Skip to content

Instantly share code, notes, and snippets.

@gridley

gridley/criticality_check_x.ipynb

Created March 17, 2025 01:16
Show Gist options
  • Save gridley/0656c7f57e5ca36ee10c2d30a1c0c520 to your computer and use it in GitHub Desktop.
Save gridley/0656c7f57e5ca36ee10c2d30a1c0c520 to your computer and use it in GitHub Desktop.
Download ZIP
An openmc model to simulate the criticality (un)safety of the case of stolen plutonium in Mission Impossible: Fallout
Display the source blob
Display the rendered blob
Raw
criticality_check_x.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "dfe46508-94ed-402b-be04-3bc06f7b6427",
"metadata": {},
"outputs": [],
"source": [
"import openmc\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ed6caed4-324e-4777-8c6b-533e31ece3df",
"metadata": {},
"outputs": [],
"source": [
"pu = openmc.Material(name='plutonium metal')\n",
"pu.add_nuclide('Pu239', 1.0)\n",
"pu.set_density('g/cc', 19.84)\n",
"\n",
"borated_poly = openmc.Material(name=\"Borated Polyethylene\")\n",
"\n",
"# Define weight fractions\n",
"wt_fraction_boron = 0.05 # 5% boron by weight\n",
"wt_fraction_poly = 1.0 - wt_fraction_boron # Remaining is polyethylene\n",
"\n",
"# Define polyethylene composition (assuming CH2 stoichiometry)\n",
"mass_hydrogen = 1.008\n",
"mass_carbon = 12.011\n",
"mass_ch2 = (mass_carbon + 2 * mass_hydrogen)\n",
"\n",
"# Convert to weight fractions\n",
"wt_fraction_carbon = wt_fraction_poly * (mass_carbon / mass_ch2)\n",
"wt_fraction_hydrogen = wt_fraction_poly * (2 * mass_hydrogen / mass_ch2)\n",
"\n",
"# Add elements to material\n",
"borated_poly.add_element(\"C\", wt_fraction_carbon, percent_type=\"wo\")\n",
"borated_poly.add_element(\"H\", wt_fraction_hydrogen, percent_type=\"wo\")\n",
"borated_poly.add_element(\"B\", wt_fraction_boron, percent_type=\"wo\")\n",
"\n",
"# Set density (approximate, varies with formulation)\n",
"borated_poly.set_density(\"g/cm3\", 0.94) # Approximate for 5% boron loading\n",
"\n",
"concrete = openmc.Material(name='concrete')\n",
"\n",
"# Add elements/compounds by weight fraction (approximate values)\n",
"concrete.add_element(\"H\", 0.01, percent_type=\"wo\") # Water content\n",
"concrete.add_element(\"O\", 0.529, percent_type=\"wo\") # Oxygen from oxides\n",
"concrete.add_element(\"Si\", 0.337, percent_type=\"wo\") # From SiO2\n",
"concrete.add_element(\"Ca\", 0.044, percent_type=\"wo\") # From CaO\n",
"concrete.add_element(\"Al\", 0.022, percent_type=\"wo\") # From Al2O3\n",
"concrete.add_element(\"Fe\", 0.012, percent_type=\"wo\") # From Fe2O3\n",
"\n",
"# Set density (typical for standard concrete)\n",
"concrete.set_density(\"g/cm3\", 2.3) # Common density for ordinary concrete\n",
"\n",
"\n",
"# Create the air material\n",
"air = openmc.Material(name=\"Air\")\n",
"\n",
"# Add elements by volume fraction (approximate composition)\n",
"air.add_element(\"N\", 0.7809, percent_type=\"ao\") # Nitrogen\n",
"air.add_element(\"O\", 0.2095, percent_type=\"ao\") # Oxygen\n",
"air.add_element(\"Ar\", 0.0093, percent_type=\"ao\") # Argon\n",
"air.add_element(\"C\", 0.0003, percent_type=\"ao\") # Carbon (from CO2)\n",
"\n",
"# Set density (standard at STP)\n",
"air.set_density(\"g/cm3\", 0.001205) # Density at 1 atm, 20°C\n",
"\n",
"mats = openmc.Materials([pu, borated_poly, air, concrete])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7ef73d92-0104-4cab-92d3-da395eb89fbf",
