diff --git a/docs/source/pythonapi/examples/images/mdgxs.png b/docs/source/pythonapi/examples/images/mdgxs.png new file mode 100644 index 0000000000..b93d0f0423 Binary files /dev/null and b/docs/source/pythonapi/examples/images/mdgxs.png differ diff --git a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb new file mode 100644 index 0000000000..5d65a0a202 --- /dev/null +++ b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb @@ -0,0 +1,1379 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This IPython Notebook introduces the use of the `openmc.mgxs` module to calculate multi-energy-group and multi-delayed-group cross sections for an infinite homogeneous medium. In particular, this Notebook introduces the the following features:\n", + "\n", + "* Creation of multi-delayed-group cross sections for an **infinite homogeneous medium**\n", + "* Calculation of delayed neutron precursor concentrations\n", + "\n", + "**Note:** This Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data. We recommend using [Pandas](http://pandas.pydata.org/) >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases of [Pandas](http://pandas.pydata.org/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to Multi-Delayed-Group Cross Sections (MDGXS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many Monte Carlo particle transport codes, including OpenMC, use continuous-energy nuclear cross section data. However, most deterministic neutron transport codes use *multi-group cross sections* defined over discretized energy bins or *energy groups*. Furthermore, kinetics calculations typically separate out parameters that involve delayed neutrons into prompt and delayed components and further subdivide delayed components by delayed groups. An example is the energy spectrum for prompt and delayed neutrons for U-235 and Pu-239 computed for a light water reactor spectrum." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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yGm+vatvquLi4MHNzc2Ztbc1cXFxYREQEKygo4NMqlsBCQ0NrdNywsDA2ePBg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\nCJ0VSenq6sKmTZuwZ88eobMiKQUFBTh8+LDQ2ZCEQ4cOISo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+ "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": { + "image/png": { + "width": 350 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='images/mdgxs.png', width=350)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A variety of tools employing different methodologies have been developed over the years to compute multi-group cross sections for certain applications, including NJOY (LANL), MC$^2$-3 (ANL), and Serpent (VTT). The `openmc.mgxs` Python module is designed to leverage OpenMC's tally system to calculate multi-group cross sections with arbitrary energy discretizations and different delayed group models (e.g. 6, 7, or 8 delayed group models) for fine-mesh heterogeneous deterministic neutron transport applications.\n", + "\n", + "Before proceeding to illustrate how one may use the `openmc.mgxs` module, it is worthwhile to define the general equations used to calculate multi-energy-group and multi-delayed-group cross sections. This is only intended as a brief overview of the methodology used by `openmc.mgxs` - we refer the interested reader to the large body of literature on the subject for a more comprehensive understanding of this complex topic." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introductory Notation\n", + "The continuous real-valued microscopic cross section may be denoted $\\sigma_{n,x}(\\mathbf{r}, E)$ for position vector $\\mathbf{r}$, energy $E$, nuclide $n$ and interaction type $x$. Similarly, the scalar neutron flux may be denoted by $\\Phi(\\mathbf{r},E)$ for position $\\mathbf{r}$ and energy $E$. **Note**: Although nuclear cross sections are dependent on the temperature $T$ of the interacting medium, the temperature variable is neglected here for brevity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatial and Energy Discretization\n", + "The energy domain for critical systems such as thermal reactors spans more than 10 orders of magnitude of neutron energies from 10$^{-5}$ - 10$^7$ eV. The multi-group approximation discretization divides this energy range into one or more energy groups. In particular, for $G$ total groups, we denote an energy group index $g$ such that $g \\in \\{1, 2, ..., G\\}$. The energy group indices are defined such that the smaller group the higher the energy, and vice versa. The integration over neutron energies across a discrete energy group is commonly referred to as **energy condensation**.\n", + "\n", + "The delayed neutrons created from fissions are created from > 30 delayed neutron precursors. Modeling each of the delayed neutron precursors is possible, but this approach has not recieved much attention due to large uncertainties in certain precursors. Therefore, the delayed neutrons are often combined into \"delayed groups\" that have a set time constant, $\\lambda_d$. Some cross section libraries use the same group time constants for all nuclides (e.g. JEFF 3.1) while other libraries use different time constants for all nuclides (e.g. ENDF/B-VII.1). Multi-delayed-group cross sections can either be created with the entire delayed group set, a subset of delayed groups, or integrated over all delayed groups.\n", + "\n", + "Multi-group cross sections are computed for discretized spatial zones in the geometry of interest. The spatial zones may be defined on a structured and regular fuel assembly or pin cell mesh, an arbitrary unstructured mesh or the constructive solid geometry used by OpenMC. For a geometry with $K$ distinct spatial zones, we designate each spatial zone an index $k$ such that $k \\in \\{1, 2, ..., K\\}$. The volume of each spatial zone is denoted by $V_{k}$. The integration over discrete spatial zones is commonly referred to as **spatial homogenization**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### General Scalar-Flux Weighted MDGXS\n", + "The multi-group cross sections computed by `openmc.mgxs` are defined as a *scalar flux-weighted average* of the microscopic cross sections across each discrete energy group. This formulation is employed in order to preserve the reaction rates within each energy group and spatial zone. In particular, spatial homogenization and energy condensation are used to compute the general multi-group cross section. For instance, the delayed-nu-fission multi-energy-group and multi-delayed-group cross section, $\\nu_d \\sigma_{f,x,k,g}$, can be computed as follows:\n", + "\n", + "$$\\nu_d \\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\nu_d \\sigma_{f,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n", + "\n", + "This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for only the delayed-nu-fission and delayed neutron fraction reaction type at the moment. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](https://mit-crpg.github.io/openmc/pythonapi/filter.html) on the energy range and spatial zone (material, cell, universe, or mesh) define the bounds of integration for both numerator and denominator." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-Group Prompt and Delayed Fission Spectrum\n", + "The energy spectrum of neutrons emitted from fission is denoted by $\\chi_{n}(\\mathbf{r},E' \\rightarrow E'')$ for incoming and outgoing energies $E'$ and $E''$, respectively. Unlike the multi-group cross sections $\\sigma_{n,x,k,g}$ considered up to this point, the fission spectrum is a probability distribution and must sum to unity. The outgoing energy is typically much less dependent on the incoming energy for fission than for scattering interactions. As a result, it is common practice to integrate over the incoming neutron energy when computing the multi-group fission spectrum. The fission spectrum may be simplified as $\\chi_{n}(\\mathbf{r},E)$ with outgoing energy $E$.\n", + "\n", + "Computing the cumulative energy spectrum of emitted neutrons, $\\chi_{n}(\\mathbf{r},E)$, has been presented in the `mgxs-part-i.ipynb` notebook. Here, we will present the energy spectrum of prompt and delayed emission neutrons, $\\chi_{n,p}(\\mathbf{r},E)$ and $\\chi_{n,d}(\\mathbf{r},E)$, respectively. Unlike the multi-group cross sections defined up to this point, the multi-group fission spectrum is weighted by the fission production rate rather than the scalar flux. This formulation is intended to preserve the total fission production rate in the multi-group deterministic calculation. In order to mathematically define the multi-group fission spectrum, we denote the microscopic fission cross section as $\\sigma_{n,f}(\\mathbf{r},E)$ and the average number of neutrons emitted from fission interactions with nuclide $n$ as $\\nu_{n,p}(\\mathbf{r},E)$ and $\\nu_{n,d}(\\mathbf{r},E)$ for prompt and delayed neutrons, respectively. The multi-group fission spectrum $\\chi_{n,k,g,d}$ is then the probability of fission neutrons emitted into energy group $g$ and delayed group $d$. There are not prompt groups, so inserting $p$ in place of $d$ just denotes all prompt neutrons. \n", + "\n", + "Similar to before, spatial homogenization and energy condensation are used to find the multi-energy-group and multi-delayed-group fission spectrum $\\chi_{n,k,g,d}$ as follows:\n", + "\n", + "$$\\chi_{n,k,g',d} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\chi_{n,d}(\\mathbf{r},E'\\rightarrow E'')\\nu_{n,d}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\nu_{n,d}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}$$\n", + "\n", + "The fission production-weighted multi-energy-group and multi-delayed-group fission spectrum for delayed neutrons is computed using OpenMC tallies with energy in, energy out, and delayed group filters. Alternatively, the delayed group filter can be omitted to compute the fission spectrum integrated over all delayed groups.\n", + "\n", + "This concludes our brief overview on the methodology to compute multi-energy-group and multi-delayed-group cross sections. The following sections detail more concretely how users may employ the `openmc.mgxs` module to power simulation workflows requiring multi-group cross sections for downstream deterministic calculations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import openmc\n", + "import openmc.mgxs as mgxs" + ] + }, + { + "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": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate some Nuclides\n", + "h1 = openmc.Nuclide('H1')\n", + "o16 = openmc.Nuclide('O16')\n", + "u235 = openmc.Nuclide('U235')\n", + "u238 = openmc.Nuclide('U238')\n", + "pu239 = openmc.Nuclide('Pu239')\n", + "zr90 = openmc.Nuclide('Zr90')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the nuclides we defined, we will now create a material for the homogeneous medium." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a Material and register the Nuclides\n", + "inf_medium = openmc.Material(name='moderator')\n", + "inf_medium.set_density('g/cc', 5.)\n", + "inf_medium.add_nuclide(h1, 0.03)\n", + "inf_medium.add_nuclide(o16, 0.015)\n", + "inf_medium.add_nuclide(u235 , 0.0001)\n", + "inf_medium.add_nuclide(u238 , 0.007)\n", + "inf_medium.add_nuclide(pu239, 0.00003)\n", + "inf_medium.add_nuclide(zr90, 0.002)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With our material, we can now create a `Materials` object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a Materials collection and export to XML\n", + "materials_file = openmc.Materials([inf_medium])\n", + "materials_file.default_xs = '71c'\n", + "materials_file.export_to_xml()" + ] + }, + { + "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": 6, + "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": 7, + "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": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate Universe\n", + "root_universe = openmc.Universe(universe_id=0, name='root universe')\n", + "root_universe.add_cell(cell)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now must create a geometry that is assigned a root universe and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create Geometry and set root Universe\n", + "openmc_geometry = openmc.Geometry()\n", + "openmc_geometry.root_universe = root_universe\n", + "\n", + "# Export to \"geometry.xml\"\n", + "openmc_geometry.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": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# OpenMC simulation parameters\n", + "batches = 50\n", + "inactive = 10\n", + "particles = 5000\n", + "\n", + "# Instantiate a Settings object\n", + "settings_file = openmc.Settings()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "settings_file.output = {'tallies': True}\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings_file.source = openmc.source.Source(space=uniform_dist)\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 100-energy-group structure and 1-energy-group structure using the built-in `EnergyGroups` class. We will also create a 6-delayed-group list." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a 100-group EnergyGroups object\n", + "energy_groups = mgxs.EnergyGroups()\n", + "energy_groups.group_edges = np.logspace(-9,1.3,101)\n", + "\n", + "# Instantiate a 1-group EnergyGroups object\n", + "one_group = mgxs.EnergyGroups()\n", + "one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])\n", + "\n", + "delayed_groups = list(range(1,7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use the `EnergyGroups` object and delayed group list, 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", + "* `NuTransportXS`\n", + "* `AbsorptionXS`\n", + "* `CaptureXS`\n", + "* `FissionXS`\n", + "* `NuFissionXS`\n", + "* `KappaFissionXS`\n", + "* `ScatterXS`\n", + "* `NuScatterXS`\n", + "* `ScatterMatrixXS`\n", + "* `NuScatterMatrixXS`\n", + "* `Chi`\n", + "* `ChiPrompt`\n", + "* `InverseVelocity`\n", + "* `PromptNuFissionXS`\n", + "\n", + "A separate abstract `MDGXS` class is used for cross-sections and parameters that involve delayed neutrons. The subclasses of `MDGXS` include:\n", + "\n", + "* `DelayedNuFissionXS`\n", + "* `ChiDelayed`\n", + "* `Beta`\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 chi-prompt, chi-delayed, and prompt-nu-fission cross sections with our 100-energy-group structure and multi-group delayed-nu-fission and beta cross sections with our 100-energy-group and 6-delayed-group structures. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a few different sections\n", + "chi_prompt = mgxs.ChiPrompt(domain=cell, groups=energy_groups, by_nuclide=True)\n", + "prompt_nu_fission = mgxs.PromptNuFissionXS(domain=cell, groups=energy_groups, by_nuclide=True)\n", + "chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n", + "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n", + "beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n", + "\n", + "chi_prompt.nuclides = ['U235', 'Pu239']\n", + "prompt_nu_fission.nuclides = ['U235', 'Pu239']\n", + "chi_delayed.nuclides = ['U235', 'Pu239']\n", + "delayed_nu_fission.nuclides = ['U235', 'Pu239']\n", + "beta.nuclides = ['U235', 'Pu239']" + ] + }, + { + "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 `Beta` object as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "OrderedDict([('nu-fission', Tally\n", + " \tID =\t10000\n", + " \tName =\t\n", + " \tFilters =\t\n", + " \t\tcell\t[1]\n", + " \t\tenergy\t[ 1.00000000e-09 1.26765187e-09 1.60694125e-09 2.03704208e-09\n", + " 2.58226019e-09 3.27340695e-09 4.14954043e-09 5.26017266e-09\n", + " 6.66806769e-09 8.45278845e-09 1.07151931e-08 1.35831345e-08\n", + " 1.72186857e-08 2.18272991e-08 2.76694165e-08 3.50751874e-08\n", + " 4.44631267e-08 5.63637656e-08 7.14496326e-08 9.05732601e-08\n", + " 1.14815362e-07 1.45545908e-07 1.84501542e-07 2.33883724e-07\n", + " 2.96483139e-07 3.75837404e-07 4.76430987e-07 6.03948629e-07\n", + " 7.65596607e-07 9.70509967e-07 1.23026877e-06 1.55955250e-06\n", + " 1.97696964e-06 2.50610925e-06 3.17687407e-06 4.02717034e-06\n", + " 5.10505000e-06 6.47142616e-06 8.20351544e-06 1.03992017e-05\n", + " 1.31825674e-05 1.67109061e-05 2.11836114e-05 2.68534445e-05\n", + " 3.40408190e-05 4.31519077e-05 5.47015963e-05 6.93425806e-05\n", + " 8.79022517e-05 1.11429453e-04 1.41253754e-04 1.79060585e-04\n", + " 2.26986485e-04 2.87739841e-04 3.64753947e-04 4.62381021e-04\n", + " 5.86138165e-04 7.43019138e-04 9.41889597e-04 1.19398810e-03\n", + " 1.51356125e-03 1.91866874e-03 2.43220401e-03 3.08318795e-03\n", + " 3.90840896e-03 4.95450191e-03 6.28058359e-03 7.96159350e-03\n", + " 1.00925289e-02 1.27938130e-02 1.62181010e-02 2.05589060e-02\n", + " 2.60615355e-02 3.30369541e-02 4.18793565e-02 5.30884444e-02\n", + " 6.72976656e-02 8.53100114e-02 1.08143395e-01 1.37088177e-01\n", + " 1.73780083e-01 2.20292646e-01 2.79254384e-01 3.53997341e-01\n", + " 4.48745390e-01 5.68852931e-01 7.21107479e-01 9.14113241e-01\n", + " 1.15877736e+00 1.46892628e+00 1.86208714e+00 2.36047823e+00\n", + " 2.99226464e+00 3.79314985e+00 4.80839348e+00 6.09536897e+00\n", + " 7.72680585e+00 9.79489985e+00 1.24165231e+01 1.57398286e+01\n", + " 1.99526231e+01]\n", + " \tNuclides =\tU235 Pu239 \n", + " \tScores =\t['nu-fission']\n", + " \tEstimator =\ttracklength), ('delayed-nu-fission', Tally\n", + " \tID =\t10001\n", + " \tName =\t\n", + " \tFilters =\t\n", + " \t\tcell\t[1]\n", + " \t\tdelayedgroup\t[1 2 3 4 5 6]\n", + " \t\tenergy\t[ 1.00000000e-09 1.26765187e-09 1.60694125e-09 2.03704208e-09\n", + " 2.58226019e-09 3.27340695e-09 4.14954043e-09 5.26017266e-09\n", + " 6.66806769e-09 8.45278845e-09 1.07151931e-08 1.35831345e-08\n", + " 1.72186857e-08 2.18272991e-08 2.76694165e-08 3.50751874e-08\n", + " 4.44631267e-08 5.63637656e-08 7.14496326e-08 9.05732601e-08\n", + " 1.14815362e-07 1.45545908e-07 1.84501542e-07 2.33883724e-07\n", + " 2.96483139e-07 3.75837404e-07 4.76430987e-07 6.03948629e-07\n", + " 7.65596607e-07 9.70509967e-07 1.23026877e-06 1.55955250e-06\n", + " 1.97696964e-06 2.50610925e-06 3.17687407e-06 4.02717034e-06\n", + " 5.10505000e-06 6.47142616e-06 8.20351544e-06 1.03992017e-05\n", + " 1.31825674e-05 1.67109061e-05 2.11836114e-05 2.68534445e-05\n", + " 3.40408190e-05 4.31519077e-05 5.47015963e-05 6.93425806e-05\n", + " 8.79022517e-05 1.11429453e-04 1.41253754e-04 1.79060585e-04\n", + " 2.26986485e-04 2.87739841e-04 3.64753947e-04 4.62381021e-04\n", + " 5.86138165e-04 7.43019138e-04 9.41889597e-04 1.19398810e-03\n", + " 1.51356125e-03 1.91866874e-03 2.43220401e-03 3.08318795e-03\n", + " 3.90840896e-03 4.95450191e-03 6.28058359e-03 7.96159350e-03\n", + " 1.00925289e-02 1.27938130e-02 1.62181010e-02 2.05589060e-02\n", + " 2.60615355e-02 3.30369541e-02 4.18793565e-02 5.30884444e-02\n", + " 6.72976656e-02 8.53100114e-02 1.08143395e-01 1.37088177e-01\n", + " 1.73780083e-01 2.20292646e-01 2.79254384e-01 3.53997341e-01\n", + " 4.48745390e-01 5.68852931e-01 7.21107479e-01 9.14113241e-01\n", + " 1.15877736e+00 1.46892628e+00 1.86208714e+00 2.36047823e+00\n", + " 2.99226464e+00 3.79314985e+00 4.80839348e+00 6.09536897e+00\n", + " 7.72680585e+00 9.79489985e+00 1.24165231e+01 1.57398286e+01\n", + " 1.99526231e+01]\n", + " \tNuclides =\tU235 Pu239 \n", + " \tScores =\t['delayed-nu-fission']\n", + " \tEstimator =\ttracklength)])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "beta.tallies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Beta` object includes tracklength tallies for the 'nu-fission' and 'delayed-nu-fission' scores in the 100-energy-group and 6-delayed-group structure in cell 1. Now that each `MGXS` and `MDGXS` object contains the tallies that it needs, we must add these tallies to a `Tallies` object to generate the \"tallies.xml\" input file for OpenMC." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate an empty Tallies object\n", + "tallies_file = openmc.Tallies()\n", + "\n", + "# Add chi-prompt tallies to the tallies file\n", + "tallies_file += chi_prompt.tallies.values()\n", + "\n", + "# Add prompt-nu-fission tallies to the tallies file\n", + "tallies_file += prompt_nu_fission.tallies.values()\n", + "\n", + "# Add chi-delayed tallies to the tallies file\n", + "tallies_file += chi_delayed.tallies.values()\n", + "\n", + "# Add delayed-nu-fission tallies to the tallies file\n", + "tallies_file += delayed_nu_fission.tallies.values()\n", + "\n", + "# Add beta tallies to the tallies file\n", + "tallies_file += beta.tallies.values()\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": 15, + "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: 2011-2016 Massachusetts Institute of Technology\n", + " License: http://openmc.readthedocs.io/en/latest/license.html\n", + " Version: 0.8.0\n", + " Git SHA1: c21ceb0aea4abc243b84106576c4f9010f608d0b\n", + " Date/Time: 2016-08-11 08:23:44\n", + " MPI Processes: 1\n", + "\n", + " ===========================================================================\n", + " ========================> INITIALIZATION <=========================\n", + " ===========================================================================\n", + "\n", + " Reading settings XML file...\n", + " Reading geometry XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading H1.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/H1_71c.h5\n", + " Reading O16.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/O16_71c.h5\n", + " Reading U235.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U238_71c.h5\n", + " Reading Pu239.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/Pu239_71c.h5\n", + " Reading Zr90.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/Zr90_71c.h5\n", + " Maximum neutron transport energy: 20.0000 MeV for H1.71c\n", + " Reading tallies XML file...\n", + " Building neighboring cells lists for each surface...\n", + " Initializing source particles...\n", + "\n", + " ===========================================================================\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + " ===========================================================================\n", + "\n", + " Bat./Gen. k Average k \n", + " ========= ======== ==================== \n", + " 1/1 1.21670 \n", + " 2/1 1.24155 \n", + " 3/1 1.21924 \n", + " 4/1 1.22486 \n", + " 5/1 1.21719 \n", + " 6/1 1.24330 \n", + " 7/1 1.22322 \n", + " 8/1 1.24133 \n", + " 9/1 1.21840 \n", + " 10/1 1.25141 \n", + " 11/1 1.21217 \n", + " 12/1 1.25625 1.23421 +/- 0.02204\n", + " 13/1 1.22056 1.22966 +/- 0.01351\n", + " 14/1 1.21757 1.22664 +/- 0.01002\n", + " 15/1 1.24571 1.23045 +/- 0.00865\n", + " 16/1 1.26489 1.23619 +/- 0.00910\n", + " 17/1 1.22323 1.23434 +/- 0.00791\n", + " 18/1 1.26108 1.23768 +/- 0.00762\n", + " 19/1 1.23145 1.23699 +/- 0.00676\n", + " 20/1 1.23548 1.23684 +/- 0.00605\n", + " 21/1 1.20446 1.23390 +/- 0.00621\n", + " 22/1 1.20533 1.23152 +/- 0.00615\n", + " 23/1 1.22520 1.23103 +/- 0.00568\n", + " 24/1 1.18367 1.22765 +/- 0.00625\n", + " 25/1 1.23614 1.22821 +/- 0.00585\n", + " 26/1 1.23746 1.22879 +/- 0.00550\n", + " 27/1 1.23626 1.22923 +/- 0.00518\n", + " 28/1 1.21334 1.22835 +/- 0.00497\n", + " 29/1 1.25169 1.22958 +/- 0.00486\n", + " 30/1 1.25579 1.23089 +/- 0.00479\n", + " 31/1 1.23828 1.23124 +/- 0.00457\n", + " 32/1 1.26911 1.23296 +/- 0.00468\n", + " 33/1 1.20090 1.23157 +/- 0.00469\n", + " 34/1 1.28606 1.23384 +/- 0.00503\n", + " 35/1 1.23129 1.23374 +/- 0.00483\n", + " 36/1 1.22535 1.23341 +/- 0.00465\n", + " 37/1 1.20367 1.23231 +/- 0.00461\n", + " 38/1 1.22886 1.23219 +/- 0.00444\n", + " 39/1 1.24056 1.23248 +/- 0.00429\n", + " 40/1 1.25038 1.23307 +/- 0.00419\n", + " 41/1 1.21504 1.23249 +/- 0.00410\n", + " 42/1 1.20762 1.23171 +/- 0.00404\n", + " 43/1 1.20597 1.23093 +/- 0.00399\n", + " 44/1 1.24424 1.23133 +/- 0.00389\n", + " 45/1 1.24767 1.23179 +/- 0.00381\n", + " 46/1 1.22998 1.23174 +/- 0.00370\n", + " 47/1 1.26352 1.23260 +/- 0.00370\n", + " 48/1 1.23155 1.23257 +/- 0.00360\n", + " 49/1 1.22059 1.23227 +/- 0.00352\n", + " 50/1 1.24724 1.23264 +/- 0.00345\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 6.1600E-01 seconds\n", + " Reading cross sections = 3.6500E-01 seconds\n", + " Total time in simulation = 8.3297E+01 seconds\n", + " Time in transport only = 8.3256E+01 seconds\n", + " Time in inactive batches = 4.4890E+00 seconds\n", + " Time in active batches = 7.8808E+01 seconds\n", + " Time synchronizing fission bank = 1.6000E-02 seconds\n", + " Sampling source sites = 1.1000E-02 seconds\n", + " SEND/RECV source sites = 3.0000E-03 seconds\n", + " Time accumulating tallies = 5.0000E-03 seconds\n", + " Total time for finalization = 8.0000E-02 seconds\n", + " Total time elapsed = 8.4019E+01 seconds\n", + " Calculation Rate (inactive) = 11138.3 neutrons/second\n", + " Calculation Rate (active) = 2537.81 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.23260 +/- 0.00309\n", + " k-effective (Track-length) = 1.23264 +/- 0.00345\n", + " k-effective (Absorption) = 1.23111 +/- 0.00186\n", + " Combined k-effective = 1.23135 +/- 0.00185\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run OpenMC\n", + "openmc.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "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. By default, a `Summary` object is automatically linked when a `StatePoint` is loaded. This is necessary for the `openmc.mgxs` module to properly process the tally data." + ] + }, + { + "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", + "chi_prompt.load_from_statepoint(sp)\n", + "prompt_nu_fission.load_from_statepoint(sp)\n", + "chi_delayed.load_from_statepoint(sp)\n", + "delayed_nu_fission.load_from_statepoint(sp)\n", + "beta.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 delayed-nu-fission section by printing it to the screen after condensing the cross section down to one group." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5.14239169e-06, 1.16429778e-06],\n", + " [ 2.65434434e-05, 7.58244504e-06],\n", + " [ 2.53406770e-05, 5.73814391e-06],\n", + " [ 5.68158884e-05, 1.04761254e-05],\n", + " [ 2.32937121e-05, 5.45676114e-06],\n", + " [ 9.75765501e-06, 1.65156185e-06]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delayed_nu_fission.get_condensed_xs(one_group).get_xs()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the `openmc.mgxs` module uses [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](http://pandas.pydata.org/) `DataFrame` of the multi-group cross section data." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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celldelayedgroupgroup innuclidemeanstd. dev.
198111U2359.533842e-114.789050e-11
199111Pu2391.606499e-118.071081e-12
398121U2351.224131e-096.149449e-10
399121Pu2392.602518e-101.307590e-10
598131U2359.033000e-104.537601e-10
599131Pu2391.522295e-107.648264e-11
798141U2351.749138e-098.786432e-10
799141Pu2392.400317e-101.205943e-10
998151U2352.724017e-101.368376e-10
999151Pu2394.749191e-112.386080e-11
\n", + "
" + ], + "text/plain": [ + " cell delayedgroup group in nuclide mean std. dev.\n", + "198 1 1 1 U235 9.533842e-11 4.789050e-11\n", + "199 1 1 1 Pu239 1.606499e-11 8.071081e-12\n", + "398 1 2 1 U235 1.224131e-09 6.149449e-10\n", + "399 1 2 1 Pu239 2.602518e-10 1.307590e-10\n", + "598 1 3 1 U235 9.033000e-10 4.537601e-10\n", + "599 1 3 1 Pu239 1.522295e-10 7.648264e-11\n", + "798 1 4 1 U235 1.749138e-09 8.786432e-10\n", + "799 1 4 1 Pu239 2.400317e-10 1.205943e-10\n", + "998 1 5 1 U235 2.724017e-10 1.368376e-10\n", + "999 1 5 1 Pu239 4.749191e-11 2.386080e-11" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = delayed_nu_fission.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": false + }, + "outputs": [], + "source": [ + "beta.export_xs_data(filename='beta', format='excel')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following code snippet shows how to export the chi-prompt and chi-delayed `MGXS` to the same HDF5 binary data store." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "chi_prompt.build_hdf5_store(filename='mdgxs', append=True)\n", + "chi_delayed.build_hdf5_store(filename='mdgxs', append=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Tally Arithmetic to Compute the Delayed Neutron Precursor Concentrations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n", + "\n", + "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n", + "\n", + "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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celldelayedgroupnuclidescoremeanstd. dev.
011(U235 / total)(((delayed-nu-fission / nu-fission) * (delayed...9.391610e-082.566220e-10
111(Pu239 / total)(((delayed-nu-fission / nu-fission) * (delayed...7.611347e-092.278727e-11
212(U235 / total)(((delayed-nu-fission / nu-fission) * (delayed...1.102594e-063.012794e-09
312(Pu239 / total)(((delayed-nu-fission / nu-fission) * (delayed...1.422470e-074.258670e-10
413(U235 / total)(((delayed-nu-fission / nu-fission) * (delayed...6.685886e-071.826892e-09
513(Pu239 / total)(((delayed-nu-fission / nu-fission) * (delayed...5.419872e-081.622631e-10
614(U235 / total)(((delayed-nu-fission / nu-fission) * (delayed...4.886781e-071.335293e-09
714(Pu239 / total)(((delayed-nu-fission / nu-fission) * (delayed...2.626687e-087.863920e-11
815(U235 / total)(((delayed-nu-fission / nu-fission) * (delayed...1.469529e-084.015430e-11
915(Pu239 / total)(((delayed-nu-fission / nu-fission) * (delayed...1.274953e-093.817026e-12
1016(U235 / total)(((delayed-nu-fission / nu-fission) * (delayed...1.185934e-093.240517e-12
1116(Pu239 / total)(((delayed-nu-fission / nu-fission) * (delayed...5.371343e-111.608102e-13
\n", + "
" + ], + "text/plain": [ + " cell delayedgroup nuclide \\\n", + "0 1 1 (U235 / total) \n", + "1 1 1 (Pu239 / total) \n", + "2 1 2 (U235 / total) \n", + "3 1 2 (Pu239 / total) \n", + "4 1 3 (U235 / total) \n", + "5 1 3 (Pu239 / total) \n", + "6 1 4 (U235 / total) \n", + "7 1 4 (Pu239 / total) \n", + "8 1 5 (U235 / total) \n", + "9 1 5 (Pu239 / total) \n", + "10 1 6 (U235 / total) \n", + "11 1 6 (Pu239 / total) \n", + "\n", + " score mean std. dev. \n", + "0 (((delayed-nu-fission / nu-fission) * (delayed... 9.39e-08 2.57e-10 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.61e-09 2.28e-11 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 1.10e-06 3.01e-09 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.42e-07 4.26e-10 \n", + "4 (((delayed-nu-fission / nu-fission) * (delayed... 6.69e-07 1.83e-09 \n", + "5 (((delayed-nu-fission / nu-fission) * (delayed... 5.42e-08 1.62e-10 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.89e-07 1.34e-09 \n", + "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 7.86e-11 \n", + "8 (((delayed-nu-fission / nu-fission) * (delayed... 1.47e-08 4.02e-11 \n", + "9 (((delayed-nu-fission / nu-fission) * (delayed... 1.27e-09 3.82e-12 \n", + "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.19e-09 3.24e-12 \n", + "11 (((delayed-nu-fission / nu-fission) * (delayed... 5.37e-11 1.61e-13 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set the time constants for the delayed precursors (in seconds^-1) using some ficticious time constant data.\n", + "precursor_halflife = np.array([55.6, 24.5, 16.3, 2.37, 0.424, 0.195])\n", + "precursor_lambda = -np.log(0.5) / precursor_halflife\n", + "\n", + "# Create a tally object with only the delayed group filter for the time constants\n", + "beta_filters = [f for f in beta.xs_tally.filters if f.type != 'delayedgroup']\n", + "lambda_tally = beta.get_condensed_xs(one_group).xs_tally.summation(nuclides=beta.xs_tally.nuclides)\n", + "for f in beta_filters:\n", + " lambda_tally = lambda_tally.summation(filter_type=f.type, remove_filter=True) * 0. + 1.\n", + "\n", + "# Set the mean of the lambda tally and reshape to account for nuclides and scores\n", + "lambda_tally._mean = precursor_lambda\n", + "lambda_tally._mean.shape = lambda_tally.std_dev.shape\n", + "\n", + "# Set a total nuclide and lambda score\n", + "lambda_tally.nuclides = [openmc.Nuclide(name='total')]\n", + "lambda_tally.scores = ['lambda']\n", + "\n", + "# Use tally arithmetic to compute the precursor concentrations\n", + "precursor_conc = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True) * \\\n", + " delayed_nu_fission.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True) / lambda_tally\n", + " \n", + "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n", + "precursor_conc.get_pandas_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the delayed neutron fractions for each nuclide." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beta (U-235) : 0.006504 +/- 0.000007\n", + "Beta (Pu-239): 0.002245 +/- 0.000002\n" + ] + }, + { + "data": { + "text/plain": [ + "(0, 7)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "energy_filter = [f for f in beta.xs_tally.filters if f.type == 'energy']\n", + "beta_integrated = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True)\n", + "beta_u235 = beta_integrated.get_values(nuclides=['U235'])\n", + "beta_pu239 = beta_integrated.get_values(nuclides=['Pu239'])\n", + "\n", + "# Reshape the betas\n", + "beta_u235.shape = (beta_u235.shape[0])\n", + "beta_pu239.shape = (beta_pu239.shape[0])\n", + "\n", + "df = beta_integrated.summation(filter_type='delayedgroup', remove_filter=True).get_pandas_dataframe()\n", + "print('Beta (U-235) : {:.6f} +/- {:.6f}'.format(df[df['nuclide'] == 'U235']['mean'][0], df[df['nuclide'] == 'U235']['std. dev.'][0]))\n", + "print('Beta (Pu-239): {:.6f} +/- {:.6f}'.format(df[df['nuclide'] == 'Pu239']['mean'][1], df[df['nuclide'] == 'Pu239']['std. dev.'][1]))\n", + "\n", + "beta_u235 = np.append(beta_u235[0], beta_u235)\n", + "beta_pu239 = np.append(beta_pu239[0], beta_pu239)\n", + "\n", + "# Create a step plot for the MGXS\n", + "plt.plot(np.arange(0.5, 7.5, 1), beta_u235, drawstyle='steps', color='b', linewidth=3)\n", + "plt.plot(np.arange(0.5, 7.5, 1), beta_pu239, drawstyle='steps', color='g', linewidth=3)\n", + "\n", + "plt.title('Delayed Neutron Fraction (beta)')\n", + "plt.xlabel('Delayed Group')\n", + "plt.ylabel('Beta(fraction total neutrons)')\n", + "plt.legend(['U-235', 'Pu-239'])\n", + "plt.xlim([0,7])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot the energy spectrum for fission emission of prompt and delayed neutrons." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.001, 20)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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oXeI190z1eA2RhpoMtUO1Jp9TEdNjMMRDpr4n7mUoNbk9KT1YgJYk5vn46/Nw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\nPR88eVMZRU2Lwvr6ce/7Tevc8QQAniyz8FnhfQV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "chi_d_u235 = np.squeeze(chi_delayed.get_xs(nuclides=['U235'], order_groups='decreasing'))\n", + "chi_d_pu239 = np.squeeze(chi_delayed.get_xs(nuclides=['Pu239'], order_groups='decreasing'))\n", + "chi_p_u235 = np.squeeze(chi_prompt.get_xs(nuclides=['U235'], order_groups='decreasing'))\n", + "chi_p_pu239 = np.squeeze(chi_prompt.get_xs(nuclides=['Pu239'], order_groups='decreasing'))\n", + "\n", + "chi_d_u235 = np.append(chi_d_u235 , chi_d_u235[0])\n", + "chi_d_pu239 = np.append(chi_d_pu239, chi_d_pu239[0])\n", + "chi_p_u235 = np.append(chi_p_u235 , chi_p_u235[0])\n", + "chi_p_pu239 = np.append(chi_p_pu239, chi_p_pu239[0])\n", + "\n", + "# Create a step plot for the MGXS\n", + "plt.semilogx(energy_groups.group_edges, chi_d_u235 , drawstyle='steps', color='b', linestyle='--', linewidth=3)\n", + "plt.semilogx(energy_groups.group_edges, chi_d_pu239, drawstyle='steps', color='g', linestyle='--', linewidth=3)\n", + "plt.semilogx(energy_groups.group_edges, chi_p_u235 , drawstyle='steps', color='b', linestyle=':', linewidth=3)\n", + "plt.semilogx(energy_groups.group_edges, chi_p_pu239, drawstyle='steps', color='g', linestyle=':', linewidth=3)\n", + "\n", + "plt.title('Energy Spectrum for Fission Neutrons')\n", + "plt.xlabel('Energy (MeV)')\n", + "plt.ylabel('Fraction on emitted neutrons')\n", + "plt.legend(['U-235 delayed', 'Pu-239 delayed', 'U-235 prompt', 'Pu-239 prompt'],loc=2)\n", + "plt.xlim(0.001,20)" + ] + }, + { + "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": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/source/pythonapi/examples/mdgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mdgxs-part-ii.ipynb new file mode 100644 index 0000000000..ee652bc1f4 --- /dev/null +++ b/docs/source/pythonapi/examples/mdgxs-part-ii.ipynb @@ -0,0 +1,1328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This IPython Notebook illustrates the use of the **`openmc.mgxs.Library`** class. The `Library` class is designed to 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-energy-group and multi-delayed-group cross sections for a **fuel assembly**\n", + "* Automated creation, manipulation and storage of `MGXS` with **`openmc.mgxs.Library`**\n", + "* Steady-state pin-by-pin **delayed neutron fractions (beta)** for each delayed group.\n", + "* Generation of surface currents on the interfaces and surfaces of a Mesh." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Input Files" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/__init__.py:1357: 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 math\n", + "import pickle\n", + "\n", + "from IPython.display import Image\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import openmc\n", + "import openmc.mgxs\n", + "import openmoc\n", + "import openmoc.process\n", + "from openmoc.opencg_compatible import get_openmoc_geometry\n", + "from openmoc.materialize import load_openmc_mgxs_lib\n", + "\n", + "%matplotlib inline" + ] + }, + { + "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('H1')\n", + "b10 = openmc.Nuclide('B10')\n", + "o16 = openmc.Nuclide('O16')\n", + "u235 = openmc.Nuclide('U235')\n", + "u238 = openmc.Nuclide('U238')\n", + "zr90 = openmc.Nuclide('Zr90')" + ] + }, + { + "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` object that can be exported to an actual XML file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Instantiate a Materials object\n", + "materials_file = openmc.Materials((fuel, water, 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": false + }, + "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": false + }, + "outputs": [], + "source": [ + "# Create a Universe to encapsulate a control rod guide tube\n", + "guide_tube_universe = openmc.Universe(name='Guide Tube')\n", + "\n", + "# Create guide tube 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 1.26 cm pitch." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create fuel assembly Lattice\n", + "assembly = openmc.RectLattice(name='1.6% Fuel Assembly')\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": false + }, + "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": false + }, + "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 and export it to XML." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "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": false + }, + "outputs": [], + "source": [ + "# Export to \"geometry.xml\"\n", + "geometry.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 Settings object\n", + "settings_file = openmc.Settings()\n", + "settings_file.batches = batches\n", + "settings_file.inactive = inactive\n", + "settings_file.particles = particles\n", + "settings_file.output = {'tallies': False}\n", + "\n", + "# Create an initial uniform spatial source distribution over fissionable zones\n", + "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n", + "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", + "settings_file.source = openmc.source.Source(space=uniform_dist)\n", + "\n", + "# Export to \"settings.xml\"\n", + "settings_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us also create a `Plots` file that we can use to verify that our fuel assembly geometry was created successfully." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a Plot\n", + "plot = openmc.Plot(plot_id=1)\n", + "plot.filename = 'materials-xy'\n", + "plot.origin = [0, 0, 0]\n", + "plot.pixels = [250, 250]\n", + "plot.width = [-10.71*2, -10.71*2]\n", + "plot.color = 'mat'\n", + "\n", + "# Instantiate a Plots object, add Plot, and export to \"plots.xml\"\n", + "plot_file = openmc.Plots([plot])\n", + "plot_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run openmc in plotting mode\n", + "openmc.plot_geometry(output=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVDE2OjQ1OjQ5LTA0OjAwh7C73wAAAABJRU5ErkJggg==\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 fuel and guide tube pin cells with fuel, cladding, and water!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create an MGXS Library" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to generate multi-group cross sections! First, let's define 20-energy-group, 1-energy-group, and 6-delayed-group structures." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Instantiate a 20-group EnergyGroups object\n", + "energy_groups = openmc.mgxs.EnergyGroups()\n", + "energy_groups.group_edges = np.logspace(-9,1.3,21)\n", + "\n", + "# Instantiate a 1-group EnergyGroups object\n", + "one_group = openmc.mgxs.EnergyGroups()\n", + "one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])\n", + "\n", + "# Instantiate a 6-delayed-group list\n", + "delayed_groups = list(range(1,7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will instantiate an `openmc.mgxs.Library` for the energy and delayed groups with our the fuel assembly geometry." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "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, 1]\n", + "mesh.lower_left = [-10.71, -10.71, -10000.]\n", + "mesh.width = [1.26, 1.26, 20000.]\n", + "\n", + "# Initialize an 20-energy-group and 6-delayed-group MGXS Library\n", + "mgxs_lib = openmc.mgxs.Library(geometry)\n", + "mgxs_lib.energy_groups = energy_groups\n", + "mgxs_lib.delayed_groups = delayed_groups\n", + "\n", + "# Specify multi-group cross section types to compute\n", + "mgxs_lib.mgxs_types = ['total', 'transport', 'nu-scatter matrix', 'kappa-fission', 'inverse-velocity', 'chi-prompt',\n", + " 'prompt-nu-fission', 'chi-delayed', 'delayed-nu-fission', 'beta']\n", + "\n", + "# Specify a \"mesh\" domain type for the cross section tally filters\n", + "mgxs_lib.domain_type = 'mesh'\n", + "\n", + "# Specify the mesh domain over which to compute multi-group cross sections\n", + "mgxs_lib.domains = [mesh]\n", + "\n", + "# Construct all tallies needed for the multi-group cross section library\n", + "mgxs_lib.build_library()\n", + "\n", + "# Create a \"tallies.xml\" file for the MGXS Library\n", + "tallies_file = openmc.Tallies()\n", + "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)\n", + "\n", + "# Instantiate a current tally\n", + "mesh_filter = openmc.Filter()\n", + "mesh_filter.mesh = mesh\n", + "current_tally = openmc.Tally(name='current tally')\n", + "current_tally.scores = ['current']\n", + "current_tally.filters = [mesh_filter]\n", + "\n", + "# Add current tally to the tallies file\n", + "tallies_file.append(current_tally)\n", + "\n", + "# Export to \"tallies.xml\"\n", + "tallies_file.export_to_xml()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can run OpenMC to generate the cross sections." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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: 2011-2016 Massachusetts Institute of Technology\n", + " License: http://openmc.readthedocs.io/en/latest/license.html\n", + " Version: 0.8.0\n", + " Git SHA1: 2636be6779b4c821dc6fb7a49de391fecb471358\n", + " Date/Time: 2016-08-16 16:45:50\n", + " MPI Processes: 1\n", + "\n", + " ===========================================================================\n", + " ========================> INITIALIZATION <=========================\n", + " ===========================================================================\n", + "\n", + " Reading settings XML file...\n", + " Reading geometry XML file...\n", + " Reading cross sections XML file...\n", + " Reading materials XML file...\n", + " Reading U235.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U238_71c.h5\n", + " Reading O16.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/O16_71c.h5\n", + " Reading H1.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/H1_71c.h5\n", + " Reading B10.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/B10_71c.h5\n", + " Reading Zr90.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/Zr90_71c.h5\n", + " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n", + " Reading tallies XML file...\n", + " WARNING: A delayedgroup filter was used on a total nuclide tally. Cross section\n", + " libraries are not guaranteed to have the same delayed group structure\n", + " across all isotopes. In particular, ENDF/B-VII.1 does not have a\n", + " consistent delayed group structure across all isotopes while the JEFF\n", + " 3.1.1 library has the same delayed group structure across all\n", + " isotopes. Use with caution!\n", + " WARNING: A delayedgroup filter was used on a total nuclide tally. Cross section\n", + " libraries are not guaranteed to have the same delayed group structure\n", + " across all isotopes. In particular, ENDF/B-VII.1 does not have a\n", + " consistent delayed group structure across all isotopes while the JEFF\n", + " 3.1.1 library has the same delayed group structure across all\n", + " isotopes. Use with caution!\n", + " WARNING: A delayedgroup filter was used on a total nuclide tally. Cross section\n", + " libraries are not guaranteed to have the same delayed group structure\n", + " across all isotopes. In particular, ENDF/B-VII.1 does not have a\n", + " consistent delayed group structure across all isotopes while the JEFF\n", + " 3.1.1 library has the same delayed group structure across all\n", + " isotopes. Use with caution!\n", + " Building neighboring cells lists for each surface...\n", + " Initializing source particles...\n", + "\n", + " ===========================================================================\n", + " ====================> K EIGENVALUE SIMULATION <====================\n", + " ===========================================================================\n", + "\n", + " Bat./Gen. k Average k \n", + " ========= ======== ==================== \n", + " 1/1 1.03852 \n", + " 2/1 0.99743 \n", + " 3/1 1.02987 \n", + " 4/1 1.04472 \n", + " 5/1 1.02183 \n", + " 6/1 1.05263 \n", + " 7/1 0.99048 \n", + " 8/1 1.02753 \n", + " 9/1 1.03159 \n", + " 10/1 1.04005 \n", + " 11/1 1.05278 \n", + " 12/1 1.02555 1.03917 +/- 0.01362\n", + " 13/1 0.99400 1.02411 +/- 0.01699\n", + " 14/1 1.03508 1.02685 +/- 0.01232\n", + " 15/1 1.00055 1.02159 +/- 0.01090\n", + " 16/1 1.01334 1.02022 +/- 0.00900\n", + " 17/1 0.99822 1.01707 +/- 0.00823\n", + " 18/1 1.01767 1.01715 +/- 0.00713\n", + " 19/1 1.05052 1.02086 +/- 0.00730\n", + " 20/1 1.03133 1.02190 +/- 0.00661\n", + " 21/1 1.04112 1.02365 +/- 0.00623\n", + " 22/1 1.04175 1.02516 +/- 0.00588\n", + " 23/1 1.01909 1.02469 +/- 0.00543\n", + " 24/1 1.07119 1.02801 +/- 0.00603\n", + " 25/1 0.97414 1.02442 +/- 0.00666\n", + " 26/1 1.04709 1.02584 +/- 0.00639\n", + " 27/1 1.05872 1.02777 +/- 0.00631\n", + " 28/1 1.03930 1.02841 +/- 0.00598\n", + " 29/1 1.01488 1.02770 +/- 0.00570\n", + " 30/1 1.04513 1.02857 +/- 0.00548\n", + " 31/1 0.99538 1.02699 +/- 0.00545\n", + " 32/1 1.00106 1.02581 +/- 0.00532\n", + " 33/1 0.99389 1.02442 +/- 0.00527\n", + " 34/1 0.99938 1.02338 +/- 0.00516\n", + " 35/1 1.02161 1.02331 +/- 0.00495\n", + " 36/1 1.04084 1.02398 +/- 0.00480\n", + " 37/1 0.98801 1.02265 +/- 0.00481\n", + " 38/1 1.01348 1.02232 +/- 0.00464\n", + " 39/1 1.06693 1.02386 +/- 0.00474\n", + " 40/1 1.07729 1.02564 +/- 0.00491\n", + " 41/1 1.03191 1.02585 +/- 0.00475\n", + " 42/1 1.05209 1.02667 +/- 0.00468\n", + " 43/1 1.02997 1.02677 +/- 0.00453\n", + " 44/1 1.07288 1.02812 +/- 0.00460\n", + " 45/1 1.01268 1.02768 +/- 0.00449\n", + " 46/1 1.03759 1.02796 +/- 0.00437\n", + " 47/1 1.02620 1.02791 +/- 0.00425\n", + " 48/1 1.02509 1.02783 +/- 0.00414\n", + " 49/1 1.01043 1.02739 +/- 0.00406\n", + " 50/1 1.01457 1.02707 +/- 0.00397\n", + " Creating state point statepoint.50.h5...\n", + "\n", + " ===========================================================================\n", + " ======================> SIMULATION FINISHED <======================\n", + " ===========================================================================\n", + "\n", + "\n", + " =======================> TIMING STATISTICS <=======================\n", + "\n", + " Total time for initialization = 4.3700E-01 seconds\n", + " Reading cross sections = 2.3700E-01 seconds\n", + " Total time in simulation = 6.7426E+01 seconds\n", + " Time in transport only = 6.7172E+01 seconds\n", + " Time in inactive batches = 4.8900E+00 seconds\n", + " Time in active batches = 6.2536E+01 seconds\n", + " Time synchronizing fission bank = 7.0000E-03 seconds\n", + " Sampling source sites = 7.0000E-03 seconds\n", + " SEND/RECV source sites = 0.0000E+00 seconds\n", + " Time accumulating tallies = 2.0100E-01 seconds\n", + " Total time for finalization = 6.0000E-03 seconds\n", + " Total time elapsed = 6.7893E+01 seconds\n", + " Calculation Rate (inactive) = 5112.47 neutrons/second\n", + " Calculation Rate (active) = 1599.08 neutrons/second\n", + "\n", + " ============================> RESULTS <============================\n", + "\n", + " k-effective (Collision) = 1.02489 +/- 0.00308\n", + " k-effective (Track-length) = 1.02707 +/- 0.00397\n", + " k-effective (Absorption) = 1.02637 +/- 0.00325\n", + " Combined k-effective = 1.02581 +/- 0.00264\n", + " Leakage Fraction = 0.00000 +/- 0.00000\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run OpenMC\n", + "openmc.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tally Data Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the last statepoint file\n", + "sp = openmc.StatePoint('statepoint.50.h5')" + ] + }, + { + "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": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize MGXS Library with OpenMC statepoint data\n", + "mgxs_lib.load_from_statepoint(sp)\n", + "\n", + "# Extrack the current tally separately\n", + "current_tally = sp.get_tally(name='current tally')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Tally Arithmetic to Compute the Delayed Neutron Precursor Concentrations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n", + "\n", + "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n", + "\n", + "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " mesh 1 delayedgroup nuclide \\\n", + " x y z \n", + "0 1 1 1 1 total \n", + "1 1 1 1 2 total \n", + "2 1 1 1 3 total \n", + "3 1 1 1 4 total \n", + "4 1 1 1 5 total \n", + "5 1 1 1 6 total \n", + "6 1 2 1 1 total \n", + "7 1 2 1 2 total \n", + "8 1 2 1 3 total \n", + "9 1 2 1 4 total \n", + "\n", + " score mean std. dev. \n", + " \n", + "0 (((delayed-nu-fission / nu-fission) * (delayed... 0.003381 0.000837 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 0.000960 0.000074 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 0.006946 0.000771 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 0.074721 0.005119 \n", + "4 (((delayed-nu-fission / nu-fission) * (delayed... 0.034106 0.002235 \n", + "5 (((delayed-nu-fission / nu-fission) * (delayed... 0.002500 0.000358 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 0.011466 0.004394 \n", + "7 (((delayed-nu-fission / nu-fission) * (delayed... 0.000960 0.000081 \n", + "8 (((delayed-nu-fission / nu-fission) * (delayed... 0.007407 0.000779 \n", + "9 (((delayed-nu-fission / nu-fission) * (delayed... 0.083327 0.005229 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set the time constants for the delayed precursors (in seconds^-1)\n", + "precursor_halflife = np.array([55.6, 24.5, 16.3, 2.37, 0.424, 0.195])\n", + "precursor_lambda = -np.log(0.5) / precursor_halflife\n", + "\n", + "beta = mgxs_lib.get_mgxs(mesh, 'beta')\n", + "\n", + "# Create a tally object with only the delayed group filter for the time constants\n", + "beta_filters = [f for f in beta.xs_tally.filters if f.type != 'delayedgroup']\n", + "lambda_tally = beta.xs_tally.summation(nuclides=beta.xs_tally.nuclides)\n", + "for f in beta_filters:\n", + " lambda_tally = lambda_tally.summation(filter_type=f.type, remove_filter=True) * 0. + 1.\n", + "\n", + "# Set the mean of the lambda tally and reshape to account for nuclides and scores\n", + "lambda_tally._mean = precursor_lambda\n", + "lambda_tally._mean.shape = lambda_tally.std_dev.shape\n", + "\n", + "# Set a total nuclide and lambda score\n", + "lambda_tally.nuclides = [openmc.Nuclide(name='total')]\n", + "lambda_tally.scores = ['lambda']\n", + "\n", + "delayed_nu_fission = mgxs_lib.get_mgxs(mesh, 'delayed-nu-fission')\n", + "\n", + "# Use tally arithmetic to compute the precursor concentrations\n", + "precursor_conc = beta.xs_tally.summation(filter_type='energy', remove_filter=True) * \\\n", + " delayed_nu_fission.xs_tally.summation(filter_type='energy', remove_filter=True) / lambda_tally\n", + " \n", + "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n", + "precursor_conc.get_pandas_dataframe().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another useful feature of the Python API is the ability to extract the surface currents for the interfaces and surfaces of a mesh. We can inspect the currents for the mesh by getting the pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " mesh 1 surface nuclide score mean std. dev.\n", + " x y z \n", + "0 1 1 1 x-min total current 0.00000 0.000000\n", + "1 1 1 1 x-max total current 0.02986 0.000678\n", + "2 1 1 1 y-min total current 0.00000 0.000000\n", + "3 1 1 1 y-max total current 0.03091 0.000636\n", + "4 1 1 1 z-min total current 0.00000 0.000000\n", + "5 1 1 1 z-max total current 0.00000 0.000000\n", + "6 1 2 1 x-min total current 0.03039 0.000670\n", + "7 1 2 1 x-max total current 0.03067 0.000567\n", + "8 1 2 1 y-min total current 0.00000 0.000000\n", + "9 1 2 1 y-max total current 0.03082 0.000617" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_tally.get_pandas_dataframe().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cross Section Visualizations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to inspecting the data in the tallies by getting the pandas dataframe, we can also plot the tally data on the domain mesh. Below is the delayed neutron fraction tallied in each mesh cell for each delayed group." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RcXQTn9PMrPf4nrgwz+FgZsV5ghwzs9ZqctLIfE6GrvXfnyCbBHKapHGS9s6r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O0R8Xx6eTspzyNiaUTMSc9fAOZTLutX438ZmFlhHi5hZtZeA+XwbT2vcntP0wnK6t1VEAX3KVJb\nv1HpTGB5RFyeNj0BjImIZyXtCfxX+uVuJNmvdr+NiFMkfQk4D/h0kXbMzIZDi9fE9Tp+xzXaJyJ6\nJeU7fvNL6fV1/KrJMc+W9FXgV8DpqVMi7+9ZvUMYAEnbAXsAdxT5YPW4w8HMCnuV1pZzS7drXUDW\nyzolIr5R8/4o4MfAXsBTwJER8Vh67wyyX8dWACdFxExJo9P+WwG9wCUR8Z20/3Rgx3ToTYFnI2JP\nSSOBfwf2BLqAaRHx9ZY+mJnZMBkoh/fqHsVe3ateXzD5pXq7LQHG5F6PJvvHf95iYBvgCUldwCap\nY2BJ2j5Q7RokHQN8mFW/0JEudp9Nz++R9BCwY3r+YkT8V9r1Z2TZb2bWMVq8Jh6Kjt96Ixf6jnl6\nRCyTtB7ZsInTgLP7G5L2B44F3rvaCWTDKWaQXXe/UOf4hbjDwcwK68Dxag3HmEXExFzb3wKeSy8P\nB0ZFxO6SNgDmSbq8r2PDzKyTDcIcDrOBHdJ8C38EJgJH1exzHXAM2S9ahwM3p+3XApdJ+jbZL2o7\nAHfm6kTNxXDqaD6VbJzwK7ntW5Dl/UpJb0nHerivfUn7R8SvgQ8A81r7yGZmg6tRFt/Z8zJ39rzc\nrHwoOn7V6JgRsSz9uVzSVOCUvp3SBJI/BA6KiGdz20eSdTZMi4hrmn2ggbjDwcwKa3F+hkGfqCwi\n7gCWQjbGTFLfGLPaSW2OAPZPzwN4fQrvDYFXgD+18sHMzIZLq/PkpFtzTyRbPaLvbrP5kiYDsyPi\nemAKMC1l7dNknRJExDxJV5B1ACwHToiIAJB0OdANbC7pMWBSREwly/FRwE1pQYvb04oU+wJnSVpO\ndofaZyOir2P49NT+t4EnyX55MzPrGI2yeK/ujdire6P+1/82+fl6uw1Fx++IRseUtFVELE2rCn0M\neCBtHwNcCXwqIh6qaf9SYF5EXDjgF1GAOxzMrLAOHK/Wr9EYM0nvA5bmgnQGWcfGH4ENgC/lLnLN\nzDraYMylExE3klsFIm2blHv+CllHbb3ac4Bz6mw/usH+Yxtsvwq4qsF7jwH7NTh9M7O2ayWLh6jj\nt+4xU5OXpbvKBMwBPpe2fxXYjFVLHC+PiHGS3gN8Epgr6V6yH+u+kv7uKM0dDmZWWKNwvavnRe7q\nqTtWOG/IJiprMsbsKOAnudfjyIZibAVsDvxW0qw006+ZWUfz5L1mZu3XahYPUcfvGsdM28c3OM4/\nAP9QZ/utMHh/2bjDwcwKazRebY/ujdmje+P+1z+c/FS93YZkorKBxpilYxxKNkFkn6OBGyNiJfCk\npFuBvwUW1f1wZmYdZBDmcDAzsxY5i4sblg6HP2jH5jvlPBF/Xamd8087s3zRibUrghRzwHHXVqqr\n4qon/650zT++qfxwm2euqLa86pZHP1K65puf/Wqltv7lB6c036nGLXpv853quIu9S9ccctj00jU7\namHpGoA/brpphapnm+8ygBbHDg/VRGUDjTE7AJgfEfmOjcfIZkq/TNLrgXcC327lg60N5uqtpWv+\nHG8oXfN/Tvlm6RqA9Sc9U7rmI4fOqNRWFd9bdHLpmlO2O7v5TnU8+eMxzXeqseWny+cwwKTTzi1d\nc/43/qlSWz0qf4f8Pav1FRbzkcOq/XfxRpUfWfXHjbcoXbM+GzffaQCtzuFg7VU2i6vkMFTL4io5\nDNWyuIsVpWt+uOSk0jUAJ41e44fgpqrkMFTL4kmnlM9hgPPPK5/Fw5XDUC2LN9PTldqqksXZYmjV\nOYuL8zdlZoV12ni1AmPMjmT14RQA/wZMlfRAej0lIh7AzGwt4CEVZmbt5ywuzh0OZlZYi2sOD/p4\ntWZjzCJijZnNI+LFRm2YmXW6VnPYzMxa5ywuzh0OZlaYx6uZmbWXc9jMrP2cxcW5w8HMCvN4NTOz\n9nIOm5m1n7O4OH9TZlaYx6uZmbWXc9jMrP2cxcW5w8HMCnO4mpm1l3PYzKz9nMXFucPBzArzeDUz\ns/ZyDpuZtZ+zuDh3OJhZYR6vZmbWXs5hM7P2cxYX52/KzArz7WNmZu3lHDYzaz9ncXHucDCzwl7x\nmsNmZm3lHDYzaz9ncXHucDCzwnz7mJlZezmHzczaz1lcnL8pMyvMt4+ZmbWXc9jMrP2cxcW5w8HM\nCnO4mpm1l3PYzKz9nMXFDUuHww8fPaHU/ieO+XaldlaeW76ma9nySm3Nuu+jpWt6365KbW04qnxb\nr1y4aemalSeVLgFgxKTtS9cc+P1rKrV1BueXrvlgXFupraseOrp0Te/fjChdsxXvLl0DcIIurlD1\nzUpt9XG4rr1+8PTnStf882b/Vrpm5XmlSwB4My+WrrnhwcMqtdW7W/ks3vSvPl665k9TtixdA7Dy\n+PI1VXIY4OCv/6x0zUl8v1Jbs2JG6Zornj6idM3Lm29SugZga/YsXfNFXVC6Znt2Acp/730GI4cl\nHQRcAIwApkTEN2reHwX8GNgLeAo4MiIeS++dARwHrABOioiZafsU4GBgWUTsnjvWucBHgVeAh4Bj\nI+JPuffHAA8CkyLi/LTtS8DxwEpgbqp5teUP3gHKZnGVHIZqWVwlh6FaFlfK4TeVz2GolsVVchiq\nZfHB36qWB1WyeLhyGKplcZUcBvi8LqpQdWGltvq0msVDlMN1jylpKrAf8DwQwGci4n5JOwFTgT2B\nr/RlcKo5Cfj79PKSiPhO1c9a/l9HZrbOWkFXoYeZmQ2NojncKIsljQAuAj4I7AYcJWnnmt2OB56J\niLFkF6/nptpdgSOAXYAPARdL6vuX49R0zFozgd0iYg9gIXBGzfvnAzfkzu+vgc8De6aOi5HAxAJf\njZnZsOm0HC5wzFMi4h0RsWdE3J+2PU2Wt6v9Iilpt9T+3wJ7AB+V9DcVvibAHQ5mVkIvIws9zMxs\naBTN4QGyeBywMCIejYjlwHRgQs0+E4AfpeczgPen54cA0yNiRUQsIutAGAcQEbcAz9Y2FhGzImJl\nenk7MLrvPUkTyO56eLCmrAt4vaSRwIbAEwN+KWZmw6wDc7jZMdf4d39EPBURd5PdKZG3C3B7RLwS\nEb3Ab4BqtxjVa9jMrJFeugo9zMxsaBTN4QGyeGtgce71krSt7j7pYvN5SZvVqX28Tu1AjgN+ASBp\nQ+BUYDLQf399RDwBnAc8lo7/XETMKtGGmdmQ68AcbnbMsyXNkXSepPWafLwHgH0lbZqy+sPANk1q\nGvJPkWZWmNccNjNrr4Fy+JGexTzSs7jh+0m9wfNRcJ8itfUblc4ElkfE5WnTZODbEfFSGpWhtN8b\nyX6V25ZsvPEMSUfn6szM2q5RFrcxh+vdSNB3zNMjYlnqaLgEOA04u9HJRcQCSd8AZgF/Buaw5l0Q\nhbnDwcwK83AJM7P2GiiHx3Rvz5juVZPW/XrybfV2WwKMyb0ezZpDFhaT/Zr1hKQuYJOIeFbSElb/\nlate7RokHUP2C9n7c5v3AQ5Lk0puCvRK+gvwv8DDEfFMqr0KeDfgDgcz6xiNsriNOaxGx4yIZenP\n5WkCyVOafDwiYirZ3DxI+hdWv3uiFA+pMLPCWh1SIekgSQsk/UHSaXXeHyVpuqSFkm5Ls5f3vXdG\n2j5f0oFp22hJN0uaJ2mupC/k9p8u6Z70eETSPWn70ZLuTdvvldQraffaczEz60SDMKRiNrCDpG3T\nLOgTgdrlnK4DjknPDwduTs+vBSamrN4e2AG4M1cnan59S7OmnwocEhGv9G2PiH0j4i0R8RayCdH+\nNSIuJhtK8U5J66cJKccD80t8RWZmQ64Dc7jhMSVtlf4U8DGyIRO1arP7TenPMWTzN/ykyPdSj3+u\nNLPCWpmfITd77niyHtfZkq6JiAW53fpn5JV0JNmMvBNrZuQdDcySNJbs9q6TI2KOpI2AuyXNjIgF\nETEx1/a3gOcA0m25l6ftbwX+Kzdbr5lZR2t1npyI6JV0ItnqEX1Lp82XNBmYHRHXA1OAaZIWks1i\nPjHVzpN0BTAPWA6cEBEBIOlyoBvYXNJjZMtcTgW+C4wCbkpDJ26PiIbrpUfEnZJmAPemNu4FftjS\nhzYzG2StZPEQ5XDdY6YmL5O0BVmnwhzgcwCStgTuAt4ArExLYe4aES8AV6Y5I/raeL7q53WHg5kV\n1uKFbv/suZDdgUA2Tjff4TABmJSezyC7UIXcjLzAohS+4yLiDmApQES8IGk+2QQ5+WNC1lmxf51z\nOooWemzNzIbbYEzMGxE3AjvVbJuUe/4KWW7Wqz0HOKfO9qMb7D+2wPlMrvN6coPdzczabhA6f4ci\nh9c4Zto+vsFxltFgMsiI2HeA0y/FHQ5mVlij9YQLqjd77rhG+6Te3/yMvPlBcGvMjC5pO7K1gu+o\n2f4+YGlEPFTnnI4k68wwM1srtJjDZmY2CJzFxbnDwcwKa3HSyCGbGT0Np5gBnJRuA8urexeDpHHA\nixExb6CTNjPrJJ6818ys/ZzFxfmbMrPCGt0+tqTnIR7vqXcDweq7MQQzo0saSdbZMC0irskfLB3j\nUGDPOuczEQ+nMLO1zGAMqTAzs9Y4i4tzh4OZFdYoXP+qe0f+qnvH/td3Tp5Vb7f+2XOBP5L9g/+o\nmn36ZuS9gzVn5L1M0rfJhlLkZ0a/FJgXERfWafMAYH5ErNaxkWbpPRx4X90PZGbWoXyRa2bWfs7i\n4oalw+Efxnyv1P5zeEeldjZ+4cnSNbHkTZXa4kv17vAeWNcbVlZqaouf194h3tz4k25uvlONrl8d\nVroG4PCvTStd87MJn67UVtd7ytfENtWG6F96dO2/hZvrurv8MuErlm3ffKc6zjq4UllLXuF1lWuH\nYkZeSe8BPgnMlXQv2TCLr6RJcyCbo6HeXQz7AosjYlHlD7SWOX6zKaVr7tfbStf89cqHS9cAPPVY\n3TmLBvblais7d21QPos3vaK3dM1Hj/9p6RqArt8cWbrm0K+Vzx6Aq47/ZOmarrdXaorY5hOlay49\ntEIOP1jtu1ixrOnchms46wPl23n5gx8sX5TTSg5b+5XN4ntV7Zq4ShZXymGolMXDlcNQLYur5DBU\ny+IqOQzVsni4chiqZXGVHIZqWdwqZ3FxvsPBzArrtBl5I+JWaHxSEXFsg+2/Ad5d+MTNzDqEf1Uz\nM2s/Z3Fx7nAws8IcrmZm7eUcNjNrP2dxce5wMLPCHK5mZu3lHDYzaz9ncXHucDCzwrzmsJlZezmH\nzczaz1lcnDsczKwwrzlsZtZezmEzs/ZzFhfnb8rMCvPtY2Zm7eUcNjNrP2dxce5wMLPCHK5mZu3l\nHDYzaz9ncXHucDCzwrzmsJlZezmHzczaz1lcnDsczKww9+aambWXc9jMrP2cxcWNaPcJmNnao5eu\nQg8zMxsaRXPYWWxmNnRazWFJB0laIOkPkk6r8/4oSdMlLZR0m6QxuffOSNvnSzqw2TElTZX0sKR7\nJd0jafe0fSdJv5P0sqSTa9rfRNLPUhsPStqn6nflOxzMrDAvAWRm1l7OYTOz9msliyWNAC4CxgNP\nALMlXRMRC3K7HQ88ExFjJR0JnAtMlLQrcASwCzAamCVpLKAmxzwlIq6uOZWngc8DH6tzmhcCN0TE\n4ZJGAhtW/by+w8HMCutlZKGHmZkNjaI5PFAWD9Eva1MkLZN0f82xzk37zpF0paSNa94fI+nP+V/X\nmp2fmVm7tZjD44CFEfFoRCwHpgMTavaZAPwoPZ8BvD89PwSYHhErImIRsDAdr9kx1/h3f0Q8FRF3\nAyvy2yW9AXhfRExN+62IiD8V+FrqcoeDmRXm23jNzNqr1SEVuV/WPgjsBhwlaeea3fp/WQMuIPtl\njZpf1j4EXCxJqWZqOmatmcBuEbEH2YXxGTXvnw/cUPL8zMzaqsVr4q2BxbnXS9K2uvtERC/wvKTN\n6tQ+nrY1O+bZqeP3PEnrNfl4bwGeSkMx7pH0Q0kbNKlpaFh+ivyPZz5Tav9zNz+1UjvLNnpz6Zru\nvX5Sqa1d/3te6Zp5sWulti456Qula975ne+Urvnq+P9bugbgnb+8r3zR9ZWaIgeYvbAAACAASURB\nVH7+3dI1b+kdX6mtqzisdE38XM13qrHipNIlAFy8clH5ohHbVWsscWfC2mvKM8eXrrlg8/L/cT49\nYovSNQBHb1s+i/e4YU6ltubG20rXfPP0/1O6Zr9z/7t0DcDZ+321dM3uv19YqS3+o3xJ6N8rNbVb\n7x6la37G4aVr4sryOQygz5WvqZLD+7M+jNiqfGPJIORw/69gAJL6fgXL38o7AZiUns8A+v7y7f9l\nDVgkqe+XtTsi4hZJ29Y2FhGzci9vh1V/uUqaADwEvFjy/NZaZbO4Sg4DfG/ECaVrquQwwNtumFu6\npso1cZUchmpZXCWHoWIW/0elpipl8c69e5WuqZLDUC2Lq+QwdNY18Z977uHPPfc2K6/35UTBfRpt\nr3cjQd8xT4+IZamj4RLgNODsAc5vJLAn8M8RcZekC4DTWfX3Qim+99nMCnOHg5lZew1CDtf7FWxc\no30ioldS/pe123L79f2yVtRxZLf5ImlD4FTgAODLJc/PzKytGmXxht17s2H33v2v/zh5ar3dlgBj\ncq9Hk827kLcY2AZ4QlIXsElEPCtpSdpeW6tGx4yIZenP5ZKmAqc0+XhLgMURcVd6PYOsk6KSph0O\nkqYABwPLIqJvRstNgZ8C2wKLgCMi4vmqJ2FmawevOdw+zmIzg4Fz+MWeu3ip566G7ydD8ctaU5LO\nBJZHxOVp02Tg2xHx0qpRGYXPry2cw2bWp8Vr4tnADumusD8CE4Gjava5DjgGuAM4HLg5bb8WuEzS\nt8k6aHcA7iS7w6HuMSVtFRFL0xC4jwEP1Dmn/uxNd0MslrRjRPyBbCLK8rf3J0XmcKg3Ju90YFZE\n7ET24WvH45nZa5DncGgrZ7GZDZi963fvw2Zf++f+RwNlflkj/8taqq33y9qAJB0DfBg4Ord5H+Bc\nSQ8DXwS+IumEgufXLs5hMwNauyZOczKcSDbHzYNkQ9XmS5os6eC02xRgizR07YtkWUNEzAOuIOsA\nuAE4ITJ1j5mOdZmk+4D7gM1JwykkbSlpMfAl4ExJj0naKNV8IdXNAd4O/GvV76rpHQ4NxuRNAPZL\nz38E9JC+BDN77XJnQvs4i80MBiWHh+KXtT6i5g4FSQeRDZ3YNyJe6dseEfvm9pkE/DkiLk4dHM3O\nry2cw2bWp9UsjogbgZ1qtk3KPX+FbJLeerXnAOcUOWbaXndCuzTUYpsG790H7F3vvbKqzuHw5txY\nkKWS3jQYJ2Nmnc3rv3ccZ7HZOqbVHE5zMvT9CjYCmNL3yxowOyKuJ/tlbVr6Ze1psn/0ExHzJPX9\nsrac9MsagKTLgW5gc0mPAZPSkmrfBUYBN6WhE7dHRMMZDRudX0sfemg5h83WQb4mLs6TRppZYQOt\n615E+qXrAlZdRH6j5v1RwI+BvYCngCMj4rH03hlkE46tAE6KiJmSRqf9twJ6gUsi4jtp/+nAjunQ\nmwLPRsSe6b3dge8DG6e6vSPi1ZY+nJnZMGg1h2HIflk7us7upKU1m53P5GbnZ2bWSQYji9cVVb+p\nZZK2TBNKbAX870A7935j1d9Les97GfHe91Vs1szKeLXnNpb33D5ox2vl9rHc2urjycbjzpZ0TUTk\nlzrrX/td0pFka79PrFn7fTQwS9JYss6HkyNiThpzdrekmRGxICIm5tr+FvBcet4FTAM+GREPpAm/\nllf+YO1VOIudw2btk8/iB1q8SPXQto7ja2KztUAnXROva4r+rVc7Ju9a4DPAN8jG+F0zUHHXaZ4/\nx6wdRnW/i1Hd7+p//dJZF7Z0vBbDddDXfo+IO4ClABHxgqT5ZOOKa9drPwLYPz0/ELgvIh5Idc+2\n8qGGWeUsdg6btU8+i9/K+sw767zKx/JFbtv5mthsLdRh18TrlCLLYq4xJg/4OvAzSccBj5FNKGRm\nr3G9K1sK1yFd+13SdsAeZJOc5be/D1gaEQ+lTTum7TcCWwA/jYhvVv5Uw8RZbGbQcg5bC5zDZtbH\nWVxckVUq6o7JAz4wyOdiZh3ulZfrrzm84r9vpfe3tzYrH7K139Nwihlkczu8ULPfUcBPcq9HAu8B\n/hZ4GfiVpLsi4tcDn357OYvNDBrnsA0957CZ9XEWF+fZLsyssN4V9Xtz9e59Gfnu/hXOWP6vdW8Y\nKLP2+xP5td8lNVz7XdJIss6GaRGx2q2s6RiHAnvWnMdv+oZSSLohvd/RHQ5mZtA4h83MbPg4i4sb\n0e4TMLO1R++KrkKPBvrXfk+rUUwkG/ua17f2O6y59vtESaMkbc/qa79fCsyLiHqD8Q4A5kdEvmPj\nl8DuktZPnRX7kS3xZmbW8YrmsC+GzcyGjnO4uGG5w+Hl3k1L7X9MfL9SO7/n7aVrRpxUvgZgg7Oe\nLl3z4iabV2rrki3L1/w6ukvXnMm3yjcE8C/17nYfmD5Xeyd9Mb0Xf750zYe5ulJbP//sYaVrVv6g\nfDtd9/eWLwJe3q58iLV689eK5dWDcyjWfpf0HuCTwFxJ95INs/hKWlIN4EhWH05BRDwn6XzgLmAl\n8POI+EXlD7aWKJvDAH8Xl5SuuZd3lq4BGHFW+brNz1xSqa0nu0aXrvlmhRy+Lj5avgj4EhdXKCqf\nwwD6/8pnce+5f1+prSpZfONnP166pkoOQ7UsfnGj8pk4ouuDXFG6apVWctjar2wWV8lhgPvZu3TN\niLPK10C1LB6uHIZqWVwph6FSFlfJYaiWxcOVwzC818RVsnjDSi2t4iwuzkMqzKywlb2tRcZgr/0e\nEbdC42mCI+LYBtsvBy4vfOJmZh2i1Rw2M7PWOYuL8zdlZsX51jAzs/ZyDpuZtZ+zuDB3OJhZcQ5X\nM7P2cg6bmbWfs7gwdziYWXErqo0TNzOzQeIcNjNrP2dxYe5wMLPiVrT7BMzM1nHOYTOz9nMWF+Zl\nMc2suJcLPszMbGgUzWFnsZnZ0GkxhyUdJGmBpD9IOq3O+6MkTZe0UNJtksbk3jsjbZ8v6cBmx5Q0\nVdLDku6VdI+k3dP2nST9TtLLkk7O7f86SXek/edK6p/gvQrf4WBmxS1v9wmYma3jnMNmZu3XQhZL\nGgFcBIwHngBmS7omIhbkdjseeCYixko6EjgXmChpV7IV3XYBRgOzJI0F1OSYp0RE7bqoTwOfBz6W\n3xgRr0jaPyJektQF3CrpFxFxZ5XP6zsczKy43oIPMzMbGkVz2FlsZjZ0WsvhccDCiHg0IpYD04EJ\nNftMAH6Uns8A3p+eHwJMj4gVEbEIWJiO1+yYa/y7PyKeioi7qTNAJCJeSk9fR3aTQjT8NE24w8HM\niltR8GFmZkOjaA47i83Mhk5rObw1sDj3eknaVnefiOgFnpe0WZ3ax9O2Zsc8W9IcSedJWq/Zx5M0\nQtK9wFLgpoiY3aymEXc4mFlxvsg1M2uvQehwGKKxw1MkLZN0f82xzk37zpF0paSN0/a90/jgvsfH\n0vbRkm6WNC+NHf5C1a/KzGzItJbD9Za4qL2DoNE+ZbcDnB4RuwB7A5sDa+T+GoURKyPiHWTDNvZJ\nQzkq8RwOZlacOxPMzNqrxRweirHDERHAVOC7wI9rmpxJdrG7UtLXgTPSYy6wV9q+FXCfpGvTJzw5\nIuZI2gi4W9LMmvMzM2uvRll8Xw/c39OsegkwJvd6NFke5y0GtgGeSPMobBIRz0pakrbX1qrRMSNi\nWfpzuaSpwCnNTrBPRPxJUg9wEDCvaF2e73Aws+J8h4OZWXu1fofDUIwdJiJuAZ6tbSwiZkXEyvTy\ndrKLYCLi5dz2DYCVafvSiJiTnr8AzGfNW43NzNqrUe7u1g1HfW3Vo77ZwA6StpU0CpgIXFuzz3XA\nMen54cDN6fm1ZB3AoyRtD+wA3DnQMVOnLpJENkHkA3XOqf8OCUlbSNokPd8A+ABQudPXdziYWXHu\nTDAza6/Wc7jeON9xjfaJiF5J+bHDt+X26xs7XNRxZB0cAEgaB1xK9qvcp3IdEH3vbwfsAdxRog0z\ns6HXQhanXD2R7A6wEcCUiJgvaTIwOyKuB6YA0yQtJFtNYmKqnSfpCrK7DZYDJ6S7zOoeMzV5maQt\nyDoV5gCfA5C0JXAX8AZgpaSTgF2BvwJ+lO6IGwH8NCJuqPp5h6XDQceVm9Ry1HWvVmqnq+vp8kXj\nN6/U1js3vr10TVfXk5Xa2n3FLqVrbj704NI1XfuULskcX74k/q1aU5f0d/QV92c+VamteH35G4C6\n7l7ZfKca/7nXoaVrAEau8TvSMPhLG9q0QaF/Kj+58EYzXihd09VVuiRT+/tqAXuPqDZ/UVfXrNI1\nu614a+mank9/qHQNQNdeFYqOrNQUMaV8zTSOqNTWSxX+sojN6w1JHVjXQ+VzGOBnu3+0dM36Vf5a\nH1WhJm+gHH6wB+b1NDvCUIwdbkrSmcDyiLi8vzBbYu2tknYCfpyWXXs17b8R2d0VJ6U7HV4TymZx\nlRyGillcIYehWhYPVw5DtSyulMNQKYur5DBUy+LhymGolsVVchgqZnGrWrwmjogbgZ1qtk3KPX8F\n6v+PHBHnAOcUOWbaPr7BcZax+vCMPnOBPQc4/VJ8h4OZFedl1szM2mugHN65O3v0uXJyvb2GYuzw\ngCQdA3yYVUMzVhMRv5f0IvBW4B5JI8k6G6ZFxDXNjm9mNux8TVyY53Aws+I8h4OZWXu1PofDUIwd\n7iNq7oKQdBBwKnBI+sWub/t2qTMDSdsCOwKL0tuXAvMi4sIBvgkzs/bxNXFhvsPBzIpzcJqZtVeL\nOTxEY4eRdDnQDWwu6TFgUkT0rVwxCrgpm6+M2yPiBOC9wOmSXiWbMPKfIuIZSe8BPgnMTWvAB/CV\ndKuwmVln8DVxYe5wMLPiHK5mZu01CDk8RGOHj26w/9gG2/8T+M86228Fqs4GY2Y2PHxNXJg7HMys\nOIermVl7OYfNzNrPWVyY53Aws+JaHK8m6SBJCyT9QdJpdd4fJWm6pIWSbpM0JvfeGWn7fEkHpm2j\nJd0saZ6kuZK+kNt/uqR70uMRSfek7dtKein33sWD8M2YmQ2P1udwMDOzVjmHC/MdDmZWXAvBmdby\nvQgYTzar+WxJ10TEgtxuxwPPRMRYSUcC55JNULYr2e29u5DNij5L0th0RidHxJy0hNrdkmZGxIKI\nmJhr+1vAc7l2/iciBm25HzOzYeMLWDOz9nMWF+YOBzMr7uWWqscBCyPiUcjuQCBb9Tvf4TAB6BtH\nPINssjGAQ4DpEbECWJQmMhsXEXcASwEi4gVJ84Gta44JWWfF/rnX1RaVNjNrt9Zy2MzMBoOzuDAP\nqTCz4lq7fWxrsrXd+yxJ2+ruExG9wPOSNqtT+3htraTtgD2AO2q2vw9YGhEP5TZvJ+luSb+W9N6G\nZ2xm1mk8pMLMrP2cw4X5DgczK65RcC7qgUd7mlXXu6sgCu4zYG0aTjEDOCkiXqjZ7yjgJ7nXTwBj\nIuJZSXsC/yVp1zp1ZmadxxewZmbt5ywuzB0OZlZco3Ad3Z09+vz35Hp7LQHG5F6PJvvHf95iYBvg\nCUldwCapY2BJ2r5GraSRZJ0N0yLimvzB0jEOBfrna4iI5cCz6fk9kh4CdgTuafDpzMw6hy9yzcza\nz1lcmIdUmFlxyws+6psN7JBWiRgFTASurdnnOuCY9Pxw4Ob0/FqyySNHSdoe2AG4M713KTAvIi6s\n0+YBwPyI6O/YkLRFmsASSW9Jx3q46Wc3M+sERXO4cRabmVmrnMOFDc8dDjuX231Hfl+tnR9sVrpk\n5d9Xa2rE6z5Svujm2rvHi5k54o2la8658ozSNRdecnrpGoDed5fvt/rep49pvlMdnz3/R6Vrxp38\nm0ptrTy//P9eG7/wVOmaCRtf03ynOi7+03EVqi6t1Fa/3uqlEdEr6URgJlln55SImC9pMjA7Iq4H\npgDT0qSQT5N1ShAR8yRdAcwji+8TIiIkvQf4JDBX0r1kwyy+EhE3pmaPZPXhFAD7AmdJWp4+0Wcj\n4jle68rHI29jbvmi8v8XBWDl35WvGbHJx6s1NrP8/7dviQ1K13zjR9Uy9etTv1a6pnevar8fXPTp\n40vXHHPuFZXa2u/UG5vvVGPlv5ZvZ4sVfyxfBBy6zS9K18xYXP5aYEv2BH5Zuq5fCzlsHaBkFlfK\nYaiUxVVyGCpm8TDlMFTL4io5DNWyuEoOQ7UsHq4chmpZXCWHAaYvPqRCVe1vXiU5iwvzkAozK67F\n28dSR8BONdsm5Z6/QraiRL3ac4BzarbdCnQN0N6xdbZdBVxV6sTNzDqFb+M1M2s/Z3Fh7nAws+Ic\nrmZm7eUcNjNrP2dxYZ7DwcyKe7ngw8zMhkbRHHYWm5kNnRZzWNJBkhZI+oOk0+q8P0rSdEkLJd0m\naUzuvTPS9vmSDmx2TElTJT0s6V5J90jaPW3fSdLvJL0s6eQy51eG73Aws+Lcm2tm1l7OYTOz9msh\ni9Pk5RcB48lWXZst6ZqIWJDb7XjgmYgYK+lI4FyyCdR3JRt+vAvZqm2zJI0lW0J+oGOeEhFX15zK\n08DngY9VOL/CfIeDmRW3ouDDzMyGRtEcdhabmQ2d1nJ4HLAwIh5Ny7VPBybU7DOBVdO/zgDen54f\nAkyPiBURsQhYmI7X7Jhr/Ls/Ip6KiLvrnGmR8yvMHQ5mVpyXADIzay8vi2lm1n6t5fDWwOLc6yVp\nW919IqIXeF7SZnVqH0/bmh3zbElzJJ0nab0mn67I+RXmIRVmVpyXADIzay/nsJlZ+zXK4id74Kme\nZtWqs612rdhG+zTaXu9Ggr5jnh4Ry1JHwyXAacDZLZ5fYb7DwcyK8228ZmbtNQhDKoZosrIpkpZJ\nur/mWOemfedIulLSxmn7ByTdJek+SbMl7V/nPK6tPZ6ZWUdolLubdsPYr6161LcEGJN7PZpsroS8\nxcA2AJK6gE0i4tlUu02d2obHjIhl6c/lwFSyIRMDKXJ+hbnDwcyKc4eDmVl7tdjhkJsM7IPAbsBR\nknau2a1/sjLgArLJyqiZrOxDwMWS+n4Jm5qOWWsmsFtE7EE21viMtP1J4OCIeDvwGWBazXl+HPjT\nQF+FmVnbtHZNPBvYQdK2kkYBE4Fra/a5DjgmPT8cuDk9v5Zs8shRkrYHdgDuHOiYkrZKf4psgsgH\n6pxT/q6GIudXmIdUmFlxHhNsZtZeredw/2RgAJL6JgPLzz4+AZiUns8Avpue909WBiyS1DdZ2R0R\ncYukbWsbi4hZuZe3A4el7ffl9nlQ0uskrRcRyyW9HvgS8I/AFS1/YjOzwdZCFkdEr6QTyTpkRwBT\nImK+pMnA7Ii4HpgCTEs5+zTZP/qJiHmSrgDmpbM4ISICqHvM1ORlkrYg61SYA3wOQNKWwF3AG4CV\nkk4Cdo2IFwY4VmnucDCz4l5p9wmYma3jWs/hepOB1d5eu9pkZZLyk5Xdltuvb7Kyoo4jm+18NZI+\nAdybbvcF+L/At4C/lDi2mdnwaTGLI+JGYKeabZNyz18hu6OsXu05wDlFjpm2j29wnGWsPjyj6bGq\ncIeDmRXn4RJmZu01UA4/3wN/6ml2hKGYrKwpSWcCyyPi8prtu5FdOB+QXr8d2CEiTpa0XYM2zcza\ny9fEhQ1Ph8MN5XZf/5vVuoxWjC//cbp+Ve2/lg+9fHXpmp/r0EptfTlOLV3znQ+sMQdUUyt/VboE\ngB0oP5/TGB6r1NbKk8vXfIqFldrqOmzf0jU7XFl+PpWT//Tt0jUAb1O94VdDzEMq1l6/KF+yssI0\nPyv26SrfENB1R/ksPvi5GZXaulaHl645na+UrvnmR75augZg5c/L1+zO7ZXa2onfl65ZWf6vJAD+\nnodK13R9svyk2Ltftrj5TnX88+Jvla7ZSeW/v/XYvHTNagbK4Q27s0efJZPr7VVmsrIn8pOVSWo0\nWdmAJB0DfJhV68j3bR8NXAV8Kq0nD/AuYE9JDwPrAW+WdHNErFa71iqZxVVyGKplcZUchmpZPFw5\nDNWyuEoOQ7UsrpLDUC2LP8Oi0jVVchhgt8vKXxNXyWGolsUt8zVxYZ400syK6y34MDOzoVE0hxtn\n8VBMVtZH1NyRIOkg4FTgkHSLcN/2TYDryZZr6/9XWkR8PyJGR8RbgPcCv3/NdDaY2WuHr4kLc4eD\nmRXnVSrMzNqrxVUqIqIX6JsM7EGySSDnS5os6eC02xRgizRZ2ReB01PtPLJJHOeR3b/aN1kZki4H\nfgfsKOkxScemY30X2Ai4SdI9ki5O208E/gb4qqR703tbtPr1mJkNC18TF+Y5HMysOAenmVl7DUIO\nD9FkZUc32H9sg+3/AvxLk/N8FNh9oH3MzNrC18SFucPBzIrzeDUzs/ZyDpuZtZ+zuDB3OJhZcR6L\nZmbWXs5hM7P2cxYX5jkczKy4FserSTpI0gJJf5C0xlIqaSKy6ZIWSrpN0pjce2ek7fMlHZi2jZZ0\ns6R5kuZK+kJu/+lpTPA9kh6RdE9NW2Mk/VlShbVPzMzapMU5HMzMbBA4hwvzHQ5mVtxfqpdKGgFc\nBIwnW0ZttqRrImJBbrfjgWciYqykI4FzyWZE35VsPPEuZMuwzZI0lizKT46IOZI2Au6WNDMiFkTE\nxFzb3wKeqzml8ym9aK+ZWZu1kMNmZjZInMWF+Q4HMyuutSWAxgELI+LRiFgOTAcm1OwzAfhRej6D\nVWu2H0I2k/qKtFb7QmBcRCyNiDkAEfECMB/Yuk7bRwA/6XshaQLwENkM7WZma4/Wl8U0M7NWOYcL\nc4eDmRXX2u1jWwOLc6+XsGbnQP8+aem25yVtVqf28dpaSdsBewB31Gx/H7A0Ih5KrzckWxN+MjXr\nxZuZdTwPqTAzaz/ncGEeUmFmxbUWnPX+cR8F9xmwNg2nmAGclO50yDuK3N0NZB0N346IlyQ1atPM\nrDP5AtbMrP2cxYW5w8HMimu0BNDKHoieZtVLgDG516PJ5nLIWwxsAzwhqQvYJCKelbQkbV+jVtJI\nss6GaRFxTf5g6RiHAnvmNu8DHCbpXGBToFfSXyLi4mYfwMys7bwUm5lZ+zmLC3OHg5kV13AsWnd6\n9Jlcb6fZwA6StgX+CEwku/sg7zrgGLJhEYcDN6ft1wKXSfo22VCKHYA703uXAvMi4sI6bR4AzI+I\n/o6NiNi377mkScCf3dlgZmsNjwk2M2s/Z3FhnsPBzIqLgo96pdmcDCcCM8kma5weEfMlTZZ0cNpt\nCrCFpIXAF4HTU+084ApgHtnKEidEREh6D/BJ4P2S7k1LYB6Ua/ZIVh9OYWa2diuaww2y2MzMBkGL\nOTzYS8UPdExJUyU9nLtW3j333nfSseZI2iNt687te6+kv0g6pOpXNSx3OHz5wbNK7X88Uyq1ozPK\n/+36/enHVGprP35TuqbrRx+v1NaSz5xXuub2X+1TuuZ0bi1dA/A9/ap0zSuMqtTWp2NJ6ZoteLpS\nW7FT+aH926w2r2Ex37vv5NI1AKfscXalunaKiBuBnWq2Tco9f4VsRYl6tecA59RsuxXoGqC9Y5uc\nT91bMV6Lvry4XA4DfJELStfohGr/yrnmpgNK17yL2yq11XXlJ0rX/O8nvlG6ZvbP9ypdA3A2PaVr\nLtDvKrX1aoUsPjb+XKmtN1K+brhyGOCim75cuubEA79ZuqaL0aVr7LWjbBZXyWEAfaF8Fl/1iw9V\nauu9/LZ0zXDlMFTL4io5DNWyuEoOQ7Us3pTnS9dUyWGolsUX/bp8DgOc+P7yWdxOQ7RUvJoc85SI\nuLrmPD4E/E1qYx/g+8A7I6IHeEfaZ1Oy1eFmVv28vsPBzMzMzMzMbHgM+lLxBY5Z79/9E4AfA0TE\nHcAmkras2ecTwC8i4uXyH7Nxw6uRNEXSMkn357ZNkrQk3WZRewuzmb1mLS/4sMHmLDazTNEcdhYP\nNuewma3SUg4PxVLxzY55dho2cZ6k9RqcxxrLzpPNudbS8OQidzhMBT5YZ/v5EbFnetzYykmY2drC\niw63kbPYzCiew87iIeAcNrOkpRweiqXiBzrm6RGxC7A3sDnQN79Ds2XntwLeCvyyzn6FNZ3DISJu\nSbPK1/La9WbrHP9i1i7OYjPLOIfbxTlsZqs0yuLfArc0Kx6KpeLV6JgRsSz9uVzSVOCU3HnUXXY+\nOQK4Ot1hUVkrczj8c7ot498lbdLKSZjZ2sK/qnUgZ7HZOsV3OHQg57DZOqdR7r4L+HLuUVf/UvGS\nRpENW7i2Zp++peJhzaXiJ6ZVLLZn1VLxDY+Z7lRAkoCPAQ/kjvXp9N47gef6OieSoxiE1d6qdjhc\nTDaj5R7AUuD8Vk/EzNYGHjfcYZzFZuuc1udwGKLl2NaY3yBtPzftO0fSlZI2Tts3k3SzpD9L+k5N\nzXqSfiDp95LmSaq2zNfwcA6brZOq5/BQLBXf6JjpWJdJug+4j2xIxdnpWDcAj0j6H+AHwAl955ju\n5hodEeWXZqxRaVnMiHgy9/ISsh6Yhn73tZv7n2/TvT3bdG9fpVkzK2lxz8Ms6XlkEI/ozoROUiaL\nncNm7fN4z//wRM9DACzijS0erbUcHorl2CIiyOY3+C5pxvOcmWTjh1dK+jpwRnq8DPz/ZOOD31pT\ncyawLCJ2Sue8WUsfegj5mths7ZDP4cHRWhYP9lLxjY6Zto8f4DxObLD9UVYfblFZ0Q4HkRufJmmr\niFiaXh7Kqtsy6nr3194/0NtmNkS26X4L23S/pf/1HZNvHmDvInyLbptVzmLnsFn7bN29A1t37wDA\nrmzHLyb/rIWjtZzD/UunAUjqWzot3+EwAei78J1B1pEAueXYgEXpl7dxwB2N5jeIiFm5l7cDh6Xt\nLwG/S+vH1zqO3EVzRDxT+lMOHV8Tm62F8jkMcNfkm1o8oq+Ji2ra4SDpcqAb2FzSY2R/Ae0vaQ9g\nJbAI+OwQnqOZdYy/tPsE1lnOYjPLtJzD9ZZOG9don4jolZRfju223H71llAbyHFka8M3lJsD4WxJ\n3cD/ACfW3EnQFs5hM1vF18RFFVml4ug6m6cOwbmYWcfzkIp2cRabWWaghn4KRQAACltJREFUHJ4N\n3NXsAEOxHFtTks4ElkfE5U12HUk2XOO3EXGKpC8B55EmNmsn57CZreJr4qIqzeFgZusq3z5mZtZe\nA+XwO9Kjz/fr7TQUy7ENSNIxwIeBpuMJIuJpSS9GxH+lTT8juzPCzKyD+Jq4qFaWxTSzdY5XqTAz\na6+WV6kYiuXY+qw2vwFkK2IApwKHpEnQ6qm9c+I6Sfun5x8gm43dzKyD+Jq4KGUTCw9hA1KsrJ2v\nuImnK/Zjr3h149I1I1XtP4SD4sbSNXfe3F2prZEvlO9B+88Jh5au+Q3dpWsAxvOr0jUXclKltqZy\nbOman3JkpbZeig1L1+yq8tdEqvj/wev00dI1V+hYIqLeLbFNSQq4peDe763cjg2+KjkM8Pgxzfep\ntfGr1W6cG9G7snTNR0cNOBl8Q7+6+eDmO9UY+cYKObxX+RyGall8IDMrtTWNvytdcxGfr9RWlSxe\nEV2la7bXotI1ACOjt3TNDfpQ6ZrdGMPJ+liljCyXw9Aoi1MnwIVkPzxNiYivS5oMzI6I6yW9DphG\ndrvE08DEiFiUas8gW8ViOXBSRMxM2/vnNwCWAZMiYmqaWHJUOg7A7RFxQqp5BHhDev854MCIWJCW\n4ZwGbAI8CRwbEUtKfPCOVCWLq+QwVMviKjkM1bK4Ug5vVO0X5f/cZ/iuiatkcZUcBriAL5auuZJP\nlK6pksNQLYur5DBUy+Ip+oKviYeJh1SYWQnuqTUza6/Wc3iIlmOrN78BEVFvFYq+9+quCRkRjwH7\nNaozM2s/XxMX5Q4HMyvB49XMzNrLOWxm1n7O4qLc4WBmJbg318ysvZzDZmbt5ywuyh0OZlaC1xw2\nM2sv57CZWfs5i4tyh4OZleDeXDOz9nIOm5m1n7O4KC+LaWYlrCj4qE/SQZIWSPqDpNPqvD9K0nRJ\nCyXdlmYq73vvjLR9vqQD07bRkm6WNE/SXElfyO0/XdI96fGIpHvS9r0l3Zt7fGwwvhkzs+FRNIc9\nvtjMbOg4h4vyHQ5mVkL13lxJI4CLgPHAE8BsSddExILcbscDz0TEWElHAueSrfm+K9mM6bsAo4FZ\nksaSJfnJETFH0kbA3ZJmRsSCiJiYa/tbZEuuAcwF9oqIlZK2Au6TdG1EVFsPzMxsWPlXNTOz9nMW\nF+U7HMyshJZ6c8cBCyPi0YhYDkwHJtTsMwH4UXo+A3h/en4IMD0iVqS14BcC4yJiaUTMAYiIF4D5\nwNZ12j4C+Ena7+Vc58IGgDsazGwt4jsczMzazzlclO9wMLMSWurN3RpYnHu9hKwTou4+EdEr6XlJ\nm6Xtt+X2e5yajgVJ2wF7AHfUbH8fsDQiHsptGwdcCowBPuW7G8xs7eFf1czM2s9ZXFRb73Domd/O\n1jtLz5xo9yl0jOd77mv3KXSM+T1PtvsUajTqvZ0PXJt71KU622r/w2+0z4C1aTjFDOCkdKdD3lGk\nuxv6CyPujIi3AnsDX5E0qtFJv9Y5h1dxDq/yVM+8dp9CR3mg5+l2n0KO73B4LXIWr+IsXsVZvEpn\n5TA4h4tzh0OH+I3/jd3PHQ6rdF6Hw/IGj+2AA3KPupaQ3VHQZzTZXA55i4FtACR1AZtExLOpdpt6\ntZJGknU2TIuIa/IHS8c4FPhpvROKiN8DLwJvbXTSr3XO4VWcw6s85f8wVvNgzzPtPoWcRjlc72Fr\nC/9fbhVn8SrO4lU6K4fBOVyc53AwsxJa6s2dDewgadt0R8FE1rwd4jrgmPT8cODm9PxasskjR0na\nHtgBuDO9dykwLyIurNPmAcD8iOjv2JC0XeqIQNK2wI7AoqYf3cysI/gOBzOz9nMOFzU8czhstmf9\n7Rs8AZv99Rqbu95RtaHXl67oqvgfws5sVL7oDQ2+B4DXPQFvWPO7ANizq3xTm/KW0jVjeHP5hoBN\n+ZvSNTvxhobvvcSohu+/jreVbmurunMINvcy65eu2YyXS9dojVEFq2zAH9mswf+W27N56bZa95fK\nlWlOhhOBmWSdnVMiYr6kycDsiLgemAJMk7QQeJqsU4KImCfpCmAeWXfxCRERkt4DfBKYK+lesmEW\nX4mIG1OzR1IznAJ4L3C6pFfJJoz8p4jotG7zwVcyhwHWGyCyGhlBhcACRqj8LbRj2bhSWw2zeKAc\n3rB8M1VyGKpl8RvZoVJbb2HTutuXsX7D99bj7ZXaevNqNykV01vhv6dNK/432DXA/LHr83Td73gM\nbyrdzpvZpHTN6qrnsHWAkllcJYehWhZXyWGomMUVron33KB8MzC818RVsrhR1sLAWTyK3Uu3NVw5\nDNWyuEoOQ7Usbp2zuChFDO04KaliepnZkIiIevMhNCVpEbBtwd0fjYjtqrRjg885bNZ5qmRxyRwG\nZ3FHcRabdRZfEw+PIe9wMDMzMzMzM7N1j+dwMDMzMzMzM7NB5w4HMzMzMzMzMxt0belwkHSQpAWS\n/iDptHacQ6eQtEjSfZLulXRn84rXFklTJC2TdH9u26aSZkr6vaRfSmp1hq21QoPvYpKkJZLuSY+D\n2nmO9triLF5lXc5i5/AqzmEbbs7hVdblHAZncZ6z+LVl2DscJI0ALgI+COwGHCVp5+E+jw6yEuiO\niHdExLh2n0wbTCX7byHvdGBWROxEtiziGcN+Vu1R77sAOD8i9kyPG+u8b1aas3gN63IWO4dXcQ7b\nsHEOr2FdzmFwFuc5i19D2nGHwzhgYUQ8GhHLgenAhDacR6cQ6/DQloi45f+1d8eqUYRRAIXPBbFQ\nO8EIihb6ACm0EQttLGwUQbCLCGKh72BrZWmjFim0sQimM6+QxkKwlRgkMYVPINdiJ+78Jms1O/9m\n5nzV7Da5THbPLpdhB/j1z9N3gNXmeBW42+tQlcw4FzB5jUhds8Wl0bbYDk/ZYfXMDpdG22GwxW22\neFhqvKnPAd9bj7eb58YqgU8RsRkRj2sPsyDOZOYuQGbuQJWb6y6SpxHxOSLejOVSOvXCFpdscckO\nl+yw5sEOl+zwQba4ZIuPoBoLh8M2U2O+N+e1zLwC3GbyJrpeeyAtlFfApcxcBnaAl5Xn0XDY4pIt\n1ix2WPNih0t2WP9ji4+oGguHbeBC6/F54EeFORZCs60kM/eANSaX143dbkQsAUTEWeBn5Xmqycy9\nzNz/8vEauFpzHg2KLW6xxQfY4YYd1hzZ4RY7fChb3LDFR1eNhcMmcDkiLkbEceABsF5hjuoi4kRE\nnGqOTwK3gC91p6oiKLf868DD5ngF+Nj3QBUV56L5cNl3j3G+PjQftrhhiwE73GaH1Rc73LDDf9ni\nKVs8EMf6/oOZ+TsingEbTBYebzPza99zLIglYC0iksn/4l1mblSeqVcR8R64AZyOiC3gOfAC+BAR\nj4At4H69Cfsz41zcjIhlJr/c/A14Um1ADYotLoy6xXZ4yg6rT3a4MOoOgy1us8XDEtMrUyRJkiRJ\nkrox2lvPSJIkSZKk+XHhIEmSJEmSOufCQZIkSZIkdc6FgyRJkiRJ6pwLB0mSJEmS1DkXDpIkSZIk\nqXMuHCRJkiRJUudcOEiSJEmSpM79AUNF5zJ7SK7pAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Extract