OpenMC_NEA_Training_nov25/course/live_notebooks/Volume-Calcs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eaf1887f-cb0b-4a84-96ac-66d40e766a9b",
   "metadata": {},
   "source": [
    "# Stochastic Volume Calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24321fe3-025c-436b-b705-6ac03ebc22ed",
   "metadata": {},
   "source": [
    "Knowing the volume of different regions in a model can be important for tally normalization, depletion, and more. In some cases, you can calculate the volume of a cell analytically (like $\\pi R^2H$ for a cylinder), but this is not always possible for more complex regions. For these regions, we can use OpenMC's stochastic volume calculation mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b7f5363-c001-49e7-b12c-fb9e1cd1429e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import openmc\n",
    "\n",
    "model = openmc.examples.pwr_core()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0f22927-9760-49d3-b4b6-02f524f33236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1290.32x1298.7 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.geometry.root_universe.plot(width=(500, 500), pixels=(1000, 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50e677a3-fd5b-4360-b091-c8f0b4fa4a50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 516.129x519.481 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.geometry.root_universe.plot(width=(22, 22), origin=(0, 0, 0), pixels=(400, 400))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c4191f-3059-4d96-9f87-f1d7057ecff6",
   "metadata": {},
   "source": [
    "This model doesn't contain any particularly complex geometry, but there are still regions for which it would be onerous to compute volumes analytically, such as the region between the baffle and barrel (dark green)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d94bda98-24fc-4ae3-aa5c-21b96a57a01e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.geometry.root_universe.plot(width=(100, 100), origin=(130, 130, 0), pixels=(400, 400))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23643fd8-2471-4dc9-aa7c-14db48ee8d25",
   "metadata": {},
   "source": [
    "Volumes can be computed using a few different domain types: materials, cells, and universes. The number of atoms for different nuclides can be computed as well based on the volumes of materials and their various atom percentages.\n",
    "\n",
    "Volume calculations are performed by randomly selecting locations in a specified bounding box. The number of locations for where a domain item (cell, material, universe) is found indicates how much of the bounding box is occupied by this item. This straightforward method is a relative to one of the most well-known uses of Monte Carlo, [the computation of $\\pi$](https://blogs.sas.com/content/iml/2016/03/14/monte-carlo-estimates-of-pi.html#:~:text=To%20compute%20Monte%20Carlo%20estimates,the%20curve%20is%20%CF%80%20%2F%204.).\n",
    "\n",
    "$$ V_{i} = \\frac{N_{i}}{N} V_{box}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3bd184b-df90-4fb4-bfdb-7ff8d9a67124",
   "metadata": {},
   "source": [
    "Let's set up a volume calculation using the materials from this PWR core model. We need to define the bounding box for the volume calculation, which we take to encompass the entire domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "24f42abe-34c4-4d4e-b8de-0cbd3d5d7088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BoundingBox(lower_left=(-249.0, -249.0, -229.0), upper_right=(249.0, 249.0, 223.0))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb = model.geometry.root_universe.bounding_box\n",
    "bb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c5f9248",
   "metadata": {},
   "source": [
    "Let's compute the volumes of the different materials in the domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8d71d38-16ae-4252-86b3-0c011a2fc00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Material\n",
       " \tID             =\t1\n",
       " \tName           =\tUOX fuel\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t10.062 [g/cm3]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tTrue\n",
       " \tS(a,b) Tables  \n",
       " \tNuclides       \n",
       " \tU234           =\t4.9476e-06   [ao]\n",
       " \tU235           =\t0.00048218   [ao]\n",
       " \tU238           =\t0.021504     [ao]\n",
       " \tXe135          =\t1.0801e-08   [ao]\n",
       " \tO16            =\t0.045737     [ao],\n",
       " Material\n",
       " \tID             =\t2\n",
       " \tName           =\tZircaloy\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t5.77 [g/cm3]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tNuclides       \n",
       " \tZr90           =\t0.5145       [ao]\n",
       " \tZr91           =\t0.1122       [ao]\n",
       " \tZr92           =\t0.1715       [ao]\n",
       " \tZr94           =\t0.1738       [ao]\n",
       " \tZr96           =\t0.028        [ao],\n",
       " Material\n",
       " \tID             =\t3\n",