"metadata": {},
"outputs": [],
"source": [
"# case dimensions (in) 20.39 x 11.2 x 7.21 in\n",
"wx = 15.0 * 2.54\n",
"wy = 11.2 * 2.54\n",
"wz = 7.21 * 2.54"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "04a93082-3817-4e65-87ff-e7118615bc02",
"metadata": {},
"outputs": [],
"source": [
"sph_diam = 9.2 # cm\n",
"sp1 = openmc.Sphere(2**-.5 * sph_diam*1.1, -0.5 * sph_diam, 0.0, sph_diam/2)\n",
"sp2 = openmc.Sphere(-2**-.5 * sph_diam*1.1, -0.5 * sph_diam, 0.0, sph_diam/2)\n",
"sp3 = openmc.Sphere(0.0, .8*sph_diam, 0.0, sph_diam/2)\n",
"\n",
"# cuts from the upper part of the case\n",
"sp4 = openmc.Sphere(sp1.x0, wy/2, wz/2 +0.8*sph_diam, sph_diam/2)\n",
"sp5 = openmc.Sphere(sp2.x0, wy/2, wz/2 +0.8*sph_diam, sph_diam/2)\n",
"sp6 = openmc.Sphere(sp3.x0, wy/2, wz/2 - 0.5 * sph_diam, sph_diam/2)\n",
"\n",
"\n",
"lower_case = openmc.model.RectangularParallelepiped(-wx/2, wx/2, -wy/2, wy/2, -wz/2, 0.0)\n",
"\n",
"# The lid is hinged open\n",
"upper_case = openmc.model.RectangularParallelepiped(-wx/2, wx/2, wy/2, wy/2 + wz/2, 0.0, wy)\n",
"\n",
"floor = openmc.ZPlane(-wz/2.)\n",
"\n",
"bbox = openmc.model.RectangularParallelepiped(-100, 100, -100, 100, -100, 100, boundary_type='vacuum')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "5ea4e51d-5fd0-4fa7-be51-e4d785a20473",
"metadata": {},
"outputs": [],
"source": [
"c1 = openmc.Cell(fill=pu, region=-sp1)\n",
"c2 = openmc.Cell(fill=pu, region=-sp2)\n",
"c3 = openmc.Cell(fill=pu, region=-sp3)\n",
"c4 = openmc.Cell(fill=borated_poly, region=-lower_case & +sp1 & +sp2 & +sp3)\n",
"c5 = openmc.Cell(fill=concrete, region=-floor & -bbox)\n",
"c6 = openmc.Cell(fill=air, region=-bbox & +sp1 & +sp2 & +sp3 & ~(-lower_case) & ~(-upper_case) & +floor)\n",
"c7 = openmc.Cell(fill=borated_poly, region=-upper_case & +sp4 & +sp5 & +sp6)\n",
"c8 = openmc.Cell(fill=air, region=-sp4)\n",
"c9 = openmc.Cell(fill=air, region=-sp5)\n",
"c10 = openmc.Cell(fill=air, region=-sp6)\n",
"\n",
"u = openmc.Universe(cells=[c1, c2, c3, c4, c5, c6, c7, c8, c9, c10])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "4ba99be6-e545-4125-9b2c-8aa7fc2ebeda",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'XZ plane view')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 645.161x649.351 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"u.plot(basis='xz', color_by='material', pixels=(500, 500))\n",
"a = plt.gca()\n",
"a.set_title('XZ plane view')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "f9c87b53-53e9-412e-bdbc-ebe97313971b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'YZ plane view')"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 645.161x649.351 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"u.plot(basis='yz', color_by='material', pixels=(500, 500))\n",
"a = plt.gca()\n",
"a.set_title('YZ plane view')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "1bd4cbab-752d-4e54-8e75-4588b7000df2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'XY plane view')"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 645.161x649.351 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"u.plot(basis='xy', color_by='material', pixels=(500, 500))\n",
"a = plt.gca()\n",
"a.set_title('XY plane view')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "7b163237-27b0-40c8-96bc-0f568dd376d8",
"metadata": {},
"outputs": [],
"source": [
"view3d = openmc.SolidRayTracePlot()\n",
