{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03228f54-7693-4b5e-be0e-66490007558d",
   "metadata": {},
   "source": [
    "# PWR Assembly Depletion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a26f26-f908-4da1-a082-3bfb37e8044a",
   "metadata": {},
   "source": [
    "In this example, we will demonstrate how to perform a PWR assembly depletion simulation based on the 3.1% enriched assembly (without any burnable poison rods) from the BEAVRS benchmark. We can begin by importing all the tools we will need from Scarabée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff60fb2e-6d6c-4569-beb3-4fa01d82c309",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scarabee import (\n",
    "    NDLibrary,\n",
    "    MaterialComposition,\n",
    "    Material,\n",
    "    Fraction,\n",
    "    DensityUnits,\n",
    ")\n",
    "from scarabee.reseau import FuelPin, GuideTube, PWRAssembly, Symmetry\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a3b77f5-ad0a-4801-a4b5-f2504eea7062",
   "metadata": {},
   "source": [
    "The `scarabee.reseau` submodule contains all of the elements needed for lattice physics calculations (reseau is French for lattice). In this case, we have directly imported only the classes needed for this example.\n",
    "\n",
    "We can now create our Nuclear Data Library (NDL) object, and define the materials we will need for the fuel pins and guide tubes. In this example, we will not provide a path to the `NDLibrary` constructor, which means it will automatically look at our `SCARABEE_ND_LIBRARY` environment variable to find the path to the NDL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "441fa30d-1f25-46d2-8faa-1cc28c74cc35",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndl = NDLibrary()\n",
    "\n",
    "# Define Fuel at 3.1 w/o enrichment\n",
    "Fuel31Comp = MaterialComposition(Fraction.Atoms, name=\"Fuel 3.1%\")\n",
    "Fuel31Comp.add_leu(3.1, 1.0) # The add_leu method assumes weight enrichment !\n",
    "Fuel31Comp.add_element(\"O\", 2.0)\n",
    "Fuel31 = Material(Fuel31Comp, 900.0, 10.30166, DensityUnits.g_cm3, ndl)\n",
    "\n",
    "# Define the cladding\n",
    "CladComp = MaterialComposition(Fraction.Weight, name=\"Zircaloy 4\")\n",
    "CladComp.add_element(\"O\", 0.00125)\n",
    "CladComp.add_element(\"Cr\", 0.0010)\n",
    "CladComp.add_element(\"Fe\", 0.0021)\n",
    "CladComp.add_element(\"Zr\", 0.98115)\n",
    "CladComp.add_element(\"Sn\", 0.0145)\n",
    "Clad = Material(CladComp, 575.0, 6.55, DensityUnits.g_cm3, ndl)\n",
    "\n",
    "# Define the helium gas\n",
    "HeComp = MaterialComposition(Fraction.Atoms, name=\"He Gas\")\n",
    "HeComp.add_element(\"He\", 1.0)\n",
    "He = Material(HeComp, 575.0, 0.0015981, DensityUnits.g_cm3, ndl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1038264-e34c-422b-8dbd-592cec50998d",
   "metadata": {},
   "source": [
    "In this simulation, we have assumed a fuel temperature of 900 K, and a cladding and gas temperature of 575 K. You might have noticed that we have not defined any moderator material. This will be taken care of automatically by the `PWRAssembly` class later on.\n",
    "\n",
    "The next step is to define the fuel pin and guide tubes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8311aee6-9c4d-49ca-9836-ae8ee0d8da70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a fuel pin\n",
    "fp = FuelPin(\n",
    "    fuel=Fuel31,\n",
    "    fuel_radius=0.39218,\n",
    "    gap=He,\n",
    "    gap_radius=0.40005,\n",
    "    clad=Clad,\n",
    "    clad_radius=0.45720,\n",
    ")\n",
    "\n",
    "# Create a guide tube\n",
    "gt = GuideTube(inner_radius=0.56134, outer_radius=0.60198, clad=Clad)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e891334e-699d-440c-b7d4-c2ee284040d0",
   "metadata": {},
   "source": [
    "Now we need to define the arrangement of the fuel pins and guide tubes in the assembly. The full 2D assembly would normally be 17x17, but since the assembly is symmetric, we can use quarter symmetry to reduce the size of the problem (and get results faster). When running with quarter symmetry, the the left hand side (-x) and the bottom (-y) pins will be half pin cells (because our full assembly had an odd number of pins on each side)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79d010d2-f63d-4a4d-a63a-de09358ccd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#         |--- Half pin cells\n",
    "#         v\n",
    "cells = [[fp, fp, fp, fp, fp, fp, fp, fp, fp],\n",
    "         [fp, fp, fp, fp, fp, fp, fp, fp, fp],\n",
    "         [gt, fp, fp, gt, fp, fp, fp, fp, fp],\n",
    "         [fp, fp, fp, fp, fp, gt, fp, fp, fp],\n",
    "         [fp, fp, fp, fp, fp, fp, fp, fp, fp],\n",
    "         [gt, fp, fp, gt, fp, fp, gt, fp, fp],\n",
    "         [fp, fp, fp, fp, fp, fp, fp, fp, fp],\n",
    "         [fp, fp, fp, fp, fp, fp, fp, fp, fp],\n",
    "         [gt, fp, fp, gt, fp, fp, gt, fp, fp]] # <- Half pin cells\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae56e835-a0a6-4a4f-8220-ba4db20b5ead",
   "metadata": {},
   "source": [