the energy-condensed delayed neutron fraction tally\n", + "beta_by_group = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True)\n", + "beta_by_group.mean.shape = (17, 17, 6)\n", + "beta_by_group.mean[beta_by_group.mean == 0] = np.nan\n", + "\n", + "# Plot the betas\n", + "plt.figure(figsize=(18,9))\n", + "fig = plt.subplot(231)\n", + "plt.imshow(beta_by_group.mean[:,:,0], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 1')\n", + "\n", + "fig = plt.subplot(232)\n", + "plt.imshow(beta_by_group.mean[:,:,1], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 2')\n", + "\n", + "fig = plt.subplot(233)\n", + "plt.imshow(beta_by_group.mean[:,:,2], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 3')\n", + "\n", + "fig = plt.subplot(234)\n", + "plt.imshow(beta_by_group.mean[:,:,3], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 4')\n", + "\n", + "fig = plt.subplot(235)\n", + "plt.imshow(beta_by_group.mean[:,:,4], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 5')\n", + "\n", + "fig = plt.subplot(236)\n", + "plt.imshow(beta_by_group.mean[:,:,5], interpolation='none', cmap='jet')\n", + "plt.colorbar()\n", + "plt.title('Beta - delayed group 6')" + ] + }, + { + "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": "2.7.12" + } + }, + "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 index cb4df0fadf..ca4832809a 100644 --- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb @@ -34,7 +34,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", + "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/__init__.py:878: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", + " warnings.warn(self.msg_depr % (key, alt_key))\n", + "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/__init__.py:1357: 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", @@ -440,9 +442,10 @@ "\n", " Copyright: 2011-2016 Massachusetts Institute of Technology\n", " License: http://openmc.readthedocs.io/en/latest/license.html\n", - " Version: 0.7.1\n", - " Git SHA1: 3d68c07625e33cd64188df03ee03e9c31b3d4b74\n", - " Date/Time: 2016-07-22 21:32:41\n", + " Version: 0.8.0\n", + " Git SHA1: be7e6e035d22944a8c80ca32f99935b6822854c9\n", + " Date/Time: 2016-08-10 15:31:07\n", + " MPI Processes: 1\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -452,11 +455,11 @@ " Reading geometry XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", - " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n", - " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n", - " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n", - " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n", - " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n", + " Reading U235.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U238_71c.h5\n", + " Reading O16.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/O16_71c.h5\n", + " Reading H1.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/H1_71c.h5\n", + " Reading Zr90.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/Zr90_71c.h5\n", " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", @@ -518,7 +521,7 @@ " 48/1 1.21610 1.22612 +/- 0.00251\n", " 49/1 1.22199 1.22602 +/- 0.00245\n", " 50/1 1.20860 1.22558 +/- 0.00243\n", - " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10054\n", + " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10052\n", " The estimated number of batches is 73\n", " Creating state point statepoint.050.h5...\n", " 51/1 1.21850 1.22541 +/- 0.00237\n", @@ -544,7 +547,7 @@ " 71/1 1.19720 1.22444 +/- 0.00195\n", " 72/1 1.23770 1.22465 +/- 0.00193\n", " 73/1 1.23894 1.22488 +/- 0.00191\n", - " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10054\n", + " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10052\n", " The estimated number of batches is 74\n", " 74/1 1.22437 1.22487 +/- 0.00188\n", " Triggers satisfied for batch 74\n", @@ -557,20 +560,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.3000E-01 seconds\n", - " Reading cross sections = 2.6000E-01 seconds\n", - " Total time in simulation = 3.4077E+02 seconds\n", - " Time in transport only = 3.4068E+02 seconds\n", - " Time in inactive batches = 2.3968E+01 seconds\n", - " Time in active batches = 3.1680E+02 seconds\n", - " Time synchronizing fission bank = 3.0000E-02 seconds\n", + " Total time for initialization = 4.0400E-01 seconds\n", + " Reading cross sections = 2.1100E-01 seconds\n", + " Total time in simulation = 2.8243E+02 seconds\n", + " Time in transport only = 2.8236E+02 seconds\n", + " Time in inactive batches = 1.8781E+01 seconds\n", + " Time in active batches = 2.6365E+02 seconds\n", + " Time synchronizing fission bank = 2.7000E-02 seconds\n", " Sampling source sites = 1.7000E-02 seconds\n", - " SEND/RECV source sites = 1.3000E-02 seconds\n", - " Time accumulating tallies = 3.0000E-03 seconds\n", - " Total time for finalization = 1.6000E-02 seconds\n", - " Total time elapsed = 3.4129E+02 seconds\n", - " Calculation Rate (inactive) = 4172.23 neutrons/second\n", - " Calculation Rate (active) = 1262.62 neutrons/second\n", + " SEND/RECV source sites = 8.0000E-03 seconds\n", + " Time accumulating tallies = 2.0000E-03 seconds\n", + " Total time for finalization = 2.4000E-02 seconds\n", + " Total time elapsed = 2.8293E+02 seconds\n", + " Calculation Rate (inactive) = 5324.53 neutrons/second\n", + " Calculation Rate (active) = 1517.17 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -769,6 +772,14 @@ "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", + " return c.reshape(shape_out)\n" + ] + }, { "data": { "text/html": [ @@ -1159,169 +1170,239 @@ "text": [ "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.574672\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.679815\tres = 4.253E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.660826\tres = 1.830E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.658941\tres = 2.793E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.643012\tres = 2.852E-03\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.625810\tres = 2.417E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.606678\tres = 2.675E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.587485\tres = 3.057E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.569029\tres = 3.164E-02\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.551707\tres = 3.142E-02\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.536035\tres = 3.044E-02\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.522274\tres = 2.841E-02\n", - "[ NORMAL ] Iteration 12:\tk_eff = 0.510609\tres = 2.567E-02\n", - "[ NORMAL ] Iteration 13:\tk_eff = 0.501106\tres = 2.234E-02\n", - "[ NORMAL ] Iteration 14:\tk_eff = 0.493831\tres 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1.220814\n", + "bias [pcm]: -266.0\n" ] } ], @@ -1430,237 +1511,346 @@ "text": [ "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.495816\tres = 0.000E+00\n", - "[ NORMAL ] Iteration 1:\tk_eff = 0.557477\tres = 5.042E-01\n", - "[ NORMAL ] Iteration 2:\tk_eff = 0.518301\tres = 1.244E-01\n", - "[ NORMAL ] Iteration 3:\tk_eff = 0.509212\tres = 7.027E-02\n", - "[ NORMAL ] Iteration 4:\tk_eff = 0.496490\tres = 1.754E-02\n", - "[ NORMAL ] Iteration 5:\tk_eff = 0.488581\tres = 2.498E-02\n", - "[ NORMAL ] Iteration 6:\tk_eff = 0.482897\tres = 1.593E-02\n", - "[ NORMAL ] Iteration 7:\tk_eff = 0.479775\tres = 1.163E-02\n", - "[ NORMAL ] Iteration 8:\tk_eff = 0.478834\tres = 6.465E-03\n", - "[ NORMAL ] Iteration 9:\tk_eff = 0.479871\tres = 1.960E-03\n", - "[ NORMAL ] Iteration 10:\tk_eff = 0.482684\tres = 2.165E-03\n", - "[ NORMAL ] Iteration 11:\tk_eff = 0.487084\tres = 5.860E-03\n", - "[ NORMAL ] Iteration 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2.045E-05\n", + "[ NORMAL ] Iteration 312:\tk_eff = 1.222576\tres = 1.995E-05\n", + "[ NORMAL ] Iteration 313:\tk_eff = 1.222599\tres = 1.946E-05\n", + "[ NORMAL ] Iteration 314:\tk_eff = 1.222622\tres = 1.899E-05\n", + "[ NORMAL ] Iteration 315:\tk_eff = 1.222644\tres = 1.852E-05\n", + "[ NORMAL ] Iteration 316:\tk_eff = 1.222665\tres = 1.807E-05\n", + "[ NORMAL ] Iteration 317:\tk_eff = 1.222686\tres = 1.763E-05\n", + "[ NORMAL ] Iteration 318:\tk_eff = 1.222707\tres = 1.720E-05\n", + "[ NORMAL ] Iteration 319:\tk_eff = 1.222727\tres = 1.678E-05\n", + "[ NORMAL ] Iteration 320:\tk_eff = 1.222746\tres = 1.637E-05\n", + "[ NORMAL ] Iteration 321:\tk_eff = 1.222766\tres = 1.597E-05\n", + "[ NORMAL ] Iteration 322:\tk_eff = 1.222784\tres = 1.558E-05\n", + "[ NORMAL ] Iteration 323:\tk_eff = 1.222802\tres = 1.520E-05\n", + "[ NORMAL ] Iteration 324:\tk_eff = 1.222820\tres = 1.483E-05\n", + "[ NORMAL ] Iteration 325:\tk_eff = 1.222837\tres = 1.446E-05\n", + "[ NORMAL ] Iteration 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keff = 1.223474\n", - "openmoc keff = 1.223227\n", - "bias [pcm]: -24.7\n" + "openmoc keff = 1.223039\n", + "bias [pcm]: -43.5\n" ] } ], @@ -1772,9 +1962,9 @@ }, { "data": { - "image/png": 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s//SZaStbolLd0ZyNidDkt7hJQUQeUNXTRGQ2zn0J4e0AqGonmuHGpEqwvEub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\nRK5CYII=\n", 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9zMSJ97Nq1ara12y9tRM0nGyp/QDo3LmYgoJ8/vhjXb1yg8vq3bsPGRkZZGdn07Pn9rWJ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\ndf37FYqK/PXGBBoGhXDS0qj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YY2pZUDDGGFPLgoIxxphaFhSMMcbUsqBgjDGmlgUFY4wx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\nYwHYAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1790,6 +1980,7 @@ "energy_groups = nufission.energy_groups\n", "x = energy_groups.group_edges\n", "y = nufission.get_xs(nuclides=['U235'], order_groups='decreasing', xs_type='micro')\n", + "y = np.squeeze(y)\n", "\n", "# Fix low energy bound to the value defined by the ACE library\n", "x[0] = fission.xs.x[0]\n", @@ -1855,9 +2046,9 @@ "outputs": [ { "data": { - "image/png": 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RMQY4DdhrkVtpVqACctt5bZXQVw9+YqvBJV0XEV03I14H+EOrMc0KMLGVNzuvrSp6LfCS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\nfn35+EIOpHUAAAAASUVORK5CYII=\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1897,21 +2088,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 2", "language": "python", - "name": "python3" + "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" + "pygments_lexer": "ipython2", + "version": "2.7.12" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb index a1eaa7ad82..af9f2878fe 100644 --- a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb @@ -32,7 +32,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning: This call to matplotlib.use() has no effect\n", + "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/__init__.py:1357: 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", @@ -458,7 +458,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] @@ -552,6 +552,9 @@ "* `ChiPrompt` (`\"chi prompt\"`)\n", "* `InverseVelocity` (`\"inverse-velocity\"`)\n", "* `PromptNuFissionXS` (`\"prompt-nu-fission\"`)\n", + "* `DelayedNuFissionXS` (`\"delayed-nu-fission\"`)\n", + "* `ChiDelayed` (`\"chi-delayed\"`)\n", + "* `Beta` (`\"beta\"`)\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\"`, `'\"fission\"`, `\"nu-scatter matrix\"` and `\"chi\"` cross sections for our `Library`.\n", "\n", @@ -727,9 +730,10 @@ "\n", " Copyright: 2011-2016 Massachusetts Institute of Technology\n", " License: http://openmc.readthedocs.io/en/latest/license.html\n", - " Version: 0.7.1\n", - " Git SHA1: 3d68c07625e33cd64188df03ee03e9c31b3d4b74\n", - " Date/Time: 2016-07-23 16:42:32\n", + " Version: 0.8.0\n", + " Git SHA1: be7e6e035d22944a8c80ca32f99935b6822854c9\n", + " Date/Time: 2016-08-10 18:33:28\n", + " MPI Processes: 1\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -739,12 +743,12 @@ " Reading geometry XML file...\n", " Reading cross sections XML file...\n", " Reading materials XML file...\n", - " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n", - " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n", - " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n", - " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n", - " Reading B10.71c from /home/romano/openmc/data/nndc_hdf5/B10_71c.h5\n", - " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n", + " Reading U235.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U235_71c.h5\n", + " Reading U238.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U238_71c.h5\n", + " Reading O16.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/O16_71c.h5\n", + " Reading H1.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/H1_71c.h5\n", + " Reading B10.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/B10_71c.h5\n", + " Reading Zr90.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/Zr90_71c.h5\n", " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", @@ -780,32 +784,32 @@ " 22/1 1.04175 1.02516 +/- 0.00588\n", " 23/1 1.01909 1.02469 +/- 0.00543\n", " 24/1 1.07119 1.02801 +/- 0.00603\n", - " 25/1 0.97445 1.02444 +/- 0.00665\n", - " 26/1 1.04737 1.02588 +/- 0.00638\n", - " 27/1 1.04656 1.02709 +/- 0.00612\n", - " 28/1 1.03464 1.02751 +/- 0.00578\n", - " 29/1 1.02528 1.02739 +/- 0.00547\n", - " 30/1 1.02799 1.02742 +/- 0.00519\n", - " 31/1 1.05846 1.02890 +/- 0.00516\n", - " 32/1 1.03811 1.02932 +/- 0.00493\n", - " 33/1 1.00894 1.02843 +/- 0.00480\n", - " 34/1 1.02049 1.02810 +/- 0.00460\n", - " 35/1 1.00690 1.02726 +/- 0.00450\n", - " 36/1 1.03129 1.02741 +/- 0.00432\n", - " 37/1 0.98864 1.02597 +/- 0.00440\n", - " 38/1 1.00017 1.02505 +/- 0.00434\n", - " 39/1 1.03635 1.02544 +/- 0.00421\n", - " 40/1 1.07090 1.02696 +/- 0.00434\n", - " 41/1 1.03141 1.02710 +/- 0.00420\n", - " 42/1 1.02624 1.02707 +/- 0.00406\n", - " 43/1 1.02668 1.02706 +/- 0.00394\n", - " 44/1 1.05940 1.02801 +/- 0.00394\n", - " 45/1 1.01149 1.02754 +/- 0.00385\n", - " 46/1 1.06958 1.02871 +/- 0.00392\n", - " 47/1 1.02674 1.02866 +/- 0.00381\n", - " 48/1 1.02542 1.02857 +/- 0.00371\n", - " 49/1 1.03516 1.02874 +/- 0.00362\n", - " 50/1 1.06818 1.02973 +/- 0.00366\n", + " 25/1 0.97414 1.02442 +/- 0.00666\n", + " 26/1 1.04709 1.02584 +/- 0.00639\n", + " 27/1 1.05872 1.02777 +/- 0.00631\n", + " 28/1 1.03930 1.02841 +/- 0.00598\n", + " 29/1 1.01488 1.02770 +/- 0.00570\n", + " 30/1 1.04513 1.02857 +/- 0.00548\n", + " 31/1 0.99538 1.02699 +/- 0.00545\n", + " 32/1 1.00106 1.02581 +/- 0.00532\n", + " 33/1 0.99389 1.02442 +/- 0.00527\n", + " 34/1 0.99938 1.02338 +/- 0.00516\n", + " 35/1 1.02161 1.02331 +/- 0.00495\n", + " 36/1 1.04084 1.02398 +/- 0.00480\n", + " 37/1 0.98801 1.02265 +/- 0.00481\n", + " 38/1 1.01348 1.02232 +/- 0.00464\n", + " 39/1 1.06693 1.02386 +/- 0.00474\n", + " 40/1 1.07729 1.02564 +/- 0.00491\n", + " 41/1 1.03191 1.02585 +/- 0.00475\n", + " 42/1 1.05209 1.02667 +/- 0.00468\n", + " 43/1 1.02997 1.02677 +/- 0.00453\n", + " 44/1 1.07288 1.02812 +/- 0.00460\n", + " 45/1 1.01268 1.02768 +/- 0.00449\n", + " 46/1 1.03759 1.02796 +/- 0.00437\n", + " 47/1 1.02620 1.02791 +/- 0.00425\n", + " 48/1 1.02509 1.02783 +/- 0.00414\n", + " 49/1 1.01043 1.02739 +/- 0.00406\n", + " 50/1 1.01457 1.02707 +/- 0.00397\n", " Creating state point statepoint.50.h5...\n", "\n", " ===========================================================================\n", @@ -815,27 +819,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.3400E-01 seconds\n", - " Reading cross sections = 2.7900E-01 seconds\n", - " Total time in simulation = 6.1121E+01 seconds\n", - " Time in transport only = 6.1101E+01 seconds\n", - " Time in inactive batches = 5.0660E+00 seconds\n", - " Time in active batches = 5.6055E+01 seconds\n", - " Time synchronizing fission bank = 5.0000E-03 seconds\n", - " Sampling source sites = 3.0000E-03 seconds\n", - " SEND/RECV source sites = 2.0000E-03 seconds\n", - " Time accumulating tallies = 0.0000E+00 seconds\n", + " Total time for initialization = 4.3000E-01 seconds\n", + " Reading cross sections = 2.2800E-01 seconds\n", + " Total time in simulation = 6.1235E+01 seconds\n", + " Time in transport only = 6.1207E+01 seconds\n", + " Time in inactive batches = 5.0280E+00 seconds\n", + " Time in active batches = 5.6207E+01 seconds\n", + " Time synchronizing fission bank = 7.0000E-03 seconds\n", + " Sampling source sites = 4.0000E-03 seconds\n", + " SEND/RECV source sites = 1.0000E-03 seconds\n", + " Time accumulating tallies = 2.0000E-03 seconds\n", " Total time for finalization = 0.0000E+00 seconds\n", - " Total time elapsed = 6.1576E+01 seconds\n", - " Calculation Rate (inactive) = 4934.86 neutrons/second\n", - " Calculation Rate (active) = 1783.96 neutrons/second\n", + " Total time elapsed = 6.1689E+01 seconds\n", + " Calculation Rate (inactive) = 4972.16 neutrons/second\n", + " Calculation Rate (active) = 1779.14 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02763 +/- 0.00343\n", - " k-effective (Track-length) = 1.02973 +/- 0.00366\n", - " k-effective (Absorption) = 1.02732 +/- 0.00319\n", - " Combined k-effective = 1.02826 +/- 0.00259\n", + " k-effective (Collision) = 1.02489 +/- 0.00308\n", + " k-effective (Track-length) = 1.02707 +/- 0.00397\n", + " k-effective (Absorption) = 1.02637 +/- 0.00325\n", + " Combined k-effective = 1.02581 +/- 0.00264\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -955,8 +959,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1941: RuntimeWarning: invalid value encountered in true_divide\n", - " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" + "/Users/sam/.local/lib/python2.7/site-packages/openmc-0.8.0-py2.7.egg/openmc/tallies.py:1944: RuntimeWarning: invalid value encountered in true_divide\n" ] }, { @@ -980,16 +983,16 @@ " 10000\n", " 1\n", " U235\n", - " 8.055246e-03\n", - " 2.857567e-05\n", + " 8.046809e-03\n", + " 2.697198e-05\n", " \n", " \n", " 4\n", " 10000\n", " 1\n", " U238\n", - " 7.339215e-03\n", - " 4.349466e-05\n", + " 7.366624e-03\n", + " 4.255197e-05\n", " \n", " \n", " 5\n", @@ -1004,16 +1007,16 @@ " 10000\n", " 2\n", " U235\n", - " 3.615565e-01\n", - " 2.050486e-03\n", + " 3.614917e-01\n", + " 2.135233e-03\n", " \n", " \n", " 1\n", " 10000\n", " 2\n", " U238\n", - " 6.742638e-07\n", - " 3.795256e-09\n", + " 6.741607e-07\n", + " 3.924924e-09\n", " \n", " \n", " 2\n", @@ -1029,11 +1032,11 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "3 10000 1 U235 8.055246e-03 2.857567e-05\n", - "4 10000 1 U238 7.339215e-03 4.349466e-05\n", + "3 10000 1 U235 8.046809e-03 2.697198e-05\n", + "4 10000 1 U238 7.366624e-03 4.255197e-05\n", "5 10000 1 O16 0.000000e+00 0.000000e+00\n", - "0 10000 2 U235 3.615565e-01 2.050486e-03\n", - "1 10000 2 U238 6.742638e-07 3.795256e-09\n", + "0 10000 2 U235 3.614917e-01 2.135233e-03\n", + "1 10000 2 U238 6.741607e-07 3.924924e-09\n", "2 10000 2 O16 0.000000e+00 0.000000e+00" ] }, @@ -1071,18 +1074,18 @@ "\tDomain ID =\t10000\n", "\tNuclide =\tU235\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t8.06e-03 +/- 3.55e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t3.62e-01 +/- 5.67e-01%\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t8.05e-03 +/- 3.35e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t3.61e-01 +/- 5.91e-01%\n", "\n", "\tNuclide =\tU238\n", "\tCross Sections [cm^-1]:\n", - " Group 1 [6.25e-07 - 20.0 MeV]:\t7.34e-03 +/- 5.93e-01%\n", - " Group 2 [0.0 - 6.25e-07 MeV]:\t6.74e-07 +/- 5.63e-01%\n", + " Group 1 [6.25e-07 - 20.0 MeV]:\t7.37e-03 +/- 5.78e-01%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t6.74e-07 +/- 5.82e-01%\n", "\n", "\tNuclide =\tO16\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", + " Group 1 [6.25e-07 - 20.0 MeV]:\t0.00e+00 +/- 0.00e+00%\n", + " Group 2 [0.0 - 6.25e-07 MeV]:\t0.00e+00 +/- 0.00e+00%\n", "\n", "\n", "\n" @@ -1193,16 +1196,16 @@ " 10000\n", " 1\n", " U235\n", - " 0.074860\n", - " 0.000303\n", + " 0.074734\n", + " 0.000325\n", " \n", " \n", " 1\n", " 10000\n", " 1\n", " U238\n", - " 0.005952\n", - " 0.000035\n", + " 0.005977\n", + " 0.000034\n", " \n", " \n", " 2\n", @@ -1218,8 +1221,8 @@ ], "text/plain": [ " cell group in nuclide mean std. dev.\n", - "0 10000 1 U235 0.074860 0.000303\n", - "1 10000 1 U238 0.005952 0.000035\n", + "0 10000 1 U235 0.074734 0.000325\n", + "1 10000 1 U238 0.005977 0.000034\n", "2 10000 1 O16 0.000000 0.000000" ] }, @@ -1300,127 +1303,133 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ NORMAL ] Ray tracing for track segmentation...\n", - "[ NORMAL ] Dumping tracks to file...\n", + "[ NORMAL ] Importing ray tracing data from file...\n", "[ NORMAL ] Computing the eigenvalue...\n", - "[ NORMAL ] Iteration 0:\tk_eff = 0.854370\tres = 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+ "[ NORMAL ] Iteration 76:\tk_eff = 1.021462\tres = 4.335E-04\n", + "[ NORMAL ] Iteration 77:\tk_eff = 1.021841\tres = 4.019E-04\n", + "[ NORMAL ] Iteration 78:\tk_eff = 1.022193\tres = 3.725E-04\n", + "[ NORMAL ] Iteration 79:\tk_eff = 1.022518\tres = 3.453E-04\n", + "[ NORMAL ] Iteration 80:\tk_eff = 1.022820\tres = 3.200E-04\n", + "[ NORMAL ] Iteration 81:\tk_eff = 1.023100\tres = 2.965E-04\n", + "[ NORMAL ] Iteration 82:\tk_eff = 1.023359\tres = 2.748E-04\n", + "[ NORMAL ] Iteration 83:\tk_eff = 1.023600\tres = 2.546E-04\n", + "[ NORMAL ] Iteration 84:\tk_eff = 1.023822\tres = 2.358E-04\n", + "[ NORMAL ] Iteration 85:\tk_eff = 1.024028\tres = 2.185E-04\n", + "[ NORMAL ] Iteration 86:\tk_eff = 1.024219\tres = 2.023E-04\n", + "[ NORMAL ] Iteration 87:\tk_eff = 1.024396\tres = 1.874E-04\n", + "[ NORMAL ] Iteration 88:\tk_eff = 1.024560\tres = 1.735E-04\n", + "[ NORMAL ] Iteration 89:\tk_eff = 1.024712\tres = 1.607E-04\n", + "[ NORMAL ] Iteration 90:\tk_eff = 1.024852\tres = 1.488E-04\n", + "[ NORMAL ] Iteration 91:\tk_eff = 1.024983\tres = 1.378E-04\n", + "[ NORMAL ] Iteration 92:\tk_eff = 1.025103\tres = 1.275E-04\n", + "[ NORMAL ] Iteration 93:\tk_eff = 1.025215\tres = 1.181E-04\n", + "[ NORMAL ] Iteration 94:\tk_eff = 1.025318\tres = 1.093E-04\n", + "[ NORMAL ] Iteration 95:\tk_eff = 1.025413\tres = 1.012E-04\n", + "[ NORMAL ] Iteration 96:\tk_eff = 1.025502\tres = 9.364E-05\n", + "[ NORMAL ] Iteration 97:\tk_eff = 1.025584\tres = 8.666E-05\n", + "[ NORMAL ] Iteration 98:\tk_eff = 1.025659\tres = 8.020E-05\n", + "[ NORMAL ] Iteration 99:\tk_eff = 1.025729\tres = 7.422E-05\n", + "[ NORMAL ] Iteration 100:\tk_eff = 1.025794\tres = 6.868E-05\n", + "[ NORMAL ] Iteration 101:\tk_eff = 1.025854\tres = 6.355E-05\n", + "[ NORMAL ] Iteration 102:\tk_eff = 1.025910\tres = 5.880E-05\n", + "[ NORMAL ] Iteration 103:\tk_eff = 1.025961\tres = 5.440E-05\n", + "[ NORMAL ] Iteration 104:\tk_eff = 1.026009\tres = 5.033E-05\n", + "[ NORMAL ] Iteration 105:\tk_eff = 1.026053\tres = 4.656E-05\n", + "[ NORMAL ] Iteration 106:\tk_eff = 1.026093\tres = 4.307E-05\n", + "[ NORMAL ] Iteration 107:\tk_eff = 1.026131\tres = 3.984E-05\n", + "[ NORMAL ] Iteration 108:\tk_eff = 1.026166\tres = 3.685E-05\n", + "[ NORMAL ] Iteration 109:\tk_eff = 1.026198\tres = 3.409E-05\n", + "[ NORMAL ] Iteration 110:\tk_eff = 1.026228\tres = 3.153E-05\n", + "[ NORMAL ] Iteration 111:\tk_eff = 1.026255\tres = 2.916E-05\n", + "[ NORMAL ] Iteration 112:\tk_eff = 1.026281\tres = 2.697E-05\n", + "[ NORMAL ] Iteration 113:\tk_eff = 1.026304\tres = 2.494E-05\n", + "[ NORMAL ] Iteration 114:\tk_eff = 1.026326\tres = 2.307E-05\n", + "[ NORMAL ] Iteration 115:\tk_eff = 1.026346\tres = 2.133E-05\n", + "[ NORMAL ] Iteration 116:\tk_eff = 1.026365\tres = 1.973E-05\n", + "[ NORMAL ] Iteration 117:\tk_eff = 1.026382\tres = 1.824E-05\n", + "[ NORMAL ] Iteration 118:\tk_eff = 1.026398\tres = 1.687E-05\n", + "[ NORMAL ] Iteration 119:\tk_eff = 1.026413\tres = 1.560E-05\n", + "[ NORMAL ] Iteration 120:\tk_eff = 1.026426\tres = 1.442E-05\n", + "[ NORMAL ] Iteration 121:\tk_eff = 1.026439\tres = 1.333E-05\n", + "[ NORMAL ] Iteration 122:\tk_eff = 1.026451\tres = 1.233E-05\n", + "[ NORMAL ] Iteration 123:\tk_eff = 1.026461\tres = 1.140E-05\n", + "[ NORMAL ] Iteration 124:\tk_eff = 1.026471\tres = 1.054E-05\n" ] } ], @@ -1452,9 +1461,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "openmc keff = 1.028263\n", - "openmoc keff = 1.028491\n", - "bias [pcm]: 22.8\n" + "openmc keff = 1.025806\n", + "openmoc keff = 1.026471\n", + "bias [pcm]: 66.5\n" ] } ], @@ -1562,7 +1571,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 43, @@ -1571,9 +1580,9 @@ }, { "data": { - "image/png": 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VNdtPwnmHuC2E1Cu0bRJH6iVyzU5JR96okOa+NACcdbbdB/qxnmvqYIn9CqQF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\nSUVORK5CYII=\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1613,7 +1622,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/index.rst b/docs/source/pythonapi/index.rst index aa4815175e..392c2c72df 100644 --- a/docs/source/pythonapi/index.rst +++ b/docs/source/pythonapi/index.rst @@ -27,6 +27,8 @@ Example Jupyter Notebooks examples/mgxs-part-ii examples/mgxs-part-iii examples/mgxs-part-iv + examples/mdgxs-part-i + examples/mdgxs-part-ii examples/nuclear-data ------------------------------------ @@ -284,6 +286,19 @@ Multi-group Cross Sections openmc.mgxs.TotalXS openmc.mgxs.TransportXS +Multi-delayed-group Cross Sections +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: myclassinherit.rst + + openmc.mgxs.MDGXS + openmc.mgxs.ChiDelayed + openmc.mgxs.DelayedNuFissionXS + openmc.mgxs.Beta + Multi-group Cross Section Libraries ----------------------------------- diff --git a/openmc/__init__.py b/openmc/__init__.py index 026ccce114..a0492ee408 100644 --- a/openmc/__init__.py +++ b/openmc/__init__.py @@ -13,10 +13,10 @@ from openmc.settings import * from openmc.surface import * from openmc.universe import * from openmc.mesh import * -from openmc.mgxs_library import * from openmc.filter import * from openmc.trigger import * from openmc.tallies import * +from openmc.mgxs_library import * from openmc.cmfd import * from openmc.executor import * from openmc.statepoint import * diff --git a/openmc/filter.py b/openmc/filter.py index 8a54e05cee..256674af3e 100644 --- a/openmc/filter.py +++ b/openmc/filter.py @@ -777,6 +777,18 @@ class Filter(object): df.loc[:, self.type + ' low'] = lo_bins df.loc[:, self.type + ' high'] = hi_bins + elif self.type == 'surface': + filter_bins = np.repeat(self.bins, self.stride) + tile_factor = data_size / len(filter_bins) + filter_bins = np.tile(filter_bins, tile_factor) + filter_bins = [x if x != 1 else 'x-min' for x in filter_bins] + filter_bins = [x if x != 2 else 'x-max' for x in filter_bins] + filter_bins = [x if x != 3 else 'y-min' for x in filter_bins] + filter_bins = [x if x != 4 else 'y-max' for x in filter_bins] + filter_bins = [x if x != 5 else 'z-min' for x in filter_bins] + filter_bins = [x if x != 6 else 'z-max' for x in filter_bins] + df = pd.concat([df, pd.DataFrame({self.type : filter_bins})]) + # universe, material, surface, cell, and cellborn filters else: filter_bins = np.repeat(self.bins, self.stride) diff --git a/openmc/mgxs/__init__.py b/openmc/mgxs/__init__.py index 4fcf8b6aed..7fd6e0a692 100644 --- a/openmc/mgxs/__init__.py +++ b/openmc/mgxs/__init__.py @@ -1,3 +1,4 @@ from openmc.mgxs.groups import EnergyGroups from openmc.mgxs.library import Library from openmc.mgxs.mgxs import * +from openmc.mgxs.mdgxs import * diff --git a/openmc/mgxs/library.py b/openmc/mgxs/library.py index ee11d0ef64..bf1195f1a9 100644 --- a/openmc/mgxs/library.py +++ b/openmc/mgxs/library.py @@ -11,6 +11,7 @@ import numpy as np import openmc import openmc.mgxs import openmc.checkvalue as cv +from openmc.tallies import ESTIMATOR_TYPES if sys.version_info[0] >= 3: @@ -18,17 +19,18 @@ if sys.version_info[0] >= 3: class Library(object): - """A multi-group cross section library for some energy group structure. + """A multi-energy-group and multi-delayed-group cross section library for + some energy group structure. This class can be used for both OpenMC input generation and tally data post-processing to compute spatially-homogenized and energy-integrated multi-group cross sections for deterministic neutronics calculations. - This class helps automate the generation of MGXS objects for some energy - group structure and domain type. The Library serves as a collection for - MGXS objects with routines to automate the initialization of tallies for - input files, the loading of tally data from statepoint files, data storage, - energy group condensation and more. + This class helps automate the generation of MGXS and MDGXS objects for some + energy group structure and domain type. The Library serves as a collection + for MGXS and MDGXS objects with routines to automate the initialization of + tallies for input files, the loading of tally data from statepoint files, + data storage, energy group condensation and more. Parameters ---------- @@ -39,7 +41,7 @@ class Library(object): mgxs_types : Iterable of str The types of cross sections in the library (e.g., ['total', 'scatter']) name : str, optional - Name of the multi-group cross section. library Used as a label to + Name of the multi-group cross section library. Used as a label to identify tallies in OpenMC 'tallies.xml' file. Attributes @@ -64,6 +66,11 @@ class Library(object): The highest legendre moment in the scattering matrices (default is 0) energy_groups : openmc.mgxs.EnergyGroups Energy group structure for energy condensation + delayed_groups : list of int + Delayed groups to filter out the xs + estimator : str or None + The tally estimator used to compute multi-group cross sections. If None, + the default for each MGXS type is used. tally_trigger : openmc.Trigger An (optional) tally precision trigger given to each tally used to compute the cross section @@ -95,6 +102,7 @@ class Library(object): self._domain_type = None self._domains = 'all' self._energy_groups = None + self._delayed_groups = None self._correction = 'P0' self._legendre_order = 0 self._tally_trigger = None @@ -102,6 +110,7 @@ class Library(object): self._sp_filename = None self._keff = None self._sparse = False + self._estimator = None self.name = name self.openmc_geometry = openmc_geometry @@ -126,6 +135,7 @@ class Library(object): clone._correction = self.correction clone._legendre_order = self.legendre_order clone._energy_groups = copy.deepcopy(self.energy_groups, memo) + clone._delayed_groups = copy.deepcopy(self.delayed_groups, memo) clone._tally_trigger = copy.deepcopy(self.tally_trigger, memo) clone._all_mgxs = copy.deepcopy(self.all_mgxs) clone._sp_filename = self._sp_filename @@ -194,6 +204,10 @@ class Library(object): def energy_groups(self): return self._energy_groups + @property + def delayed_groups(self): + return self._delayed_groups + @property def correction(self): return self._correction @@ -206,10 +220,21 @@ class Library(object): def tally_trigger(self): return self._tally_trigger + @property + def estimator(self): + return self._estimator + @property def num_groups(self): return self.energy_groups.num_groups + @property + def num_delayed_groups(self): + if self.delayed_groups == None: + return 0 + else: + return len(self.delayed_groups) + @property def all_mgxs(self): return self._all_mgxs @@ -239,22 +264,33 @@ class Library(object): @mgxs_types.setter def mgxs_types(self, mgxs_types): + all_mgxs_types = openmc.mgxs.MGXS_TYPES + openmc.mgxs.MDGXS_TYPES if mgxs_types == 'all': - self._mgxs_types = openmc.mgxs.MGXS_TYPES + self._mgxs_types = all_mgxs_types else: cv.check_iterable_type('mgxs_types', mgxs_types, basestring) for mgxs_type in mgxs_types: - cv.check_value('mgxs_type', mgxs_type, openmc.mgxs.MGXS_TYPES) + cv.check_value('mgxs_type', mgxs_type, all_mgxs_types) self._mgxs_types = mgxs_types @by_nuclide.setter def by_nuclide(self, by_nuclide): cv.check_type('by_nuclide', by_nuclide, bool) + + if by_nuclide == True and self.domain_type == 'mesh': + raise ValueError('Unable to create MGXS library by nuclide with ' + 'mesh domain') + self._by_nuclide = by_nuclide @domain_type.setter def domain_type(self, domain_type): cv.check_value('domain type', domain_type, openmc.mgxs.DOMAIN_TYPES) + + if self.by_nuclide == True and domain_type == 'mesh': + raise ValueError('Unable to create MGXS library by nuclide with ' + 'mesh domain') + self._domain_type = domain_type @domains.setter @@ -298,6 +334,23 @@ class Library(object): cv.check_type('energy groups', energy_groups, openmc.mgxs.EnergyGroups) self._energy_groups = energy_groups + @delayed_groups.setter + def delayed_groups(self, delayed_groups): + + if delayed_groups != None: + + cv.check_type('delayed groups', delayed_groups, list, int) + cv.check_greater_than('num delayed groups', len(delayed_groups), 0) + + # Check that the groups are within [1, MAX_DELAYED_GROUPS] + for group in delayed_groups: + cv.check_greater_than('delayed group', group, 0) + cv.check_less_than('delayed group', group, + openmc.mgxs.MAX_DELAYED_GROUPS, + equality=True) + + self._delayed_groups = delayed_groups + @correction.setter def correction(self, correction): cv.check_value('correction', correction, ('P0', None)) @@ -327,6 +380,11 @@ class Library(object): cv.check_type('tally trigger', tally_trigger, openmc.Trigger) self._tally_trigger = tally_trigger + @estimator.setter + def estimator(self, estimator): + cv.check_value('estimator', estimator, ESTIMATOR_TYPES) + self._estimator = estimator + @sparse.setter def sparse(self, sparse): """Convert tally data from NumPy arrays to SciPy list of lists (LIL) @@ -363,14 +421,23 @@ class Library(object): for domain in self.domains: self.all_mgxs[domain.id] = OrderedDict() for mgxs_type in self.mgxs_types: - mgxs = openmc.mgxs.MGXS.get_mgxs(mgxs_type, name=self.name) + if mgxs_type in openmc.mgxs.MDGXS_TYPES: + mgxs = openmc.mgxs.MDGXS.get_mgxs(mgxs_type, name=self.name) + else: + mgxs = openmc.mgxs.MGXS.get_mgxs(mgxs_type, name=self.name) + mgxs.domain = domain mgxs.domain_type = self.domain_type mgxs.energy_groups = self.energy_groups mgxs.by_nuclide = self.by_nuclide + if self.estimator is not None: + mgxs.estimator = self.estimator + + if mgxs_type in openmc.mgxs.MDGXS_TYPES: + mgxs.delayed_groups = self.delayed_groups # If a tally trigger was specified, add it to the MGXS - if self.tally_trigger: + if self.tally_trigger is not None: mgxs.tally_trigger = self.tally_trigger # Specify whether to use a transport ('P0') correction @@ -460,7 +527,7 @@ class Library(object): ---------- domain : Material or Cell or Universe or Integral The material, cell, or universe object of interest (or its ID) - mgxs_type : {'total', 'transport', 'nu-transport', 'absorption', 'capture', 'fission', 'nu-fission', 'kappa-fission', 'scatter', 'nu-scatter', 'scatter matrix', 'nu-scatter matrix', 'multiplicity matrix', 'nu-fission matrix', chi', 'chi-prompt', 'inverse-velocity', 'prompt-nu-fission'} + mgxs_type : {'total', 'transport', 'nu-transport', 'absorption', 'capture', 'fission', 'nu-fission', 'kappa-fission', 'scatter', 'nu-scatter', 'scatter matrix', 'nu-scatter matrix', 'multiplicity matrix', 'nu-fission matrix', chi', 'chi-prompt', 'inverse-velocity', 'prompt-nu-fission', 'delayed-nu-fission', 'chi-delayed', 'beta'} The type of multi-group cross section object to return Returns @@ -763,7 +830,7 @@ class Library(object): Parameters ---------- - domain : openmc.Material or openmc.Cell or openmc.Universe + domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh The domain for spatial homogenization xsdata_name : str Name to apply to the "xsdata" entry produced by this method @@ -811,7 +878,7 @@ class Library(object): """ cv.check_type('domain', domain, (openmc.Material, openmc.Cell, - openmc.Cell)) + openmc.Cell, openmc.Mesh)) cv.check_type('xsdata_name', xsdata_name, basestring) cv.check_type('nuclide', nuclide, basestring) cv.check_value('xs_type', xs_type, ['macro', 'micro']) diff --git a/openmc/mgxs/mdgxs.py b/openmc/mgxs/mdgxs.py new file mode 100644 index 0000000000..0a2f898c07 --- /dev/null +++ b/openmc/mgxs/mdgxs.py @@ -0,0 +1,1573 @@ +from __future__ import division + +from collections import Iterable, OrderedDict +from numbers import Integral +import warnings +import os +import sys +import copy +import abc + +import numpy as np + +import openmc +from openmc.mgxs import MGXS +import openmc.checkvalue as cv + +if sys.version_info[0] >= 3: + basestring = str + +# Supported cross section types +MDGXS_TYPES = ['delayed-nu-fission', + 'chi-delayed', + 'beta'] + +# Maximum number of delayed groups, from src/constants.F90 +MAX_DELAYED_GROUPS = 8 + + +class MDGXS(MGXS): + """An abstract multi-delayed-group cross section for some energy and delayed + group structures within some spatial domain. + + This class can be used for both OpenMC input generation and tally data + post-processing to compute spatially-homogenized and energy-integrated + multi-group and multi-delayed-group cross sections for downstream + neutronics calculations. + + NOTE: Users should instantiate the subclasses of this abstract class. + + Parameters + ---------- + domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh + The domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + The domain type for spatial homogenization + energy_groups : openmc.mgxs.EnergyGroups + The energy group structure for energy condensation + by_nuclide : bool + If true, computes cross sections for each nuclide in domain + name : str, optional + Name of the multi-group cross section. Used as a label to identify + tallies in OpenMC 'tallies.xml' file. + delayed_groups : list of int + Delayed groups to filter out the xs + + Attributes + ---------- + name : str, optional + Name of the multi-group cross section + rxn_type : str + Reaction type (e.g., 'chi-delayed', 'beta', etc.) + by_nuclide : bool + If true, computes cross sections for each nuclide in domain + domain : Material or Cell or Universe or Mesh + Domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + Domain type for spatial homogenization + energy_groups : openmc.mgxs.EnergyGroups + Energy group structure for energy condensation + delayed_groups : list of int + Delayed groups to filter out the xs + tally_trigger : openmc.Trigger + An (optional) tally precision trigger given to each tally used to + compute the cross section + scores : list of str + The scores in each tally used to compute the multi-group cross section + filters : list of openmc.Filter + The filters in each tally used to compute the multi-group cross section + tally_keys : list of str + The keys into the tallies dictionary for each tally used to compute + the multi-group cross section + estimator : {'tracklength', 'analog'} + The tally estimator used to compute the multi-group cross section + tallies : collections.OrderedDict + OpenMC tallies needed to compute the multi-group cross section + rxn_rate_tally : openmc.Tally + Derived tally for the reaction rate tally used in the numerator to + compute the multi-group cross section. This attribute is None + unless the multi-group cross section has been computed. + xs_tally : openmc.Tally + Derived tally for the multi-group cross section. This attribute + is None unless the multi-group cross section has been computed. + num_subdomains : int + The number of subdomains is unity for 'material', 'cell', and 'universe' + domain types. This is equal to the number of cell instances for + 'distribcell' domain types (it is equal to unity prior to loading + tally data from a statepoint file) and the number of mesh cells for + 'mesh' domain types. + num_nuclides : int + The number of nuclides for which the multi-group cross section is + being tracked. This is unity if the by_nuclide attribute is False. + nuclides : Iterable of str or 'sum' + The optional user-specified nuclides for which to compute cross + sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides + are not specified by the user, all nuclides in the spatial domain + are included. This attribute is 'sum' if by_nuclide is false. + sparse : bool + Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format + for compressed data storage + loaded_sp : bool + Whether or not a statepoint file has been loaded with tally data + derived : bool + Whether or not the MGXS is merged from one or more other MGXS + hdf5_key : str + The key used to index multi-group cross sections in an HDF5 data store + + """ + + # This is an abstract class which cannot be instantiated + __metaclass__ = abc.ABCMeta + + def __init__(self, domain=None, domain_type=None, energy_groups=None, + delayed_groups=None, by_nuclide=False, name=''): + super(MDGXS, self).__init__(domain, domain_type, energy_groups, + by_nuclide, name) + + self._delayed_groups = None + + if delayed_groups is not None: + self.delayed_groups = delayed_groups + + def __deepcopy__(self, memo): + existing = memo.get(id(self)) + + # If this is the first time we have tried to copy this object, copy it + if existing is None: + clone = type(self).__new__(type(self)) + clone._name = self.name + clone._rxn_type = self.rxn_type + clone._by_nuclide = self.by_nuclide + clone._nuclides = copy.deepcopy(self._nuclides) + clone._domain = self.domain + clone._domain_type = self.domain_type + clone._energy_groups = copy.deepcopy(self.energy_groups, memo) + clone._delayed_groups = copy.deepcopy(self.delayed_groups, memo) + clone._tally_trigger = copy.deepcopy(self.tally_trigger, memo) + clone._rxn_rate_tally = copy.deepcopy(self._rxn_rate_tally, memo) + clone._xs_tally = copy.deepcopy(self._xs_tally, memo) + clone._sparse = self.sparse + clone._derived = self.derived + + clone._tallies = OrderedDict() + for tally_type, tally in self.tallies.items(): + clone.tallies[tally_type] = copy.deepcopy(tally, memo) + + memo[id(self)] = clone + + return clone + + # If this object has been copied before, return the first copy made + else: + return existing + + @property + def delayed_groups(self): + return self._delayed_groups + + @property + def num_delayed_groups(self): + if self.delayed_groups == None: + return 1 + else: + return len(self.delayed_groups) + + @delayed_groups.setter + def delayed_groups(self, delayed_groups): + + if delayed_groups != None: + + cv.check_type('delayed groups', delayed_groups, list, int) + cv.check_greater_than('num delayed groups', len(delayed_groups), 0) + + # Check that the groups are within [1, MAX_DELAYED_GROUPS] + for group in delayed_groups: + cv.check_greater_than('delayed group', group, 0) + cv.check_less_than('delayed group', group, MAX_DELAYED_GROUPS, + equality=True) + + self._delayed_groups = delayed_groups + + @property + def filters(self): + + # Create the non-domain specific Filters for the Tallies + group_edges = self.energy_groups.group_edges + energy_filter = openmc.Filter('energy', group_edges) + + if self.delayed_groups != None: + delayed_filter = openmc.Filter('delayedgroup', self.delayed_groups) + return [[energy_filter], [delayed_filter, energy_filter]] + else: + return [[energy_filter], [energy_filter]] + + @staticmethod + def get_mgxs(mdgxs_type, domain=None, domain_type=None, + energy_groups=None, delayed_groups=None, + by_nuclide=False, name=''): + """Return a MDGXS subclass object for some energy group structure within + some spatial domain for some reaction type. + + This is a factory method which can be used to quickly create MDGXS + subclass objects for various reaction types. + + Parameters + ---------- + mdgxs_type : {'delayed-nu-fission', 'chi-delayed', 'beta'} + The type of multi-delayed-group cross section object to return + domain : openmc.Material or openmc.Cell or openmc.Universe or + openmc.Mesh + The domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + The domain type for spatial homogenization + energy_groups : openmc.mgxs.EnergyGroups + The energy group structure for energy condensation + by_nuclide : bool + If true, computes cross sections for each nuclide in domain. + Defaults to False + name : str, optional + Name of the multi-group cross section. Used as a label to identify + tallies in OpenMC 'tallies.xml' file. Defaults to the empty string. + delayed_groups : list of int + Delayed groups to filter out the xs + + Returns + ------- + openmc.mgxs.MDGXS + A subclass of the abstract MDGXS class for the multi-delayed-group + cross section type requested by the user + + """ + + cv.check_value('mdgxs_type', mdgxs_type, MDGXS_TYPES) + + if mdgxs_type == 'delayed-nu-fission': + mdgxs = DelayedNuFissionXS(domain, domain_type, energy_groups, + delayed_groups) + elif mdgxs_type == 'chi-delayed': + mdgxs = ChiDelayed(domain, domain_type, energy_groups, + delayed_groups) + elif mdgxs_type == 'beta': + mdgxs = Beta(domain, domain_type, energy_groups, delayed_groups) + + mdgxs.by_nuclide = by_nuclide + mdgxs.name = name + return mdgxs + + def get_xs(self, groups='all', subdomains='all', nuclides='all', + xs_type='macro', order_groups='increasing', + value='mean', delayed_groups='all', squeeze=True, **kwargs): + """Returns an array of multi-delayed-group cross sections. + + This method constructs a 4D NumPy array for the requested + multi-delayed-group cross section data for one or more + subdomains (1st dimension), delayed groups (2nd demension), + energy groups (3rd dimension), and nuclides (4th dimension). + + Parameters + ---------- + groups : Iterable of Integral or 'all' + Energy groups of interest. Defaults to 'all'. + subdomains : Iterable of Integral or 'all' + Subdomain IDs of interest. Defaults to 'all'. + nuclides : Iterable of str or 'all' or 'sum' + A list of nuclide name strings (e.g., ['U-235', 'U-238']). The + special string 'all' will return the cross sections for all nuclides + in the spatial domain. The special string 'sum' will return the + cross section summed over all nuclides. Defaults to 'all'. + xs_type: {'macro', 'micro'} + Return the macro or micro cross section in units of cm^-1 or barns. + Defaults to 'macro'. + order_groups: {'increasing', 'decreasing'} + Return the cross section indexed according to increasing or + decreasing energy groups (decreasing or increasing energies). + Defaults to 'increasing'. + value : {'mean', 'std_dev', 'rel_err'} + A string for the type of value to return. Defaults to 'mean'. + delayed_groups : list of int or 'all' + Delayed groups of interest. Defaults to 'all'. + squeeze : bool + A boolean representing whether to eliminate the extra dimensions + of the multi-dimensional array to be returned. Defaults to True. + + Returns + ------- + numpy.ndarray + A NumPy array of the multi-group cross section indexed in the order + each group, subdomain and nuclide is listed in the parameters. + + Raises + ------ + ValueError + When this method is called before the multi-delayed-group cross + section is computed from tally data. + + """ + + cv.check_value('value', value, ['mean', 'std_dev', 'rel_err']) + cv.check_value('xs_type', xs_type, ['macro', 'micro']) + + # FIXME: Unable to get microscopic xs for mesh domain because the mesh + # cells do not know the nuclide densities in each mesh cell. + if self.domain_type == 'mesh' and xs_type == 'micro': + msg = 'Unable to get micro xs for mesh domain since the mesh ' \ + 'cells do not know the nuclide densities in each mesh cell.' + raise ValueError(msg) + + filters = [] + filter_bins = [] + + # Construct a collection of the domain filter bins + if not isinstance(subdomains, basestring): + cv.check_iterable_type('subdomains', subdomains, Integral, + max_depth=3) + for subdomain in subdomains: + filters.append(self.domain_type) + filter_bins.append((subdomain,)) + + # Construct list of energy group bounds tuples for all requested groups + if not isinstance(groups, basestring): + cv.check_iterable_type('groups', groups, Integral) + for group in groups: + filters.append('energy') + filter_bins.append( + (self.energy_groups.get_group_bounds(group),)) + + # Construct list of delayed group tuples for all requested groups + if not isinstance(delayed_groups, basestring): + cv.check_type('delayed groups', delayed_groups, list, int) + for delayed_group in delayed_groups: + filters.append('delayedgroup') + filter_bins.append((delayed_group,)) + + # Construct a collection of the nuclides to retrieve from the xs tally + if self.by_nuclide: + if nuclides == 'all' or nuclides == 'sum' or nuclides == ['sum']: + query_nuclides = self.get_all_nuclides() + else: + query_nuclides = nuclides + else: + query_nuclides = ['total'] + + # If user requested the sum for all nuclides, use tally summation + if nuclides == 'sum' or nuclides == ['sum']: + xs_tally = self.xs_tally.summation(nuclides=query_nuclides) + xs = xs_tally.get_values(filters=filters, + filter_bins=filter_bins, value=value) + else: + xs = self.xs_tally.get_values(filters=filters, + filter_bins=filter_bins, + nuclides=query_nuclides, value=value) + + # Divide by atom number densities for microscopic cross sections + if xs_type == 'micro': + if self.by_nuclide: + densities = self.get_nuclide_densities(nuclides) + else: + densities = self.get_nuclide_densities('sum') + if value == 'mean' or value == 'std_dev': + xs /= densities[np.newaxis, :, np.newaxis] + + # Eliminate the trivial score dimension + xs = np.squeeze(xs, axis=len(xs.shape) - 1) + xs = np.nan_to_num(xs) + + if groups == 'all': + num_groups = self.num_groups + else: + num_groups = len(groups) + + if delayed_groups == 'all': + num_delayed_groups = self.num_delayed_groups + else: + num_delayed_groups = len(delayed_groups) + + # Reshape tally data array with separate axes for domain, energy groups, + # delayed groups, and nuclides + num_subdomains = int(xs.shape[0] / (num_groups * num_delayed_groups)) + new_shape = (num_subdomains, num_delayed_groups, num_groups) + new_shape += xs.shape[1:] + xs = np.reshape(xs, new_shape) + + # Reverse data if user requested increasing energy groups since + # tally data is stored in order of increasing energies + if order_groups == 'increasing': + xs = xs[:, :, ::-1, :] + + if squeeze: + xs = np.squeeze(xs) + xs = np.atleast_1d(xs) + + return xs + + def get_slice(self, nuclides=[], groups=[], delayed_groups=[]): + """Build a sliced MDGXS for the specified nuclides, energy groups, + and delayed groups. + + This method constructs a new MDGXS to encapsulate a subset of the data + represented by this MDGXS. The subset of data to include in the tally + slice is determined by the nuclides, energy groups, delayed groups + specified in the input parameters. + + Parameters + ---------- + nuclides : list of str + A list of nuclide name strings + (e.g., ['U-235', 'U-238']; default is []) + groups : list of int + A list of energy group indices starting at 1 for the high energies + (e.g., [1, 2, 3]; default is []) + delayed_groups : list of int + A list of delayed group indices + (e.g., [1, 2, 3]; default is []) + + Returns + ------- + openmc.mgxs.MDGXS + A new MDGXS object which encapsulates the subset of data requested + for the nuclide(s) and/or energy group(s) and/or delayed group(s) + requested in the parameters. + + """ + + cv.check_iterable_type('nuclides', nuclides, basestring) + cv.check_iterable_type('energy_groups', groups, Integral) + cv.check_type('delayed groups', delayed_groups, list, int) + + # Build lists of filters and filter bins to slice + filters = [] + filter_bins = [] + + if len(groups) != 0: + energy_bins = [] + for group in groups: + group_bounds = self.energy_groups.get_group_bounds(group) + energy_bins.append(group_bounds) + filter_bins.append(tuple(energy_bins)) + filters.append('energy') + + if len(delayed_groups) != 0: + filter_bins.append(tuple(delayed_groups)) + filters.append('delayedgroup') + + # Clone this MGXS to initialize the sliced version + slice_xs = copy.deepcopy(self) + slice_xs._rxn_rate_tally = None + slice_xs._xs_tally = None + + # Slice each of the tallies across nuclides and energy groups + for tally_type, tally in slice_xs.tallies.items(): + slice_nuclides = [nuc for nuc in nuclides if nuc in tally.nuclides] + if filters != []: + tally_slice = tally.get_slice(filters=filters, + filter_bins=filter_bins, + nuclides=slice_nuclides) + else: + tally_slice = tally.get_slice(nuclides=slice_nuclides) + slice_xs.tallies[tally_type] = tally_slice + + # Assign sliced energy group structure to sliced MDGXS + if groups: + new_group_edges = [] + for group in groups: + group_edges = self.energy_groups.get_group_bounds(group) + new_group_edges.extend(group_edges) + new_group_edges = np.unique(new_group_edges) + slice_xs.energy_groups.group_edges = sorted(new_group_edges) + + # Assign sliced delayed group structure to sliced MDGXS + if delayed_groups: + slice_xs.delayed_groups = delayed_groups + + # Assign sliced nuclides to sliced MGXS + if nuclides: + slice_xs.nuclides = nuclides + + slice_xs.sparse = self.sparse + return slice_xs + + def merge(self, other): + """Merge another MGXS with this one + + MGXS are only mergeable if their energy groups and nuclides are either + identical or mutually exclusive. If results have been loaded from a + statepoint, then MGXS are only mergeable along one and only one of + energy groups or nuclides. + + Parameters + ---------- + other : openmc.mgxs.MDGXS + MDGXS to merge with this one + + Returns + ------- + merged_mdgxs : openmc.mgxs.MDGXS + Merged MDGXS + + """ + + merged_mdgxs = super(MDGXS, self).merge(other) + + # Merge delayed groups + if self.delayed_groups != other.delayed_groups: + merged_mdgxs.delayed_groups = list(set(self.delayed_groups + + other.delayed_groups)) + + return merged_mdgxs + + def print_xs(self, subdomains='all', nuclides='all', xs_type='macro'): + """Print a string representation for the multi-group cross section. + + Parameters + ---------- + subdomains : Iterable of Integral or 'all' + The subdomain IDs of the cross sections to include in the report. + Defaults to 'all'. + nuclides : Iterable of str or 'all' or 'sum' + The nuclides of the cross-sections to include in the report. This + may be a list of nuclide name strings (e.g., ['U-235', 'U-238']). + The special string 'all' will report the cross sections for all + nuclides in the spatial domain. The special string 'sum' will report + the cross sections summed over all nuclides. Defaults to 'all'. + xs_type: {'macro', 'micro'} + Return the macro or micro cross section in units of cm^-1 or barns. + Defaults to 'macro'. + + """ + + if self.delayed_groups == None: + super(MDGXS, self).print_xs(subdomains, nuclides, xs_type) + return + + # Construct a collection of the subdomains to report + if not isinstance(subdomains, basestring): + cv.check_iterable_type('subdomains', subdomains, Integral) + elif self.domain_type == 'distribcell': + subdomains = np.arange(self.num_subdomains, dtype=np.int) + elif self.domain_type == 'mesh': + xyz = [range(1, x+1) for x in self.domain.dimension] + subdomains = list(itertools.product(*xyz)) + else: + subdomains = [self.domain.id] + + # Construct a collection of the nuclides to report + if self.by_nuclide: + if nuclides == 'all': + nuclides = self.get_all_nuclides() + elif nuclides == 'sum': + nuclides = ['sum'] + else: + cv.check_iterable_type('nuclides', nuclides, basestring) + else: + nuclides = ['sum'] + + cv.check_value('xs_type', xs_type, ['macro', 'micro']) + + # Build header for string with type and domain info + string = 'Multi-Delayed-Group XS\n' + string += '{0: <16}=\t{1}\n'.format('\tReaction Type', self.rxn_type) + string += '{0: <16}=\t{1}\n'.format('\tDomain Type', self.domain_type) + string += '{0: <16}=\t{1}\n'.format('\tDomain ID', self.domain.id) + + # Generate the header for an individual XS + xs_header = '\tCross Sections [{0}]:'.format(self.get_units(xs_type)) + + # If cross section data has not been computed, only print string header + if self.tallies is None: + print(string) + return + + # Loop over all subdomains + for subdomain in subdomains: + + if self.domain_type == 'distribcell' or self.domain_type == 'mesh': + string += '{0: <16}=\t{1}\n'.format('\tSubdomain', subdomain) + + # Loop over all Nuclides + for nuclide in nuclides: + + # Build header for nuclide type + if nuclide != 'sum': + string += '{0: <16}=\t{1}\n'.format('\tNuclide', nuclide) + + # Add the cross section header + string += '{0: <16}\n'.format(xs_header) + + for delayed_group in self.delayed_groups: + + template = '{0: <12}Delayed Group {1}:\t' + string += template.format('', delayed_group) + string += '\n' + + template = '{0: <12}Group {1} [{2: <10} - {3: <10}MeV]:\t' + + # Loop over energy groups ranges + for group in range(1, self.num_groups+1): + bounds = self.energy_groups.get_group_bounds(group) + string += template.format('', group, bounds[0], bounds[1]) + average = self.get_xs([group], [subdomain], [nuclide], + xs_type=xs_type, value='mean', + delayed_groups=[delayed_group]) + rel_err = self.get_xs([group], [subdomain], [nuclide], + xs_type=xs_type, value='rel_err', + delayed_groups=[delayed_group]) + average = average.flatten()[0] + rel_err = rel_err.flatten()[0] * 100. + string += '{:.2e} +/- {:1.2e}%'.format(average, rel_err) + string += '\n' + string += '\n' + string += '\n' + + print(string) + + def export_xs_data(self, filename='mgxs', directory='mgxs', + format='csv', groups='all', xs_type='macro', + delayed_groups='all'): + """Export the multi-delayed-group cross section data to a file. + + This method leverages the functionality in the Pandas library to export + the multi-group cross section data in a variety of output file formats + for storage and/or post-processing. + + Parameters + ---------- + filename : str + Filename for the exported file. Defaults to 'mgxs'. + directory : str + Directory for the exported file. Defaults to 'mgxs'. + format : {'csv', 'excel', 'pickle', 'latex'} + The format for the exported data file. Defaults to 'csv'. + groups : Iterable of Integral or 'all' + Energy groups of interest. Defaults to 'all'. + xs_type: {'macro', 'micro'} + Store the macro or micro cross section in units of cm^-1 or barns. + Defaults to 'macro'. + delayed_groups : list of int or 'all' + Delayed groups of interest. Defaults to 'all'. + + """ + + cv.check_type('filename', filename, basestring) + cv.check_type('directory', directory, basestring) + cv.check_value('format', format, ['csv', 'excel', 'pickle', 'latex']) + cv.check_value('xs_type', xs_type, ['macro', 'micro']) + + # Make directory if it does not exist + if not os.path.exists(directory): + os.makedirs(directory) + + filename = os.path.join(directory, filename) + filename = filename.replace(' ', '-') + + # Get a Pandas DataFrame for the data + df = self.get_pandas_dataframe(groups=groups, xs_type=xs_type, + delayed_groups=delayed_groups) + + # Export the data using Pandas IO API + if format == 'csv': + df.to_csv(filename + '.csv', index=False) + elif format == 'excel': + if self.domain_type == 'mesh': + df.to_excel(filename + '.xls') + else: + df.to_excel(filename + '.xls', index=False) + elif format == 'pickle': + df.to_pickle(filename + '.pkl') + elif format == 'latex': + if self.domain_type == 'distribcell': + msg = 'Unable to export distribcell multi-group cross section' \ + 'data to a LaTeX table' + raise NotImplementedError(msg) + + df.to_latex(filename + '.tex', bold_rows=True, + longtable=True, index=False) + + # Surround LaTeX table with code needed to run pdflatex + with open(filename + '.tex','r') as original: + data = original.read() + with open(filename + '.tex','w') as modified: + modified.write( + '\\documentclass[preview, 12pt, border=1mm]{standalone}\n') + modified.write('\\usepackage{caption}\n') + modified.write('\\usepackage{longtable}\n') + modified.write('\\usepackage{booktabs}\n') + modified.write('\\begin{document}\n\n') + modified.write(data) + modified.write('\n\\end{document}') + + def get_pandas_dataframe(self, groups='all', nuclides='all', + xs_type='macro', distribcell_paths=True, + delayed_groups='all'): + """Build a Pandas DataFrame for the MDGXS data. + + This method leverages :meth:`openmc.Tally.get_pandas_dataframe`, but + renames the columns with terminology appropriate for cross section data. + + Parameters + ---------- + groups : Iterable of Integral or 'all' + Energy groups of interest. Defaults to 'all'. + nuclides : Iterable of str or 'all' or 'sum' + The nuclides of the cross-sections to include in the dataframe. This + may be a list of nuclide name strings (e.g., ['U-235', 'U-238']). + The special string 'all' will include the cross sections for all + nuclides in the spatial domain. The special string 'sum' will + include the cross sections summed over all nuclides. Defaults + to 'all'. + xs_type: {'macro', 'micro'} + Return macro or micro cross section in units of cm^-1 or barns. + Defaults to 'macro'. + distribcell_paths : bool, optional + Construct columns for distribcell tally filters (default is True). + The geometric information in the Summary object is embedded into + a Multi-index column with a geometric "path" to each distribcell + instance. + delayed_groups : list of int or 'all' + Delayed groups of interest. Defaults to 'all'. + + Returns + ------- + pandas.DataFrame + A Pandas DataFrame for the cross section data. + + Raises + ------ + ValueError + When this method is called before the multi-delayed-group cross + section is computed from tally data. + + """ + + if not isinstance(groups, basestring): + cv.check_iterable_type('groups', groups, Integral) + if nuclides != 'all' and nuclides != 'sum': + cv.check_iterable_type('nuclides', nuclides, basestring) + if not isinstance(delayed_groups, basestring): + cv.check_type('delayed groups', delayed_groups, list, int) + + cv.check_value('xs_type', xs_type, ['macro', 'micro']) + + # Get a Pandas DataFrame from the derived xs tally + if self.by_nuclide and nuclides == 'sum': + + # Use tally summation to sum across all nuclides + query_nuclides = self.get_all_nuclides() + xs_tally = self.xs_tally.summation(nuclides=query_nuclides) + df = xs_tally.get_pandas_dataframe( + distribcell_paths=distribcell_paths) + + # Remove nuclide column since it is homogeneous and redundant + if self.domain_type == 'mesh': + df.drop('nuclide', axis=1, level=0, inplace=True) + else: + df.drop('nuclide', axis=1, inplace=True) + + # If the user requested a specific set of nuclides + elif self.by_nuclide and nuclides != 'all': + xs_tally = self.xs_tally.get_slice(nuclides=nuclides) + df = xs_tally.get_pandas_dataframe( + distribcell_paths=distribcell_paths) + + # If the user requested all nuclides, keep nuclide column in dataframe + else: + df = self.xs_tally.get_pandas_dataframe( + distribcell_paths=distribcell_paths) + + # Remove the score column since it is homogeneous and redundant + if self.domain_type == 'mesh': + df = df.drop('score', axis=1, level=0) + else: + df = df.drop('score', axis=1) + + # Override energy groups bounds with indices + all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int) + all_groups = np.repeat(all_groups, self.num_nuclides) + if 'energy low [MeV]' in df and 'energyout low [MeV]' in df: + df.rename(columns={'energy low [MeV]': 'group in'}, + inplace=True) + in_groups = np.tile(all_groups, int(self.num_subdomains * + self.num_delayed_groups)) + in_groups = np.repeat(in_groups, int(df.shape[0] / in_groups.size)) + df['group in'] = in_groups + del df['energy high [MeV]'] + + df.rename(columns={'energyout low [MeV]': 'group out'}, + inplace=True) + out_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size)) + df['group out'] = out_groups + del df['energyout high [MeV]'] + columns = ['group in', 'group out'] + + elif 'energyout low [MeV]' in df: + df.rename(columns={'energyout low [MeV]': 'group out'}, + inplace=True) + in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size)) + df['group out'] = in_groups + del df['energyout high [MeV]'] + columns = ['group out'] + + elif 'energy low [MeV]' in df: + df.rename(columns={'energy low [MeV]': 'group in'}, inplace=True) + in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size)) + df['group in'] = in_groups + del df['energy high [MeV]'] + columns = ['group in'] + + # Select out those groups the user requested + if not isinstance(groups, basestring): + if 'group in' in df: + df = df[df['group in'].isin(groups)] + if 'group out' in df: + df = df[df['group out'].isin(groups)] + + # If user requested micro cross sections, divide out the atom densities + if xs_type == 'micro': + if self.by_nuclide: + densities = self.get_nuclide_densities(nuclides) + else: + densities = self.get_nuclide_densities('sum') + densities = np.repeat(densities, len(self.rxn_rate_tally.scores)) + tile_factor = df.shape[0] / len(densities) + df['mean'] /= np.tile(densities, tile_factor) + df['std. dev.'] /= np.tile(densities, tile_factor) + + # Sort the dataframe by domain type id (e.g., distribcell id) and + # energy groups such that data is from fast to thermal + if self.domain_type == 'mesh': + mesh_str = 'mesh {0}'.format(self.domain.id) + df.sort_values(by=[(mesh_str, 'x'), (mesh_str, 'y'), \ + (mesh_str, 'z')] + columns, inplace=True) + else: + df.sort_values(by=[self.domain_type] + columns, inplace=True) + + return df + + +class ChiDelayed(MDGXS): + r"""The delayed fission spectrum. + + This class can be used for both OpenMC input generation and tally data + post-processing to compute spatially-homogenized and energy-integrated + multi-group and multi-delayed-group cross sections for multi-group + neutronics calculations. At a minimum, one needs to set the + :attr:`ChiDelayed.energy_groups` and :attr:`ChiDelayed.domain` properties. + Tallies for the flux and appropriate reaction rates over the specified + domain are generated automatically via the :attr:`ChiDelayed.tallies` + property, which can then be appended to a :class:`openmc.Tallies` instance. + + For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the + necessary data to compute multi-group cross sections from a + :class:`openmc.StatePoint` instance. The derived multi-group cross + section can then be obtained from the :attr:`ChiDelayed.xs_tally` property. + + For a spatial domain :math:`V`, energy group :math:`[E_g,E_{g-1}]`, and + delayed group :math:`d`, the delayed fission spectrum is calculated as: + + .. math:: + + \langle \nu^d \sigma_{f,g' \rightarrow g} \phi \rangle &= \int_{r \in V} + dr \int_{4\pi} d\Omega' \int_0^\infty dE' \int_{E_g}^{E_{g-1}} dE \; + \chi(E) \nu^d \sigma_f (r, E') \psi(r, E', \Omega')\\ + \langle \nu^d \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi} + d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu^d \sigma_f (r, + E') \psi(r, E', \Omega') \\ + \chi_g^d &= \frac{\langle \nu^d \sigma_{f,g' \rightarrow g} \phi \rangle} + {\langle \nu^d \sigma_f \phi \rangle} + + Parameters + ---------- + domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh + The domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + The domain type for spatial homogenization + groups : openmc.mgxs.EnergyGroups + The energy group structure for energy condensation + by_nuclide : bool + If true, computes cross sections for each nuclide in domain + name : str, optional + Name of the multi-group cross section. Used as a label to identify + tallies in OpenMC 'tallies.xml' file. + delayed_groups : list of int + Delayed groups to filter out the xs + + Attributes + ---------- + name : str, optional + Name of the multi-group cross section + rxn_type : str + Reaction type (e.g., 'total', 'nu-fission', etc.) + by_nuclide : bool + If true, computes cross sections for each nuclide in domain + domain : Material or Cell or Universe or Mesh + Domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + Domain type for spatial homogenization + energy_groups : openmc.mgxs.EnergyGroups + Energy group structure for energy condensation + delayed_groups : list of int + Delayed groups to filter out the xs + tally_trigger : openmc.Trigger + An (optional) tally precision trigger given to each tally used to + compute the cross section + scores : list of str + The scores in each tally used to compute the multi-group cross section + filters : list of openmc.Filter + The filters in each tally used to compute the multi-group cross section + tally_keys : list of str + The keys into the tallies dictionary for each tally used to compute + the multi-group cross section + estimator : {'tracklength', 'analog'} + The tally estimator used to compute the multi-group cross section + tallies : collections.OrderedDict + OpenMC tallies needed to compute the multi-group cross section. The keys + are strings listed in the :attr:`ChiDelayed.tally_keys` property and + values are instances of :class:`openmc.Tally`. + rxn_rate_tally : openmc.Tally + Derived tally for the reaction rate tally used in the numerator to + compute the multi-group cross section. This attribute is None + unless the multi-group cross section has been computed. + xs_tally : openmc.Tally + Derived tally for the multi-group cross section. This attribute + is None unless the multi-group cross section has been computed. + num_subdomains : int + The number of subdomains is unity for 'material', 'cell' and 'universe' + domain types. When the This is equal to the number of cell instances + for 'distribcell' domain types (it is equal to unity prior to loading + tally data from a statepoint file). + num_nuclides : int + The number of nuclides for which the multi-group cross section is + being tracked. This is unity if the by_nuclide attribute is False. + nuclides : Iterable of str or 'sum' + The optional user-specified nuclides for which to compute cross + sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides + are not specified by the user, all nuclides in the spatial domain + are included. This attribute is 'sum' if by_nuclide is false. + sparse : bool + Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format + for compressed data storage + loaded_sp : bool + Whether or not a statepoint file has been loaded with tally data + derived : bool + Whether or not the MGXS is merged from one or more other MGXS + hdf5_key : str + The key used to index multi-group cross sections in an HDF5 data store + + """ + + def __init__(self, domain=None, domain_type=None, energy_groups=None, + delayed_groups=None, by_nuclide=False, name=''): + super(ChiDelayed, self).