triso.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling TRISO Particles\n",
    "OpenMC includes a few convenience functions for generationing TRISO particle locations and placing them in a lattice. To be clear, this capability is not a stochastic geometry capability like that included in MCNP. It's also important to note that OpenMC does not use delta tracking, which would normally speed up calculations in geometries with tons of surfaces and cells. However, the computational burden can be eased by placing TRISO particles in a lattice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from math import pi\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import openmc\n",
    "import openmc.model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first start by creating materials that will be used in our TRISO particles and the background material."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fuel = openmc.Material(name='Fuel')\n",
    "fuel.set_density('g/cm3', 10.5)\n",
    "fuel.add_nuclide('U235', 4.6716e-02)\n",
    "fuel.add_nuclide('U238', 2.8697e-01)\n",
    "fuel.add_nuclide('O16',  5.0000e-01)\n",
    "fuel.add_element('C', 1.6667e-01)\n",
    "\n",
    "buff = openmc.Material(name='Buffer')\n",
    "buff.set_density('g/cm3', 1.0)\n",
    "buff.add_element('C', 1.0)\n",
    "buff.add_s_alpha_beta('c_Graphite')\n",
    "\n",
    "PyC1 = openmc.Material(name='PyC1')\n",
    "PyC1.set_density('g/cm3', 1.9)\n",
    "PyC1.add_element('C', 1.0)\n",
    "PyC1.add_s_alpha_beta('c_Graphite')\n",
    "\n",
    "PyC2 = openmc.Material(name='PyC2')\n",
    "PyC2.set_density('g/cm3', 1.87)\n",
    "PyC2.add_element('C', 1.0)\n",
    "PyC2.add_s_alpha_beta('c_Graphite')\n",
    "\n",
    "SiC = openmc.Material(name='SiC')\n",
    "SiC.set_density('g/cm3', 3.2)\n",
    "SiC.add_element('C', 0.5)\n",
    "SiC.add_element('Si', 0.5)\n",
    "\n",
    "graphite = openmc.Material()\n",
    "graphite.set_density('g/cm3', 1.1995)\n",
    "graphite.add_element('C', 1.0)\n",
    "graphite.add_s_alpha_beta('c_Graphite')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To actually create individual TRISO particles, we first need to create a universe that will be used within each particle. The reason we use the same universe for each TRISO particle is to reduce the total number of cells/surfaces needed which can substantially improve performance over using unique cells/surfaces in each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create TRISO universe\n",
    "spheres = [openmc.Sphere(r=1e-4*r)\n",
    "           for r in [215., 315., 350., 385.]]\n",
    "cells = [openmc.Cell(fill=fuel, region=-spheres[0]),\n",
    "         openmc.Cell(fill=buff, region=+spheres[0] & -spheres[1]),\n",
    "         openmc.Cell(fill=PyC1, region=+spheres[1] & -spheres[2]),\n",
    "         openmc.Cell(fill=SiC, region=+spheres[2] & -spheres[3]),\n",
    "         openmc.Cell(fill=PyC2, region=+spheres[3])]\n",
    "triso_univ = openmc.Universe(cells=cells)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need a region to pack the TRISO particles in. We will use a 1 cm x 1 cm x 1 cm box centered at the origin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_x = openmc.XPlane(x0=-0.5, boundary_type='reflective')\n",
    "max_x = openmc.XPlane(x0=0.5, boundary_type='reflective')\n",
    "min_y = openmc.YPlane(y0=-0.5, boundary_type='reflective')\n",
    "max_y = openmc.YPlane(y0=0.5, boundary_type='reflective')\n",
    "min_z = openmc.ZPlane(z0=-0.5, boundary_type='reflective')\n",
    "max_z = openmc.ZPlane(z0=0.5, boundary_type='reflective')\n",
    "region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to select locations for the TRISO particles using `openmc.model.pack_spheres`. In this example, we will select locations, at random, within the above box with a packing fraction of 30%. A seed has been included so that the randomly generated sphere locations will remain the same, even if the script is rerun. The user is not strictly required to use a seed or the exact same seed used here, but it is a best practice to include one for repeatability. Note that `pack_spheres` can handle up to the theoretical maximum of 60% (it will just be slow)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "outer_radius = 425.*1e-4\n",
    "centers = openmc.model.pack_spheres(radius=outer_radius, region=region, pf=0.3, seed=124848351)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the locations of the TRISO particles determined and a universe that can be used for each particle, we can create the TRISO particles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "trisos = [openmc.model.TRISO(outer_radius, triso_univ, center) for center in centers]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each TRISO object actually **is** a Cell, in fact; we can look at the properties of the TRISO just as we would a cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cell\n",
      "\tID             =\t6\n",
      "\tName           =\t\n",
      "\tFill           =\t1\n",
      "\tRegion         =\t-11\n",
      "\tRotation       =\tNone\n",
      "\tTranslation    =\t[-0.4557226  -0.00821291 -0.29390205]\n",
      "\tVolume         =\tNone\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(trisos[0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's confirm that all our TRISO particles are within the box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.45748859 -0.45742868 -0.45747   ]\n",
      "[0.45582441 0.45746362 0.4562236 ]\n"
     ]
    }
   ],
   "source": [
    "centers = np.vstack([triso.center for triso in trisos])\n",
    "print(centers.min(axis=0))\n",
    "print(centers.max(axis=0))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look at what the actual packing fraction turned out to be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2996893513959326"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trisos)*4/3*pi*outer_radius**3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our TRISO particles created, we need to place them in a lattice to provide optimal tracking performance in OpenMC. We can use the box we created above to place the lattice in. Actually creating a lattice containing TRISO particles can be done with the `model.create_triso_lattice()` function. This function requires that we give it a list of TRISO particles, the lower-left coordinates of the lattice, the pitch of each lattice cell, the overall shape of the lattice (number of cells in each direction), and a background material."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "box = openmc.Cell(region=region)\n",
    "lower_left, upper_right = box.region.bounding_box\n",
    "shape = (3, 3, 3)\n",
    "pitch = (upper_right - lower_left)/shape\n",
    "lattice = openmc.model.create_triso_lattice(\n",
    "    trisos, lower_left, pitch, shape, graphite)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can set the fill of our box cell to be the lattice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "box.fill = lattice"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's take a look at our geometry by putting the box in a universe and plotting it. We're going to use the Fortran-side plotter since it's much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "universe = openmc.Universe(cells=[box])\n",
    "\n",
    "geometry = openmc.Geometry(universe)\n",
    "geometry.export_to_xml()\n",
    "\n",
    "materials = list(geometry.get_all_materials().values())\n",
    "openmc.Materials(materials).export_to_xml()\n",
    "\n",
    "settings = openmc.Settings()\n",
    "settings.run_mode = 'plot'\n",
    "settings.export_to_xml()\n",
    "\n",
    "plot = openmc.Plot.from_geometry(geometry)\n",
    "plot.to_ipython_image()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot the universe by material rather than by cell, we can see that the entire background is just graphite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot.color_by = 'material'\n",
    "plot.colors = {graphite: 'gray'}\n",
    "plot.to_ipython_image()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}