    "It is now time to define the `PWRAssembly` object, which will be responsible for orchestrating the entire depletion calculation. It requires all the assembly information (such a pin pitch, assembly pitch, moderator parameters, linear power density, and more). We will assume the moderator is at 575 K and a pressure of 15.5132 MPa with a soluble boron concentration of 975 parts per million. By default, Scarabée assumes a linear power density of 42 kW/cm, which is reasonable for most PWRs, and we have kept this choice. Keep in mind that **the linear power density that is provided is for the linear power of the entire assembly** and not just for a single fule pin !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce6790a9-5f32-49bc-b6ae-7a34353c0c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "asmbly = PWRAssembly(\n",
    "    pitch=1.25984,\n",
    "    assembly_pitch=21.50364,\n",
    "    shape=(17, 17),\n",
    "    symmetry=Symmetry.Quarter,\n",
    "    moderator_pressure=15.5132,\n",
    "    moderator_temp=575.0,\n",
    "    boron_ppm=975.0,\n",
    "    cells=cells,\n",
    "    ndl=ndl,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68db3ddc-88eb-4163-b311-42b54812c0ec",
   "metadata": {},
   "source": [
    "At this point, if we were to call `asmbly.solve()`, it would run a single k-eigenvalue problem, and not actually perform any depletion. For that, we need to tell it what the series of time steps should be. We can either set `asmbly.depletion_time_steps` to be an array containing the durration of all time steps in days, or we can set `asmbly.depletion_exposure_steps` to be an array containing the durration of all time steps in MWd/kg of initial heavy mental. We will use the later of these two option here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b213724-66b0-45f4-be7f-9c37f71881b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "asmbly.depletion_exposure_steps = np.array(5*[0.01] + [0.15] + [0.8] + [1.] + 15*[2.])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6c676a-94a9-422c-a8da-1d82eff33219",
   "metadata": {},
   "source": [
    "The first few time steps are rather small (on the order of about 0.5 days). This is to accurately predict the build up of strong fission product poisons, particularly Xe-135.\n",
    "\n",
    "Scarabée uses an LE/QI predictor-corrector method for depletion. That means for each time step a transport calculation is performed for the predictor *and* the corrector. This is the most accurate option, but it might not be necessary for your problem. In our case, we don't have any burnable poisons so we likely don't need to perform a second transport calcualtion on the corrector step. We will instead just use the flux from the predictor step with the nuclide concentrations obtained with the predictor step. This will help the simulation run almost twice as fast, but should likely only be used if you know what you are doing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3a7b2a5-81b2-4113-83b2-63a3bd26178f",
   "metadata": {},
   "outputs": [],
   "source": [
    "asmbly.corrector_transport = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8935843d-584f-43b7-a4dc-dd2fa6884e4f",
   "metadata": {},
   "source": [
    "We can now finally launch the simulation, which will take approximately 10-30 minutes depending on your computer and the group strucutre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db9d88fa-7581-4c32-bab3-0a918721f71b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 0\n",
      "[info] Exposure: 0.000E+00 MWd/kg\n",
      "[info] Time    : 0.000E+00 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Initializing Dancoff correction calculation components.\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] Creating quadrature\n",
      "[info] Number of azimuthal angles: 64\n",
      "[info] Maximum track spacing: 0.05 cm\n",
      "[info] Tracing tracks\n",
      "[info] Renormalizing segment lengths\n",
      "[info] Determining track connections\n",
      "[info] Time spent drawing tracks: 0.088601 s.\n",
      "[info] \n",
      "[info] Kinf: 1.20997\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.20997\n",
      "[info] Buckling: 0.00355\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.19067\n",
      "[info] Buckling: 0.00325\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 1\n",
      "[info] Exposure: 1.000E-02 MWd/kg\n",
      "[info] Time    : 2.758E-01 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.19590\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.19590\n",
      "[info] Buckling: 0.00333\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.19255\n",
      "[info] Buckling: 0.00328\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 2\n",
      "[info] Exposure: 2.000E-02 MWd/kg\n",
      "[info] Time    : 5.516E-01 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.18638\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.18638\n",
      "[info] Buckling: 0.00318\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17923\n",
      "[info] Buckling: 0.00306\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 3\n",
      "[info] Exposure: 3.000E-02 MWd/kg\n",
      "[info] Time    : 8.274E-01 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.17899\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17899\n",
      "[info] Buckling: 0.00306\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17548\n",
      "[info] Buckling: 0.00300\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 4\n",
      "[info] Exposure: 4.000E-02 MWd/kg\n",
      "[info] Time    : 1.103E+00 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.17538\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17538\n",
      "[info] Buckling: 0.00300\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17350\n",
