├── .github ├── environment.yml └── workflows │ └── ci.yml ├── .gitignore ├── .test ├── clear_notebooks_test.py ├── download-xs.sh ├── notebook-fails ├── notebook-pass └── test_notebooks.py ├── README.md ├── c5g7.h5 ├── cad-based-geometry.ipynb ├── candu.ipynb ├── capi.ipynb ├── chain_simple.xml ├── depletion.ipynb ├── expansion-filters.ipynb ├── flux-spectrum.ipynb ├── gamma-detector.ipynb ├── hexagonal-lattice.ipynb ├── images ├── cs137_decay.png ├── cylinder_mesh.png ├── flux3d.png ├── manifold-cad.png ├── manifold_flux.png ├── mdgxs.png ├── mgxs.png ├── pin_mesh.png ├── teapot.jpg ├── umesh_flux.png ├── umesh_heating.png └── umesh_w_assembly.png ├── mdgxs-part-i.ipynb ├── mdgxs-part-ii.ipynb ├── mg-mode-part-i.ipynb ├── mg-mode-part-ii.ipynb ├── mg-mode-part-iii.ipynb ├── mgxs-part-i.ipynb ├── mgxs-part-ii.ipynb ├── mgxs-part-iii.ipynb ├── nuclear-data-resonance-covariance.ipynb ├── nuclear-data.ipynb ├── pandas-dataframes.ipynb ├── pincell.ipynb ├── post-processing.ipynb ├── search.ipynb ├── shielded_room_weight_window.ipynb ├── tally-arithmetic.ipynb ├── tally-power-normalization.ipynb ├── triso.ipynb ├── unstructured-mesh-part-i.ipynb └── unstructured-mesh-part-ii.ipynb /.github/environment.yml: -------------------------------------------------------------------------------- 1 | name: jupyter-actions 2 | channels: 3 | - conda-forge 4 | dependencies: 5 | - jupyter=1.0 6 | - nbformat 7 | - nbconvert 8 | - numpy 9 | - seaborn 10 | - requests=2.24.0 11 | - pytest 12 | - vtk 13 | - pyevtk 14 | - swig==4.2.1 -------------------------------------------------------------------------------- /.github/workflows/ci.yml: -------------------------------------------------------------------------------- 1 | name: Test Notebooks 2 | 3 | on: 4 | # Allow this workflow to be run manually 5 | workflow_dispatch: 6 | # Run for pull requests 7 | pull_request: 8 | branches: 9 | - main 10 | - develop 11 | # Run every time a new commit is pushed 12 | push: 13 | branches: 14 | - main 15 | - develop 16 | # Run every week on Sunday at midnight 17 | schedule: 18 | - cron: "0 0 * * 1" 19 | 20 | jobs: 21 | # Set the job key 22 | test-notebooks: 23 | # Name the job 24 | name: Test Jupyter Notebooks 25 | # Set the type of machine to run on 26 | runs-on: ubuntu-latest 27 | 28 | steps: 29 | - name: Checkout code 30 | uses: actions/checkout@v2 31 | 32 | 33 | - name: Install conda environment 34 | uses: conda-incubator/setup-miniconda@v2 35 | with: 36 | activate-environment: jupyter-actions 37 | environment-file: .github/environment.yml 38 | python-version: "3.10" 39 | 40 | - name: Install OpenMC 41 | shell: bash -l {0} 42 | run: | 43 | conda install -n base conda-libmamba-solver 44 | conda config --set solver libmamba 45 | conda activate jupyter-actions 46 | conda install -c conda-forge openmc==0.15.0 47 | 48 | - name: Cache Cross Sections 49 | id: xs-cache 50 | uses: actions/cache@v3 51 | with: 52 | # use this cache as long as the download script doesn't change 53 | key: xs-cache-${{ hashfiles('.test/download-xs.sh') }} 54 | path: ~/endfb-vii.1-hdf5/* 55 | 56 | - name: Download OpenMC Cross Sections 57 | shell: bash -l {0} 58 | if: steps.xs-cache.outputs.cache-hit != 'true' 59 | run: | 60 | ./.test/download-xs.sh 61 | 62 | - name: Set Environment Variables 63 | shell: bash -l {0} 64 | run: | 65 | echo "OPENMC_CROSS_SECTIONS=$HOME/endfb-vii.1-hdf5/cross_sections.xml" >> $GITHUB_ENV 66 | 67 | - name: Install OpenMOC 68 | shell: bash -l {0} 69 | run: | 70 | conda init bash 71 | source ~/.bashrc 72 | conda activate jupyter-actions 73 | cd ~ 74 | git clone https://github.com/mit-crpg/openmoc && 75 | cd openmoc 76 | # OpenMOC has some custom commands that rely on setuptools/distutils 77 | python setup.py install 78 | # install twice to make sure openmoc.py is copied into the installation location 79 | python setup.py install 80 | 81 | - name: Execute all Notebooks 82 | shell: bash -l {0} 83 | run: | 84 | conda activate jupyter-actions 85 | pytest -v .test 86 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # IPython notebook checkpoints 2 | .ipynb_checkpoints 3 | 4 | # Jupyter notebooks 5 | *.xml 6 | *.h5 7 | *.h5m 8 | *.vtk 9 | *.png 10 | *.xls 11 | *.ace 12 | *.endf 13 | *.out 14 | mgxs/ 15 | tracks/ 16 | fission-rates/ 17 | plots/ 18 | *~ 19 | *__pycache__* -------------------------------------------------------------------------------- /.test/clear_notebooks_test.py: -------------------------------------------------------------------------------- 1 | import os 2 | import subprocess 3 | 4 | import nbformat 5 | from nbconvert.preprocessors import ExecutePreprocessor 6 | from nbconvert.preprocessors import CellExecutionError 7 | import pytest 8 | 9 | TEST_DIR = os.path.dirname(os.path.abspath(__file__)) 10 | 11 | 12 | def process_notebook(notebook_filename, html_directory = 'notebook-html'): 13 | '''Checks if an IPython notebook runs without error from start to finish. If so, 14 | writes the notebook to HTML (with outputs) and overwrites the .ipynb file 15 | (without outputs). 16 | ''' 17 | with open(notebook_filename) as f: 18 | nb = nbformat.read(f, as_version=4) 19 | 20 | ep = ExecutePreprocessor(timeout=600, kernel_name='python3') 21 | 22 | try: 23 | # Check that the notebook runs 24 | ep.preprocess(nb, {'metadata': {'path': ''}}) 25 | except CellExecutionError: 26 | msg = f'Error executing the notebook {notebook_filename}.\n\n' 27 | msg += f'See notebook "{notebook_filename}" for the traceback.' 28 | print(msg) 29 | raise 30 | 31 | print(f"Successfully executed {notebook_filename}") 32 | return 33 | 34 | 35 | def test_process_notebook(): 36 | 37 | with pytest.raises(CellExecutionError): 38 | process_notebook(os.path.join(TEST_DIR,'notebook-fails')) 39 | 40 | assert(process_notebook(os.path.join(TEST_DIR,'notebook-pass')) is None) 41 | 42 | 43 | if __name__ == '__main__': 44 | pytest.main() 45 | -------------------------------------------------------------------------------- /.test/download-xs.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | 3 | set -ex 4 | 5 | cd $HOME 6 | wget https://anl.box.com/shared/static/9igk353zpy8fn9ttvtrqgzvw1vtejoz6.xz -O endf7b_xs.tar.xz 7 | tar xf endf7b_xs.tar.xz 8 | -------------------------------------------------------------------------------- /.test/notebook-fails: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "tags": [ 8 | "save_html" 9 | ] 10 | }, 11 | "outputs": [], 12 | "source": [] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": null, 17 | "metadata": { 18 | "execution": { 19 | "iopub.execute_input": "2020-09-25T12:29:32.718006Z", 20 | "iopub.status.busy": "2020-09-25T12:29:32.716176Z", 21 | "iopub.status.idle": "2020-09-25T12:29:32.838050Z", 22 | "shell.execute_reply": "2020-09-25T12:29:32.836977Z" 23 | } 24 | }, 25 | "outputs": [], 26 | "source": [ 27 | "1 + \"a\"" 28 | ] 29 | } 30 | ], 31 | "metadata": { 32 | "celltoolbar": "Tags", 33 | "kernelspec": { 34 | "display_name": "Python 3", 35 | "language": "python", 36 | "name": "python3" 37 | }, 38 | "language_info": { 39 | "codemirror_mode": { 40 | "name": "ipython", 41 | "version": 3 42 | }, 43 | "file_extension": ".py", 44 | "mimetype": "text/x-python", 45 | "name": "python", 46 | "nbconvert_exporter": "python", 47 | "pygments_lexer": "ipython3", 48 | "version": "3.8.5" 49 | } 50 | }, 51 | "nbformat": 4, 52 | "nbformat_minor": 4 53 | } 54 | -------------------------------------------------------------------------------- /.test/notebook-pass: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "tags": [ 7 | "save_html" 8 | ] 9 | }, 10 | "source": [ 11 | "# Test notebook" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "This notebook passes the test." 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": null, 24 | "metadata": { 25 | "execution": { 26 | "iopub.execute_input": "2020-09-25T17:55:34.624627Z", 27 | "iopub.status.busy": "2020-09-25T17:55:34.622763Z", 28 | "iopub.status.idle": "2020-09-25T17:55:34.631046Z", 29 | "shell.execute_reply": "2020-09-25T17:55:34.632276Z" 30 | } 31 | }, 32 | "outputs": [], 33 | "source": [ 34 | "1 + 2" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": null, 40 | "metadata": { 41 | "execution": { 42 | "iopub.execute_input": "2020-09-25T17:55:34.640643Z", 43 | "iopub.status.busy": "2020-09-25T17:55:34.638873Z", 44 | "iopub.status.idle": "2020-09-25T17:55:34.644490Z", 45 | "shell.execute_reply": "2020-09-25T17:55:34.645412Z" 46 | } 47 | }, 48 | "outputs": [], 49 | "source": [ 50 | "1" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": null, 56 | "metadata": {}, 57 | "outputs": [], 58 | "source": [] 59 | } 60 | ], 61 | "metadata": { 62 | "celltoolbar": "Tags", 63 | "kernelspec": { 64 | "display_name": "Python 3", 65 | "language": "python", 66 | "name": "python3" 67 | }, 68 | "language_info": { 69 | "codemirror_mode": { 70 | "name": "ipython", 71 | "version": 3 72 | }, 73 | "file_extension": ".py", 74 | "mimetype": "text/x-python", 75 | "name": "python", 76 | "nbconvert_exporter": "python", 77 | "pygments_lexer": "ipython3", 78 | "version": "3.8.5" 79 | } 80 | }, 81 | "nbformat": 4, 82 | "nbformat_minor": 4 83 | } 84 | -------------------------------------------------------------------------------- /.test/test_notebooks.py: -------------------------------------------------------------------------------- 1 | import os 2 | import subprocess 3 | 4 | import nbformat 5 | from nbconvert.preprocessors import ExecutePreprocessor 6 | from nbconvert.preprocessors import CellExecutionError 7 | import pytest 8 | 9 | 10 | TEST_DIR = os.path.dirname(os.path.abspath(__file__)) 11 | PARENT_DIR = os.path.join(TEST_DIR, '..') 12 | 13 | 14 | def process_notebook(notebook_filename, html_directory='notebook-html'): 15 | '''Checks if an IPython notebook runs without error from start to finish. If so, 16 | writes the notebook to HTML (with outputs) and overwrites the .ipynb file 17 | (without outputs). 18 | ''' 19 | 20 | with open(notebook_filename) as f: 21 | nb = nbformat.read(f, as_version=4) 22 | 23 | ep = ExecutePreprocessor(timeout=1200, 24 | kernel_name='python3') 25 | 26 | try: 27 | # Check that the notebook runs 28 | ep.preprocess(nb, {'metadata': {'path': ''}}) 29 | except CellExecutionError: 30 | raise 31 | 32 | print(f"Successfully executed {notebook_filename}") 33 | return 34 | 35 | 36 | def find_notebooks(): 37 | # Get all files included in the git repository 38 | git_files = (subprocess 39 | .check_output("git ls-tree --full-tree --name-only -r HEAD", shell=True) 40 | .decode('utf-8') 41 | .splitlines()) 42 | 43 | # Get just the notebooks from the git files 44 | notebooks = [fn for fn in git_files if fn.endswith(".ipynb")] 45 | # remove the MGXS notebooks that use on OepnMOC for now 46 | return notebooks 47 | 48 | 49 | @pytest.mark.parametrize('notebook', find_notebooks()) 50 | def test_all_notebooks(notebook, remove_fail_test=True): 51 | '''Runs `process_notebook` on all notebooks in the git repository. 52 | ''' 53 | 54 | print("Testing", notebook) 55 | process_notebook(os.path.join(PARENT_DIR, notebook)) 56 | 57 | # clean out files when test is complete 58 | subprocess.check_output("git clean -dxf", shell=True) 59 | 60 | 61 | if __name__ == '__main__': 62 | pytest.main() 63 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | OpenMC Notebooks 2 | ---------------- 3 | 4 | This repository contains a set of example Jupyter notebooks demonstrating 5 | various capabilities in OpenMC from basic model building to advanced features 6 | such as depletion/activation, CAD geometry universes, etc. 7 | 8 | Notebook versioning 9 | ------------------- 10 | 11 | Notebooks contained in the `main` branch of this repository can be expected to 12 | work with the latest release of OpenMC. Note, `main` **is not the default branch 13 | of this repository** and must be checked out after cloning the repo. 14 | 15 | Previous versions notebooks will be tagged with the corresponding OpenMC version 16 | with which they are expected to work. These tags can be used to checkout 17 | notebooks ensured to be compatible with the corresponding release of OpenMC. 