       " \tName           =\tCold borated water\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t0.07416 [atom/b-cm]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tS(a,b)         =\t('c_H_in_H2O', 1.0)\n",
       " \tNuclides       \n",
       " \tH1             =\t2.0          [ao]\n",
       " \tO16            =\t1.0          [ao]\n",
       " \tB10            =\t0.000649     [ao]\n",
       " \tB11            =\t0.002689     [ao],\n",
       " Material\n",
       " \tID             =\t4\n",
       " \tName           =\tHot borated water\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t0.06614 [atom/b-cm]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tS(a,b)         =\t('c_H_in_H2O', 1.0)\n",
       " \tNuclides       \n",
       " \tH1             =\t2.0          [ao]\n",
       " \tO16            =\t1.0          [ao]\n",
       " \tB10            =\t0.000649     [ao]\n",
       " \tB11            =\t0.002689     [ao],\n",
       " Material\n",
       " \tID             =\t5\n",
       " \tName           =\tReactor pressure vessel steel\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t7.9 [g/cm3]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tNuclides       \n",
       " \tFe54           =\t0.05437098   [wo]\n",
       " \tFe56           =\t0.88500663   [wo]\n",
       " \tFe57           =\t0.0208008    [wo]\n",
       " \tFe58           =\t0.00282159   [wo]\n",
       " \tNi58           =\t0.0067198    [wo]\n",
       " \tNi60           =\t0.0026776    [wo]\n",
       " \tMn55           =\t0.01         [wo]\n",
       " \tCr52           =\t0.002092475  [wo]\n",
       " \tC0             =\t0.0025       [wo]\n",
       " \tCu63           =\t0.0013696    [wo],\n",
       " Material\n",
       " \tID             =\t6\n",
       " \tName           =\tLower radial reflector\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t4.32 [g/cm3]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tS(a,b)         =\t('c_H_in_H2O', 1.0)\n",
       " \tNuclides       \n",
       " \tH1             =\t0.0095661    [wo]\n",
       " \tO16            =\t0.0759107    [wo]\n",
       " \tB10            =\t3.08409e-05  [wo]\n",
       " \tB11            =\t0.000140499  [wo]\n",
       " \tFe54           =\t0.035620772088 [wo]\n",
       " \tFe56           =\t0.579805982228 [wo]\n",
       " \tFe57           =\t0.01362750048 [wo]\n",
       " \tFe58           =\t0.001848545204 [wo]\n",
       " \tNi58           =\t0.055298376566 [wo]\n",
       " \tMn55           =\t0.018287     [wo]\n",
       " \tCr52           =\t0.145407678031 [wo],\n",
       " Material\n",
       " \tID             =\t7\n",
       " \tName           =\tUpper radial reflector / Top plate region\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t4.28 [g/cm3]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tS(a,b)         =\t('c_H_in_H2O', 1.0)\n",
       " \tNuclides       \n",
       " \tH1             =\t0.0086117    [wo]\n",
       " \tO16            =\t0.0683369    [wo]\n",
       " \tB10            =\t2.77638e-05  [wo]\n",
       " \tB11            =\t0.000126481  [wo]\n",
       " \tFe54           =\t0.035953677186 [wo]\n",
       " \tFe56           =\t0.585224740891 [wo]\n",
       " \tFe57           =\t0.01375486056 [wo]\n",
       " \tFe58           =\t0.001865821363 [wo]\n",
       " \tNi58           =\t0.055815129186 [wo]\n",
       " \tMn55           =\t0.0184579    [wo]\n",
       " \tCr52           =\t0.146766614995 [wo],\n",
       " Material\n",
       " \tID             =\t8\n",
       " \tName           =\tBottom plate region\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t7.184 [g/cm3]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tS(a,b)         =\t('c_H_in_H2O', 1.0)\n",
       " \tNuclides       \n",
       " \tH1             =\t0.0011505    [wo]\n",
       " \tO16            =\t0.0091296    [wo]\n",
       " \tB10            =\t3.70915e-06  [wo]\n",
       " \tB11            =\t1.68974e-05  [wo]\n",
       " \tFe54           =\t0.03855611055 [wo]\n",
       " \tFe56           =\t0.627585036425 [wo]\n",
       " \tFe57           =\t0.014750478  [wo]\n",
       " \tFe58           =\t0.002000875025 [wo]\n",
       " \tNi58           =\t0.059855207342 [wo]\n",
       " \tMn55           =\t0.019794     [wo]\n",
       " \tCr52           =\t0.157390026871 [wo],\n",
       " Material\n",
       " \tID             =\t9\n",
       " \tName           =\tBottom nozzle region\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t2.53 [g/cm3]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tS(a,b)         =\t('c_H_in_H2O', 1.0)\n",
       " \tNuclides       \n",
       " \tH1             =\t0.0245014    [wo]\n",
       " \tO16            =\t0.1944274    [wo]\n",
       " \tB10            =\t7.89917e-05  [wo]\n",
       " \tB11            =\t0.000359854  [wo]\n",
       " \tFe54           =\t0.030411411144 [wo]\n",
       " \tFe56           =\t0.495012237964 [wo]\n",
       " \tFe57           =\t0.01163454624 [wo]\n",