      "[info] Buckling: 0.00297\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 5\n",
      "[info] Exposure: 5.000E-02 MWd/kg\n",
      "[info] Time    : 1.379E+00 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.17345\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17345\n",
      "[info] Buckling: 0.00297\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.16724\n",
      "[info] Buckling: 0.00287\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 6\n",
      "[info] Exposure: 2.000E-01 MWd/kg\n",
      "[info] Time    : 5.516E+00 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.16724\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.16724\n",
      "[info] Buckling: 0.00287\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.15677\n",
      "[info] Buckling: 0.00270\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 7\n",
      "[info] Exposure: 1.000E+00 MWd/kg\n",
      "[info] Time    : 2.758E+01 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.15654\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.15654\n",
      "[info] Buckling: 0.00270\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.14773\n",
      "[info] Buckling: 0.00255\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 8\n",
      "[info] Exposure: 2.000E+00 MWd/kg\n",
      "[info] Time    : 5.516E+01 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.14747\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.14747\n",
      "[info] Buckling: 0.00255\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.12653\n",
      "[info] Buckling: 0.00220\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 9\n",
      "[info] Exposure: 4.000E+00 MWd/kg\n",
      "[info] Time    : 1.103E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.12627\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.12627\n",
      "[info] Buckling: 0.00220\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.10363\n",
      "[info] Buckling: 0.00181\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 10\n",
      "[info] Exposure: 6.000E+00 MWd/kg\n",
      "[info] Time    : 1.655E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.10401\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.10401\n",
      "[info] Buckling: 0.00182\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.08123\n",
      "[info] Buckling: 0.00143\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 11\n",
      "[info] Exposure: 8.000E+00 MWd/kg\n",
      "[info] Time    : 2.206E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.08220\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.08220\n",
      "[info] Buckling: 0.00145\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.05981\n",
      "[info] Buckling: 0.00106\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 12\n",
      "[info] Exposure: 1.000E+01 MWd/kg\n",
      "[info] Time    : 2.758E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.06118\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.06118\n",
      "[info] Buckling: 0.00108\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.03936\n",
      "[info] Buckling: 0.00070\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 13\n",
      "[info] Exposure: 1.200E+01 MWd/kg\n",
      "[info] Time    : 3.310E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.04096\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.04096\n",
      "[info] Buckling: 0.00073\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.01973\n",
      "[info] Buckling: 0.00035\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 14\n",
      "[info] Exposure: 1.400E+01 MWd/kg\n",
      "[info] Time    : 3.861E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.02145\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.02145\n",
      "[info] Buckling: 0.00038\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.00075\n",
      "[info] Buckling: 0.00001\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 15\n",
      "[info] Exposure: 1.600E+01 MWd/kg\n",
      "[info] Time    : 4.413E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.00252\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.00252\n",
      "[info] Buckling: 0.00005\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.98232\n",
      "[info] Buckling: -0.00032\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 16\n",
      "[info] Exposure: 1.800E+01 MWd/kg\n",
      "[info] Time    : 4.964E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.98409\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.98409\n",
      "[info] Buckling: -0.00029\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.96435\n",
      "[info] Buckling: -0.00065\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 17\n",
      "[info] Exposure: 2.000E+01 MWd/kg\n",
      "[info] Time    : 5.516E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.96608\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.96608\n",
      "[info] Buckling: -0.00061\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.94680\n",
      "[info] Buckling: -0.00097\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 18\n",
      "[info] Exposure: 2.200E+01 MWd/kg\n",
      "[info] Time    : 6.068E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.94847\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.94847\n",
      "[info] Buckling: -0.00094\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.92966\n",