__init__(domain, domain_type, energy_groups, + delayed_groups, by_nuclide, name) + self._rxn_type = 'chi-delayed' + + @property + def scores(self): + return ['delayed-nu-fission', 'delayed-nu-fission'] + + @property + def filters(self): + # Create the non-domain specific Filters for the Tallies + group_edges = self.energy_groups.group_edges + energyout = openmc.Filter('energyout', group_edges) + energyin = openmc.Filter('energy', [group_edges[0], group_edges[-1]]) + if self.delayed_groups != None: + delayed_filter = openmc.Filter('delayedgroup', self.delayed_groups) + return [[delayed_filter, energyin], [delayed_filter, energyout]] + else: + return [[energyin], [energyout]] + + @property + def tally_keys(self): + return ['delayed-nu-fission-in', 'delayed-nu-fission-out'] + + @property + def estimator(self): + return 'analog' + + @property + def rxn_rate_tally(self): + if self._rxn_rate_tally is None: + self._rxn_rate_tally = self.tallies['delayed-nu-fission-out'] + self._rxn_rate_tally.sparse = self.sparse + return self._rxn_rate_tally + + @property + def xs_tally(self): + + if self._xs_tally is None: + delayed_nu_fission_in = self.tallies['delayed-nu-fission-in'] + + # Remove coarse energy filter to keep it out of tally arithmetic + energy_filter = delayed_nu_fission_in.find_filter('energy') + delayed_nu_fission_in.remove_filter(energy_filter) + + # Compute chi + self._xs_tally = self.rxn_rate_tally / delayed_nu_fission_in + super(ChiDelayed, self)._compute_xs() + + # Add the coarse energy filter back to the nu-fission tally + delayed_nu_fission_in.filters.append(energy_filter) + + return self._xs_tally + + def get_slice(self, nuclides=[], groups=[], delayed_groups=[]): + """Build a sliced ChiDelayed for the specified nuclides and energy + groups. + + This method constructs a new MGXS to encapsulate a subset of the data + represented by this MGXS. The subset of data to include in the tally + slice is determined by the nuclides and energy groups specified in + the input parameters. + + Parameters + ---------- + nuclides : list of str + A list of nuclide name strings + (e.g., ['U-235', 'U-238']; default is []) + groups : list of Integral + A list of energy group indices starting at 1 for the high energies + (e.g., [1, 2, 3]; default is []) + delayed_groups : list of int + A list of delayed group indices + (e.g., [1, 2, 3]; default is []) + + Returns + ------- + openmc.mgxs.MDGXS + A new MDGXS which encapsulates the subset of data requested + for the nuclide(s) and/or energy group(s) and/or delayed group(s) + requested in the parameters. + + """ + + # Temporarily remove energy filter from delayed-nu-fission-in since its + # group structure will work in super MGXS.get_slice(...) method + delayed_nu_fission_in = self.tallies['delayed-nu-fission-in'] + energy_filter = delayed_nu_fission_in.find_filter('energy') + delayed_nu_fission_in.remove_filter(energy_filter) + + # Call super class method and null out derived tallies + slice_xs = super(ChiDelayed, self).get_slice(nuclides, groups, + delayed_groups) + slice_xs._rxn_rate_tally = None + slice_xs._xs_tally = None + + # Slice energy groups if needed + filters = [] + filter_bins = [] + + if len(groups) != 0: + energy_bins = [] + for group in groups: + group_bounds = self.energy_groups.get_group_bounds(group) + energy_bins.append(group_bounds) + filter_bins.append(tuple(energy_bins)) + filters.append('energyout') + + if len(delayed_groups) != 0: + filter_bins.append(tuple(delayed_groups)) + filters.append('delayedgroup') + + if filters != []: + + # Slice nu-fission-out tally along energyout filter + delayed_nu_fission_out = slice_xs.tallies['delayed-nu-fission-out'] + tally_slice = delayed_nu_fission_out.get_slice \ + (filters=filters, filter_bins=filter_bins) + slice_xs._tallies['delayed-nu-fission-out'] = tally_slice + + # Add energy filter back to nu-fission-in tallies + self.tallies['delayed-nu-fission-in'].add_filter(energy_filter) + slice_xs._tallies['delayed-nu-fission-in'].add_filter(energy_filter) + + slice_xs.sparse = self.sparse + return slice_xs + + def merge(self, other): + """Merge another ChiDelayed with this one + + If results have been loaded from a statepoint, then ChiDelayed are only + mergeable along one and only one of energy groups or nuclides. + + Parameters + ---------- + other : openmc.mdgxs.MGXS + MGXS to merge with this one + + Returns + ------- + merged_mdgxs : openmc.mgxs.MDGXS + Merged MDGXS + """ + + if not self.can_merge(other): + raise ValueError('Unable to merge ChiDelayed') + + # Create deep copy of tally to return as merged tally + merged_mdgxs = copy.deepcopy(self) + merged_mdgxs._derived = True + merged_mdgxs._rxn_rate_tally = None + merged_mdgxs._xs_tally = None + + # Merge energy groups + if self.energy_groups != other.energy_groups: + merged_groups = self.energy_groups.merge(other.energy_groups) + merged_mdgxs.energy_groups = merged_groups + + # Merge delayed groups + if self.delayed_groups != other.delayed_groups: + merged_mdgxs.delayed_groups = list(set(self.delayed_groups + + other.delayed_groups)) + + # Merge nuclides + if self.nuclides != other.nuclides: + + # The nuclides must be mutually exclusive + for nuclide in self.nuclides: + if nuclide in other.nuclides: + msg = 'Unable to merge Chi Delayed with shared nuclides' + raise ValueError(msg) + + # Concatenate lists of nuclides for the merged MGXS + merged_mdgxs.nuclides = self.nuclides + other.nuclides + + # Merge tallies + for tally_key in self.tallies: + merged_tally = self.tallies[tally_key].merge\ + (other.tallies[tally_key]) + merged_mdgxs.tallies[tally_key] = merged_tally + + return merged_mdgxs + + def get_xs(self, groups='all', subdomains='all', nuclides='all', + xs_type='macro', order_groups='increasing', + value='mean', delayed_groups='all', squeeze=True, **kwargs): + """Returns an array of the delayed fission spectrum. + + This method constructs a 4D NumPy array for the requested + multi-delayed-group cross section data for one or more + subdomains (1st dimension), delayed groups (2nd demension), + energy groups (3rd dimension), and nuclides (4th dimension). + + Parameters + ---------- + groups : Iterable of Integral or 'all' + Energy groups of interest. Defaults to 'all'. + delayed_groups : list of int or 'all' + Delayed groups of interest. Defaults to 'all'. + subdomains : Iterable of Integral or 'all' + Subdomain IDs of interest. Defaults to 'all'. + nuclides : Iterable of str or 'all' or 'sum' + A list of nuclide name strings (e.g., ['U-235', 'U-238']). The + special string 'all' will return the cross sections for all nuclides + in the spatial domain. The special string 'sum' will return the + cross section summed over all nuclides. Defaults to 'all'. + xs_type: {'macro', 'micro'} + This parameter is not relevant for chi but is included here to + mirror the parent MGXS.get_xs(...) class method + order_groups: {'increasing', 'decreasing'} + Return the cross section indexed according to increasing or + decreasing energy groups (decreasing or increasing energies). + Defaults to 'increasing'. + value : {'mean', 'std_dev', 'rel_err'} + A string for the type of value to return. Defaults to 'mean'. + squeeze : bool + A boolean representing whether to eliminate the extra dimensions + of the multi-dimensional array to be returned. Defaults to True. + + Returns + ------- + numpy.ndarray + A NumPy array of the multi-group and multi-delayed-group cross + section indexed in the order each group, subdomain and nuclide is + listed in the parameters. + + Raises + ------ + ValueError + When this method is called before the multi-group cross section is + computed from tally data. + + """ + + cv.check_value('value', value, ['mean', 'std_dev', 'rel_err']) + cv.check_value('xs_type', xs_type, ['macro', 'micro']) + + # FIXME: Unable to get microscopic xs for mesh domain because the mesh + # cells do not know the nuclide densities in each mesh cell. + if self.domain_type == 'mesh' and xs_type == 'micro': + msg = 'Unable to get micro xs for mesh domain since the mesh ' \ + 'cells do not know the nuclide densities in each mesh cell.' + raise ValueError(msg) + + filters = [] + filter_bins = [] + + # Construct a collection of the domain filter bins + if not isinstance(subdomains, basestring): + cv.check_iterable_type('subdomains', subdomains, Integral, + max_depth=3) + for subdomain in subdomains: + filters.append(self.domain_type) + filter_bins.append((subdomain,)) + + # Construct list of energy group bounds tuples for all requested groups + if not isinstance(groups, basestring): + cv.check_iterable_type('groups', groups, Integral) + for group in groups: + filters.append('energyout') + filter_bins.append( + (self.energy_groups.get_group_bounds(group),)) + + # Construct list of delayed group tuples for all requested groups + if not isinstance(delayed_groups, basestring): + cv.check_type('delayed groups', delayed_groups, list, int) + for delayed_group in delayed_groups: + filters.append('delayedgroup') + filter_bins.append((delayed_group,)) + + # If chi delayed was computed for each nuclide in the domain + if self.by_nuclide: + + # Get the sum as the fission source weighted average chi for all + # nuclides in the domain + if nuclides == 'sum' or nuclides == ['sum']: + + # Retrieve the fission production tallies + delayed_nu_fission_in = self.tallies['delayed-nu-fission-in'] + delayed_nu_fission_out = self.tallies['delayed-nu-fission-out'] + + # Sum out all nuclides + nuclides = self.get_all_nuclides() + delayed_nu_fission_in = delayed_nu_fission_in.summation\ + (nuclides=nuclides) + delayed_nu_fission_out = delayed_nu_fission_out.summation\ + (nuclides=nuclides) + + # Remove coarse energy filter to keep it out of tally arithmetic + energy_filter = delayed_nu_fission_in.find_filter('energy') + delayed_nu_fission_in.remove_filter(energy_filter) + + # Compute chi and store it as the xs_tally attribute so we can + # use the generic get_xs(...) method + xs_tally = delayed_nu_fission_out / delayed_nu_fission_in + + # Add the coarse energy filter back to the nu-fission tally + delayed_nu_fission_in.filters.append(energy_filter) + + xs = xs_tally.get_values(filters=filters, + filter_bins=filter_bins, value=value) + + # Get chi delayed for all nuclides in the domain + elif nuclides == 'all': + nuclides = self.get_all_nuclides() + xs = self.xs_tally.get_values(filters=filters, + filter_bins=filter_bins, + nuclides=nuclides, value=value) + + # Get chi delayed for user-specified nuclides in the domain + else: + cv.check_iterable_type('nuclides', nuclides, basestring) + xs = self.xs_tally.get_values(filters=filters, + filter_bins=filter_bins, + nuclides=nuclides, value=value) + + # If chi delayed was computed as an average of nuclides in the domain + else: + xs = self.xs_tally.get_values(filters=filters, + filter_bins=filter_bins, value=value) + + # Eliminate the trivial score dimension + xs = np.squeeze(xs, axis=len(xs.shape) - 1) + xs = np.nan_to_num(xs) + + # Reshape tally data array with separate axes for domain and energy + if groups == 'all': + num_groups = self.num_groups + else: + num_groups = len(groups) + + if delayed_groups == 'all': + num_delayed_groups = self.num_delayed_groups + else: + num_delayed_groups = len(delayed_groups) + + # Reshape tally data array with separate axes for domain, energy groups, + # delayed groups, and nuclides + num_subdomains = int(xs.shape[0] / (num_groups * num_delayed_groups)) + new_shape = (num_subdomains, num_delayed_groups, num_groups) + new_shape += xs.shape[1:] + xs = np.reshape(xs, new_shape) + + # Reverse data if user requested increasing energy groups since + # tally data is stored in order of increasing energies + if order_groups == 'increasing': + xs = xs[:, :, ::-1, :] + + if squeeze: + xs = np.squeeze(xs) + xs = np.atleast_1d(xs) + + return xs + + +class DelayedNuFissionXS(MDGXS): + r"""A fission delayed neutron production multi-group cross section. + + This class can be used for both OpenMC input generation and tally data + post-processing to compute spatially-homogenized and energy-integrated + multi-group fission neutron production cross sections for multi-group + neutronics calculations. At a minimum, one needs to set the + :attr:`DelayedNuFissionXS.energy_groups` and :attr:`DelayedNuFissionXS.domain` + properties. Tallies for the flux and appropriate reaction rates over the + specified domain are generated automatically via the + :attr:`DelayedNuFissionXS.tallies` property, which can then be appended to a + :class:`openmc.Tallies` instance. + + For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the + necessary data to compute multi-group cross sections from a + :class:`openmc.StatePoint` instance. The derived multi-group cross section + can then be obtained from the :attr:`DelayedNuFissionXS.xs_tally` property. + + For a spatial domain :math:`V`, energy group :math:`[E_g,E_{g-1}]`, and + delayed group :math:`d`, the fission delayed neutron production cross + section is calculated as: + + .. math:: + + \frac{\int_{r \in V} dr \int_{4\pi} d\Omega \int_{E_g}^{E_{g-1}} dE \; + \nu^d \sigma_f (r, E) \psi (r, E, \Omega)}{\int_{r \in V} dr \int_{4\pi} + d\Omega \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega)}. + + + Parameters + ---------- + domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh + The domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + The domain type for spatial homogenization + groups : openmc.mgxs.EnergyGroups + The energy group structure for energy condensation + by_nuclide : bool + If true, computes cross sections for each nuclide in domain + name : str, optional + Name of the multi-group cross section. Used as a label to identify + tallies in OpenMC 'tallies.xml' file. + delayed_groups : list of int + Delayed groups to filter out the xs + + Attributes + ---------- + name : str, optional + Name of the multi-group cross section + rxn_type : str + Reaction type (e.g., 'total', 'nu-fission', etc.) + by_nuclide : bool + If true, computes cross sections for each nuclide in domain + domain : Material or Cell or Universe or Mesh + Domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + Domain type for spatial homogenization + energy_groups : openmc.mgxs.EnergyGroups + Energy group structure for energy condensation + delayed_groups : list of int + Delayed groups to filter out the xs + tally_trigger : openmc.Trigger + An (optional) tally precision trigger given to each tally used to + compute the cross section + scores : list of str + The scores in each tally used to compute the multi-group cross section + filters : list of openmc.Filter + The filters in each tally used to compute the multi-group cross section + tally_keys : list of str + The keys into the tallies dictionary for each tally used to compute + the multi-group cross section + estimator : {'tracklength', 'analog'} + The tally estimator used to compute the multi-group cross section + tallies : collections.OrderedDict + OpenMC tallies needed to compute the multi-group cross section. The keys + are strings listed in the :attr:`DelayedNuFissionXS.tally_keys` property + and values are instances of :class:`openmc.Tally`. + rxn_rate_tally : openmc.Tally + Derived tally for the reaction rate tally used in the numerator to + compute the multi-group cross section. This attribute is None + unless the multi-group cross section has been computed. + xs_tally : openmc.Tally + Derived tally for the multi-group cross section. This attribute + is None unless the multi-group cross section has been computed. + num_subdomains : int + The number of subdomains is unity for 'material', 'cell' and 'universe' + domain types. When the This is equal to the number of cell instances + for 'distribcell' domain types (it is equal to unity prior to loading + tally data from a statepoint file). + num_nuclides : int + The number of nuclides for which the multi-group cross section is + being tracked. This is unity if the by_nuclide attribute is False. + nuclides : Iterable of str or 'sum' + The optional user-specified nuclides for which to compute cross + sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides + are not specified by the user, all nuclides in the spatial domain + are included. This attribute is 'sum' if by_nuclide is false. + sparse : bool + Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format + for compressed data storage + loaded_sp : bool + Whether or not a statepoint file has been loaded with tally data + derived : bool + Whether or not the MGXS is merged from one or more other MGXS + hdf5_key : str + The key used to index multi-group cross sections in an HDF5 data store + + """ + + def __init__(self, domain=None, domain_type=None, energy_groups=None, + delayed_groups=None, by_nuclide=False, name=''): + super(DelayedNuFissionXS, self).__init__(domain, domain_type, + energy_groups, delayed_groups, + by_nuclide, name) + self._rxn_type = 'delayed-nu-fission' + + +class Beta(MDGXS): + r"""The delayed neutron fraction. + + This class can be used for both OpenMC input generation and tally data + post-processing to compute spatially-homogenized and energy-integrated + multi-group and multi-delayed group cross sections for multi-group + neutronics calculations. At a minimum, one needs to set the + :attr:`Beta.energy_groups` and :attr:`Beta.domain` properties. Tallies for + the flux and appropriate reaction rates over the specified domain are + generated automatically via the :attr:`Beta.tallies` property, which can + then be appended to a :class:`openmc.Tallies` instance. + + For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the + necessary data to compute multi-group cross sections from a + :class:`openmc.StatePoint` instance. The derived multi-group cross section + can then be obtained from the :attr:`Beta.xs_tally` property. + + For a spatial domain :math:`V`, energy group :math:`[E_g,E_{g-1}]`, and + delayed group :math:`d`, the delayed neutron fraction is calculated as: + + .. math:: + + \langle \nu^d \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi} + d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu^d + \sigma_f (r, E') \psi(r, E', \Omega') \\ + \langle \nu \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi} + d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu + \sigma_f (r, E') \psi(r, E', \Omega') \\ + \beta_{d,g} &= \frac{\langle \nu^d \sigma_f \phi \rangle} + {\langle \nu \sigma_f \phi \rangle} + + Parameters + ---------- + domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh + The domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + The domain type for spatial homogenization + groups : openmc.mgxs.EnergyGroups + The energy group structure for energy condensation + by_nuclide : bool + If true, computes cross sections for each nuclide in domain + name : str, optional + Name of the multi-group cross section. Used as a label to identify + tallies in OpenMC 'tallies.xml' file. + delayed_groups : list of int + Delayed groups to filter out the xs + + Attributes + ---------- + name : str, optional + Name of the multi-group cross section + rxn_type : str + Reaction type (e.g., 'total', 'nu-fission', etc.) + by_nuclide : bool + If true, computes cross sections for each nuclide in domain + domain : Material or Cell or Universe or Mesh + Domain for spatial homogenization + domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} + Domain type for spatial homogenization + energy_groups : openmc.mgxs.EnergyGroups + Energy group structure for energy condensation + delayed_groups : list of int + Delayed groups to filter out the xs + tally_trigger : openmc.Trigger + An (optional) tally precision trigger given to each tally used to + compute the cross section + scores : list of str + The scores in each tally used to compute the multi-group cross section + filters : list of openmc.Filter + The filters in each tally used to compute the multi-group cross section + tally_keys : list of str + The keys into the tallies dictionary for each tally used to compute + the multi-group cross section + estimator : {'tracklength', 'analog'} + The tally estimator used to compute the multi-group cross section + tallies : collections.OrderedDict + OpenMC tallies needed to compute the multi-group cross section. The keys + are strings listed in the :attr:`Beta.tally_keys` property and + values are instances of :class:`openmc.Tally`. + rxn_rate_tally : openmc.Tally + Derived tally for the reaction rate tally used in the numerator to + compute the multi-group cross section. This attribute is None + unless the multi-group cross section has been computed. + xs_tally : openmc.Tally + Derived tally for the multi-group cross section. This attribute + is None unless the multi-group cross section has been computed. + num_subdomains : int + The number of subdomains is unity for 'material', 'cell' and 'universe' + domain types. When the This is equal to the number of cell instances + for 'distribcell' domain types (it is equal to unity prior to loading + tally data from a statepoint file). + num_nuclides : int + The number of nuclides for which the multi-group cross section is + being tracked. This is unity if the by_nuclide attribute is False. + nuclides : Iterable of str or 'sum' + The optional user-specified nuclides for which to compute cross + sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides + are not specified by the user, all nuclides in the spatial domain + are included. This attribute is 'sum' if by_nuclide is false. + sparse : bool + Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format + for compressed data storage + loaded_sp : bool + Whether or not a statepoint file has been loaded with tally data + derived : bool + Whether or not the MGXS is merged from one or more other MGXS + hdf5_key : str + The key used to index multi-group cross sections in an HDF5 data store + + """ + + def __init__(self, domain=None, domain_type=None, energy_groups=None, + delayed_groups=None, by_nuclide=False, name=''): + super(Beta, self).__init__(domain, domain_type, energy_groups, + delayed_groups, by_nuclide, name) + self._rxn_type = 'beta' + + @property + def scores(self): + return ['nu-fission', 'delayed-nu-fission'] + + @property + def tally_keys(self): + return ['nu-fission', 'delayed-nu-fission'] + + @property + def rxn_rate_tally(self): + if self._rxn_rate_tally is None: + self._rxn_rate_tally = self.tallies['delayed-nu-fission'] + self._rxn_rate_tally.sparse = self.sparse + return self._rxn_rate_tally + + @property + def xs_tally(self): + + if self._xs_tally is None: + nu_fission = self.tallies['nu-fission'] + + # Compute beta + self._xs_tally = self.rxn_rate_tally / nu_fission + super(Beta, self)._compute_xs() + + return self._xs_tally diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 137ab62f07..4b465a19d5 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -13,6 +13,7 @@ import numpy as np import openmc import openmc.checkvalue as cv +from openmc.tallies import ESTIMATOR_TYPES from openmc.mgxs import EnergyGroups if sys.version_info[0] >= 3: @@ -39,7 +40,6 @@ MGXS_TYPES = ['total', 'inverse-velocity', 'prompt-nu-fission'] - # Supported domain types DOMAIN_TYPES = ['cell', 'distribcell', @@ -102,7 +102,7 @@ class MGXS(object): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section @@ -144,11 +144,11 @@ class MGXS(object): def __init__(self, domain=None, domain_type=None, energy_groups=None, by_nuclide=False, name=''): - self._name = '' self._rxn_type = None self._by_nuclide = None self._nuclides = None + self._estimator = 'tracklength' self._domain = None self._domain_type = None self._energy_groups = None @@ -160,6 +160,7 @@ class MGXS(object): self._loaded_sp = False self._derived = False self._hdf5_key = None + self._valid_estimators = ESTIMATOR_TYPES self.name = name self.by_nuclide = by_nuclide @@ -250,7 +251,7 @@ class MGXS(object): @property def estimator(self): - return 'tracklength' + return self._estimator @property def tallies(self): @@ -368,6 +369,11 @@ class MGXS(object): cv.check_iterable_type('nuclides', nuclides, basestring) self._nuclides = nuclides + @estimator.setter + def estimator(self, estimator): + cv.check_value('estimator', estimator, self._valid_estimators) + self._estimator = estimator + @domain.setter def domain(self, domain): cv.check_type('domain', domain, _DOMAINS) @@ -710,11 +716,13 @@ class MGXS(object): def get_xs(self, groups='all', subdomains='all', nuclides='all', xs_type='macro', order_groups='increasing', - value='mean', **kwargs): + value='mean', squeeze=True, **kwargs): r"""Returns an array of multi-group cross sections. - This method constructs a 2D NumPy array for the requested multi-group - cross section data data for one or more energy groups and subdomains. + This method constructs a 3D NumPy array for the requested + multi-group cross section data for one or more subdomains + (1st dimension), energy groups (2nd dimension), and nuclides + (3rd dimension). Parameters ---------- @@ -736,6 +744,9 @@ class MGXS(object): Defaults to 'increasing'. value : {'mean', 'std_dev', 'rel_err'} A string for the type of value to return. Defaults to 'mean'. + squeeze : bool + A boolean representing whether to eliminate the extra dimensions + of the multi-dimensional array to be returned. Defaults to True. Returns ------- @@ -806,25 +817,29 @@ class MGXS(object): xs /= densities[np.newaxis, :, np.newaxis] xs[np.isnan(xs)] = 0.0 + # Eliminate the trivial score dimension + xs = np.squeeze(xs, axis=len(xs.shape) - 1) + xs = np.nan_to_num(xs) + + if groups == 'all': + num_groups = self.num_groups + else: + num_groups = len(groups) + + # Reshape tally data array with separate axes for domain and energy + num_subdomains = int(xs.shape[0] / num_groups) + new_shape = (num_subdomains, num_groups) + xs.shape[1:] + xs = np.reshape(xs, new_shape) + # Reverse data if user requested increasing energy groups since # tally data is stored in order of increasing energies if order_groups == 'increasing': - if groups == 'all': - num_groups = self.num_groups - else: - num_groups = len(groups) - - # Reshape tally data array with separate axes for domain and energy - num_subdomains = int(xs.shape[0] / num_groups) - new_shape = (num_subdomains, num_groups) + xs.shape[1:] - xs = np.reshape(xs, new_shape) - - # Reverse energies to align with increasing energy groups xs = xs[:, ::-1, :] - # Eliminate trivial dimensions - xs = np.squeeze(xs) - xs = np.atleast_1d(xs) + if squeeze: + xs = np.squeeze(xs) + xs = np.atleast_1d(xs) + return xs def get_condensed_xs(self, coarse_groups): @@ -1337,8 +1352,6 @@ class MGXS(object): std_dev = self.get_xs(subdomains=[subdomain], nuclides=[nuclide], xs_type=xs_type, value='std_dev', row_column=row_column) - average = average.squeeze() - std_dev = std_dev.squeeze() # Add MGXS results data to the HDF5 group nuclide_group.require_dataset('average', dtype=np.float64, @@ -1504,15 +1517,14 @@ class MGXS(object): if 'energy low [MeV]' in df and 'energyout low [MeV]' in df: df.rename(columns={'energy low [MeV]': 'group in'}, inplace=True) - in_groups = np.tile(all_groups, self.num_subdomains) - in_groups = np.repeat(in_groups, df.shape[0] / in_groups.size) + in_groups = np.tile(all_groups, int(self.num_subdomains)) + in_groups = np.repeat(in_groups, int(df.shape[0] / in_groups.size)) df['group in'] = in_groups del df['energy high [MeV]'] df.rename(columns={'energyout low [MeV]': 'group out'}, inplace=True) - out_groups = np.repeat(all_groups, self.xs_tally.num_scores) - out_groups = np.tile(out_groups, df.shape[0] / out_groups.size) + out_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size)) df['group out'] = out_groups del df['energyout high [MeV]'] columns = ['group in', 'group out'] @@ -1520,14 +1532,14 @@ class MGXS(object): elif 'energyout low [MeV]' in df: df.rename(columns={'energyout low [MeV]': 'group out'}, inplace=True) - in_groups = np.tile(all_groups, self.num_subdomains) + in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size)) df['group out'] = in_groups del df['energyout high [MeV]'] columns = ['group out'] elif 'energy low [MeV]' in df: df.rename(columns={'energy low [MeV]': 'group in'}, inplace=True) - in_groups = np.tile(all_groups, self.num_subdomains) + in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size)) df['group in'] = in_groups del df['energy high [MeV]'] columns = ['group in'] @@ -1562,6 +1574,7 @@ class MGXS(object): (mesh_str, 'z')] + columns, inplace=True) else: df.sort_values(by=[self.domain_type] + columns, inplace=True) + return df def get_units(self, xs_type='macro'): @@ -1636,7 +1649,7 @@ class MatrixMGXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section @@ -1685,18 +1698,16 @@ class MatrixMGXS(MGXS): return [[energy], [energy, energyout]] - @property - def estimator(self): - return 'analog' - def get_xs(self, in_groups='all', out_groups='all', subdomains='all', nuclides='all', xs_type='macro', order_groups='increasing', - row_column='inout', value='mean', **kwargs): + row_column='inout', value='mean', squeeze=True, **kwargs): """Returns an array of multi-group cross sections. - This method constructs a 2D NumPy array for the requested multi-group - matrix data for one or more energy groups and subdomains. + This method constructs a 4D NumPy array for the requested + multi-group cross section data for one or more subdomains + (1st dimension), energy groups in (2nd dimension), energy groups out + (3rd dimension), and nuclides (4th dimension). Parameters ---------- @@ -1725,6 +1736,9 @@ class MatrixMGXS(MGXS): Defaults to 'inout'. value : {'mean', 'std_dev', 'rel_err'} A string for the type of value to return. Defaults to 'mean'. + squeeze : bool + A boolean representing whether to eliminate the extra dimensions + of the multi-dimensional array to be returned. Defaults to True. Returns ------- @@ -1796,8 +1810,6 @@ class MatrixMGXS(MGXS): filter_bins=filter_bins, nuclides=query_nuclides, value=value) - xs = np.nan_to_num(xs) - # Divide by atom number densities for microscopic cross sections if xs_type == 'micro': if self.by_nuclide: @@ -1808,33 +1820,36 @@ class MatrixMGXS(MGXS): xs /= densities[np.newaxis, :, np.newaxis] xs[np.isnan(xs)] = 0.0 + # Eliminate the trivial score dimension + xs = np.squeeze(xs, axis=len(xs.shape) - 1) + xs = np.nan_to_num(xs) + + if in_groups == 'all': + num_in_groups = self.num_groups + else: + num_in_groups = len(in_groups) + + if out_groups == 'all': + num_out_groups = self.num_groups + else: + num_out_groups = len(out_groups) + + # Reshape tally data array with separate axes for domain and energy + num_subdomains = int(xs.shape[0] / (num_in_groups * num_out_groups)) + new_shape = (num_subdomains, num_in_groups, num_out_groups) + new_shape += xs.shape[1:] + xs = np.reshape(xs, new_shape) + + # Transpose the matrix if requested by user + if row_column == 'outin': + xs = np.swapaxes(xs, 1, 2) + # Reverse data if user requested increasing energy groups since # tally data is stored in order of increasing energies if order_groups == 'increasing': - if in_groups == 'all': - num_in_groups = self.num_groups - else: - num_in_groups = len(in_groups) - if out_groups == 'all': - num_out_groups = self.num_groups - else: - num_out_groups = len(out_groups) - - # Reshape tally data array with separate axes for domain and energy - num_subdomains = int(xs.shape[0] / - (num_in_groups * num_out_groups)) - new_shape = (num_subdomains, num_in_groups, num_out_groups) - new_shape += xs.shape[1:] - xs = np.reshape(xs, new_shape) - - # Transpose the matrix if requested by user - if row_column == 'outin': - xs = np.swapaxes(xs, 1, 2) - - # Reverse energies to align with increasing energy groups xs = xs[:, ::-1, ::-1, :] - # Eliminate trivial dimensions + if squeeze: xs = np.squeeze(xs) xs = np.atleast_2d(xs) @@ -2066,7 +2081,7 @@ class TotalXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -2184,7 +2199,7 @@ class TransportXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : 'analog' The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -2227,6 +2242,8 @@ class TransportXS(MGXS): super(TransportXS, self).