       " \tFe58           =\t0.001578204652 [wo]\n",
       " \tNi58           =\t0.047211231662 [wo]\n",
       " \tMn55           =\t0.0156126    [wo]\n",
       " \tCr52           =\t0.124142524198 [wo],\n",
       " Material\n",
       " \tID             =\t10\n",
       " \tName           =\tTop nozzle region\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t1.746 [g/cm3]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tS(a,b)         =\t('c_H_in_H2O', 1.0)\n",
       " \tNuclides       \n",
       " \tH1             =\t0.035887     [wo]\n",
       " \tO16            =\t0.2847761    [wo]\n",
       " \tB10            =\t0.000115699  [wo]\n",
       " \tB11            =\t0.000527075  [wo]\n",
       " \tFe54           =\t0.02644016154 [wo]\n",
       " \tFe56           =\t0.43037146399 [wo]\n",
       " \tFe57           =\t0.0101152584 [wo]\n",
       " \tFe58           =\t0.00137211607 [wo]\n",
       " \tNi58           =\t0.04104621835 [wo]\n",
       " \tMn55           =\t0.0135739    [wo]\n",
       " \tCr52           =\t0.107931450781 [wo],\n",
       " Material\n",
       " \tID             =\t11\n",
       " \tName           =\tTop of fuel assemblies\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t3.044 [g/cm3]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tS(a,b)         =\t('c_H_in_H2O', 1.0)\n",
       " \tNuclides       \n",
       " \tH1             =\t0.0162913    [wo]\n",
       " \tO16            =\t0.1292776    [wo]\n",
       " \tB10            =\t5.25228e-05  [wo]\n",
       " \tB11            =\t0.000239272  [wo]\n",
       " \tZr90           =\t0.43313403903 [wo]\n",
       " \tZr91           =\t0.09549277374 [wo]\n",
       " \tZr92           =\t0.14759527104 [wo]\n",
       " \tZr94           =\t0.15280552077 [wo]\n",
       " \tZr96           =\t0.02511169542 [wo],\n",
       " Material\n",
       " \tID             =\t12\n",
       " \tName           =\tBottom of fuel assemblies\n",
       " \tTemperature    =\tNone\n",
       " \tDensity        =\t1.762 [g/cm3]\n",
       " \tVolume         =\tNone [cm^3]\n",
       " \tDepletable     =\tFalse\n",
       " \tS(a,b) Tables  \n",
       " \tS(a,b)         =\t('c_H_in_H2O', 1.0)\n",
       " \tNuclides       \n",
       " \tH1             =\t0.0292856    [wo]\n",
       " \tO16            =\t0.2323919    [wo]\n",
       " \tB10            =\t9.44159e-05  [wo]\n",
       " \tB11            =\t0.00043012   [wo]\n",
       " \tZr90           =\t0.3741373658 [wo]\n",
       " \tZr91           =\t0.0824858164 [wo]\n",
       " \tZr92           =\t0.1274914944 [wo]\n",
       " \tZr94           =\t0.1319920622 [wo]\n",
       " \tZr96           =\t0.0216912612 [wo]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.materials"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd176295-ff3a-4924-833e-c2afc2a6cff6",
   "metadata": {},
   "source": [
    "There are a number of methods/attributes of the `openmc.Material` class which either return `None` or do not work unless OpenMC knows about the volume of the material."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "82fbfc5e-2c8d-46eb-8359-eb48179be745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Material\n",
       "\tID             =\t1\n",
       "\tName           =\tUOX fuel\n",
       "\tTemperature    =\tNone\n",
       "\tDensity        =\t10.062 [g/cm3]\n",
       "\tVolume         =\tNone [cm^3]\n",
       "\tDepletable     =\tTrue\n",
       "\tS(a,b) Tables  \n",
       "\tNuclides       \n",
       "\tU234           =\t4.9476e-06   [ao]\n",
       "\tU235           =\t0.00048218   [ao]\n",
       "\tU238           =\t0.021504     [ao]\n",
       "\tXe135          =\t1.0801e-08   [ao]\n",
       "\tO16            =\t0.045737     [ao]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.materials[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d42e036-2b56-4846-8110-b9a9636cac8d",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Volume must be set in order to determine mass.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaterials\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_mass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnuclide\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mU234\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/openmc/openmc/material.py:1341\u001b[0m, in \u001b[0;36mMaterial.get_mass\u001b[0;34m(self, nuclide, volume)\u001b[0m\n\u001b[1;32m   1339\u001b[0m     volume \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvolume\n\u001b[1;32m   1340\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m volume \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1341\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVolume must be set in order to determine mass.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m volume\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_mass_density(nuclide)\n",
      "\u001b[0;31mValueError\u001b[0m: Volume must be set in order to determine mass."
