{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Annular Collision Probabilities Pin Cell Problem\n", "\n", "In this example, we will look at simulating a single UO2 pin-cell using the annular collision probabilities solver in Scarabée. To start, we will load the libraries we will be using, and initialize the nuclear data library object (`NDLibrary`) which contains the nuclear data for the simulation. We will be using the ENDF/B-VIII.0 library in the SHEM-281 group structure." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import scarabee as scrb\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "ndl = scrb.NDLibrary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will start by defining the materials for the simulation. Firstly, we will need fuel, which will be 3 w/o enriched UO2. Scarabée has several helper functions to facilitate this process:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "UO2_comp = scrb.MaterialComposition(name=\"UO2\")\n", "UO2_comp.add_leu(enrichment=3., fraction=1.)\n", "UO2_comp.add_element(name=\"O\", fraction=2.)\n", "UO2 = scrb.Material(UO2_comp, 800., 10.96, scrb.DensityUnits.g_cm3, ndl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `MaterialComposition.add_leu` method allows you to add low enriched Uranium up to 5 w/o to the material composition. The last line of this cell makes the final material object, where we have taken the fuel to be at a temperature of 800 K and a density of 10.96 g/cm$^3$. Next, we need natural He gas for the gap between the fuel pellet and the cladding:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "He_comp = scrb.MaterialComposition(name=\"He\")\n", "He_comp.add_element(\"He\", 1.)\n", "He = scrb.Material(He_comp, 600., 1.66322E-4, scrb.DensityUnits.g_cm3, ndl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the PNNL Materials Compendium, we can also create a material for Zircaloy-4 for the fuel rod cladding." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "Zirc_comp = scrb.MaterialComposition(fractions=scrb.Fraction.Weight, name=\"Zircaoloy-4\")\n", "Zirc_comp.add_element(\"O\", 0.001196)\n", "Zirc_comp.add_element(\"Cr\", 0.000997)\n", "Zirc_comp.add_element(\"Fe\", 0.001993)\n", "Zirc_comp.add_element(\"Zr\", 0.981859)\n", "Zirc_comp.add_element(\"Sn\", 0.013955)\n", "Zirc = scrb.Material(Zirc_comp, 590., 6.56, scrb.DensityUnits.g_cm3, ndl)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, we need the moderator. We will use light water at 575 K under a pressure of 15.5 MPa and with 650 PPM boron. This is fairly representative of the moderator conditions one might find in a PWR. Scarabée has a helper function to generate such a water material for us at the correct density and nuclide fractions." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "H2O = scrb.borated_water(temperature=575., pressure=15.5, boron_ppm=650., ndl=ndl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will assume that the fuel pellet has a radius of 0.3975 cm, the gap a radius of 0.4125 cm, and the cladding a radius of 0.4750 cm. The pin pitch is 1.26 cm. Since we can only simulate a cylindrical system of rings with white boundary conditions when using the annular collision probabilities solver, we need to determine the equivalent radius for our pin cell. If the pin pitch is denoted as $p$, then the equivalent radius $R_{cell}$ can be found with\n", "\n", "$$\n", " p^2 = \\pi R_{cell}^2\n", "$$\n", "\n", "$$\n", " R_{cell} = \\frac{p}{\\sqrt{\\pi}}\n", "$$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "R_fuel = 0.3975\n", "R_gap = 0.4125\n", "R_clad = 0.4750\n", "R_cell = 1.26 / np.sqrt(np.pi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before proceeding, we must first do the self-shielding calculations for the fuel and cladding. This requires the appropriate Dancoff corrections that account for shadowing in the fuel lattice. While Dancoff factors are best calculated for the true square geometry using the method of characteristics, we can get reasonable corrections using the annular collision probabilities solver for our use case here. To compute the Dancoff corrections we will use the neutron current method which requires four calculations in total (two for the fuel and two for the cladding). For each material, a simulation with an \"isolated\" fuel pin is performed, and then the \"true\" geometry. Additionally, for the non-resonant materials, the cross sections will only be the potential scattering cross section of the material. Here is the calculation for determining the fuel Dancoff correction:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[info] Solving cylindrical cell.