__init__(domain, domain_type, groups, by_nuclide, name) self._rxn_type = 'transport' + self._estimator = 'analog' + self._valid_estimators = ['analog'] @property def scores(self): @@ -2239,10 +2256,6 @@ class TransportXS(MGXS): energyout_filter = openmc.Filter('energyout', group_edges) return [[energy_filter], [energy_filter], [energyout_filter]] - @property - def estimator(self): - return 'analog' - @property def rxn_rate_tally(self): if self._rxn_rate_tally is None: @@ -2314,7 +2327,7 @@ class NuTransportXS(TransportXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : 'analog' The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -2435,7 +2448,7 @@ class AbsorptionXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -2452,7 +2465,8 @@ class AbsorptionXS(MGXS): The number of subdomains is unity for 'material', 'cell' and 'universe' domain types. This is equal to the number of cell instances for 'distribcell' domain types (it is equal to unity prior to loading - tally data from a statepoint file). + tally data from a statepoint file) and the number of mesh cells for + 'mesh' domain types. num_nuclides : int The number of nuclides for which the multi-group cross section is being tracked. This is unity if the by_nuclide attribute is False. @@ -2551,7 +2565,7 @@ class CaptureXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -2673,7 +2687,7 @@ class FissionXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -2784,7 +2798,7 @@ class NuFissionXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -2828,7 +2842,6 @@ class NuFissionXS(MGXS): groups, by_nuclide, name) self._rxn_type = 'nu-fission' - class KappaFissionXS(MGXS): r"""A recoverable fission energy production rate multi-group cross section. @@ -2900,7 +2913,7 @@ class KappaFissionXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -3013,7 +3026,7 @@ class ScatterXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -3128,7 +3141,7 @@ class NuScatterXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -3171,10 +3184,8 @@ class NuScatterXS(MGXS): super(NuScatterXS, self).__init__(domain, domain_type, groups, by_nuclide, name) self._rxn_type = 'nu-scatter' - - @property - def estimator(self): - return 'analog' + self._estimator = 'analog' + self._valid_estimators = ['analog'] class ScatterMatrixXS(MatrixMGXS): @@ -3262,7 +3273,7 @@ class ScatterMatrixXS(MatrixMGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : 'analog' The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -3308,6 +3319,8 @@ class ScatterMatrixXS(MatrixMGXS): self._correction = 'P0' self._legendre_order = 0 self._hdf5_key = 'scatter matrix' + self._estimator = 'analog' + self._valid_estimators = ['analog'] def __deepcopy__(self, memo): clone = super(ScatterMatrixXS, self).__deepcopy__(memo) @@ -3511,11 +3524,13 @@ class ScatterMatrixXS(MatrixMGXS): def get_xs(self, in_groups='all', out_groups='all', subdomains='all', nuclides='all', moment='all', xs_type='macro', order_groups='increasing', - row_column='inout', value='mean'): + row_column='inout', value='mean', squeeze=True): r"""Returns an array of multi-group cross sections. - This method constructs a 2D NumPy array for the requested scattering - matrix data data for one or more energy groups and subdomains. + This method constructs a 5D NumPy array for the requested + multi-group cross section data for one or more subdomains + (1st dimension), energy groups in (2nd dimension), energy groups out + (3rd dimension), nuclides (4th dimension), and moments (5th dimension). NOTE: The scattering moments are not multiplied by the :math:`(2l+1)/2` prefactor in the expansion of the scattering source into Legendre @@ -3551,6 +3566,9 @@ class ScatterMatrixXS(MatrixMGXS): Defaults to 'inout'. value : {'mean', 'std_dev', 'rel_err'} A string for the type of value to return. Defaults to 'mean'. + squeeze : bool + A boolean representing whether to eliminate the extra dimensions + of the multi-dimensional array to be returned. Defaults to True. Returns ------- @@ -3629,8 +3647,6 @@ class ScatterMatrixXS(MatrixMGXS): filter_bins=filter_bins, nuclides=query_nuclides, value=value) - xs = np.nan_to_num(xs) - # Divide by atom number densities for microscopic cross sections if xs_type == 'micro': if self.by_nuclide: @@ -3641,32 +3657,35 @@ class ScatterMatrixXS(MatrixMGXS): xs /= densities[np.newaxis, :, np.newaxis] xs[np.isnan(xs)] = 0.0 + # Convert and nans to zero + xs = np.nan_to_num(xs) + + if in_groups == 'all': + num_in_groups = self.num_groups + else: + num_in_groups = len(in_groups) + + if out_groups == 'all': + num_out_groups = self.num_groups + else: + num_out_groups = len(out_groups) + + # Reshape tally data array with separate axes for domain and energy + num_subdomains = int(xs.shape[0] / (num_in_groups * num_out_groups)) + new_shape = (num_subdomains, num_in_groups, num_out_groups) + new_shape += xs.shape[1:] + xs = np.reshape(xs, new_shape) + + # Transpose the scattering matrix if requested by user + if row_column == 'outin': + xs = np.swapaxes(xs, 1, 2) + # Reverse data if user requested increasing energy groups since # tally data is stored in order of increasing energies if order_groups == 'increasing': - if in_groups == 'all': - num_in_groups = self.num_groups - else: - num_in_groups = len(in_groups) - if out_groups == 'all': - num_out_groups = self.num_groups - else: - num_out_groups = len(out_groups) - - # Reshape tally data array with separate axes for domain and energy - num_subdomains = int(xs.shape[0] / (num_in_groups * num_out_groups)) - new_shape = (num_subdomains, num_in_groups, num_out_groups) - new_shape += xs.shape[1:] - xs = np.reshape(xs, new_shape) - - # Transpose the scattering matrix if requested by user - if row_column == 'outin': - xs = np.swapaxes(xs, 1, 2) - - # Reverse energies to align with increasing energy groups xs = xs[:, ::-1, ::-1, :] - # Eliminate trivial dimensions + if squeeze: xs = np.squeeze(xs) xs = np.atleast_2d(xs) @@ -3723,7 +3742,7 @@ class ScatterMatrixXS(MatrixMGXS): if self.legendre_order > 0: # Insert a column corresponding to the Legendre moments moments = ['P{}'.format(i) for i in range(self.legendre_order+1)] - moments = np.tile(moments, df.shape[0] / len(moments)) + moments = np.tile(moments, int(df.shape[0] / len(moments))) df['moment'] = moments # Place the moment column before the mean column @@ -3924,7 +3943,7 @@ class NuScatterMatrixXS(ScatterMatrixXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : 'analog' The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -4046,7 +4065,7 @@ class MultiplicityMatrixXS(MatrixMGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : 'analog' The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -4089,6 +4108,8 @@ class MultiplicityMatrixXS(MatrixMGXS): super(MultiplicityMatrixXS, self).__init__(domain, domain_type, groups, by_nuclide, name) self._rxn_type = 'multiplicity matrix' + self._estimator = 'analog' + self._valid_estimators = ['analog'] @property def scores(self): @@ -4193,7 +4214,7 @@ class NuFissionMatrixXS(MatrixMGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : 'analog' The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -4237,6 +4258,8 @@ class NuFissionMatrixXS(MatrixMGXS): groups, by_nuclide, name) self._rxn_type = 'nu-fission' self._hdf5_key = 'nu-fission matrix' + self._estimator = 'analog' + self._valid_estimators = ['analog'] class Chi(MGXS): @@ -4308,7 +4331,7 @@ class Chi(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : 'analog' The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -4350,6 +4373,8 @@ class Chi(MGXS): groups=None, by_nuclide=False, name=''): super(Chi, self).__init__(domain, domain_type, groups, by_nuclide, name) self._rxn_type = 'chi' + self._estimator = 'analog' + self._valid_estimators = ['analog'] @property def scores(self): @@ -4367,10 +4392,6 @@ class Chi(MGXS): def tally_keys(self): return ['nu-fission-in', 'nu-fission-out'] - @property - def estimator(self): - return 'analog' - @property def rxn_rate_tally(self): if self._rxn_rate_tally is None: @@ -4507,11 +4528,13 @@ class Chi(MGXS): def get_xs(self, groups='all', subdomains='all', nuclides='all', xs_type='macro', order_groups='increasing', - value='mean', **kwargs): + value='mean', squeeze=True, **kwargs): """Returns an array of the fission spectrum. - This method constructs a 2D NumPy array for the requested multi-group - cross section data data for one or more energy groups and subdomains. + This method constructs a 3D NumPy array for the requested + multi-group cross section data for one or more subdomains + (1st dimension), energy groups (2nd dimension), and nuclides + (3rd dimension). Parameters ---------- @@ -4533,6 +4556,9 @@ class Chi(MGXS): Defaults to 'increasing'. value : {'mean', 'std_dev', 'rel_err'} A string for the type of value to return. Defaults to 'mean'. + squeeze : bool + A boolean representing whether to eliminate the extra dimensions + of the multi-dimensional array to be returned. Defaults to True. Returns ------- @@ -4624,27 +4650,29 @@ class Chi(MGXS): xs = self.xs_tally.get_values(filters=filters, filter_bins=filter_bins, value=value) + # Eliminate the trivial score dimension + xs = np.squeeze(xs, axis=len(xs.shape) - 1) + xs = np.nan_to_num(xs) + + # Reshape tally data array with separate axes for domain and energy + if groups == 'all': + num_groups = self.num_groups + else: + num_groups = len(groups) + + num_subdomains = int(xs.shape[0] / num_groups) + new_shape = (num_subdomains, num_groups) + xs.shape[1:] + xs = np.reshape(xs, new_shape) + # Reverse data if user requested increasing energy groups since # tally data is stored in order of increasing energies if order_groups == 'increasing': - - # Reshape tally data array with separate axes for domain and energy - if groups == 'all': - num_groups = self.num_groups - else: - num_groups = len(groups) - num_subdomains = int(xs.shape[0] / num_groups) - new_shape = (num_subdomains, num_groups) + xs.shape[1:] - xs = np.reshape(xs, new_shape) - - # Reverse energies to align with increasing energy groups xs = xs[:, ::-1, :] - # Eliminate trivial dimensions + if squeeze: xs = np.squeeze(xs) xs = np.atleast_1d(xs) - xs = np.nan_to_num(xs) return xs def get_pandas_dataframe(self, groups='all', nuclides='all', @@ -4753,7 +4781,7 @@ class ChiPrompt(Chi): \langle \nu^p \sigma_{f,g' \rightarrow g} \phi \rangle &= \int_{r \in V} dr \int_{4\pi} d\Omega' \int_0^\infty dE' \int_{E_g}^{E_{g-1}} dE \; - \chi(E) \nu^p \sigma_f (r, E') \psi(r, E', \Omega')\\ + \chi(E)^p \nu^p \sigma_f (r, E') \psi(r, E', \Omega')\\ \langle \nu^p \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi} d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu^p \sigma_f (r, E') \psi(r, E', \Omega') \\ @@ -4798,7 +4826,7 @@ class ChiPrompt(Chi): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : 'analog' The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -4913,7 +4941,7 @@ class InverseVelocity(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys @@ -4930,7 +4958,8 @@ class InverseVelocity(MGXS): The number of subdomains is unity for 'material', 'cell' and 'universe' domain types. This is equal to the number of cell instances for 'distribcell' domain types (it is equal to unity prior to loading - tally data from a statepoint file). + tally data from a statepoint file) and the number of mesh cells for + 'mesh' domain types. num_nuclides : int The number of nuclides for which the multi-group cross section is being tracked. This is unity if the by_nuclide attribute is False. @@ -5047,7 +5076,7 @@ class PromptNuFissionXS(MGXS): tally_keys : list of str The keys into the tallies dictionary for each tally used to compute the multi-group cross section - estimator : {'tracklength', 'analog'} + estimator : {'tracklength', 'collision', 'analog'} The tally estimator used to compute the multi-group cross section tallies : collections.OrderedDict OpenMC tallies needed to compute the multi-group cross section. The keys diff --git a/openmc/statepoint.py b/openmc/statepoint.py index b20ea25908..01154b33d9 100644 --- a/openmc/statepoint.py +++ b/openmc/statepoint.py @@ -701,7 +701,7 @@ class StatePoint(object): if tally_filter.type == 'surface': surface_ids = [] for bin in tally_filter.bins: - surface_ids.append(summary.surfaces[bin].id) + surface_ids.append(bin) tally_filter.bins = surface_ids if tally_filter.type in ['cell', 'distribcell']: diff --git a/openmc/tallies.py b/openmc/tallies.py index c68b0faacb..4ced2fe282 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -40,6 +40,9 @@ _SCORE_CLASSES = (basestring, CrossScore, AggregateScore) _NUCLIDE_CLASSES = (basestring, Nuclide, CrossNuclide, AggregateNuclide) _FILTER_CLASSES = (Filter, CrossFilter, AggregateFilter) +# Valid types of estimators +ESTIMATOR_TYPES = ['tracklength', 'collision', 'analog'] + def reset_auto_tally_id(): """Reset counter for auto-generated tally IDs.""" @@ -387,8 +390,7 @@ class Tally(object): @estimator.setter def estimator(self, estimator): - cv.check_value('estimator', estimator, - ['analog', 'tracklength', 'collision']) + cv.check_value('estimator', estimator, ESTIMATOR_TYPES) self._estimator = estimator @triggers.setter @@ -795,6 +797,9 @@ class Tally(object): else: no_scores_match = False + if score == 'current' and score not in self.scores: + return False + # Nuclides cannot be specified on 'flux' scores if 'flux' in self.scores or 'flux' in other.scores: if self.nuclides != other.nuclides: @@ -2197,8 +2202,8 @@ class Tally(object): """ - cv.check_type('filter1', filter1, (Filter, CrossFilter, AggregateFilter)) - cv.check_type('filter2', filter2, (Filter, CrossFilter, AggregateFilter)) + cv.check_type('filter1', filter1, _FILTER_CLASSES) + cv.check_type('filter2', filter2, _FILTER_CLASSES) # Check that the filters exist in the tally and are not the same if filter1 == filter2: @@ -2280,8 +2285,8 @@ class Tally(object): 'since it does not contain any results.'.format(self.id) raise ValueError(msg) - cv.check_type('nuclide1', nuclide1, Nuclide) - cv.check_type('nuclide2', nuclide2, Nuclide) + cv.check_type('nuclide1', nuclide1, _NUCLIDE_CLASSES) + cv.check_type('nuclide2', nuclide2, _NUCLIDE_CLASSES) # Check that the nuclides exist in the tally and are not the same if nuclide1 == nuclide2: @@ -3318,7 +3323,7 @@ class Tally(object): """ - cv.check_type('new_filter', new_filter, Filter) + cv.check_type('new_filter', new_filter, _FILTER_CLASSES) if new_filter in self.filters: msg = 'Unable to diagonalize Tally ID="{0}" which already ' \ diff --git a/src/cmfd_data.F90 b/src/cmfd_data.F90 index 0f1d9420be..d74c1b17e0 100644 --- a/src/cmfd_data.F90 +++ b/src/cmfd_data.F90 @@ -51,9 +51,9 @@ contains subroutine compute_xs() use constants, only: FILTER_MESH, FILTER_ENERGYIN, FILTER_ENERGYOUT, & - FILTER_SURFACE, IN_RIGHT, OUT_RIGHT, IN_FRONT, & - OUT_FRONT, IN_TOP, OUT_TOP, CMFD_NOACCEL, ZERO, & - ONE, TINY_BIT + FILTER_SURFACE, OUT_LEFT, OUT_RIGHT, OUT_BACK, & + OUT_FRONT, OUT_BOTTOM, OUT_TOP, CMFD_NOACCEL, & + ZERO, ONE, TINY_BIT use error, only: fatal_error use global, only: cmfd, n_cmfd_tallies, cmfd_tallies, meshes,& matching_bins @@ -236,23 +236,33 @@ contains ! Left surface matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & - (/ i-1, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_RIGHT + (/ i, j, k /)) + matching_bins(i_filter_surf) = OUT_LEFT score_index = sum((matching_bins(1:size(t % filters)) - 1) & * t%stride) + 1 ! outgoing cmfd % current(1,h,i,j,k) = t % results(1,score_index) % sum - matching_bins(i_filter_surf) = OUT_RIGHT - score_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 ! incoming - cmfd % current(2,h,i,j,k) = t % results(1,score_index) % sum + + if (i > 1) then + matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & + (/ i-1, j, k /)) + matching_bins(i_filter_surf) = OUT_RIGHT + score_index = sum((matching_bins(1:size(t % filters)) - 1) & + * t % stride) + 1 ! incoming + cmfd % current(2,h,i,j,k) = t % results(1,score_index) % sum + end if ! Right surface + if (i < nx) then + matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & + (/ i+1, j, k /) ) + matching_bins(i_filter_surf) = OUT_LEFT + score_index = sum((matching_bins(1:size(t % filters)) - 1) & + * t % stride) + 1 ! incoming + cmfd % current(3,h,i,j,k) = t % results(1,score_index) % sum + end if + matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & - (/ i, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_RIGHT - score_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 ! incoming - cmfd % current(3,h,i,j,k) = t % results(1,score_index) % sum + (/ i, j, k /) ) matching_bins(i_filter_surf) = OUT_RIGHT score_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 ! outgoing @@ -260,23 +270,33 @@ contains ! Back surface matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & - (/ i, j-1, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_FRONT + (/ i, j, k /)) + matching_bins(i_filter_surf) = OUT_BACK score_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 ! outgoing cmfd % current(5,h,i,j,k) = t % results(1,score_index) % sum - matching_bins(i_filter_surf) = OUT_FRONT - score_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 ! incoming - cmfd % current(6,h,i,j,k) = t % results(1,score_index) % sum + + if (j > 1) then + matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & + (/ i, j-1, k /)) + matching_bins(i_filter_surf) = OUT_FRONT + score_index = sum((matching_bins(1:size(t % filters)) - 1) & + * t % stride) + 1 ! incoming + cmfd % current(6,h,i,j,k) = t % results(1,score_index) % sum + end if ! Front surface + if (j < ny) then + matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & + (/ i, j+1, k /)) + matching_bins(i_filter_surf) = OUT_BACK + score_index = sum((matching_bins(1:size(t % filters)) - 1) & + * t % stride) + 1 ! incoming + cmfd % current(7,h,i,j,k) = t % results(1,score_index) % sum + end if + matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & - (/ i, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_FRONT - score_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 ! incoming - cmfd % current(7,h,i,j,k) = t % results(1,score_index) % sum + (/ i, j, k /)) matching_bins(i_filter_surf) = OUT_FRONT score_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 ! outgoing @@ -284,23 +304,33 @@ contains ! Bottom surface matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & - (/ i, j, k-1 /) + 1, .true.) - matching_bins(i_filter_surf) = IN_TOP + (/ i, j, k /)) + matching_bins(i_filter_surf) = OUT_BOTTOM score_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 ! outgoing cmfd % current(9,h,i,j,k) = t % results(1,score_index) % sum - matching_bins(i_filter_surf) = OUT_TOP - score_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 ! incoming - cmfd % current(10,h,i,j,k) = t % results(1,score_index) % sum + + if (k > 1) then + matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & + (/ i, j, k-1 /)) + matching_bins(i_filter_surf) = OUT_TOP + score_index = sum((matching_bins(1:size(t % filters)) - 1) & + * t % stride) + 1 ! incoming + cmfd % current(10,h,i,j,k) = t % results(1,score_index) % sum + end if ! Top surface + if (k < nz) then + matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & + (/ i, j, k+1 /)) + matching_bins(i_filter_surf) = OUT_BOTTOM + score_index = sum((matching_bins(1:size(t % filters)) - 1) & + * t % stride) + 1 ! incoming + cmfd % current(11,h,i,j,k) = t % results(1,score_index) % sum + end if + matching_bins(i_filter_mesh) = mesh_indices_to_bin(m, & - (/ i, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_TOP - score_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 ! incoming - cmfd % current(11,h,i,j,k) = t % results(1,score_index) % sum + (/ i, j, k /)) matching_bins(i_filter_surf) = OUT_TOP score_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 ! outgoing diff --git a/src/cmfd_input.F90 b/src/cmfd_input.F90 index baa08e76e8..5fe44ad436 100644 --- a/src/cmfd_input.F90 +++ b/src/cmfd_input.F90 @@ -534,10 +534,10 @@ contains filt % n_bins = 2 * m % n_dimension allocate(filt % surfaces(2 * m % n_dimension)) if (m % n_dimension == 2) then - filt % surfaces = (/ IN_RIGHT, OUT_RIGHT, IN_FRONT, OUT_FRONT /) + filt % surfaces = (/ OUT_LEFT, OUT_RIGHT, OUT_BACK, OUT_FRONT /) elseif (m % n_dimension == 3) then - filt % surfaces = (/ IN_RIGHT, OUT_RIGHT, IN_FRONT, OUT_FRONT, & - IN_TOP, OUT_TOP /) + filt % surfaces = (/ OUT_LEFT, OUT_RIGHT, OUT_BACK, OUT_FRONT, & + OUT_BOTTOM, OUT_TOP /) end if end select t % find_filter(FILTER_SURFACE) = n_filters diff --git a/src/constants.F90 b/src/constants.F90 index fca45d1458..6d5f2fc107 100644 --- a/src/constants.F90 +++ b/src/constants.F90 @@ -359,12 +359,12 @@ module constants ! Tally surface current directions integer, parameter :: & - IN_RIGHT = 1, & - OUT_RIGHT = 2, & - IN_FRONT = 3, & - OUT_FRONT = 4, & - IN_TOP = 5, & - OUT_TOP = 6 + OUT_LEFT = 1, & ! x min + OUT_RIGHT = 2, & ! x max + OUT_BACK = 3, & ! y min + OUT_FRONT = 4, & ! y max + OUT_BOTTOM = 5, & ! z min + OUT_TOP = 6 ! z max ! Tally trigger types and threshold integer, parameter :: & diff --git a/src/input_xml.F90 b/src/input_xml.F90 index b33f0a105b..a60d8196ea 100644 --- a/src/input_xml.F90 +++ b/src/input_xml.F90 @@ -3047,18 +3047,18 @@ contains // " specified on tally " // trim(to_str(t % id))) end if - ! Determine number of bins -- this is assuming that the tally is - ! a volume tally and not a surface current tally. If it is a - ! surface current tally, the number of bins will get reset later + ! Determine number of bins filt % n_bins = product(m % dimension) ! Store the index of the mesh filt % mesh = i_mesh end select + ! Set the filter index in the tally find_filter array t % find_filter(FILTER_MESH) = j case ('energy') + ! Allocate and declare the filter type allocate(EnergyFilter::t % filters(j) % obj) select type (filt => t % filters(j) % obj) @@ -3676,10 +3676,6 @@ contains &same tally as surface currents") end if - ! Since the number of bins for the mesh filter was already set - ! assuming it was a volume tally, we need to adjust the number - ! of bins - ! Get index of mesh filter k = t % find_filter(FILTER_MESH) @@ -3689,19 +3685,6 @@ contains &filter.") end if - ! Declare the type of the mesh filter - select type(filt => t % filters(k) % obj) - type is (MeshFilter) - - ! Get pointer to mesh - i_mesh = filt % mesh - m => meshes(i_mesh) - - ! We need to increase the dimension by one since we also need - ! currents coming into and out of the boundary mesh cells. - filt % n_bins = product(m % dimension + 1) - end select - ! Copy filters to temporary array allocate(filters(size(t % filters) + 1)) filters(1:size(t % filters)) = t % filters @@ -3718,10 +3701,10 @@ contains filt % n_bins = 2 * m % n_dimension allocate(filt % surfaces(2 * m % n_dimension)) if (m % n_dimension == 2) then - filt % surfaces = (/ IN_RIGHT, OUT_RIGHT, IN_FRONT, OUT_FRONT /) + filt % surfaces = (/ OUT_LEFT, OUT_RIGHT, OUT_BACK, OUT_FRONT /) elseif (m % n_dimension == 3) then - filt % surfaces = (/ IN_RIGHT, OUT_RIGHT, IN_FRONT, OUT_FRONT,& - IN_TOP, OUT_TOP /) + filt % surfaces = (/ OUT_LEFT, OUT_RIGHT, OUT_BACK, OUT_FRONT,& + OUT_BOTTOM, OUT_TOP /) end if end select t % find_filter(FILTER_SURFACE) = size(t % filters) diff --git a/src/mesh.F90 b/src/mesh.F90 index 0b227f4f5d..cee29aa1ca 100644 --- a/src/mesh.F90 +++ b/src/mesh.F90 @@ -93,30 +93,21 @@ contains ! use in a TallyObject results array !=============================================================================== - pure function mesh_indices_to_bin(m, ijk, surface_current) result(bin) + pure function mesh_indices_to_bin(m, ijk) result(bin) type(RegularMesh), intent(in) :: m integer, intent(in) :: ijk(:) - logical, intent(in), optional :: surface_current integer :: bin + integer :: n_x ! number of mesh cells in x direction integer :: n_y ! number of mesh cells in y direction - integer :: n_z ! number of mesh cells in z direction - if (present(surface_current)) then - n_y = m % dimension(2) + 1 - else - n_y = m % dimension(2) - end if + n_x = m % dimension(1) + n_y = m % dimension(2) if (m % n_dimension == 2) then - bin = (ijk(1) - 1)*n_y + ijk(2) + bin = (ijk(2) - 1)*n_x + ijk(1) elseif (m % n_dimension == 3) then - if (present(surface_current)) then - n_z = m % dimension(3) + 1 - else - n_z = m % dimension(3) - end if - bin = (ijk(1) - 1)*n_y*n_z + (ijk(2) - 1)*n_z + ijk(3) + bin = (ijk(3) - 1)*n_y*n_x + (ijk(2) - 1)*n_x + ijk(1) end if end function mesh_indices_to_bin @@ -131,19 +122,19 @@ contains integer, intent(in) :: bin integer, intent(out) :: ijk(:) + integer :: n_x ! number of mesh cells in x direction integer :: n_y ! number of mesh cells in y direction - integer :: n_z ! number of mesh cells in z direction + n_x = m % dimension(1) n_y = m % dimension(2) if (m % n_dimension == 2) then - ijk(1) = (bin - 1)/n_y + 1 - ijk(2) = mod(bin - 1, n_y) + 1 + ijk(1) = mod(bin - 1, n_x) + 1 + ijk(2) = (bin - 1)/n_x + 1 else if (m % n_dimension == 3) then - n_z = m % dimension(3) - ijk(1) = (bin - 1)/(n_y*n_z) + 1 - ijk(2) = mod(bin - 1, n_y*n_z)/n_z + 1 - ijk(3) = mod(bin - 1, n_z) + 1 + ijk(1) = mod(bin - 1, n_x) + 1 + ijk(2) = mod(bin - 1, n_x*n_y)/n_x + 1 + ijk(3) = (bin - 1)/(n_x*n_y) + 1 end if end subroutine bin_to_mesh_indices diff --git a/src/output.F90 b/src/output.F90 index 2ed25092e9..9e23f11d88 100644 --- a/src/output.F90 +++ b/src/output.F90 @@ -1036,8 +1036,8 @@ contains ! Left Surface matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i-1, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_RIGHT + mesh_indices_to_bin(m, (/ i, j, k /)) + matching_bins(i_filter_surf) = OUT_LEFT filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & @@ -1045,25 +1045,9 @@ contains to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) - matching_bins(i_filter_surf) = OUT_RIGHT - filter_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 - write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & - "Incoming Current from Left", & - to_str(t % results(1,filter_index) % sum), & - trim(to_str(t % results(1,filter_index) % sum_sq)) - ! Right Surface matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_RIGHT - filter_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 - write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & - "Incoming Current from Right", & - to_str(t % results(1,filter_index) % sum), & - trim(to_str(t % results(1,filter_index) % sum_sq)) - + mesh_indices_to_bin(m, (/ i, j, k /)) matching_bins(i_filter_surf) = OUT_RIGHT filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 @@ -1074,8 +1058,8 @@ contains ! Back Surface matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i, j-1, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_FRONT + mesh_indices_to_bin(m, (/ i, j, k /)) + matching_bins(i_filter_surf) = OUT_BACK filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & @@ -1083,25 +1067,9 @@ contains to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) - matching_bins(i_filter_surf) = OUT_FRONT - filter_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 - write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & - "Incoming Current from Back", & - to_str(t % results(1,filter_index) % sum), & - trim(to_str(t % results(1,filter_index) % sum_sq)) - ! Front Surface matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_FRONT - filter_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 - write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & - "Incoming Current from Front", & - to_str(t % results(1,filter_index) % sum), & - trim(to_str(t % results(1,filter_index) % sum_sq)) - + mesh_indices_to_bin(m, (/ i, j, k /)) matching_bins(i_filter_surf) = OUT_FRONT filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 @@ -1112,8 +1080,8 @@ contains ! Bottom Surface matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i, j, k-1 /) + 1, .true.) - matching_bins(i_filter_surf) = IN_TOP + mesh_indices_to_bin(m, (/ i, j, k /)) + matching_bins(i_filter_surf) = OUT_BOTTOM filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & @@ -1121,25 +1089,9 @@ contains to_str(t % results(1,filter_index) % sum), & trim(to_str(t % results(1,filter_index) % sum_sq)) - matching_bins(i_filter_surf) = OUT_TOP - filter_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 - write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & - "Incoming Current from Bottom", & - to_str(t % results(1,filter_index) % sum), & - trim(to_str(t % results(1,filter_index) % sum_sq)) - ! Top Surface matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_TOP - filter_index = sum((matching_bins(1:size(t % filters)) - 1) & - * t % stride) + 1 - write(UNIT=unit_tally, FMT='(5X,A,T35,A,"+/- ",A)') & - "Incoming Current from Top", & - to_str(t % results(1,filter_index) % sum), & - trim(to_str(t % results(1,filter_index) % sum_sq)) - + mesh_indices_to_bin(m, (/ i, j, k /)) matching_bins(i_filter_surf) = OUT_TOP filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 diff --git a/src/tally.F90 b/src/tally.F90 index 503ebe325f..3c3dc6a693 100644 --- a/src/tally.F90 +++ b/src/tally.F90 @@ -2327,6 +2327,7 @@ contains integer :: filter_index ! index of scoring bin integer :: i_filter_mesh ! index of mesh filter in filters array integer :: i_filter_surf ! index of surface filter in filters + integer :: i_filter_energy ! index of energy filter in filters real(8) :: uvw(3) ! cosine of angle of particle real(8) :: xyz0(3) ! starting/intermediate coordinates real(8) :: xyz1(3) ! ending coordinates of particle @@ -2351,9 +2352,10 @@ contains i_tally = active_current_tallies % get_item(i) t => tallies(i_tally) - ! Get index for mesh and surface filters + ! Get index for mesh, surface, and energy filters i_filter_mesh = t % find_filter(FILTER_MESH) i_filter_surf = t % find_filter(FILTER_SURFACE) + i_filter_energy = t % find_filter(FILTER_ENERGYIN) ! Get pointer to mesh select type(filt => t % filters(i_filter_mesh) % obj) @@ -2386,11 +2388,11 @@ contains ! Determine incoming energy bin. We need to tell the energy filter this ! is a tracklength tally so it uses the pre-collision energy. - j = t % find_filter(FILTER_ENERGYIN) - if (j > 0) then - call t % filters(i) % obj % get_next_bin(p, ESTIMATOR_TRACKLENGTH, & - & NO_BIN_FOUND, matching_bins(j), filt_score) - if (matching_bins(j) == NO_BIN_FOUND) cycle + if (i_filter_energy > 0) then + call t % filters(i_filter_energy) % obj % get_next_bin(p, & + ESTIMATOR_TRACKLENGTH, NO_BIN_FOUND, & + matching_bins(i_filter_energy), filt_score) + if (matching_bins(i_filter_energy) == NO_BIN_FOUND) cycle end if ! ======================================================================= @@ -2405,10 +2407,10 @@ contains if (uvw(3) > 0) then do j = ijk0(3), ijk1(3) - 1 ijk0(3) = j - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then matching_bins(i_filter_surf) = OUT_TOP matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) + mesh_indices_to_bin(m, ijk0) filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 !$omp atomic @@ -2417,12 +2419,12 @@ contains end if end do else - do j = ijk0(3) - 1, ijk1(3), -1 + do j = ijk0(3), ijk1(3) + 1, -1 ijk0(3) = j - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then - matching_bins(i_filter_surf) = IN_TOP + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then + matching_bins(i_filter_surf) = OUT_BOTTOM matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) + mesh_indices_to_bin(m, ijk0) filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 !$omp atomic @@ -2437,10 +2439,10 @@ contains if (uvw(2) > 0) then do j = ijk0(2), ijk1(2) - 1 ijk0(2) = j - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then matching_bins(i_filter_surf) = OUT_FRONT matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) + mesh_indices_to_bin(m, ijk0) filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 !$omp atomic @@ -2449,12 +2451,12 @@ contains end if end do else - do j = ijk0(2) - 1, ijk1(2), -1 + do j = ijk0(2), ijk1(2) + 1, -1 ijk0(2) = j - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then - matching_bins(i_filter_surf) = IN_FRONT + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then + matching_bins(i_filter_surf) = OUT_BACK matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) + mesh_indices_to_bin(m, ijk0) filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 !$omp atomic @@ -2469,10 +2471,10 @@ contains if (uvw(1) > 0) then do j = ijk0(1), ijk1(1) - 1 ijk0(1) = j - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then matching_bins(i_filter_surf) = OUT_RIGHT matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) + mesh_indices_to_bin(m, ijk0) filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 !$omp atomic @@ -2481,12 +2483,12 @@ contains end if end do else - do j = ijk0(1) - 1, ijk1(1), -1 + do j = ijk0(1), ijk1(1) + 1, -1 ijk0(1) = j - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then - matching_bins(i_filter_surf) = IN_RIGHT + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then + matching_bins(i_filter_surf) = OUT_LEFT matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) + mesh_indices_to_bin(m, ijk0) filter_index = sum((matching_bins(1:size(t % filters)) - 1) & * t % stride) + 1 !