     ]
    }
   ],
   "source": [
    "model.materials[0].get_mass(nuclide='U234')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "045a37dc-0cdd-4c81-bbdc-ec640b1cef1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "vc = openmc.VolumeCalculation(model.materials, samples=10000, lower_left = bb.lower_left, upper_right = bb.upper_right)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99b53f1d-3119-4621-a55a-f7d30e0d6a08",
   "metadata": {},
   "source": [
    "Now we apply the volume calculation to the model's settings and we're ready to run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9132870d-e2cf-40fa-8ebd-8c693907c9f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.settings.volume_calculations = [vc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2d7666a-ce46-4206-ad8b-9064a7f60087",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
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      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\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 | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.15.3\n",
      "     Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n",
      "       Date/Time | 2025-11-27 14:29:14\n",
      "  OpenMP Threads | 2\n",
      "\n",
      " Reading model XML file 'model.xml' ...\n",
      " Reading chain file: /home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml...\n",
      " Reading cross sections XML file...\n",
      " Reading U234 from /home/ubuntu/data/endfb71_hdf5/U234.h5\n",
      " Reading U235 from /home/ubuntu/data/endfb71_hdf5/U235.h5\n",
      " Reading U238 from /home/ubuntu/data/endfb71_hdf5/U238.h5\n",
      " Reading Xe135 from /home/ubuntu/data/endfb71_hdf5/Xe135.h5\n",
      " Reading O16 from /home/ubuntu/data/endfb71_hdf5/O16.h5\n",
      " Reading Zr90 from /home/ubuntu/data/endfb71_hdf5/Zr90.h5\n",
      " Reading Zr91 from /home/ubuntu/data/endfb71_hdf5/Zr91.h5\n",
      " Reading Zr92 from /home/ubuntu/data/endfb71_hdf5/Zr92.h5\n",
      " Reading Zr94 from /home/ubuntu/data/endfb71_hdf5/Zr94.h5\n",
      " Reading Zr96 from /home/ubuntu/data/endfb71_hdf5/Zr96.h5\n",
      " Reading H1 from /home/ubuntu/data/endfb71_hdf5/H1.h5\n",
      " Reading B10 from /home/ubuntu/data/endfb71_hdf5/B10.h5\n",
      " Reading B11 from /home/ubuntu/data/endfb71_hdf5/B11.h5\n",
      " Reading Fe54 from /home/ubuntu/data/endfb71_hdf5/Fe54.h5\n",
      " Reading Fe56 from /home/ubuntu/data/endfb71_hdf5/Fe56.h5\n",
      " Reading Fe57 from /home/ubuntu/data/endfb71_hdf5/Fe57.h5\n",
      " Reading Fe58 from /home/ubuntu/data/endfb71_hdf5/Fe58.h5\n",
      " Reading Ni58 from /home/ubuntu/data/endfb71_hdf5/Ni58.h5\n",
      " Reading Ni60 from /home/ubuntu/data/endfb71_hdf5/Ni60.h5\n",
      " Reading Mn55 from /home/ubuntu/data/endfb71_hdf5/Mn55.h5\n",
      " Reading Cr52 from /home/ubuntu/data/endfb71_hdf5/Cr52.h5\n",
      " Reading C0 from /home/ubuntu/data/endfb71_hdf5/C0.h5\n",
      " Reading Cu63 from /home/ubuntu/data/endfb71_hdf5/Cu63.h5\n",
      " Reading c_H_in_H2O from /home/ubuntu/data/endfb71_hdf5/c_H_in_H2O.h5\n",
      " Minimum neutron data temperature: 1.7976931348623157e+308 K\n",
      " Maximum neutron data temperature: 0 K\n",
      " Preparing distributed cell instances...\n",
      " Writing summary.h5 file...\n",
      "\n",
      " =================>     STOCHASTIC VOLUME CALCULATION     <=================\n",
      "\n",
      " Running volume calculation 1\n",
      "   Material 1 UOX fuel: 12308339.3184 +/- 350462.9282728749 cm^3\n",
      "   Material 2 Zircaloy: 4696898.1552 +/- 224599.89655314686 cm^3\n",
      "   Material 3 Cold borated water: 15660063.7776 +/- 388615.6488216423 cm^3\n",
      "   Material 4 Hot borated water: 17128545.0624 +/- 403321.8689545108 cm^3\n",
      "   Material 5 Reactor pressure vessel steel: 13776820.6032 +/- 368042.1993325224\n",
      " cm^3\n",
      "   Material 6 Lower radial reflector: 425971.6704 +/- 68970.3114810678 cm^3\n",
      "   Material 7 Upper radial reflector / Top plate region: 2129858.352 +/-\n",
