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@paulromano
Created May 5, 2022 15:26
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Example of using IRDFF-II ACE file data for dosimetry
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e3d70574",
"metadata": {},
"outputs": [],
"source": [
"import openmc\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "75cef3a4",
"metadata": {},
"outputs": [],
"source": [
"# Get ACE file and assign variables for NXS, JXS, and XSS arrays\n",
"ace_table = openmc.data.ace.get_table('dos-irdff2-1125.acef')\n",
"nxs = ace_table.nxs\n",
"jxs = ace_table.jxs\n",
"xss = ace_table.xss"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0b366a5d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MTs: [ 2 16 102]\n"
]
}
],
"source": [
"# Get MT values and locators for cross section blocks\n",
"lmt = jxs[3]\n",
"nmt = nxs[4]\n",
"lxs = jxs[6]\n",
"mts = xss[lmt : lmt+nmt].astype(int)\n",
"locators = xss[lxs : lxs+nmt].astype(int)\n",
"print(f'MTs: {mts}')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "12db5473",
"metadata": {},
"outputs": [],
"source": [
"# Create dictionary mapping MT to Tabulated1D object\n",
"cross_sections = {}\n",
"for mt, loca in zip(mts, locators):\n",
" # Determine starting index on energy grid\n",
" nr = int(xss[jxs[7] + loca - 1])\n",
" if nr == 0:\n",
" breakpoints = None\n",
" interpolation = None\n",
" else:\n",
" breakpoints = xss[jxs[7] + loca : jxs[7] + loca + nr].astype(int)\n",
" interpolation = xss[jxs[7] + loca + nr : jxs[7] + loca + 2*nr].astype(int)\n",
"\n",
" # Determine number of energies in reaction\n",
" ne = int(xss[jxs[7] + loca + 2*nr])\n",
"\n",
" # Read reaction cross section\n",
" start = jxs[7] + loca + 1 + 2*nr\n",
" energy = xss[start : start + ne] * 1e6\n",
" xs = xss[start + ne : start + 2*ne]\n",
"\n",
" cross_sections[mt] = openmc.data.Tabulated1D(energy, xs, breakpoints, interpolation)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7a83c9c7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Cross section [b]')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.loglog(cross_sections[102].x, cross_sections[102].y)\n",
"plt.xlabel('Energy [eV]')\n",
"plt.ylabel('Cross section [b]')"
]
},
{
"cell_type": "markdown",
"id": "2a55b8ea",
"metadata": {},
"source": [
"Now we can create an energy function filter and use it in a tally:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0324b6c8",
"metadata": {},
"outputs": [],
"source": [
"tally = openmc.Tally()\n",
"multiplier = openmc.EnergyFunctionFilter.from_tabulated1d(cross_sections[102])\n",
"tally.filters = [multiplier]\n",
"tally.scores = ['flux']"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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