { if (index < 0 || index >= model::tallies.size()) { set_errmsg("Index in tallies array is out of bounds."); @@ -1301,7 +1307,7 @@ openmc_tally_get_filters(int32_t index, const int32_t** indices, int* n) } extern "C" int -openmc_tally_set_filters(int32_t index, int n, const int32_t* indices) +openmc_tally_set_filters(int32_t index, size_t n, const int32_t* indices) { // Make sure the index fits in the array bounds. if (index < 0 || index >= model::tallies.size()) { @@ -1311,9 +1317,15 @@ openmc_tally_set_filters(int32_t index, int n, const int32_t* indices) // Set the filters. try { - model::tallies[index]->set_filters(indices, n); + // Convert indices to filter pointers + std::vector filters; + for (gsl::index i = 0; i < n; ++i) { + int32_t i_filt = indices[i]; + filters.push_back(model::tally_filters.at(i_filt).get()); + } + model::tallies[index]->set_filters(filters); } catch (const std::out_of_range& ex) { - set_errmsg(ex.what()); + set_errmsg("Index in tally filter array out of bounds."); return OPENMC_E_OUT_OF_BOUNDS; } diff --git a/src/tallies/tally_scoring.cpp b/src/tallies/tally_scoring.cpp index a69ab8ca5..6bf9ca6e9 100644 --- a/src/tallies/tally_scoring.cpp +++ b/src/tallies/tally_scoring.cpp @@ -69,7 +69,7 @@ FilterBinIter::FilterBinIter(const Tally& tally, bool end) if (!match.bins_present_) { match.bins_.clear(); match.weights_.clear(); - for (auto i = 0; i < model::tally_filters[i_filt]->n_bins_; ++i) { + for (auto i = 0; i < model::tally_filters[i_filt]->n_bins(); ++i) { match.bins_.push_back(i); match.weights_.push_back(1.0); } @@ -209,14 +209,14 @@ score_fission_eout(const Particle* p, int i_tally, int i_score, int score_bin) if (tally.deriv_ != C_NONE) apply_derivative_to_score(p, i_tally, 0, 0., SCORE_NU_FISSION, score); - if (!settings::run_CE && eo_filt.matches_transport_groups_) { + if (!settings::run_CE && eo_filt.matches_transport_groups()) { // determine outgoing energy group from fission bank auto g_out = static_cast(bank.E); // modify the value so that g_out = 1 corresponds to the highest energy // bin - g_out = eo_filt.n_bins_ - g_out; + g_out = eo_filt.n_bins() - g_out; // change outgoing energy bin simulation::filter_matches[i_eout_filt].bins_[i_bin] = g_out; @@ -231,11 +231,11 @@ score_fission_eout(const Particle* p, int i_tally, int i_score, int score_bin) } // Set EnergyoutFilter bin index - if (E_out < eo_filt.bins_.front() || E_out > eo_filt.bins_.back()) { + if (E_out < eo_filt.bins().front() || E_out > eo_filt.bins().back()) { continue; } else { - auto i_match = lower_bound_index(eo_filt.bins_.begin(), - eo_filt.bins_.end(), E_out); + auto i_match = lower_bound_index(eo_filt.bins().begin(), + eo_filt.bins().end(), E_out); simulation::filter_matches[i_eout_filt].bins_[i_bin] = i_match; } @@ -272,8 +272,8 @@ score_fission_eout(const Particle* p, int i_tally, int i_score, int score_bin) model::tally_filters[i_dg_filt].get())}; // Loop over delayed group bins until the corresponding bin is found - for (auto d_bin = 0; d_bin < dg_filt.n_bins_; ++d_bin) { - if (dg_filt.groups_[d_bin] == g) { + for (auto d_bin = 0; d_bin < dg_filt.n_bins(); ++d_bin) { + if (dg_filt.groups()[d_bin] == g) { // Find the filter index and weight for this filter combination double filter_weight = 1.; for (auto j = 0; j < tally.filters().size(); ++j) { @@ -632,8 +632,8 @@ score_general_ce(Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; auto yield = data::nuclides[p->event_nuclide_] ->nu(E, ReactionProduct::EmissionMode::delayed, d); score = p->wgt_absorb_ * yield @@ -670,8 +670,8 @@ score_general_ce(Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; score = simulation::keff * p->wgt_bank_ / p->n_bank_ * p->n_delayed_bank_[d-1] * flux; score_fission_delayed_dg(i_tally, d_bin, score, score_index); @@ -693,8 +693,8 @@ score_general_ce(Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; auto yield = data::nuclides[i_nuclide] ->nu(E, ReactionProduct::EmissionMode::delayed, d); score = p->neutron_xs_[i_nuclide].fission * yield @@ -722,8 +722,8 @@ score_general_ce(Particle* p, int i_tally, int start_index, auto j_nuclide = material.nuclide_[i]; auto atom_density = material.atom_density_(i); // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; auto yield = data::nuclides[j_nuclide] ->nu(E, ReactionProduct::EmissionMode::delayed, d); score = p->neutron_xs_[j_nuclide].fission * yield @@ -770,8 +770,8 @@ score_general_ce(Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; auto yield = nuc.nu(E, ReactionProduct::EmissionMode::delayed, d); auto rate = rxn.products_[d].decay_rate_; @@ -830,8 +830,8 @@ score_general_ce(Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Find the corresponding filter bin and then score - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; if (d == g) score_fission_delayed_dg(i_tally, d_bin, score, score_index); @@ -852,8 +852,8 @@ score_general_ce(Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; auto yield = nuc.nu(E, ReactionProduct::EmissionMode::delayed, d); auto rate = rxn.products_[d].decay_rate_; @@ -892,8 +892,8 @@ score_general_ce(Particle* p, int i_tally, int start_index, if (nuc.fissionable_) { const auto& rxn {*nuc.fission_rx_[0]}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; auto yield = nuc.nu(E, ReactionProduct::EmissionMode::delayed, d); auto rate = rxn.products_[d].decay_rate_; @@ -1757,8 +1757,8 @@ score_general_mg(const Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; score = p->wgt_absorb_ * flux; if (i_nuclide >= 0) { score *= @@ -1807,8 +1807,8 @@ score_general_mg(const Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; score = simulation::keff * p->wgt_bank_ / p->n_bank_ * p->n_delayed_bank_[d-1] * flux; if (i_nuclide >= 0) { @@ -1839,8 +1839,8 @@ score_general_mg(const Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; if (i_nuclide >= 0) { score = flux * atom_density * get_nuclide_xs(i_nuclide, MG_GET_XS_DELAYED_NU_FISSION, @@ -1879,8 +1879,8 @@ score_general_mg(const Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; score = p->wgt_absorb_ * flux; if (i_nuclide >= 0) { score *= @@ -1959,8 +1959,8 @@ score_general_mg(const Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Find the corresponding filter bin and then score - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; if (d == g) score_fission_delayed_dg(i_tally, d_bin, score, score_index); @@ -1978,8 +1978,8 @@ score_general_mg(const Particle* p, int i_tally, int start_index, {*dynamic_cast( model::tally_filters[i_dg_filt].get())}; // Tally each delayed group bin individually - for (auto d_bin = 0; d_bin < filt.n_bins_; ++d_bin) { - auto d = filt.groups_[d_bin]; + for (auto d_bin = 0; d_bin < filt.n_bins(); ++d_bin) { + auto d = filt.groups()[d_bin]; if (i_nuclide >= 0) { score += atom_density * flux * get_nuclide_xs(i_nuclide, MG_GET_XS_DECAY_RATE, diff --git a/tests/regression_tests/complex_cell/test.py b/tests/regression_tests/complex_cell/test.py index 77cbd6cb7..b43ccd72f 100755 --- a/tests/regression_tests/complex_cell/test.py +++ b/tests/regression_tests/complex_cell/test.py @@ -1,5 +1,8 @@ from tests.testing_harness import TestHarness +import sys + +import openmc.capi def test_complex_cell(): harness = TestHarness('statepoint.10.h5') diff --git a/tests/regression_tests/dagmc/legacy/dagmc.h5m b/tests/regression_tests/dagmc/legacy/dagmc.h5m index c90b6d674..fbbe9a34a 