      " 153041.22190119507 cm^3\n",
      "   Material 8 Bottom plate region: 5537631.7151999995 +/- 242917.88978418434\n",
      " cm^3\n",
      "   Material 9 Bottom nozzle region: 12173821.9488 +/- 348777.4096184676 cm^3\n",
      "   Material 10 Top nozzle region: 1221866.1072 +/- 116393.96698398459 cm^3\n",
      "   Material 11 Top of fuel assemblies: 1939292.0784 +/- 146160.71199028115 cm^3\n",
      "   Material 12 Bottom of fuel assemblies: 1087348.7376 +/- 109866.77511393827\n",
      " cm^3\n",
      " Elapsed time: 0.00363598 s\n"
     ]
    }
   ],
   "source": [
    "model.calculate_volumes()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7619b750",
   "metadata": {},
   "source": [
    "We can see that the volume information was printed to the screen after the solve completes. When we perform a volume calculation, that volume information is automatically applied to the materials/cells for which the volume calculation was performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "21f04a4e-d898-4b9c-92a1-bce437110e51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Material\n",
       "\tID             =\t1\n",
       "\tName           =\tUOX fuel\n",
       "\tTemperature    =\tNone\n",
       "\tDensity        =\t10.062 [g/cm3]\n",
       "\tVolume         =\t12308339.3184 [cm^3]\n",
       "\tDepletable     =\tTrue\n",
       "\tS(a,b) Tables  \n",
       "\tNuclides       \n",
       "\tU234           =\t4.9476e-06   [ao]\n",
       "\tU235           =\t0.00048218   [ao]\n",
       "\tU238           =\t0.021504     [ao]\n",
       "\tXe135          =\t1.0801e-08   [ao]\n",
       "\tO16            =\t0.045737     [ao]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.materials[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e13f61c-4cca-494f-9472-4535b9537036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(24041.011695559042)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.materials[0].get_mass(nuclide='U234')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f71dc9a-ac36-4da9-9a21-a191d2c4e87f",
   "metadata": {},
   "source": [
    "If you want to run a volume calculation just one time and then load that information for later runs, you can load the HDF5 file we created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8dbbedcf-3806-4a00-ab08-f614daed8739",
   "metadata": {},
   "outputs": [],
   "source": [
    "vc.load_results('volume_1.h5')\n",
    "model.geometry.add_volume_information(vc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdf6e736-d448-4edc-a436-e3ee75e61c15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "80019844-292c-4d61-afc5-d383fead589c",
   "metadata": {},
   "source": [
    "Pretty easy overall! Now we'll look use a different volume calculation to compute volumes according to the different cells in this PWR. In this case, we will set `domains` to all of the cells in the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c67a9fde-c53f-41fc-a503-814afd80b5a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "cells = list(model.geometry.get_all_cells().values())\n",
    "print(cells[0].volume)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "103d4c93-8c73-4b58-b4aa-5f3afcacd8ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "cell_vc = openmc.VolumeCalculation(domains=cells, samples=100000, \n",
    "                                   lower_left = bb.lower_left, upper_right=bb.upper_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "13335317-e618-441c-a360-c66238262e29",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.settings.volume_calculations = [cell_vc]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "310f432e-f070-4290-bf0b-d3986af3a054",
   "metadata": {},
   "source": [
    "Similar to the case for materials, the cell volume calculation will automatically load the volumes onto the cells - or, if you want to run just one time and load into a model, you can load the HDF5 file generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "882538fc-e351-4431-b776-d327256b6222",