\n", "[info] Calculating collision probabilities.\n", "[info] Solving system of equations for cylindrical cell.\n", "[info] Solving for keff.\n", "[info] keff tolerance: 1.00000E-05\n", "[info] Flux tolerance: 1.00000E-05\n", "[info] \n", "[info] Simulation Time: 1.64708E-03 s\n", "[info] Solving cylindrical cell.\n", "[info] Calculating collision probabilities.\n", "[info] Solving system of equations for cylindrical cell.\n", "[info] Solving for keff.\n", "[info] keff tolerance: 1.00000E-05\n", "[info] Flux tolerance: 1.00000E-05\n", "[info] \n", "[info] Simulation Time: 1.46512E-03 s\n", "\n", "Fuel Dancoff Correction: 0.3322517616608382\n" ] } ], "source": [ "# Define 1-group cross sections\n", "Et = np.array([1.E5])\n", "Ea = Et\n", "Es = np.array([[0.]])\n", "FuelXS = scrb.CrossSection(Et, Ea, Es)\n", "\n", "Et = np.array([He.lambda_potential_xs])\n", "Ea = Et\n", "HeXS = scrb.CrossSection(Et, Ea, Es)\n", "\n", "Et = np.array([Zirc.lambda_potential_xs])\n", "Ea = Et\n", "ZircXS = scrb.CrossSection(Et, Ea, Es)\n", "\n", "Et = np.array([H2O.lambda_potential_xs])\n", "Ea = Et\n", "H2OXS = scrb.CrossSection(Et, Ea, Es)\n", "\n", "# Define the cell geometry for an isolated pin\n", "isolated_cell = scrb.CylindricalCell([R_fuel, R_gap, R_clad, 50.*R_cell],\n", " [FuelXS, HeXS, ZircXS, H2OXS])\n", "isolated_cell.solve()\n", "\n", "# Create the flux solver for the isolated pin\n", "isolated_flux = scrb.CylindricalFluxSolver(isolated_cell)\n", "isolated_flux.albedo = 0.\n", "isolated_flux.sim_mode = scrb.SimulationMode.FixedSource\n", "# Set the sources of the non-resonant materials to the potential xs\n", "isolated_flux.set_extern_src(1, 0, HeXS.Et(0))\n", "isolated_flux.set_extern_src(2, 0, ZircXS.Et(0))\n", "isolated_flux.set_extern_src(3, 0, H2OXS.Et(0))\n", "isolated_flux.solve()\n", "\n", "# Define true cell geometry\n", "true_cell = scrb.CylindricalCell([R_fuel, R_gap, R_clad, R_cell],\n", " [FuelXS, HeXS, ZircXS, H2OXS])\n", "true_cell.solve()\n", "\n", "# Solve flux for true pin\n", "true_flux = scrb.CylindricalFluxSolver(true_cell)\n", "true_flux.sim_mode = scrb.SimulationMode.FixedSource\n", "true_flux.set_extern_src(1, 0, HeXS.Et(0))\n", "true_flux.set_extern_src(2, 0, ZircXS.Et(0))\n", "true_flux.set_extern_src(3, 0, H2OXS.Et(0))\n", "true_flux.solve()\n", "\n", "# Compute the Dancoff correction for the fuel\n", "C_fuel = (isolated_flux.flux(0, 0) - true_flux.flux(0, 0)) / isolated_flux.flux(0, 0)\n", "print(\"\\nFuel Dancoff Correction: {:}\".format(C_fuel))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We follow a similar procedure to get the Dancoff correction for the cladding:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[info] Solving cylindrical cell.\n", "[info] Calculating collision probabilities.\n", "[info] Solving system of equations for cylindrical cell.\n", "[info] Solving for keff.\n", "[info] keff tolerance: 1.00000E-05\n", "[info] Flux tolerance: 1.00000E-05\n", "[info] \n", "[info] Simulation Time: 9.59783E-04 s\n", "[info] Solving cylindrical cell.\n", "[info] Calculating collision probabilities.\n", "[info] Solving system of equations for cylindrical cell.\n", "[info] Solving for keff.\n", "[info] keff tolerance: 1.00000E-05\n", "[info] Flux tolerance: 1.00000E-05\n", "[info] \n", "[info] Simulation Time: 2.06069E-03 s\n", "\n", "Clad Dancoff Correction: 0.32347535668762445\n" ] } ], "source": [ "# Define 1-group cross sections\n", "Et = np.array([UO2.lambda_potential_xs])\n", "Ea = Et\n", "Es = np.array([[0.]])