diff --git a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb index b15ace951..03bf15897 100644 --- a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb @@ -23,7 +23,7 @@ "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 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." + "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." ] }, { @@ -78,7 +78,7 @@ "### 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 3.2) 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", + "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**." ] @@ -92,7 +92,7 @@ "\n", "$$\\nu_d \\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\nu_d \\sigma_{f,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n", "\n", - "This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for only the delayed-nu-fission and delayed neutron fraction reaction type at the oment. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](https://mit-crpg.github.io/openmc/pythonapi/filter.html) on the energy range and spatial zone (material, cell, universe, or mesh) define the bounds of integration for both numerator and denominator." + "This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for only the delayed-nu-fission and delayed neutron fraction reaction type at the moment. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](https://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." ] }, { @@ -102,7 +102,7 @@ "### 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 $g$. \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", @@ -337,7 +337,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we are ready to generate multi-group cross sections! First, let's define a 2-energy-group structure using the built-in `EnergyGroups` class. We will also create a 6-delayed-group structure using the built-in `DelayedGroups` class." + "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 structure using the built-in `DelayedGroups` class." ] }, { @@ -389,7 +389,7 @@ "* `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 and prompt-nu-fission cross sections with our 2-energy-group structure and multi-group chi-delayed, delayed-nu-fission, and beta cross sections with our 2-energy-group and 6-delayed-group structures. " + "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. " ] }, { @@ -402,7 +402,7 @@ "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=one_group, 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", @@ -514,7 +514,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `Beta` object includes tracklength tallies for the 'nu-fission' and 'delayed-nu-fission' scores in the 2-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." + "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." ] }, { @@ -784,7 +784,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's first inspect our delayed-nu-fission section by printing it to the screen." + "Let's first inspect our delayed-nu-fission section by printing it to the screen after condensing the cross section down to one group." ] }, { @@ -1043,7 +1043,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following code snippet shows how to export the chi `MGXS` to the same HDF5 binary data store." + "The following code snippet shows how to export the chi-prompt and chi-delayed `MGXS` to the same HDF5 binary data store." ] }, { @@ -1248,7 +1248,7 @@ } ], "source": [ - "# Set the time constants for the delayed precursors (in seconds^-1)\n", + "# 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", @@ -1349,7 +1349,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also plot the fission spectrum for the prompt and delayed neutrons." + "We can also plot the energy spectrum for fission emission of prompt and delayed neutrons." ] }, { 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 000000000..bb740b7bc --- /dev/null +++ b/docs/source/pythonapi/examples/mdgxs-part-ii.ipynb @@ -0,0 +1,1395 @@ +{ + "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:1350: 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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\nVDAwOjExOjE1LTA0OjAwsFn7CQAAAABJRU5ErkJggg==\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 using the built-in `EnergyGroups` and `DelayedGroups` classes." + ] + }, + { + "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-group DelayedGroups object\n", + "delayed_groups = openmc.mgxs.DelayedGroups()\n", + "delayed_groups.groups = 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: e8819e6a77f2e998dcce937e80fbdd8dd430b667\n", + " Date/Time: 2016-08-03 00:11:15\n", + " MPI Processes: 4\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", + " 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", + " 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", + " 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", + " 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", + " 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 = 6.4100E-01 seconds\n", + " Reading cross sections = 3.6400E-01 seconds\n", + " Total time in simulation = 2.6284E+01 seconds\n", + " Time in transport only = 2.5289E+01 seconds\n", + " Time in inactive batches = 1.3870E+00 seconds\n", + " Time in active batches = 2.4897E+01 seconds\n", + " Time synchronizing fission bank = 3.9900E-01 seconds\n", + " Sampling source sites = 1.0000E-03 seconds\n", + " SEND/RECV source sites = 0.0000E+00 seconds\n", + " Time accumulating tallies = 5.6100E-01 seconds\n", + " Total time for finalization = 1.1000E-02 seconds\n", + " Total time elapsed = 2.6944E+01 seconds\n", + " Calculation Rate (inactive) = 18024.5 neutrons/second\n", + " Calculation Rate (active) = 4016.55 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(mpi_procs=4)" + ] + }, + { + "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": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/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", + "/Users/sam/.local/lib/python2.7/site-packages/openmc-0.8.0-py2.7.egg/openmc/tallies.py:1945: RuntimeWarning: invalid value encountered in true_divide\n", + "/Users/sam/.local/lib/python2.7/site-packages/openmc-0.8.0-py2.7.egg/openmc/tallies.py:1946: RuntimeWarning: divide by zero encountered in true_divide\n", + "/Users/sam/.local/lib/python2.7/site-packages/openmc-0.8.0-py2.7.egg/openmc/tallies.py:1946: RuntimeWarning: invalid value encountered in true_divide\n", + "/Users/sam/.local/lib/python2.7/site-packages/openmc-0.8.0-py2.7.egg/openmc/tallies.py:1938: RuntimeWarning: invalid value encountered in true_divide\n", + "/Users/sam/.local/lib/python2.7/site-packages/openmc-0.8.0-py2.7.egg/openmc/tallies.py:1939: RuntimeWarning: invalid value encountered in true_divide\n" + ] + }, + { + "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 0.00000 0.000000\n", + "7 1 2 1 x-max total current 0.03134 0.000669\n", + "8 1 2 1 y-min total current 0.03055 0.000605\n", + "9 1 2 1 y-max total current 0.03113 0.000634" + ] + }, + "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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FfAdYOSKObOJ9mpn1HV8TF+Y9HMysOG+QY2bWWk1uGpnvydB9//eHyTaBfETS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\nDmZmZmZmZmbW5TzgYGZmZmZmZmZdzgMOZmZmZmZmZtblPOBgZmZmZmZ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4LCLGphcquwJ4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vBL4PTUKFHpX1i3QRgASbsAY4Db8nywWtzgYGa5vcTwpupTd63zyVpZp0bEV6veHw78ENgXeAKY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\nGjgXDpIkSZIkaeBcOEiSJEmSpIH7Db/BFUInlBauAAAAAElFTkSuQmCC\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": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/source/pythonapi/index.rst b/docs/source/pythonapi/index.rst index df50eee0b..3be64bd1c 100644 --- a/docs/source/pythonapi/index.rst +++ b/docs/source/pythonapi/index.rst @@ -257,6 +257,16 @@ Energy Groups openmc.mgxs.EnergyGroups +Delayed Groups +------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: myclass.rst + + openmc.mgxs.DelayedGroups + Multi-group Cross Sections -------------------------- @@ -284,6 +294,19 @@ 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 -----------------------------------