      "[info] Buckling: -0.00129\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 19\n",
      "[info] Exposure: 2.400E+01 MWd/kg\n",
      "[info] Time    : 6.619E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.93125\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.93125\n",
      "[info] Buckling: -0.00126\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.91294\n",
      "[info] Buckling: -0.00160\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 20\n",
      "[info] Exposure: 2.600E+01 MWd/kg\n",
      "[info] Time    : 7.171E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.91445\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.91445\n",
      "[info] Buckling: -0.00157\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.89665\n",
      "[info] Buckling: -0.00191\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 21\n",
      "[info] Exposure: 2.800E+01 MWd/kg\n",
      "[info] Time    : 7.722E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.89807\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.89807\n",
      "[info] Buckling: -0.00188\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.88083\n",
      "[info] Buckling: -0.00221\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 22\n",
      "[info] Exposure: 3.000E+01 MWd/kg\n",
      "[info] Time    : 8.274E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.88215\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.88215\n",
      "[info] Buckling: -0.00218\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.86549\n",
      "[info] Buckling: -0.00250\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 23\n",
      "[info] Exposure: 3.200E+01 MWd/kg\n",
      "[info] Time    : 8.825E+02 days\n",
      "[info] \n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.86673\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.86673\n",
      "[info] Buckling: -0.00248\n",
      "[info] \n"
     ]
    }
   ],
   "source": [
    "asmbly.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a9e6d35-7138-42e9-b2bd-fb860210332b",
   "metadata": {},
   "source": [
    "Now comes the fun part: making plots ! Let's first take a look at $k_\\text{eff}$ as a function of the burn-up of the assembly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b0b146b-229c-4427-80fa-4f6d2617debc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(asmbly.exposures, asmbly.keff)\n",
    "plt.xlabel(\"Exposure [MWd/kg]\")\n",
    "plt.ylabel(r\"$k_\\text{eff}$\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae83ccb-f878-4123-a369-f352d100b96f",
   "metadata": {},
   "source": [
    "At the beginning, there is a sharp decrease in reactivity due to the Xe-135 build up, but then the reactivity decreases in almost a linear fashion with burn-up (which is where the linear reactivity model for fuel management comes from). In addition to the reactivity, we can also look at our fuel composition. Helper methods are provided to obtain the average atom density of a desired nuclide in all the fuel across the assembly. Let's look at just a few important ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d291126b-3c58-4c29-9dff-97b252e97d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "U235 = np.zeros(asmbly.exposures.size)\n",
    "U238 = np.zeros(asmbly.exposures.size)\n",
    "Pu239 = np.zeros(asmbly.exposures.size)\n",
    "Xe135 = np.zeros(asmbly.exposures.size)\n",
    "for t in range(asmbly.exposures.size):\n",
    "    U235[t] = asmbly.get_average_fuel_nuclide_density(t, \"U235\")\n",
    "    U238[t] = asmbly.get_average_fuel_nuclide_density(t, \"U238\")\n",
    "    Pu239[t] = asmbly.get_average_fuel_nuclide_density(t, \"Pu239\")\n",
    "    Xe135[t] = asmbly.get_average_fuel_nuclide_density(t, \"Xe135\")\n",
    "\n",
    "plt.plot(asmbly.exposures, Xe135, label=\"Xe135\")\n",
    "plt.xlabel(\"Exposure [MWd/kg]\")\n",
    "plt.ylabel(\"Average Atom Density in Fuel [atoms / (barn-cm)]\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6dac48-8f30-489d-bd4f-2e10766e19a7",
   "metadata": {},
   "source": [
    "Here, we can see the rapid build up of Xe-135. Let's take a look at U-235 and Pu-239 next:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8fa0e5cf-f003-4007-ba3d-5dd8612e2e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(asmbly.exposures, U235, label=\"U235\")\n",
    "plt.plot(asmbly.exposures, Pu239, label=\"Pu239\")\n",
    "plt.xlabel(\"Exposure [MWd/kg]\")\n",
    "plt.ylabel(\"Average Atom Density in Fuel [atoms / (barn-cm)]\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daeaadb4-2ed9-41d2-86d7-428487577451",
   "metadata": {},
   "source": [
    "At a burn-up of 30 MWd/kg, we can see that there is now a comparable amount of Pu-239 and U-235. Lastly, we can look at how the quantity of U-238 has decreased:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48d29047-005f-4b38-8c23-8593fa9ff94a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(asmbly.exposures, U238, label=\"U238\")\n",
    "plt.xlabel(\"Exposure [MWd/kg]\")\n",
    "plt.ylabel(\"Average Atom Density in Fuel [atoms / (barn-cm)]\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72612f3a-6d99-4d1d-94d3-e12b39610c0f",
   "metadata": {},
   "source": [
    "There are many other things which can be done with Scarabée, but this should be sufficient for you to get started with lattice physics calculations !"
   ]
  }
 ],
 "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": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}