"view3d.camera_position = (75.0, -75.0, 50.0)\n",
"view3d.look_at = (0.0, 0.0, 0.0)\n",
"view3d.color_by = 'material'\n",
"view3d.pixels = (800, 800)\n",
"view3d.opaque_domains = [borated_poly, pu, concrete]\n",
"\n",
"plots = openmc.Plots([view3d])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "5a8c7fe8-ecef-4e9f-9b85-852ae368115b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/gavin/Code/gavin-env/lib/python3.12/site-packages/openmc/source.py:658: FutureWarning: This class is deprecated in favor of 'IndependentSource'\n",
" warnings.warn(\"This class is deprecated in favor of 'IndependentSource'\", FutureWarning)\n"
]
}
],
"source": [
"# Create a settings object\n",
"settings = openmc.Settings()\n",
"\n",
"# Run in criticality mode\n",
"settings.run_mode = \"eigenvalue\"\n",
"\n",
"# Number of particles per batch\n",
"settings.particles = 100000 # Increase for better statistics\n",
"\n",
"# Number of inactive and active batches\n",
"settings.batches = 500 # Total batches\n",
"settings.inactive = 100 # Inactive batches (skipped for convergence)\n",
"\n",
"# Source distribution: Uniform in a 10x10x10 cm cube centered at the origin\n",
"source = openmc.Source()\n",
"source.space = openmc.stats.Box([-5, -5, -5], [5, 5, 5])\n",
"source.energy = openmc.stats.Watt(a=0.988, b=2.249) # Fission neutron spectrum\n",
"settings.source = source\n",
"\n",
"# Set up for reproducibility (optional)\n",
"settings.seed = 1234 # Ensures identical runs"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "57f0b8ef-1bd3-458b-9d3c-0596ac0d1787",
"metadata": {},
"outputs": [],
"source": [
"# Create a mesh for the tally\n",
"mesh = openmc.RegularMesh()\n",
"mesh.lower_left = [-100, -100, 1] # (x_min, y_min, z_min)\n",
"mesh.upper_right = [100, 100, 3] # (x_max, y_max, z_max)\n",
"mesh.dimension = [100, 100, 1] # 100x100 bins over XY, 1 bin in Z\n",
"\n",
"# Create a mesh filter\n",
"mesh_filter = openmc.MeshFilter(mesh)\n",
"\n",
"# Define a tally to measure neutron flux\n",
"tally = openmc.Tally(name=\"neutron_flux_xy\")\n",
"tally.filters = [mesh_filter]\n",
"tally.scores = [\"flux\"] # Flux = neutron track length per volume\n",
"\n",
"# Create a tally collection and export to XML\n",
"tallies = openmc.Tallies([tally])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "b467af31-182c-41a0-8c79-63864116f8fc",
"metadata": {},
"outputs": [],
"source": [
"model = openmc.Model(geometry=openmc.Geometry(u), plots=plots, materials=mats, settings=settings, tallies=tallies)\n",
"model.export_to_model_xml()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "0b7dab60-79f5-4eea-ae2d-66ee0046e77c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<?xml version='1.0' encoding='utf-8'?>\n",
"<model>\n",
" <materials>\n",
" <material depletable=\"true\" id=\"1\" name=\"plutonium metal\">\n",
" <density units=\"g/cc\" value=\"19.84\"/>\n",
" <nuclide ao=\"1.0\" name=\"Pu239\"/>\n",
" </material>\n",
" <material id=\"2\" name=\"Borated Polyethylene\">\n",
" <density units=\"g/cm3\" value=\"0.94\"/>\n",
" <nuclide name=\"C12\" wo=\"0.803707329115846\"/>\n",
" <nuclide name=\"C13\" wo=\"0.009755991622729606\"/>\n",
" <nuclide name=\"H1\" wo=\"0.1364941900911384\"/>\n",
" <nuclide name=\"H2\" wo=\"4.248917028598714e-05\"/>\n",
" <nuclide name=\"B10\" wo=\"0.00917774793753663\"/>\n",
" <nuclide name=\"B11\" wo=\"0.04082225206246337\"/>\n",