$omp atomic @@ -2538,67 +2540,67 @@ contains if (uvw(1) > 0) then ! Crossing into right mesh cell -- this is treated as outgoing ! current from (i,j,k) - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then matching_bins(i_filter_surf) = OUT_RIGHT matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) + mesh_indices_to_bin(m, ijk0) end if ijk0(1) = ijk0(1) + 1 xyz_cross(1) = xyz_cross(1) + m % width(1) else - ! Crossing into left mesh cell -- this is treated as incoming - ! current in (i-1,j,k) + ! Crossing into left mesh cell -- this is treated as outgoing + ! current in (i,j,k) + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then + matching_bins(i_filter_surf) = OUT_LEFT + matching_bins(i_filter_mesh) = & + mesh_indices_to_bin(m, ijk0) + end if ijk0(1) = ijk0(1) - 1 xyz_cross(1) = xyz_cross(1) - m % width(1) - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then - matching_bins(i_filter_surf) = IN_RIGHT - matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) - end if end if elseif (distance == d(2)) then if (uvw(2) > 0) then ! Crossing into front mesh cell -- this is treated as outgoing ! current in (i,j,k) - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then matching_bins(i_filter_surf) = OUT_FRONT matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) + mesh_indices_to_bin(m, ijk0) end if ijk0(2) = ijk0(2) + 1 xyz_cross(2) = xyz_cross(2) + m % width(2) else - ! Crossing into back mesh cell -- this is treated as incoming - ! current in (i,j-1,k) + ! Crossing into back mesh cell -- this is treated as outgoing + ! current in (i,j,k) + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then + matching_bins(i_filter_surf) = OUT_BACK + matching_bins(i_filter_mesh) = & + mesh_indices_to_bin(m, ijk0) + end if ijk0(2) = ijk0(2) - 1 xyz_cross(2) = xyz_cross(2) - m % width(2) - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then - matching_bins(i_filter_surf) = IN_FRONT - matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) - end if end if else if (distance == d(3)) then if (uvw(3) > 0) then ! Crossing into top mesh cell -- this is treated as outgoing ! current in (i,j,k) - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then matching_bins(i_filter_surf) = OUT_TOP matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) + mesh_indices_to_bin(m, ijk0) end if ijk0(3) = ijk0(3) + 1 xyz_cross(3) = xyz_cross(3) + m % width(3) else - ! Crossing into bottom mesh cell -- this is treated as incoming - ! current in (i,j,k-1) + ! Crossing into bottom mesh cell -- this is treated as outgoing + ! current in (i,j,k) + if (all(ijk0 >= 1) .and. all(ijk0 <= m % dimension)) then + matching_bins(i_filter_surf) = OUT_BOTTOM + matching_bins(i_filter_mesh) = & + mesh_indices_to_bin(m, ijk0) + end if ijk0(3) = ijk0(3) - 1 xyz_cross(3) = xyz_cross(3) - m % width(3) - if (all(ijk0 >= 0) .and. all(ijk0 <= m % dimension)) then - matching_bins(i_filter_surf) = IN_TOP - matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, ijk0 + 1, .true.) - end if end if end if diff --git a/src/trigger.F90 b/src/trigger.F90 index 2fd0e2a121..4af03a2dea 100644 --- a/src/trigger.F90 +++ b/src/trigger.F90 @@ -328,10 +328,11 @@ contains matching_bins(i_filter_ein) = l end if - ! Left Surface matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i-1, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_RIGHT + mesh_indices_to_bin(m, (/ i, j, k /)) + + ! Left Surface + matching_bins(i_filter_surf) = OUT_LEFT filter_index = & sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 call get_trigger_uncertainty(std_dev, rel_err, 1, filter_index, t) @@ -343,33 +344,7 @@ contains end if trigger % variance = std_dev**2 - matching_bins(i_filter_surf) = OUT_RIGHT - filter_index = & - sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 - call get_trigger_uncertainty(std_dev, rel_err, 1, filter_index, t) - if (trigger % std_dev < std_dev) then - trigger % std_dev = std_dev - end if - if (trigger % rel_err < rel_err) then - trigger % rel_err = rel_err - end if - trigger % variance = trigger % std_dev**2 - ! Right Surface - matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_RIGHT - filter_index = & - sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 - call get_trigger_uncertainty(std_dev, rel_err, 1, filter_index, t) - if (trigger % std_dev < std_dev) then - trigger % std_dev = std_dev - end if - if (trigger % rel_err < rel_err) then - trigger % rel_err = rel_err - end if - trigger % variance = trigger % std_dev**2 - matching_bins(i_filter_surf) = OUT_RIGHT filter_index = & sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 @@ -383,22 +358,7 @@ contains trigger % variance = trigger % std_dev**2 ! Back Surface - matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i, j-1, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_FRONT - filter_index = & - sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 - call get_trigger_uncertainty(std_dev, rel_err, 1, filter_index, t) - if (trigger % std_dev < std_dev) then - trigger % std_dev = std_dev - end if - if (trigger % rel_err < rel_err) then - trigger % rel_err = rel_err - end if - trigger % variance = trigger % std_dev**2 - - - matching_bins(i_filter_surf) = OUT_FRONT + matching_bins(i_filter_surf) = OUT_BACK filter_index = & sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 call get_trigger_uncertainty(std_dev, rel_err, 1, filter_index, t) @@ -411,20 +371,6 @@ contains trigger % variance = trigger % std_dev**2 ! Front Surface - matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_FRONT - filter_index = & - sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 - call get_trigger_uncertainty(std_dev, rel_err, 1, filter_index, t) - if (trigger % std_dev < std_dev) then - trigger % std_dev = std_dev - end if - if (trigger % rel_err < rel_err) then - trigger % rel_err = rel_err - end if - trigger % variance = trigger % std_dev**2 - matching_bins(i_filter_surf) = OUT_FRONT filter_index = & sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 @@ -438,21 +384,7 @@ contains trigger % variance = trigger % std_dev**2 ! Bottom Surface - matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i, j, k-1 /) + 1, .true.) - matching_bins(i_filter_surf) = IN_TOP - filter_index = & - sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 - call get_trigger_uncertainty(std_dev, rel_err, 1, filter_index, t) - if (trigger % std_dev < std_dev) then - trigger % std_dev = std_dev - end if - if (trigger % rel_err < rel_err) then - trigger % rel_err = rel_err - end if - trigger % variance = trigger % std_dev**2 - - matching_bins(i_filter_surf) = OUT_TOP + matching_bins(i_filter_surf) = OUT_BOTTOM filter_index = & sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 call get_trigger_uncertainty(std_dev, rel_err, 1, filter_index, t) @@ -465,20 +397,6 @@ contains trigger % variance = trigger % std_dev**2 ! Top Surface - matching_bins(i_filter_mesh) = & - mesh_indices_to_bin(m, (/ i, j, k /) + 1, .true.) - matching_bins(i_filter_surf) = IN_TOP - filter_index = & - sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 - call get_trigger_uncertainty(std_dev, rel_err, 1, filter_index, t) - if (trigger % std_dev < std_dev) then - trigger % std_dev = std_dev - end if - if (trigger % rel_err < rel_err) then - trigger % rel_err = rel_err - end if - trigger % variance = trigger % std_dev**2 - matching_bins(i_filter_surf) = OUT_TOP filter_index = & sum((matching_bins(1:size(t % filters)) - 1) * t % stride) + 1 diff --git a/tests/input_set.py b/tests/input_set.py index 827484022d..8d650cafbc 100644 --- a/tests/input_set.py +++ b/tests/input_set.py @@ -2,6 +2,7 @@ import openmc from openmc.source import Source from openmc.stats import Box +import numpy as np class InputSet(object): def __init__(self): @@ -673,6 +674,158 @@ class PinCellInputSet(object): self.plots.add_plot(plot) +class AssemblyInputSet(object): + def __init__(self): + self.settings = openmc.Settings() + self.materials = openmc.Materials() + self.geometry = openmc.Geometry() + self.tallies = None + self.plots = None + + def export(self): + self.settings.export_to_xml() + self.materials.export_to_xml() + self.geometry.export_to_xml() + if self.tallies is not None: + self.tallies.export_to_xml() + if self.plots is not None: + self.plots.export_to_xml() + + def build_default_materials_and_geometry(self): + # Define materials. + fuel = openmc.Material(name='Fuel') + fuel.set_density('g/cm3', 10.29769) + fuel.add_nuclide("U234", 4.4843e-6) + fuel.add_nuclide("U235", 5.5815e-4) + fuel.add_nuclide("U238", 2.2408e-2) + fuel.add_nuclide("O16", 4.5829e-2) + + clad = openmc.Material(name='Cladding') + clad.set_density('g/cm3', 6.55) + clad.add_nuclide("Zr90", 2.1827e-2) + clad.add_nuclide("Zr91", 4.7600e-3) + clad.add_nuclide("Zr92", 7.2758e-3) + clad.add_nuclide("Zr94", 7.3734e-3) + clad.add_nuclide("Zr96", 1.1879e-3) + + hot_water = openmc.Material(name='Hot borated water') + hot_water.set_density('g/cm3', 0.740582) + hot_water.add_nuclide("H1", 4.9457e-2) + hot_water.add_nuclide("O16", 2.4672e-2) + hot_water.add_nuclide("B10", 8.0042e-6) + hot_water.add_nuclide("B11", 3.2218e-5) + hot_water.add_s_alpha_beta('c_H_in_H2O', '71t') + + # Define the materials file. + self.materials.default_xs = '71c' + self.materials += (fuel, clad, hot_water) + + # Instantiate ZCylinder surfaces + fuel_or = openmc.ZCylinder(x0=0, y0=0, R=0.39218, name='Fuel OR') + clad_or = openmc.ZCylinder(x0=0, y0=0, R=0.45720, name='Clad OR') + + # Create boundary planes to surround the geometry + min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective') + max_x = openmc.XPlane(x0=+10.71, boundary_type='reflective') + min_y = openmc.YPlane(y0=-10.71, boundary_type='reflective') + max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective') + + # Create a Universe to encapsulate a fuel pin + fuel_pin_universe = openmc.Universe(name='Fuel Pin') + + # Create fuel Cell + fuel_cell = openmc.Cell(name='fuel') + fuel_cell.fill = fuel + fuel_cell.region = -fuel_or + fuel_pin_universe.add_cell(fuel_cell) + + # Create a clad Cell + clad_cell = openmc.Cell(name='clad') + clad_cell.fill = clad + clad_cell.region = +fuel_or & -clad_or + fuel_pin_universe.add_cell(clad_cell) + + # Create a moderator Cell + hot_water_cell = openmc.Cell(name='hot water') + hot_water_cell.fill = hot_water + hot_water_cell.region = +clad_or + fuel_pin_universe.add_cell(hot_water_cell) + + # Create a Universe to encapsulate a control rod guide tube + guide_tube_universe = openmc.Universe(name='Guide Tube') + + # Create guide tube inner Cell + gt_inner_cell = openmc.Cell(name='guide tube inner water') + gt_inner_cell.fill = hot_water + gt_inner_cell.region = -fuel_or + guide_tube_universe.add_cell(gt_inner_cell) + + # Create a clad Cell + gt_clad_cell = openmc.Cell(name='guide tube clad') + gt_clad_cell.fill = clad + gt_clad_cell.region = +fuel_or & -clad_or + guide_tube_universe.add_cell(gt_clad_cell) + + # Create a guide tube outer Cell + gt_outer_cell = openmc.Cell(name='guide tube outer water') + gt_outer_cell.fill = hot_water + gt_outer_cell.region = +clad_or + guide_tube_universe.add_cell(gt_outer_cell) + + # Create fuel assembly Lattice + assembly = openmc.RectLattice(name='Fuel Assembly') + assembly.pitch = (1.26, 1.26) + assembly.lower_left = [-1.26 * 17. / 2.0] * 2 + + # Create array indices for guide tube locations in lattice + template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8, + 11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11]) + template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8, + 8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14]) + + # Initialize an empty 17x17 array of the lattice universes + universes = np.empty((17, 17), dtype=openmc.Universe) + + # Fill the array with the fuel pin and guide tube universes + universes[:,:] = fuel_pin_universe + universes[template_x, template_y] = guide_tube_universe + + # Store the array of universes in the lattice + assembly.universes = universes + + # Create root Cell + root_cell = openmc.Cell(name='root cell') + root_cell.fill = assembly + + # Add boundary planes + root_cell.region = +min_x & -max_x & +min_y & -max_y + + # Create root Universe + root_universe = openmc.Universe(universe_id=0, name='root universe') + root_universe.add_cell(root_cell) + + # Instantiate a Geometry, register the root Universe, and export to XML + self.geometry.root_universe = root_universe + + def build_default_settings(self): + self.settings.batches = 10 + self.settings.inactive = 5 + self.settings.particles = 100 + self.settings.source = Source(space=Box([-10.71, -10.71, -1], + [10.71, 10.71, 1], + only_fissionable=True)) + + def build_defualt_plots(self): + plot = openmc.Plot() + plot.filename = 'mat' + plot.origin = (0.0, 0.0, 0) + plot.width = (21.42, 21.42) + plot.pixels = (300, 300) + plot.color = 'mat' + + self.plots.add_plot(plot) + + class MGInputSet(InputSet): def build_default_materials_and_geometry(self): # Define materials needed for 1D/1G slab problem diff --git a/tests/test_cmfd_feed/results_true.dat b/tests/test_cmfd_feed/results_true.dat index 4579fa5459..04103129dd 100644 --- a/tests/test_cmfd_feed/results_true.dat +++ b/tests/test_cmfd_feed/results_true.dat @@ -124,92 +124,8 @@ tally 3: 1.020705E+00 5.413570E-02 tally 4: -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 3.049469E+00 4.677325E-01 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -5.514939E+00 -1.528899E+00 2.770358E+00 3.879191E-01 0.000000E+00 @@ -220,44 +136,8 @@ tally 4: 0.000000E+00 0.000000E+00 0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -7.294002E+00 -2.675589E+00 +5.514939E+00 +1.528899E+00 5.032131E+00 1.275040E+00 0.000000E+00 @@ -268,44 +148,8 @@ tally 4: 0.000000E+00 0.000000E+00 0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -8.668860E+00 -3.776102E+00 +7.294002E+00 +2.675589E+00 7.036008E+00 2.490719E+00 0.000000E+00 @@ -316,44 +160,8 @@ tally 4: 0.000000E+00 0.000000E+00 0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -9.345868E+00 -4.380719E+00 +8.668860E+00 +3.776102E+00 8.352414E+00 3.501945E+00 0.000000E+00 @@ -364,44 +172,8 @@ tally 4: 0.000000E+00 0.000000E+00 0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -9.223771E+00 -4.270119E+00 +9.345868E+00 +4.380719E+00 9.093766E+00 4.158282E+00 0.000000E+00 @@ -412,44 +184,8 @@ tally 4: 0.000000E+00 0.000000E+00 0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -8.530966E+00 -3.651778E+00 +9.223771E+00 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+0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +2.585363E+00 +1.416531E+00 +7.208386E-02 +1.068367E-03 +3.721777E+00 +2.868016E+00 +2.981132E-02 +1.871051E-04 +7.390860E-02 +1.150041E-03 +3.227353E+00 +2.189454E+00 +8.161989E-02 +1.366996E-03 +4.465292E+00 +4.143037E+00 +3.169508E-02 +2.069605E-04 +7.857885E-02 +1.272082E-03 +3.101358E+00 +2.022582E+00 +8.316566E-02 +1.469875E-03 +4.433641E+00 +4.167950E+00 +3.363316E-02 +2.428472E-04 +8.338377E-02 +1.492660E-03 +3.342682E+00 +2.353374E+00 +9.542381E-02 +1.892530E-03 +4.916807E+00 +5.068483E+00 +4.010858E-02 +3.330650E-04 +9.943770E-02 +2.047183E-03 +2.733720E+00 +1.608810E+00 +7.575232E-02 +1.234497E-03 +3.955386E+00 +3.353987E+00 +3.126476E-02 +2.114507E-04 +7.751200E-02 +1.299681E-03 +2.532385E+00 +1.341692E+00 +7.908024E-02 +1.289149E-03 +3.816608E+00 +3.020090E+00 +3.480705E-02 +2.494950E-04 +8.629409E-02 +1.533520E-03 +2.651467E+00 +1.472579E+00 +7.547436E-02 +1.201468E-03 +3.877983E+00 +3.154531E+00 +3.166886E-02 +2.150564E-04 +7.851384E-02 +1.321843E-03 2.404559E+00 1.241962E+00 6.952417E-02 @@ -1251,6 +931,166 @@ tally 1: 1.929942E-04 7.306514E-02 1.186238E-03 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +2.740199E+00 +1.663131E+00 +8.719535E-02 +1.596752E-03 +4.146740E+00 +3.682203E+00 +3.870935E-02 +3.136109E-04 +9.596871E-02 +1.927608E-03 +3.006650E+00 +1.972749E+00 +8.846181E-02 +1.643195E-03 +4.449165E+00 +4.222523E+00 +3.779771E-02 +2.999515E-04 +9.370858E-02 +1.843651E-03 +2.669269E+00 +1.482479E+00 +7.250461E-02 +1.097959E-03 +3.815372E+00 +3.039000E+00 +2.952621E-02 +1.837209E-04 +7.320176E-02 +1.129240E-03 +2.906350E+00 +1.760817E+00 +8.559496E-02 +1.527401E-03 +4.338708E+00 +3.888336E+00 +3.663301E-02 +2.834528E-04 +9.082102E-02 +1.742242E-03 +2.633801E+00 +1.566751E+00 +7.851898E-02 +1.276803E-03 +3.867974E+00 +3.237952E+00 +3.370851E-02 +2.331450E-04 +8.357057E-02 +1.433025E-03 +2.946914E+00 +1.857842E+00 +7.586572E-02 +1.217999E-03 +4.089763E+00 +3.522165E+00 +2.980738E-02 +1.901498E-04 +7.389883E-02 +1.168755E-03 +2.884789E+00 +1.801722E+00 +7.517555E-02 +1.168857E-03 +4.085136E+00 +3.522983E+00 +2.985774E-02 +1.859368E-04 +7.402368E-02 +1.142860E-03 2.740499E+00 1.588007E+00 7.592011E-02 @@ -1261,6 +1101,166 @@ tally 1: 2.121705E-04 7.770960E-02 1.304106E-03 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +2.212799E+00 +1.000035E+00 +7.449165E-02 +1.135538E-03 +3.466616E+00 +2.419923E+00 +3.399202E-02 +2.463541E-04 +8.427346E-02 +1.514215E-03 +2.591189E+00 +1.530310E+00 +7.305643E-02 +1.114250E-03 +3.732882E+00 +3.031120E+00 +3.039294E-02 +1.919600E-04 +7.535057E-02 +1.179882E-03 +2.719836E+00 +1.670890E+00 +8.176774E-02 +1.472662E-03 +4.030045E+00 +3.607044E+00 +3.530219E-02 +2.721266E-04 +8.752165E-02 +1.672625E-03 +2.201534E+00 +1.026489E+00 +7.361463E-02 +1.156634E-03 +3.444005E+00 +2.510736E+00 +3.352726E-02 +2.491193E-04 +8.312121E-02 +1.531211E-03 +2.649820E+00 +1.500516E+00 +6.849773E-02 +9.733415E-04 +3.744876E+00 +2.926356E+00 +2.706771E-02 +1.530408E-04 +6.710661E-02 +9.406650E-04 +2.788908E+00 +1.667764E+00 +7.477958E-02 +1.148212E-03 +4.029335E+00 +3.403340E+00 +3.033277E-02 +1.914118E-04 +7.520139E-02 +1.176512E-03 +2.810664E+00 +1.609682E+00 +6.917229E-02 +9.875658E-04 +3.854296E+00 +3.029615E+00 +2.634304E-02 +1.483398E-04 +6.531000E-02 +9.117703E-04 2.647586E+00 1.416013E+00 7.421099E-02 diff --git a/tests/test_mgxs_library_condense/inputs_true.dat b/tests/test_mgxs_library_condense/inputs_true.dat index 0c648376e8..e58015868a 100644 --- a/tests/test_mgxs_library_condense/inputs_true.dat +++ b/tests/test_mgxs_library_condense/inputs_true.dat @@ -1 +1 @@ -e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e \ No newline at end of file +08c5f1c783dd88c5fed51c054718ca09fc4e99aa4560a6f928b3902991948f3a878d055ac46c07548904285c2c5f22dc2a3d8c1bb82b8e73d76dd790820117df \ No newline at end of file diff --git a/tests/test_mgxs_library_condense/results_true.dat b/tests/test_mgxs_library_condense/results_true.dat index ae768cbe67..d1a964a860 100644 --- a/tests/test_mgxs_library_condense/results_true.dat +++ b/tests/test_mgxs_library_condense/results_true.dat @@ -40,6 +40,27 @@ 0 10000 1 total 4.996730e-07 3.650635e-08 material group in nuclide mean std. dev. 0 10000 1 total 0.090004 0.006367 + material delayedgroup group in nuclide mean std. dev. +0 10000 1 1 total 0.000021 0.000001 +1 10000 2 1 total 0.000110 0.000008 +2 10000 3 1 total 0.000107 0.000007 +3 10000 4 1 total 0.000249 0.000017 +4 10000 5 1 total 0.000112 0.000007 +5 10000 6 1 total 0.000046 0.000003 + material delayedgroup group out nuclide mean std. dev. +0 10000 1 1 total 0.0 0.000000 +1 10000 2 1 total 1.0 0.869128 +2 10000 3 1 total 1.0 1.414214 +3 10000 4 1 total 1.0 0.360359 +4 10000 5 1 total 0.0 0.000000 +5 10000 6 1 total 0.0 0.000000 + material delayedgroup group in nuclide mean std. dev. +0 10000 1 1 total 0.000227 0.000020 +1 10000 2 1 total 0.001214 0.000108 +2 10000 3 1 total 0.001184 0.000104 +3 10000 4 1 total 0.002752 0.000240 +4 10000 5 1 total 0.001231 0.000105 +5 10000 6 1 total 0.000512 0.000044 material group in nuclide mean std. dev. 0 10001 1 total 0.311594 0.013793 material group in nuclide mean std. dev. @@ -82,6 +103,27 @@ 0 10001 1 total 5.454760e-07 4.949800e-08 material group in nuclide mean std. dev. 0 10001 1 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +0 10001 1 1 total 0.0 0.0 +1 10001 2 1 total 0.0 0.0 +2 10001 3 1 total 0.0 0.0 +3 10001 4 1 total 0.0 0.0 +4 10001 5 1 total 0.0 0.0 +5 10001 6 1 total 0.0 0.0 + material delayedgroup group out nuclide mean std. dev. +0 10001 1 1 total 0.0 0.0 +1 10001 2 1 total 0.0 0.0 +2 10001 3 1 total 0.0 0.0 +3 10001 4 1 total 0.0 0.0 +4 10001 5 1 total 0.0 0.0 +5 10001 6 1 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +0 10001 1 1 total 0.0 0.0 +1 10001 2 1 total 0.0 0.0 +2 10001 3 1 total 0.0 0.0 +3 10001 4 1 total 0.0 0.0 +4 10001 5 1 total 0.0 0.0 +5 10001 6 1 total 0.0 0.0 material group in nuclide mean std. dev. 0 10002 1 total 0.904999 0.043964 material group in nuclide mean std. dev. @@ -124,3 +166,24 @@ 0 10002 1 total 5.773006e-07 5.322132e-08 material group in nuclide mean std. dev. 0 10002 1 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +0 10002 1 1 total 0.0 0.0 +1 10002 2 1 total 0.0 0.0 +2 10002 3 1 total 0.0 0.0 +3 10002 4 1 total 0.0 0.0 +4 10002 5 1 total 0.0 0.0 +5 10002 6 1 total 0.0 0.0 + material delayedgroup group out nuclide mean std. dev. +0 10002 1 1 total 0.0 0.0 +1 10002 2 1 total 0.0 0.0 +2 10002 3 1 total 0.0 0.0 +3 10002 4 1 total 0.0 0.0 +4 10002 5 1 total 0.0 0.0 +5 10002 6 1 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +0 10002 1 1 total 0.0 0.0 +1 10002 2 1 total 0.0 0.0 +2 10002 3 1 total 0.0 0.0 +3 10002 4 1 total 0.0 0.0 +4 10002 5 1 total 0.0 0.0 +5 10002 6 1 total 0.0 0.0 diff --git a/tests/test_mgxs_library_condense/test_mgxs_library_condense.py b/tests/test_mgxs_library_condense/test_mgxs_library_condense.py index 5571b59f2e..d3f2bc08e8 100644 --- a/tests/test_mgxs_library_condense/test_mgxs_library_condense.py +++ b/tests/test_mgxs_library_condense/test_mgxs_library_condense.py @@ -23,12 +23,18 @@ class MGXSTestHarness(PyAPITestHarness): energy_groups = openmc.mgxs.EnergyGroups(group_edges=[0, 0.625e-6, 20.]) + # Initialize a six-delayed-group structure + delayed_groups = list(range(1,7)) + # Initialize MGXS Library for a few cross section types self.mgxs_lib = openmc.mgxs.Library(self._input_set.geometry) self.mgxs_lib.by_nuclide = False + # Test all MGXS types - self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES + self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES + \ + openmc.mgxs.MDGXS_TYPES self.mgxs_lib.energy_groups = energy_groups + self.mgxs_lib.delayed_groups = delayed_groups self.mgxs_lib.legendre_order = 3 self.mgxs_lib.domain_type = 'material' self.mgxs_lib.build_library() diff --git a/tests/test_mgxs_library_distribcell/inputs_true.dat b/tests/test_mgxs_library_distribcell/inputs_true.dat index 055ce35a57..924c53838f 100644 --- a/tests/test_mgxs_library_distribcell/inputs_true.dat +++ b/tests/test_mgxs_library_distribcell/inputs_true.dat @@ -1 +1 @@ -2d948f3b12293294eaeca231a3df9d51195379e8bb38dd3e68d3bc512a7d08ed52a1109054ca381684ec127268710f6d6e9210ac8154c9b379608e996627624a \ No newline at end of file +9ce3d6987d67e92b0924916bb54288429d2bd6dfd12a69f86c5dbefb407f7eb72adb0e44d558c09e9a39610ffeb651aee4aedc629cf3a28a181d62ca4cfbcd5a \ No newline at end of file diff --git a/tests/test_mgxs_library_distribcell/results_true.dat b/tests/test_mgxs_library_distribcell/results_true.dat index c21ca09e99..5a996c8fa9 100644 --- a/tests/test_mgxs_library_distribcell/results_true.dat +++ b/tests/test_mgxs_library_distribcell/results_true.dat @@ -1,42 +1,63 @@ avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 1.145934 0.553822 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.457353 0.010474 avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.718919 0.520644 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.405649 0.015784 avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.718919 0.520644 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.405641 0.015787 avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.019762 0.010629 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.066556 0.00251 avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.019762 0.010629 - avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.0 0.0 - avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.0 0.0 - avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.0 0.0 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.028979 0.002712 avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 1.126172 0.54344 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.037577 0.001487 avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 1.142547 0.570131 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.092377 0.003628 + avg(distribcell) group in nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 7.276707 0.287579 + avg(distribcell) group in nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.390797 0.008717 + avg(distribcell) group in nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.387332 0.014241 avg(distribcell) group in group out nuclide moment mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P0 1.142547 0.570131 -1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P1 0.447381 0.216322 -2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P2 0.141202 0.066504 -3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P3 0.039228 0.024621 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P0 0.387009 0.014230 +1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P1 0.047179 0.004923 +2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P2 0.015713 0.003654 +3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P3 0.005378 0.003137 avg(distribcell) group in group out nuclide moment mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P0 1.142547 0.570131 -1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P1 0.447381 0.216322 -2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P2 0.141202 0.066504 -3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P3 0.039228 0.024621 - avg(distribcell) group in group out nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 1.0 0.529717 - avg(distribcell) group in group out nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.0 0.0 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P0 0.387332 0.014241 +1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P1 0.047187 0.004933 +2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P2 0.015727 0.003654 +3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P3 0.005387 0.003141 + avg(distribcell) group in group out nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 1.000834 0.037242 + avg(distribcell) group in group out nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.094516 0.0059 avg(distribcell) group out nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.0 0.0 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 1.0 0.080455 avg(distribcell) group out nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.0 0.0 - avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.000001 6.946255e-07 - avg(distribcell) group in nuclide mean std. dev. -0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.0 0.0 +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 1.0 0.080541 + avg(distribcell) group in nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 5.139437e-07 2.133314e-08 + avg(distribcell) group in nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.091725 0.003604 + avg(distribcell) delayedgroup group in nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.000021 8.253907e-07 +1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 2 1 total 0.000112 4.284000e-06 +2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 3 1 total 0.000109 4.105197e-06 +3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4 1 total 0.000252 9.271420e-06 +4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 5 1 total 0.000112 3.888625e-06 +5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 6 1 total 0.000047 1.625563e-06 + avg(distribcell) delayedgroup group out nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.0 0.000000 +1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 2 1 total 1.0 1.414214 +2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 3 1 total 1.0 1.414214 +3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4 1 total 0.0 0.000000 +4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 5 1 total 0.0 0.000000 +5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 6 1 total 1.0 1.414214 + avg(distribcell) delayedgroup group in nuclide mean std. dev. +0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.000227 0.000012 +1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 2 1 total 0.001209 0.000061 +2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 3 1 total 0.001177 0.000059 +3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4 1 total 0.002727 0.000135 +4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 5 1 total 0.001210 0.000058 +5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 6 1 total 0.000504 0.000024 diff --git a/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py b/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py index 30593e54b5..940985d916 100644 --- a/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py +++ b/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py @@ -6,29 +6,39 @@ import glob import hashlib sys.path.insert(0, os.pardir) from testing_harness import PyAPITestHarness +from input_set import AssemblyInputSet import openmc import openmc.mgxs class MGXSTestHarness(PyAPITestHarness): def _build_inputs(self): + # Set the input set to use the pincell model + self._input_set = AssemblyInputSet() + # Generate inputs using parent class routine super(MGXSTestHarness, self)._build_inputs() # Initialize a one-group structure energy_groups = openmc.mgxs.EnergyGroups(group_edges=[0, 20.]) + # Initialize a six-delayed-group structure + delayed_groups = list(range(1,7)) + # Initialize MGXS Library for a few cross section types # for one material-filled cell in the geometry self.mgxs_lib = openmc.mgxs.Library(self._input_set.geometry) self.mgxs_lib.by_nuclide = False + # Test all MGXS types - self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES + self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES + \ + openmc.mgxs.MDGXS_TYPES self.mgxs_lib.energy_groups = energy_groups + self.mgxs_lib.delayed_groups = delayed_groups self.mgxs_lib.legendre_order = 3 self.mgxs_lib.domain_type = 'distribcell' - material_cells = self.mgxs_lib.openmc_geometry.get_all_material_cells() - self.mgxs_lib.domains = [material_cells[-1]] + cells = self.mgxs_lib.openmc_geometry.get_all_material_cells() + self.mgxs_lib.domains = [c for c in cells if c.name == 'fuel'] self.mgxs_lib.build_library() # Initialize a tallies file diff --git a/tests/test_mgxs_library_hdf5/inputs_true.dat b/tests/test_mgxs_library_hdf5/inputs_true.dat index 0c648376e8..e58015868a 100644 --- a/tests/test_mgxs_library_hdf5/inputs_true.dat +++ b/tests/test_mgxs_library_hdf5/inputs_true.dat @@ -1 +1 @@ -e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e \ No newline at end of file +08c5f1c783dd88c5fed51c054718ca09fc4e99aa4560a6f928b3902991948f3a878d055ac46c07548904285c2c5f22dc2a3d8c1bb82b8e73d76dd790820117df \ No newline at end of file diff --git a/tests/test_mgxs_library_hdf5/results_true.dat b/tests/test_mgxs_library_hdf5/results_true.dat index b2bd28f279..3108573b35 100644 --- a/tests/test_mgxs_library_hdf5/results_true.dat +++ b/tests/test_mgxs_library_hdf5/results_true.dat @@ -72,6 +72,45 @@ domain=10000 type=inverse-velocity domain=10000 type=prompt-nu-fission [ 0.01923922 0.46671903] [ 0.00130951 0.04141087] +domain=10000 type=delayed-nu-fission +[[ 2.29808234e-05 1.06974158e-04] + [ 1.43606337e-04 5.52167907e-04] + [ 1.51382216e-04 5.27147681e-04] + [ 7.42603178e-05 2.22018043e-04] + [ 4.14908454e-05 9.10244403e-05] + [ 1.70016000e-05 3.81298119e-05]] +[[ 1.66363133e-06 9.49156242e-06] + [ 1.05907806e-05 4.89925426e-05] + [ 1.12671238e-05 4.67725567e-05] + [ 5.22610273e-06 1.87563195e-05] + [ 2.99830766e-06 7.68984041e-06] + [ 1.22654684e-06 3.22124663e-06]] +domain=10000 type=chi-delayed +[[ 0. 0.] + [ 1. 0.] + [ 1. 0.] + [ 1. 0.] + [ 0. 0.] + [ 0. 0.]] +[[ 0. 0. ] + [ 0.86912776 0. ] + [ 1.41421356 0. ] + [ 0.36035904 0. ] + [ 0. 0. ] + [ 0. 0. ]] +domain=10000 type=beta +[[ 4.89188107e-05 2.27713711e-04] + [ 3.05691886e-04 1.17538858e-03] + [ 3.22244241e-04 1.12212853e-03] + [ 3.82159891e-03 1.14255357e-02] + [ 2.13520995e-03 4.68431744e-03] + [ 8.74939644e-04 1.96224379e-03]] +[[ 4.67388620e-06 2.46946810e-05] + [ 2.95223877e-05 1.27466393e-04] + [ 3.12885004e-05 1.21690543e-04] + [ 3.21434855e-04 1.09939816e-03] + [ 1.82980497e-04 4.50738567e-04] + [ 7.48899920e-05 1.88812772e-04]] domain=10001 type=total [ 0.31373767 0.3008214 ] [ 0.0155819 0.02805245] @@ -146,6 +185,45 @@ domain=10001 type=inverse-velocity domain=10001 type=prompt-nu-fission [ 0. 0.] [ 0. 0.] +domain=10001 type=delayed-nu-fission +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +domain=10001 type=chi-delayed +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +domain=10001 type=beta +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] domain=10002 type=total [ 0.66457226 2.05238401] [ 0.03121475 0.22434291] @@ -220,3 +298,42 @@ domain=10002 type=inverse-velocity domain=10002 type=prompt-nu-fission [ 0. 0.] [ 0. 0.] +domain=10002 type=delayed-nu-fission +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +domain=10002 type=chi-delayed +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +domain=10002 type=beta +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] +[[ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.] + [ 0. 