18 | 19 | Continuous development of these notebooks occurs in the `develop` branch, the 20 | default branch of this repository. We ask that any updates or contributions to 21 | the repository occur based on this branch. -------------------------------------------------------------------------------- /c5g7.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/openmc-dev/openmc-notebooks/bb39f2509d47d8f4c997cc320685ad06207fdcc9/c5g7.h5 -------------------------------------------------------------------------------- /capi.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Using the C/C++ API\n", 8 | "This notebook shows how to use the OpenMC C/C++ API through the openmc.lib module. This module is particularly useful for multiphysics coupling because it allows you to update the density of materials and the temperatures of cells in memory, without stopping the simulation.\n", 9 | "\n", 10 | "Warning: these bindings are still somewhat experimental and may be subject to change in future versions of OpenMC." 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 1, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "%matplotlib inline\n", 20 | "import openmc\n", 21 | "import openmc.lib" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "Generate Input Files\n", 29 | "\n", 30 | "Let's start by creating a fuel rod geometry. We will make 10 zones in the z-direction which will allow us to make changes to each zone. Changes in temperature have to be made on the cell, so will make 10 cells in the axial direction. Changes in density have to be made on the material, so we will make 10 water materials. " 31 | ] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "metadata": {}, 36 | "source": [ 37 | "Materials: we will make a fuel, helium, zircaloy, and 10 water materials. " 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 2, 43 | "metadata": {}, 44 | "outputs": [], 45 | "source": [ 46 | "material_list = []" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 3, 52 | "metadata": {}, 53 | "outputs": [], 54 | "source": [ 55 | "uo2 = openmc.Material(material_id=1, name='UO2 fuel at 2.4% wt enrichment')\n", 56 | "uo2.set_density('g/cm3', 10.29769)\n", 57 | "uo2.add_element('U', 1., enrichment=2.4)\n", 58 | "uo2.add_element('O', 2.)\n", 59 | "material_list.append(uo2)\n", 60 | "\n", 61 | "helium = openmc.Material(material_id=2, name='Helium for gap')\n", 62 | "helium.set_density('g/cm3', 0.001598)\n", 63 | "helium.add_element('He', 2.4044e-4)\n", 64 | "material_list.append(helium)\n", 65 | "\n", 66 | "zircaloy = openmc.Material(material_id=3, name='Zircaloy 4')\n", 67 | "zircaloy.set_density('g/cm3', 6.55)\n", 68 | "zircaloy.add_element('Sn', 0.014, 'wo')\n", 69 | "zircaloy.add_element('Fe', 0.00165, 'wo')\n", 70 | "zircaloy.add_element('Cr', 0.001, 'wo')\n", 71 | "zircaloy.add_element('Zr', 0.98335, 'wo')\n", 72 | "material_list.append(zircaloy)\n", 73 | "\n", 74 | "for i in range(4, 14):\n", 75 | " water = openmc.Material(material_id=i)\n", 76 | " water.set_density('g/cm3', 0.7)\n", 77 | " water.add_element('H', 2.0)\n", 78 | " water.add_element('O', 1.0)\n", 79 | " water.add_s_alpha_beta('c_H_in_H2O')\n", 80 | " material_list.append(water)\n", 81 | " \n", 82 | "materials_file = openmc.Materials(material_list)\n", 83 | "materials_file.export_to_xml()" 84 | ] 85 | }, 86 | { 87 | "cell_type": "markdown", 88 | "metadata": {}, 89 | "source": [ 90 | "Cells: we will make a fuel cylinder, a gap cylinder, a cladding cylinder, and a water exterior. Each one will be broken into 10 cells which are the 10 axial zones. The z_list is the list of axial positions that delimit those 10 zones. To keep track of all the cells, we will create lists: fuel_list, gap_list, clad_list, and water_list. " 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 4, 96 | "metadata": {}, 97 | "outputs": [], 98 | "source": [ 99 | "pitch = 1.25984\n", 100 | "fuel_or = openmc.ZCylinder(r=0.39218)\n", 101 | "clad_ir = openmc.ZCylinder(r=0.40005)\n", 102 | "clad_or = openmc.ZCylinder(r=0.4572)\n", 103 | "left = openmc.XPlane(x0=-pitch/2)\n", 104 | "right = openmc.XPlane(x0=pitch/2)\n", 105 | "back = openmc.YPlane(y0=-pitch/2)\n", 106 | "front = openmc.YPlane(y0=pitch/2)\n", 107 | "z = [0., 30., 60., 90., 120., 150., 180., 210., 240., 270., 300.]\n", 108 | "z_list = [openmc.ZPlane(z0=z_i) for z_i in z]" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 5, 114 | "metadata": {}, 115 | "outputs": [], 116 | "source": [ 117 | "left.boundary_type = 'reflective'\n", 118 | "right.boundary_type = 'reflective'\n", 119 | "front.boundary_type = 'reflective'\n", 120 | "back.boundary_type = 'reflective'\n", 121 | "z_list[0].boundary_type = 'vacuum'\n", 122 | "z_list[-1].boundary_type = 'vacuum'" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": 6, 128 | "metadata": {}, 129 | "outputs": [], 130 | "source": [ 131 | "fuel_list = []\n", 132 | "gap_list = []\n", 133 | "clad_list = []\n", 134 | "water_list = []\n", 135 | "for i in range(1, 11):\n", 136 | " fuel_list.append(openmc.Cell(cell_id=i))\n", 137 | " gap_list.append(openmc.Cell(cell_id=i+10))\n", 138 | " clad_list.append(openmc.Cell(cell_id=i+20))\n", 139 | " water_list.append(openmc.Cell(cell_id=i+30))\n", 140 | " \n", 141 | "for j, fuels in enumerate(fuel_list):\n", 142 | " fuels.region = -fuel_or & +z_list[j] & -z_list[j+1]\n", 143 | " fuels.fill = uo2\n", 144 | " fuels.temperature = 800.\n", 145 | "\n", 146 | "for j, gaps in enumerate(gap_list):\n", 147 | " gaps.region = +fuel_or & -clad_ir & +z_list[j] & -z_list[j+1]\n", 148 | " gaps.fill = helium\n", 149 | " gaps.temperature = 700.\n", 150 | "\n", 151 | "for j, clads in enumerate(clad_list):\n", 152 | " clads.region = +clad_ir & -clad_or & +z_list[j] & -z_list[j+1]\n", 153 | " clads.fill = zircaloy\n", 154 | " clads.temperature = 600.\n", 155 | "\n", 156 | "for j, waters in enumerate(water_list):\n", 157 | " waters.region = +clad_or & +left & -right & +back & -front & +z_list[j] & -z_list[j+1]\n", 158 | " waters.fill = material_list[j+3]\n", 159 | " waters.temperature = 500." 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": 7, 165 | "metadata": {}, 166 | "outputs": [], 167 | "source": [ 168 | "root = openmc.Universe(name='root universe')\n", 169 | "root.add_cells(fuel_list)\n", 170 | "root.add_cells(gap_list)\n", 171 | "root.add_cells(clad_list)\n", 172 | "root.add_cells(water_list)\n", 173 | "geometry_file = openmc.Geometry(root)\n", 174 | "geometry_file.export_to_xml()" 175 | ] 176 | }, 177 | { 178 | "cell_type": "markdown", 179 | "metadata": {}, 180 | "source": [ 181 | "If you are coupling this externally to a heat transfer solver, you will want to know the heat deposited by each fuel cell. So let's create a cell filter for the recoverable fission heat. " 182 | ] 183 | }, 184 | { 185 | "cell_type": "code", 186 | "execution_count": 8, 187 | "metadata": {}, 188 | "outputs": [], 189 | "source": [ 190 | "cell_filter = openmc.CellFilter(fuel_list)\n", 191 | "t = openmc.Tally(tally_id=1)\n", 192 | "t.filters.append(cell_filter)\n", 193 | "t.scores = ['fission-q-recoverable']\n", 194 | "tallies = openmc.Tallies([t])\n", 195 | "tallies.export_to_xml()" 196 | ] 197 | }, 198 | { 199 | "cell_type": "markdown", 200 | "metadata": {}, 201 | "source": [ 202 | "Let's plot our geometry to make sure it looks like we expect. Since we made new water materials in each axial cell, and we have centered the plot at 150, we should see one color for the water material in the bottom half and a different color for the water material in the top half. " 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": 9, 208 | "metadata": {}, 209 | "outputs": [ 210 | { 211 | "data": { 212 | "text/plain": [ 213 | "" 214 | ] 215 | }, 216 | "execution_count": 9, 217 | "metadata": {}, 218 | "output_type": "execute_result" 219 | }, 220 | { 221 | "data": { 222 | "image/png": "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\n", 223 | "text/plain": [ 224 | "
" 225 | ] 226 | }, 227 | "metadata": { 228 | "needs_background": "light" 229 | }, 230 | "output_type": "display_data" 231 | } 232 | ], 233 | "source": [ 234 | "root.plot(basis='yz', width=[2, 10], color_by='material', origin=[0., 0., 150.], pixels=[400, 400])" 235 | ] 236 | }, 237 | { 238 | "cell_type": "markdown", 239 | "metadata": {}, 240 | "source": [ 241 | "Settings: everything will be standard except for the temperature settings. Since we will be working with specified temperatures, you will need temperature dependent data. I typically use the endf data found here: https://openmc.org/official-data-libraries/\n", 242 | "Make sure your cross sections environment variable is pointing to temperature-dependent data before using the following settings." 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 10, 248 | "metadata": {}, 249 | "outputs": [], 250 | "source": [ 251 | "lower_left = [-0.62992, -pitch/2, 0]\n", 252 | "upper_right = [+0.62992, +pitch/2, +300]\n", 253 | "uniform_dist = openmc.stats.Box(lower_left, upper_right, only_fissionable=True)\n", 254 | "\n", 255 | "settings_file = openmc.Settings()\n", 256 | "settings_file.batches = 100\n", 257 | "settings_file.inactive = 10\n", 258 | "settings_file.particles = 10000\n", 259 | "settings_file.temperature = {'multipole': True, 'method': 'interpolation', 'range': [290, 2500]}\n", 260 | "settings_file.source = openmc.source.IndependentSource(space=uniform_dist)\n", 261 | "settings_file.export_to_xml()" 262 | ] 263 | }, 264 | { 265 | "cell_type": "markdown", 266 | "metadata": {}, 267 | "source": [ 268 | "To run a regular simulation, just use openmc.run(). \n", 269 | "However, we want to run a simulation that we can stop in the middle and update the material and cell properties. So we will use openmc.lib." 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "execution_count": 11, 275 | "metadata": {}, 276 | "outputs": [ 277 | { 278 | "name": "stdout", 279 | "output_type": "stream", 280 | "text": [ 281 | " %%%%%%%%%%%%%%%\n", 282 | " %%%%%%%%%%%%%%%%%%%%%%%%\n", 283 | " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", 284 | " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", 285 | " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", 286 | " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", 287 | " %%%%%%%%%%%%%%%%%%%%%%%%\n", 288 | " %%%%%%%%%%%%%%%%%%%%%%%%\n", 289 | " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", 290 | " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", 291 | " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", 292 | " #################### %%%%%%%%%%%%%%%%%%%%%%\n", 293 | " ##################### %%%%%%%%%%%%%%%%%%%%%\n", 294 | " ###################### %%%%%%%%%%%%%%%%%%%%\n", 295 | " ####################### %%%%%%%%%%%%%%%%%%\n", 296 | " ####################### %%%%%%%%%%%%%%%%%\n", 297 | " ###################### %%%%%%%%%%%%%%%%%\n", 298 | " #################### %%%%%%%%%%%%%%%%%\n", 299 | " ################# %%%%%%%%%%%%%%%%%\n", 300 | " ############### %%%%%%%%%%%%%%%%\n", 301 | " ############ %%%%%%%%%%%%%%%\n", 302 | " ######## %%%%%%%%%%%%%%\n", 303 | " %%%%%%%%%%%\n", 304 | "\n", 305 | " | The OpenMC Monte Carlo Code\n", 306 | " Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n", 307 | " License | https://docs.openmc.org/en/latest/license.html\n", 308 | " Version | 0.13.1\n", 309 | " Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n", 310 | " Date/Time | 2022-10-03 22:36:24\n", 311 | " OpenMP Threads | 2\n", 312 | "\n", 313 | " Reading settings XML file...