openmc-designs/stress-test/multi-group-xs/mdgxs-part-ii.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multigroup (Delayed) Cross Section Generation Part II: Advanced Features\n",
    "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": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import openmc\n",
    "import openmc.mgxs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to define materials that will be used in the problem: fuel, water, and cladding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a materials collection and export to XML\n",
    "materials = openmc.Materials((fuel, water, zircaloy))"
   ]
  },
  {
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create cylinders for the fuel and clad\n",
    "fuel_outer_radius = openmc.ZCylinder(r=0.39218)\n",
    "clad_outer_radius = openmc.ZCylinder(r=0.45720)\n",
    "\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": 5,
   "metadata": {},
   "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": 6,
   "metadata": {},
   "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": 7,
   "metadata": {},
   "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": 8,
   "metadata": {},
   "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",
    "# Create universes array with the fuel pin and guide tube universes\n",
    "universes = np.tile(fuel_pin_universe, (17,17))\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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create root Cell\n",
    "root_cell = openmc.Cell(name='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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Geometry and export to XML\n",
    "geometry = openmc.Geometry(root_universe)"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OpenMC simulation parameters\n",
    "batches = 50\n",
    "inactive = 10\n",
    "particles = 2500\n",
    "\n",
    "# Instantiate a Settings object\n",
    "settings = openmc.Settings()\n",
    "settings.batches = batches\n",
    "settings.inactive = inactive\n",
    "settings.particles = particles\n",
    "settings.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.source = openmc.IndependentSource(space=uniform_dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll tie the materials, geometry, and settings into a single model object and export the necessary XML files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = openmc.Model(geometry, materials, settings)\n",
    "model.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also create a plot to verify that our fuel assembly geometry was created successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot our geometry\n",
    "plot = openmc.Plot.from_geometry(geometry)\n",
    "plot.pixels = (250, 250)\n",
    "plot.color_by = 'material'\n",
    "openmc.plot_inline(plot)"
   ]
  },
  {
   "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 a 20-energy-group and 1-energy-group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a 20-group EnergyGroups object\n",
    "energy_groups = openmc.mgxs.EnergyGroups()\n",
    "energy_groups.group_edges = np.logspace(-3, 7.3, 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]])"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a tally mesh\n",
    "mesh = openmc.RegularMesh(mesh_id=1)\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.num_delayed_groups = 6\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 = openmc.Tallies()\n",
    "mgxs_lib.add_to_tallies_file(tallies, merge=True)\n",
    "\n",
    "# Instantiate a current tally\n",
    "mesh_filter = openmc.MeshSurfaceFilter(mesh)\n",
    "current_tally = openmc.Tally(name='current tally')\n",
    "current_tally.scores = ['current']\n",
    "current_tally.filters = [mesh_filter]\n",
    "\n",
    "# Add current tally to the tallies file\n",
    "tallies.append(current_tally)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can run OpenMC to generate the cross sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tie geometry, materials, settings, and tallies together into a model object\n",
    "model = openmc.Model(geometry=geometry,\n",
    "                     materials=materials,\n",
    "                     settings=settings,\n",
    "                     tallies=tallies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=1.\n",
      "  warn(msg, IDWarning)\n",
      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=2.\n",
      "  warn(msg, IDWarning)\n",
      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=5.\n",
      "  warn(msg, IDWarning)\n",
      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=6.\n",
      "  warn(msg, IDWarning)\n",
      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=17.\n",
      "  warn(msg, IDWarning)\n",
      "/home/aparler/.conda/envs/openmc-env/lib/python3.11/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=23.\n",
      "  warn(msg, IDWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.3\n",
      "        Git SHA1 | 50e39a4e20dc9e0f3d7ccf07333f6a5e6c797c8c\n",
      "       Date/Time | 2023-11-06 20:56:04\n",
      "  OpenMP Threads | 4\n",
      "\n",
      " Reading settings XML file...\n",
      " Reading cross sections XML file...\n",
      " Reading materials XML file...\n",
      " Reading geometry XML file...\n",
      " Reading U235 from /opt/xdata/endfb-vii.1-hdf5/neutron/U235.h5\n",
      " Reading U238 from /opt/xdata/endfb-vii.1-hdf5/neutron/U238.h5\n",
      " Reading O16 from /opt/xdata/endfb-vii.1-hdf5/neutron/O16.h5\n",
      " Reading H1 from /opt/xdata/endfb-vii.1-hdf5/neutron/H1.h5\n",
      " Reading B10 from /opt/xdata/endfb-vii.1-hdf5/neutron/B10.h5\n",
      " Reading Zr90 from /opt/xdata/endfb-vii.1-hdf5/neutron/Zr90.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Reading tallies XML file...\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for U235\n",
      " Initializing source particles...\n",
      "\n",
      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
      "\n",
      "  Bat./Gen.      k            Average k\n",
      "  =========   ========   ====================\n",
      "        1/1    0.99606\n",
      "        2/1    1.04749\n",
      "        3/1    1.02998\n",
      "        4/1    1.01909\n",
      "        5/1    1.01068\n",
      "        6/1    1.04796\n",
      "        7/1    1.02180\n",
      "        8/1    1.03041\n",
      "        9/1    0.99557\n",
      "       10/1    1.02019\n",
      "       11/1    1.00722\n",
      "       12/1    1.02203    1.01462 +/- 0.00741\n",
      "       13/1    1.01177    1.01367 +/- 0.00438\n",
      "       14/1    1.03241    1.01836 +/- 0.00562\n",
      "       15/1    1.03434    1.02155 +/- 0.00540\n",
      "       16/1    1.02384    1.02193 +/- 0.00442\n",
      "       17/1    1.02815    1.02282 +/- 0.00384\n",
      "       18/1    1.04619    1.02574 +/- 0.00443\n",
      "       19/1    1.03496    1.02677 +/- 0.00404\n",
      "       20/1    1.01787    1.02588 +/- 0.00372\n",
      "       21/1    1.04886    1.02797 +/- 0.00396\n",
      "       22/1    1.00158    1.02577 +/- 0.00423\n",
      "       23/1    1.01636    1.02504 +/- 0.00396\n",
      "       24/1    1.01508    1.02433 +/- 0.00373\n",
      "       25/1    0.95954    1.02001 +/- 0.00554\n",
      "       26/1    1.03097    1.02070 +/- 0.00523\n",
      "       27/1    1.03576    1.02158 +/- 0.00499\n",
      "       28/1    1.01183    1.02104 +/- 0.00474\n",
      "       29/1    1.03387    1.02172 +/- 0.00453\n",
      "       30/1    1.01176    1.02122 +/- 0.00433\n",
      "       31/1    1.03633    1.02194 +/- 0.00418\n",
      "       32/1    1.04652    1.02306 +/- 0.00414\n",
      "       33/1    0.98985    1.02161 +/- 0.00421\n",
      "       34/1    1.01121    1.02118 +/- 0.00405\n",
      "       35/1    1.03004    1.02153 +/- 0.00391\n",
      "       36/1    1.03398    1.02201 +/- 0.00378\n",
      "       37/1    1.04675    1.02293 +/- 0.00375\n",
      "       38/1    0.99813    1.02204 +/- 0.00372\n",
      "       39/1    1.05411    1.02315 +/- 0.00376\n",
      "       40/1    1.05007    1.02405 +/- 0.00374\n",
      "       41/1    0.99881    1.02323 +/- 0.00371\n",
      "       42/1    1.04085    1.02378 +/- 0.00363\n",
      "       43/1    1.01012    1.02337 +/- 0.00355\n",
      "       44/1    1.01510    1.02313 +/- 0.00345\n",
      "       45/1    1.03833    1.02356 +/- 0.00338\n",
      "       46/1    1.00385    1.02301 +/- 0.00333\n",
      "       47/1    1.02360    1.02303 +/- 0.00324\n",
      "       48/1    1.01066    1.02270 +/- 0.00317\n",
      "       49/1    0.98707    1.02179 +/- 0.00322\n",
      "       50/1    1.01133    1.02153 +/- 0.00315\n",
      " Creating state point statepoint.50.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 3.6394e-01 seconds\n",
      "   Reading cross sections          = 3.4823e-01 seconds\n",
      " Total time in simulation          = 8.2881e+00 seconds\n",
      "   Time in transport only          = 8.1978e+00 seconds\n",
      "   Time in inactive batches        = 7.4927e-01 seconds\n",
      "   Time in active batches          = 7.5389e+00 seconds\n",
      "   Time synchronizing fission bank = 1.0994e-02 seconds\n",
      "     Sampling source sites         = 9.4419e-03 seconds\n",
      "     SEND/RECV source sites        = 1.5279e-03 seconds\n",
      "   Time accumulating tallies       = 3.5476e-02 seconds\n",
      "   Time writing statepoints        = 3.2617e-02 seconds\n",
      " Total time for finalization       = 1.6240e-06 seconds\n",
      " Total time elapsed                = 8.6614e+00 seconds\n",
      " Calculation Rate (inactive)       = 33365.8 particles/second\n",
      " Calculation Rate (active)         = 13264.6 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " k-effective (Collision)     = 1.02733 +/- 0.00284\n",
      " k-effective (Track-length)  = 1.02153 +/- 0.00315\n",
      " k-effective (Absorption)    = 1.02638 +/- 0.00281\n",
      " Combined k-effective        = 1.02616 +/- 0.00228\n",
      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Run OpenMC\n",
    "statepoint_filename = model.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": 18,
   "metadata": {},
   "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": 19,
   "metadata": {},
   "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')\n",
    "\n",
    "# Close statepoint file now that we have the info we need\n",
    "sp.close()"
   ]
  },
  {
   "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](tally-arithmetic.ipynb) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
    "\n",
    "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n",
    "\n",
    "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
       "      <th>delayedgroup</th>\n",
       "      <th>nuclide</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>3.807030e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.001547</td>\n",
       "      <td>4.775303e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000980</td>\n",
       "      <td>3.006970e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000770</td>\n",
       "      <td>2.340328e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>8.143610e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>6.498748e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>2.343214e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>2.937441e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>1.849319e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>1.439161e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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      2  1  1            1   total   \n",
       "7      2  1  1            2   total   \n",
       "8      2  1  1            3   total   \n",
       "9      2  1  1            4   total   \n",
       "\n",
       "                                               score      mean     std. dev.  \n",
       "                                                                              \n",
       "0  (((delayed-nu-fission / nu-fission) * (delayed...  0.000122  3.807030e-05  \n",
       "1  (((delayed-nu-fission / nu-fission) * (delayed...  0.001547  4.775303e-04  \n",
       "2  (((delayed-nu-fission / nu-fission) * (delayed...  0.000980  3.006970e-04  \n",
       "3  (((delayed-nu-fission / nu-fission) * (delayed...  0.000770  2.340328e-04  \n",
       "4  (((delayed-nu-fission / nu-fission) * (delayed...  0.000027  8.143610e-06  \n",
       "5  (((delayed-nu-fission / nu-fission) * (delayed...  0.000002  6.498748e-07  \n",
       "6  (((delayed-nu-fission / nu-fission) * (delayed...  0.000101  2.343214e-05  \n",
       "7  (((delayed-nu-fission / nu-fission) * (delayed...  0.001282  2.937441e-04  \n",
       "8  (((delayed-nu-fission / nu-fission) * (delayed...  0.000813  1.849319e-04  \n",
       "9  (((delayed-nu-fission / nu-fission) * (delayed...  0.000640  1.439161e-04  "
      ]
     },
     "execution_count": 20,
     "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 = math.log(2.0) / 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 type(f) is not openmc.DelayedGroupFilter]\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=type(f), 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=openmc.EnergyFilter, remove_filter=True) * \\\n",
    "    delayed_nu_fission.xs_tally.summation(filter_type=openmc.EnergyFilter, 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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">mesh 1</th>\n",
       "      <th>nuclide</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>surf</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-min out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-min in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-max out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03147</td>\n",
       "      <td>0.000703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-max in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03109</td>\n",
       "      <td>0.000648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-min out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-min in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-max out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03183</td>\n",
       "      <td>0.000776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-max in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03220</td>\n",
       "      <td>0.000812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>z-min out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>z-min in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mesh 1                  nuclide    score     mean std. dev.\n",
       "       x  y  z       surf                                    \n",
       "0      1  1  1  x-min out   total  current  0.00000  0.000000\n",
       "1      1  1  1   x-min in   total  current  0.00000  0.000000\n",
       "2      1  1  1  x-max out   total  current  0.03147  0.000703\n",
       "3      1  1  1   x-max in   total  current  0.03109  0.000648\n",
       "4      1  1  1  y-min out   total  current  0.00000  0.000000\n",
       "5      1  1  1   y-min in   total  current  0.00000  0.000000\n",
       "6      1  1  1  y-max out   total  current  0.03183  0.000776\n",
       "7      1  1  1   y-max in   total  current  0.03220  0.000812\n",
       "8      1  1  1  z-min out   total  current  0.00000  0.000000\n",
       "9      1  1  1   z-min in   total  current  0.00000  0.000000"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Beta - delayed group 6')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x900 with 12 Axes>"
      ]
     },
     "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')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}