100644 Binary files a/tests/regression_tests/dagmc/legacy/dagmc.h5m and b/tests/regression_tests/dagmc/legacy/dagmc.h5m differ diff --git a/tests/regression_tests/dagmc/legacy/test.py b/tests/regression_tests/dagmc/legacy/test.py index b6f2f55e2..db062d36e 100644 --- a/tests/regression_tests/dagmc/legacy/test.py +++ b/tests/regression_tests/dagmc/legacy/test.py @@ -1,6 +1,5 @@ import openmc import openmc.capi -from openmc.stats import Box import pytest from tests.testing_harness import PyAPITestHarness @@ -17,8 +16,10 @@ def test_dagmc(): model.settings.inactive = 0 model.settings.particles = 100 - source = openmc.Source(space=Box([-4, -4, -4], - [ 4, 4, 4])) + source_box = openmc.stats.Box([-4, -4, -4], + [ 4, 4, 4]) + source = openmc.Source(space=source_box) + model.settings.source = source model.settings.dagmc = True @@ -45,5 +46,4 @@ def test_dagmc(): mats = openmc.Materials([u235, water]) model.materials = mats - harness = PyAPITestHarness('statepoint.5.h5', model=model) - harness.main() + model.export_to_xml() diff --git a/tests/regression_tests/mg_tallies/results_true.dat b/tests/regression_tests/mg_tallies/results_true.dat index bc2393f73..50ec653e2 100644 --- a/tests/regression_tests/mg_tallies/results_true.dat +++ b/tests/regression_tests/mg_tallies/results_true.dat @@ -1 +1 @@ -15e00a46742e973d7c3c5defe427e6f76a4f1b661538cf54957712751410218dc64301fe12ccb8dbed3f2d51d19b3e99d2b615a3e98c46f9954894e96667dc23 \ No newline at end of file +508cd056f2d9409a536e487512df34c683720ea45b9100316efb31c19927890c4cacd92f38817d74fcf491bbe4bdb78a0215a798d5a078e0575e3fd94cedf0bd \ No newline at end of file diff --git a/tests/regression_tests/photon_production/inputs_true.dat b/tests/regression_tests/photon_production/inputs_true.dat index f82b4a6a7..1821bea92 100644 --- a/tests/regression_tests/photon_production/inputs_true.dat +++ b/tests/regression_tests/photon_production/inputs_true.dat @@ -41,7 +41,7 @@ 9 - 2 + photon 1 2 diff --git a/tests/regression_tests/photon_source/inputs_true.dat b/tests/regression_tests/photon_source/inputs_true.dat index 425738a47..89f4de0e0 100644 --- a/tests/regression_tests/photon_source/inputs_true.dat +++ b/tests/regression_tests/photon_source/inputs_true.dat @@ -36,7 +36,7 @@ - 2 + photon 1 diff --git a/tests/regression_tests/source/inputs_true.dat b/tests/regression_tests/source/inputs_true.dat index 860a36bc8..7eeefbc00 100644 --- a/tests/regression_tests/source/inputs_true.dat +++ b/tests/regression_tests/source/inputs_true.dat @@ -5,8 +5,7 @@ - - 294 + diff --git a/tests/unit_tests/dagmc/__init__.py b/tests/unit_tests/dagmc/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/unit_tests/dagmc/dagmc.h5m b/tests/unit_tests/dagmc/dagmc.h5m new file mode 120000 index 000000000..0c5ab5da8 --- /dev/null +++ b/tests/unit_tests/dagmc/dagmc.h5m @@ -0,0 +1 @@ +../../regression_tests/dagmc/legacy/dagmc.h5m \ No newline at end of file diff --git a/tests/unit_tests/dagmc/test.py b/tests/unit_tests/dagmc/test.py new file mode 100644 index 000000000..bfe054dd8 --- /dev/null +++ b/tests/unit_tests/dagmc/test.py @@ -0,0 +1,74 @@ +import shutil + +import numpy as np +import pytest + +import openmc +import openmc.capi + +from tests import cdtemp + +pytestmark = pytest.mark.skipif( + not openmc.capi._dagmc_enabled(), + reason="DAGMC CAD geometry is not enabled.") + + +@pytest.fixture(scope="module", autouse=True) +def dagmc_model(request): + + model = openmc.model.Model() + + # settings + model.settings.batches = 5 + model.settings.inactive = 0 + model.settings.particles = 100 + model.settings.temperature = {'tolerance': 50.0} + model.settings.verbosity = 1 + source_box = openmc.stats.Box([ -4, -4, -4 ], + [ 4, 4, 4 ]) + source = openmc.Source(space=source_box) + model.settings.source = source + + model.settings.dagmc = True + + # tally + tally = openmc.Tally() + tally.scores = ['total'] + tally.filters = [openmc.CellFilter(1)] + model.tallies = [tally] + + # materials + u235 = openmc.Material(name="fuel") + u235.add_nuclide('U235', 1.0, 'ao') + u235.set_density('g/cc', 11) + u235.id = 40 + u235.temperature = 320 + + water = openmc.Material(name="water") + water.add_nuclide('H1', 2.0, 'ao') + water.add_nuclide('O16', 1.0, 'ao') + water.set_density('g/cc', 1.0) + water.add_s_alpha_beta('c_H_in_H2O') + water.id = 41 + + mats = openmc.Materials([u235, water]) + model.materials = mats + + # location of dagmc file in test directory + dagmc_file = request.fspath.dirpath() + "/dagmc.h5m" + # move to a temporary directory + with cdtemp(): + shutil.copyfile(dagmc_file, "./dagmc.h5m") + model.export_to_xml() + openmc.capi.init() + yield + + openmc.capi.finalize() + + +@pytest.mark.parametrize("cell_id,exp_temp", ((1, 320.0), # assigned by material + (2, 300.0), # assigned in dagmc file + (3, 293.6))) # assigned by default +def test_dagmc_temperatures(cell_id, exp_temp): + cell = openmc.capi.cells[cell_id] + assert np.isclose(cell.get_temperature(), exp_temp) diff --git a/tests/unit_tests/test_capi.py b/tests/unit_tests/test_capi.py index 53ad09e90..ed0bfd441 100644 --- a/tests/unit_tests/test_capi.py +++ b/tests/unit_tests/test_capi.py @@ -35,6 +35,14 @@ def pincell_model(): zernike_tally.scores = ['fission'] pincell.tallies.append(zernike_tally) + # Add an energy function tally + energyfunc_tally = openmc.Tally() + energyfunc_filter = openmc.EnergyFunctionFilter( + [0.0, 20e6], [0.0, 20e6]) + energyfunc_tally.scores = ['fission'] + energyfunc_tally.filters = [energyfunc_filter] + pincell.tallies.append(energyfunc_tally) + # Write XML files in tmpdir with cdtemp(): pincell.export_to_xml() @@ -73,7 +81,9 @@ def test_cell(capi_init): assert isinstance(cell.fill, openmc.capi.Material) cell.fill = openmc.capi.materials[1] assert str(cell) == 'Cell[0]' - + assert cell.name == "Fuel" + cell.name = "Not fuel" + assert cell.name == "Not fuel" def test_cell_temperature(capi_init): cell = openmc.capi.cells[1] @@ -113,7 +123,7 @@ def test_material(capi_init): m.volume = 10.0 assert m.volume == 10.0 - with pytest.raises(exc.InvalidArgumentError): + with pytest.raises(exc.OpenMCError): m.set_density(1.0, 'goblins') rho = 2.25e-2 @@ -122,7 +132,9 @@ def test_material(capi_init): m.set_density(0.1, 'g/cm3') assert m.density == pytest.approx(0.1) - + assert m.name == "Hot borated water" + m.name = "Not hot borated water" + assert m.name == "Not hot borated water" def test_material_add_nuclide(capi_init): m = openmc.capi.materials[3] @@ -166,12 +178,21 @@ def test_settings(capi_init): def test_tally_mapping(capi_init): tallies = openmc.capi.tallies assert isinstance(tallies, Mapping) - assert len(tallies) == 2 + assert len(tallies) == 3 for tally_id, tally in tallies.items(): assert isinstance(tally, openmc.capi.Tally) assert tally_id == tally.id +def test_energy_function_filter(capi_init): + """Test special __new__ and __init__ for EnergyFunctionFilter""" + efunc = openmc.capi.EnergyFunctionFilter([0.0, 1.0], [0.0, 2.0]) + assert len(efunc.energy) == 2 + assert (efunc.energy == [0.0, 1.0]).all() + assert len(efunc.y) == 2 + assert (efunc.y == [0.0, 2.0]).all() + + def test_tally(capi_init): t = openmc.capi.tallies[1] assert t.type == 'volume' @@ -207,6 +228,16 @@ def test_tally(capi_init): assert len(t2.filters[1].bins) == 3 assert t2.filters[0].order == 5 + t3 = openmc.capi.tallies[3] + assert len(t3.filters) == 1 + t3_f = t3.filters[0] + assert isinstance(t3_f, openmc.capi.EnergyFunctionFilter) + assert len(t3_f.energy) == 2 + assert len(t3_f.y) == 2 + t3_f.set_data([0.0, 1.0, 2.0], [0.0, 1.0, 4.0]) + assert len(t3_f.energy) == 3 + assert len(t3_f.y) == 3 + def test_new_tally(capi_init): with pytest.raises(exc.AllocationError): @@ -215,7 +246,7 @@ def test_new_tally(capi_init): new_tally.scores = ['flux'] new_tally_with_id = openmc.capi.Tally(10) new_tally_with_id.scores = ['flux'] - assert len(openmc.capi.tallies) == 4 + assert len(openmc.capi.tallies) == 5 def test_tally_activate(capi_simulation_init): @@ -469,3 +500,13 @@ def test_position(capi_init): pos[2] = 3.3 assert tuple(pos) == (1.3, 2.3, 3.3) + + +def test_global_bounding_box(capi_init): + expected_llc = (-0.63, -0.63, -np.inf) + expected_urc = (0.63, 0.63, np.inf) + + llc, urc = openmc.capi.global_bounding_box() + + assert tuple(llc) == expected_llc + assert tuple(urc) == expected_urc diff --git a/tests/unit_tests/test_complex_cell_capi.py b/tests/unit_tests/test_complex_cell_capi.py new file mode 100644 index 000000000..365ce4808 --- /dev/null +++ b/tests/unit_tests/test_complex_cell_capi.py @@ -0,0 +1,98 @@ +import numpy as np +import openmc.capi +import pytest + +@pytest.fixture(autouse=True) +def complex_cell(run_in_tmpdir): + + openmc.reset_auto_ids() + + model = openmc.model.Model() + + u235 = openmc.Material() + u235.set_density('g/cc', 4.5) + u235.add_nuclide("U235", 1.0) + + u238 = openmc.Material() + u238.set_density('g/cc', 4.5) + u238.add_nuclide("U238", 1.0) + + zr90 = openmc.Material() + zr90.set_density('g/cc', 2.0) + zr90.add_nuclide("Zr90", 1.0) + + n14 = openmc.Material() + n14.set_density('g/cc', 0.1) + n14.add_nuclide("N14", 1.0) + + model.materials = (u235, u238, zr90, n14) + + s1 = openmc.XPlane(x0=-10.0, boundary_type='vacuum') + s2 = openmc.XPlane(x0=-7.0) + s3 = openmc.XPlane(x0=-4.0) + s4 = openmc.XPlane(x0=4.0) + s5 = openmc.XPlane(x0=7.0) + s6 = openmc.XPlane(x0=10.0, boundary_type='vacuum') + s7 = openmc.XPlane(x0=0.0) + + s11 = openmc.YPlane(y0=-10.0, boundary_type='vacuum') + s12 = openmc.YPlane(y0=-7.0) + s13 = openmc.YPlane(y0=-4.0) + s14 = openmc.YPlane(y0=4.0) + s15 = openmc.YPlane(y0=7.0) + s16 = openmc.YPlane(y0=10.0, boundary_type='vacuum') + s17 = openmc.YPlane(y0=0.0) + + c1 = openmc.Cell(fill=u235) + c1.region = ~(-s3 | +s4 | ~(+s13 & -s14)) + + c2 = openmc.Cell(fill=u238) + c2.region = +s2 & -s5 & +s12 & -s15 & ~(+s3 & -s4 & +s13 & -s14) + + c3 = openmc.Cell(fill=zr90) + c3.region = ((+s1 & -s7 & +s17 & -s16) | (+s7 & -s6 & +s11 & -s17)) \ + & (-s2 | +s5 | -s12 | +s15) + + c4 = openmc.Cell(fill=n14) + c4.region = ((+s1 & -s7 & +s11 & -s17) | (+s7 & -s6 & +s17 & -s16)) & \ + ~(+s2 & -s5 & +s12 & -s15) + + c5 = openmc.Cell(fill=n14) + c5.region = ~(+s1 & -s6 & +s11 & -s16) + + model.geometry.root_universe = openmc.Universe() + model.geometry.root_universe.add_cells([c1, c2, c3, c4, c5]) + + model.settings.batches = 10 + model.settings.inactive = 5 + model.settings.particles = 100 + model.settings.source = openmc.Source(space=openmc.stats.Box( + [-10., -10., -1.], [10., 10., 1.])) + + model.settings.verbosity = 1 + + model.export_to_xml() + + openmc.capi.finalize() + openmc.capi.init() + + yield + + openmc.capi.finalize() + + +expected_results = ( (1, (( -4., -4., -np.inf), + ( 4., 4., np.inf))), + (2, (( -7., -7., -np.inf), + ( 7., 7., np.inf))), + (3, ((-10., -10., -np.inf), + ( 10., 10., np.inf))), + (4, ((-10., -10., -np.inf), + ( 10., 10., np.inf))), + (5, ((-np.inf, -np.inf, -np.inf), + ( np.inf, np.inf, np.inf))) ) +@pytest.mark.parametrize("cell_id,expected_box", expected_results) +def test_cell_box(cell_id, expected_box): + cell_box = openmc.capi.cells[cell_id].bounding_box + assert tuple(cell_box[0]) == expected_box[0] + assert tuple(cell_box[1]) == expected_box[1] diff --git a/tests/unit_tests/test_deplete_chain.py b/tests/unit_tests/test_deplete_chain.py index dd6817a8e..33b13b596 100644 --- a/tests/unit_tests/test_deplete_chain.py +++ b/tests/unit_tests/test_deplete_chain.py @@ -243,3 +243,89 @@ def test_set_fiss_q(): for rx in chain_nuc.reactions: if rx.type == 'fission': assert rx.Q == q + + +def test_get_set_chain_br(simple_chain): + """Test minor modifications to capture branch ratios""" + expected = {"C": {"A": 0.7, "B": 0.3}} + assert simple_chain.get_capture_branches() == expected + + # safely modify + new_chain = Chain.from_xml("chain_test.xml") + new_br = {"C": {"A": 0.5, "B": 0.5}, "A": {"C": 0.99, "B": 0.01}} + new_chain.set_capture_branches(new_br) + assert new_chain.get_capture_branches() == new_br + + # write, re-read + new_chain.export_to_xml("chain_mod.xml") + assert Chain.from_xml("chain_mod.xml").get_capture_branches() == new_br + + # Test non-strict [warn, not error] setting + bad_br = {"B": {"X": 0.6, "A": 0.4}, "X": {"A": 0.5, "C": 0.5}} + bad_br.update(new_br) + new_chain.set_capture_branches(bad_br, strict=False) + assert new_chain.get_capture_branches() == new_br + + # Ensure capture reactions are removed + rem_br = {"A": {"C": 1.0}} + new_chain.set_capture_branches(rem_br) + # A is not in returned dict because there is no branch + assert "A" not in new_chain.get_capture_branches() + + +def test_capture_branch_infer_ground(): + """Ensure the ground state is infered if not given""" + # Make up a metastable capture transition: + infer_br = {"Xe135": {"Xe136_m1": 0.5}} + set_br = {"Xe135": {"Xe136": 0.5, "Xe136_m1": 0.5}} + + chain_file = Path(__file__).parents[1] / "chain_simple.xml" + chain = Chain.from_xml(chain_file) + + # Create nuclide to be added into the chain + xe136m = nuclide.Nuclide() + xe136m.name = "Xe136_m1" + + chain.nuclides.append(xe136m) + chain.nuclide_dict[xe136m.name] = len(chain.nuclides) - 1 + + chain.set_capture_branches(infer_br) + + assert chain.get_capture_branches() == set_br + + +def test_capture_branch_no_rxn(): + """Ensure capture reactions that don't exist aren't created""" + u4br = {"U234": {"U235": 0.5, "U235_m1": 0.5}} + + chain_file = Path(__file__).parents[1] / "chain_simple.xml" + chain = Chain.from_xml(chain_file) + + u5m = nuclide.Nuclide() + u5m.name = "U235_m1" + + chain.nuclides.append(u5m) + chain.nuclide_dict[u5m.name] = len(chain.nuclides) - 1 + + phrase = "U234 does not have capture reactions" + with pytest.raises(AttributeError, match=phrase): + chain.set_capture_branches(u4br) + + +def test_capture_branch_failures(simple_chain): + """Test failure modes for setting capture branch ratios""" + + # Parent isotope not present + br = {"X": {"A": 0.6, "B": 0.7}} + with pytest.raises(KeyError, match="X"): + simple_chain.set_capture_branches(br) + + # Product isotope not present + br = {"C": {"X": 0.4, "A": 0.2, "B": 0.4}} + with pytest.raises(KeyError, match="X"): + simple_chain.set_capture_branches(br) + + # Sum of ratios > 1.0 + br = {"C": {"A": 1.0, "B": 1.0}} + with pytest.raises(ValueError, match="C ratios"): + simple_chain.set_capture_branches(br) diff --git a/tests/unit_tests/test_material.py b/tests/unit_tests/test_material.py index 30a3e2498..62fff7cdf 100644 --- a/tests/unit_tests/test_material.py +++ b/tests/unit_tests/test_material.py @@ -189,6 +189,8 @@ def test_from_xml(run_in_tmpdir): m1.add_nuclide('H1', 1.0) m1.add_nuclide('O16', 2.0) m1.add_s_alpha_beta('c_H_in_H2O') + m1.temperature = 300 + m1.volume = 100 m1.set_density('g/cm3', 0.9) m1.isotropic = ['H1'] m2 = openmc.Material(2, 'zirc') @@ -209,6 +211,8 @@ def test_from_xml(run_in_tmpdir): assert m1.name == 'water' assert m1.nuclides == [('H1', 1.0, 'ao'), ('O16', 2.0, 'ao')] assert m1.isotropic == ['H1'] + assert m1.temperature == 300 + assert m1.volume == 100 m2 = mats[1] assert m2.nuclides == [('Zr90', 1.0, 'wo')] assert m2.density == 10.0 diff --git a/tests/unit_tests/test_pin.py b/tests/unit_tests/test_pin.py new file mode 100644 index 000000000..42241de5f --- /dev/null +++ b/tests/unit_tests/test_pin.py @@ -0,0 +1,117 @@ +""" +Tests for constructing Pin universes +""" + +import numpy +import pytest + +import openmc +from openmc.model import pin + + +def get_pin_radii(pin_univ): + """Return a sorted list of all radii from pin""" + rads = set() + + for cell in pin_univ.get_all_cells().values(): + surfs = cell.region.get_surfaces().values() + rads.update(set(s.r for s in surfs)) + + return list(sorted(rads)) + + +@pytest.fixture +def pin_mats(): + fuel = openmc.Material(name="UO2") + fuel.volume = 100 + clad = openmc.Material(name="zirc") + clad.volume = 100 + water = openmc.Material(name="water") + return fuel, clad, water + + +@pytest.fixture +def good_radii(): + return (0.4, 0.42) + + +def test_failure(pin_mats, good_radii): + """Check for various failure modes""" + good_surfaces = [openmc.ZCylinder(r=r) for r in good_radii] + # Bad material type + with pytest.raises(TypeError): + pin(good_surfaces, [mat.name for mat in pin_mats]) + + # Incorrect lengths + with pytest.raises(ValueError, match="length"): + pin(good_surfaces[:len(pin_mats) - 2], pin_mats) + + # Non-positive radii + rad = [openmc.ZCylinder(r=-0.1)] + good_surfaces[1:] + with pytest.raises(ValueError, match="index 0"): + pin(rad, pin_mats) + + # Non-increasing radii + surfs = tuple(reversed(good_surfaces)) + with pytest.raises(ValueError, match="index 1"): + pin(surfs, pin_mats) + + # Bad orientation + surfs = [openmc.XCylinder(r=good_surfaces[0].r)] + good_surfaces[1:] + with pytest.raises(TypeError, match="surfaces"): + pin(surfs, pin_mats) + + # Passing cells argument + with pytest.raises(SyntaxError, match="Cells"): + pin(surfs, pin_mats, cells=[]) + + +def test_pins_of_universes(pin_mats, good_radii): + """Build a pin with a Universe in one ring""" + u1 = openmc.Universe(cells=[openmc.Cell(fill=pin_mats[1])]) + new_items = pin_mats[:1] + (u1, ) + pin_mats[2:] + new_pin = pin( + [openmc.ZCylinder(r=r) for r in good_radii], new_items, + subdivisions={0: 2}, divide_vols=True) + assert len(new_pin.cells) == len(pin_mats) + 1 + + +@pytest.mark.parametrize( + "surf_type", [openmc.ZCylinder, openmc.XCylinder, openmc.YCylinder]) +def test_subdivide(pin_mats, good_radii, surf_type): + """Test the subdivision with various orientations""" + surfs = [surf_type(r=r) for r in good_radii] + fresh = pin(surfs, pin_mats, name="fresh pin") + assert len(fresh.cells) == len(pin_mats) + assert fresh.name == "fresh pin" + + # subdivide inner region + N = 5 + div0 = pin(surfs, pin_mats, {0: N}) + assert len(div0.cells) == len(pin_mats) + N - 1 + + # Check volume of fuel material + for mid, mat in div0.get_all_materials().items(): + if mat.name == "UO2": + assert mat.volume == pytest.approx(100 / N) + + # check volumes of new rings + radii = get_pin_radii(div0) + bounds = [0] + radii[:N] + sqrs = numpy.square(bounds) + assert sqrs[1:] - sqrs[:-1] == pytest.approx(good_radii[0] ** 2 / N) + + # subdivide non-inner most region + new_pin = pin(surfs, pin_mats, {1: N}) + assert len(new_pin.cells) == len(pin_mats) + N - 1 + + # Check volume of clad material + for mid, mat in div0.get_all_materials().items(): + if mat.name == "zirc": + assert mat.volume == pytest.approx(100 / N) + + # check volumes of new rings + radii = get_pin_radii(new_pin) + sqrs = numpy.square(radii[:N + 1]) + assert sqrs[1:] - sqrs[:-1] == pytest.approx( + (good_radii[1] ** 2 - good_radii[0] ** 2) / N) diff --git a/tests/unit_tests/test_surface.py b/tests/unit_tests/test_surface.py index 6ed657a4c..8ce104021 100644 --- a/tests/unit_tests/test_surface.py +++ b/tests/unit_tests/test_surface.py @@ -1,3 +1,6 @@ +from functools import partial +from random import uniform, seed + import numpy as np import openmc import pytest @@ -329,14 +332,61 @@ def test_quadric(): def test_cylinder_from_points(): - # Generate 45-degree rotated cylinder in x-y plane with radius 1 - p1 = (0, 0, 0) - p2 = (1, 1, 0) - s = openmc.model.cylinder_from_points(p1, p2, 1) + seed(1) # Make random numbers reproducible + for _ in range(100): + # Generate cylinder in random direction + xi = partial(uniform, -10.0, 10.0) + p1 = np.array([xi(), xi(), xi()]) + p2 = np.array([xi(), xi(), xi()]) + r = uniform(1.0, 100.0) + s = openmc.model.cylinder_from_points(p1, p2, r) - # Points p1 and p2 need to be inside cylinder - assert p1 in -s - assert p2 in -s - assert (-1, 1, 0) in +s - assert (1, -1, 0) in +s - assert (0, 0, 1.5) in +s + # Points p1 and p2 need to be inside cylinder + assert p1 in -s + assert p2 in -s + + # Points further along the line should be inside cylinder as well + t = uniform(-100.0, 100.0) + p = p1 + t*(p2 - p1) + assert p in -s + + # Check that points outside cylinder are in positive half-space and + # inside are in negative half-space. We do this by constructing a plane + # that includes the cylinder's axis, finding the normal to the plane, + # and using it to find a point slightly more/less than one radius away + # from the axis. + plane = openmc.Plane.from_points(p1, p2, (0., 0., 0.)) + n = np.array([plane.a, plane.b, plane.c]) + n /= np.linalg.norm(n) + assert p1 + 1.1*r*n in +s + assert p2 + 1.1*r*n in +s + assert p1 + 0.9*r*n in -s + assert p2 + 0.9*r*n in -s + + +def test_cylinder_from_points_axis(): + # Create axis-aligned cylinders and confirm the coefficients are as expected + + # (x - 3)^2 + (y - 4)^2 = 2^2 + # x^2 + y^2 - 6x - 8y + 21 = 0 + s = openmc.model.cylinder_from_points((3., 4., 0.), (3., 4., 1.), 2.) + assert (s.a, s.b, s.c) == pytest.approx((1., 1., 0.)) + assert (s.d, s.e, s.f) == pytest.approx((0., 0., 0.)) + assert (s.g, s.h, s.j) == pytest.approx((-6., -8., 0.)) + assert s.k == pytest.approx(21.) + + # (y + 7)^2 + (z - 1)^2 = 3^2 + # y^2 + z^2 + 14y - 2z + 41 = 0 + s = openmc.model.cylinder_from_points((0., -7, 1.), (1., -7., 1.), 3.) + assert (s.a, s.b, s.c) == pytest.approx((0., 1., 1.)) + assert (s.d, s.e, s.f) == pytest.approx((0., 0., 0.)) + assert (s.g, s.h, s.j) == pytest.approx((0., 14., -2.)) + assert s.k == 41. + + # (x - 2)^2 + (z - 5)^2 = 4^2 + # x^2 + z^2 - 4x - 10z + 13 = 0 + s = openmc.model.cylinder_from_points((2., 0., 5.), (2., 1., 5.), 4.) + assert (s.a, s.b, s.c) == pytest.approx((1., 0., 1.)) + assert (s.d, s.e, s.f) == pytest.approx((0., 0., 0.)) + assert (s.g, s.h, s.j) == pytest.approx((-4., 0., -10.)) + assert s.k == pytest.approx(13.) diff --git a/tools/ci/travis-install-dagmc.sh b/tools/ci/travis-install-dagmc.sh index ca3e8b3c7..1797e9dba 100755 --- a/tools/ci/travis-install-dagmc.sh +++ b/tools/ci/travis-install-dagmc.sh @@ -20,7 +20,7 @@ mkdir MOAB && cd MOAB git clone -b $MOAB_BRANCH $MOAB_REPO mkdir build && cd build cmake ../moab -DENABLE_HDF5=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_INSTALL_PREFIX=$MOAB_INSTALL_DIR -make -j && make -j test install +make -j && make -j install cmake ../moab -DBUILD_SHARED_LIBS=OFF make -j install rm -rf $HOME/MOAB/moab