\n", 314 | " Reading cross sections XML file...\n", 315 | " Reading materials XML file...\n", 316 | " Reading geometry XML file...\n", 317 | " Reading U234 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U234.h5\n", 318 | " Reading U234 WMP data from\n", 319 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/092234.h5\n", 320 | " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n", 321 | " Reading U235 WMP data from\n", 322 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/092235.h5\n", 323 | " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n", 324 | " Reading U238 WMP data from\n", 325 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/092238.h5\n", 326 | " Reading U236 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U236.h5\n", 327 | " Reading U236 WMP data from\n", 328 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/092236.h5\n", 329 | " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n", 330 | " Reading O16 WMP data from\n", 331 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/008016.h5\n", 332 | " Reading O17 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O17.h5\n", 333 | " Reading O17 WMP data from\n", 334 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/008017.h5\n", 335 | " Reading He3 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/He3.h5\n", 336 | " Reading He3 WMP data from\n", 337 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/002003.h5\n", 338 | " Reading He4 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/He4.h5\n", 339 | " Reading He4 WMP data from\n", 340 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/002004.h5\n", 341 | " Reading Sn112 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Sn112.h5\n", 342 | " Reading Sn112 WMP data from\n", 343 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/050112.h5\n", 344 | " Reading Sn114 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Sn114.h5\n", 345 | " Reading Sn114 WMP data from\n", 346 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/050114.h5\n", 347 | " Reading Sn115 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Sn115.h5\n", 348 | " Reading Sn115 WMP data from\n", 349 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/050115.h5\n", 350 | " Reading Sn116 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Sn116.h5\n", 351 | " Reading Sn116 WMP data from\n", 352 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/050116.h5\n", 353 | " Reading Sn117 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Sn117.h5\n", 354 | " Reading Sn117 WMP data from\n", 355 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/050117.h5\n", 356 | " Reading Sn118 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Sn118.h5\n", 357 | " Reading Sn118 WMP data from\n", 358 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/050118.h5\n", 359 | " Reading Sn119 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Sn119.h5\n", 360 | " Reading Sn119 WMP data from\n", 361 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/050119.h5\n", 362 | " Reading Sn120 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Sn120.h5\n", 363 | " Reading Sn120 WMP data from\n", 364 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/050120.h5\n", 365 | " Reading Sn122 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Sn122.h5\n", 366 | " Reading Sn122 WMP data from\n", 367 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/050122.h5\n", 368 | " Reading Sn124 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Sn124.h5\n", 369 | " Reading Sn124 WMP data from\n", 370 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/050124.h5\n", 371 | " Reading Fe54 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Fe54.h5\n", 372 | " Reading Fe54 WMP data from\n", 373 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/026054.h5\n", 374 | " Reading Fe56 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Fe56.h5\n", 375 | " Reading Fe56 WMP data from\n", 376 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/026056.h5\n", 377 | " Reading Fe57 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Fe57.h5\n", 378 | " Reading Fe57 WMP data from\n", 379 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/026057.h5\n", 380 | " Reading Fe58 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Fe58.h5\n", 381 | " Reading Fe58 WMP data from\n", 382 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/026058.h5\n", 383 | " Reading Cr50 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Cr50.h5\n", 384 | " Reading Cr50 WMP data from\n", 385 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/024050.h5\n", 386 | " Reading Cr52 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Cr52.h5\n", 387 | " Reading Cr52 WMP data from\n", 388 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/024052.h5\n", 389 | " Reading Cr53 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Cr53.h5\n", 390 | " Reading Cr53 WMP data from\n", 391 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/024053.h5\n", 392 | " Reading Cr54 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Cr54.h5\n", 393 | " Reading Cr54 WMP data from\n", 394 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/024054.h5\n", 395 | " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n", 396 | " Reading Zr90 WMP data from\n", 397 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/040090.h5\n", 398 | " Reading Zr91 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr91.h5\n", 399 | " Reading Zr91 WMP data from\n", 400 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/040091.h5\n", 401 | " Reading Zr92 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr92.h5\n", 402 | " Reading Zr92 WMP data from\n", 403 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/040092.h5\n", 404 | " Reading Zr94 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr94.h5\n", 405 | " Reading Zr94 WMP data from\n", 406 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/040094.h5\n", 407 | " Reading Zr96 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr96.h5\n", 408 | " Reading Zr96 WMP data from\n", 409 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/040096.h5\n", 410 | " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n", 411 | " Reading H1 WMP data from\n", 412 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/001001.h5\n", 413 | " Reading H2 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H2.h5\n", 414 | " Reading H2 WMP data from\n", 415 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/wmp/001002.h5\n", 416 | " Reading c_H_in_H2O from\n", 417 | " /home/pshriwise/data/xs/openmc/endfb71_hdf5/c_H_in_H2O.h5\n", 418 | " Minimum neutron data temperature: 250 K\n", 419 | " Maximum neutron data temperature: 2500 K\n", 420 | " Reading tallies XML file...\n", 421 | " Preparing distributed cell instances...\n", 422 | " Reading plot XML file...\n", 423 | " Writing summary.h5 file...\n", 424 | " Maximum neutron transport energy: 20000000 eV for U235\n", 425 | " Initializing source particles...\n", 426 | "\n", 427 | " ====================> K EIGENVALUE SIMULATION <====================\n", 428 | "\n" 429 | ] 430 | } 431 | ], 432 | "source": [ 433 | "openmc.lib.init()\n", 434 | "openmc.lib.simulation_init()" 435 | ] 436 | }, 437 | { 438 | "cell_type": "markdown", 439 | "metadata": {}, 440 | "source": [ 441 | "There are 10 inactive batches, so we need to run next_batch() at least 10 times before the tally is activated. " 442 | ] 443 | }, 444 | { 445 | "cell_type": "code", 446 | "execution_count": 12, 447 | "metadata": {}, 448 | "outputs": [ 449 | { 450 | "name": "stdout", 451 | "output_type": "stream", 452 | "text": [ 453 | " Bat./Gen. k Average k\n", 454 | " ========= ======== ====================\n", 455 | " 1/1 1.23436\n", 456 | " 2/1 1.26521\n", 457 | " 3/1 1.22463\n", 458 | " 4/1 1.27677\n", 459 | " 5/1 1.23851\n", 460 | " 6/1 1.28804\n", 461 | " 7/1 1.26625\n", 462 | " 8/1 1.28035\n", 463 | " 9/1 1.28143\n", 464 | " 10/1 1.25761\n", 465 | " 11/1 1.27147\n", 466 | " 12/1 1.27749 1.27448 +/- 0.00301\n", 467 | " 13/1 1.27870 1.27589 +/- 0.00224\n", 468 | " 14/1 1.26985 1.27438 +/- 0.00219\n" 469 | ] 470 | } 471 | ], 472 | "source": [ 473 | "for _ in range(14):\n", 474 | " openmc.lib.next_batch()" 475 | ] 476 | }, 477 | { 478 | "cell_type": "markdown", 479 | "metadata": {}, 480 | "source": [ 481 | "Let's take a look at the tally. There are 10 entries, one for each cell in the fuel." 482 | ] 483 | }, 484 | { 485 | "cell_type": "code", 486 | "execution_count": 13, 487 | "metadata": {}, 488 | "outputs": [ 489 | { 490 | "name": "stdout", 491 | "output_type": "stream", 492 | "text": [ 493 | "[[ 3931482.80669347]\n", 494 | " [ 9131299.17209842]\n", 495 | " [11481356.26853649]\n", 496 | " [12467135.97736016]\n", 497 | " [11306527.77160696]\n", 498 | " [12652743.75842057]\n", 499 | " [12641528.78488241]\n", 500 | " [11430393.02418431]\n", 501 | " [10539158.01402584]\n", 502 | " [ 4703235.03712948]]\n" 503 | ] 504 | } 505 | ], 506 | "source": [ 507 | "t = openmc.lib.tallies[1]\n", 508 | "print(t.mean)" 509 | ] 510 | }, 511 | { 512 | "cell_type": "markdown", 513 | "metadata": {}, 514 | "source": [ 515 | "Now, let's make some changes to the temperatures. For this, we need to identify each cell by its id. We can use get_temperature() to compare the temperatures of the cells before and after the change. " 516 | ] 517 | }, 518 | { 519 | "cell_type": "code", 520 | "execution_count": 14, 521 | "metadata": {}, 522 | "outputs": [ 523 | { 524 | "name": "stdout", 525 | "output_type": "stream", 526 | "text": [ 527 | "fuel temperature is: \n", 528 | "799.9999999999999\n", 529 | "gap temperature is: \n", 530 | "700.0\n", 531 | "clad temperature is: \n", 532 | "600.0\n", 533 | "water temperature is: \n", 534 | "500.00000000000006\n" 535 | ] 536 | } 537 | ], 538 | "source": [ 539 | "print(\"fuel temperature is: \")\n", 540 | "print(openmc.lib.cells[5].get_temperature())\n", 541 | "print(\"gap temperature is: \")\n", 542 | "print(openmc.lib.cells[15].get_temperature())\n", 543 | "print(\"clad temperature is: \")\n", 544 | "print(openmc.lib.cells[25].get_temperature())\n", 545 | "print(\"water temperature is: \")\n", 546 | "print(openmc.lib.cells[35].get_temperature())" 547 | ] 548 | }, 549 | { 550 | "cell_type": "code", 551 | "execution_count": 15, 552 | "metadata": {}, 553 | "outputs": [], 554 | "source": [ 555 | "for i in range(1, 11):\n", 556 | " temp = 900.0\n", 557 | " openmc.lib.cells[i].set_temperature(temp)" 558 | ] 559 | }, 560 | { 561 | "cell_type": "code", 562 | "execution_count": 16, 563 | "metadata": {}, 564 | "outputs": [ 565 | { 566 | "name": "stdout", 567 | "output_type": "stream", 568 | "text": [ 569 | "fuel temperature is: \n", 570 | "900.0\n" 571 | ] 572 | } 573 | ], 574 | "source": [ 575 | "print(\"fuel temperature is: \")\n", 576 | "print(openmc.lib.cells[5].get_temperature())" 577 | ] 578 | }, 579 | { 580 | "cell_type": "markdown", 581 | "metadata": {}, 582 | "source": [ 583 | "Let's make a similar change for the water density. Again, we need to identify each material by its id." 584 | ] 585 | }, 586 | { 587 | "cell_type": "code", 588 | "execution_count": 17, 589 | "metadata": {}, 590 | "outputs": [], 591 | "source": [ 592 | "for i in range(4, 14):\n", 593 | " density = 0.65\n", 594 | " openmc.lib.materials[i].set_density(density, units='g/cm3')" 595 | ] 596 | }, 597 | { 598 | "cell_type": "markdown", 599 | "metadata": {}, 600 | "source": [ 601 | "The new batches we run will use the new material and cell properties." 