\n", "FuelXS = scrb.CrossSection(Et, Ea, Es)\n", "\n", "Et = np.array([1.E5])\n", "Ea = Et\n", "ZircXS = scrb.CrossSection(Et, Ea, Es)\n", "\n", "# Define the cell geometry for an isolated pin\n", "isolated_cell = scrb.CylindricalCell([R_fuel, R_gap, R_clad, 50.*R_cell],\n", " [FuelXS, HeXS, ZircXS, H2OXS])\n", "isolated_cell.solve()\n", "\n", "# Create the flux solver for the isolated pin\n", "isolated_flux = scrb.CylindricalFluxSolver(isolated_cell)\n", "isolated_flux.albedo = 0.\n", "isolated_flux.sim_mode = scrb.SimulationMode.FixedSource\n", "# Set the sources of the non-resonant materials to the potential xs\n", "isolated_flux.set_extern_src(0, 0, FuelXS.Et(0))\n", "isolated_flux.set_extern_src(1, 0, HeXS.Et(0))\n", "isolated_flux.set_extern_src(3, 0, H2OXS.Et(0))\n", "isolated_flux.solve()\n", "\n", "# Define true cell geometry\n", "true_cell = scrb.CylindricalCell([R_fuel, R_gap, R_clad, R_cell],\n", " [FuelXS, HeXS, ZircXS, H2OXS])\n", "true_cell.solve()\n", "\n", "# Solve flux for true pin\n", "true_flux = scrb.CylindricalFluxSolver(true_cell)\n", "true_flux.sim_mode = scrb.SimulationMode.FixedSource\n", "true_flux.set_extern_src(0, 0, FuelXS.Et(0))\n", "true_flux.set_extern_src(1, 0, HeXS.Et(0))\n", "true_flux.set_extern_src(3, 0, H2OXS.Et(0))\n", "true_flux.solve()\n", "\n", "# Compute the Dancoff correction for the clad\n", "C_clad = (isolated_flux.flux(2, 0) - true_flux.flux(2, 0)) / isolated_flux.flux(2, 0)\n", "print(\"\\nClad Dancoff Correction: {:}\".format(C_clad))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the Dancoff corrections obtained, we can generate the `CrossSection` objects for each material. For the fuel region, we will use Carlvik's two-term rational approximation, and for the cladding we will use Roman's two term rational approximation. The He gas and the water moderator do not contain resonant isotopes, so they do not need to be self-shielded. We will simply use infinite dilution for all nuclides in the material.\n", "\n", "For the fuel and cladding, we need the escape cross section for each region which is defined as\n", "$$\n", " \\Sigma_e = \\frac{1}{\\bar{l}}\n", "$$\n", "where $\\bar{l}$ is the mean chord length of the region. For the fuel, $\\bar{l}_f = 2R_{fuel}$ and for the cladding $\\bar{l}_c = 2(R_{clad} - R_{gap})$." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "Ee_fuel = 1. / (2.*R_fuel) # Fuel escape cross section\n", "FuelXS = UO2.carlvik_xs(C_fuel, Ee_fuel, ndl)\n", "\n", "Ee_clad = 1. / (2.*(R_clad - R_gap)) # Clad escape cross section\n", "CladXS = Zirc.roman_xs(C_clad, Ee_clad, ndl)\n", "\n", "HeXS = He.dilution_xs(He.size*[1.E10], ndl)\n", "\n", "H2OXS = H2O.dilution_xs(H2O.size*[1.E10], ndl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have the self-shielded cross sections, so we can construct the fuel pin geometry and run the simulation:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[info] Solving cylindrical cell.\n", "[info] Calculating collision probabilities.\n", "[info] Solving system of equations for cylindrical cell.\n", "[info] Solving for keff.\n", "[info] keff tolerance: 1.00000E-08\n", "[info] Flux tolerance: 1.00000E-08\n", "[info] -------------------------------------\n", "[info] Iteration 1 keff: 1.23670\n", "[info] keff difference: 1.60172E+01\n", "[info] max flux difference: 9.99967E-01\n", "[info] -------------------------------------\n", "[info] Iteration 2 keff: 1.23670\n", "[info] keff difference: 5.36923E-09\n", "[info] max flux difference: 8.83373E-09\n", "[info] \n", "[info] Simulation Time: 1.00087E-01 s\n" ] } ], "source": [ "pin_cell = scrb.CylindricalCell([R_fuel, R_gap, R_clad, R_cell], [FuelXS, HeXS, CladXS, H2OXS])\n", "pin_cell.solve()\n", "\n", "pin = scrb.CylindricalFluxSolver(pin_cell)\n", "pin.keff_tolerance = 1.E-8\n", "pin.flux_tolerance = 1.E-8\n", "pin.solve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That was fast ! This is the power of the annular collision probabilities solver, as it does an excellent job for single pin-cell problem domains with very fine energy group structures. Let's use the results to make a plot of the flux spectrum in the fuel, moderator, and the average flux spectrum in the entire pin." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# The moderator is at the ring with index = 3\n", "moderator_flux = pin.homogenize_flux_spectrum([3])\n", "\n", "# The fuel is at the center with index = 0\n", "fuel_flux = pin.homogenize_flux_spectrum([0])\n", "\n", "# Without a list of region indices, this method will average over the entire pin cell\n", "avg_flux = pin.homogenize_flux_spectrum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These 1D arrays contain the resulting energy-integrated flux in each group of the data library. The indices follow the standard convention, where g=0 corresponds to the fastest energy group and as g increases the energy decreases (lethargy increases). If we plot these spectra right now (using the group bounds from the nuclear data library), they will look rather odd:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.stairs(fuel_flux, edges=ndl.group_bounds, label=\"Fuel\")\n", "plt.stairs(moderator_flux, edges=ndl.group_bounds, label=\"Moderator\")\n", "plt.stairs(avg_flux, edges=ndl.group_bounds, label=\"Pin-Cell Average\")\n", "plt.xscale(\"log\")\n", "plt.yscale(\"log\")\n", "plt.xlabel(\"Energy [eV]\")\n", "plt.ylabel(\"Energy-Integrated Flux [Arb. Units]\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since these values of the flux spectrum are integrated over the energy range of each group, they each have a unique normalization which depends on the energy width of the group. To fix this, we will divide each group by the energy width of the bin:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "norm_fuel_flux = fuel_flux.copy()\n", "norm_moderator_flux = moderator_flux.copy()\n", "norm_avg_flux = avg_flux.copy()\n", "\n", "for g in range(ndl.ngroups):\n", " # Group bounds also go from high to low energy !\n", " delta_E = ndl.group_bounds[g] - ndl.group_bounds[g+1]\n", " norm_fuel_flux[g] /= delta_E\n", " norm_moderator_flux[g] /= delta_E\n", " norm_avg_flux[g] /= delta_E\n", "\n", "plt.stairs(norm_fuel_flux, edges=ndl.group_bounds, label=\"Fuel\")\n", "plt.stairs(norm_moderator_flux, edges=ndl.group_bounds, label=\"Moderator\")\n", "plt.stairs(norm_avg_flux, edges=ndl.group_bounds, label=\"Pin-Cell Average\")\n", "plt.xscale(\"log\")\n", "plt.yscale(\"log\")\n", "plt.xlabel(\"Energy [eV]\")\n", "plt.ylabel(\"Flux [Arb. Units]\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This looks much more reasonable ! However, you might also be more accustomed to looking at the *lethargy normalized* flux spectrum, which we can also plot here:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "leth_norm_fuel_flux = fuel_flux.copy()\n", "leth_norm_moderator_flux = moderator_flux.copy()\n", "leth_norm_avg_flux = avg_flux.copy()\n", "\n", "for g in range(ndl.ngroups):\n", " # Group bounds also go from high to low energy !\n", " u = np.log(ndl.group_bounds[g] / ndl.group_bounds[g+1])\n", " leth_norm_fuel_flux[g] /= u\n", " leth_norm_moderator_flux[g] /= u\n", " leth_norm_avg_flux[g] /= u\n", "\n", "plt.stairs(leth_norm_fuel_flux, edges=ndl.group_bounds, label=\"Fuel\")\n", "plt.stairs(leth_norm_moderator_flux, edges=ndl.group_bounds, label=\"Moderator\")\n", "plt.stairs(leth_norm_avg_flux, edges=ndl.group_bounds, label=\"Pin-Cell Average\")\n", "plt.xscale(\"log\")\n", "plt.xlabel(\"Energy [eV]\")\n", "plt.ylabel(\"Flux per Unit Lethargy [Arb. Units]\")\n", "plt.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is also likely to be a familiar results to most seasoned nuclear engineers. There is of course far more that can be done. We could use the `CylindricalFluxSolver` to homogenize the cross sections across the entire pin-cell for use in subsequent simulations. These flux spectra could also be used to condense the original cross sections for another calculation, like is done in the lattice physics calculation chain." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 }