0.]] diff --git a/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py b/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py index 000a1f8cb9..b4d7e5dbbe 100644 --- a/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py +++ b/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py @@ -24,12 +24,18 @@ class MGXSTestHarness(PyAPITestHarness): energy_groups = openmc.mgxs.EnergyGroups(group_edges=[0, 0.625e-6, 20.]) + # Initialize a six-delayed-group structure + delayed_groups = list(range(1,7)) + # Initialize MGXS Library for a few cross section types self.mgxs_lib = openmc.mgxs.Library(self._input_set.geometry) self.mgxs_lib.by_nuclide = False + # Test all MGXS types - self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES + self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES + \ + openmc.mgxs.MDGXS_TYPES self.mgxs_lib.energy_groups = energy_groups + self.mgxs_lib.delayed_groups = delayed_groups self.mgxs_lib.legendre_order = 3 self.mgxs_lib.domain_type = 'material' self.mgxs_lib.build_library() diff --git a/tests/test_mgxs_library_mesh/inputs_true.dat b/tests/test_mgxs_library_mesh/inputs_true.dat index e036b49a26..f62e0aa05e 100644 --- a/tests/test_mgxs_library_mesh/inputs_true.dat +++ b/tests/test_mgxs_library_mesh/inputs_true.dat @@ -1 +1 @@ -a4cd030bea212e45fdb159e75a7fb3d1947e9bf3d0384ac5d37a72298d67dcfdd1b9eb5c6af8ac6e5983bd5b47de9c17a2ea472b467b7222a4909ee070bf1ca3 \ No newline at end of file +5f167bdd4d6ae5873d48483e85aceaec8a934239ed5a50ef6f6500ce204f5851ae330621a5007f3b3d6bdab49f2cd627d011c1f6e6983fec958a6984eb9cb7ca \ No newline at end of file diff --git a/tests/test_mgxs_library_mesh/results_true.dat b/tests/test_mgxs_library_mesh/results_true.dat index 03019cfd3d..b6656d67b4 100644 --- a/tests/test_mgxs_library_mesh/results_true.dat +++ b/tests/test_mgxs_library_mesh/results_true.dat @@ -1,62 +1,62 @@ mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 0.640786 0.044177 -1 1 2 1 1 total 0.660597 0.128423 -2 2 1 1 1 total 0.615276 0.104046 +1 1 2 1 1 total 0.615276 0.104046 +2 2 1 1 1 total 0.660597 0.128423 3 2 2 1 1 total 0.646999 0.186709 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 0.36665 0.048814 -1 1 2 1 1 total 0.40784 0.096486 -2 2 1 1 1 total 0.36356 0.074111 +1 1 2 1 1 total 0.36356 0.074111 +2 2 1 1 1 total 0.40784 0.096486 3 2 2 1 1 total 0.41456 0.160443 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 0.366650 0.048814 -1 1 2 1 1 total 0.407840 0.096486 -2 2 1 1 1 total 0.363560 0.074111 +1 1 2 1 1 total 0.363560 0.074111 +2 2 1 1 1 total 0.407840 0.096486 3 2 2 1 1 total 0.414593 0.160436 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 0.025749 0.002863 -1 1 2 1 1 total 0.028400 0.005275 -2 2 1 1 1 total 0.022988 0.004099 +1 1 2 1 1 total 0.022988 0.004099 +2 2 1 1 1 total 0.028400 0.005275 3 2 2 1 1 total 0.027589 0.010350 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 0.015861 0.002876 -1 1 2 1 1 total 0.017280 0.004371 -2 2 1 1 1 total 0.014403 0.003542 +1 1 2 1 1 total 0.014403 0.003542 +2 2 1 1 1 total 0.017280 0.004371 3 2 2 1 1 total 0.018061 0.010110 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 0.009888 0.001077 -1 1 2 1 1 total 0.011121 0.002456 -2 2 1 1 1 total 0.008585 0.001552 +1 1 2 1 1 total 0.008585 0.001552 +2 2 1 1 1 total 0.011121 0.002456 3 2 2 1 1 total 0.009527 0.003659 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 0.026065 0.002907 -1 1 2 1 1 total 0.029084 0.006430 -2 2 1 1 1 total 0.022596 0.004062 +1 1 2 1 1 total 0.022596 0.004062 +2 2 1 1 1 total 0.029084 0.006430 3 2 2 1 1 total 0.025066 0.009687 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 1.938476 0.211550 -1 1 2 1 1 total 2.177360 0.480780 -2 2 1 1 1 total 1.682799 0.303764 +1 1 2 1 1 total 1.682799 0.303764 +2 2 1 1 1 total 2.177360 0.480780 3 2 2 1 1 total 1.864890 0.715661 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 0.615037 0.041754 -1 1 2 1 1 total 0.632196 0.123878 -2 2 1 1 1 total 0.592288 0.100439 +1 1 2 1 1 total 0.592288 0.100439 +2 2 1 1 1 total 0.632196 0.123878 3 2 2 1 1 total 0.619410 0.177190 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 0.584014 0.054315 -1 1 2 1 1 total 0.622514 0.111323 -2 2 1 1 1 total 0.587256 0.084833 +1 1 2 1 1 total 0.587256 0.084833 +2 2 1 1 1 total 0.622514 0.111323 3 2 2 1 1 total 0.613792 0.168612 mesh 1 group in group out nuclide moment mean std. dev. x y z @@ -64,14 +64,14 @@ 1 1 1 1 1 1 total P1 0.243427 0.025488 2 1 1 1 1 1 total P2 0.089236 0.007357 3 1 1 1 1 1 total P3 0.008994 0.005768 -4 1 2 1 1 1 total P0 0.622514 0.111323 -5 1 2 1 1 1 total P1 0.239376 0.042594 -6 1 2 1 1 1 total P2 0.088386 0.017200 -7 1 2 1 1 1 total P3 -0.001243 0.005639 -8 2 1 1 1 1 total P0 0.587256 0.084833 -9 2 1 1 1 1 total P1 0.245120 0.041033 -10 2 1 1 1 1 total P2 0.086784 0.016255 -11 2 1 1 1 1 total P3 0.008660 0.004755 +4 1 2 1 1 1 total P0 0.587256 0.084833 +5 1 2 1 1 1 total P1 0.245120 0.041033 +6 1 2 1 1 1 total P2 0.086784 0.016255 +7 1 2 1 1 1 total P3 0.008660 0.004755 +8 2 1 1 1 1 total P0 0.622514 0.111323 +9 2 1 1 1 1 total P1 0.239376 0.042594 +10 2 1 1 1 1 total P2 0.088386 0.017200 +11 2 1 1 1 1 total P3 -0.001243 0.005639 12 2 2 1 1 1 total P0 0.612950 0.167940 13 2 2 1 1 1 total P1 0.226176 0.061882 14 2 2 1 1 1 total P2 0.086593 0.026126 @@ -82,14 +82,14 @@ 1 1 1 1 1 1 total P1 0.243427 0.025488 2 1 1 1 1 1 total P2 0.089236 0.007357 3 1 1 1 1 1 total P3 0.008994 0.005768 -4 1 2 1 1 1 total P0 0.622514 0.111323 -5 1 2 1 1 1 total P1 0.239376 0.042594 -6 1 2 1 1 1 total P2 0.088386 0.017200 -7 1 2 1 1 1 total P3 -0.001243 0.005639 -8 2 1 1 1 1 total P0 0.587256 0.084833 -9 2 1 1 1 1 total P1 0.245120 0.041033 -10 2 1 1 1 1 total P2 0.086784 0.016255 -11 2 1 1 1 1 total P3 0.008660 0.004755 +4 1 2 1 1 1 total P0 0.587256 0.084833 +5 1 2 1 1 1 total P1 0.245120 0.041033 +6 1 2 1 1 1 total P2 0.086784 0.016255 +7 1 2 1 1 1 total P3 0.008660 0.004755 +8 2 1 1 1 1 total P0 0.622514 0.111323 +9 2 1 1 1 1 total P1 0.239376 0.042594 +10 2 1 1 1 1 total P2 0.088386 0.017200 +11 2 1 1 1 1 total P3 -0.001243 0.005639 12 2 2 1 1 1 total P0 0.613792 0.168612 13 2 2 1 1 1 total P1 0.226142 0.061856 14 2 2 1 1 1 total P2 0.086174 0.025979 @@ -97,36 +97,114 @@ mesh 1 group in group out nuclide mean std. dev. x y z 0 1 1 1 1 1 total 1.000000 0.088094 -1 1 2 1 1 1 total 1.000000 0.160891 -2 2 1 1 1 1 total 1.000000 0.126864 +1 1 2 1 1 1 total 1.000000 0.126864 +2 2 1 1 1 1 total 1.000000 0.160891 3 2 2 1 1 1 total 1.001374 0.305883 mesh 1 group in group out nuclide mean std. dev. x y z 0 1 1 1 1 1 total 0.027395 0.004680 -1 1 2 1 1 1 total 0.022914 0.006025 -2 2 1 1 1 1 total 0.019384 0.002846 +1 1 2 1 1 1 total 0.019384 0.002846 +2 2 1 1 1 1 total 0.022914 0.006025 3 2 2 1 1 1 total 0.029629 0.006292 mesh 1 group out nuclide mean std. dev. x y z 0 1 1 1 1 total 1.0 0.220956 -1 1 2 1 1 total 1.0 0.316565 -2 2 1 1 1 total 1.0 0.132140 +1 1 2 1 1 total 1.0 0.132140 +2 2 1 1 1 total 1.0 0.316565 3 2 2 1 1 total 1.0 0.181577 mesh 1 group out nuclide mean std. dev. x y z 0 1 1 1 1 total 1.0 0.222246 -1 1 2 1 1 total 1.0 0.316565 -2 2 1 1 1 total 1.0 0.132140 +1 1 2 1 1 total 1.0 0.132140 +2 2 1 1 1 total 1.0 0.316565 3 2 2 1 1 total 1.0 0.181577 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 3.610522e-07 3.169931e-08 -1 1 2 1 1 total 3.942353e-07 8.459167e-08 -2 2 1 1 1 total 3.097784e-07 5.252025e-08 +1 1 2 1 1 total 3.097784e-07 5.252025e-08 +2 2 1 1 1 total 3.942353e-07 8.459167e-08 3 2 2 1 1 total 3.799163e-07 1.806470e-07 mesh 1 group in nuclide mean std. dev. x y z 0 1 1 1 1 total 0.025920 0.002893 -1 1 2 1 1 total 0.028922 0.006394 -2 2 1 1 1 total 0.022467 0.004039 +1 1 2 1 1 total 0.022467 0.004039 +2 2 1 1 1 total 0.028922 0.006394 3 2 2 1 1 total 0.024923 0.009632 + mesh 1 delayedgroup group in nuclide mean std. dev. + x y z +0 1 1 1 1 1 total 0.000004 4.432732e-07 +1 1 1 1 2 1 total 0.000026 2.653319e-06 +2 1 1 1 3 1 total 0.000024 2.402270e-06 +3 1 1 1 4 1 total 0.000054 5.464055e-06 +4 1 1 1 5 1 total 0.000026 2.663025e-06 +5 1 1 1 6 1 total 0.000010 1.038005e-06 +6 1 2 1 1 1 total 0.000004 6.987770e-07 +7 1 2 1 2 1 total 0.000023 4.115234e-06 +8 1 2 1 3 1 total 0.000021 3.816392e-06 +9 1 2 1 4 1 total 0.000049 8.885822e-06 +10 1 2 1 5 1 total 0.000024 4.378290e-06 +11 1 2 1 6 1 total 0.000009 1.745695e-06 +12 2 1 1 1 1 total 0.000005 1.098837e-06 +13 2 1 1 2 1 total 0.000029 6.436855e-06 +14 2 1 1 3 1 total 0.000027 5.926286e-06 +15 2 1 1 4 1 total 0.000061 1.359391e-05 +16 2 1 1 5 1 total 0.000029 6.489015e-06 +17 2 1 1 6 1 total 0.000011 2.574270e-06 +18 2 2 1 1 1 total 0.000004 1.660497e-06 +19 2 2 1 2 1 total 0.000025 9.701974e-06 +20 2 2 1 3 1 total 0.000023 9.005217e-06 +21 2 2 1 4 1 total 0.000054 2.084107e-05 +22 2 2 1 5 1 total 0.000026 9.981045e-06 +23 2 2 1 6 1 total 0.000010 3.987979e-06 + mesh 1 delayedgroup group out nuclide mean std. dev. + x y z +0 1 1 1 1 1 total 0.0 0.000000 +1 1 1 1 2 1 total 0.0 0.000000 +2 1 1 1 3 1 total 0.0 0.000000 +3 1 1 1 4 1 total 1.0 1.414214 +4 1 1 1 5 1 total 0.0 0.000000 +5 1 1 1 6 1 total 0.0 0.000000 +6 1 2 1 1 1 total 0.0 0.000000 +7 1 2 1 2 1 total 0.0 0.000000 +8 1 2 1 3 1 total 0.0 0.000000 +9 1 2 1 4 1 total 0.0 0.000000 +10 1 2 1 5 1 total 0.0 0.000000 +11 1 2 1 6 1 total 0.0 0.000000 +12 2 1 1 1 1 total 0.0 0.000000 +13 2 1 1 2 1 total 0.0 0.000000 +14 2 1 1 3 1 total 0.0 0.000000 +15 2 1 1 4 1 total 0.0 0.000000 +16 2 1 1 5 1 total 0.0 0.000000 +17 2 1 1 6 1 total 0.0 0.000000 +18 2 2 1 1 1 total 0.0 0.000000 +19 2 2 1 2 1 total 0.0 0.000000 +20 2 2 1 3 1 total 0.0 0.000000 +21 2 2 1 4 1 total 0.0 0.000000 +22 2 2 1 5 1 total 0.0 0.000000 +23 2 2 1 6 1 total 0.0 0.000000 + mesh 1 delayedgroup group in nuclide mean std. dev. + x y z +0 1 1 1 1 1 total 0.000166 0.000023 +1 1 1 1 2 1 total 0.000989 0.000136 +2 1 1 1 3 1 total 0.000907 0.000123 +3 1 1 1 4 1 total 0.002087 0.000282 +4 1 1 1 5 1 total 0.001014 0.000137 +5 1 1 1 6 1 total 0.000400 0.000054 +6 1 2 1 1 1 total 0.000167 0.000030 +7 1 2 1 2 1 total 0.001002 0.000178 +8 1 2 1 3 1 total 0.000926 0.000165 +9 1 2 1 4 1 total 0.002149 0.000384 +10 1 2 1 5 1 total 0.001056 0.000189 +11 1 2 1 6 1 total 0.000417 0.000076 +12 2 1 1 1 1 total 0.000171 0.000039 +13 2 1 1 2 1 total 0.001003 0.000226 +14 2 1 1 3 1 total 0.000918 0.000208 +15 2 1 1 4 1 total 0.002100 0.000477 +16 2 1 1 5 1 total 0.000996 0.000228 +17 2 1 1 6 1 total 0.000394 0.000090 +18 2 2 1 1 1 total 0.000171 0.000082 +19 2 2 1 2 1 total 0.001007 0.000480 +20 2 2 1 3 1 total 0.000929 0.000445 +21 2 2 1 4 1 total 0.002143 0.001028 +22 2 2 1 5 1 total 0.001026 0.000492 +23 2 2 1 6 1 total 0.000408 0.000196 diff --git a/tests/test_mgxs_library_mesh/test_mgxs_library_mesh.py b/tests/test_mgxs_library_mesh/test_mgxs_library_mesh.py index df7a0a5ae8..1f31bd5660 100644 --- a/tests/test_mgxs_library_mesh/test_mgxs_library_mesh.py +++ b/tests/test_mgxs_library_mesh/test_mgxs_library_mesh.py @@ -18,14 +18,19 @@ class MGXSTestHarness(PyAPITestHarness): # Initialize a one-group structure energy_groups = openmc.mgxs.EnergyGroups(group_edges=[0, 20.]) + # Initialize a six-delayed-group structure + delayed_groups = list(range(1,7)) + # Initialize MGXS Library for a few cross section types # for one material-filled cell in the geometry self.mgxs_lib = openmc.mgxs.Library(self._input_set.geometry) self.mgxs_lib.by_nuclide = False # Test all MGXS types - self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES + self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES + \ + openmc.mgxs.MDGXS_TYPES self.mgxs_lib.energy_groups = energy_groups + self.mgxs_lib.delayed_groups = delayed_groups self.mgxs_lib.legendre_order = 3 self.mgxs_lib.domain_type = 'mesh' diff --git a/tests/test_mgxs_library_no_nuclides/inputs_true.dat b/tests/test_mgxs_library_no_nuclides/inputs_true.dat index 0c648376e8..e58015868a 100644 --- a/tests/test_mgxs_library_no_nuclides/inputs_true.dat +++ b/tests/test_mgxs_library_no_nuclides/inputs_true.dat @@ -1 +1 @@ -e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e \ No newline at end of file +08c5f1c783dd88c5fed51c054718ca09fc4e99aa4560a6f928b3902991948f3a878d055ac46c07548904285c2c5f22dc2a3d8c1bb82b8e73d76dd790820117df \ No newline at end of file diff --git a/tests/test_mgxs_library_no_nuclides/results_true.dat b/tests/test_mgxs_library_no_nuclides/results_true.dat index 141143c8c3..3563f141b1 100644 --- a/tests/test_mgxs_library_no_nuclides/results_true.dat +++ b/tests/test_mgxs_library_no_nuclides/results_true.dat @@ -29,39 +29,39 @@ 1 10000 1 total 0.385188 0.026946 0 10000 2 total 0.412389 0.015425 material group in group out nuclide moment mean std. dev. -12 10000 1 1 total P0 0.384199 0.027001 -13 10000 1 1 total P1 0.051870 0.006983 -14 10000 1 1 total P2 0.020069 0.002846 +9 10000 1 1 total P0 -0.000207 0.000149 +11 10000 1 1 total P1 0.000234 0.000128 +13 10000 1 1 total P2 0.051870 0.006983 15 10000 1 1 total P3 0.009478 0.002234 8 10000 1 2 total P0 0.000989 0.000482 -9 10000 1 2 total P1 -0.000207 0.000149 -10 10000 1 2 total P2 -0.000103 0.000184 -11 10000 1 2 total P3 0.000234 0.000128 -4 10000 2 1 total P0 0.000925 0.000925 -5 10000 2 1 total P1 -0.000768 0.000768 -6 10000 2 1 total P2 0.000494 0.000494 +10 10000 1 2 total P1 -0.000103 0.000184 +12 10000 1 2 total P2 0.384199 0.027001 +14 10000 1 2 total P3 0.020069 0.002846 +1 10000 2 1 total P0 0.016482 0.004502 +3 10000 2 1 total P1 -0.010499 0.010438 +5 10000 2 1 total P2 -0.000768 0.000768 7 10000 2 1 total P3 -0.000171 0.000172 0 10000 2 2 total P0 0.411465 0.015245 -1 10000 2 2 total P1 0.016482 0.004502 -2 10000 2 2 total P2 0.006371 0.010551 -3 10000 2 2 total P3 -0.010499 0.010438 +2 10000 2 2 total P1 0.006371 0.010551 +4 10000 2 2 total P2 0.000925 0.000925 +6 10000 2 2 total P3 0.000494 0.000494 material group in group out nuclide moment mean std. dev. -12 10000 1 1 total P0 0.384199 0.027001 -13 10000 1 1 total P1 0.051870 0.006983 -14 10000 1 1 total P2 0.020069 0.002846 +9 10000 1 1 total P0 -0.000207 0.000149 +11 10000 1 1 total P1 0.000234 0.000128 +13 10000 1 1 total P2 0.051870 0.006983 15 10000 1 1 total P3 0.009478 0.002234 8 10000 1 2 total P0 0.000989 0.000482 -9 10000 1 2 total P1 -0.000207 0.000149 -10 10000 1 2 total P2 -0.000103 0.000184 -11 10000 1 2 total P3 0.000234 0.000128 -4 10000 2 1 total P0 0.000925 0.000925 -5 10000 2 1 total P1 -0.000768 0.000768 -6 10000 2 1 total P2 0.000494 0.000494 +10 10000 1 2 total P1 -0.000103 0.000184 +12 10000 1 2 total P2 0.384199 0.027001 +14 10000 1 2 total P3 0.020069 0.002846 +1 10000 2 1 total P0 0.016482 0.004502 +3 10000 2 1 total P1 -0.010499 0.010438 +5 10000 2 1 total P2 -0.000768 0.000768 7 10000 2 1 total P3 -0.000171 0.000172 0 10000 2 2 total P0 0.411465 0.015245 -1 10000 2 2 total P1 0.016482 0.004502 -2 10000 2 2 total P2 0.006371 0.010551 -3 10000 2 2 total P3 -0.010499 0.010438 +2 10000 2 2 total P1 0.006371 0.010551 +4 10000 2 2 total P2 0.000925 0.000925 +6 10000 2 2 total P3 0.000494 0.000494 material group in group out nuclide mean std. dev. 3 10000 1 1 total 1.0 0.078516 2 10000 1 2 total 1.0 0.687184 @@ -84,6 +84,45 @@ material group in nuclide mean std. dev. 1 10000 1 total 0.019239 0.001310 0 10000 2 total 0.466719 0.041411 + material delayedgroup group in nuclide mean std. dev. +1 10000 1 1 total 0.000023 0.000002 +3 10000 2 1 total 0.000144 0.000011 +5 10000 3 1 total 0.000151 0.000011 +7 10000 4 1 total 0.000074 0.000005 +9 10000 5 1 total 0.000041 0.000003 +11 10000 6 1 total 0.000017 0.000001 +0 10000 1 2 total 0.000107 0.000009 +2 10000 2 2 total 0.000552 0.000049 +4 10000 3 2 total 0.000527 0.000047 +6 10000 4 2 total 0.000222 0.000019 +8 10000 5 2 total 0.000091 0.000008 +10 10000 6 2 total 0.000038 0.000003 + material delayedgroup group out nuclide mean std. dev. +1 10000 1 1 total 0.0 0.000000 +3 10000 2 1 total 1.0 0.869128 +5 10000 3 1 total 1.0 1.414214 +7 10000 4 1 total 1.0 0.360359 +9 10000 5 1 total 0.0 0.000000 +11 10000 6 1 total 0.0 0.000000 +0 10000 1 2 total 0.0 0.000000 +2 10000 2 2 total 0.0 0.000000 +4 10000 3 2 total 0.0 0.000000 +6 10000 4 2 total 0.0 0.000000 +8 10000 5 2 total 0.0 0.000000 +10 10000 6 2 total 0.0 0.000000 + material delayedgroup group in nuclide mean std. dev. +1 10000 1 1 total 0.000049 0.000005 +3 10000 2 1 total 0.000306 0.000030 +5 10000 3 1 total 0.000322 0.000031 +7 10000 4 1 total 0.003822 0.000321 +9 10000 5 1 total 0.002135 0.000183 +11 10000 6 1 total 0.000875 0.000075 +0 10000 1 2 total 0.000228 0.000025 +2 10000 2 2 total 0.001175 0.000127 +4 10000 3 2 total 0.001122 0.000122 +6 10000 4 2 total 0.011426 0.001099 +8 10000 5 2 total 0.004684 0.000451 +10 10000 6 2 total 0.001962 0.000189 material group in nuclide mean std. dev. 1 10001 1 total 0.313738 0.015582 0 10001 2 total 0.300821 0.028052 @@ -115,39 +154,39 @@ 1 10001 1 total 0.310121 0.033788 0 10001 2 total 0.296264 0.043792 material group in group out nuclide moment mean std. dev. -12 10001 1 1 total P0 0.310121 0.033788 -13 10001 1 1 total P1 0.038230 0.008484 -14 10001 1 1 total P2 0.020745 0.004696 +9 10001 1 1 total P0 0.000000 0.000000 +11 10001 1 1 total P1 0.000000 0.000000 +13 10001 1 1 total P2 0.038230 0.008484 15 10001 1 1 total P3 0.007964 0.003732 8 10001 1 2 total P0 0.000000 0.000000 -9 10001 1 2 total P1 0.000000 0.000000 -10 10001 1 2 total P2 0.000000 0.000000 -11 10001 1 2 total P3 0.000000 0.000000 -4 10001 2 1 total P0 0.000000 0.000000 -5 10001 2 1 total P1 0.000000 0.000000 -6 10001 2 1 total P2 0.000000 0.000000 +10 10001 1 2 total P1 0.000000 0.000000 +12 10001 1 2 total P2 0.310121 0.033788 +14 10001 1 2 total P3 0.020745 0.004696 +1 10001 2 1 total P0 -0.011214 0.016180 +3 10001 2 1 total P1 -0.003270 0.007329 +5 10001 2 1 total P2 0.000000 0.000000 7 10001 2 1 total P3 0.000000 0.000000 0 10001 2 2 total P0 0.296264 0.043792 -1 10001 2 2 total P1 -0.011214 0.016180 -2 10001 2 2 total P2 0.008837 0.011504 -3 10001 2 2 total P3 -0.003270 0.007329 +2 10001 2 2 total P1 0.008837 0.011504 +4 10001 2 2 total P2 0.000000 0.000000 +6 10001 2 2 total P3 0.000000 0.000000 material group in group out nuclide moment mean std. dev. -12 10001 1 1 total P0 0.310121 0.033788 -13 10001 1 1 total P1 0.038230 0.008484 -14 10001 1 1 total P2 0.020745 0.004696 +9 10001 1 1 total P0 0.000000 0.000000 +11 10001 1 1 total P1 0.000000 0.000000 +13 10001 1 1 total P2 0.038230 0.008484 15 10001 1 1 total P3 0.007964 0.003732 8 10001 1 2 total P0 0.000000 0.000000 -9 10001 1 2 total P1 0.000000 0.000000 -10 10001 1 2 total P2 0.000000 0.000000 -11 10001 1 2 total P3 0.000000 0.000000 -4 10001 2 1 total P0 0.000000 0.000000 -5 10001 2 1 total P1 0.000000 0.000000 -6 10001 2 1 total P2 0.000000 0.000000 +10 10001 1 2 total P1 0.000000 0.000000 +12 10001 1 2 total P2 0.310121 0.033788 +14 10001 1 2 total P3 0.020745 0.004696 +1 10001 2 1 total P0 -0.011214 0.016180 +3 10001 2 1 total P1 -0.003270 0.007329 +5 10001 2 1 total P2 0.000000 0.000000 7 10001 2 1 total P3 0.000000 0.000000 0 10001 2 2 total P0 0.296264 0.043792 -1 10001 2 2 total P1 -0.011214 0.016180 -2 10001 2 2 total P2 0.008837 0.011504 -3 10001 2 2 total P3 -0.003270 0.007329 +2 10001 2 2 total P1 0.008837 0.011504 +4 10001 2 2 total P2 0.000000 0.000000 +6 10001 2 2 total P3 0.000000 0.000000 material group in group out nuclide mean std. dev. 3 10001 1 1 total 1.0 0.108779 2 10001 1 2 total 0.0 0.000000 @@ -170,6 +209,45 @@ material group in nuclide mean std. dev. 1 10001 1 total 0.0 0.0 0 10001 2 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +1 10001 1 1 total 0.0 0.0 +3 10001 2 1 total 0.0 0.0 +5 10001 3 1 total 0.0 0.0 +7 10001 4 1 total 0.0 0.0 +9 10001 5 1 total 0.0 0.0 +11 10001 6 1 total 0.0 0.0 +0 10001 1 2 total 0.0 0.0 +2 10001 2 2 total 0.0 0.0 +4 10001 3 2 total 0.0 0.0 +6 10001 4 2 total 0.0 0.0 +8 10001 5 2 total 0.0 0.0 +10 10001 6 2 total 0.0 0.0 + material delayedgroup group out nuclide mean std. dev. +1 10001 1 1 total 0.0 0.0 +3 10001 2 1 total 0.0 0.0 +5 10001 3 1 total 0.0 0.0 +7 10001 4 1 total 0.0 0.0 +9 10001 5 1 total 0.0 0.0 +11 10001 6 1 total 0.0 0.0 +0 10001 1 2 total 0.0 0.0 +2 10001 2 2 total 0.0 0.0 +4 10001 3 2 total 0.0 0.0 +6 10001 4 2 total 0.0 0.0 +8 10001 5 2 total 0.0 0.0 +10 10001 6 2 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +1 10001 1 1 total 0.0 0.0 +3 10001 2 1 total 0.0 0.0 +5 10001 3 1 total 0.0 0.0 +7 10001 4 1 total 0.0 0.0 +9 10001 5 1 total 0.0 0.0 +11 10001 6 1 total 0.0 0.0 +0 10001 1 2 total 0.0 0.0 +2 10001 2 2 total 0.0 0.0 +4 10001 3 2 total 0.0 0.0 +6 10001 4 2 total 0.0 0.0 +8 10001 5 2 total 0.0 0.0 +10 10001 6 2 total 0.0 0.0 material group in nuclide mean std. dev. 1 10002 1 total 0.664572 0.031215 0 10002 2 total 2.052384 0.224343 @@ -201,39 +279,39 @@ 1 10002 1 total 0.671269 0.026186 0 10002 2 total 2.035388 0.258060 material group in group out nuclide moment mean std. dev. -12 10002 1 1 total P0 0.639901 0.024709 -13 10002 1 1 total P1 0.381167 0.016243 -14 10002 1 1 total P2 0.152392 0.008156 +9 10002 1 1 total P0 0.008758 0.000926 +11 10002 1 1 total P1 -0.003785 0.000817 +13 10002 1 1 total P2 0.381167 0.016243 15 10002 1 1 total P3 0.009148 0.003889 8 10002 1 2 total P0 0.031368 0.001728 -9 10002 1 2 total P1 0.008758 0.000926 -10 10002 1 2 total P2 -0.002568 0.001014 -11 10002 1 2 total P3 -0.003785 0.000817 -4 10002 2 1 total P0 0.000443 0.000445 -5 10002 2 1 total P1 0.000400 0.000401 -6 10002 2 1 total P2 0.000320 0.000321 +10 10002 1 2 total P1 -0.002568 0.001014 +12 10002 1 2 total P2 0.639901 0.024709 +14 10002 1 2 total P3 0.152392 0.008156 +1 10002 2 1 total P0 0.509941 0.051236 +3 10002 2 1 total P1 0.024988 0.008312 +5 10002 2 1 total P2 0.000400 0.000401 7 10002 2 1 total P3 0.000214 0.000215 0 10002 2 2 total P0 2.034945 0.257800 -1 10002 2 2 total P1 0.509941 0.051236 -2 10002 2 2 total P2 0.111175 0.013020 -3 10002 2 2 total P3 0.024988 0.008312 +2 10002 2 2 total P1 0.111175 0.013020 +4 10002 2 2 total P2 0.000443 0.000445 +6 10002 2 2 total P3 0.000320 0.000321 material group in group out nuclide moment mean std. dev. -12 10002 1 1 total P0 0.639901 0.024709 -13 10002 1 1 total P1 0.381167 0.016243 -14 10002 1 1 total P2 0.152392 0.008156 +9 10002 1 1 total P0 0.008758 0.000926 +11 10002 1 1 total P1 -0.003785 0.000817 +13 10002 1 1 total P2 0.381167 0.016243 15 10002 1 1 total P3 0.009148 0.003889 8 10002 1 2 total P0 0.031368 0.001728 -9 10002 1 2 total P1 0.008758 0.000926 -10 10002 1 2 total P2 -0.002568 0.001014 -11 10002 1 2 total P3 -0.003785 0.000817 -4 10002 2 1 total P0 0.000443 0.000445 -5 10002 2 1 total P1 0.000400 0.000401 -6 10002 2 1 total P2 0.000320 0.000321 +10 10002 1 2 total P1 -0.002568 0.001014 +12 10002 1 2 total P2 0.639901 0.024709 +14 10002 1 2 total P3 0.152392 0.008156 +1 10002 2 1 total P0 0.509941 0.051236 +3 10002 2 1 total P1 0.024988 0.008312 +5 10002 2 1 total P2 0.000400 0.000401 7 10002 2 1 total P3 0.000214 0.000215 0 10002 2 2 total P0 2.034945 0.257800 -1 10002 2 2 total P1 0.509941 0.051236 -2 10002 2 2 total P2 0.111175 0.013020 -3 10002 2 2 total P3 0.024988 0.008312 +2 10002 2 2 total P1 0.111175 0.013020 +4 10002 2 2 total P2 0.000443 0.000445 +6 10002 2 2 total P3 0.000320 0.000321 material group in group out nuclide mean std. dev. 3 10002 1 1 total 1.0 0.038609 2 10002 1 2 total 1.0 0.067667 @@ -256,3 +334,42 @@ material group in nuclide mean std. dev. 1 10002 1 total 0.0 0.0 0 10002 2 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +1 10002 1 1 total 0.0 0.0 +3 10002 2 1 total 0.0 0.0 +5 10002 3 1 total 0.0 0.0 +7 10002 4 1 total 0.0 0.0 +9 10002 5 1 total 0.0 0.0 +11 10002 6 1 total 0.0 0.0 +0 10002 1 2 total 0.0 0.0 +2 10002 2 2 total 0.0 0.0 +4 10002 3 2 total 0.0 0.0 +6 10002 4 2 total 0.0 0.0 +8 10002 5 2 total 0.0 0.0 +10 10002 6 2 total 0.0 0.0 + material delayedgroup group out nuclide mean std. dev. +1 10002 1 1 total 0.0 0.0 +3 10002 2 1 total 0.0 0.0 +5 10002 3 1 total 0.0 0.0 +7 10002 4 1 total 0.0 0.0 +9 10002 5 1 total 0.0 0.0 +11 10002 6 1 total 0.0 0.0 +0 10002 1 2 total 0.0 0.0 +2 10002 2 2 total 0.0 0.0 +4 10002 3 2 total 0.0 0.0 +6 10002 4 2 total 0.0 0.0 +8 10002 5 2 total 0.0 0.0 +10 10002 6 2 total 0.0 0.0 + material delayedgroup group in nuclide mean std. dev. +1 10002 1 1 total 0.0 0.0 +3 10002 2 1 total 0.0 0.0 +5 10002 3 1 total 0.0 0.0 +7 10002 4 1 total 0.0 0.0 +9 10002 5 1 total 0.0 0.0 +11 10002 6 1 total 0.0 0.0 +0 10002 1 2 total 0.0 0.0 +2 10002 2 2 total 0.0 0.0 +4 10002 3 2 total 0.0 0.0 +6 10002 4 2 total 0.0 0.0 +8 10002 5 2 total 0.0 0.0 +10 10002 6 2 total 0.0 0.0 diff --git a/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py b/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py index 2c0a2e278c..edd41f1c56 100644 --- a/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py +++ b/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py @@ -23,12 +23,18 @@ class MGXSTestHarness(PyAPITestHarness): energy_groups = openmc.mgxs.EnergyGroups(group_edges=[0, 0.625e-6, 20.]) + # Initialize a six-delayed-group structure + delayed_groups = list(range(1,7)) + # Initialize MGXS Library for a few cross section types self.mgxs_lib = openmc.mgxs.Library(self._input_set.geometry) self.mgxs_lib.by_nuclide = False + # Test all MGXS types - self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES + self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES + \ + openmc.mgxs.MDGXS_TYPES self.mgxs_lib.energy_groups = energy_groups + self.mgxs_lib.delayed_groups = delayed_groups self.mgxs_lib.legendre_order = 3 self.mgxs_lib.domain_type = 'material' self.mgxs_lib.build_library() diff --git a/tests/test_mgxs_library_nuclides/results_true.dat b/tests/test_mgxs_library_nuclides/results_true.dat index 3da8146042..d3e3235ccc 100644 --- a/tests/test_mgxs_library_nuclides/results_true.dat +++ b/tests/test_mgxs_library_nuclides/results_true.dat @@ -1 +1 @@ -e494320a213b5704a2ac915a2ba504857be91961ceb6735b6ad05d81eb31c44c9584d5bd9d40baececf1dcb5b030e6ecec63cfbd20639baf69bcb596c5c46591 \ No newline at end of file +8142ae4e107002a835999e4ace85c17376f262a7059fc224f3756a2de19aba6ca4c4fa14ca2085c87d7729aa8d6d6f78fdae21ac6dfe33ca303449c769076074 \ No newline at end of file diff --git a/tests/test_score_current/results_true.dat b/tests/test_score_current/results_true.dat index 5d26226ae2..68dbe069cf 100644 --- a/tests/test_score_current/results_true.dat +++ b/tests/test_score_current/results_true.dat @@ -1 +1 @@ -bafab1921a12146abb2bb29603b52b9cc28a5a950a7a6bb1e3f012c05891c310fad643760d4f148b04d0fef3d1f3e141d146e3a278d81cc6fc8187c37717c5e7 \ No newline at end of file +c2921f159dac64099862c1cd9c6d421c977991f621f954f893ec1351cfcea6794ca2c98c9c2dcc3411f1b8dac91ec83bd895788ba33179222c450a7df1d64f1e \ No newline at end of file diff --git a/tests/test_sourcepoint_restart/results_true.dat b/tests/test_sourcepoint_restart/results_true.dat index 49afeb1d52..0e83121c58 100644 --- a/tests/test_sourcepoint_restart/results_true.dat +++ b/tests/test_sourcepoint_restart/results_true.dat @@ -41,16 +41,16 @@ tally 1: 1.416293E-07 0.000000E+00 0.000000E+00 -7.000000E-03 -1.500000E-05 -3.445754E-03 -3.819507E-06 -2.124056E-03 -1.976201E-06 -1.542203E-03 -1.531669E-06 -4.135720E-03 -4.532612E-06 +2.100000E-02 +1.150000E-04 +5.280651E-03 +1.222273E-05 +5.235520E-03 +1.202448E-05 +5.064093E-03 +1.787892E-05 +1.071093E-02 +2.748613E-05 0.000000E+00 0.000000E+00 0.000000E+00 @@ -59,8 +59,118 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 -5.874391E-04 -1.725424E-07 +8.954046E-04 +2.673852E-07 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +3.100000E-02 +2.130000E-04 +1.472240E-02 +5.500913E-05 +1.077445E-02 +2.987369E-05 +6.729425E-03 +1.249089E-05 +1.363637E-02 +4.345511E-05 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +1.182717E-03 +5.268981E-07 +2.000000E-03 +2.000000E-06 +-1.367978E-03 +9.381191E-07 +4.071787E-04 +9.316064E-08 +4.394728E-04 +1.064342E-07 +2.110881E-03 +1.737594E-06 +1.000000E-03 +1.000000E-06 +9.347357E-04 +8.737309E-07 +8.105963E-04 +6.570664E-07 +6.396651E-04 +4.091714E-07 +2.938723E-04 +8.636093E-08 +2.300000E-02 +1.330000E-04 +1.081756E-02 +3.675127E-05 +2.530156E-03 +6.960955E-06 +-1.930911E-03 +4.910249E-06 +1.162826E-02 +3.490280E-05 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +8.957101E-04 +4.402865E-07 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +1.000000E-02 +3.400000E-05 +4.086838E-03 +5.900874E-06 +1.812330E-03 +3.716159E-06 +2.138941E-03 +3.006748E-06 +5.414129E-03 +8.079335E-06 0.000000E+00 0.000000E+00 0.000000E+00 @@ -71,76 +181,6 @@ tally 1: 0.000000E+00 3.079655E-04 9.484274E-08 -1.000000E-03 -1.000000E-06 -9.451745E-04 -8.933548E-07 -8.400323E-04 -7.056542E-07 -6.931788E-04 -4.804969E-07 -0.000000E+00 -0.000000E+00 -3.000000E-03 -3.000000E-06 -1.134842E-03 -1.040850E-06 -6.127525E-05 -3.988312E-07 -4.938488E-05 -2.738492E-07 -1.484493E-03 -9.722888E-07 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 @@ -201,16 +241,16 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 -1.200000E-02 -4.600000E-05 -9.302157E-04 -3.853850E-06 -1.874541E-03 -3.092623E-06 --1.511552E-03 -4.053466E-06 -5.941986E-03 -9.853873E-06 +2.700000E-02 +1.670000E-04 +1.789444E-02 +8.260005E-05 +1.049872E-02 +2.774537E-05 +5.665111E-03 +8.560197E-06 +1.100708E-02 +2.835296E-05 0.000000E+00 0.000000E+00 0.000000E+00 @@ -219,60 +259,38 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 -1.186549E-03 -5.291648E-07 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -2.935668E-04 -8.618149E-08 +3.079655E-04 +9.484274E-08 1.000000E-03 1.000000E-06 -9.893707E-04 -9.788543E-07 -9.682814E-04 -9.375689E-07 -9.370683E-04 -8.780970E-07 -0.000000E+00 -0.000000E+00 -8.000000E-03 -2.000000E-05 -3.721382E-03 -4.736982E-06 --1.037031E-04 -6.648392E-07 --5.996856E-04 -9.642316E-07 -3.248622E-03 -4.063214E-06 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -2.938723E-04 -8.636093E-08 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 +-2.856031E-04 +8.156913E-08 +-3.776463E-04 +1.426167E-07 +3.701637E-04 +1.370211E-07 +1.203065E-03 +7.240969E-07 +1.000000E-03 +1.000000E-06 +9.705482E-04 +9.419638E-07 +9.129457E-04 +8.334699E-07 +8.297310E-04 +6.884535E-07 0.000000E+00 0.000000E+00 +4.400000E-02 +4.320000E-04 +1.141886E-02 +4.208707E-05 +9.213446E-03 +2.259305E-05 +9.177440E-03 +2.088782E-05 +2.116869E-02 +9.629049E-05 0.000000E+00 0.000000E+00 0.000000E+00 @@ -281,16 +299,38 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 +1.501009E-03 +6.405023E-07 +1.000000E-03 +1.000000E-06 +5.882614E-04 +3.460515E-07 +1.907719E-05 +3.639390E-10 +-3.734703E-04 +1.394801E-07 +1.472277E-03 +9.506220E-07 2.000000E-03 2.000000E-06 -1.502634E-03 -1.146555E-06 -7.198331E-04 -3.484978E-07 --3.426513E-05 -1.342349E-07 -1.511030E-03 -1.198311E-06 +1.830192E-03 +1.679505E-06 +1.519257E-03 +1.189525E-06 +1.118506E-03 +7.332971E-07 +2.977039E-04 +8.862764E-08 +2.000000E-02 +1.080000E-04 +8.640372E-03 +1.765553E-05 +5.688468E-03 +1.038555E-05 +2.447898E-03 +4.466055E-06 +8.949668E-03 +1.935056E-05 0.000000E+00 0.000000E+00 0.000000E+00 @@ -299,6 +339,38 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 +2.977039E-04 +8.862764E-08 +1.000000E-03 +1.000000E-06 +-3.805163E-04 +1.447926E-07 +-2.828111E-04 +7.998210E-08 +4.330345E-04 +1.875189E-07 +2.121142E-03 +1.958355E-06 +1.000000E-03 +1.000000E-06 +9.260022E-04 +8.574800E-07 +7.862200E-04 +6.181419E-07 +5.960676E-04 +3.552966E-07 +2.938723E-04 +8.636093E-08 +1.000000E-02 +3.400000E-05 +4.840884E-03 +1.080853E-05 +3.402096E-03 +4.113972E-06 +1.374077E-03 +2.333511E-06 +4.754696E-03 +7.172310E-06 0.000000E+00 0.000000E+00 0.000000E+00 @@ -307,6 +379,14 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 +3.079655E-04 +9.484274E-08 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 0.000000E+00 0.000000E+00 0.000000E+00 @@ -361,16 +441,136 @@ tally 1: 1.052422E-07 0.000000E+00 0.000000E+00 -1.500000E-02 -5.500000E-05 -2.209492E-03 -2.545503E-06 -5.991182E-03 -1.290780E-05 -1.772063E-03 -2.006265E-06 -5.944487E-03 -1.042417E-05 +2.900000E-02 +2.230000E-04 +6.260565E-03 +1.544092E-05 +7.061757E-03 +2.562385E-05 +3.982541E-03 +7.962565E-06 +1.486928E-02 +5.763902E-05 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +5.915762E-04 +1.749886E-07 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +5.954079E-04 +3.545105E-07 +1.000000E-03 +1.000000E-06 +9.938157E-04 +9.876697E-07 +9.815046E-04 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-0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 2.000000E-03 2.000000E-06 1.447007E-04 @@ -2241,16 +2201,56 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 -1.900000E-02 -1.030000E-04 -1.035735E-02 -3.119381E-05 -6.483551E-03 -1.164471E-05 -3.924334E-03 -6.047577E-06 -9.748673E-03 -2.956634E-05 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +0.000000E+00 +1.000000E-02 +2.600000E-05 +5.875085E-04 +4.563904E-07 +-9.207198E-05 +5.154496E-07 +3.674257E-05 +1.178281E-06 +5.048984E-03 +5.880390E-06 0.000000E+00 0.000000E+00 0.000000E+00 @@ -2269,28 +2269,28 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 -2.935668E-04 -8.618149E-08 +5.952778E-04 +3.543557E-07 1.000000E-03 1.000000E-06 -8.623139E-04 -7.435852E-07 -6.153778E-04 -3.786899E-07 -3.095388E-04 -9.581428E-08 +9.362621E-04 +8.765867E-07 +8.148801E-04 +6.640295E-07 +6.473941E-04 +4.191191E-07 0.000000E+00 0.000000E+00 -1.900000E-02 -9.900000E-05 -7.385212E-03 -2.033270E-05 -6.336514E-03 -2.028060E-05 -3.967026E-03 -1.027239E-05 -1.066281E-02 -2.937591E-05 +2.000000E-02 +9.000000E-05 +5.358616E-03 +1.697599E-05 +3.060277E-03 +7.132281E-06 +2.485730E-03 +7.247489E-06 +9.248313E-03 +1.738407E-05 0.000000E+00 0.000000E+00 0.000000E+00 @@ -2299,8 +2299,8 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 -2.976389E-04 -8.858892E-08 +8.991712E-04 +2.696131E-07 0.000000E+00 0.000000E+00 0.000000E+00 @@ -2309,6 +2309,28 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 +5.954079E-04 +3.545105E-07 +1.000000E-03 +1.000000E-06 +9.816220E-04 +9.635817E-07 +9.453726E-04 +8.937294E-07 +8.922496E-04 +7.961093E-07 +0.000000E+00 +0.000000E+00 +8.000000E-03 +1.800000E-05 +4.925975E-03 +6.260377E-06 +3.176938E-03 +2.631319E-06 +2.008278E-03 +1.484516E-06 +3.844641E-03 +4.075869E-06 0.000000E+00 0.000000E+00 0.000000E+00 @@ -2317,32 +2339,8 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -6.000000E-03 -1.000000E-05 -1.316884E-03 -2.894217E-06 -2.095957E-03 -1.439521E-06 -1.013831E-04 -8.405300E-07 -2.404012E-03 -1.641294E-06 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 -0.000000E+00 +2.977039E-04 +8.862764E-08 0.000000E+00 0.000000E+00 0.000000E+00 @@ -2351,6 +2349,8 @@ tally 1: 0.000000E+00 0.000000E+00 0.000000E+00 +9.238964E-04 +8.535846E-07 0.000000E+00 0.000000E+00 0.000000E+00 diff --git a/tests/test_tallies/results_true.dat b/tests/test_tallies/results_true.dat index d018a65da9..108c7ee800 100644 --- a/tests/test_tallies/results_true.dat +++ b/tests/test_tallies/results_true.dat @@ -1 +1 @@ -5a0f3f1ae244ada7d8c9f444d7a98c2589a9720e78174a276cf96162df6728bab6d534f136f65cb0386c3235eb7d5db47a0dd6504636ca77e7eb375e6978a84d \ No newline at end of file +a6afd2f11affce2467d77b8477881ab20091f67df4f632226ec2dd5d4cd7fabb9ac3e182563bb467ed249e4b3fe95b319cb688d653757f8ea154759b8a7f50e1 \ No newline at end of file diff --git a/tests/test_tally_slice_merge/results_true.dat b/tests/test_tally_slice_merge/results_true.dat index 89d415b0d6..278d8ee108 100644 --- a/tests/test_tally_slice_merge/results_true.dat +++ b/tests/test_tally_slice_merge/results_true.dat @@ -49,19 +49,19 @@ 14 (500, 5000, 50000) 6.25e-07 2.00e+01 U238 fission 0.00e+00 0.00e+00 15 (500, 5000, 50000) 6.25e-07 2.00e+01 U238 nu-fission 0.00e+00 0.00e+00 sum(mesh) energy low [MeV] energy high [MeV] nuclide score mean std. dev. -0 ((1, 1, 1), (1, 2, 1)) 0.00e+00 6.25e-07 U235 fission 9.18e-03 1.62e-03 -1 ((1, 1, 1), (1, 2, 1)) 0.00e+00 6.25e-07 U235 nu-fission 2.24e-02 3.94e-03 -2 ((1, 1, 1), (1, 2, 1)) 0.00e+00 6.25e-07 U238 fission 1.31e-08 2.08e-09 -3 ((1, 1, 1), (1, 2, 1)) 0.00e+00 6.25e-07 U238 nu-fission 3.26e-08 5.19e-09 -4 ((1, 1, 1), (1, 2, 1)) 6.25e-07 2.00e+01 U235 fission 8.40e-04 2.13e-04 -5 ((1, 1, 1), (1, 2, 1)) 6.25e-07 2.00e+01 U235 nu-fission 2.06e-03 5.17e-04 -6 ((1, 1, 1), (1, 2, 1)) 6.25e-07 2.00e+01 U238 fission 7.05e-04 3.42e-04 -7 ((1, 1, 1), (1, 2, 1)) 6.25e-07 2.00e+01 U238 nu-fission 1.99e-03 1.01e-03 -8 ((2, 1, 1), (2, 2, 1)) 0.00e+00 6.25e-07 U235 fission 8.77e-03 1.30e-03 -9 ((2, 1, 1), (2, 2, 1)) 0.00e+00 6.25e-07 U235 nu-fission 2.14e-02 3.18e-03 -10 ((2, 1, 1), (2, 2, 1)) 0.00e+00 6.25e-07 U238 fission 1.24e-08 1.74e-09 -11 ((2, 1, 1), (2, 2, 1)) 0.00e+00 6.25e-07 U238 nu-fission 3.08e-08 4.33e-09 -12 ((2, 1, 1), (2, 2, 1)) 6.25e-07 2.00e+01 U235 fission 2.30e-03 6.20e-04 -13 ((2, 1, 1), (2, 2, 1)) 6.25e-07 2.00e+01 U235 nu-fission 5.63e-03 1.52e-03 -14 ((2, 1, 1), (2, 2, 1)) 6.25e-07 2.00e+01 U238 fission 1.45e-03 7.19e-04 -15 ((2, 1, 1), (2, 2, 1)) 6.25e-07 2.00e+01 U238 nu-fission 3.97e-03 1.98e-03 +0 ((1, 1, 1), (1, 2, 1)) 0.00e+00 6.25e-07 U235 fission 8.54e-03 1.30e-03 +1 ((1, 1, 1), (1, 2, 1)) 0.00e+00 6.25e-07 U235 nu-fission 2.08e-02 3.17e-03 +2 ((1, 1, 1), (1, 2, 1)) 0.00e+00 6.25e-07 U238 fission 1.21e-08 1.74e-09 +3 ((1, 1, 1), (1, 2, 1)) 0.00e+00 6.25e-07 U238 nu-fission 3.01e-08 4.34e-09 +4 ((1, 1, 1), (1, 2, 1)) 6.25e-07 2.00e+01 U235 fission 2.20e-03 6.05e-04 +5 ((1, 1, 1), (1, 2, 1)) 6.25e-07 2.00e+01 U235 nu-fission 5.38e-03 1.48e-03 +6 ((1, 1, 1), (1, 2, 1)) 6.25e-07 2.00e+01 U238 fission 1.40e-03 7.17e-04 +7 ((1, 1, 1), (1, 2, 1)) 6.25e-07 2.00e+01 U238 nu-fission 3.84e-03 1.97e-03 +8 ((2, 1, 1), (2, 2, 1)) 0.00e+00 6.25e-07 U235 fission 9.40e-03 1.62e-03 +9 ((2, 1, 1), (2, 2, 1)) 0.00e+00 6.25e-07 U235 nu-fission 2.29e-02 3.95e-03 +10 ((2, 1, 1), (2, 2, 1)) 0.00e+00 6.25e-07 U238 fission 1.34e-08 2.08e-09 +11 ((2, 1, 1), (2, 2, 1)) 0.00e+00 6.25e-07 U238 nu-fission 3.33e-08 5.18e-09 +12 ((2, 1, 1), (2, 2, 1)) 6.25e-07 2.00e+01 U235 fission 9.41e-04 2.52e-04 +13 ((2, 1, 1), (2, 2, 1)) 6.25e-07 2.00e+01 U235 nu-fission 2.31e-03 6.13e-04 +14 ((2, 1, 1), (2, 2, 1)) 6.25e-07 2.00e+01 U238 fission 7.54e-04 3.45e-04 +15 ((2, 1, 1), (2, 2, 1)) 6.25e-07 2.00e+01 U238 nu-fission 2.12e-03 1.02e-03 diff --git a/tests/test_track_output/results_true.dat b/tests/test_track_output/results_true.dat index 6ded87a0ec..1d0ca80399 100644 --- a/tests/test_track_output/results_true.dat +++ b/tests/test_track_output/results_true.dat @@ -1,5 +1,5 @@ - +