602 | ] 603 | }, 604 | { 605 | "cell_type": "code", 606 | "execution_count": 18, 607 | "metadata": {}, 608 | "outputs": [ 609 | { 610 | "name": "stdout", 611 | "output_type": "stream", 612 | "text": [ 613 | " 15/1 1.26681 1.27286 +/- 0.00227\n", 614 | " 16/1 1.22664 1.26516 +/- 0.00792\n", 615 | " 17/1 1.24385 1.26212 +/- 0.00736\n", 616 | " 18/1 1.25415 1.26112 +/- 0.00645\n", 617 | " 19/1 1.28509 1.26378 +/- 0.00628\n", 618 | " 20/1 1.26223 1.26363 +/- 0.00562\n", 619 | " 21/1 1.22983 1.26056 +/- 0.00594\n", 620 | " 22/1 1.25030 1.25970 +/- 0.00549\n", 621 | " 23/1 1.25071 1.25901 +/- 0.00510\n", 622 | " 24/1 1.25387 1.25864 +/- 0.00473\n", 623 | " 25/1 1.24512 1.25774 +/- 0.00450\n", 624 | " 26/1 1.27716 1.25895 +/- 0.00438\n", 625 | " 27/1 1.25292 1.25860 +/- 0.00413\n", 626 | " 28/1 1.27613 1.25957 +/- 0.00401\n" 627 | ] 628 | } 629 | ], 630 | "source": [ 631 | "for _ in range(14):\n", 632 | " openmc.lib.next_batch()" 633 | ] 634 | }, 635 | { 636 | "cell_type": "markdown", 637 | "metadata": {}, 638 | "source": [ 639 | "When you're ready to end the simulation, use the following:" 640 | ] 641 | }, 642 | { 643 | "cell_type": "code", 644 | "execution_count": 19, 645 | "metadata": {}, 646 | "outputs": [ 647 | { 648 | "name": "stdout", 649 | "output_type": "stream", 650 | "text": [ 651 | "\n", 652 | " =======================> TIMING STATISTICS <=======================\n", 653 | "\n", 654 | " Total time for initialization = 2.9988e+01 seconds\n", 655 | " Reading cross sections = 2.9972e+01 seconds\n", 656 | " Total time in simulation = 1.3853e+02 seconds\n", 657 | " Time in transport only = 1.3841e+02 seconds\n", 658 | " Time in inactive batches = 4.4211e+01 seconds\n", 659 | " Time in active batches = 9.4319e+01 seconds\n", 660 | " Time synchronizing fission bank = 2.2818e-02 seconds\n", 661 | " Sampling source sites = 2.1518e-02 seconds\n", 662 | " SEND/RECV source sites = 1.2778e-03 seconds\n", 663 | " Time accumulating tallies = 1.2354e-04 seconds\n", 664 | " Time writing statepoints = 0.0000e+00 seconds\n", 665 | " Total time for finalization = 2.0531e-04 seconds\n", 666 | " Total time elapsed = 2.9988e+01 seconds\n", 667 | " Calculation Rate (inactive) = 2261.88 particles/second\n", 668 | " Calculation Rate (active) = 1908.41 particles/second\n", 669 | "\n", 670 | " ============================> RESULTS <============================\n", 671 | "\n", 672 | " k-effective (Collision) = 1.26047 +/- 0.00360\n", 673 | " k-effective (Track-length) = 1.25957 +/- 0.00401\n", 674 | " k-effective (Absorption) = 1.26095 +/- 0.00280\n", 675 | " Combined k-effective = 1.26075 +/- 0.00292\n", 676 | " Leakage Fraction = 0.00915 +/- 0.00032\n", 677 | "\n" 678 | ] 679 | } 680 | ], 681 | "source": [ 682 | "openmc.lib.simulation_finalize()\n", 683 | "openmc.lib.finalize()" 684 | ] 685 | } 686 | ], 687 | "metadata": { 688 | "kernelspec": { 689 | "display_name": "Python 3 (ipykernel)", 690 | "language": "python", 691 | "name": "python3" 692 | }, 693 | "language_info": { 694 | "codemirror_mode": { 695 | "name": "ipython", 696 | "version": 3 697 | }, 698 | "file_extension": ".py", 699 | "mimetype": "text/x-python", 700 | "name": "python", 701 | "nbconvert_exporter": "python", 702 | "pygments_lexer": "ipython3", 703 | "version": "3.9.1" 704 | } 705 | }, 706 | "nbformat": 4, 707 | "nbformat_minor": 4 708 | } 709 | -------------------------------------------------------------------------------- /chain_simple.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 283500.0 4.235177914969822e-15 30 | 31 | 32 | 513971.0 892130.0 1175630.0 6.915023250821416e-10 4.235177914969822e-15 3.870076370575872e-11 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 4470.0 31817.0 32194.0 36304.0 36378.0 37255.0 661657.0 4.372683136122041e-05 9.525884763557368e-05 0.00017377297596020668 1.6633087848867745e-05 3.2112650589648466e-05 1.0158463048637348e-05 0.004069614128287558 52 | 53 | 54 | 3670.0 26400.0 624216.4 655668.2 660364.2 661404.0 661636.9 0.0003537897992698539 3.7210487357645964e-05 0.00037210482830822686 6.708752100247119e-05 1.4350025269251869e-05 3.08729347663194e-06 4.6173597451093537e-07 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 2.53000e-02 64 | 65 | Gd157 Gd156 I135 Xe135 Xe136 Cs135 66 | 1.093250e-04 2.087260e-04 2.780820e-02 6.759540e-03 2.392300e-02 4.356330e-05 67 | 68 | 69 | 70 | 71 | 72 | 73 | 2.53000e-02 74 | 75 | Gd157 Gd156 I135 Xe135 Xe136 Cs135 76 | 6.142710e-5 1.483250e-04 0.0292737 0.002566345 0.0219242 4.9097e-6 77 | 78 | 79 | 80 | 81 | 82 | 83 | 2.53000e-02 84 | 85 | Gd157 Gd156 I135 Xe135 Xe136 Cs135 86 | 4.141120e-04 7.605360e-04 0.0135457 0.00026864 0.0024432 3.7100E-07 87 | 88 | 89 | 90 | 91 | -------------------------------------------------------------------------------- /flux-spectrum.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Tally a Flux Spectrum\n", 8 | "\n", 9 | "In this example, we will demonstrate how to get the neutron flux as a function of energy (commonly called a flux spectrum). We will use a pre-built module from the `openmc.examples` package." 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 1, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "%matplotlib inline\n", 19 | "import openmc.examples\n", 20 | "import numpy as np\n", 21 | "import matplotlib.pyplot as plt" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "First, we'll generate a pin-cell model:" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 2, 34 | "metadata": {}, 35 | "outputs": [], 36 | "source": [ 37 | "model = openmc.examples.pwr_pin_cell()" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "By default, the model has no tallies." 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 3, 50 | "metadata": { 51 | "tags": [] 52 | }, 53 | "outputs": [ 54 | { 55 | "data": { 56 | "text/plain": [ 57 | "[]" 58 | ] 59 | }, 60 | "execution_count": 3, 61 | "metadata": {}, 62 | "output_type": "execute_result" 63 | } 64 | ], 65 | "source": [ 66 | "model.tallies" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "To get the flux spectrum, we just need to create a flux tally with an energy filter. We can take advantage of numpy to get an energy filter specifying equal-lethargy bins. Let's create an energy filter with 500 energy bins." 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 4, 79 | "metadata": {}, 80 | "outputs": [], 81 | "source": [ 82 | "# Create equal-lethargy energies to put in filter\n", 83 | "energies = np.logspace(np.log10(1e-5), np.log10(20.0e6), 501)\n", 84 | "e_filter = openmc.EnergyFilter(energies)\n", 85 | "\n", 86 | "# Create tally with energy filter\n", 87 | "tally = openmc.Tally()\n", 88 | "tally.filters = [e_filter]\n", 89 | "tally.scores = ['flux']\n", 90 | "\n", 91 | "# Set model tallies\n", 92 | "model.tallies = [tally]" 93 | ] 94 | }, 95 | { 96 | "cell_type": "markdown", 97 | "metadata": {}, 98 | "source": [ 99 | "OpenMC also has a set of predefined energy group structures that you can take advantage of. Let's see what's available:" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 5, 105 | "metadata": {}, 106 | "outputs": [ 107 | { 108 | "data": { 109 | "text/plain": [ 110 | "dict_keys(['CASMO-2', 'CASMO-4', 'CASMO-8', 'CASMO-16', 'CASMO-25', 'CASMO-40', 'VITAMIN-J-42', 'SCALE-44', 'CASMO-70', 'XMAS-172', 'VITAMIN-J-175', 'TRIPOLI-315', 'SHEM-361', 'CCFE-709', 'UKAEA-1102', 'ECCO-1968'])" 111 | ] 112 | }, 113 | "execution_count": 5, 114 | "metadata": {}, 115 | "output_type": "execute_result" 116 | } 117 | ], 118 | "source": [ 119 | "openmc.mgxs.GROUP_STRUCTURES.keys()" 120 | ] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "metadata": {}, 125 | "source": [ 126 | "Let's also add a flux spectrum tally using the SHEM-361 group structure." 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": 6, 132 | "metadata": {}, 133 | "outputs": [], 134 | "source": [ 135 | "# Create energy filter using SHEM-361 group structure\n", 136 | "energies_shem = openmc.mgxs.GROUP_STRUCTURES['SHEM-361']\n", 137 | "shem_filter = openmc.EnergyFilter(openmc.mgxs.GROUP_STRUCTURES['SHEM-361'])\n", 138 | "\n", 139 | "tally_shem = openmc.Tally()\n", 140 | "tally_shem.filters = [shem_filter]\n", 141 | "tally_shem.scores = ['flux']\n", 142 | "\n", 143 | "model.tallies.append(tally_shem)" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "Now let's run the model (making sure to set the number of particles/batches slightly higher than the default values)." 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": 7, 156 | "metadata": {}, 157 | "outputs": [], 158 | "source": [ 159 | "model.settings.particles = 10000\n", 160 | "model.settings.batches = 50\n", 161 | "sp_path = model.run(output=False)" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | "Great, the simulation is done. To get our results, we need to load data from the statepoint file. We can get the corresponding tallies from the statepoint file and get the mean values for each energy bin by using the `mean` attribute on the tallies." 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": 8, 174 | "metadata": {}, 175 | "outputs": [], 176 | "source": [ 177 | "with openmc.StatePoint(sp_path) as sp:\n", 178 | " t = sp.tallies[tally.id]\n", 179 | " flux500_mean = t.mean.ravel()\n", 180 | " flux500_unc = t.std_dev.ravel()\n", 181 | " \n", 182 | " t_shem = sp.tallies[tally_shem.id]\n", 183 | " flux_shem_mean = t_shem.mean.ravel()\n", 184 | " flux_shem_unc = t_shem.std_dev.ravel()" 185 | ] 186 | }, 187 | { 188 | "cell_type": "markdown", 189 | "metadata": {}, 190 | "source": [ 191 | "Now we can use matplotlib to plot the flux versus the energy. Note that we divide by the energy bin width so that integrating the curve makes sense. This appropriately highlights the fact that most of the spectrum is thermal." 192 | ] 193 | }, 194 | { 195 | "cell_type": "code", 196 | "execution_count": 9, 197 | "metadata": {}, 198 | "outputs": [ 199 | { 200 | "data": { 201 | "text/plain": [ 202 | "" 203 | ] 204 | }, 205 | "execution_count": 9, 206 | "metadata": {}, 207 | "output_type": "execute_result" 208 | }, 209 | { 210 | "data": { 211 | "image/png": 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\n", 212 | "text/plain": [ 213 | "
" 214 | ] 215 | }, 216 | "metadata": { 217 | "needs_background": "light" 218 | }, 219 | "output_type": "display_data" 220 | } 221 | ], 222 | "source": [ 223 | "fig, ax = plt.subplots()\n", 224 | "ax.step(energies[:-1], flux500_mean/np.diff(energies), where='post', label='500 group')\n", 225 | "ax.step(energies_shem[:-1], flux_shem_mean/np.diff(energies_shem), where='post', label='SHEM-361')\n", 226 | "ax.set_xscale('log')\n", 227 | "ax.set_yscale('log')\n", 228 | "ax.set_xlabel('Energy [eV]')\n", 229 | "ax.set_ylabel('Flux [n-cm/eV-src]')\n", 230 | "ax.grid()\n", 231 | "ax.legend()" 232 | ] 233 | }, 234 | { 235 | "cell_type": "markdown", 236 | "metadata": {}, 237 | "source": [ 238 | "If we don't divide by the energy bin width, we obtain a plot where the different curves aren't directly comparable." 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": 10, 244 | "metadata": {}, 245 | "outputs": [ 246 | { 247 | "data": { 248 | "text/plain": [ 249 | "" 250 | ] 251 | }, 252 | "execution_count": 10, 253 | "metadata": {}, 254 | "output_type": "execute_result" 255 | }, 256 | { 257 | "data": { 258 | "image/png": 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\n", 259 | "text/plain": [ 260 | "