" </material>\n",
" <material id=\"3\" name=\"concrete\">\n",
" <density units=\"g/cm3\" value=\"2.3\"/>\n",
" <nuclide name=\"H1\" wo=\"0.009996888076484954\"/>\n",
" <nuclide name=\"H2\" wo=\"3.111923515045644e-06\"/>\n",
" <nuclide name=\"O16\" wo=\"0.5275964957161898\"/>\n",
" <nuclide name=\"O17\" wo=\"0.0002130200673981905\"/>\n",
" <nuclide name=\"O18\" wo=\"0.0011904842164120126\"/>\n",
" <nuclide name=\"Si28\" wo=\"0.3096137532431599\"/>\n",
" <nuclide name=\"Si29\" wo=\"0.016282998486690153\"/>\n",
" <nuclide name=\"Si30\" wo=\"0.01110324827014996\"/>\n",
" <nuclide name=\"Ca40\" wo=\"0.042531188977142385\"/>\n",
" <nuclide name=\"Ca42\" wo=\"0.0002980381407470517\"/>\n",
" <nuclide name=\"Ca43\" wo=\"6.366957659320949e-05\"/>\n",
" <nuclide name=\"Ca44\" wo=\"0.001006638966121805\"/>\n",
" <nuclide name=\"Ca46\" wo=\"2.018025974341998e-06\"/>\n",
" <nuclide name=\"Ca48\" wo=\"9.844631342120591e-05\"/>\n",
" <nuclide name=\"Al27\" wo=\"0.022\"/>\n",
" <nuclide name=\"Fe54\" wo=\"0.0006774669867248227\"/>\n",
" <nuclide name=\"Fe56\" wo=\"0.011028183453547495\"/>\n",
" <nuclide name=\"Fe57\" wo=\"0.00025924423364095635\"/>\n",
" <nuclide name=\"Fe58\" wo=\"3.510532608672706e-05\"/>\n",
" </material>\n",
" <material id=\"4\" name=\"Air\">\n",
" <density units=\"g/cm3\" value=\"0.001205\"/>\n",
" <nuclide ao=\"0.7780395633\" name=\"N14\"/>\n",
" <nuclide ao=\"0.0028604367000000003\" name=\"N15\"/>\n",
" <nuclide ao=\"0.20900151569999997\" name=\"O16\"/>\n",
" <nuclide ao=\"7.94005e-05\" name=\"O17\"/>\n",
" <nuclide ao=\"0.00041908379999999995\" name=\"O18\"/>\n",
" <nuclide ao=\"3.1024799999999995e-05\" name=\"Ar36\"/>\n",
" <nuclide ao=\"5.849699999999999e-06\" name=\"Ar38\"/>\n",
" <nuclide ao=\"0.009263125499999999\" name=\"Ar40\"/>\n",
" <nuclide ao=\"0.0002966766\" name=\"C12\"/>\n",
" <nuclide ao=\"3.3233999999999994e-06\" name=\"C13\"/>\n",
" </material>\n",
" </materials>\n",
" <geometry>\n",
" <cell id=\"21\" material=\"1\" region=\"-54\" universe=\"3\"/>\n",
" <cell id=\"22\" material=\"1\" region=\"-55\" universe=\"3\"/>\n",
" <cell id=\"23\" material=\"1\" region=\"-56\" universe=\"3\"/>\n",
" <cell id=\"24\" material=\"2\" region=\"-61 60 -63 62 -65 64 54 55 56\" universe=\"3\"/>\n",
" <cell id=\"25\" material=\"3\" region=\"-72 -74 73 -76 75 -78 77\" universe=\"3\"/>\n",
" <cell id=\"26\" material=\"4\" region=\"-74 73 -76 75 -78 77 54 55 56 (61 | -60 | 63 | -62 | 65 | -64) (67 | -66 | 69 | -68 | 71 | -70) 72\" universe=\"3\"/>\n",
" <cell id=\"27\" material=\"2\" region=\"-67 66 -69 68 -71 70 57 58 59\" universe=\"3\"/>\n",
" <cell id=\"28\" material=\"4\" region=\"-57\" universe=\"3\"/>\n",
" <cell id=\"29\" material=\"4\" region=\"-58\" universe=\"3\"/>\n",
" <cell id=\"30\" material=\"4\" region=\"-59\" universe=\"3\"/>\n",
" <surface coeffs=\"7.155920625607861 -4.6 0.0 4.6\" id=\"54\" type=\"sphere\"/>\n",
" <surface coeffs=\"-7.155920625607861 -4.6 0.0 4.6\" id=\"55\" type=\"sphere\"/>\n",
" <surface coeffs=\"0.0 7.359999999999999 0.0 4.6\" id=\"56\" type=\"sphere\"/>\n",
" <surface coeffs=\"7.155920625607861 14.223999999999998 16.5167 4.6\" id=\"57\" type=\"sphere\"/>\n",
" <surface coeffs=\"-7.155920625607861 14.223999999999998 16.5167 4.6\" id=\"58\" type=\"sphere\"/>\n",
" <surface coeffs=\"0.0 14.223999999999998 4.556700000000001 4.6\" id=\"59\" type=\"sphere\"/>\n",