   "metadata": {},
   "outputs": [
    {
     "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 | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.15.3\n",
      "     Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n",
      "       Date/Time | 2025-11-27 14:34:28\n",
      "  OpenMP Threads | 2\n",
      "\n",
      " Reading model XML file 'model.xml' ...\n",
      " Reading chain file: /home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml...\n",
      " Reading cross sections XML file...\n",
      " Reading U234 from /home/ubuntu/data/endfb71_hdf5/U234.h5\n",
      " Reading U235 from /home/ubuntu/data/endfb71_hdf5/U235.h5\n",
      " Reading U238 from /home/ubuntu/data/endfb71_hdf5/U238.h5\n",
      " Reading Xe135 from /home/ubuntu/data/endfb71_hdf5/Xe135.h5\n",
      " Reading O16 from /home/ubuntu/data/endfb71_hdf5/O16.h5\n",
      " Reading Zr90 from /home/ubuntu/data/endfb71_hdf5/Zr90.h5\n",
      " Reading Zr91 from /home/ubuntu/data/endfb71_hdf5/Zr91.h5\n",
      " Reading Zr92 from /home/ubuntu/data/endfb71_hdf5/Zr92.h5\n",
      " Reading Zr94 from /home/ubuntu/data/endfb71_hdf5/Zr94.h5\n",
      " Reading Zr96 from /home/ubuntu/data/endfb71_hdf5/Zr96.h5\n",
      " Reading H1 from /home/ubuntu/data/endfb71_hdf5/H1.h5\n",
      " Reading B10 from /home/ubuntu/data/endfb71_hdf5/B10.h5\n",
      " Reading B11 from /home/ubuntu/data/endfb71_hdf5/B11.h5\n",
      " Reading Fe54 from /home/ubuntu/data/endfb71_hdf5/Fe54.h5\n",
      " Reading Fe56 from /home/ubuntu/data/endfb71_hdf5/Fe56.h5\n",
      " Reading Fe57 from /home/ubuntu/data/endfb71_hdf5/Fe57.h5\n",
      " Reading Fe58 from /home/ubuntu/data/endfb71_hdf5/Fe58.h5\n",
      " Reading Ni58 from /home/ubuntu/data/endfb71_hdf5/Ni58.h5\n",
      " Reading Ni60 from /home/ubuntu/data/endfb71_hdf5/Ni60.h5\n",
      " Reading Mn55 from /home/ubuntu/data/endfb71_hdf5/Mn55.h5\n",
      " Reading Cr52 from /home/ubuntu/data/endfb71_hdf5/Cr52.h5\n",
      " Reading C0 from /home/ubuntu/data/endfb71_hdf5/C0.h5\n",
      " Reading Cu63 from /home/ubuntu/data/endfb71_hdf5/Cu63.h5\n",
      " Reading c_H_in_H2O from /home/ubuntu/data/endfb71_hdf5/c_H_in_H2O.h5\n",
      " Minimum neutron data temperature: 1.7976931348623157e+308 K\n",
      " Maximum neutron data temperature: 0 K\n",
      " Preparing distributed cell instances...\n",
      " Writing summary.h5 file...\n",
      "\n",
      " =================>     STOCHASTIC VOLUME CALCULATION     <=================\n",
      "\n",
      " Running volume calculation 1\n",
      "   Cell 1: 25188377.4576 +/- 147956.3294059109 cm^3\n",
      "   Cell 2: 24972028.68816 +/- 147502.79524312634 cm^3\n",
      "   Cell 3: 5074667.7681599995 +/- 73695.78550919898 cm^3\n",
      "   Cell 4: 686038.58496 +/- 27646.477648587323 cm^3\n",
      "   Cell 5: 1077259.93488 +/- 34582.942092738886 cm^3\n",
      "   Cell 6: 2231867.35728 +/- 49518.3003138586 cm^3\n",
      "   Cell 7: 1340689.78368 +/- 38534.521777551396 cm^3\n",
      "   Cell 8: 1291366.7481600002 +/- 37827.47066809658 cm^3\n",
      "   Cell 9: 11353265.994239999 +/- 106947.63124348657 cm^3\n",
      "   Cell 10: 13562713.78992 +/- 115602.91004270257 cm^3\n",
      "   Cell 11: 431576.56080000004 +/- 21952.796660568507 cm^3\n",
      "   Cell 12: 829523.7792 +/- 30380.863653283086 cm^3\n",
      "   Cell 50: 4840383.34944 +/- 72053.24783674821 cm^3\n",
      "   Cell 60: 20347994.10816 +/- 136635.4519330886 cm^3\n",
      "   Cell 21: 6175468.24272 +/- 80877.68823140785 cm^3\n",
      "   Cell 22: 2086140.2068800002 +/- 47906.13357485169 cm^3\n",
      "   Cell 23: 10344385.722240001 +/- 102595.15819955239 cm^3\n",
      "   Cell 24: 1107526.34304 +/- 35060.61333697864 cm^3\n",
      "   Cell 25: 241010.2872 +/- 16419.086132313892 cm^3\n",
      "   Cell 26: 393463.30608 +/- 20964.627534676038 cm^3\n",
      "   Cell 70: 4798907.16048 +/- 71757.74965468232 cm^3\n",
      "   Cell 80: 20173121.527680002 +/- 136176.64526635958 cm^3\n",
      "   Cell 27: 6138475.966080001 +/- 80649.1669561508 cm^3\n",
      "   Cell 28: 2152277.9135999996 +/- 48644.97261835008 cm^3\n",
      "   Cell 29: 10170634.11984 +/- 101816.69766813397 cm^3\n",
      "   Cell 30: 1045872.5486399999 +/- 34080.224582874835 cm^3\n",
      "   Cell 31: 241010.2872 +/- 16419.086132313892 cm^3\n",
      "   Cell 32: 424850.69232 +/- 21781.720139968205 cm^3\n",
      " Elapsed time: 0.027262431 s\n"
     ]
    }
   ],
   "source": [
    "model.calculate_volumes()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7768f2e5",
   "metadata": {},
   "source": [
    "We can also view the number of atoms of each nuclide in each cell, through a Pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ff76f606-e13d-43dd-9ba7-1af57be78b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25188377.4576"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cells[0].volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7c61a258-c066-41de-9386-d22628013bb8",
   "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 th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cell</th>\n",
       "      <th>Nuclide</th>\n",
       "      <th>Atoms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>U234</td>\n",
       "      <td>(3.10+/-0.04)e+25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>U235</td>\n",
       "      <td>(3.02+/-0.04)e+27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>U238</td>\n",
       "      <td>(1.349+/-0.018)e+29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Xe135</td>\n",
       "      <td>(6.78+/-0.09)e+22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>O16</td>\n",
       "      <td>(6.99+/-0.05)e+29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>9</td>\n",
       "      <td>Fe57</td>\n",
       "      <td>(3.741+/-0.035)e+27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>9</td>\n",
       "      <td>Fe58</td>\n",
       "      <td>(4.99+/-0.05)e+26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>9</td>\n",
       "      <td>Ni58</td>\n",
       "      <td>(1.492+/-0.014)e+28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>9</td>\n",
       "      <td>Mn55</td>\n",
       "      <td>(5.20+/-0.05)e+27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>9</td>\n",
       "      <td>Cr52</td>\n",
       "      <td>(4.38+/-0.04)e+28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cell Nuclide                Atoms\n",
       "0       1    U234    (3.10+/-0.04)e+25\n",
       "1       1    U235    (3.02+/-0.04)e+27\n",
       "2       1    U238  (1.349+/-0.018)e+29\n",
       "3       1   Xe135    (6.78+/-0.09)e+22\n",
       "4       1     O16    (6.99+/-0.05)e+29\n",
       "..    ...     ...                  ...\n",
       "214     9    Fe57  (3.741+/-0.035)e+27\n",
       "215     9    Fe58    (4.99+/-0.05)e+26\n",
       "216     9    Ni58  (1.492+/-0.014)e+28\n",
       "217     9    Mn55    (5.20+/-0.05)e+27\n",
       "218     9    Cr52    (4.38+/-0.04)e+28\n",
       "\n",
       "[219 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_vc.atoms_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8232fd4-5e82-438b-87aa-327fae528afd",
   "metadata": {},
   "source": [
    "As we see in the simulation output, there is uncertainty associated with these results. The OpenMC volume calculation is *stochastic*, and is essentially obtained by \"throwing darts\" at the problem. The more samples used, the lower the statistical uncertainty.\n",
    "\n",
    "## Triggers\n",
    "\n",
    "All OpenMC tallies support triggers, which will continue increasing the number of samples taken until reaching some desired statistical uncertainty. We will also use these features for other tally types in a later tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7fed574e-8c41-4ad8-a3dc-1ed846aefabf",
   "metadata": {},
   "outputs": [],
   "source": [
    "vc.set_trigger(15_000, 'std_dev')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "be8d4d67-3adb-4fee-82f8-2b36b471498a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.settings.volume_calculations = [vc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3ae69e8b-0efd-4455-bc92-d3445b049346",
   "metadata": {},
   "outputs": [
    {
     "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 | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.15.3\n",
      "     Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n",
      "       Date/Time | 2025-11-27 14:37:10\n",
      "  OpenMP Threads | 2\n",
      "\n",
      " Reading model XML file 'model.xml' ...\n",
      " Reading chain file: /home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml...\n",
      " Reading cross sections XML file...\n",
      " Reading U234 from /home/ubuntu/data/endfb71_hdf5/U234.h5\n",
      " Reading U235 from /home/ubuntu/data/endfb71_hdf5/U235.h5\n",
      " Reading U238 from /home/ubuntu/data/endfb71_hdf5/U238.h5\n",
      " Reading Xe135 from /home/ubuntu/data/endfb71_hdf5/Xe135.h5\n",
      " Reading O16 from /home/ubuntu/data/endfb71_hdf5/O16.h5\n",
      " Reading Zr90 from /home/ubuntu/data/endfb71_hdf5/Zr90.h5\n",
      " Reading Zr91 from /home/ubuntu/data/endfb71_hdf5/Zr91.h5\n",
      " Reading Zr92 from /home/ubuntu/data/endfb71_hdf5/Zr92.h5\n",
      " Reading Zr94 from /home/ubuntu/data/endfb71_hdf5/Zr94.h5\n",
      " Reading Zr96 from /home/ubuntu/data/endfb71_hdf5/Zr96.h5\n",
      " Reading H1 from /home/ubuntu/data/endfb71_hdf5/H1.h5\n",
      " Reading B10 from /home/ubuntu/data/endfb71_hdf5/B10.h5\n",
      " Reading B11 from /home/ubuntu/data/endfb71_hdf5/B11.h5\n",
      " Reading Fe54 from /home/ubuntu/data/endfb71_hdf5/Fe54.h5\n",
      " Reading Fe56 from /home/ubuntu/data/endfb71_hdf5/Fe56.h5\n",
      " Reading Fe57 from /home/ubuntu/data/endfb71_hdf5/Fe57.h5\n",
      " Reading Fe58 from /home/ubuntu/data/endfb71_hdf5/Fe58.h5\n",
      " Reading Ni58 from /home/ubuntu/data/endfb71_hdf5/Ni58.h5\n",
      " Reading Ni60 from /home/ubuntu/data/endfb71_hdf5/Ni60.h5\n",
      " Reading Mn55 from /home/ubuntu/data/endfb71_hdf5/Mn55.h5\n",
      " Reading Cr52 from /home/ubuntu/data/endfb71_hdf5/Cr52.h5\n",
      " Reading C0 from /home/ubuntu/data/endfb71_hdf5/C0.h5\n",
      " Reading Cu63 from /home/ubuntu/data/endfb71_hdf5/Cu63.h5\n",
      " Reading c_H_in_H2O from /home/ubuntu/data/endfb71_hdf5/c_H_in_H2O.h5\n",
      " Minimum neutron data temperature: 1.7976931348623157e+308 K\n",
      " Maximum neutron data temperature: 0 K\n",
      " Preparing distributed cell instances...\n",
      " Writing summary.h5 file...\n",
      "\n",
      " =================>     STOCHASTIC VOLUME CALCULATION     <=================\n",
      "\n",
      " Running volume calculation 1\n",
      "   Material 1 UOX fuel: 12315198.561042493 +/- 13193.067585670624 cm^3\n",
      "   Material 2 Zircaloy: 4699835.562350142 +/- 8455.455772521776 cm^3\n",
      "   Material 3 Cold borated water: 16613419.11552408 +/- 14989.713997008372 cm^3\n",
      "   Material 4 Hot borated water: 16612196.519034559 +/- 14989.258395016583 cm^3\n",
      "   Material 5 Reactor pressure vessel steel: 13577632.642667422 +/-\n",
      " 13764.874771083209 cm^3\n",
      "   Material 6 Lower radial reflector: 431004.9572464589 +/- 2610.963637225479\n",
      " cm^3\n",
      "   Material 7 Upper radial reflector / Top plate region: 2180778.7018946176 +/-\n",
      " 5826.878187747633 cm^3\n",
      "   Material 8 Bottom plate region: 4939686.764566572 +/- 8658.843693309671 cm^3\n",
      "   Material 9 Bottom nozzle region: 12049990.39007592 +/- 13067.569228628307\n",
      " cm^3\n",
      "   Material 10 Top nozzle region: 1328406.6584294618 +/- 4565.3394620795625 cm^3\n",
      "   Material 11 Top of fuel assemblies: 2203039.484729745 +/- 5855.949181998552\n",
      " cm^3\n",
      "   Material 12 Bottom of fuel assemblies: 1097145.3873926345 +/-\n",
      " 4153.292577306855 cm^3\n",
      " Elapsed time: 1.129395206 s\n"
     ]
    }
   ],
   "source": [
    "model.calculate_volumes()"
   ]
  },
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