" 261 | ] 262 | }, 263 | "metadata": { 264 | "needs_background": "light" 265 | }, 266 | "output_type": "display_data" 267 | } 268 | ], 269 | "source": [ 270 | "fig, ax = plt.subplots()\n", 271 | "ax.loglog(energies[:-1], flux500_mean, '.', color='C0', label='500 group')\n", 272 | "ax.loglog(energies_shem[:-1], flux_shem_mean, '.', color='C1', label='SHEM-361')\n", 273 | "ax.set_xlabel('Energy [eV]')\n", 274 | "ax.set_ylabel('Flux [n-cm/src]')\n", 275 | "ax.grid()\n", 276 | "ax.legend()" 277 | ] 278 | } 279 | ], 280 | "metadata": { 281 | "kernelspec": { 282 | "display_name": "Python 3 (ipykernel)", 283 | "language": "python", 284 | "name": "python3" 285 | }, 286 | "language_info": { 287 | "codemirror_mode": { 288 | "name": "ipython", 289 | "version": 3 290 | }, 291 | "file_extension": ".py", 292 | "mimetype": "text/x-python", 293 | "name": "python", 294 | "nbconvert_exporter": "python", 295 | "pygments_lexer": "ipython3", 296 | "version": "3.9.1" 297 | } 298 | }, 299 | "nbformat": 4, 300 | "nbformat_minor": 4 301 | } 302 | -------------------------------------------------------------------------------- /hexagonal-lattice.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Modeling Hexagonal Lattices\n", 8 | "In this example, we will create a hexagonal lattice and show how the orientation can be changed via the cell rotation property. Let's first just set up some materials and universes that we will use to fill the lattice." 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [], 16 | "source": [ 17 | "%matplotlib inline\n", 18 | "import openmc" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 2, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "fuel = openmc.Material(name='fuel')\n", 28 | "fuel.add_nuclide('U235', 1.0)\n", 29 | "fuel.set_density('g/cm3', 10.0)\n", 30 | "\n", 31 | "fuel2 = openmc.Material(name='fuel2')\n", 32 | "fuel2.add_nuclide('U238', 1.0)\n", 33 | "fuel2.set_density('g/cm3', 10.0)\n", 34 | "\n", 35 | "water = openmc.Material(name='water')\n", 36 | "water.add_nuclide('H1', 2.0)\n", 37 | "water.add_nuclide('O16', 1.0)\n", 38 | "water.set_density('g/cm3', 1.0)\n", 39 | "\n", 40 | "materials = openmc.Materials((fuel, fuel2, water))\n", 41 | "materials.export_to_xml()" 42 | ] 43 | }, 44 | { 45 | "cell_type": "markdown", 46 | "metadata": {}, 47 | "source": [ 48 | "With our three materials, we will set up two universes that represent pin-cells: one with a small pin and one with a big pin. Since we will be using these universes in a lattice, it's always a good idea to have an \"outer\" universe as well that is applied outside the defined lattice." 49 | ] 50 | }, 51 | { 52 | "cell_type": "code", 53 | "execution_count": 3, 54 | "metadata": {}, 55 | "outputs": [], 56 | "source": [ 57 | "r_pin = openmc.ZCylinder(r=0.25)\n", 58 | "fuel_cell = openmc.Cell(fill=fuel, region=-r_pin)\n", 59 | "water_cell = openmc.Cell(fill=water, region=+r_pin)\n", 60 | "pin_universe = openmc.Universe(cells=(fuel_cell, water_cell))\n", 61 | "\n", 62 | "r_big_pin = openmc.ZCylinder(r=0.5)\n", 63 | "fuel2_cell = openmc.Cell(fill=fuel2, region=-r_big_pin)\n", 64 | "water2_cell = openmc.Cell(fill=water, region=+r_big_pin)\n", 65 | "big_pin_universe = openmc.Universe(cells=(fuel2_cell, water2_cell))\n", 66 | "\n", 67 | "all_water_cell = openmc.Cell(fill=water)\n", 68 | "outer_universe = openmc.Universe(cells=(all_water_cell,))" 69 | ] 70 | }, 71 | { 72 | "cell_type": "markdown", 73 | "metadata": {}, 74 | "source": [ 75 | "Now let's create a hexagonal lattice using the `HexLattice` class:" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": 4, 81 | "metadata": {}, 82 | "outputs": [], 83 | "source": [ 84 | "lattice = openmc.HexLattice()" 85 | ] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "metadata": {}, 90 | "source": [ 91 | "We need to set the `center` of the lattice, the `pitch`, an `outer` universe (which is applied to all lattice elements outside of those that are defined), and a list of `universes`. Let's start with the easy ones first. Note that for a 2D lattice, we only need to specify a single number for the pitch." 92 | ] 93 | }, 94 | { 95 | "cell_type": "code", 96 | "execution_count": 5, 97 | "metadata": {}, 98 | "outputs": [], 99 | "source": [ 100 | "lattice.center = (0., 0.)\n", 101 | "lattice.pitch = (1.25,)\n", 102 | "lattice.outer = outer_universe" 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "Now we need to set the `universes` property on our lattice. It needs to be set to a list of lists of Universes, where each list of Universes corresponds to a ring of the lattice. The rings are ordered from outermost to innermost, and within each ring the indexing starts at the \"top\". To help visualize the proper indices, we can use the `show_indices()` helper method." 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "execution_count": 6, 115 | "metadata": {}, 116 | "outputs": [ 117 | { 118 | "name": "stdout", 119 | "output_type": "stream", 120 | "text": [ 121 | " (0, 0)\n", 122 | " (0,17) (0, 1)\n", 123 | " (0,16) (1, 0) (0, 2)\n", 124 | "(0,15) (1,11) (1, 1) (0, 3)\n", 125 | " (1,10) (2, 0) (1, 2)\n", 126 | "(0,14) (2, 5) (2, 1) (0, 4)\n", 127 | " (1, 9) (3, 0) (1, 3)\n", 128 | "(0,13) (2, 4) (2, 2) (0, 5)\n", 129 | " (1, 8) (2, 3) (1, 4)\n", 130 | "(0,12) (1, 7) (1, 5) (0, 6)\n", 131 | " (0,11) (1, 6) (0, 7)\n", 132 | " (0,10) (0, 8)\n", 133 | " (0, 9)\n" 134 | ] 135 | } 136 | ], 137 | "source": [ 138 | "print(lattice.show_indices(num_rings=4))" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "Let's set up a lattice where the first element in each ring is the big pin universe and all other elements are regular pin universes. \n", 146 | "\n", 147 | "From the diagram above, we see that the outer ring has 18 elements, the first ring has 12, and the second ring has 6 elements. The innermost ring of any hexagonal lattice will have only a single element. \n", 148 | "\n", 149 | "We build these rings through 'list concatenation' as follows: " 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 7, 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "outer_ring = [big_pin_universe] + [pin_universe]*17 # Adds up to 18\n", 159 | "\n", 160 | "ring_1 = [big_pin_universe] + [pin_universe]*11 # Adds up to 12\n", 161 | "\n", 162 | "ring_2 = [big_pin_universe] + [pin_universe]*5 # Adds up to 6\n", 163 | "\n", 164 | "inner_ring = [big_pin_universe]" 165 | ] 166 | }, 167 | { 168 | "cell_type": "markdown", 169 | "metadata": {}, 170 | "source": [ 171 | "We can now assign the rings (and the universes they contain) to our lattice. " 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": 8, 177 | "metadata": {}, 178 | "outputs": [ 179 | { 180 | "name": "stdout", 181 | "output_type": "stream", 182 | "text": [ 183 | "HexLattice\n", 184 | "\tID =\t4\n", 185 | "\tName =\t\n", 186 | "\tOrientation =\ty\n", 187 | "\t# Rings =\t4\n", 188 | "\t# Axial =\tNone\n", 189 | "\tCenter =\t(0.0, 0.0)\n", 190 | "\tPitch =\t(1.25,)\n", 191 | "\tOuter =\t3\n", 192 | "\tUniverses \n", 193 | " 2\n", 194 | " 1 1\n", 195 | " 1 2 1\n", 196 | "1 1 1 1\n", 197 | " 1 2 1\n", 198 | "1 1 1 1\n", 199 | " 1 2 1\n", 200 | "1 1 1 1\n", 201 | " 1 1 1\n", 202 | "1 1 1 1\n", 203 | " 1 1 1\n", 204 | " 1 1\n", 205 | " 1\n" 206 | ] 207 | } 208 | ], 209 | "source": [ 210 | "lattice.universes = [outer_ring, \n", 211 | " ring_1, \n", 212 | " ring_2,\n", 213 | " inner_ring]\n", 214 | "print(lattice)" 215 | ] 216 | }, 217 | { 218 | "cell_type": "markdown", 219 | "metadata": {}, 220 | "source": [ 221 | "Now let's put our lattice inside a circular cell that will serve as the top-level cell for our geometry." 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": 9, 227 | "metadata": {}, 228 | "outputs": [], 229 | "source": [ 230 | "outer_surface = openmc.ZCylinder(r=5.0, boundary_type='vacuum')\n", 231 | "main_cell = openmc.Cell(fill=lattice, region=-outer_surface)\n", 232 | "geometry = openmc.Geometry([main_cell])\n", 233 | "geometry.export_to_xml()" 234 | ] 235 | }, 236 | { 237 | "cell_type": "markdown", 238 | "metadata": {}, 239 | "source": [ 240 | "Now let's create a plot to see what our geometry looks like." 241 | ] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "execution_count": 10, 246 | "metadata": {}, 247 | "outputs": [ 248 | { 249 | "data": { 250 | "image/png": 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\n", 251 | "text/plain": [ 252 | "" 253 | ] 254 | }, 255 | "execution_count": 10, 256 | "metadata": {}, 257 | "output_type": "execute_result" 258 | } 259 | ], 260 | "source": [ 261 | "plot = openmc.Plot.from_geometry(geometry)\n", 262 | "plot.color_by = 'material'\n", 263 | "plot.colors = colors = {\n", 264 | " water: 'blue',\n", 265 | " fuel: 'olive',\n", 266 | " fuel2: 'yellow'\n", 267 | "}\n", 268 | "plot.to_ipython_image()" 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "metadata": {}, 274 | "source": [ 275 | "At this point, if we wanted to simulate the model, we would need to create an instance of `openmc.Settings`, export it to XML, and run." 276 | ] 277 | }, 278 | { 279 | "cell_type": "markdown", 280 | "metadata": {}, 281 | "source": [ 282 | "## Lattice orientation\n", 283 | "\n", 284 | "Now let's say we want our hexagonal lattice orientated such that two sides of the lattice are parallel to the x-axis. This can be achieved by two means: either we can rotate the cell that contains the lattice, or we can can change the `HexLattice.orientation` attribute. By default, the `orientation` is set to \"y\", indicating that two sides of the lattice are parallel to the y-axis, but we can also change it to \"x\" to make them parallel to the x-axis." 