" <surface coeffs=\"-19.05\" id=\"60\" type=\"x-plane\"/>\n",
" <surface coeffs=\"19.05\" id=\"61\" type=\"x-plane\"/>\n",
" <surface coeffs=\"-14.223999999999998\" id=\"62\" type=\"y-plane\"/>\n",
" <surface coeffs=\"14.223999999999998\" id=\"63\" type=\"y-plane\"/>\n",
" <surface coeffs=\"-9.1567\" id=\"64\" type=\"z-plane\"/>\n",
" <surface coeffs=\"0.0\" id=\"65\" type=\"z-plane\"/>\n",
" <surface coeffs=\"-19.05\" id=\"66\" type=\"x-plane\"/>\n",
" <surface coeffs=\"19.05\" id=\"67\" type=\"x-plane\"/>\n",
" <surface coeffs=\"14.223999999999998\" id=\"68\" type=\"y-plane\"/>\n",
" <surface coeffs=\"23.380699999999997\" id=\"69\" type=\"y-plane\"/>\n",
" <surface coeffs=\"0.0\" id=\"70\" type=\"z-plane\"/>\n",
" <surface coeffs=\"28.447999999999997\" id=\"71\" type=\"z-plane\"/>\n",
" <surface coeffs=\"-9.1567\" id=\"72\" type=\"z-plane\"/>\n",
" <surface boundary=\"vacuum\" coeffs=\"-100\" id=\"73\" type=\"x-plane\"/>\n",
" <surface boundary=\"vacuum\" coeffs=\"100\" id=\"74\" type=\"x-plane\"/>\n",
" <surface boundary=\"vacuum\" coeffs=\"-100\" id=\"75\" type=\"y-plane\"/>\n",
" <surface boundary=\"vacuum\" coeffs=\"100\" id=\"76\" type=\"y-plane\"/>\n",
" <surface boundary=\"vacuum\" coeffs=\"-100\" id=\"77\" type=\"z-plane\"/>\n",
" <surface boundary=\"vacuum\" coeffs=\"100\" id=\"78\" type=\"z-plane\"/>\n",
" </geometry>\n",
" <settings>\n",
" <run_mode>eigenvalue</run_mode>\n",
" <particles>100000</particles>\n",
" <batches>500</batches>\n",
" <inactive>100</inactive>\n",
" <source particle=\"neutron\" strength=\"1.0\" type=\"independent\">\n",
" <space type=\"box\">\n",
" <parameters>-5 -5 -5 5 5 5</parameters>\n",
" </space>\n",
" <energy parameters=\"0.988 2.249\" type=\"watt\"/>\n",
" </source>\n",
" <seed>1234</seed>\n",
" </settings>\n",
" <tallies>\n",
" <mesh id=\"3\">\n",
" <dimension>100 100 1</dimension>\n",
" <lower_left>-100 -100 1</lower_left>\n",
" <upper_right>100 100 3</upper_right>\n",
" </mesh>\n",
" <filter id=\"3\" type=\"mesh\">\n",
" <bins>3</bins>\n",
" </filter>\n",
" <tally id=\"3\" name=\"neutron_flux_xy\">\n",
" <filters>3</filters>\n",
" <scores>flux</scores>\n",
" </tally>\n",
" </tallies>\n",
" <plots>\n",
" <plot color_by=\"material\" id=\"12\" type=\"solid_raytrace\">\n",
" <pixels>800 800</pixels>\n",
" <camera_position>75.0 -75.0 50.0</camera_position>\n",
" <look_at>0.0 0.0 0.0</look_at>\n",
" <horizontal_field_of_view>70.0</horizontal_field_of_view>\n",
" <diffuse_fraction>0.1</diffuse_fraction>\n",
" <opaque_ids>2 1 3</opaque_ids>\n",
" </plot>\n",
" </plots>\n",
"</model>\n"
]
}
],
"source": [
"!cat model.xml"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "66a87ef1-8483-4230-969a-17f88a85d759",
"metadata": {},
"outputs": [],
"source": [
"sp = openmc.StatePoint('statepoint.500.h5')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "a82ec42e-c896-4279-ad97-9e2bf73068c8",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "584a2e7e-443e-4b15-9185-4824eeeb9bc6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x318ea40e0>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.pcolormesh(np.log(sp.tallies[3].mean.reshape((100, 100))))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b98aa24-9278-4dfe-9e06-81d4788f0df4",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
@catastropiyush
Copy link

Is there a way to run openmc in google colab?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment