diff --git a/.gitignore b/.gitignore index b2bdeba7a..136491a4b 100644 --- a/.gitignore +++ b/.gitignore @@ -71,4 +71,6 @@ docs/source/pythonapi/examples/*.xml docs/source/pythonapi/examples/*.png docs/source/pythonapi/examples/*.xls docs/source/pythonapi/examples/mgxs -docs/source/pythonapi/examples/tracks \ No newline at end of file +docs/source/pythonapi/examples/tracks +docs/source/pythonapi/examples/fission-rates +docs/source/pythonapi/examples/plots \ No newline at end of file diff --git a/docs/source/pythonapi/examples/MGXS-Part-I.ipynb b/docs/source/pythonapi/examples/MGXS-Part-I.ipynb new file mode 100644 index 000000000..0bba3bfe0 --- /dev/null +++ b/docs/source/pythonapi/examples/MGXS-Part-I.ipynb @@ -0,0 +1,1091 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This IPython Notebook introduces the use of the `openmc.mgxs` module to calculate multi-group cross sections for an infinite homogeneous medium. In particular, this Notebook introduces the the following features:\n", + "\n", + "* Creation of multi-group cross sections for an **infinite homogeneous medium**\n", + "* Use of **tally arithmetic** to manipulate multi-group cross sections\n", + "\n", + "**Note:** This Notebook illustrates the use of Pandas DataFrames to containerize multi-group cross section data. We recommend using Pandas >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import openmc\n", + "import openmc.mgxs as mgxs\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first construct a simple homogeneous infinite medium problem to illustrate use of the `openmc.mgxs` module to generate multi-group cross sections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate some Nuclides\n", + "h1 = openmc.Nuclide('H-1')\n", + "o16 = openmc.Nuclide('O-16')\n", + "u235 = openmc.Nuclide('U-235')\n", + "u238 = openmc.Nuclide('U-238')\n", + "zr90 = openmc.Nuclide('Zr-90')" + ] + }, + { + "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": 3, + "metadata": { + "collapsed": true + }, + "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, 0.028999667)\n", + "inf_medium.add_nuclide(o16, 0.01450188)\n", + "inf_medium.add_nuclide(u235, 0.000114142)\n", + "inf_medium.add_nuclide(u238, 0.006886019)\n", + "inf_medium.add_nuclide(zr90, 0.002116053)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our material, we can now create a materials file object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a MaterialsFile, register all Materials, and export to XML\n", + "materials_file = openmc.MaterialsFile()\n", + "materials_file.default_xs = '71c'\n", + "materials_file.add_material(inf_medium)\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. This problem will be a simple square cell with reflective boundary conditions to simulate an infinite homogeneous medium. The first step is to create the outer bounding surfaces of the problem." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate boundary Planes\n", + "min_x = openmc.XPlane(boundary_type='reflective', x0=-0.63)\n", + "max_x = openmc.XPlane(boundary_type='reflective', x0=0.63)\n", + "min_y = openmc.YPlane(boundary_type='reflective', y0=-0.63)\n", + "max_y = openmc.YPlane(boundary_type='reflective', y0=0.63)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now create a cell that is defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a Cell\n", + "cell = openmc.Cell(cell_id=1, name='cell')\n", + "\n", + "# Register bounding Surfaces with the Cell\n", + "cell.region = +min_x & -max_x & +min_y & -max_y\n", + "\n", + "# Fill the Cell with the Material\n", + "cell.fill = inf_medium" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate Universe\n", + "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", + "root_universe.add_cell(cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "openmc_geometry = openmc.Geometry()\n", + "openmc_geometry.root_universe = root_universe\n", + "\n", + "# Instantiate a GeometryFile\n", + "geometry_file = openmc.GeometryFile()\n", + "geometry_file.geometry = openmc_geometry\n", + "\n", + "# Export to \"geometry.xml\"\n", + "geometry_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 2500\n", + "\n", + "# Instantiate a SettingsFile\n", + "settings_file = openmc.SettingsFile()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "settings_file.output = {'tallies': True, 'summary': True}\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "settings_file.set_source_space('fission', bounds)\n", + "\n", + "# Export to \"settings.xml\"\n", + "settings_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to generate multi-group cross sections! First, let's define a 2-group structure using the built-in `EnergyGroups` class." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a 2-group EnergyGroups object\n", + "groups = mgxs.EnergyGroups()\n", + "groups.group_edges = np.array([0., 0.625e-6, 20.])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use the fine and coarse `EnergyGroups` objects, along with our previously created materials and geometry, to instantiate some `MGXS` objects from the `openmc.mgxs` module. In particular, the following are subclasses of the generic and abstract `MGXS` class:\n", + "\n", + "* `TotalXS`\n", + "* `TransportXS`\n", + "* `AbsorptionXS`\n", + "* `CaptureXS`\n", + "* `FissionXS`\n", + "* `NuFissionXS`\n", + "* `ScatterXS`\n", + "* `NuScatterXS`\n", + "* `ScatterMatrixXS`\n", + "* `NuScatterMatrixXS`\n", + "* `Chi`\n", + "\n", + "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group total, absorption and scattering cross sections with our 2-group structure." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a few different sections\n", + "total = mgxs.TotalXS(domain=cell, domain_type='cell', groups=groups)\n", + "absorption = mgxs.AbsorptionXS(domain=cell, domain_type='cell', groups=groups)\n", + "scattering = mgxs.ScatterXS(domain=cell, domain_type='cell', groups=groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `Absorption` object as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([('flux', Tally\n", + "\tID =\t10000\n", + "\tName =\t\n", + "\tFilters =\t\n", + " \t\tcell\t[1]\n", + " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", + "\tNuclides =\ttotal \n", + "\tScores =\t['flux']\n", + "\tEstimator =\ttracklength\n", + "), ('absorption', Tally\n", + "\tID =\t10001\n", + "\tName =\t\n", + "\tFilters =\t\n", + " \t\tcell\t[1]\n", + " \t\tenergy\t[ 0.00000000e+00 6.25000000e-07 2.00000000e+01]\n", + "\tNuclides =\ttotal \n", + "\tScores =\t['absorption']\n", + "\tEstimator =\ttracklength\n", + ")])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "absorption.tallies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Absorption` object includes tracklength tallies for the 'absorption' and 'flux' scores in the 2-group structure in cell 1. Now that each multi-group cross section object contains the tallies that it needs, we must add these tallies to a `TalliesFile` object to generate the \"tallies.xml\" input file for OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate an empty TalliesFile\n", + "tallies_file = openmc.TalliesFile()\n", + "\n", + "# Add total tallies to the tallies file\n", + "for tally in total.tallies.values():\n", + " tallies_file.add_tally(tally)\n", + "\n", + "# Add absorption tallies to the tallies file\n", + "for tally in absorption.tallies.values():\n", + " tallies_file.add_tally(tally)\n", + "\n", + "# Add scattering tallies to the tallies file\n", + "for tally in scattering.tallies.values():\n", + " tallies_file.add_tally(tally)\n", + " \n", + "# Export to \"tallies.xml\"\n", + "tallies_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we a have a complete set of inputs, so we can go ahead and run our simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " .d88888b. 888b d888 .d8888b.\n", + " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", + " 888 888 88888b.d88888 888 888\n", + " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", + " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", + " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", + " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", + " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", + "__________________888______________________________________________________\n", + " 888\n", + " 888\n", + "\n", + " Copyright: 2011-2015 Massachusetts Institute of Technology\n", + " License: http://mit-crpg.github.io/openmc/license.html\n", + " Version: 0.7.0\n", + " Git SHA1: c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n", + " Date/Time: 2015-11-29 17:50:29\n", + " MPI Processes: 1\n", + "\n", + " ===========================================================================\n", + " ========================> INITIALIZATION <=========================\n", + " ===========================================================================\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading geometry XML file...\n", + " Reading materials XML file...\n", + " Reading tallies XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Loading ACE cross section table: 1001.71c\n", + " Loading ACE cross section table: 8016.71c\n", + " Loading ACE cross section table: 92235.71c\n", + " Loading ACE cross section table: 92238.71c\n", + " Loading ACE cross section table: 40090.71c\n", + " Maximum neutron transport energy: 20.0000 MeV for 1001.71c\n", + " Initializing source particles...\n", + "\n", + " ===========================================================================\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + " ===========================================================================\n", + "\n", + " Bat./Gen. k Average k \n", + " ========= ======== ==================== \n", + " 1/1 1.19804 \n", + " 2/1 1.12945 \n", + " 3/1 1.15573 \n", + " 4/1 1.13929 \n", + " 5/1 1.16300 \n", + " 6/1 1.22117 \n", + " 7/1 1.19012 \n", + " 8/1 1.11299 \n", + " 9/1 1.16066 \n", + " 10/1 1.12566 \n", + " 11/1 1.20854 \n", + " 12/1 1.14691 1.17773 +/- 0.03082\n", + " 13/1 1.17204 1.17583 +/- 0.01789\n", + " 14/1 1.14148 1.16724 +/- 0.01529\n", + " 15/1 1.17272 1.16834 +/- 0.01189\n", + " 16/1 1.18575 1.17124 +/- 0.01014\n", + " 17/1 1.20498 1.17606 +/- 0.00983\n", + " 18/1 1.14754 1.17249 +/- 0.00923\n", + " 19/1 1.18141 1.17348 +/- 0.00820\n", + " 20/1 1.15074 1.17121 +/- 0.00768\n", + " 21/1 1.15914 1.17011 +/- 0.00703\n", + " 22/1 1.14586 1.16809 +/- 0.00673\n", + " 23/1 1.18999 1.16978 +/- 0.00642\n", + " 24/1 1.15101 1.16844 +/- 0.00609\n", + " 25/1 1.13791 1.16640 +/- 0.00602\n", + " 26/1 1.19791 1.16837 +/- 0.00597\n", + " 27/1 1.19818 1.17012 +/- 0.00587\n", + " 28/1 1.14160 1.16854 +/- 0.00576\n", + " 29/1 1.11487 1.16571 +/- 0.00614\n", + " 30/1 1.17538 1.16620 +/- 0.00584\n", + " 31/1 1.20210 1.16791 +/- 0.00581\n", + " 32/1 1.20078 1.16940 +/- 0.00574\n", + " 33/1 1.14624 1.16839 +/- 0.00558\n", + " 34/1 1.14618 1.16747 +/- 0.00542\n", + " 35/1 1.16866 1.16752 +/- 0.00520\n", + " 36/1 1.18565 1.16821 +/- 0.00504\n", + " 37/1 1.16824 1.16821 +/- 0.00485\n", + " 38/1 1.18299 1.16874 +/- 0.00471\n", + " 39/1 1.21418 1.17031 +/- 0.00480\n", + " 40/1 1.11167 1.16835 +/- 0.00504\n", + " 41/1 1.11545 1.16665 +/- 0.00516\n", + " 42/1 1.11114 1.16491 +/- 0.00529\n", + " 43/1 1.14227 1.16423 +/- 0.00517\n", + " 44/1 1.14104 1.16355 +/- 0.00506\n", + " 45/1 1.16756 1.16366 +/- 0.00492\n", + " 46/1 1.13065 1.16274 +/- 0.00487\n", + " 47/1 1.11251 1.16139 +/- 0.00492\n", + " 48/1 1.14731 1.16101 +/- 0.00481\n", + " 49/1 1.16691 1.16117 +/- 0.00469\n", + " 50/1 1.19679 1.16206 +/- 0.00465\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 4.2800E-01 seconds\n", + " Reading cross sections = 8.9000E-02 seconds\n", + " Total time in simulation = 1.4506E+01 seconds\n", + " Time in transport only = 1.4496E+01 seconds\n", + " Time in inactive batches = 1.7910E+00 seconds\n", + " Time in active batches = 1.2715E+01 seconds\n", + " Time synchronizing fission bank = 1.0000E-03 seconds\n", + " Sampling source sites = 0.0000E+00 seconds\n", + " SEND/RECV source sites = 1.0000E-03 seconds\n", + " Time accumulating tallies = 0.0000E+00 seconds\n", + " Total time for finalization = 0.0000E+00 seconds\n", + " Total time elapsed = 1.4943E+01 seconds\n", + " Calculation Rate (inactive) = 13958.7 neutrons/second\n", + " Calculation Rate (active) = 7864.73 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.16131 +/- 0.00453\n", + " k-effective (Track-length) = 1.16206 +/- 0.00465\n", + " k-effective (Absorption) = 1.16096 +/- 0.00364\n", + " Combined k-effective = 1.16120 +/- 0.00325\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Remove old HDF5 (summary, statepoint) files\n", + "!rm statepoint.*\n", + "\n", + "# Run OpenMC\n", + "executor = openmc.Executor()\n", + "executor.run_simulation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and \"reading\" the results. By default, data from the statepoint file is only read into memory when it is requested. This helps keep the memory use to a minimum even when a statepoint file may be huge." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the last statepoint file\n", + "sp = openmc.StatePoint('statepoint.50.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry which is necessary for the `openmc.mgxs` module to properly process the tally data. We first create a summary object and link it with the statepoint." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the summary file and link it with the statepoint\n", + "su = openmc.Summary('summary.h5')\n", + "sp.link_with_summary(su)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the statepoint into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the tallies from the statepoint into each MGXS object\n", + "total.load_from_statepoint(sp)\n", + "absorption.load_from_statepoint(sp)\n", + "scattering.load_from_statepoint(sp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voila! Our multi-group cross sections are now ready to rock 'n roll!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extracting and Storing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first inspect our total cross section by printing it to the screen." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\ttotal\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t1\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t6.81e-01 +/- 1.88e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t1.40e+00 +/- 5.91e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "total.print_xs()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the `openmc.mgxs` module uses tally arithmetic under-the-hood, the cross section is stored as a \"derived\" tally. This means that it can be queried and manipulated using all of the same method supported for the `Tally` class in the OpenMC Python API. For example, we can construct a Pandas DataFrame of the multi-group cross section data." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1254: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "1 1 1 total 0.668323 0.001264\n", + "0 1 2 total 1.293258 0.007624" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = scattering.get_pandas_dataframe()\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each multi-group cross section object can be easily exported to a variety of file formats, including CSV, Excel, and LaTeX for storage or data processing." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "absorption.export_xs_data(filename='absorption-xs', format='excel')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code snippet shows how to export all of three cross sections to the same HDF5 binary data store." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "total.build_hdf5_store(filename='mgxs', append=True)\n", + "absorption.build_hdf5_store(filename='mgxs', append=True)\n", + "scattering.build_hdf5_store(filename='mgxs', append=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing MGXS with Tally Arithmetic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we illustrate how one can leverage OpenMC's tally arithmetic data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" tally based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to confirm that the `TotalXS` is equal to the sum of the `AbsorptionXS` and `ScatterXS` objects." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy [MeV]nuclidescoremeanstd. dev.
01(0.0e+00 - 6.3e-07)total(((total / flux) - (absorption / flux)) - (sca...4.884981e-150.011274
11(6.3e-07 - 2.0e+01)total(((total / flux) - (absorption / flux)) - (sca...1.221245e-150.001802
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" + ], + "text/plain": [ + " cell energy [MeV] nuclide \\\n", + "0 1 (0.0e+00 - 6.3e-07) total \n", + "1 1 (6.3e-07 - 2.0e+01) total \n", + "\n", + " score mean std. dev. \n", + "0 (((total / flux) - (absorption / flux)) - (sca... 4.884981e-15 0.011274 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... 1.221245e-15 0.001802 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the difference between the total, absorption and scattering\n", + "difference = total.xs_tally - absorption.xs_tally - scattering.xs_tally\n", + "\n", + "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n", + "difference.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can use tally arithmetic to compute the ratio of `AbsorptionXS` and `ScatterXS` to the `TotalXS`." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy [MeV]nuclidescoremeanstd. dev.
01(0.0e+00 - 6.3e-07)total((absorption / flux) / (total / flux))0.0762190.000651
11(6.3e-07 - 2.0e+01)total((absorption / flux) / (total / flux))0.0193190.000086
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" + ], + "text/plain": [ + " cell energy [MeV] nuclide score \\\n", + "0 1 (0.0e+00 - 6.3e-07) total ((absorption / flux) / (total / flux)) \n", + "1 1 (6.3e-07 - 2.0e+01) total ((absorption / flux) / (total / flux)) \n", + "\n", + " mean std. dev. \n", + "0 0.076219 0.000651 \n", + "1 0.019319 0.000086 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the absorption-to-total MGXS ratio\n", + "absorption_to_total = absorption.xs_tally / total.xs_tally\n", + "\n", + "# The absorption-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "absorption_to_total.get_pandas_dataframe()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellenergy [MeV]nuclidescoremeanstd. dev.
01(0.0e+00 - 6.3e-07)total((scatter / flux) / (total / flux))0.9237810.007714
11(6.3e-07 - 2.0e+01)total((scatter / flux) / (total / flux))0.9806810.002617
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" + ], + "text/plain": [ + " cell energy [MeV] nuclide score \\\n", + "0 1 (0.0e+00 - 6.3e-07) total ((scatter / flux) / (total / flux)) \n", + "1 1 (6.3e-07 - 2.0e+01) total ((scatter / flux) / (total / flux)) \n", + "\n", + " mean std. dev. \n", + "0 0.923781 0.007714 \n", + "1 0.980681 0.002617 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to compute the scattering-to-total MGXS ratio\n", + "scattering_to_total = scattering.xs_tally / total.xs_tally\n", + "\n", + "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "scattering_to_total.get_pandas_dataframe()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell energy [MeV] nuclide \\\n", + "0 1 (0.0e+00 - 6.3e-07) total \n", + "1 1 (6.3e-07 - 2.0e+01) total \n", + "\n", + " score mean std. dev. \n", + "0 (((absorption / flux) / (total / flux)) + ((sc... 1 0.007741 \n", + "1 (((absorption / flux) / (total / flux)) + ((sc... 1 0.002619 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Use tally arithmetic to ensure that the absorption- and scattering-to-total MGXS ratios sum to unity\n", + "sum_ratio = absorption_to_total + scattering_to_total\n", + "\n", + "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n", + "sum_ratio.get_pandas_dataframe()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/source/pythonapi/examples/MGXS-Part-II.ipynb b/docs/source/pythonapi/examples/MGXS-Part-II.ipynb new file mode 100644 index 000000000..2976df22b --- /dev/null +++ b/docs/source/pythonapi/examples/MGXS-Part-II.ipynb @@ -0,0 +1,1972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This IPython Notebook illustrates the use of the `openmc.mgxs` module to calculate multi-group cross sections for a heterogeneous fuel pin cell geometry. In particular, this Notebook illustrates the following features:\n", + "\n", + "* Creation of multi-group cross sections on a **heterogeneous geometry**\n", + "* Calculation of cross sections on a **nuclide-by-nuclide basis**\n", + "* Built-in features for **energy condensation** in downstream data processing\n", + "* The use of **PyNE for plot** continuous energy vs. multi-group cross sections\n", + "* **Validation** of multi-group cross sections with **OpenMOC**\n", + "\n", + "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook in its entirety, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries. In addition, this Notebook illustrates the use of Pandas DataFrames to containerize multi-group cross section data. We recommend using Pandas >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/pymodules/python2.7/matplotlib/__init__.py:1173: 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", + "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.rxname is not yet QA compliant.\n", + "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.ace is not yet QA compliant.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "import openmc\n", + "import openmc.mgxs as mgxs\n", + "import openmoc\n", + "from openmoc.compatible import get_openmoc_geometry\n", + "import pyne.ace\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this section we show how to compute multi-group cross sections for a fuel pin cell. In addition, we will illustrate how to use some of the more advanced features in `openmc.mgxs` such as nuclide-by-nuclide microscopic cross section tallies and downstream energy group condensation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate some Nuclides\n", + "h1 = openmc.Nuclide('H-1')\n", + "o16 = openmc.Nuclide('O-16')\n", + "u235 = openmc.Nuclide('U-235')\n", + "u238 = openmc.Nuclide('U-238')\n", + "zr90 = openmc.Nuclide('Zr-90')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the nuclides we defined, we will now create three distinct materials for water, clad and fuel." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# 1.6 enriched fuel\n", + "fuel = openmc.Material(name='1.6% Fuel')\n", + "fuel.set_density('g/cm3', 10.31341)\n", + "fuel.add_nuclide(u235, 3.7503e-4)\n", + "fuel.add_nuclide(u238, 2.2625e-2)\n", + "fuel.add_nuclide(o16, 4.6007e-2)\n", + "\n", + "# borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide(h1, 4.9457e-2)\n", + "water.add_nuclide(o16, 2.4732e-2)\n", + "\n", + "# zircaloy\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide(zr90, 7.2758e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our materials, we can now create a materials file object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a MaterialsFile, add Materials\n", + "materials_file = openmc.MaterialsFile()\n", + "materials_file.add_material(fuel)\n", + "materials_file.add_material(water)\n", + "materials_file.add_material(zircaloy)\n", + "materials_file.default_xs = '71c'\n", + "\n", + "# Export to \"materials.xml\"\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "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", + "\n", + "# Create boundary planes to surround the geometry\n", + "# Use both reflective and vacuum boundaries to make life interesting\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')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a fuel pin\n", + "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", + "\n", + "# Create fuel Cell\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", + "fuel_cell.fill = fuel\n", + "fuel_cell.region = -fuel_outer_radius\n", + "pin_cell_universe.add_cell(fuel_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='1.6% Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "pin_cell_universe.add_cell(clad_cell)\n", + "\n", + "# 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", + "pin_cell_universe.add_cell(moderator_cell)" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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, put the geometry into a geometry file, and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "openmc_geometry = openmc.Geometry()\n", + "openmc_geometry.root_universe = root_universe\n", + "\n", + "# Instantiate a GeometryFile\n", + "geometry_file = openmc.GeometryFile()\n", + "geometry_file.geometry = openmc_geometry\n", + "\n", + "# Export to \"geometry.xml\"\n", + "geometry_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 190 active batches each with 10000 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 10000\n", + "\n", + "# Instantiate a SettingsFile\n", + "settings_file = openmc.SettingsFile()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "settings_file.output = {'tallies': True, 'summary': True}\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "settings_file.set_source_space('fission', bounds)\n", + "\n", + "# Activate tally precision triggers\n", + "settings_file.trigger_active = True\n", + "settings_file.trigger_max_batches = settings_file.batches * 4\n", + "\n", + "# Export to \"settings.xml\"\n", + "settings_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are finally ready to make use of the `openmc.mgxs` module to generate multi-group cross sections! First, let's define a \"fine\" 8-group and \"coarse\" 2-group structures using the built-in `EnergyGroups` class." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a \"fine\" 8-group EnergyGroups object\n", + "fine_groups = mgxs.EnergyGroups()\n", + "fine_groups.group_edges = np.array([0., 0.058e-6, 0.14e-6, 0.28e-6,\n", + " 0.625e-6, 4.e-6, 5.53e-3, 821.e-3, 20.])\n", + "\n", + "# Instantiate a \"coarse\" 2-group EnergyGroups object\n", + "coarse_groups = mgxs.EnergyGroups()\n", + "coarse_groups.group_edges = np.array([0., 0.625e-6, 20.])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will instantiate a variety of `MGXS` objects needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we will define transport, nu-fission, nu-scatter and chi cross sections for each of the three cells in the fuel pin with the 8-group structure as our energy groups." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Extract all Cells filled by Materials\n", + "openmc_cells = openmc_geometry.get_all_material_cells()\n", + "\n", + "# Create dictionary to store multi-group cross sections for all cells\n", + "xs_library = {}\n", + "\n", + "# Instantiate 8-group cross sections for each cell\n", + "for cell in openmc_cells:\n", + " xs_library[cell.id] = {}\n", + " xs_library[cell.id]['transport'] = mgxs.TransportXS(groups=fine_groups)\n", + " xs_library[cell.id]['fission'] = mgxs.FissionXS(groups=fine_groups)\n", + " xs_library[cell.id]['nu-fission'] = mgxs.NuFissionXS(groups=fine_groups)\n", + " xs_library[cell.id]['nu-scatter'] = mgxs.NuScatterMatrixXS(groups=fine_groups)\n", + " xs_library[cell.id]['chi'] = mgxs.Chi(groups=fine_groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we showcase the use of OpenMC's tally trigger feature in conjunction with the `openmc.mgxs` module. In particular, we will assign a tally trigger of 1E-2 on the standard deviation for each of the tallies used to compute multi-group cross sections." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "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", + "\n", + "# Add the tally trigger to each of the multi-group cross section tallies\n", + "for cell in openmc_cells:\n", + " for mgxs_type in xs_library[cell.id]:\n", + " xs_library[cell.id][mgxs_type].tally_trigger = tally_trigger" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we must loop over all cells to set the cross section domains to the various cells - fuel, clad and moderator - included in the geometry. In addition, we will set each cross section to tally cross sections on a per-nuclide basis through the use of the `by_nuclide` instance attribute. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate an empty TalliesFile\n", + "tallies_file = openmc.TalliesFile()\n", + "\n", + "# Iterate over all cells and cross section types\n", + "for cell in openmc_cells:\n", + " for rxn_type in xs_library[cell.id]:\n", + "\n", + " # Set the cross sections domain type to the cell\n", + " xs_library[cell.id][rxn_type].domain = cell\n", + " xs_library[cell.id][rxn_type].domain_type = 'cell'\n", + " \n", + " # Tally cross sections by nuclide (e.g., micro cross sections)\n", + " xs_library[cell.id][rxn_type].by_nuclide = True\n", + " \n", + " # Add OpenMC tallies to the tallies file for XML generation\n", + " for tally in xs_library[cell.id][rxn_type].tallies.values():\n", + " tallies_file.add_tally(tally, merge=True)\n", + "\n", + "# Export to \"tallies.xml\"\n", + "tallies_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we a have a complete set of inputs, so we can go ahead and run our simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " .d88888b. 888b d888 .d8888b.\n", + " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", + " 888 888 88888b.d88888 888 888\n", + " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", + " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", + " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", + " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", + " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", + "__________________888______________________________________________________\n", + " 888\n", + " 888\n", + "\n", + " Copyright: 2011-2015 Massachusetts Institute of Technology\n", + " License: http://mit-crpg.github.io/openmc/license.html\n", + " Version: 0.7.0\n", + " Git SHA1: c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n", + " Date/Time: 2015-11-29 21:22:25\n", + " MPI Processes: 1\n", + "\n", + " ===========================================================================\n", + " ========================> INITIALIZATION <=========================\n", + " ===========================================================================\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading geometry XML file...\n", + " Reading materials XML file...\n", + " Reading tallies XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Loading ACE cross section table: 92235.71c\n", + " Loading ACE cross section table: 92238.71c\n", + " Loading ACE cross section table: 8016.71c\n", + " Loading ACE cross section table: 1001.71c\n", + " Loading ACE cross section table: 40090.71c\n", + " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", + " Initializing source particles...\n", + "\n", + " ===========================================================================\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + " ===========================================================================\n", + "\n", + " Bat./Gen. k Average k \n", + " ========= ======== ==================== \n", + " 1/1 1.22593 \n", + " 2/1 1.24245 \n", + " 3/1 1.24545 \n", + " 4/1 1.21868 \n", + " 5/1 1.22429 \n", + " 6/1 1.22607 \n", + " 7/1 1.21456 \n", + " 8/1 1.23816 \n", + " 9/1 1.25060 \n", + " 10/1 1.22806 \n", + " 11/1 1.19821 \n", + " 12/1 1.19897 1.19859 +/- 0.00038\n", + " 13/1 1.22119 1.20612 +/- 0.00754\n", + " 14/1 1.20701 1.20634 +/- 0.00533\n", + " 15/1 1.24784 1.21464 +/- 0.00927\n", + " 16/1 1.22413 1.21622 +/- 0.00773\n", + " 17/1 1.25050 1.22112 +/- 0.00817\n", + " 18/1 1.22006 1.22099 +/- 0.00707\n", + " 19/1 1.22813 1.22178 +/- 0.00629\n", + " 20/1 1.22791 1.22239 +/- 0.00566\n", + " 21/1 1.22729 1.22284 +/- 0.00514\n", + " 22/1 1.19867 1.22083 +/- 0.00510\n", + " 23/1 1.23796 1.22214 +/- 0.00488\n", + " 24/1 1.22412 1.22228 +/- 0.00452\n", + " 25/1 1.22638 1.22256 +/- 0.00421\n", + " 26/1 1.22181 1.22251 +/- 0.00394\n", + " 27/1 1.19055 1.22063 +/- 0.00415\n", + " 28/1 1.20683 1.21986 +/- 0.00399\n", + " 29/1 1.21689 1.21971 +/- 0.00378\n", + " 30/1 1.23670 1.22056 +/- 0.00368\n", + " 31/1 1.21396 1.22024 +/- 0.00352\n", + " 32/1 1.21389 1.21995 +/- 0.00337\n", + " 33/1 1.24649 1.22111 +/- 0.00342\n", + " 34/1 1.23204 1.22156 +/- 0.00330\n", + " 35/1 1.20768 1.22101 +/- 0.00322\n", + " 36/1 1.22271 1.22107 +/- 0.00309\n", + " 37/1 1.21796 1.22096 +/- 0.00298\n", + " 38/1 1.23842 1.22158 +/- 0.00293\n", + " 39/1 1.23080 1.22190 +/- 0.00285\n", + " 40/1 1.23572 1.22236 +/- 0.00279\n", + " 41/1 1.21691 1.22218 +/- 0.00271\n", + " 42/1 1.24616 1.22293 +/- 0.00272\n", + " 43/1 1.21903 1.22282 +/- 0.00264\n", + " 44/1 1.22967 1.22302 +/- 0.00257\n", + " 45/1 1.22053 1.22295 +/- 0.00250\n", + " 46/1 1.24087 1.22344 +/- 0.00248\n", + " 47/1 1.20251 1.22288 +/- 0.00248\n", + " 48/1 1.20331 1.22236 +/- 0.00246\n", + " 49/1 1.22724 1.22249 +/- 0.00240\n", + " 50/1 1.24798 1.22313 +/- 0.00243\n", + " Triggers unsatisfied, max unc./thresh. is 1.32110 for scatter-p1 in tally 10054\n", + " The estimated number of batches is 80\n", + " Creating state point statepoint.050.h5...\n", + " 51/1 1.22253 1.22311 +/- 0.00237\n", + " 52/1 1.24330 1.22359 +/- 0.00236\n", + " 53/1 1.23251 1.22380 +/- 0.00231\n", + " 54/1 1.21133 1.22352 +/- 0.00228\n", + " 55/1 1.24503 1.22399 +/- 0.00228\n", + " 56/1 1.22013 1.22391 +/- 0.00223\n", + " 57/1 1.23877 1.22423 +/- 0.00220\n", + " 58/1 1.23793 1.22451 +/- 0.00218\n", + " 59/1 1.21018 1.22422 +/- 0.00215\n", + " 60/1 1.22417 1.22422 +/- 0.00211\n", + " 61/1 1.23094 1.22435 +/- 0.00207\n", + " 62/1 1.23310 1.22452 +/- 0.00204\n", + " 63/1 1.22488 1.22453 +/- 0.00200\n", + " 64/1 1.22702 1.22457 +/- 0.00196\n", + " 65/1 1.18834 1.22391 +/- 0.00204\n", + " 66/1 1.23112 1.22404 +/- 0.00200\n", + " 67/1 1.21611 1.22390 +/- 0.00197\n", + " 68/1 1.22513 1.22392 +/- 0.00194\n", + " 69/1 1.21741 1.22381 +/- 0.00191\n", + " 70/1 1.22484 1.22383 +/- 0.00188\n", + " 71/1 1.19662 1.22338 +/- 0.00190\n", + " 72/1 1.23315 1.22354 +/- 0.00187\n", + " 73/1 1.22796 1.22361 +/- 0.00185\n", + " 74/1 1.21417 1.22346 +/- 0.00182\n", + " 75/1 1.21020 1.22326 +/- 0.00181\n", + " 76/1 1.23413 1.22343 +/- 0.00179\n", + " 77/1 1.22184 1.22340 +/- 0.00176\n", + " 78/1 1.20309 1.22310 +/- 0.00176\n", + " 79/1 1.23458 1.22327 +/- 0.00174\n", + " 80/1 1.20724 1.22304 +/- 0.00173\n", + " Triggers satisfied for batch 80\n", + " Creating state point statepoint.080.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 4.1800E-01 seconds\n", + " Reading cross sections = 8.7000E-02 seconds\n", + " Total time in simulation = 2.3349E+02 seconds\n", + " Time in transport only = 2.3343E+02 seconds\n", + " Time in inactive batches = 1.4263E+01 seconds\n", + " Time in active batches = 2.1923E+02 seconds\n", + " Time synchronizing fission bank = 2.5000E-02 seconds\n", + " Sampling source sites = 2.1000E-02 seconds\n", + " SEND/RECV source sites = 4.0000E-03 seconds\n", + " Time accumulating tallies = 0.0000E+00 seconds\n", + " Total time for finalization = 9.0000E-03 seconds\n", + " Total time elapsed = 2.3396E+02 seconds\n", + " Calculation Rate (inactive) = 7011.15 neutrons/second\n", + " Calculation Rate (active) = 1824.61 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.22327 +/- 0.00148\n", + " k-effective (Track-length) = 1.22304 +/- 0.00173\n", + " k-effective (Absorption) = 1.22407 +/- 0.00129\n", + " Combined k-effective = 1.22373 +/- 0.00113\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Delete old HDF5 files\n", + "!rm *.h5\n", + "\n", + "# Run OpenMC with the output throttled!\n", + "executor = openmc.Executor()\n", + "executor.run_simulation(output=True, mpi_procs=3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and \"reading\" the results. By default, data from the statepoint file is only read into memory when it is requested. This helps keep the memory use to a minimum even when a statepoint file may be huge." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the last statepoint file\n", + "sp = openmc.StatePoint('statepoint.080.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry which is necessary for the `openmc.mgxs` module to properly process the tally data. We first create a summary object and link it with the statepoint." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Load the summary file and link it with the statepoint\n", + "su = openmc.Summary('summary.h5')\n", + "sp.link_with_summary(su)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint is now ready to be analyzed by our multi-group cross sections. Next, we load the tallies from the statepoint into each object to compute the cross sections using tally arithmetic." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Iterate over all cells and cross section types\n", + "for cell in openmc_cells:\n", + " for rxn_type in xs_library[cell.id]:\n", + " xs_library[cell.id][rxn_type].load_from_statepoint(sp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it! Our multi-group cross sections are now ready for the big spotlight. This time we have cross sections in three distinct spatial zones - fuel, clad and moderator - on a per-nuclide basis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extracting and Storing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first inspect one of our cross sections by printing it to the screen as a microscopic cross section in units of barns." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tNuclide =\tU-235\n", + "\tCross Sections [barns]:\n", + " Group 1 [0.821 - 20.0 MeV]:\t3.31e+00 +/- 1.88e-01%\n", + " Group 2 [0.00553 - 0.821 MeV]:\t3.97e+00 +/- 1.24e-01%\n", + " Group 3 [4e-06 - 0.00553 MeV]:\t5.50e+01 +/- 2.02e-01%\n", + " Group 4 [6.25e-07 - 4e-06 MeV]:\t8.83e+01 +/- 3.56e-01%\n", + " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t2.90e+02 +/- 4.54e-01%\n", + " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t4.49e+02 +/- 4.10e-01%\n", + " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t6.87e+02 +/- 2.56e-01%\n", + " Group 8 [0.0 - 5.8e-08 MeV]:\t1.44e+03 +/- 2.82e-01%\n", + "\n", + "\tNuclide =\tU-238\n", + "\tCross Sections [barns]:\n", + " Group 1 [0.821 - 20.0 MeV]:\t1.06e+00 +/- 2.30e-01%\n", + " Group 2 [0.00553 - 0.821 MeV]:\t1.21e-03 +/- 2.25e-01%\n", + " Group 3 [4e-06 - 0.00553 MeV]:\t5.82e-04 +/- 3.09e+00%\n", + " Group 4 [6.25e-07 - 4e-06 MeV]:\t6.54e-06 +/- 3.27e-01%\n", + " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.07e-05 +/- 4.39e-01%\n", + " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.55e-05 +/- 4.12e-01%\n", + " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.30e-05 +/- 2.57e-01%\n", + " Group 8 [0.0 - 5.8e-08 MeV]:\t4.24e-05 +/- 2.81e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "nufission = xs_library[fuel_cell.id]['nu-fission']\n", + "nufission.print_xs(xs_type='micro', nuclides=['U-235', 'U-238'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our multi-group cross sections are capable of summing across all nuclides to provide us with macroscopic cross sections as well." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [0.821 - 20.0 MeV]:\t2.52e-02 +/- 2.19e-01%\n", + " Group 2 [0.00553 - 0.821 MeV]:\t1.51e-03 +/- 1.22e-01%\n", + " Group 3 [4e-06 - 0.00553 MeV]:\t2.06e-02 +/- 2.02e-01%\n", + " Group 4 [6.25e-07 - 4e-06 MeV]:\t3.31e-02 +/- 3.56e-01%\n", + " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.09e-01 +/- 4.54e-01%\n", + " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.69e-01 +/- 4.10e-01%\n", + " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.58e-01 +/- 2.56e-01%\n", + " Group 8 [0.0 - 5.8e-08 MeV]:\t5.40e-01 +/- 2.82e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "nufission = xs_library[fuel_cell.id]['nu-fission']\n", + "nufission.print_xs(xs_type='macro', nuclides='sum')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although a printed report is nice, it is not scalable or flexible. Let's extract the cross section data for the moderator as a Pandas DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1254: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell group in group out nuclide mean std. dev.\n", + "126 10002 1 1 H-1 0.234022 0.003645\n", + "127 10002 1 1 O-16 1.560305 0.006280\n", + "124 10002 1 2 H-1 1.588025 0.002815\n", + "125 10002 1 2 O-16 0.285147 0.001392\n", + "122 10002 1 3 H-1 0.010776 0.000186\n", + "123 10002 1 3 O-16 0.000000 0.000000\n", + "120 10002 1 4 H-1 0.000023 0.000010\n", + "121 10002 1 4 O-16 0.000000 0.000000\n", + "118 10002 1 5 H-1 0.000000 0.000000\n", + "119 10002 1 5 O-16 0.000000 0.000000" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", + "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we illustate how one can easily take multi-group cross sections and condense them down to a coarser energy group structure using. The `get_condensed_xs(...)` class method takes in as a parameter an `EnergyGroups` object with a coarse(r) group structure and returns a new multi-group cross section condensed to the coarse groups. We illustrate this process below using the 2-group structure created earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Extract the 16-group transport cross section for the fuel\n", + "fine_xs = xs_library[fuel_cell.id]['transport']\n", + "\n", + "# Condense to the 2-group structure\n", + "condense_xs = fine_xs.get_condensed_xs(coarse_groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Group condensation is as simple as that! We now have a new coarse 2-group cross section in addition to our original 16-group cross section. Let's inspect the 2-group cross section by printing it to the screen and extracting a Pandas DataFrame as we have already learned how to do." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\ttransport\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tNuclide =\tU-235\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t7.81e-03 +/- 4.75e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t1.82e-01 +/- 1.89e-01%\n", + "\n", + "\tNuclide =\tU-238\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t2.17e-01 +/- 1.31e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t2.53e-01 +/- 2.08e-01%\n", + "\n", + "\tNuclide =\tO-16\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t1.45e-01 +/- 1.50e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t1.74e-01 +/- 2.66e-01%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "condense_xs.print_xs()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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2100002O-163.7881670.010090
\n", + "
" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "3 10000 1 U-235 20.828127 0.098842\n", + "4 10000 1 U-238 9.582295 0.012550\n", + "5 10000 1 O-16 3.157358 0.004725\n", + "0 10000 2 U-235 485.217649 0.916465\n", + "1 10000 2 U-238 11.176081 0.023196\n", + "2 10000 2 O-16 3.788167 0.010090" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = condense_xs.get_pandas_dataframe(xs_type='micro')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verification with OpenMOC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's verify our cross sections using OpenMOC. First, we use OpenCG construct an equivalent OpenMOC geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create an OpenMOC Geometry from the OpenCG Geometry\n", + "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we we can inject the multi-group cross sections into the equivalent fuel pin cell OpenMOC geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Get all OpenMOC cells in the gometry\n", + "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", + "\n", + "# Inject multi-group cross sections into OpenMOC Materials\n", + "# NOTE: This code will work for 1, 10, or 1,000s of cells\n", + "# as is the case for a complicated geometry like BEAVRS\n", + "for cell_id, cell in openmoc_cells.items():\n", + " \n", + " # Ignore the root cell\n", + " if cell.getName() == 'root cell':\n", + " continue\n", + " \n", + " # Get a reference to the Material filling this Cell\n", + " openmoc_material = cell.getFillMaterial()\n", + " \n", + " # Set the number of energy groups for the Material\n", + " openmoc_material.setNumEnergyGroups(fine_groups.num_groups)\n", + " \n", + " # Extract the appropriate cross section objects for this cell\n", + " transport = xs_library[cell_id]['transport']\n", + " nufission = xs_library[cell_id]['nu-fission']\n", + " nuscatter = xs_library[cell_id]['nu-scatter']\n", + " chi = xs_library[cell_id]['chi']\n", + " \n", + " # Inject NumPy arrays of cross section data into the Material\n", + " # NOTE: In each case we must sum across nuclides to get the\n", + " # macroscopic cross sections needed by OpenMOC\n", + " openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to run OpenMOC to verify our cross-sections from OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Ray tracing for track segmentation...\n", + "[ NORMAL ] Dumping tracks to file...\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0:\tk_eff = 0.574633\tres = 0.000E+00\n", + "[ NORMAL ] Iteration 1:\tk_eff = 0.679931\tres = 4.254E-01\n", + "[ NORMAL ] Iteration 2:\tk_eff = 0.660910\tres = 1.832E-01\n", + "[ NORMAL ] Iteration 3:\tk_eff = 0.658975\tres = 2.798E-02\n", + "[ NORMAL ] Iteration 4:\tk_eff = 0.642976\tres = 2.927E-03\n", + "[ NORMAL ] Iteration 5:\tk_eff = 0.625710\tres = 2.428E-02\n", + "[ NORMAL ] Iteration 6:\tk_eff = 0.606521\tres = 2.685E-02\n", + "[ NORMAL ] Iteration 7:\tk_eff = 0.587277\tres = 3.067E-02\n", + "[ NORMAL ] Iteration 8:\tk_eff = 0.568777\tres = 3.173E-02\n", + "[ NORMAL ] Iteration 9:\tk_eff = 0.551415\tres = 3.150E-02\n", + "[ NORMAL ] Iteration 10:\tk_eff = 0.535708\tres = 3.052E-02\n", + "[ NORMAL ] Iteration 11:\tk_eff = 0.521916\tres = 2.849E-02\n", + "[ NORMAL ] Iteration 12:\tk_eff = 0.510222\tres = 2.575E-02\n", + "[ NORMAL ] Iteration 13:\tk_eff = 0.500691\tres = 2.241E-02\n", + "[ NORMAL ] Iteration 14:\tk_eff = 0.493392\tres = 1.868E-02\n", + "[ NORMAL ] Iteration 15:\tk_eff = 0.488318\tres = 1.458E-02\n", + "[ NORMAL ] Iteration 16:\tk_eff = 0.485438\tres = 1.028E-02\n", + "[ NORMAL ] Iteration 17:\tk_eff = 0.484705\tres = 5.896E-03\n", + "[ NORMAL ] Iteration 18:\tk_eff = 0.486046\tres = 1.510E-03\n", + "[ NORMAL ] Iteration 19:\tk_eff = 0.489362\tres = 2.766E-03\n", + "[ NORMAL ] Iteration 20:\tk_eff = 0.494546\tres = 6.824E-03\n", + "[ NORMAL ] Iteration 21:\tk_eff = 0.501482\tres = 1.059E-02\n", + "[ NORMAL ] Iteration 22:\tk_eff = 0.510042\tres = 1.402E-02\n", + "[ NORMAL ] Iteration 23:\tk_eff = 0.520095\tres = 1.707E-02\n", + "[ NORMAL ] Iteration 24:\tk_eff = 0.531508\tres = 1.971E-02\n", + "[ NORMAL ] Iteration 25:\tk_eff = 0.544145\tres = 2.194E-02\n", + "[ NORMAL ] Iteration 26:\tk_eff = 0.557872\tres = 2.378E-02\n", + "[ NORMAL ] Iteration 27:\tk_eff = 0.572558\tres = 2.523E-02\n", + "[ NORMAL ] Iteration 28:\tk_eff = 0.588073\tres = 2.632E-02\n", + "[ NORMAL ] Iteration 29:\tk_eff = 0.604293\tres = 2.710E-02\n", + "[ NORMAL ] Iteration 30:\tk_eff = 0.621101\tres = 2.758E-02\n", + "[ NORMAL ] Iteration 31:\tk_eff = 0.638383\tres = 2.781E-02\n", + "[ NORMAL ] Iteration 32:\tk_eff = 0.656033\tres = 2.782E-02\n", + "[ NORMAL ] Iteration 33:\tk_eff = 0.673951\tres = 2.765E-02\n", + "[ NORMAL ] Iteration 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+ "[ NORMAL ] Iteration 79:\tk_eff = 1.170925\tres = 2.936E-03\n", + "[ NORMAL ] Iteration 80:\tk_eff = 1.173962\tres = 2.759E-03\n", + "[ NORMAL ] Iteration 81:\tk_eff = 1.176824\tres = 2.594E-03\n", + "[ NORMAL ] Iteration 82:\tk_eff = 1.179518\tres = 2.437E-03\n", + "[ NORMAL ] Iteration 83:\tk_eff = 1.182054\tres = 2.289E-03\n", + "[ NORMAL ] Iteration 84:\tk_eff = 1.184440\tres = 2.150E-03\n", + "[ NORMAL ] Iteration 85:\tk_eff = 1.186684\tres = 2.018E-03\n", + "[ NORMAL ] Iteration 86:\tk_eff = 1.188794\tres = 1.895E-03\n", + "[ NORMAL ] Iteration 87:\tk_eff = 1.190777\tres = 1.778E-03\n", + "[ NORMAL ] Iteration 88:\tk_eff = 1.192641\tres = 1.668E-03\n", + "[ NORMAL ] Iteration 89:\tk_eff = 1.194391\tres = 1.565E-03\n", + "[ NORMAL ] Iteration 90:\tk_eff = 1.196034\tres = 1.467E-03\n", + "[ NORMAL ] Iteration 91:\tk_eff = 1.197577\tres = 1.376E-03\n", + "[ NORMAL ] Iteration 92:\tk_eff = 1.199024\tres = 1.290E-03\n", + "[ NORMAL ] Iteration 93:\tk_eff = 1.200382\tres = 1.209E-03\n", + "[ NORMAL ] Iteration 94:\tk_eff = 1.201656\tres = 1.133E-03\n", + "[ NORMAL ] Iteration 95:\tk_eff = 1.202850\tres = 1.061E-03\n", + "[ NORMAL ] Iteration 96:\tk_eff = 1.203970\tres = 9.941E-04\n", + "[ NORMAL ] Iteration 97:\tk_eff = 1.205018\tres = 9.307E-04\n", + "[ NORMAL ] Iteration 98:\tk_eff = 1.206002\tres = 8.706E-04\n", + "[ NORMAL ] Iteration 99:\tk_eff = 1.206923\tres = 8.158E-04\n", + "[ NORMAL ] Iteration 100:\tk_eff = 1.207785\tres = 7.636E-04\n", + "[ NORMAL ] Iteration 101:\tk_eff = 1.208592\tres = 7.144E-04\n", + "[ NORMAL ] Iteration 102:\tk_eff = 1.209347\tres = 6.684E-04\n", + "[ NORMAL ] Iteration 103:\tk_eff = 1.210055\tres = 6.249E-04\n", + "[ NORMAL ] Iteration 104:\tk_eff = 1.210716\tres = 5.849E-04\n", + "[ NORMAL ] Iteration 105:\tk_eff = 1.211335\tres = 5.465E-04\n", + "[ NORMAL ] Iteration 106:\tk_eff = 1.211915\tres = 5.115E-04\n", + "[ NORMAL ] Iteration 107:\tk_eff = 1.212455\tres = 4.782E-04\n", + "[ NORMAL ] Iteration 108:\tk_eff = 1.212962\tres = 4.461E-04\n", + "[ NORMAL ] Iteration 109:\tk_eff = 1.213435\tres = 4.176E-04\n", + "[ NORMAL ] Iteration 110:\tk_eff = 1.213877\tres = 3.900E-04\n", + "[ NORMAL ] Iteration 111:\tk_eff = 1.214290\tres = 3.643E-04\n", + "[ NORMAL ] Iteration 112:\tk_eff = 1.214676\tres = 3.404E-04\n", + "[ NORMAL ] Iteration 113:\tk_eff = 1.215038\tres = 3.181E-04\n", + "[ NORMAL ] Iteration 114:\tk_eff = 1.215375\tres = 2.973E-04\n", + "[ NORMAL ] Iteration 115:\tk_eff = 1.215689\tres = 2.771E-04\n", + "[ NORMAL ] Iteration 116:\tk_eff = 1.215982\tres = 2.585E-04\n", + "[ NORMAL ] Iteration 117:\tk_eff = 1.216257\tres = 2.416E-04\n", + "[ NORMAL ] Iteration 118:\tk_eff = 1.216513\tres = 2.258E-04\n", + "[ NORMAL ] Iteration 119:\tk_eff = 1.216752\tres = 2.105E-04\n", + "[ NORMAL ] Iteration 120:\tk_eff = 1.216975\tres = 1.963E-04\n", + "[ NORMAL ] Iteration 121:\tk_eff = 1.217183\tres = 1.834E-04\n", + "[ NORMAL ] Iteration 122:\tk_eff = 1.217377\tres = 1.709E-04\n", + "[ NORMAL ] 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5.977E-05\n", + "[ NORMAL ] Iteration 138:\tk_eff = 1.219183\tres = 5.590E-05\n", + "[ NORMAL ] Iteration 139:\tk_eff = 1.219242\tres = 5.170E-05\n", + "[ NORMAL ] Iteration 140:\tk_eff = 1.219296\tres = 4.832E-05\n", + "[ NORMAL ] Iteration 141:\tk_eff = 1.219347\tres = 4.486E-05\n", + "[ NORMAL ] Iteration 142:\tk_eff = 1.219395\tres = 4.185E-05\n", + "[ NORMAL ] Iteration 143:\tk_eff = 1.219439\tres = 3.868E-05\n", + "[ NORMAL ] Iteration 144:\tk_eff = 1.219480\tres = 3.650E-05\n", + "[ NORMAL ] Iteration 145:\tk_eff = 1.219518\tres = 3.379E-05\n", + "[ NORMAL ] Iteration 146:\tk_eff = 1.219554\tres = 3.107E-05\n", + "[ NORMAL ] Iteration 147:\tk_eff = 1.219587\tres = 2.929E-05\n", + "[ NORMAL ] Iteration 148:\tk_eff = 1.219618\tres = 2.709E-05\n", + "[ NORMAL ] Iteration 149:\tk_eff = 1.219647\tres = 2.542E-05\n", + "[ NORMAL ] Iteration 150:\tk_eff = 1.219674\tres = 2.362E-05\n", + "[ NORMAL ] Iteration 151:\tk_eff = 1.219699\tres = 2.199E-05\n", + "[ NORMAL ] Iteration 152:\tk_eff = 1.219722\tres = 2.070E-05\n", + "[ NORMAL ] Iteration 153:\tk_eff = 1.219743\tres = 1.878E-05\n", + "[ NORMAL ] Iteration 154:\tk_eff = 1.219763\tres = 1.770E-05\n", + "[ NORMAL ] Iteration 155:\tk_eff = 1.219782\tres = 1.628E-05\n", + "[ NORMAL ] Iteration 156:\tk_eff = 1.219799\tres = 1.526E-05\n", + "[ NORMAL ] Iteration 157:\tk_eff = 1.219815\tres = 1.387E-05\n", + "[ NORMAL ] Iteration 158:\tk_eff = 1.219830\tres = 1.319E-05\n", + "[ NORMAL ] Iteration 159:\tk_eff = 1.219844\tres = 1.208E-05\n", + "[ NORMAL ] Iteration 160:\tk_eff = 1.219857\tres = 1.150E-05\n", + "[ NORMAL ] Iteration 161:\tk_eff = 1.219869\tres = 1.055E-05\n", + "[ NORMAL ] Iteration 162:\tk_eff = 1.219880\tres = 1.008E-05\n" + ] + } + ], + "source": [ + "# Generate tracks for OpenMOC\n", + "openmoc_geometry.initializeFlatSourceRegions()\n", + "track_generator = openmoc.TrackGenerator(openmoc_geometry, 128, 0.1)\n", + "track_generator.generateTracks()\n", + "\n", + "# Run OpenMOC\n", + "solver = openmoc.CPUSolver(track_generator)\n", + "solver.computeEigenvalue()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.223729\n", + "openmoc keff = 1.219880\n", + "bias [pcm]: -384.9\n" + ] + } + ], + "source": [ + "# Print report of keff and bias with OpenMC\n", + "openmoc_keff = solver.getKeff()\n", + "openmc_keff = sp.k_combined[0]\n", + "bias = (openmoc_keff - openmc_keff) * 1e5\n", + "\n", + "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", + "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", + "print('bias [pcm]: {0:1.1f}'.format(bias))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a sanity check, let's run a simulation with the coarse 2-group cross sections to ensure that they produce a reasonable result." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)\n", + "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", + "\n", + "# Inject multi-group cross sections into OpenMOC Materials\n", + "for cell_id, cell in openmoc_cells.items():\n", + " \n", + " # Ignore the root cell\n", + " if cell.getName() == 'root cell':\n", + " continue\n", + " \n", + " openmoc_material = cell.getFillMaterial()\n", + " openmoc_material.setNumEnergyGroups(coarse_groups.num_groups)\n", + " \n", + " # Extract the appropriate cross section objects for this cell\n", + " transport = xs_library[cell_id]['transport']\n", + " nufission = xs_library[cell_id]['nu-fission']\n", + " nuscatter = xs_library[cell_id]['nu-scatter']\n", + " chi = xs_library[cell_id]['chi']\n", + " \n", + " # Perform group condensation\n", + " transport = transport.get_condensed_xs(coarse_groups)\n", + " nufission = nufission.get_condensed_xs(coarse_groups)\n", + " nuscatter = nuscatter.get_condensed_xs(coarse_groups)\n", + " chi = chi.get_condensed_xs(coarse_groups)\n", + " \n", + " # Inject NumPy arrays of cross section data into the Material\n", + " openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n", + " openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Ray tracing for track segmentation...\n", + "[ NORMAL ] Dumping tracks to file...\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0:\tk_eff = 0.495594\tres = 0.000E+00\n", + "[ NORMAL ] Iteration 1:\tk_eff = 0.557313\tres = 5.044E-01\n", + "[ NORMAL ] Iteration 2:\tk_eff = 0.518115\tres = 1.245E-01\n", + "[ NORMAL ] Iteration 3:\tk_eff = 0.509017\tres = 7.033E-02\n", + "[ NORMAL ] Iteration 4:\tk_eff = 0.496280\tres = 1.756E-02\n", + "[ NORMAL ] Iteration 5:\tk_eff = 0.488358\tres = 2.502E-02\n", + "[ NORMAL ] Iteration 6:\tk_eff = 0.482660\tres = 1.596E-02\n", + "[ NORMAL ] Iteration 7:\tk_eff = 0.479524\tres = 1.167E-02\n", + "[ NORMAL ] Iteration 8:\tk_eff = 0.478569\tres = 6.497E-03\n", + "[ NORMAL ] Iteration 9:\tk_eff = 0.479591\tres = 1.992E-03\n", + "[ NORMAL ] Iteration 10:\tk_eff = 0.482389\tres = 2.136E-03\n", + "[ NORMAL ] Iteration 11:\tk_eff = 0.486774\tres = 5.834E-03\n", + "[ NORMAL ] Iteration 12:\tk_eff = 0.492576\tres = 9.091E-03\n", + "[ NORMAL ] Iteration 13:\tk_eff = 0.499632\tres = 1.192E-02\n", + "[ NORMAL ] Iteration 14:\tk_eff = 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1.497E-05\n", + "[ NORMAL ] Iteration 221:\tk_eff = 1.222322\tres = 1.452E-05\n", + "[ NORMAL ] Iteration 222:\tk_eff = 1.222338\tres = 1.381E-05\n", + "[ NORMAL ] Iteration 223:\tk_eff = 1.222354\tres = 1.330E-05\n", + "[ NORMAL ] Iteration 224:\tk_eff = 1.222369\tres = 1.285E-05\n", + "[ NORMAL ] Iteration 225:\tk_eff = 1.222383\tres = 1.230E-05\n", + "[ NORMAL ] Iteration 226:\tk_eff = 1.222397\tres = 1.170E-05\n", + "[ NORMAL ] Iteration 227:\tk_eff = 1.222410\tres = 1.124E-05\n", + "[ NORMAL ] Iteration 228:\tk_eff = 1.222423\tres = 1.095E-05\n", + "[ NORMAL ] Iteration 229:\tk_eff = 1.222436\tres = 1.047E-05\n", + "[ NORMAL ] Iteration 230:\tk_eff = 1.222448\tres = 1.009E-05\n" + ] + } + ], + "source": [ + "# Generate tracks for OpenMOC\n", + "openmoc_geometry.initializeFlatSourceRegions()\n", + "track_generator = openmoc.TrackGenerator(openmoc_geometry, 128, 0.1)\n", + "track_generator.generateTracks()\n", + "\n", + "# Run OpenMOC\n", + "solver = openmoc.CPUSolver(track_generator)\n", + "solver.computeEigenvalue()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.223729\n", + "openmoc keff = 1.222448\n", + "bias [pcm]: -128.1\n" + ] + } + ], + "source": [ + "# Print report of keff and bias with OpenMC\n", + "openmoc_keff = solver.getKeff()\n", + "openmc_keff = sp.k_combined[0]\n", + "bias = (openmoc_keff - openmc_keff) * 1e5\n", + "\n", + "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", + "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", + "print('bias [pcm]: {0:1.1f}'.format(bias))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a non-trivial bias in both the 2-group and 8-group cases. In the case of the pin cell, one can show that these biases do not converge to <100 pcm with more particle histories. In the case of heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n", + "\n", + "* Appropriate transport-corrected cross sections\n", + "* Spatial discretization of OpenMOC's mesh\n", + "* Constant-in-angle multi-group cross sections" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Visualizing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is often insightful to generate visual depictions of multi-group cross sections. There are many different types of plots which may be useful for MGXS visualization, only a few of which will be shown here for inspiration.\n", + "\n", + "One particularly useful visualization is a comparison of the continuous energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the open source PyNE library to parse continuous energy multi-group cross sections from the cross section data library provided with OpenMC. First, we instantiate a `pyne.ace.Library` object for U-235 as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a PyNE ACE continuous energy cross sections library\n", + "pyne_lib = pyne.ace.Library('../../../../data/nndc/293.6K/U_235_293.6K.ace')\n", + "pyne_lib.read('92235.71c')\n", + "\n", + "# Extract the U-235 data from the library\n", + "u235 = pyne_lib.tables['92235.71c']\n", + "\n", + "# Extract the continuous energy fission U-235 cross section data\n", + "fission = u235.reactions[18]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we use matplotlib to plot the multi-group and continuous energy cross sections on a single plot." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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xhDFjapk1qzoqPTucM9VJf414SceNnJQ0xOmEyy5zM21aDaecUsCSJW0jRXs0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\nfj+uTTvoZzy8s6y80fuhP5+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a loglog plot of the U-235 continuous energy fission cross section \n", + "plt.loglog(u235.energy, fission.sigma, color='b', linewidth=1)\n", + "\n", + "# Extract energy group bounds and MGXS values to plot\n", + "nufission = xs_library[fuel_cell.id]['fission']\n", + "energy_groups = nufission.energy_groups\n", + "x = energy_groups.group_edges\n", + "y = nufission.get_xs(nuclides=['U-235'], order_groups='decreasing', xs_type='micro')\n", + "\n", + "# Fix low energy bound to the value defined by the ACE library\n", + "x[0] = u235.energy[0]\n", + "\n", + "# Extend the mgxs values array for matplotlib's step plot\n", + "y = np.insert(y, 0, y[0])\n", + "\n", + "# Create a step plot for the MGXS\n", + "plt.plot(x, y, drawstyle='steps', color='r', linewidth=3)\n", + "\n", + "plt.title('U-235 Fission Cross Section')\n", + "plt.xlabel('Energy [MeV]')\n", + "plt.ylabel('Micro Fission XS')\n", + "plt.legend(['Continuous', 'Multi-Group'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful illustration are scattering matrix sparsity structures. First, we extract Pandas DataFrames for the H-1 and O-16 scattering matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Construct a Pandas DataFrame for the microscopic nu-scattering matrix\n", + "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", + "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", + "\n", + "# Slice DataFrame in two for each nuclide's mean values\n", + "h1 = df[df['nuclide'] == 'H-1']['mean']\n", + "o16 = df[df['nuclide'] == 'O-16']['mean']\n", + "\n", + "# Cast DataFrames as NumPy arrays\n", + "h1 = h1.as_matrix()\n", + "o16 = o16.as_matrix()\n", + "\n", + "# Reshape arrays to 2D matrix for plotting\n", + "h1.shape = (fine_groups.num_groups, fine_groups.num_groups)\n", + "o16.shape = (fine_groups.num_groups, fine_groups.num_groups)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matplotlib's `imshow` routine can be used to plot the matrices to illustrate their sparsity structures." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create plot of the H-1 scattering matrix\n", + "fig = plt.subplot(121)\n", + "fig.imshow(h1, interpolation='nearest')\n", + "plt.title('H-1 Scattering Matrix')\n", + "\n", + "# Create plot of the O-16 scattering matrix\n", + "fig2 = plt.subplot(122)\n", + "fig2.imshow(o16, interpolation='nearest')\n", + "plt.title('O-16 Scattering Matrix')\n", + "\n", + "# Show the plot on screen\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/source/pythonapi/examples/MGXS-Part-III.ipynb b/docs/source/pythonapi/examples/MGXS-Part-III.ipynb new file mode 100644 index 000000000..4a8cfbc6b --- /dev/null +++ b/docs/source/pythonapi/examples/MGXS-Part-III.ipynb @@ -0,0 +1,1633 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This IPython Notebook illustrates the use of the **`openmc.mgxs.Library`** class. The `Library` class is designed to help automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, and/or nuclides. In particular, this Notebook illustrates the following features:\n", + "\n", + "* Calculation of multi-group cross sections for a **fuel assembly**\n", + "* Automated creation, manipulation and storage of `MGXS` with **`openmc.mgxs.Library`**\n", + "* **Validation** of multi-group cross sections with **OpenMOC**\n", + "* Steady-state pin-by-pin **fission rates comparison** between OpenMC and OpenMOC\n", + "\n", + "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook in its entirety, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries. In addition, this Notebook illustrates the use of Pandas DataFrames to containerize multi-group cross section data. We recommend using Pandas >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import math\n", + "import pickle\n", + "from IPython.display import Image\n", + "import matplotlib.pylab as pylab\n", + "import numpy as np\n", + "\n", + "import openmc\n", + "import openmc.mgxs\n", + "from openmc.statepoint import StatePoint\n", + "from openmc.summary import Summary\n", + "\n", + "import openmoc\n", + "import openmoc.process\n", + "from openmoc.compatible import get_openmoc_geometry\n", + "from openmoc.materialize import load_openmc_mgxs_lib\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate some Nuclides\n", + "h1 = openmc.Nuclide('H-1')\n", + "b10 = openmc.Nuclide('B-10')\n", + "o16 = openmc.Nuclide('O-16')\n", + "u235 = openmc.Nuclide('U-235')\n", + "u238 = openmc.Nuclide('U-238')\n", + "zr90 = openmc.Nuclide('Zr-90')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the nuclides we defined, we will now create three materials for the fuel, water, and cladding of the fuel pins." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 1.6 enriched fuel\n", + "fuel = openmc.Material(name='1.6% Fuel')\n", + "fuel.set_density('g/cm3', 10.31341)\n", + "fuel.add_nuclide(u235, 3.7503e-4)\n", + "fuel.add_nuclide(u238, 2.2625e-2)\n", + "fuel.add_nuclide(o16, 4.6007e-2)\n", + "\n", + "# borated water\n", + "water = openmc.Material(name='Borated Water')\n", + "water.set_density('g/cm3', 0.740582)\n", + "water.add_nuclide(h1, 4.9457e-2)\n", + "water.add_nuclide(o16, 2.4732e-2)\n", + "water.add_nuclide(b10, 8.0042e-6)\n", + "\n", + "# zircaloy\n", + "zircaloy = openmc.Material(name='Zircaloy')\n", + "zircaloy.set_density('g/cm3', 6.55)\n", + "zircaloy.add_nuclide(zr90, 7.2758e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our three materials, we can now create a materials file object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a MaterialsFile, add Materials\n", + "materials_file = openmc.MaterialsFile()\n", + "materials_file.add_material(fuel)\n", + "materials_file.add_material(water)\n", + "materials_file.add_material(zircaloy)\n", + "materials_file.default_xs = '71c'\n", + "\n", + "# Export to \"materials.xml\"\n", + "materials_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's move on to the geometry. This problem will be a square array of fuel pins and control rod guide tubes for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "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", + "\n", + "# Create boundary planes to surround the geometry\n", + "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n", + "max_x = openmc.XPlane(x0=+10.71, boundary_type='reflective')\n", + "min_y = openmc.YPlane(y0=-10.71, boundary_type='reflective')\n", + "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n", + "min_z = openmc.ZPlane(z0=-10., boundary_type='reflective')\n", + "max_z = openmc.ZPlane(z0=+10., boundary_type='reflective')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a fuel pin\n", + "fuel_pin_universe = openmc.Universe(name='1.6% Fuel Pin')\n", + "\n", + "# Create fuel Cell\n", + "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", + "fuel_cell.fill = fuel\n", + "fuel_cell.region = -fuel_outer_radius\n", + "fuel_pin_universe.add_cell(fuel_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='1.6% Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "fuel_pin_universe.add_cell(clad_cell)\n", + "\n", + "# 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", + "fuel_pin_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Likewise, we can construct a control rod guide tube with the same surfaces." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a control rod guide tube\n", + "guide_tube_universe = openmc.Universe(name='Guide Tube')\n", + "\n", + "# Create fuel Cell\n", + "guide_tube_cell = openmc.Cell(name='Guide Tube Water')\n", + "guide_tube_cell.fill = water\n", + "guide_tube_cell.region = -fuel_outer_radius\n", + "guide_tube_universe.add_cell(guide_tube_cell)\n", + "\n", + "# Create a clad Cell\n", + "clad_cell = openmc.Cell(name='Guide Clad')\n", + "clad_cell.fill = zircaloy\n", + "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", + "guide_tube_universe.add_cell(clad_cell)\n", + "\n", + "# Create a moderator Cell\n", + "moderator_cell = openmc.Cell(name='Guide Tube Moderator')\n", + "moderator_cell.fill = water\n", + "moderator_cell.region = +clad_outer_radius\n", + "guide_tube_universe.add_cell(moderator_cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26cm pitch." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create fuel assembly Lattice\n", + "assembly = openmc.RectLattice(name='1.6% Fuel Assembly')\n", + "assembly.dimension = (17, 17)\n", + "assembly.pitch = (1.26, 1.26)\n", + "assembly.lower_left = [-1.26 * 17. / 2.0] * 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we create a NumPy array of fuel pin and guide tube universes for the lattice." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create array indices for guide tube locations in lattice\n", + "template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\n", + " 11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n", + "template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\n", + " 8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n", + "\n", + "# Initialize an empty 17x17 array of the lattice universes\n", + "universes = np.empty((17, 17), dtype=openmc.Universe)\n", + "\n", + "# Fill the array with the fuel pin and guide tube universes\n", + "universes[:,:] = fuel_pin_universe\n", + "universes[template_x, template_y] = guide_tube_universe\n", + "\n", + "# Store the array of universes in the lattice\n", + "assembly.universes = universes" + ] + }, + { + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create root Cell\n", + "root_cell = openmc.Cell(name='root cell')\n", + "root_cell.fill = assembly\n", + "\n", + "# Add boundary planes\n", + "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\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, put the geometry into a geometry file, and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "geometry = openmc.Geometry()\n", + "geometry.root_universe = root_universe" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a GeometryFile\n", + "geometry_file = openmc.GeometryFile()\n", + "geometry_file.geometry = geometry\n", + "\n", + "# Export to \"geometry.xml\"\n", + "geometry_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 2500\n", + "\n", + "# Instantiate a SettingsFile\n", + "settings_file = openmc.SettingsFile()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "settings_file.output = {'tallies': False, 'summary': True}\n", + "source_bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", + "settings_file.set_source_space('fission', source_bounds)\n", + "\n", + "# Export to \"settings.xml\"\n", + "settings_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "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 = [21.5, 21.5]\n", + "plot.pixels = [250, 250]\n", + "plot.color = 'mat'\n", + "\n", + "# Instantiate a PlotsFile, add Plot, and export to \"plots.xml\"\n", + "plot_file = openmc.PlotsFile()\n", + "plot_file.add_plot(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": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run openmc in plotting mode\n", + "executor = openmc.Executor()\n", + "executor.plot_geometry(output=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nMS0yOVQxNzoyMDowMS0wNTowMASPc8AAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "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')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see from the plot, we have a nice array of pin cells with fuel, cladding, and water!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an MGXS Library" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are finally ready to make use of the `openmc.mgxs` module to generate multi-group cross sections! First, let's define a 2-group structure using the built-in `EnergyGroups` class." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a 2-group EnergyGroups object\n", + "groups = openmc.mgxs.EnergyGroups()\n", + "groups.group_edges = np.array([0., 0.625e-6, 20.])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will instantiate an `openmc.mgxs.Library` for the energy groups with our the fuel assembly geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize an 2-group MGXS Library for OpenMOC\n", + "mgxs_lib = openmc.mgxs.Library(geometry)\n", + "mgxs_lib.energy_groups = groups" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we must specify to the `Library` which types of cross sections to compute. In particular, the following are the multi-group cross section `MGXS` subclasses are mapped to type string codes mapped accepted by the `Library` class:\n", + "\n", + "* `TotalXS` (`\"total\"`)\n", + "* `TransportXS` (`\"transport\"`)\n", + "* `AbsorptionXS` (`\"absorption\"`)\n", + "* `CaptureXS` (`\"capture\"`)\n", + "* `FissionXS` (`\"fission\"`)\n", + "* `NuFissionXS` (`\"nu-fission\"`)\n", + "* `ScatterXS` (`\"scatter\"`)\n", + "* `NuScatterXS` (`\"nu-scatter\"`)\n", + "* `ScatterMatrixXS` (`\"scatter matrix\"`)\n", + "* `NuScatterMatrixXS` (`\"nu-scatter matrix\"`)\n", + "* `Chi` (`\"chi\"`)\n", + "\n", + "In this case, let's create the multi-group cross sections needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we will define `\"transport\"`, `\"nu-fission\"`, `\"nu-scatter matrix\"` and `\"chi\"` cross sections for our `Library`.\n", + "\n", + "**Note**: A variety of different approximate transport-corrected total multi-group cross sections (and corresponding scattering matrices) can be found in the literature. At the present time, the `openmc.mgxs` module only supports the \"P0\" transport correction. This correction can be turned on or off through the boolean `Library.correction` property which may take values of `\"P0\"` (default) or `None`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Specify multi-group cross section types to compute\n", + "mgxs_lib.mgxs_types = [\"transport\", \"nu-fission\", \"nu-scatter matrix\", \"chi\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports `\"material,\"` `\"cell,\"` and `\"universe\"` domain types. We will use a `\"cell\"` domain type here to compute cross sections in each of the cells for our fuel and guide tube pin cells." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Specify a \"cell\" domain type for the cross section tally filters\n", + "mgxs_lib.domain_type = \"cell\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can easily instruct the `Library` to compute multi-group cross sections on a nuclide-by-nuclide basis as was first illustrated in MGXS: Part II with the boolean `Library.by_nuclide` property. By default, `by_nuclide` is set to `False`, but we will set it to `True` here." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Compute cross sections on a nuclide-by-nuclide basis\n", + "mgxs_lib.by_nuclide = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we use the `Library` to construct all of the tallies needed to compute all of the requested multi-group cross sections in each domain and nuclide." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Construct all tallies needed for the multi-group cross section library\n", + "mgxs_lib.build_library()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tallies can now be export to a \"tallies.xml\" input file for OpenMC. \n", + "\n", + "**NOTE**: At this point the `Library` has constructed nearly 100 distinct `Tally`. The overhead to tally in OpenMC scales as $O(N)$ for $N$ tallies, which can become a bottleneck for large tally datasets. To compensate for this, the Python API's `Tally`, `Filter` and `TalliesFile` classes allow for the smart *merging* of tallies when possible. The `Library` class supports this runtime optimization with the use of the optional `merge` paramter (`False` by default) for the `Library.add_to_tallies_file(...)` method, as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create a \"tallies.xml\" file for the MGXS Library\n", + "tallies_file = openmc.TalliesFile()\n", + "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, we instantiate a fission rate mesh tally to compare with OpenMOC." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a tally Mesh\n", + "mesh = openmc.Mesh(mesh_id=1)\n", + "mesh.type = 'regular'\n", + "mesh.dimension = [17, 17]\n", + "mesh.lower_left = [-10.71, -10.71]\n", + "mesh.width = [1.26, 1.26]\n", + "\n", + "# Instantiate tally Filter\n", + "mesh_filter = openmc.Filter()\n", + "mesh_filter.mesh = mesh\n", + "\n", + "# Instantiate the Tally\n", + "tally = openmc.Tally(name='mesh tally')\n", + "tally.add_filter(mesh_filter)\n", + "tally.add_score('fission')\n", + "tally.add_score('nu-fission')\n", + "\n", + "# Add mesh and Tally to TalliesFile\n", + "tallies_file.add_mesh(mesh)\n", + "tallies_file.add_tally(tally)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Export all tallies to a \"tallies.xml\" file\n", + "tallies_file.export_to_xml()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " .d88888b. 888b d888 .d8888b.\n", + " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", + " 888 888 88888b.d88888 888 888\n", + " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", + " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", + " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", + " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", + " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", + "__________________888______________________________________________________\n", + " 888\n", + " 888\n", + "\n", + " Copyright: 2011-2015 Massachusetts Institute of Technology\n", + " License: http://mit-crpg.github.io/openmc/license.html\n", + " Version: 0.7.0\n", + " Git SHA1: c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n", + " Date/Time: 2015-11-29 17:20:02\n", + " MPI Processes: 1\n", + "\n", + " ===========================================================================\n", + " ========================> INITIALIZATION <=========================\n", + " ===========================================================================\n", + "\n", + " Reading settings XML file...\n", + " Reading cross sections XML file...\n", + " Reading geometry XML file...\n", + " Reading materials XML file...\n", + " Reading tallies XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Loading ACE cross section table: 92235.71c\n", + " Loading ACE cross section table: 92238.71c\n", + " Loading ACE cross section table: 8016.71c\n", + " Loading ACE cross section table: 1001.71c\n", + " Loading ACE cross section table: 5010.71c\n", + " Loading ACE cross section table: 40090.71c\n", + " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", + " Initializing source particles...\n", + "\n", + " ===========================================================================\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + " ===========================================================================\n", + "\n", + " Bat./Gen. k Average k \n", + " ========= ======== ==================== \n", + " 1/1 1.02650 \n", + " 2/1 1.01386 \n", + " 3/1 1.01045 \n", + " 4/1 1.05511 \n", + " 5/1 1.04873 \n", + " 6/1 1.04558 \n", + " 7/1 1.03840 \n", + " 8/1 1.02086 \n", + " 9/1 1.08845 \n", + " 10/1 1.03932 \n", + " 11/1 1.01271 \n", + " 12/1 1.03448 1.02360 +/- 0.01088\n", + " 13/1 1.04395 1.03038 +/- 0.00925\n", + " 14/1 1.05477 1.03648 +/- 0.00894\n", + " 15/1 1.00485 1.03015 +/- 0.00938\n", + " 16/1 1.04523 1.03267 +/- 0.00806\n", + " 17/1 1.01328 1.02990 +/- 0.00735\n", + " 18/1 1.01476 1.02800 +/- 0.00664\n", + " 19/1 1.01490 1.02655 +/- 0.00604\n", + " 20/1 1.00926 1.02482 +/- 0.00567\n", + " 21/1 0.98504 1.02120 +/- 0.00627\n", + " 22/1 1.00397 1.01977 +/- 0.00591\n", + " 23/1 1.02556 1.02021 +/- 0.00545\n", + " 24/1 0.99808 1.01863 +/- 0.00529\n", + " 25/1 0.99638 1.01715 +/- 0.00514\n", + " 26/1 0.99615 1.01584 +/- 0.00499\n", + " 27/1 1.01843 1.01599 +/- 0.00469\n", + " 28/1 1.00315 1.01528 +/- 0.00447\n", + " 29/1 1.00633 1.01480 +/- 0.00426\n", + " 30/1 1.02159 1.01514 +/- 0.00405\n", + " 31/1 1.03395 1.01604 +/- 0.00396\n", + " 32/1 1.02672 1.01652 +/- 0.00381\n", + " 33/1 1.03778 1.01745 +/- 0.00375\n", + " 34/1 1.03807 1.01831 +/- 0.00369\n", + " 35/1 1.07854 1.02072 +/- 0.00428\n", + " 36/1 1.03524 1.02128 +/- 0.00415\n", + " 37/1 1.03100 1.02164 +/- 0.00401\n", + " 38/1 1.03853 1.02224 +/- 0.00391\n", + " 39/1 1.04089 1.02288 +/- 0.00383\n", + " 40/1 1.02150 1.02284 +/- 0.00370\n", + " 41/1 0.98470 1.02161 +/- 0.00379\n", + " 42/1 1.00658 1.02114 +/- 0.00370\n", + " 43/1 0.98652 1.02009 +/- 0.00373\n", + " 44/1 1.02787 1.02032 +/- 0.00363\n", + " 45/1 0.98800 1.01939 +/- 0.00364\n", + " 46/1 1.00286 1.01893 +/- 0.00357\n", + " 47/1 1.02559 1.01911 +/- 0.00348\n", + " 48/1 1.03729 1.01959 +/- 0.00342\n", + " 49/1 1.02538 1.01974 +/- 0.00333\n", + " 50/1 1.01478 1.01962 +/- 0.00325\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 4.0000E-01 seconds\n", + " Reading cross sections = 8.4000E-02 seconds\n", + " Total time in simulation = 3.8366E+01 seconds\n", + " Time in transport only = 3.8351E+01 seconds\n", + " Time in inactive batches = 3.6930E+00 seconds\n", + " Time in active batches = 3.4673E+01 seconds\n", + " Time synchronizing fission bank = 1.0000E-03 seconds\n", + " Sampling source sites = 1.0000E-03 seconds\n", + " SEND/RECV source sites = 0.0000E+00 seconds\n", + " Time accumulating tallies = 0.0000E+00 seconds\n", + " Total time for finalization = 0.0000E+00 seconds\n", + " Total time elapsed = 3.8780E+01 seconds\n", + " Calculation Rate (inactive) = 6769.56 neutrons/second\n", + " Calculation Rate (active) = 2884.09 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.01805 +/- 0.00261\n", + " k-effective (Track-length) = 1.01962 +/- 0.00325\n", + " k-effective (Absorption) = 1.01554 +/- 0.00339\n", + " Combined k-effective = 1.01711 +/- 0.00235\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Remove old HDF5 (summary, statepoint) files\n", + "!rm statepoint.*\n", + "\n", + "# Run OpenMC\n", + "executor.run_simulation()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and \"reading\" the results. By default, data from the statepoint file is only read into memory when it is requested. This helps keep the memory use to a minimum even when a statepoint file may be huge." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the last statepoint file\n", + "sp = openmc.StatePoint('statepoint.50.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry which is necessary for the `openmc.mgxs` module to properly process the tally data. We first create a summary object and link it with the statepoint." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "su = openmc.Summary('summary.h5')\n", + "sp.link_with_summary(su)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The statepoint is now ready to be analyzed by the `Library`. We simply have to load the tallies from the statepoint into the `Library` and our `MGXS` objects will compute the cross sections for us under-the-hood." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize MGXS Library with OpenMC statepoint data\n", + "mgxs_lib.load_from_statepoint(sp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Voila! Our multi-group cross sections are now ready to rock 'n roll!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extracting and Storing MGXS Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Library` supports a rich API to automate a variety of tasks, including multi-group cross section data retrieval and storage. We will highlight a few of these features here. First, the `Library.get_mgxs(...)` method allows one to extract an `MGXS` object from the `Library` for a particular domain and cross section type. The following cell illustrates how one may extract the `NuFissionXS` object for the fuel cell." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Retrieve the NuFissionXS object for the fuel cell from the library\n", + "fuel_mgxs = mgxs_lib.get_mgxs(fuel_cell, 'nu-fission')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `NuFissionXS` object supports all of the methods described previously the `openmc.mgxs` tutorials, such as Pandas DataFrames:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1254: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "3 10000 1 U-235 8.063513e-03 4.062984e-05\n", + "4 10000 1 U-238 7.335515e-03 4.459335e-05\n", + "5 10000 1 O-16 0.000000e+00 0.000000e+00\n", + "0 10000 2 U-235 3.613274e-01 1.902492e-03\n", + "1 10000 2 U-238 6.738424e-07 3.536787e-09\n", + "2 10000 2 O-16 0.000000e+00 0.000000e+00" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = fuel_mgxs.get_pandas_dataframe()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, we can use the `MGXS.print_xs(...)` method to view a string representation of the multi-group cross section data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multi-Group XS\n", + "\tReaction Type =\tnu-fission\n", + "\tDomain Type =\tcell\n", + "\tDomain ID =\t10000\n", + "\tNuclide =\tU-235\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t8.06e-03 +/- 5.04e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t3.61e-01 +/- 5.27e-01%\n", + "\n", + "\tNuclide =\tU-238\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t7.34e-03 +/- 6.08e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t6.74e-07 +/- 5.25e-01%\n", + "\n", + "\tNuclide =\tO-16\n", + "\tCross Sections [cm^-1]:\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t0.00e+00 +/- nan%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t0.00e+00 +/- nan%\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "fuel_mgxs.print_xs()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can export the entire `Library` to HDF5 with the `Library.build_hdf5_store(...)` method as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store the cross section data in an \"mgxs/mgxs.h5\" HDF5 binary file\n", + "mgxs_lib.build_hdf5_store(filename='mgxs.h5', directory='mgxs')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The HDF5 store will contain the numerical multi-group cross section data indexed by domain, nuclide and cross section type. Some data workflows may be optimized by storing and retrieving binary representations of the `MGXS` objects in the `Library`. This feature is supported through the `Library.dump_to_file(...)` and `Library.load_from_file(...)` routines which use Python's `pickle` module. This is illustrated as follows." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store a complete binary representation of the Library and\n", + "# its MGXS objects in a pickled binary file \"mgxs/mgxs.pkl\"\n", + "mgxs_lib.dump_to_file(filename='mgxs', directory='mgxs')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a new MGXS Library from the complete binary representation\n", + "# stored in the pickled binary file \"mgxs/mgxs.pkl\"\n", + "mgxs_lib = openmc.mgxs.Library.load_from_file(filename='mgxs', directory='mgxs')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Library` class may be used to leverage the energy condensation features supported by the `MGXS` class and illutrated in earlier tutorials on `openmc.mgxs`. In particular, one can use the `Library.get_condensed_library(...)` with a coarse group structure which is a subset of the original \"fine\" group structure as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create a 1-group structure\n", + "coarse_groups = openmc.mgxs.EnergyGroups(group_edges=[0., 20.])\n", + "\n", + "# Create a new MGXS Library on the coarse 1-group structure\n", + "coarse_mgxs_lib = mgxs_lib.get_condensed_library(coarse_groups)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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cellgroup innuclidemeanstd. dev.
0100001U-2350.0743830.000280
1100001U-2380.0059590.000036
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" + ], + "text/plain": [ + " cell group in nuclide mean std. dev.\n", + "0 10000 1 U-235 0.074383 0.000280\n", + "1 10000 1 U-238 0.005959 0.000036\n", + "2 10000 1 O-16 0.000000 0.000000" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Retrieve the NuFissionXS object for the fuel cell from the 1-group library\n", + "coarse_fuel_mgxs = coarse_mgxs_lib.get_mgxs(fuel_cell, 'nu-fission')\n", + "\n", + "# Show the Pandas DataFrame for the 1-group MGXS\n", + "coarse_fuel_mgxs.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Verification with OpenMOC" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Of course it is always a good idea to verify that one's cross sections are accurate. We can easily do so here with the deterministic transport code OpenMOC. We will extract an OpenCG geometry from the summary file and convert it into an equivalent OpenMOC geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create an OpenMOC Geometry from the OpenCG Geometry\n", + "openmoc_geometry = get_openmoc_geometry(mgxs_lib.opencg_geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can inject the multi-group cross sections into the equivalent fuel assembly OpenMOC geometry. The `openmoc.materialize` module is seamlessly integrated to support the loading of `Library` objects from OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the library into the OpenMOC geometry\n", + "materials = load_openmc_mgxs_lib(mgxs_lib, openmoc_geometry)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to run OpenMOC to verify our cross-sections from OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ NORMAL ] Ray tracing for track segmentation...\n", + "[ NORMAL ] Dumping tracks to file...\n", + "[ NORMAL ] Computing the eigenvalue...\n", + "[ NORMAL ] Iteration 0:\tk_eff = 0.854316\tres = 0.000E+00\n", + "[ NORMAL ] Iteration 1:\tk_eff = 0.801593\tres = 1.522E-01\n", + "[ 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+ "[ NORMAL ] Iteration 32:\tk_eff = 0.916523\tres = 8.745E-03\n", + "[ NORMAL ] Iteration 33:\tk_eff = 0.923546\tres = 8.194E-03\n", + "[ NORMAL ] Iteration 34:\tk_eff = 0.930162\tres = 7.669E-03\n", + "[ NORMAL ] Iteration 35:\tk_eff = 0.936387\tres = 7.171E-03\n", + "[ NORMAL ] Iteration 36:\tk_eff = 0.942236\tres = 6.698E-03\n", + "[ NORMAL ] Iteration 37:\tk_eff = 0.947725\tres = 6.252E-03\n", + "[ NORMAL ] Iteration 38:\tk_eff = 0.952869\tres = 5.830E-03\n", + "[ NORMAL ] Iteration 39:\tk_eff = 0.957687\tres = 5.433E-03\n", + "[ NORMAL ] Iteration 40:\tk_eff = 0.962193\tres = 5.060E-03\n", + "[ NORMAL ] Iteration 41:\tk_eff = 0.966404\tres = 4.710E-03\n", + "[ NORMAL ] Iteration 42:\tk_eff = 0.970337\tres = 4.381E-03\n", + "[ NORMAL ] Iteration 43:\tk_eff = 0.974006\tres = 4.073E-03\n", + "[ NORMAL ] Iteration 44:\tk_eff = 0.977426\tres = 3.785E-03\n", + "[ NORMAL ] Iteration 45:\tk_eff = 0.980613\tres = 3.515E-03\n", + "[ NORMAL ] Iteration 46:\tk_eff = 0.983580\tres = 3.264E-03\n", + "[ NORMAL ] Iteration 47:\tk_eff = 0.986341\tres = 3.029E-03\n", + "[ NORMAL ] Iteration 48:\tk_eff = 0.988908\tres = 2.809E-03\n", + "[ NORMAL ] Iteration 49:\tk_eff = 0.991293\tres = 2.605E-03\n", + "[ NORMAL ] Iteration 50:\tk_eff = 0.993509\tres = 2.415E-03\n", + "[ NORMAL ] Iteration 51:\tk_eff = 0.995566\tres = 2.238E-03\n", + "[ NORMAL ] Iteration 52:\tk_eff = 0.997475\tres = 2.073E-03\n", + "[ NORMAL ] Iteration 53:\tk_eff = 0.999246\tres = 1.920E-03\n", + "[ NORMAL ] Iteration 54:\tk_eff = 1.000888\tres = 1.777E-03\n", + "[ NORMAL ] Iteration 55:\tk_eff = 1.002409\tres = 1.645E-03\n", + "[ NORMAL ] Iteration 56:\tk_eff = 1.003818\tres = 1.522E-03\n", + "[ NORMAL ] Iteration 57:\tk_eff = 1.005123\tres = 1.408E-03\n", + "[ NORMAL ] Iteration 58:\tk_eff = 1.006331\tres = 1.302E-03\n", + "[ NORMAL ] Iteration 59:\tk_eff = 1.007450\tres = 1.203E-03\n", + "[ NORMAL ] Iteration 60:\tk_eff = 1.008484\tres = 1.112E-03\n", + "[ NORMAL ] Iteration 61:\tk_eff = 1.009440\tres = 1.028E-03\n", + "[ NORMAL ] Iteration 62:\tk_eff = 1.010324\tres = 9.496E-04\n", + "[ NORMAL ] Iteration 63:\tk_eff = 1.011141\tres = 8.771E-04\n", + "[ NORMAL ] Iteration 64:\tk_eff = 1.011897\tres = 8.100E-04\n", + "[ NORMAL ] Iteration 65:\tk_eff = 1.012594\tres = 7.478E-04\n", + "[ NORMAL ] Iteration 66:\tk_eff = 1.013238\tres = 6.903E-04\n", + "[ NORMAL ] Iteration 67:\tk_eff = 1.013833\tres = 6.371E-04\n", + "[ NORMAL ] Iteration 68:\tk_eff = 1.014382\tres = 5.879E-04\n", + "[ NORMAL ] Iteration 69:\tk_eff = 1.014889\tres = 5.424E-04\n", + "[ NORMAL ] Iteration 70:\tk_eff = 1.015357\tres = 5.004E-04\n", + "[ NORMAL ] Iteration 71:\tk_eff = 1.015789\tres = 4.615E-04\n", + "[ NORMAL ] Iteration 72:\tk_eff = 1.016187\tres = 4.255E-04\n", + "[ NORMAL ] Iteration 73:\tk_eff = 1.016554\tres = 3.923E-04\n", + "[ NORMAL ] Iteration 74:\tk_eff = 1.016892\tres = 3.617E-04\n", + "[ NORMAL ] Iteration 75:\tk_eff = 1.017204\tres = 3.333E-04\n", + "[ NORMAL ] Iteration 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Iteration 91:\tk_eff = 1.019869\tres = 8.895E-05\n", + "[ NORMAL ] Iteration 92:\tk_eff = 1.019946\tres = 8.183E-05\n", + "[ NORMAL ] Iteration 93:\tk_eff = 1.020016\tres = 7.528E-05\n", + "[ NORMAL ] Iteration 94:\tk_eff = 1.020081\tres = 6.922E-05\n", + "[ NORMAL ] Iteration 95:\tk_eff = 1.020141\tres = 6.368E-05\n", + "[ NORMAL ] Iteration 96:\tk_eff = 1.020195\tres = 5.857E-05\n", + "[ NORMAL ] Iteration 97:\tk_eff = 1.020246\tres = 5.385E-05\n", + "[ NORMAL ] Iteration 98:\tk_eff = 1.020292\tres = 4.954E-05\n", + "[ NORMAL ] Iteration 99:\tk_eff = 1.020335\tres = 4.553E-05\n", + "[ NORMAL ] Iteration 100:\tk_eff = 1.020374\tres = 4.185E-05\n", + "[ NORMAL ] Iteration 101:\tk_eff = 1.020410\tres = 3.848E-05\n", + "[ NORMAL ] Iteration 102:\tk_eff = 1.020443\tres = 3.537E-05\n", + "[ NORMAL ] Iteration 103:\tk_eff = 1.020474\tres = 3.253E-05\n", + "[ NORMAL ] Iteration 104:\tk_eff = 1.020502\tres = 2.989E-05\n", + "[ NORMAL ] Iteration 105:\tk_eff = 1.020527\tres = 2.746E-05\n", + "[ NORMAL ] Iteration 106:\tk_eff = 1.020551\tres = 2.526E-05\n", + "[ NORMAL ] Iteration 107:\tk_eff = 1.020573\tres = 2.319E-05\n", + "[ NORMAL ] Iteration 108:\tk_eff = 1.020593\tres = 2.134E-05\n", + "[ NORMAL ] Iteration 109:\tk_eff = 1.020611\tres = 1.960E-05\n", + "[ NORMAL ] Iteration 110:\tk_eff = 1.020628\tres = 1.800E-05\n", + "[ NORMAL ] Iteration 111:\tk_eff = 1.020643\tres = 1.652E-05\n", + "[ NORMAL ] Iteration 112:\tk_eff = 1.020657\tres = 1.518E-05\n", + "[ NORMAL ] Iteration 113:\tk_eff = 1.020670\tres = 1.398E-05\n", + "[ NORMAL ] Iteration 114:\tk_eff = 1.020682\tres = 1.283E-05\n", + "[ NORMAL ] Iteration 115:\tk_eff = 1.020693\tres = 1.178E-05\n", + "[ NORMAL ] Iteration 116:\tk_eff = 1.020704\tres = 1.083E-05\n" + ] + } + ], + "source": [ + "# Generate tracks for OpenMOC\n", + "openmoc_geometry.initializeFlatSourceRegions()\n", + "track_generator = openmoc.TrackGenerator(openmoc_geometry, 32, 0.1)\n", + "track_generator.generateTracks()\n", + "\n", + "# Run OpenMOC\n", + "solver = openmoc.CPUSolver(track_generator)\n", + "solver.computeEigenvalue()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "openmc keff = 1.017105\n", + "openmoc keff = 1.020704\n", + "bias [pcm]: 359.8\n" + ] + } + ], + "source": [ + "# Print report of keff and bias with OpenMC\n", + "openmoc_keff = solver.getKeff()\n", + "openmc_keff = sp.k_combined[0]\n", + "bias = (openmoc_keff - openmc_keff) * 1e5\n", + "\n", + "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", + "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", + "print('bias [pcm]: {0:1.1f}'.format(bias))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a non-trivial bias between the eigenvalues computed by OpenMC and OpenMOC. One can show that these biases do not converge to <100 pcm with more particle histories. For heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n", + "\n", + "* Appropriate transport-corrected cross sections\n", + "* Spatial discretization of OpenMOC's mesh\n", + "* Constant-in-angle multi-group cross sections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flux and Pin Power Visualizations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will conclude this tutorial by illustrating how to visualize the fission rates computed by OpenMOC and OpenMC. First, we extract OpenMC's volume-averaged fission rates from each fuel pin into a 2D 17x17 NumPy array." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Get the OpenMC fission rate mesh tally data\n", + "mesh_tally = sp.get_tally(name='mesh tally')\n", + "openmc_fission_rates = mesh_tally.get_values(scores=['nu-fission'])\n", + "\n", + "# Reshape array to 2D for plotting\n", + "openmc_fission_rates.shape = (17,17)\n", + "\n", + "# Compute volume-average rates from OpenMC's volume-integrated fission rates\n", + "openmc_fission_rates /= math.pi * fuel_outer_radius.r**2\n", + "\n", + "# Normalize to the average pin power\n", + "openmc_fission_rates /= np.mean(openmc_fission_rates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we extract OpenMOC's volume-averaged fission rates into a 2D 17x17 NumPy array." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Export OpenMOC's fission rates for each pin cell instance in the fuel assembly\n", + "openmoc.process.compute_fission_rates(solver)\n", + "\n", + "# Open the pickle file with the fission rates\n", + "fission_rates = pickle.load(open('fission-rates/fission-rates.pkl', 'rb' ))\n", + "\n", + "# Allocate array for fission rates in each fuel pin\n", + "openmoc_fission_rates = np.zeros((17, 17))\n", + "\n", + "# Extract fission rates for each fuel pin\n", + "for key, value in fission_rates.items():\n", + " lat_x = int(key.split(':')[1].split()[3][1:-1])\n", + " lat_y = int(key.split(':')[1].split()[4][:-1]) \n", + " openmoc_fission_rates[lat_x, lat_y] = value\n", + "\n", + "# Normalize to the average pin fission rate\n", + "openmoc_fission_rates /= np.mean(openmoc_fission_rates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can easily use Matplotlib to visualize the fission rates from OpenMC and OpenMOC side-by-side." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot OpenMC's fission rates in the left subplot\n", + "fig = pylab.subplot(121)\n", + "pylab.imshow(openmc_fission_rates, interpolation='none', cmap='jet')\n", + "pylab.grid()\n", + "pylab.title('OpenMC Fission Rates')\n", + "\n", + "# Plot OpenMOC's fission rates in the right subplot\n", + "fig2 = pylab.subplot(122)\n", + "pylab.imshow(openmoc_fission_rates, interpolation='none', cmap='jet')\n", + "pylab.grid()\n", + "pylab.title('OpenMOC Fission Rates')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb b/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb deleted file mode 100644 index f4e4b59ef..000000000 --- a/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb +++ /dev/null @@ -1,2454 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook demonstrates how to use the **``openmc.mgxs``** module to generate multi-group cross sections with OpenMC.\n", - "\n", - "**Note:** that this Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. In order to run this Notebook, you must have [OpenMOC](https://mit-crpg.github.io/OpenMOC/) installed on your system, along with OpenCG to convert the OpenMC geometries into OpenMOC geometries." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/lib/pymodules/python2.7/matplotlib/__init__.py:1173: 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" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import openmc\n", - "import openmc.mgxs as mgxs\n", - "import openmoc\n", - "from openmoc.compatible import get_openmoc_geometry\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Infinite Homogeneous Medium" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We first construct a simple homogeneous infinite medium problem to illustrate use of the `openmc.mgxs` module to generate multi-group cross sections." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate Inputs" - ] - }, - { - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate some Nuclides\n", - "h1 = openmc.Nuclide('H-1')\n", - "o16 = openmc.Nuclide('O-16')\n", - "u235 = openmc.Nuclide('U-235')\n", - "u238 = openmc.Nuclide('U-238')\n", - "zr90 = openmc.Nuclide('Zr-90')" - ] - }, - { - "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": 3, - "metadata": { - "collapsed": true - }, - "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, 0.028999667)\n", - "inf_medium.add_nuclide(o16, 0.01450188)\n", - "inf_medium.add_nuclide(u235, 0.000114142)\n", - "inf_medium.add_nuclide(u238, 0.006886019)\n", - "inf_medium.add_nuclide(zr90, 0.002116053)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our material, we can now create a materials file object that can be exported to an actual XML file." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate a MaterialsFile, register all Materials, and export to XML\n", - "materials_file = openmc.MaterialsFile()\n", - "materials_file.default_xs = '71c'\n", - "materials_file.add_material(inf_medium)\n", - "materials_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's move on to the geometry. This problem will be a simple square cell with reflective boundary conditions to simulate an infinite homogeneous medium. The first step is to create the outer bounding surfaces of the problem." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate boundary Planes\n", - "min_x = openmc.XPlane(boundary_type='reflective', x0=-0.63)\n", - "max_x = openmc.XPlane(boundary_type='reflective', x0=0.63)\n", - "min_y = openmc.YPlane(boundary_type='reflective', y0=-0.63)\n", - "max_y = openmc.YPlane(boundary_type='reflective', y0=0.63)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the surfaces defined, we can now create a cell that is defined by intersections of half-spaces created by the surfaces." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Instantiate a Cell\n", - "cell = openmc.Cell(cell_id=1, name='cell')\n", - "\n", - "# Register bounding Surfaces with the Cell\n", - "cell.region = +min_x & -max_x & +min_y & -max_y\n", - "\n", - "# Fill the Cell with the Material\n", - "cell.fill = inf_medium" - ] - }, - { - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate Universe\n", - "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", - "root_universe.add_cell(cell)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create Geometry and set root Universe\n", - "openmc_geometry = openmc.Geometry()\n", - "openmc_geometry.root_universe = root_universe\n", - "\n", - "# Instantiate a GeometryFile\n", - "geometry_file = openmc.GeometryFile()\n", - "geometry_file.geometry = openmc_geometry\n", - "\n", - "# Export to \"geometry.xml\"\n", - "geometry_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# OpenMC simulation parameters\n", - "batches = 50\n", - "inactive = 10\n", - "particles = 2500\n", - "\n", - "# Instantiate a SettingsFile\n", - "settings_file = openmc.SettingsFile()\n", - "settings_file.batches = batches\n", - "settings_file.inactive = inactive\n", - "settings_file.particles = particles\n", - "settings_file.output = {'tallies': True, 'summary': True}\n", - "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", - "settings_file.set_source_space('box', bounds)\n", - "\n", - "# Export to \"settings.xml\"\n", - "settings_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are finally ready to make use of the `openmc.mgxs` module to generate multi-group cross sections! First, let's define a \"fine\" 8-group and \"coarse\" 2-group structures using the built-in `EnergyGroups` class." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate a \"fine\" 8-group EneryGroups object\n", - "fine_groups = mgxs.EnergyGroups()\n", - "fine_groups.group_edges = np.array([0., 0.058e-6, 0.14e-6, 0.28e-6,\n", - " 0.625e-6, 4.e-6, 5.53e-3, 821.e-3, 20.])\n", - "\n", - "# Instantiate a \"coarse\" 2-group EneryGroups object\n", - "coarse_groups = mgxs.EnergyGroups()\n", - "coarse_groups.group_edges = np.array([0., 0.625e-6, 20.])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now use the fine and coarse `EnergyGroups` objects, along with our previously created materials and geometry, to instantiate some `MGXS` objects from the `openmc.mgxs` module. In particular, the following are subclasses of the generic and abstract `MGXS` class:\n", - "\n", - "* `TotalXS`\n", - "* `TransportXS`\n", - "* `AbsorptionXS`\n", - "* `CaptureXS`\n", - "* `FissionXS`\n", - "* `NuFissionXS`\n", - "* `ScatterXS`\n", - "* `NuScatterXS`\n", - "* `ScatterMatrixXS`\n", - "* `NuScatterMatrixXS`\n", - "* `Chi`\n", - "\n", - "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group cross sections needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we will define total, nu-fission, nu-scatter and chi cross sections for our infinite medium cell as the domain and our fine 8-group structure as our energy groups." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Instantiate cross sections needed for an OpenMOC simulation\n", - "transport = mgxs.TransportXS(domain=cell, domain_type='cell', groups=fine_groups)\n", - "nufission = mgxs.NuFissionXS(domain=cell, domain_type='cell', groups=fine_groups)\n", - "nuscatter = mgxs.NuScatterMatrixXS(domain=cell, domain_type='cell', groups=fine_groups)\n", - "chi = mgxs.Chi(domain=cell, domain_type='cell', groups=fine_groups)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `NuFission` object as follows. " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OrderedDict([('flux', Tally\n", - "\tID =\t10000\n", - "\tName =\t\n", - "\tFilters =\t\n", - " \t\tcell\t[1]\n", - " \t\tenergy\t[ 0.00000000e+00 5.80000000e-08 1.40000000e-07 2.80000000e-07\n", - " 6.25000000e-07 4.00000000e-06 5.53000000e-03 8.21000000e-01\n", - " 2.00000000e+01]\n", - "\tNuclides =\ttotal \n", - "\tScores =\t['flux']\n", - "\tEstimator =\ttracklength\n", - "), ('nu-fission', Tally\n", - "\tID =\t10001\n", - "\tName =\t\n", - "\tFilters =\t\n", - " \t\tcell\t[1]\n", - " \t\tenergy\t[ 0.00000000e+00 5.80000000e-08 1.40000000e-07 2.80000000e-07\n", - " 6.25000000e-07 4.00000000e-06 5.53000000e-03 8.21000000e-01\n", - " 2.00000000e+01]\n", - "\tNuclides =\ttotal \n", - "\tScores =\t['nu-fission']\n", - "\tEstimator =\ttracklength\n", - ")])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nufission.tallies" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `NuFission` object includes tracklength tallies for the 'nu-fission' and 'flux' scores in the 8-group structure in cell 1. Now that each multi-group cross section object contains the tallies that it needs, we must add these tallies to a `TalliesFile` object to generate the \"tallies.xml\" input file for OpenMC." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Instantiate an empty TalliesFile\n", - "tallies_file = openmc.TalliesFile()\n", - "\n", - "# Add transport tallies to the tallies file\n", - "for tally in transport.tallies.values():\n", - " tallies_file.add_tally(tally, merge=True)\n", - "\n", - "# Add nu-fission tallies to the tallies file\n", - "for tally in nufission.tallies.values():\n", - " tallies_file.add_tally(tally, merge=True)\n", - "\n", - "# Add nu-scatter tallies to the tallies file\n", - "for tally in nuscatter.tallies.values():\n", - " tallies_file.add_tally(tally, merge=True)\n", - "\n", - "# Add chi tallies to the tallies file \n", - "for tally in chi.tallies.values():\n", - " tallies_file.add_tally(tally, merge=True)\n", - " \n", - "# Export to \"tallies.xml\"\n", - "tallies_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we a have a complete set of inputs, so we can go ahead and run our simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " .d88888b. 888b d888 .d8888b.\n", - " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", - " 888 888 88888b.d88888 888 888\n", - " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", - " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", - " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", - " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", - " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", - "__________________888______________________________________________________\n", - " 888\n", - " 888\n", - "\n", - " Copyright: 2011-2015 Massachusetts Institute of Technology\n", - " License: http://mit-crpg.github.io/openmc/license.html\n", - " Version: 0.7.0\n", - " Git SHA1: 21738db07debeabde824c9b955bd3bf0c9a16366\n", - " Date/Time: 2015-11-01 21:28:30\n", - " MPI Processes: 1\n", - "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", - " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 1001.71c\n", - " Initializing source particles...\n", - "\n", - " ===========================================================================\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", - "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.19804 \n", - " 2/1 1.12945 \n", - " 3/1 1.15573 \n", - " 4/1 1.13929 \n", - " 5/1 1.16300 \n", - " 6/1 1.22117 \n", - " 7/1 1.19012 \n", - " 8/1 1.11299 \n", - " 9/1 1.16066 \n", - " 10/1 1.12566 \n", - " 11/1 1.20854 \n", - " 12/1 1.14691 1.17773 +/- 0.03082\n", - " 13/1 1.17204 1.17583 +/- 0.01789\n", - " 14/1 1.14148 1.16724 +/- 0.01529\n", - " 15/1 1.17272 1.16834 +/- 0.01189\n", - " 16/1 1.18575 1.17124 +/- 0.01014\n", - " 17/1 1.20498 1.17606 +/- 0.00983\n", - " 18/1 1.14754 1.17249 +/- 0.00923\n", - " 19/1 1.18141 1.17348 +/- 0.00820\n", - " 20/1 1.15074 1.17121 +/- 0.00768\n", - " 21/1 1.15914 1.17011 +/- 0.00703\n", - " 22/1 1.14586 1.16809 +/- 0.00673\n", - " 23/1 1.18999 1.16978 +/- 0.00642\n", - " 24/1 1.15101 1.16844 +/- 0.00609\n", - " 25/1 1.13791 1.16640 +/- 0.00602\n", - " 26/1 1.19791 1.16837 +/- 0.00597\n", - " 27/1 1.19818 1.17012 +/- 0.00587\n", - " 28/1 1.14160 1.16854 +/- 0.00576\n", - " 29/1 1.11487 1.16571 +/- 0.00614\n", - " 30/1 1.17538 1.16620 +/- 0.00584\n", - " 31/1 1.20210 1.16791 +/- 0.00581\n", - " 32/1 1.20078 1.16940 +/- 0.00574\n", - " 33/1 1.14624 1.16839 +/- 0.00558\n", - " 34/1 1.14618 1.16747 +/- 0.00542\n", - " 35/1 1.16866 1.16752 +/- 0.00520\n", - " 36/1 1.18565 1.16821 +/- 0.00504\n", - " 37/1 1.16824 1.16821 +/- 0.00485\n", - " 38/1 1.18299 1.16874 +/- 0.00471\n", - " 39/1 1.21418 1.17031 +/- 0.00480\n", - " 40/1 1.11167 1.16835 +/- 0.00504\n", - " 41/1 1.11545 1.16665 +/- 0.00516\n", - " 42/1 1.11114 1.16491 +/- 0.00529\n", - " 43/1 1.14227 1.16423 +/- 0.00517\n", - " 44/1 1.14104 1.16355 +/- 0.00506\n", - " 45/1 1.16756 1.16366 +/- 0.00492\n", - " 46/1 1.13065 1.16274 +/- 0.00487\n", - " 47/1 1.11251 1.16139 +/- 0.00492\n", - " 48/1 1.14731 1.16101 +/- 0.00481\n", - " 49/1 1.16691 1.16117 +/- 0.00469\n", - " 50/1 1.19679 1.16206 +/- 0.00465\n", - " Creating state point statepoint.50.h5...\n", - "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 1.9120E+00 seconds\n", - " Reading cross sections = 4.8600E-01 seconds\n", - " Total time in simulation = 6.1145E+01 seconds\n", - " Time in transport only = 6.0977E+01 seconds\n", - " Time in inactive batches = 8.1390E+00 seconds\n", - " Time in active batches = 5.3006E+01 seconds\n", - " Time synchronizing fission bank = 1.6000E-02 seconds\n", - " Sampling source sites = 1.2000E-02 seconds\n", - " SEND/RECV source sites = 3.0000E-03 seconds\n", - " Time accumulating tallies = 1.0000E-03 seconds\n", - " Total time for finalization = 1.0000E-02 seconds\n", - " Total time elapsed = 6.3104E+01 seconds\n", - " Calculation Rate (inactive) = 3071.63 neutrons/second\n", - " Calculation Rate (active) = 1886.58 neutrons/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 1.16131 +/- 0.00453\n", - " k-effective (Track-length) = 1.16206 +/- 0.00465\n", - " k-effective (Absorption) = 1.16096 +/- 0.00364\n", - " Combined k-effective = 1.16120 +/- 0.00325\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Run OpenMC!\n", - "executor = openmc.Executor()\n", - "executor.run_simulation()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tally Data Processing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and 'reading' the results. By default, data from the statepoint file is only read into memory when it is requested. This helps keep the memory use to a minimum even when a statepoint file may be huge." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the last statepoint file\n", - "sp = openmc.StatePoint('statepoint.50.h5')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry which is necessary for the `openmc.mgxs` module to properly process the tally data. We first create a summary object and link it with the statepoint." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the summary file and link it with the statepoint\n", - "su = openmc.Summary('summary.h5')\n", - "sp.link_with_summary(su)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the statepoint into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.py:1514: RuntimeWarning: invalid value encountered in true_divide\n" - ] - } - ], - "source": [ - "# Load the tallies from the statepoint into each MGXS object\n", - "transport.load_from_statepoint(sp)\n", - "nufission.load_from_statepoint(sp)\n", - "nuscatter.load_from_statepoint(sp)\n", - "chi.load_from_statepoint(sp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Voila! Our multi-group cross sections are now ready to rock 'n roll!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cross Section Data Visualization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's first inspect our fission production cross section by printing it to the screen." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\tnu-fission\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t1\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t1.11e-02 +/- 7.69e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t6.59e-04 +/- 2.97e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t8.95e-03 +/- 5.12e-01%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t1.45e-02 +/- 7.10e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t4.71e-02 +/- 1.02e+00%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t7.29e-02 +/- 8.86e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t1.11e-01 +/- 6.67e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t2.38e-01 +/- 7.71e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "nufission.print_xs()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the `openmc.mgxs` module uses tally arithmetic under-the-hood, the cross section is stored as a \"derived\" tally. This means that it can be queried and manipulated using all of the same method supported for the `Tally` class in the OpenMC Python API. For example, we can construct a Pandas DataFrame of the multi-group cross section data." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1264: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" - ] - }, - { - "data": { - "text/html": [ - "
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cellgroup ingroup outnuclidemeanstd. dev.
63111total0.0769700.001012
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\n", - "
" - ], - "text/plain": [ - " cell group in group out nuclide mean std. dev.\n", - "63 1 1 1 total 0.076970 0.001012\n", - "62 1 1 2 total 0.087876 0.000344\n", - "61 1 1 3 total 0.000418 0.000023\n", - "60 1 1 4 total 0.000000 0.000000\n", - "59 1 1 5 total 0.000000 0.000000\n", - "58 1 1 6 total 0.000000 0.000000\n", - "57 1 1 7 total 0.000000 0.000000\n", - "56 1 1 8 total 0.000000 0.000000\n", - "55 1 2 1 total 0.000000 0.000000\n", - "54 1 2 2 total 0.266499 0.001265" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = nuscatter.get_pandas_dataframe()\n", - "df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each multi-group cross section object can be easily exported to a variety of file formats, including CSV, Excel, and LaTeX for storage or data processing." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "transport.export_xs_data(filename='transport-xs', format='excel')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following code snippet shows how to export all of four cross sections to the same HDF5 binary data store." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "transport.build_hdf5_store(filename='mgxs', append=True)\n", - "nufission.build_hdf5_store(filename='mgxs', append=True)\n", - "nuscatter.build_hdf5_store(filename='mgxs', append=True)\n", - "chi.build_hdf5_store(filename='mgxs', append=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Verification with OpenMOC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course it is always a good idea to verify that one's cross sections are accurate. We can easily do so here with the deterministic transport code OpenMOC. We will extract an OpenCG geometry from the summary file and convert it into an equivalent OpenMOC geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create an OpenMOC Geometry from the OpenCG Geometry\n", - "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can inject the multi-group cross sections into the equivalent infinite homogeneous medium OpenMOC geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Get all OpenMOC cells in the gometry\n", - "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", - "\n", - "# Inject multi-group cross sections into OpenMOC Materials\n", - "for cell_id, cell in openmoc_cells.items():\n", - " \n", - " # Get a reference to the Material filling this Cell\n", - " openmoc_material = cell.getFillMaterial()\n", - " \n", - " # Set the number of energy groups for the Material\n", - " openmoc_material.setNumEnergyGroups(fine_groups.num_groups)\n", - " \n", - " # Inject NumPy arrays of cross section data into the Material\n", - " openmoc_material.setSigmaT(transport.get_xs().flatten())\n", - " openmoc_material.setNuSigmaF(nufission.get_xs().flatten())\n", - " openmoc_material.setSigmaS(nuscatter.get_xs().flatten())\n", - " openmoc_material.setChi(chi.get_xs().flatten())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to run OpenMOC to verify our cross-sections from OpenMC." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ NORMAL ] Ray tracing for track segmentation...\n", - "[ NORMAL ] Dumping tracks to file...\n", - "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.685184\tres = 1.483E-316\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.785642\tres = 3.148E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.750185\tres = 1.466E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.728846\tres = 4.513E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.695632\tres = 2.844E-02\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.663357\tres = 4.557E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.632339\tres = 4.640E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.604187\tres = 4.676E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.579451\tres = 4.452E-02\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.558474\tres = 4.094E-02\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.541436\tres = 3.620E-02\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.528380\tres = 3.051E-02\n", - "[ NORMAL ] Iteration 12:\tk_eff = 0.519273\tres = 2.411E-02\n", - "[ NORMAL ] Iteration 13:\tk_eff = 0.513991\tres = 1.724E-02\n", - "[ NORMAL ] Iteration 14:\tk_eff = 0.512364\tres = 1.017E-02\n", - "[ NORMAL ] Iteration 15:\tk_eff = 0.514171\tres = 3.165E-03\n", - "[ NORMAL ] Iteration 16:\tk_eff = 0.519155\tres = 3.527E-03\n", - "[ NORMAL ] Iteration 17:\tk_eff = 0.527038\tres = 9.693E-03\n", - "[ NORMAL ] Iteration 18:\tk_eff = 0.537524\tres = 1.518E-02\n", - "[ NORMAL ] Iteration 19:\tk_eff = 0.550310\tres = 1.990E-02\n", - "[ NORMAL ] Iteration 20:\tk_eff = 0.565096\tres = 2.379E-02\n", - "[ NORMAL ] Iteration 21:\tk_eff = 0.581585\tres = 2.687E-02\n", - "[ NORMAL ] Iteration 22:\tk_eff = 0.599493\tres = 2.918E-02\n", - "[ NORMAL ] Iteration 23:\tk_eff = 0.618548\tres = 3.079E-02\n", - "[ NORMAL ] Iteration 24:\tk_eff = 0.638497\tres = 3.179E-02\n", - "[ NORMAL ] Iteration 25:\tk_eff = 0.659105\tres = 3.225E-02\n", - "[ NORMAL ] Iteration 26:\tk_eff = 0.680156\tres = 3.228E-02\n", - "[ NORMAL ] Iteration 27:\tk_eff = 0.701457\tres = 3.194E-02\n", - "[ NORMAL ] Iteration 28:\tk_eff = 0.722832\tres = 3.132E-02\n", - "[ NORMAL ] Iteration 29:\tk_eff = 0.744127\tres = 3.047E-02\n", - "[ NORMAL ] Iteration 30:\tk_eff = 0.765209\tres = 2.946E-02\n", - "[ NORMAL ] Iteration 31:\tk_eff = 0.785961\tres = 2.833E-02\n", - "[ NORMAL ] Iteration 32:\tk_eff = 0.806283\tres = 2.712E-02\n", - "[ NORMAL ] Iteration 33:\tk_eff = 0.826093\tres = 2.586E-02\n", - "[ NORMAL ] Iteration 34:\tk_eff = 0.845324\tres = 2.457E-02\n", - "[ NORMAL ] Iteration 35:\tk_eff = 0.863921\tres = 2.328E-02\n", - "[ NORMAL ] Iteration 36:\tk_eff = 0.881841\tres = 2.200E-02\n", - "[ NORMAL ] Iteration 37:\tk_eff = 0.899055\tres = 2.074E-02\n", - "[ NORMAL ] Iteration 38:\tk_eff = 0.915540\tres = 1.952E-02\n", - "[ NORMAL ] Iteration 39:\tk_eff = 0.931284\tres = 1.834E-02\n", - "[ NORMAL ] Iteration 40:\tk_eff = 0.946283\tres = 1.720E-02\n", - "[ NORMAL ] Iteration 41:\tk_eff = 0.960536\tres = 1.610E-02\n", - "[ NORMAL ] Iteration 42:\tk_eff = 0.974052\tres = 1.506E-02\n", - "[ NORMAL ] Iteration 43:\tk_eff = 0.986841\tres = 1.407E-02\n", - "[ NORMAL ] Iteration 44:\tk_eff = 0.998920\tres = 1.313E-02\n", - "[ NORMAL ] Iteration 45:\tk_eff = 1.010307\tres = 1.224E-02\n", - "[ NORMAL ] Iteration 46:\tk_eff = 1.021023\tres = 1.140E-02\n", - "[ NORMAL ] Iteration 47:\tk_eff = 1.031091\tres = 1.061E-02\n", - "[ NORMAL ] Iteration 48:\tk_eff = 1.040537\tres = 9.861E-03\n", - "[ NORMAL ] Iteration 49:\tk_eff = 1.049386\tres = 9.161E-03\n", - "[ NORMAL ] Iteration 50:\tk_eff = 1.057664\tres = 8.504E-03\n", - "[ NORMAL ] Iteration 51:\tk_eff = 1.065399\tres = 7.889E-03\n", - "[ NORMAL ] Iteration 52:\tk_eff = 1.072617\tres = 7.313E-03\n", - "[ NORMAL ] Iteration 53:\tk_eff = 1.079345\tres = 6.775E-03\n", - "[ NORMAL ] Iteration 54:\tk_eff = 1.085610\tres = 6.273E-03\n", - "[ NORMAL ] Iteration 55:\tk_eff = 1.091437\tres = 5.804E-03\n", - "[ NORMAL ] Iteration 56:\tk_eff = 1.096851\tres = 5.367E-03\n", - "[ NORMAL ] Iteration 57:\tk_eff = 1.101878\tres = 4.961E-03\n", - "[ NORMAL ] Iteration 58:\tk_eff = 1.106540\tres = 4.583E-03\n", - "[ NORMAL ] Iteration 59:\tk_eff = 1.110861\tres = 4.231E-03\n", - "[ NORMAL ] Iteration 60:\tk_eff = 1.114862\tres = 3.905E-03\n", - "[ NORMAL ] Iteration 61:\tk_eff = 1.118564\tres = 3.602E-03\n", - "[ NORMAL ] Iteration 62:\tk_eff = 1.121987\tres = 3.321E-03\n", - "[ NORMAL ] Iteration 63:\tk_eff = 1.125150\tres = 3.060E-03\n", - "[ NORMAL ] Iteration 64:\tk_eff = 1.128070\tres = 2.819E-03\n", - "[ NORMAL ] Iteration 65:\tk_eff = 1.130764\tres = 2.595E-03\n", - "[ NORMAL ] Iteration 66:\tk_eff = 1.133249\tres = 2.389E-03\n", - "[ NORMAL ] Iteration 67:\tk_eff = 1.135539\tres = 2.197E-03\n", - "[ NORMAL ] Iteration 68:\tk_eff = 1.137649\tres = 2.021E-03\n", - "[ NORMAL ] Iteration 69:\tk_eff = 1.139591\tres = 1.858E-03\n", - "[ NORMAL ] Iteration 70:\tk_eff = 1.141378\tres = 1.707E-03\n", - "[ NORMAL ] Iteration 71:\tk_eff = 1.143021\tres = 1.568E-03\n", - "[ NORMAL ] Iteration 72:\tk_eff = 1.144532\tres = 1.440E-03\n", - "[ NORMAL ] Iteration 73:\tk_eff = 1.145920\tres = 1.322E-03\n", - "[ NORMAL ] Iteration 74:\tk_eff = 1.147196\tres = 1.213E-03\n", - "[ NORMAL ] Iteration 75:\tk_eff = 1.148366\tres = 1.113E-03\n", - "[ NORMAL ] Iteration 76:\tk_eff = 1.149440\tres = 1.020E-03\n", - 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"[ NORMAL ] Iteration 122:\tk_eff = 1.160825\tres = 1.533E-05\n", - "[ NORMAL ] Iteration 123:\tk_eff = 1.160840\tres = 1.395E-05\n", - "[ NORMAL ] Iteration 124:\tk_eff = 1.160853\tres = 1.270E-05\n", - "[ NORMAL ] Iteration 125:\tk_eff = 1.160865\tres = 1.156E-05\n", - "[ NORMAL ] Iteration 126:\tk_eff = 1.160876\tres = 1.052E-05\n" - ] - } - ], - "source": [ - "# Generate tracks for OpenMOC\n", - "openmoc_geometry.initializeFlatSourceRegions()\n", - "track_generator = openmoc.TrackGenerator(openmoc_geometry, 128, 0.1)\n", - "track_generator.generateTracks()\n", - "\n", - "# Run OpenMOC\n", - "solver = openmoc.CPUSolver(track_generator)\n", - "solver.computeEigenvalue()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "openmc keff = 1.161200\n", - "openmoc keff = 1.160876\n", - "bias [pcm]: -32.4\n" - ] - } - ], - "source": [ - "# Print report of keff and bias with OpenMC\n", - "openmoc_keff = solver.getKeff()\n", - "openmc_keff = sp.k_combined[0]\n", - "bias = (openmoc_keff - openmc_keff) * 1e5\n", - "\n", - "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", - "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", - "print('bias [pcm]: {0:1.1f}'.format(bias))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although there is a non-trivial bias, one can easily run the preceding code with more particle histories to show that both codes converge to the same eigenvalue with <10 pcm bias. It should be noted that this discrepancy is due to use of tracklength tallies for `NuFission`, while one must use more slowly converging analog tallies for `TransportXS`, `NuScatterMatrixXS` and `Chi` (which require an 'energyout' filter)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Fuel Pin Cell" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section we show how to compute multi-group cross sections for a fuel pin cell. In addition, we will illustrate how to use some of the more advanced features in `openmc.mgxs` such as nuclide-by-nuclide microscopic cross section tallies and downstream energy group condensation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate Inputs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "this time we separate our nuclides into three distinct materials for water, clad and fuel." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# 1.6 enriched fuel\n", - "fuel = openmc.Material(name='1.6% Fuel')\n", - "fuel.set_density('g/cm3', 10.31341)\n", - "fuel.add_nuclide(u235, 3.7503e-4)\n", - "fuel.add_nuclide(u238, 2.2625e-2)\n", - "fuel.add_nuclide(o16, 4.6007e-2)\n", - "\n", - "# borated water\n", - "water = openmc.Material(name='Borated Water')\n", - "water.set_density('g/cm3', 0.740582)\n", - "water.add_nuclide(h1, 4.9457e-2)\n", - "water.add_nuclide(o16, 2.4732e-2)\n", - "\n", - "# zircaloy\n", - "zircaloy = openmc.Material(name='Zircaloy')\n", - "zircaloy.set_density('g/cm3', 6.55)\n", - "zircaloy.add_nuclide(zr90, 7.2758e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With our materials, we can now create a materials file object that can be exported to an actual XML file." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Instantiate a MaterialsFile, add Materials\n", - "materials_file = openmc.MaterialsFile()\n", - "materials_file.add_material(fuel)\n", - "materials_file.add_material(water)\n", - "materials_file.add_material(zircaloy)\n", - "materials_file.default_xs = '71c'\n", - "\n", - "# Export to \"materials.xml\"\n", - "materials_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": true - }, - "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", - "\n", - "# Create boundary planes to surround the geometry\n", - "# Use both reflective and vacuum boundaries to make life interesting\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')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create a Universe to encapsulate a fuel pin\n", - "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n", - "\n", - "# Create fuel Cell\n", - "fuel_cell = openmc.Cell(name='1.6% Fuel')\n", - "fuel_cell.fill = fuel\n", - "fuel_cell.region = -fuel_outer_radius\n", - "pin_cell_universe.add_cell(fuel_cell)\n", - "\n", - "# Create a clad Cell\n", - "clad_cell = openmc.Cell(name='1.6% Clad')\n", - "clad_cell.fill = zircaloy\n", - "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", - "pin_cell_universe.add_cell(clad_cell)\n", - "\n", - "# 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", - "pin_cell_universe.add_cell(moderator_cell)" - ] - }, - { - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "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, put the geometry into a geometry file, and export it to XML." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Create Geometry and set root Universe\n", - "openmc_geometry = openmc.Geometry()\n", - "openmc_geometry.root_universe = root_universe\n", - "\n", - "# Instantiate a GeometryFile\n", - "geometry_file = openmc.GeometryFile()\n", - "geometry_file.geometry = openmc_geometry\n", - "\n", - "# Export to \"geometry.xml\"\n", - "geometry_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will reuse our settings from the previous simulation. Now, we let's create transport, nu-fission, nu-scatter and chi multi-group cross sections for each cell." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Extract all Cells filled by Materials\n", - "openmc_cells = openmc_geometry.get_all_material_cells()\n", - "\n", - "# Create dictionary to store multi-group cross sections for all cells\n", - "xs_library = {}\n", - "\n", - "# Instantiate 8-group cross sections for each cell\n", - "for cell in openmc_cells:\n", - " xs_library[cell.id] = {}\n", - " xs_library[cell.id]['transport'] = mgxs.TransportXS(groups=fine_groups)\n", - " xs_library[cell.id]['nu-fission'] = mgxs.NuFissionXS(groups=fine_groups)\n", - " xs_library[cell.id]['nu-scatter'] = mgxs.NuScatterMatrixXS(groups=fine_groups)\n", - " xs_library[cell.id]['chi'] = mgxs.Chi(groups=fine_groups)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create a tally trigger set to +/- 0.01 for each tally\n", - "# used to compute the multi-group cross sections\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", - " for mgxs_type in xs_library[cell.id]:\n", - " xs_library[cell.id][mgxs_type].tally_trigger = tally_trigger\n", - " \n", - "# Set the trigger to active in the \"settings.xml\" file\n", - "settings_file.trigger_active = True\n", - "settings_file.particles *= 4\n", - "settings_file.trigger_max_batches = settings_file.batches * 4\n", - "settings_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we did not give our cross sections a spatial domain in their constructors. Instead, we will loop over all cells to set each cross sections domain. In addition, we will set each cross section to tally cross sections on a per-nuclide basis through the use of the `by_nuclide` instance attribute. " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Instantiate an empty TalliesFile\n", - "tallies_file = openmc.TalliesFile()\n", - "\n", - "# Iterate over all cells and cross section types\n", - "for cell in openmc_cells:\n", - " for rxn_type in xs_library[cell.id]:\n", - "\n", - " # Set the cross sections domain type to the cell\n", - " xs_library[cell.id][rxn_type].domain = cell\n", - " xs_library[cell.id][rxn_type].domain_type = 'cell'\n", - " \n", - " # Tally cross sections by nuclide (e.g., micro cross sections)\n", - " xs_library[cell.id][rxn_type].by_nuclide = True\n", - " \n", - " # Add OpenMC tallies to the tallies file for XML generation\n", - " for tally in xs_library[cell.id][rxn_type].tallies.values():\n", - " tallies_file.add_tally(tally, merge=True)\n", - "\n", - "# Export to \"tallies.xml\"\n", - "tallies_file.export_to_xml()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we a have a complete set of inputs, so we can go ahead and run our simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " .d88888b. 888b d888 .d8888b.\n", - " d88P\" \"Y88b 8888b d8888 d88P Y88b\n", - " 888 888 88888b.d88888 888 888\n", - " 888 888 88888b. .d88b. 88888b. 888Y88888P888 888 \n", - " 888 888 888 \"88b d8P Y8b 888 \"88b 888 Y888P 888 888 \n", - " 888 888 888 888 88888888 888 888 888 Y8P 888 888 888\n", - " Y88b. .d88P 888 d88P Y8b. 888 888 888 \" 888 Y88b d88P\n", - " \"Y88888P\" 88888P\" \"Y8888 888 888 888 888 \"Y8888P\"\n", - "__________________888______________________________________________________\n", - " 888\n", - " 888\n", - "\n", - " Copyright: 2011-2015 Massachusetts Institute of Technology\n", - " License: http://mit-crpg.github.io/openmc/license.html\n", - " Version: 0.7.0\n", - " Git SHA1: 21738db07debeabde824c9b955bd3bf0c9a16366\n", - " Date/Time: 2015-11-01 21:29:37\n", - " MPI Processes: 1\n", - "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", - " Reading settings XML file...\n", - " Reading cross sections XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", - " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Loading ACE cross section table: 92235.71c\n", - " Loading ACE cross section table: 92238.71c\n", - " Loading ACE cross section table: 8016.71c\n", - " Loading ACE cross section table: 1001.71c\n", - " Loading ACE cross section table: 40090.71c\n", - " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n", - " Initializing source particles...\n", - "\n", - " ===========================================================================\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", - "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.23985 \n", - " 2/1 1.24082 \n", - " 3/1 1.22031 \n", - " 4/1 1.21649 \n", - " 5/1 1.23229 \n", - " 6/1 1.21957 \n", - " 7/1 1.22515 \n", - " 8/1 1.21309 \n", - " 9/1 1.23939 \n", - " 10/1 1.23865 \n", - " 11/1 1.22776 \n", - " 12/1 1.21661 1.22219 +/- 0.00558\n", - " 13/1 1.22202 1.22213 +/- 0.00322\n", - " 14/1 1.23251 1.22473 +/- 0.00345\n", - " 15/1 1.23965 1.22771 +/- 0.00401\n", - " 16/1 1.21441 1.22549 +/- 0.00395\n", - " 17/1 1.23348 1.22663 +/- 0.00353\n", - " 18/1 1.21121 1.22471 +/- 0.00361\n", - " 19/1 1.20506 1.22252 +/- 0.00386\n", - " 20/1 1.22275 1.22255 +/- 0.00346\n", - " 21/1 1.21700 1.22204 +/- 0.00317\n", - " 22/1 1.20841 1.22091 +/- 0.00311\n", - " 23/1 1.21302 1.22030 +/- 0.00292\n", - " 24/1 1.22504 1.22064 +/- 0.00272\n", - " 25/1 1.22325 1.22081 +/- 0.00254\n", - " 26/1 1.22988 1.22138 +/- 0.00244\n", - " 27/1 1.21374 1.22093 +/- 0.00234\n", - " 28/1 1.21434 1.22056 +/- 0.00224\n", - " 29/1 1.24678 1.22194 +/- 0.00253\n", - " 30/1 1.22600 1.22215 +/- 0.00240\n", - " 31/1 1.22783 1.22242 +/- 0.00230\n", - " 32/1 1.23107 1.22281 +/- 0.00223\n", - " 33/1 1.23041 1.22314 +/- 0.00216\n", - " 34/1 1.21147 1.22266 +/- 0.00212\n", - " 35/1 1.23184 1.22302 +/- 0.00207\n", - " 36/1 1.22513 1.22310 +/- 0.00199\n", - " 37/1 1.22969 1.22335 +/- 0.00193\n", - " 38/1 1.21288 1.22297 +/- 0.00190\n", - " 39/1 1.23967 1.22355 +/- 0.00192\n", - " 40/1 1.21419 1.22324 +/- 0.00188\n", - " 41/1 1.23212 1.22352 +/- 0.00184\n", - " 42/1 1.20703 1.22301 +/- 0.00185\n", - " 43/1 1.24153 1.22357 +/- 0.00188\n", - " 44/1 1.23561 1.22392 +/- 0.00186\n", - " 45/1 1.20369 1.22335 +/- 0.00190\n", - " 46/1 1.24517 1.22395 +/- 0.00194\n", - " 47/1 1.22985 1.22411 +/- 0.00189\n", - " 48/1 1.23570 1.22442 +/- 0.00187\n", - " 49/1 1.22288 1.22438 +/- 0.00182\n", - " 50/1 1.20470 1.22389 +/- 0.00184\n", - " Triggers unsatisfied, max unc./thresh. is 1.18932 for flux in tally 10080\n", - " The estimated number of batches is 67\n", - " Creating state point statepoint.050.h5...\n", - " 51/1 1.24158 1.22432 +/- 0.00185\n", - " 52/1 1.24407 1.22479 +/- 0.00186\n", - " 53/1 1.23412 1.22500 +/- 0.00183\n", - " 54/1 1.25172 1.22561 +/- 0.00189\n", - " 55/1 1.22653 1.22563 +/- 0.00185\n", - " 56/1 1.24741 1.22610 +/- 0.00187\n", - " 57/1 1.24342 1.22647 +/- 0.00186\n", - " 58/1 1.20365 1.22600 +/- 0.00189\n", - " 59/1 1.23576 1.22620 +/- 0.00186\n", - " 60/1 1.21398 1.22595 +/- 0.00184\n", - " 61/1 1.22186 1.22587 +/- 0.00180\n", - " 62/1 1.23502 1.22605 +/- 0.00178\n", - " 63/1 1.23328 1.22618 +/- 0.00175\n", - " 64/1 1.23990 1.22644 +/- 0.00173\n", - " 65/1 1.23283 1.22655 +/- 0.00171\n", - " 66/1 1.21605 1.22637 +/- 0.00169\n", - " 67/1 1.22322 1.22631 +/- 0.00166\n", - " Triggers satisfied for batch 67\n", - " Creating state point statepoint.067.h5...\n", - "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", - " =======================> TIMING STATISTICS <=======================\n", - "\n", - " Total time for initialization = 2.0080E+00 seconds\n", - " Reading cross sections = 4.3400E-01 seconds\n", - " Total time in simulation = 9.0060E+02 seconds\n", - " Time in transport only = 9.0020E+02 seconds\n", - " Time in inactive batches = 7.3259E+01 seconds\n", - " Time in active batches = 8.2734E+02 seconds\n", - " Time synchronizing fission bank = 6.9000E-02 seconds\n", - " Sampling source sites = 4.7000E-02 seconds\n", - " SEND/RECV source sites = 2.2000E-02 seconds\n", - " Time accumulating tallies = 5.0000E-03 seconds\n", - " Total time for finalization = 7.7000E-02 seconds\n", - " Total time elapsed = 9.0285E+02 seconds\n", - " Calculation Rate (inactive) = 1365.02 neutrons/second\n", - " Calculation Rate (active) = 483.479 neutrons/second\n", - "\n", - " ============================> RESULTS <============================\n", - "\n", - " k-effective (Collision) = 1.22548 +/- 0.00143\n", - " k-effective (Track-length) = 1.22631 +/- 0.00166\n", - " k-effective (Absorption) = 1.22204 +/- 0.00138\n", - " Combined k-effective = 1.22386 +/- 0.00114\n", - " Leakage Fraction = 0.00000 +/- 0.00000\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Delete old HDF5 files\n", - "!rm *.h5\n", - "\n", - "# Run OpenMC with the output throttled!\n", - "executor = openmc.Executor()\n", - "executor.run_simulation(output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tally Data Processing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our simulation ran successfully and created a statepoint file with all the tally data in it. As before, we begin our analysis here loading the statepoint file." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the last statepoint and summary files\n", - "sp = openmc.StatePoint('statepoint.067.h5')\n", - "su = openmc.Summary('summary.h5')\n", - "sp.link_with_summary(su)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The statepoint is now ready to be analyzed by our multi-group cross sections. Next, we load the tallies from the statepoint into each object and to compute the cross sections using tally arithmetic." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Iterate over all cells and cross section types\n", - "for cell in openmc_cells:\n", - " for rxn_type in xs_library[cell.id]:\n", - " xs_library[cell.id][rxn_type].load_from_statepoint(sp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it! Our multi-group cross sections are now ready for the big spotlight. This time we have cross sections in three distinct spatial zones - fuel, clad and moderator - on a per-nuclide basis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Cross Section Data Visualization" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's first inspect one of our cross sections by printing it to the screen as a microscopic cross section in units of barns." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\tnu-fission\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", - "\tNuclide =\tU-235\n", - "\tCross Sections [barns]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t3.31e+00 +/- 2.13e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t3.96e+00 +/- 1.54e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t5.51e+01 +/- 2.36e-01%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t8.83e+01 +/- 3.76e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t2.89e+02 +/- 4.10e-01%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t4.49e+02 +/- 4.94e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t6.87e+02 +/- 3.44e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t1.44e+03 +/- 2.37e-01%\n", - "\n", - "\tNuclide =\tU-238\n", - "\tCross Sections [barns]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t1.06e+00 +/- 2.47e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t1.21e-03 +/- 3.07e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t5.72e-04 +/- 3.47e+00%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t6.54e-06 +/- 3.29e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.07e-05 +/- 4.20e-01%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.55e-05 +/- 4.94e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.30e-05 +/- 3.44e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t4.24e-05 +/- 2.37e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "nufission = xs_library[fuel_cell.id]['nu-fission']\n", - "nufission.print_xs(xs_type='micro', nuclides=['U-235', 'U-238'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our multi-group cross sections are capable of summing across all nuclides to provide us with macroscopic cross sections as well." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\tnu-fission\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [0.821 - 20.0 MeV]:\t2.53e-02 +/- 2.35e-01%\n", - " Group 2 [0.00553 - 0.821 MeV]:\t1.51e-03 +/- 1.51e-01%\n", - " Group 3 [4e-06 - 0.00553 MeV]:\t2.07e-02 +/- 2.36e-01%\n", - " Group 4 [6.25e-07 - 4e-06 MeV]:\t3.31e-02 +/- 3.76e-01%\n", - " Group 5 [2.8e-07 - 6.25e-07 MeV]:\t1.09e-01 +/- 4.10e-01%\n", - " Group 6 [1.4e-07 - 2.8e-07 MeV]:\t1.69e-01 +/- 4.94e-01%\n", - " Group 7 [5.8e-08 - 1.4e-07 MeV]:\t2.58e-01 +/- 3.44e-01%\n", - " Group 8 [0.0 - 5.8e-08 MeV]:\t5.40e-01 +/- 2.37e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "nufission = xs_library[fuel_cell.id]['nu-fission']\n", - "nufission.print_xs(xs_type='macro', nuclides='sum')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although a printed report is nice, it is not scalable or flexible. Let's extract the cross section data for the moderator as a Pandas DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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cellgroup ingroup outnuclidemeanstd. dev.
1261000211H-10.2338960.004410
1271000211O-161.5644880.007478
1241000212H-11.5899750.003196
1251000212O-160.2836970.001986
1221000213H-10.0108460.000225
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" - ], - "text/plain": [ - " cell group in group out nuclide mean std. dev.\n", - "126 10002 1 1 H-1 0.233896 0.004410\n", - "127 10002 1 1 O-16 1.564488 0.007478\n", - "124 10002 1 2 H-1 1.589975 0.003196\n", - "125 10002 1 2 O-16 0.283697 0.001986\n", - "122 10002 1 3 H-1 0.010846 0.000225\n", - "123 10002 1 3 O-16 0.000000 0.000000\n", - "120 10002 1 4 H-1 0.000000 0.000000\n", - "121 10002 1 4 O-16 0.000000 0.000000\n", - "118 10002 1 5 H-1 0.000000 0.000000\n", - "119 10002 1 5 O-16 0.000000 0.000000" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n", - "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n", - "df.head(10)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can easily use the Pandas DataFrame to extract the H-1 and O-16 scattering matrices separately." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Slice DataFrame in two for each nuclide's mean values\n", - "h1 = df[df['nuclide'] == 'H-1']['mean']\n", - "o16 = df[df['nuclide'] == 'O-16']['mean']\n", - "\n", - "# Cast DataFrames as NumPy arrays\n", - "h1 = h1.as_matrix()\n", - "o16 = o16.as_matrix()\n", - "\n", - "# Reshape arrays to 2D matrix for plotting\n", - "h1.shape = (fine_groups.num_groups, fine_groups.num_groups)\n", - "o16.shape = (fine_groups.num_groups, fine_groups.num_groups)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Matplotlib's `imshow` routine can be used to plot the matrices to illustrate their sparsity structures." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create plot of the H-1 scattering matrix\n", - "fig = plt.subplot(121)\n", - "fig.imshow(h1, interpolation='nearest')\n", - "plt.title('H-1 Scattering Matrix')\n", - "\n", - "# Create plot of the O-16 scattering matrix\n", - "fig2 = plt.subplot(122)\n", - "fig2.imshow(o16, interpolation='nearest')\n", - "plt.title('O-16 Scattering Matrix')\n", - "\n", - "# Show the plot on screen\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we illustate how one can easily take multi-group cross sections and condense them down to a coarser energy group structure using. The `get_condensed_xs(...)` class method takes in as a parameter an `EnergyGroups` object with a coarse(r) group structure and returns a new multi-group cross section condensed to the coarse groups. We illustrate this process below using the 2-group structure created earlier." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Extract the 16-group transport cross section for the fuel\n", - "fine_xs = xs_library[fuel_cell.id]['transport']\n", - "\n", - "# Condense to the 2-group structure\n", - "condense_xs = fine_xs.get_condensed_xs(coarse_groups)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Group condensation is as simple as that! We now have a new coarse 2-group cross section in addition to our original 16-group cross section. Let's inspect the 2-group cross section by printing it to the screen and extracting a Pandas DataFrame as we have already learned how to do." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Multi-Group XS\n", - "\tReaction Type =\ttransport\n", - "\tDomain Type =\tcell\n", - "\tDomain ID =\t10000\n", - "\tNuclide =\tU-235\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t7.81e-03 +/- 4.72e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t1.82e-01 +/- 2.09e-01%\n", - "\n", - "\tNuclide =\tU-238\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t2.17e-01 +/- 1.58e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t2.54e-01 +/- 2.46e-01%\n", - "\n", - "\tNuclide =\tO-16\n", - "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t1.45e-01 +/- 1.73e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t1.75e-01 +/- 2.64e-01%\n", - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "condense_xs.print_xs()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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2100002O-163.7988330.010031
\n", - "
" - ], - "text/plain": [ - " cell group in nuclide mean std. dev.\n", - "3 10000 1 U-235 20.832704 0.098310\n", - "4 10000 1 U-238 9.574435 0.015117\n", - "5 10000 1 O-16 3.161919 0.005466\n", - "0 10000 2 U-235 484.133513 1.011870\n", - "1 10000 2 U-238 11.215152 0.027565\n", - "2 10000 2 O-16 3.798833 0.010031" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = condense_xs.get_pandas_dataframe(xs_type='micro')\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Verification with OpenMOC" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's verify our cross sections using OpenMOC. First, we use OpenCG construct an equivalent OpenMOC geometry just as we did before." - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create an OpenMOC Geometry from the OpenCG Geometry\n", - "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Likewise, we can inject the multi-group cross sections into the equivalent fuel pin cell OpenMOC geometry." - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Get all OpenMOC cells in the gometry\n", - "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", - "\n", - "# Inject multi-group cross sections into OpenMOC Materials\n", - "# NOTE: This code will work for 1, 10, or 1,000s of cells\n", - "# as is the case for a complicated geometry like BEAVRS\n", - "for cell_id, cell in openmoc_cells.items():\n", - " \n", - " # Ignore the root cell\n", - " if cell.getName() == 'root cell':\n", - " continue\n", - " \n", - " # Get a reference to the Material filling this Cell\n", - " openmoc_material = cell.getFillMaterial()\n", - " \n", - " # Set the number of energy groups for the Material\n", - " openmoc_material.setNumEnergyGroups(fine_groups.num_groups)\n", - " \n", - " # Extract the appropriate cross section objects for this cell\n", - " transport = xs_library[cell_id]['transport']\n", - " nufission = xs_library[cell_id]['nu-fission']\n", - " nuscatter = xs_library[cell_id]['nu-scatter']\n", - " chi = xs_library[cell_id]['chi']\n", - " \n", - " # Inject NumPy arrays of cross section data into the Material\n", - " # NOTE: In each case we must sum across nuclides to get the\n", - " # macroscopic cross sections needed by OpenMOC\n", - " openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to run OpenMOC to verify our cross-sections from OpenMC." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Throttle OpenMOC output to screen\n", - "openmoc.log.set_log_level('WARNING')\n", - "\n", - "# Generate tracks for OpenMOC\n", - "openmoc_geometry.initializeFlatSourceRegions()\n", - "track_generator = openmoc.TrackGenerator(openmoc_geometry, 128, 0.1)\n", - "track_generator.generateTracks()\n", - "\n", - "# Run OpenMOC\n", - "solver = openmoc.CPUSolver(track_generator)\n", - "solver.computeEigenvalue()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results." - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "openmc keff = 1.223863\n", - "openmoc keff = 1.222517\n", - "bias [pcm]: -134.7\n" - ] - } - ], - "source": [ - "# Print report of keff and bias with OpenMC\n", - "openmoc_keff = solver.getKeff()\n", - "openmc_keff = sp.k_combined[0]\n", - "bias = (openmoc_keff - openmc_keff) * 1e5\n", - "\n", - "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", - "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", - "print('bias [pcm]: {0:1.1f}'.format(bias))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a sanity check, let's run a simulation with the coarse 2-group cross sections to ensure that they produce a reasonable result." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)\n", - "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n", - "\n", - "# Inject multi-group cross sections into OpenMOC Materials\n", - "for cell_id, cell in openmoc_cells.items():\n", - " \n", - " # Ignore the root cell\n", - " if cell.getName() == 'root cell':\n", - " continue\n", - " \n", - " openmoc_material = cell.getFillMaterial()\n", - " openmoc_material.setNumEnergyGroups(coarse_groups.num_groups)\n", - " \n", - " # Extract the appropriate cross section objects for this cell\n", - " transport = xs_library[cell_id]['transport']\n", - " nufission = xs_library[cell_id]['nu-fission']\n", - " nuscatter = xs_library[cell_id]['nu-scatter']\n", - " chi = xs_library[cell_id]['chi']\n", - " \n", - " # Perform group condensation\n", - " transport = transport.get_condensed_xs(coarse_groups)\n", - " nufission = nufission.get_condensed_xs(coarse_groups)\n", - " nuscatter = nuscatter.get_condensed_xs(coarse_groups)\n", - " chi = chi.get_condensed_xs(coarse_groups)\n", - " \n", - " # Inject NumPy arrays of cross section data into the Material\n", - " openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n", - " openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Generate tracks for OpenMOC\n", - "openmoc_geometry.initializeFlatSourceRegions()\n", - "track_generator = openmoc.TrackGenerator(openmoc_geometry, 128, 0.1)\n", - "track_generator.generateTracks()\n", - "\n", - "# Run OpenMOC\n", - "solver = openmoc.CPUSolver(track_generator)\n", - "solver.computeEigenvalue()" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "openmc keff = 1.223863\n", - "openmoc keff = 1.225691\n", - "bias [pcm]: 182.7\n" - ] - } - ], - "source": [ - "# Print report of keff and bias with OpenMC\n", - "openmoc_keff = solver.getKeff()\n", - "openmc_keff = sp.k_combined[0]\n", - "bias = (openmoc_keff - openmc_keff) * 1e5\n", - "\n", - "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n", - "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n", - "print('bias [pcm]: {0:1.1f}'.format(bias))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is a non-trivial bias in both the 2-group and 8-group cases. In the case of the pin cell, one can show that these biases do not converge to <100 pcm with more particle histories. In the case of heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n", - "\n", - "* Appropriate transport-corrected cross sections\n", - "* Spatial discretization of OpenMOC's mesh\n", - "* Constant-in-angle multi-group cross sections" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb index 1e4c3c9cd..763cdd9ef 100644 --- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb +++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb @@ -126,7 +126,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now let's move on to the geometry. This problem will be a square array of fuel pins, which we can use OpenMC's lattice/universe feature for. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem." + "Now let's move on to the geometry. This problem will be a square array of fuel pins and control rod guide tubes for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem." ] }, { @@ -155,7 +155,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces." + "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces." ] }, { diff --git a/docs/source/pythonapi/examples/post-processing.ipynb b/docs/source/pythonapi/examples/post-processing.ipynb index 87ae42b41..6e2dd9429 100644 --- a/docs/source/pythonapi/examples/post-processing.ipynb +++ b/docs/source/pythonapi/examples/post-processing.ipynb @@ -347,7 +347,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -413,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": true }, @@ -432,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true @@ -458,8 +458,8 @@ " Copyright: 2011-2015 Massachusetts Institute of Technology\n", " License: http://mit-crpg.github.io/openmc/license.html\n", " Version: 0.7.0\n", - " Git SHA1: 21738db07debeabde824c9b955bd3bf0c9a16366\n", - " Date/Time: 2015-10-28 21:04:43\n", + " Git SHA1: c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n", + " Date/Time: 2015-11-29 16:46:53\n", " MPI Processes: 1\n", "\n", " ===========================================================================\n", @@ -520,8 +520,116 @@ " 31/1 1.04883 1.04094 +/- 0.00388\n", " 32/1 1.03557 1.04070 +/- 0.00371\n", " 33/1 1.02947 1.04021 +/- 0.00358\n", - " 34/1 1.03651 1.04006 +/- 0.00343\n" + " 34/1 1.03651 1.04006 +/- 0.00343\n", + " 35/1 1.03331 1.03979 +/- 0.00330\n", + " 36/1 1.05947 1.04054 +/- 0.00326\n", + " 37/1 1.05093 1.04093 +/- 0.00316\n", + " 38/1 1.06787 1.04189 +/- 0.00319\n", + " 39/1 1.01451 1.04095 +/- 0.00322\n", + " 40/1 1.02351 1.04037 +/- 0.00317\n", + " 41/1 1.04826 1.04062 +/- 0.00307\n", + " 42/1 1.04228 1.04067 +/- 0.00298\n", + " 43/1 1.03214 1.04041 +/- 0.00290\n", + " 44/1 1.04950 1.04068 +/- 0.00282\n", + " 45/1 1.06616 1.04141 +/- 0.00284\n", + " 46/1 1.07039 1.04221 +/- 0.00287\n", + " 47/1 1.00292 1.04115 +/- 0.00299\n", + " 48/1 1.04477 1.04125 +/- 0.00291\n", + " 49/1 1.03360 1.04105 +/- 0.00284\n", + " 50/1 1.04783 1.04122 +/- 0.00277\n", + " 51/1 1.03985 1.04119 +/- 0.00271\n", + " 52/1 1.02507 1.04080 +/- 0.00267\n", + " 53/1 1.03477 1.04066 +/- 0.00261\n", + " 54/1 1.00412 1.03983 +/- 0.00268\n", + " 55/1 1.02239 1.03945 +/- 0.00265\n", + " 56/1 1.04308 1.03952 +/- 0.00259\n", + " 57/1 1.05534 1.03986 +/- 0.00256\n", + " 58/1 1.06667 1.04042 +/- 0.00257\n", + " 59/1 1.06458 1.04091 +/- 0.00256\n", + " 60/1 1.00304 1.04015 +/- 0.00262\n", + " 61/1 1.05038 1.04036 +/- 0.00258\n", + " 62/1 1.02904 1.04014 +/- 0.00254\n", + " 63/1 1.00249 1.03943 +/- 0.00259\n", + " 64/1 1.01779 1.03903 +/- 0.00257\n", + " 65/1 1.05335 1.03929 +/- 0.00254\n", + " 66/1 1.06231 1.03970 +/- 0.00253\n", + " 67/1 1.02382 1.03942 +/- 0.00250\n", + " 68/1 1.03796 1.03939 +/- 0.00245\n", + " 69/1 1.03672 1.03935 +/- 0.00241\n", + " 70/1 1.02926 1.03918 +/- 0.00238\n", + " 71/1 1.05834 1.03950 +/- 0.00236\n", + " 72/1 1.04332 1.03956 +/- 0.00232\n", + " 73/1 1.05613 1.03982 +/- 0.00230\n", + " 74/1 1.01963 1.03950 +/- 0.00228\n", + " 75/1 1.02228 1.03924 +/- 0.00226\n", + " 76/1 1.04842 1.03938 +/- 0.00223\n", + " 77/1 1.02157 1.03911 +/- 0.00222\n", + " 78/1 1.02810 1.03895 +/- 0.00219\n", + " 79/1 1.05030 1.03912 +/- 0.00216\n", + " 80/1 1.02391 1.03890 +/- 0.00214\n", + " 81/1 1.02488 1.03870 +/- 0.00212\n", + " 82/1 1.04957 1.03885 +/- 0.00210\n", + " 83/1 1.03499 1.03880 +/- 0.00207\n", + " 84/1 1.05922 1.03907 +/- 0.00206\n", + " 85/1 1.05898 1.03934 +/- 0.00205\n", + " 86/1 1.02242 1.03912 +/- 0.00204\n", + " 87/1 1.03278 1.03904 +/- 0.00201\n", + " 88/1 1.06134 1.03932 +/- 0.00201\n", + " 89/1 1.04521 1.03940 +/- 0.00198\n", + " 90/1 1.04277 1.03944 +/- 0.00196\n", + " 91/1 1.04214 1.03947 +/- 0.00193\n", + " 92/1 1.05610 1.03967 +/- 0.00192\n", + " 93/1 1.04531 1.03974 +/- 0.00190\n", + " 94/1 1.01534 1.03945 +/- 0.00190\n", + " 95/1 1.03971 1.03945 +/- 0.00187\n", + " 96/1 1.07183 1.03983 +/- 0.00189\n", + " 97/1 1.07214 1.04020 +/- 0.00191\n", + " 98/1 1.03710 1.04017 +/- 0.00188\n", + " 99/1 1.02532 1.04000 +/- 0.00187\n", + " 100/1 1.03965 1.04000 +/- 0.00185\n", + " Creating state point statepoint.100.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 3.7900E-01 seconds\n", + " Reading cross sections = 8.7000E-02 seconds\n", + " Total time in simulation = 2.2064E+02 seconds\n", + " Time in transport only = 2.2060E+02 seconds\n", + " Time in inactive batches = 8.7100E+00 seconds\n", + " Time in active batches = 2.1193E+02 seconds\n", + " Time synchronizing fission bank = 1.4000E-02 seconds\n", + " Sampling source sites = 8.0000E-03 seconds\n", + " SEND/RECV source sites = 2.0000E-03 seconds\n", + " Time accumulating tallies = 1.3000E-02 seconds\n", + " Total time for finalization = 1.6600E-01 seconds\n", + " Total time elapsed = 2.2120E+02 seconds\n", + " Calculation Rate (inactive) = 5740.53 neutrons/second\n", + " Calculation Rate (active) = 2123.37 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.03912 +/- 0.00160\n", + " k-effective (Track-length) = 1.04000 +/- 0.00185\n", + " k-effective (Absorption) = 1.04240 +/- 0.00156\n", + " Combined k-effective = 1.04078 +/- 0.00127\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -545,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false, "scrolled": true @@ -565,11 +673,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t10000\n", + "\tName =\t\n", + "\tFilters =\t\n", + " \t\tmesh\t[10000]\n", + "\tNuclides =\ttotal \n", + "\tScores =\t[u'flux', u'fission']\n", + "\tEstimator =\ttracklength\n", + "\n" + ] + } + ], "source": [ "tally = sp.get_tally(scores=['flux'])\n", "print(tally)" @@ -584,11 +708,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 0.4107676 , 0. ]],\n", + "\n", + " [[ 0.40849402, 0. ]],\n", + "\n", + " [[ 0.41014343, 0. ]],\n", + "\n", + " ..., \n", + " [[ 0.41049467, 0. ]],\n", + "\n", + " [[ 0.40982242, 0. ]],\n", + "\n", + " [[ 0.40996987, 0. ]]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tally.sum" ] @@ -602,11 +748,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 1, 2)\n" + ] + }, + { + "data": { + "text/plain": [ + "(array([[[ 0.00456408, 0. ]],\n", + " \n", + " [[ 0.00453882, 0. ]],\n", + " \n", + " [[ 0.00455715, 0. ]],\n", + " \n", + " ..., \n", + " [[ 0.00456105, 0. ]],\n", + " \n", + " [[ 0.00455358, 0. ]],\n", + " \n", + " [[ 0.00455522, 0. ]]]),\n", + " array([[[ 1.95085625e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.78129859e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.89709648e-05, 0.00000000e+00]],\n", + " \n", + " ..., \n", + " [[ 1.56286612e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.65813279e-05, 0.00000000e+00]],\n", + " \n", + " [[ 1.67530331e-05, 0.00000000e+00]]]))" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(tally.mean.shape)\n", "(tally.mean, tally.std_dev)" @@ -621,11 +808,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tally\n", + "\tID =\t10000\n", + "\tName =\t\n", + "\tFilters =\t\n", + " \t\tmesh\t[10000]\n", + "\tNuclides =\ttotal \n", + "\tScores =\t[u'flux']\n", + "\tEstimator =\ttracklength\n", + "\n" + ] + } + ], "source": [ "flux = tally.get_slice(scores=['flux'])\n", "fission = tally.get_slice(scores=['fission'])\n", @@ -641,7 +844,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "collapsed": false }, @@ -655,11 +858,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PFI0uXxK+SsPl49ro4/zR5SHsnEyg3WQksIGs9WhIPrqoPON+mVOjt7jx7GPs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\nunTp0qVLly5dunTp0qVLl585/j/rdUfNxuwKnQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.subplot(121)\n", "fig.imshow(flux.mean)\n", @@ -676,11 +900,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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7EWD5LB9DklSidgznjNN4Om39eqmi/CqqqmO2XzsdBa4ETgBLgddj+WvAisx2V8WyBgMD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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Determine relative error\n", "relative_error = np.zeros_like(flux.std_dev)\n", @@ -707,11 +942,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ (1.0, [0.2712169917165897, -0.04844236597355761, -0.1887902218343974], [0.3889598463000694, 0.8470657529949065, 0.36220139158953857], 2.2746035619924734, 0),\n", + " (1.0, [0.080729018085932, 0.19838688738571317, -0.38053428394017363], [-0.6604834049157511, -0.6893239101986768, 0.2976478097673534], 0.7833467555325838, 0),\n", + " (1.0, [0.019430574216787868, 0.06594180627832635, 0.23329810254580194], [-0.7472138923667574, 0.13227244377548197, -0.651287493870243], 1.1632342240714935, 0),\n", + " ...,\n", + " (1.0, [0.18544614514351207, -0.0113070561851496, 0.5468392238881264], [-0.8006491411918817, 0.43855795172388223, -0.4082007786475368], 1.4358240241589555, 0),\n", + " (1.0, [0.18544614514351207, -0.0113070561851496, 0.5468392238881264], [-0.5150076397044656, -0.34922134026850293, 0.7828228321575105], 1.5771133724329802, 0),\n", + " (1.0, [-0.2722999793764598, 0.22680062445008103, 0.2987060438567475], [0.9207818175032396, -0.2884020326181676, 0.26265017063984586], 2.932342523379745, 0)], \n", + " dtype=[('wgt', '" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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GlUqYsKkZZ/mbetoFju0tliVNMWOz/EnQvh/v08Djdeszgd8lyzuJY2Y+3+OQuiCW9w/K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\nJEmSJEmSJEmSJEmSJEmSJHXm/wHv3X8uqw7iTQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Create log-spaced energy bins from 1 keV to 100 MeV\n", "energy_bins = np.logspace(-3,1)\n", @@ -773,11 +1066,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.5, 0.5)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == 'face':\n" + ] + }, + { + "data": { + "image/png": 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XE9lKiVrnA2uehFuXwuE18Pb90D4Qer4CVXWefJuKPX3Q5wqh70BIbA1RFkRB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\nD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O/JQ8OBPXYkplAEh0GlUdpJ7oZsNHUQwSu8+LIOE39Gj+TZAjRBqAaR0h/zqEnIQ8ai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\nNhv274ZnHobEthAVB0NHnls6VRWFC6eEXwHSULwkjgEPnq6xqoR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.quiver(sp.source['xyz'][:,0], sp.source['xyz'][:,1],\n", " sp.source['uvw'][:,0], sp.source['uvw'][:,1],\n", diff --git a/openmc/opencg_compatible.py b/openmc/opencg_compatible.py index 6430b2424..afa57c78d 100644 --- a/openmc/opencg_compatible.py +++ b/openmc/opencg_compatible.py @@ -882,7 +882,7 @@ def get_opencg_lattice(openmc_lattice): outer = openmc_lattice.outer if len(pitch) == 2: - new_pitch = np.ones(3, dtype=np.float64) + new_pitch = np.ones(3, dtype=np.float64) * np.inf new_pitch[:2] = pitch pitch = new_pitch