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "execution_count": 11, 290 | "metadata": {}, 291 | "outputs": [ 292 | { 293 | "data": { 294 | "image/png": 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\n", 295 | "text/plain": [ 296 | "" 297 | ] 298 | }, 299 | "execution_count": 11, 300 | "metadata": {}, 301 | "output_type": "execute_result" 302 | } 303 | ], 304 | "source": [ 305 | "# Change the orientation of the lattice and re-export the geometry\n", 306 | "lattice.orientation = 'x'\n", 307 | "geometry.export_to_xml()\n", 308 | "\n", 309 | "# Run OpenMC in plotting mode\n", 310 | "plot.to_ipython_image()" 311 | ] 312 | }, 313 | { 314 | "cell_type": "markdown", 315 | "metadata": {}, 316 | "source": [ 317 | "When we change the orientation to 'x', you can see that the first universe in each ring starts to the right along the x-axis. As before, the universes are defined in a clockwise fashion around each ring. To see the proper indices for a hexagonal lattice in this orientation, we can again call `show_indices` but pass an extra orientation argument:" 318 | ] 319 | }, 320 | { 321 | "cell_type": "code", 322 | "execution_count": 12, 323 | "metadata": {}, 324 | "outputs": [ 325 | { 326 | "name": "stdout", 327 | "output_type": "stream", 328 | "text": [ 329 | " (0,12) (0,13) (0,14) (0,15)\n", 330 | "\n", 331 | " (0,11) (1, 8) (1, 9) (1,10) (0,16)\n", 332 | "\n", 333 | " (0,10) (1, 7) (2, 4) (2, 5) (1,11) (0,17)\n", 334 | "\n", 335 | "(0, 9) (1, 6) (2, 3) (3, 0) (2, 0) (1, 0) (0, 0)\n", 336 | "\n", 337 | " (0, 8) (1, 5) (2, 2) (2, 1) (1, 1) (0, 1)\n", 338 | "\n", 339 | " (0, 7) (1, 4) (1, 3) (1, 2) (0, 2)\n", 340 | "\n", 341 | " (0, 6) (0, 5) (0, 4) (0, 3)\n" 342 | ] 343 | } 344 | ], 345 | "source": [ 346 | "print(lattice.show_indices(4, orientation='x'))" 347 | ] 348 | }, 349 | { 350 | "cell_type": "markdown", 351 | "metadata": {}, 352 | "source": [ 353 | "## Hexagonal prisms\n", 354 | "\n", 355 | "OpenMC also contains a convenience function that can create a hexagonal prism representing the interior region of six surfaces defining a hexagon. This can be useful as a bounding surface of a hexagonal lattice. For example, if we wanted the outer boundary of our geometry to be hexagonal, we could change the `region` of the main cell:" 356 | ] 357 | }, 358 | { 359 | "cell_type": "code", 360 | "execution_count": 13, 361 | "metadata": {}, 362 | "outputs": [ 363 | { 364 | "data": { 365 | "image/png": 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\n", 366 | "text/plain": [ 367 | "" 368 | ] 369 | }, 370 | "execution_count": 13, 371 | "metadata": {}, 372 | "output_type": "execute_result" 373 | } 374 | ], 375 | "source": [ 376 | "main_cell.region = -openmc.model.HexagonalPrism(\n", 377 | " edge_length=4*lattice.pitch[0],\n", 378 | " orientation='x',\n", 379 | " boundary_type='vacuum'\n", 380 | ")\n", 381 | "geometry.export_to_xml()\n", 382 | "\n", 383 | "# Run OpenMC in plotting mode\n", 384 | "plot.color_by = 'cell'\n", 385 | "plot.to_ipython_image()" 386 | ] 387 | } 388 | ], 389 | "metadata": { 390 | "anaconda-cloud": {}, 391 | "kernelspec": { 392 | "display_name": "Python 3 (ipykernel)", 393 | "language": "python", 394 | "name": "python3" 395 | }, 396 | "language_info": { 397 | "codemirror_mode": { 398 | "name": "ipython", 399 | "version": 3 400 | }, 401 | "file_extension": ".py", 402 | "mimetype": "text/x-python", 403 | "name": "python", 404 | "nbconvert_exporter": "python", 405 | "pygments_lexer": "ipython3", 406 | "version": 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In this Notebook, we will do a critical boron concentration search of a typical PWR pin cell.\n", 9 | "\n", 10 | "To use the search functionality, we must create a function which creates our model according to the input parameter we wish to search for (in this case, the boron concentration). \n", 11 | "\n", 12 | "This notebook will first create that function, and then, run the search." 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 1, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "# Initialize third-party libraries and the OpenMC Python API\n", 22 | "import matplotlib.pyplot as plt\n", 23 | "import numpy as np\n", 24 | "\n", 25 | "import openmc\n", 26 | "import openmc.model\n", 27 | "\n", 28 | "%matplotlib inline" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "## Create Parametrized Model\n", 36 | "\n", 37 | "To perform the search we will use the `openmc.search_for_keff` function. This function requires a different function be defined which creates an parametrized model to analyze. This model is required to be stored in an `openmc.model.Model` object. The first parameter of this function will be modified during the search process for our critical eigenvalue.\n", 38 | "\n", 39 | "Our model will be a pin-cell from the [Multi-Group Mode Part II](mg-mode-part-ii.ipynb) assembly, except this time the entire model building process will be contained within a function, and the Boron concentration will be parametrized." 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 2, 45 | "metadata": {}, 46 | "outputs": [], 47 | "source": [ 48 | "# Create the model. `ppm_Boron` will be the parametric variable.\n", 49 | "\n", 50 | "def build_model(ppm_Boron):\n", 51 | " \n", 52 | " # Create the pin materials\n", 53 | " fuel = openmc.Material(name='1.6% Fuel')\n", 54 | " fuel.set_density('g/cm3', 10.31341)\n", 55 | " fuel.add_element('U', 1., enrichment=1.6)\n", 56 | " fuel.add_element('O', 2.)\n", 57 | "\n", 58 | " zircaloy = openmc.Material(name='Zircaloy')\n", 59 | " zircaloy.set_density('g/cm3', 6.55)\n", 60 | " zircaloy.add_element('Zr', 1.)\n", 61 | "\n", 62 | " water = openmc.Material(name='Borated Water')\n", 63 | " water.set_density('g/cm3', 0.741)\n", 64 | " water.add_element('H', 2.)\n", 65 | " water.add_element('O', 1.)\n", 66 | "\n", 67 | " # Include the amount of boron in the water based on the ppm,\n", 68 | " # neglecting the other constituents of boric acid\n", 69 | " water.add_element('B', ppm_Boron * 1e-6)\n", 70 | " \n", 71 | " # Instantiate a Materials object\n", 72 | " materials = openmc.Materials([fuel, zircaloy, water])\n", 73 | " \n", 74 | " # Create cylinders for the fuel and clad\n", 75 | " fuel_outer_radius = openmc.ZCylinder(r=0.39218)\n", 76 | " clad_outer_radius = openmc.ZCylinder(r=0.45720)\n", 77 | "\n", 78 | " # Create boundary planes to surround the geometry\n", 79 | " min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n", 80 | " max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n", 81 | " min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n", 82 | " max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n", 83 | "\n", 84 | " # Create fuel Cell\n", 85 | " fuel_cell = openmc.Cell(name='1.6% Fuel')\n", 86 | " fuel_cell.fill = fuel\n", 87 | " fuel_cell.region = -fuel_outer_radius\n", 88 | "\n", 89 | " # Create a clad Cell\n", 90 | " clad_cell = openmc.Cell(name='1.6% Clad')\n", 91 | " clad_cell.fill = zircaloy\n", 92 | " clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n", 93 | "\n", 94 | " # Create a moderator Cell\n", 95 | " moderator_cell = openmc.Cell(name='1.6% Moderator')\n", 96 | " moderator_cell.fill = water\n", 97 | " moderator_cell.region = +clad_outer_radius & (+min_x & -max_x & +min_y & -max_y)\n", 98 | "\n", 99 | " # Create root Universe\n", 100 | " root_universe = openmc.Universe(name='root universe')\n", 101 | " root_universe.add_cells([fuel_cell, clad_cell, moderator_cell])\n", 102 | "\n", 103 | " # Create Geometry and set root universe\n", 104 | " geometry = openmc.Geometry(root_universe)\n", 105 | " \n", 106 | " # Instantiate a Settings object\n", 107 | " settings = openmc.Settings()\n", 108 | " \n", 109 | " # Set simulation parameters\n", 110 | " settings.batches = 300\n", 111 | " settings.inactive = 20\n", 112 | " settings.particles = 1000\n", 113 | " \n", 114 | " # Create an initial uniform spatial source distribution over fissionable zones\n", 115 | " bounds = [-0.63, -0.63, -10, 0.63, 0.63, 10.]\n", 116 | " uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n", 117 | " settings.source = openmc.source.IndependentSource(space=uniform_dist)\n", 118 | " \n", 119 | " # We dont need a tallies file so dont waste the disk input/output time\n", 120 | " settings.output = {'tallies': False}\n", 121 | " \n", 122 | " model = openmc.model.Model(geometry, materials, settings)\n", 123 | " \n", 124 | " return model" 125 | ] 126 | }, 127 | { 128 | "cell_type": "markdown", 129 | "metadata": {}, 130 | "source": [ 131 | "## Search for the Critical Boron Concentration\n", 132 | "\n", 133 | "To perform the search we imply call the `openmc.search_for_keff` function and pass in the relvant arguments. For our purposes we will be passing in the model building function (`build_model` defined above), a bracketed range for the expected critical Boron concentration (1,000 to 2,500 ppm), the tolerance, and the method we wish to use. \n", 134 | "\n", 135 | "Instead of the bracketed range we could have used a single initial guess, but have elected not to in this example. Finally, due to the high noise inherent in using as few histories as are used in this example, our tolerance on the final keff value will be rather large (1.e-2) and the default 'bisection' method will be used for the search." 136 | ] 137 | }, 138 | { 139 | "cell_type": "code", 140 | "execution_count": 3, 141 | "metadata": {}, 142 | "outputs": [ 143 | { 144 | "name": "stdout", 145 | "output_type": "stream", 146 | "text": [ 147 | "Iteration: 1; Guess of 1.00e+03 produced a keff of 1.08971 +/- 0.00163\n", 148 | "Iteration: 2; Guess of 2.50e+03 produced a keff of 0.95309 +/- 0.00154\n", 149 | "Iteration: 3; Guess of 1.75e+03 produced a keff of 1.01511 +/- 0.00156\n", 150 | "Iteration: 4; Guess of 2.12e+03 produced a keff of 0.98400 +/- 0.00172\n", 151 | "Iteration: 5; Guess of 1.94e+03 produced a keff of 0.99913 +/- 0.00174\n", 152 | "Iteration: 6; Guess of 1.84e+03 produced a keff of 1.00690 +/- 0.00169\n", 153 | "Iteration: 7; Guess of 1.89e+03 produced a keff of 1.00394 +/- 0.00162\n", 154 | "Iteration: 8; Guess of 1.91e+03 produced a keff of 1.00241 +/- 0.00170\n", 155 | "Iteration: 9; Guess of 1.93e+03 produced a keff of 1.00204 +/- 0.00154\n", 156 | "Critical Boron Concentration: 1926 ppm\n" 157 | ] 158 | } 159 | ], 160 | "source": [ 161 | "# Perform the search\n", 162 | "crit_ppm, guesses, keffs = openmc.search_for_keff(build_model, bracket=[1000., 2500.],\n", 163 | " tol=1e-2, print_iterations=True, \n", 164 | " run_args={'output': False})\n", 165 | "\n", 166 | "print('Critical Boron Concentration: {:4.0f} ppm'.format(crit_ppm))" 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": {}, 172 | "source": [ 173 | "Finally, the `openmc.search_for_keff` function also provided us with `List`s of the guesses and corresponding keff values generated during the search process with OpenMC. Let's use that information to make a quick plot of the value of keff versus the boron concentration." 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 4, 179 | "metadata": {}, 180 | "outputs": [ 181 | { 182 | "data": { 183 | "image/png": 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\n", 184 | "text/plain": [ 185 | "
" 186 | ] 187 | }, 188 | "metadata": { 189 | "needs_background": "light" 190 | }, 191 | "output_type": "display_data" 192 | } 193 | ], 194 | "source": [ 195 | "plt.figure(figsize=(8, 4.5))\n", 196 | "plt.title('Eigenvalue versus Boron Concentration')\n", 197 | "# Create a scatter plot using the mean value of keff\n", 198 | "plt.scatter(guesses, [keffs[i].nominal_value for i in range(len(keffs))])\n", 199 | "plt.xlabel('Boron Concentration [ppm]')\n", 200 | "plt.ylabel('Eigenvalue')\n", 201 | "plt.show()" 202 | ] 203 | }, 204 | { 205 | "cell_type": "markdown", 206 | "metadata": {}, 207 | "source": [ 208 | "We see a nearly linear reactivity coefficient for the boron concentration, exactly as one would expect for a pure 1/v absorber at small concentrations." 209 | ] 210 | } 211 | ], 212 | "metadata": { 213 | "kernelspec": { 214 | "display_name": "Python 3 (ipykernel)", 215 | "language": "python", 216 | "name": "python3" 217 | }, 218 | "language_info": { 219 | "codemirror_mode": { 220 | "name": "ipython", 221 | "version": 3 222 | }, 223 | "file_extension": ".py", 224 | "mimetype": "text/x-python", 225 | "name": "python", 226 | "nbconvert_exporter": "python", 227 | "pygments_lexer": "ipython3", 228 | "version": "3.9.1" 229 | } 230 | }, 231 | "nbformat": 4, 232 | "nbformat_minor": 4 233 | } 234 | -------------------------------------------------------------------------------- /tally-power-normalization.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "1c9efafb", 6 | "metadata": {}, 7 | "source": [ 8 | "# Tally Power Normalization\n", 9 | "\n", 10 | "In this notebook, we demonstrate how to normalize tally values in OpenMC assuming you have a reactor operating at a known power. We'll begin by creating a very simple \"reactor\" model consisting of a sphere of U235 surrounded by water." 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 1, 16 | "id": "aa112441", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "import openmc" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "id": "d3e05e9d", 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "u235 = openmc.Material()\n", 31 | "u235.add_nuclide('U235', 1.0)\n", 32 | "u235.set_density('g/cm3', 10.0)\n", 33 | "\n", 34 | "water = openmc.Material()\n", 35 | "water.add_components({'H': 2.0, 'O': 1.0})\n", 36 | "water.add_s_alpha_beta('c_H_in_H2O')\n", 37 | "\n", 38 | "fuel_sph = openmc.Sphere(r=6.5)\n", 39 | "outer_sph = openmc.Sphere(r=15.0, boundary_type='vacuum')\n", 40 | "\n", 41 | "fuel = openmc.Cell(fill=u235, region=-fuel_sph)\n", 42 | "moderator = openmc.Cell(fill=water, region=+fuel_sph & -outer_sph)\n", 43 | "geometry = openmc.Geometry([fuel, moderator])\n", 44 | "model = openmc.Model(geometry=geometry)\n", 45 | "\n", 46 | "model.settings.batches = 100\n", 47 | "model.settings.inactive = 10\n", 48 | "model.settings.particles = 1000\n", 49 | "model.settings.source = openmc.IndependentSource(space=openmc.stats.Point())" 50 | ] 51 | }, 52 | { 53 | "cell_type": "markdown", 54 | "id": "4346c07d", 55 | "metadata": {}, 56 | "source": [ 57 | "Let's run OpenMC to see what we get for $k_\\text{eff}$:" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 3, 63 | "id": "e3669be6", 64 | "metadata": {}, 65 | "outputs": [ 66 | { 67 | "name": "stdout", 68 | "output_type": "stream", 69 | "text": [ 70 | "k-effective = 0.9943+/-0.0030\n" 71 | ] 72 | } 73 | ], 74 | "source": [ 75 | "sp_path = model.run(output=False)\n", 76 | "with openmc.StatePoint(sp_path) as sp:\n", 77 | " keff = sp.keff\n", 78 | "print(f'k-effective = {keff}')" 79 | ] 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "id": "a48ca59a", 84 | "metadata": {}, 85 | "source": [ 86 | "Now let's say we want to know the fission reaction rate in \\[fissions/sec\\] in the fuel. We'll create a tally for this using the \"fission\" score:" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 4, 92 | "id": "72905d10", 93 | "metadata": {}, 94 | "outputs": [], 95 | "source": [ 96 | "fission_tally = openmc.Tally()\n", 97 | "fission_tally.filters = [openmc.CellFilter([fuel])]\n", 98 | "fission_tally.scores = ['fission']\n", 99 | "model.tallies = [fission_tally]" 100 | ] 101 | }, 102 | { 103 | "cell_type": "markdown", 104 | "id": "6f5817ae", 105 | "metadata": {}, 106 | "source": [ 107 | "We then run OpenMC and get the result from the statepoint:" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 5, 113 | "id": "149b4d65", 114 | "metadata": {}, 115 | "outputs": [ 116 | { 117 | "name": "stdout", 118 | "output_type": "stream", 119 | "text": [ 120 | "Fission rate = 0.3955335940378975 [reactions/source]\n" 121 | ] 122 | } 123 | ], 124 | "source": [ 125 | "sp_path = model.run(output=False)\n", 126 | "with openmc.StatePoint(sp_path) as sp:\n", 127 | " tally = sp.tallies[fission_tally.id]\n", 128 | " fission_rate = tally.mean.ravel()[0]\n", 129 | " \n", 130 | "print(f'Fission rate = {fission_rate} [reactions/source]')" 131 | ] 132 | }, 133 | { 134 | "cell_type": "markdown", 135 | "id": "cc37fdde", 136 | "metadata": {}, 137 | "source": [ 138 | "So, the number we get here is not quite what we wanted—it's the fission reaction rate *per source particle*. We want the reaction rate *per second*. In order to determine that, we need to figure out the source rate, i.e., the number of source particles emitted per second. The method for doing this is described in the OpenMC [user's guide](https://docs.openmc.org/en/stable/usersguide/tallies.html#normalization-of-tally-results) but here we'll give a practical demonstration.\n", 139 | "\n", 140 | "We need two things in order to determine our desired normalization factor in units of \\[source/sec\\]. First, we need to know the power in \\[W\\] or \\[J/sec\\]. This is not something OpenMC or any other neutronics code will provide for you—that is something you have to provide as a user. In addition to the power, we need the observed heating rate from the simulation in \\[J/source\\]. To get that, we can add another tally for the heating rate. We'll use the `fission-q-recoverable` score but there are some pitfalls as to which score you choose (see further discussion below)." 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": 6, 146 | "id": "48bd4b14", 147 | "metadata": {}, 148 | "outputs": [], 149 | "source": [ 150 | "heating_tally = openmc.Tally()\n", 151 | "heating_tally.scores = ['fission-q-recoverable']\n", 152 | "model.tallies = [fission_tally, heating_tally]" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 7, 158 | "id": "4c804fc7", 159 | "metadata": {}, 160 | "outputs": [ 161 | { 162 | "name": "stdout", 163 | "output_type": "stream", 164 | "text": [ 165 | "Heating rate = 76235017.82874997 [eV/source]\n" 166 | ] 167 | } 168 | ], 169 | "source": [ 170 | "sp_path = model.run(output=False)\n", 171 | "with openmc.StatePoint(sp_path) as sp:\n", 172 | " tally = sp.tallies[heating_tally.id]\n", 173 | " heating_rate_ev = tally.mean.ravel()[0]\n", 174 | " \n", 175 | "print(f'Heating rate = {heating_rate_ev} [eV/source]')" 176 | ] 177 | }, 178 | { 179 | "cell_type": "markdown", 180 | "id": "310229cd", 181 | "metadata": {}, 182 | "source": [ 183 | "The OpenMC tally gives us units of \\[eV/source\\] and we need to convert this to \\[J/source\\]:" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 8, 189 | "id": "e684b4c9", 190 | "metadata": {}, 191 | "outputs": [ 192 | { 193 | "name": "stdout", 194 | "output_type": "stream", 195 | "text": [ 196 | "Heating rate = 1.2214222086486662e-11 [J/source]\n" 197 | ] 198 | } 199 | ], 200 | "source": [ 201 | "joule_per_ev = 1.60218e-19\n", 202 | "heating_rate = heating_rate_ev * joule_per_ev\n", 203 | "print(f'Heating rate = {heating_rate} [J/source]')" 204 | ] 205 | }, 206 | { 207 | "cell_type": "markdown", 208 | "id": "1e9c1002", 209 | "metadata": {}, 210 | "source": [ 211 | "At this point, let's assume that our spherical reactor has a power of 30 kW. That is:" 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": 9, 217 | "id": "a7f4ae45", 218 | "metadata": {}, 219 | "outputs": [], 220 | "source": [ 221 | "power = 30e3 # [J/s]" 222 | ] 223 | }, 224 | { 225 | "cell_type": "markdown", 226 | "id": "03d1211b", 227 | "metadata": {}, 228 | "source": [ 229 | "The tally normalization factor is simply the power in \\[J/s\\] divided by the heating rate in \\[J/source\\]:" 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": 10, 235 | "id": "ef03e05b", 236 | "metadata": {}, 237 | "outputs": [], 238 | "source": [ 239 | "source_per_sec = power / heating_rate" 240 | ] 241 | }, 242 | { 243 | "cell_type": "markdown", 244 | "id": "f6989d40", 245 | "metadata": {}, 246 | "source": [ 247 | "Whenever we want our tallied quantities in units of \"per sec\" rather than \"per source neutron\", all we have to do is multiply by the normalization factor. For example, our fission rate would be:" 248 | ] 249 | }, 250 | { 251 | "cell_type": "code", 252 | "execution_count": 11, 253 | "id": "218ad621", 254 | "metadata": {}, 255 | "outputs": [ 256 | { 257 | "name": "stdout", 258 | "output_type": "stream", 259 | "text": [ 260 | "Fission rate = 9.715e+14 [reactions/sec]\n" 261 | ] 262 | } 263 | ], 264 | "source": [ 265 | "fission_rate_sec = fission_rate * source_per_sec\n", 266 | "print(f'Fission rate = {fission_rate_sec:.3e} [reactions/sec]')" 267 | ] 268 | }, 269 | { 270 | "cell_type": "markdown", 271 | "id": "19dd5d6c", 272 | "metadata": {}, 273 | "source": [ 274 | "As a quick sanity check, we can multiply by 200 MeV/reaction to see how much heating we'd expect from this many fission reactions:" 275 | ] 276 | }, 277 | { 278 | "cell_type": "code", 279 | "execution_count": 12, 280 | "id": "a6f5943a", 281 | "metadata": {}, 282 | "outputs": [ 283 | { 284 | "name": "stdout", 285 | "output_type": "stream", 286 | "text": [ 287 | "Expected power = 31130.1 [W]\n" 288 | ] 289 | } 290 | ], 291 | "source": [ 292 | "expected_power = fission_rate_sec * 200e6 * joule_per_ev\n", 293 | "print(f'Expected power = {expected_power:.1f} [W]')" 294 | ] 295 | }, 296 | { 297 | "cell_type": "markdown", 298 | "id": "f3364440", 299 | "metadata": {}, 300 | "source": [ 301 | "The value is fairly close to the expected 30 kW but not quite exact since we used a crude estimate of 200 MeV/reaction. The actual amount of heat being deposited per reaction can be determined by dividing the heating rate by the fission rate." 302 | ] 303 | }, 304 | { 305 | "cell_type": "code", 306 | "execution_count": 13, 307 | "id": "19fbe062", 308 | "metadata": {}, 309 | "outputs": [ 310 | { 311 | "name": "stdout", 312 | "output_type": "stream", 313 | "text": [ 314 | "Heat per reaction = 192.7 [MeV]\n" 315 | ] 316 | } 317 | ], 318 | "source": [ 319 | "mev_per_reaction = heating_rate_ev / fission_rate * 1e-6\n", 320 | "print(f'Heat per reaction = {mev_per_reaction:.1f} [MeV]')" 321 | ] 322 | }, 323 | { 324 | "cell_type": "markdown", 325 | "id": "d7b63bb5", 326 | "metadata": {}, 327 | "source": [ 328 | "How much heat you observe will depend on which [score](https://docs.openmc.org/en/stable/usersguide/tallies.html#scores) you choose to estimate the heating. OpenMC has a variety of scores that allow you to tally heating that differ in subtle ways, so you should exercise some caution when picking a score. A few notes about the available scores:\n", 329 | "\n", 330 | "- `\"heating\"` — This score gives you the heating from *all* reactions, not just fission, so it gives you a way of accounting for heat from (n,$\\gamma$) reactions. It will also include photon energy deposition but only if you are running a coupled neutron–photon calculation; for a neutron-only calculation, you should use the `\"heating-local\"` score to account for photon energy deposition.\n", 331 | "- `\"heating-local\"` — This score is the same as `\"heating\"` except that it assumes photon energy is deposited locally and thus can be used in a neutron-only calculation. This score requires special cross section data (MT=901) that is not present in all data libraries, particularly those that were converted straight from ACE files. The \"official\" data libraries from https://openmc.org do have the necessary data included.\n", 332 | "- `\"fission-q-recoverable\"` — This score represents the recoverable energy release from fission, which is basically the total fission energy release less neutrinos. Do note that it does not account for heating from non-fission reactions.\n", 333 | "- `\"kappa-fission\"` — This older score is basically equivalent to `\"fission-q-recoverable\"` but does not include an energy-dependence on the incident neutron energy." 334 | ] 335 | }, 336 | { 337 | "cell_type": "code", 338 | "execution_count": null, 339 | "id": "35f457bf", 340 | "metadata": {}, 341 | "outputs": [], 342 | "source": [] 343 | } 344 | ], 345 | "metadata": { 346 | "kernelspec": { 347 | "display_name": "Python 3 (ipykernel)", 348 | "language": "python", 349 | "name": "python3" 350 | }, 351 | "language_info": { 352 | "codemirror_mode": { 353 | "name": "ipython", 354 | "version": 3 355 | }, 356 | "file_extension": ".py", 357 | "mimetype": "text/x-python", 358 | "name": "python", 359 | "nbconvert_exporter": "python", 360 | "pygments_lexer": "ipython3", 361 | "version": "3.10.6" 362 | } 363 | }, 364 | "nbformat": 4, 365 | "nbformat_minor": 5 366 | } 367 | -------------------------------------------------------------------------------- /triso.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "metadata": {}, 7 | "source": [ 8 | "# Modeling TRISO Particles\n", 9 | "OpenMC includes a few convenience functions for generationing TRISO particle locations and placing them in a lattice. To be clear, this capability is not a stochastic geometry capability like that included in MCNP. It's also important to note that OpenMC does not use delta tracking, which would normally speed up calculations in geometries with tons of surfaces and cells. However, the computational burden can be eased by placing TRISO particles in a lattice." 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 1, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "%matplotlib inline\n", 19 | "from math import pi\n", 20 | "import numpy as np\n", 21 | "import matplotlib.pyplot as plt\n", 22 | "import openmc\n", 23 | "import openmc.model" 24 | ] 25 | }, 26 | { 27 | "attachments": {}, 28 | "cell_type": "markdown", 29 | "metadata": {}, 30 | "source": [ 31 | "Let's first start by creating materials that will be used in our TRISO particles and the background material." 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 2, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [ 40 | "fuel = openmc.Material(name='Fuel')\n", 41 | "fuel.set_density('g/cm3', 10.5)\n", 42 | "fuel.add_nuclide('U235', 4.6716e-02)\n", 43 | "fuel.add_nuclide('U238', 2.8697e-01)\n", 44 | "fuel.add_nuclide('O16', 5.0000e-01)\n", 45 | "fuel.add_element('C', 1.6667e-01)\n", 46 | "\n", 47 | "buff = openmc.Material(name='Buffer')\n", 48 | "buff.set_density('g/cm3', 1.0)\n", 49 | "buff.add_element('C', 1.0)\n", 50 | "buff.add_s_alpha_beta('c_Graphite')\n", 51 | "\n", 52 | "PyC1 = openmc.Material(name='PyC1')\n", 53 | "PyC1.set_density('g/cm3', 1.9)\n", 54 | "PyC1.add_element('C', 1.0)\n", 55 | "PyC1.add_s_alpha_beta('c_Graphite')\n", 56 | "\n", 57 | "PyC2 = openmc.Material(name='PyC2')\n", 58 | "PyC2.set_density('g/cm3', 1.87)\n", 59 | "PyC2.add_element('C', 1.0)\n", 60 | "PyC2.add_s_alpha_beta('c_Graphite')\n", 61 | "\n", 62 | "SiC = openmc.Material(name='SiC')\n", 63 | "SiC.set_density('g/cm3', 3.2)\n", 64 | "SiC.add_element('C', 0.5)\n", 65 | "SiC.add_element('Si', 0.5)\n", 66 | "\n", 67 | "graphite = openmc.Material()\n", 68 | "graphite.set_density('g/cm3', 1.1995)\n", 69 | "graphite.add_element('C', 1.0)\n", 70 | "graphite.add_s_alpha_beta('c_Graphite')" 71 | ] 72 | }, 73 | { 74 | "attachments": {}, 75 | "cell_type": "markdown", 76 | "metadata": {}, 77 | "source": [ 78 | "To actually create individual TRISO particles, we first need to create a universe that will be used within each particle. The reason we use the same universe for each TRISO particle is to reduce the total number of cells/surfaces needed which can substantially improve performance over using unique cells/surfaces in each." 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": 3, 84 | "metadata": {}, 85 | "outputs": [], 86 | "source": [ 87 | "# Create TRISO universe\n", 88 | "spheres = [openmc.Sphere(r=1e-4*r)\n", 89 | " for r in [215., 315., 350., 385.]]\n", 90 | "cells = [openmc.Cell(fill=fuel, region=-spheres[0]),\n", 91 | " openmc.Cell(fill=buff, region=+spheres[0] & -spheres[1]),\n", 92 | " openmc.Cell(fill=PyC1, region=+spheres[1] & -spheres[2]),\n", 93 | " openmc.Cell(fill=SiC, region=+spheres[2] & -spheres[3]),\n", 94 | " openmc.Cell(fill=PyC2, region=+spheres[3])]\n", 95 | "triso_univ = openmc.Universe(cells=cells)" 96 | ] 97 | }, 98 | { 99 | "attachments": {}, 100 | "cell_type": "markdown", 101 | "metadata": {}, 102 | "source": [ 103 | "Next, we need a region to pack the TRISO particles in. We will use a 1 cm x 1 cm x 1 cm box centered at the origin." 104 | ] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "execution_count": 4, 109 | "metadata": {}, 110 | "outputs": [], 111 | "source": [ 112 | "min_x = openmc.XPlane(x0=-0.5, boundary_type='reflective')\n", 113 | "max_x = openmc.XPlane(x0=0.5, boundary_type='reflective')\n", 114 | "min_y = openmc.YPlane(y0=-0.5, boundary_type='reflective')\n", 115 | "max_y = openmc.YPlane(y0=0.5, boundary_type='reflective')\n", 116 | "min_z = openmc.ZPlane(z0=-0.5, boundary_type='reflective')\n", 117 | "max_z = openmc.ZPlane(z0=0.5, boundary_type='reflective')\n", 118 | "region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z" 119 | ] 120 | }, 121 | { 122 | "attachments": {}, 123 | "cell_type": "markdown", 124 | "metadata": {}, 125 | "source": [ 126 | "Now we need to select locations for the TRISO particles using `openmc.model.pack_spheres`. In this example, we will select locations, at random, within the above box with a packing fraction of 30%. A seed has been included so that the randomly generated sphere locations will remain the same, even if the script is rerun. The user is not strictly required to use a seed or the exact same seed used here, but it is a best practice to include one for repeatability. Note that `pack_spheres` can handle up to the theoretical maximum of 60% (it will just be slow)." 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": 5, 132 | "metadata": {}, 133 | "outputs": [], 134 | "source": [ 135 | "outer_radius = 425.*1e-4\n", 136 | "centers = openmc.model.pack_spheres(radius=outer_radius, region=region, pf=0.3, seed=124848351)" 137 | ] 138 | }, 139 | { 140 | "attachments": {}, 141 | "cell_type": "markdown", 142 | "metadata": {}, 143 | "source": [ 144 | "Now that we have the locations of the TRISO particles determined and a universe that can be used for each particle, we can create the TRISO particles." 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 6, 150 | "metadata": {}, 151 | "outputs": [], 152 | "source": [ 153 | "trisos = [openmc.model.TRISO(outer_radius, triso_univ, center) for center in centers]" 154 | ] 155 | }, 156 | { 157 | "attachments": {}, 158 | "cell_type": "markdown", 159 | "metadata": {}, 160 | "source": [ 161 | "Each TRISO object actually **is** a Cell, in fact; we can look at the properties of the TRISO just as we would a cell:" 162 | ] 163 | }, 164 | { 165 | "cell_type": "code", 166 | "execution_count": 7, 167 | "metadata": {}, 168 | "outputs": [ 169 | { 170 | "name": "stdout", 171 | "output_type": "stream", 172 | "text": [ 173 | "Cell\n", 174 | "\tID =\t6\n", 175 | "\tName =\t\n", 176 | "\tFill =\t1\n", 177 | "\tRegion =\t-11\n", 178 | "\tRotation =\tNone\n", 179 | "\tTranslation =\t[-0.4557226 -0.00821291 -0.29390205]\n", 180 | "\tVolume =\tNone\n", 181 | "\n" 182 | ] 183 | } 184 | ], 185 | "source": [ 186 | "print(trisos[0])" 187 | ] 188 | }, 189 | { 190 | "attachments": {}, 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "Let's confirm that all our TRISO particles are within the box." 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 8, 200 | "metadata": {}, 201 | "outputs": [ 202 | { 203 | "name": "stdout", 204 | "output_type": "stream", 205 | "text": [ 206 | "[-0.45748859 -0.45742868 -0.45747 ]\n", 207 | "[0.45582441 0.45746362 0.4562236 ]\n" 208 | ] 209 | } 210 | ], 211 | "source": [ 212 | "centers = np.vstack([triso.center for triso in trisos])\n", 213 | "print(centers.min(axis=0))\n", 214 | "print(centers.max(axis=0))" 215 | ] 216 | }, 217 | { 218 | "attachments": {}, 219 | "cell_type": "markdown", 220 | "metadata": {}, 221 | "source": [ 222 | "We can also look at what the actual packing fraction turned out to be:" 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "execution_count": 9, 228 | "metadata": {}, 229 | "outputs": [ 230 | { 231 | "data": { 232 | "text/plain": [ 233 | "0.2996893513959326" 234 | ] 235 | }, 236 | "execution_count": 9, 237 | "metadata": {}, 238 | "output_type": "execute_result" 239 | } 240 | ], 241 | "source": [ 242 | "len(trisos)*4/3*pi*outer_radius**3" 243 | ] 244 | }, 245 | { 246 | "attachments": {}, 247 | "cell_type": "markdown", 248 | "metadata": {}, 249 | "source": [ 250 | "Now that we have our TRISO particles created, we need to place them in a lattice to provide optimal tracking performance in OpenMC. We can use the box we created above to place the lattice in. Actually creating a lattice containing TRISO particles can be done with the `model.create_triso_lattice()` function. This function requires that we give it a list of TRISO particles, the lower-left coordinates of the lattice, the pitch of each lattice cell, the overall shape of the lattice (number of cells in each direction), and a background material." 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": 10, 256 | "metadata": {}, 257 | "outputs": [], 258 | "source": [ 259 | "box = openmc.Cell(region=region)\n", 260 | "lower_left, upper_right = box.region.bounding_box\n", 261 | "shape = (3, 3, 3)\n", 262 | "pitch = (upper_right - lower_left)/shape\n", 263 | "lattice = openmc.model.create_triso_lattice(\n", 264 | " trisos, lower_left, pitch, shape, graphite)" 265 | ] 266 | }, 267 | { 268 | "attachments": {}, 269 | "cell_type": "markdown", 270 | "metadata": {}, 271 | "source": [ 272 | "Now we can set the fill of our box cell to be the lattice:" 273 | ] 274 | }, 275 | { 276 | "cell_type": "code", 277 | "execution_count": 11, 278 | "metadata": {}, 279 | "outputs": [], 280 | "source": [ 281 | "box.fill = lattice" 282 | ] 283 | }, 284 | { 285 | "attachments": {}, 286 | "cell_type": "markdown", 287 | "metadata": {}, 288 | "source": [ 289 | "Finally, let's take a look at our geometry by putting the box in a universe and plotting it. We're going to use the Fortran-side plotter since it's much faster." 290 | ] 291 | }, 292 | { 293 | "cell_type": "code", 294 | "execution_count": 12, 295 | "metadata": {}, 296 | "outputs": [ 297 | { 298 | "data": { 299 | "image/png": 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", 300 | "text/plain": [ 301 | "" 302 | ] 303 | }, 304 | "execution_count": 12, 305 | "metadata": {}, 306 | "output_type": "execute_result" 307 | } 308 | ], 309 | "source": [ 310 | "universe = openmc.Universe(cells=[box])\n", 311 | "\n", 312 | "geometry = openmc.Geometry(universe)\n", 313 | "geometry.export_to_xml()\n", 314 | "\n", 315 | "materials = list(geometry.get_all_materials().values())\n", 316 | "openmc.Materials(materials).export_to_xml()\n", 317 | "\n", 318 | "settings = openmc.Settings()\n", 319 | "settings.run_mode = 'plot'\n", 320 | "settings.export_to_xml()\n", 321 | "\n", 322 | "plot = openmc.Plot.from_geometry(geometry)\n", 323 | "plot.to_ipython_image()" 324 | ] 325 | }, 326 | { 327 | "attachments": {}, 328 | "cell_type": "markdown", 329 | "metadata": {}, 330 | "source": [ 331 | "If we plot the universe by material rather than by cell, we can see that the entire background is just graphite." 332 | ] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": 13, 337 | "metadata": {}, 338 | "outputs": [ 339 | { 340 | "data": { 341 | "image/png": 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", 342 | "text/plain": [ 343 | "" 344 | ] 345 | }, 346 | "execution_count": 13, 347 | "metadata": {}, 348 | "output_type": "execute_result" 349 | } 350 | ], 351 | "source": [ 352 | "plot.color_by = 'material'\n", 353 | "plot.colors = {graphite: 'gray'}\n", 354 | "plot.to_ipython_image()" 355 | ] 356 | } 357 | ], 358 | "metadata": { 359 | "anaconda-cloud": {}, 360 | "kernelspec": { 361 | "display_name": "Python 3 (ipykernel)", 362 | "language": "python", 363 | "name": "python3" 364 | }, 365 | "language_info": { 366 | "codemirror_mode": { 367 | "name": "ipython", 368 | "version": 3 369 | }, 370 | "file_extension": ".py", 371 | "mimetype": "text/x-python", 372 | "name": "python", 373 | "nbconvert_exporter": "python", 374 | "pygments_lexer": "ipython3", 375 | "version": "3.10.5" 376 | } 377 | }, 378 | "nbformat": 4, 379 | "nbformat_minor": 4 380 | } 381 | --------------------------------------------------------------------------------