1298 lines
302 KiB
Plaintext
1298 lines
302 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ece82854-c6ff-4bc5-8b3f-2c6ad2dddea7",
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"metadata": {},
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"source": [
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"# Defining an External Source\n",
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"\n",
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"By default, if you do not specify the initial source distribution, OpenMC considers a point source (at the origin) with an isotropic angular distribution and the Watt fission spectrum. Regardless of the source distribution, OpenMC will randomly sample from the distribution and \"accept\" the particle for transport if it is within the confines of the problem.\n",
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"\n",
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"For criticality problems, any initial source distribution is fine, provided you have a source rejection rate above 95% (i.e. at least 5% of your attempts to sample a neutron are successful, meaning that you're not automatically sampling a neutron outside the domain of the problem) - you'll still need to be sure to run enough inactive batches to converge away from this guessed source, so choosing a more physically realistic source could help you to run overall fewer inactive batches.\n",
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"\n",
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"Of course for a fixed source calculation, you will always be defining a source distribution corresponding to the known neutron/photon source of your problem (unless your starting source really is an isotropic point source at the origin of U235 fission neutrons!).\n",
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"\n",
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"OpenMC provides several options for defining an external source definition. There are currently four primary classes for defining a source:\n",
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"\n",
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"- `openmc.IndependentSource`\n",
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"- `openmc.FileSource`\n",
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"- `openmc.MeshSource`\n",
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"- `openmc.CompiledSource`\n",
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"\n",
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"We'll briefly go through each of these classes with the exception of `CompiledSource`, which is a bit more involved as it requires compiling a source class written in C++. `CompiledSource` is typically used when the source definition does not fit cleanly in any of the other formalisms; common examples here would be the neutron source in a tokamak or stellarator, or a beam-target source with a complex angle-energy distribution."
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]
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},
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{
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"cell_type": "markdown",
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"id": "127811a6-5560-4301-b160-f07e1a16d517",
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"metadata": {},
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"source": [
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"My note:\n",
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"For efficiency you need a propper realistic source, less inactive batches\n",
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"Source in a mesh with density for each point\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "997762f9-5b48-4893-b832-90b348e34c86",
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"metadata": {},
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"source": [
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"## `IndepedentSource`\n",
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"\n",
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"The `IndependentSource` allows you to define distributions in space, angle, and energy that are sampled _independently_ (hence the name). To demonstrate this, we first need a model to work with. We'll define a very simple problem composed of two cylinders, one made of U235 and one made of water."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "aa706f41-c675-4263-9f56-167404a7b486",
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"metadata": {},
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"outputs": [],
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"source": [
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"import openmc\n",
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"import random\n",
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"import math\n",
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"\n",
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"u235 = openmc.Material()\n",
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"u235.add_nuclide('U235', 1.0)\n",
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"u235.set_density('g/cm3', 5.0)\n",
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"\n",
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"h2o = openmc.Material()\n",
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"h2o.add_element('H', 2.0)\n",
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"h2o.add_element('O', 1.0)\n",
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"h2o.set_density('g/cm3', 1.0)\n",
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"\n",
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"cyl1 = openmc.ZCylinder(r=10)\n",
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"cyl2 = openmc.ZCylinder(r=20, boundary_type='vacuum')\n",
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"height= 200\n",
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"bottom = openmc.ZPlane(z0=-height/2, boundary_type='vacuum')\n",
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"top = openmc.ZPlane(z0=height/2, boundary_type='vacuum')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "b5c7cddc-2b2d-478a-b16e-b6fa34adf337",
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"metadata": {},
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"outputs": [],
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"source": [
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"inner = openmc.Cell(fill=u235, region=-cyl1 & +bottom & -top)\n",
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"outer = openmc.Cell(fill=h2o, region=+cyl1 & -cyl2 & +bottom & -top)\n",
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"model = openmc.Model()\n",
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"model.geometry = openmc.Geometry([inner, outer])\n",
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"# you can pass the cell and it doens not need a root universe"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f63b7815-5417-4845-afd5-a074d6391296",
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"metadata": {},
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"source": [
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"Now let's define the source. We'll start with the simplest source possible, which is a point source. All spatial, angular, and energy distributions can be found in the `openmc.stats` submodule. For a point source located at (1, 1, 0), we use `openmc.stats.Point`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "733366b2-c629-4931-87fb-46f4736190e9",
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"metadata": {},
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"outputs": [],
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"source": [
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"space = openmc.stats.Point((1., 1., 0))\n",
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"model.settings.source = openmc.IndependentSource(space=space)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "027e2c4a-113f-49f8-b01d-8423d3d7ce05",
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"metadata": {},
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"source": [
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"As with the `Cell` and `Universe` classes, the `Model` class also has a `plot` method, but with a `Model` we can also plot sampled source sites alongside the geometry. We specify the number of samples we'd like to take, and to use a dot for each location in our source. The `plane_tolerance` will plot all source particles within +/- 10 cm of our plot plane (this is just an efficiency metric - if we ran enough sample we'd get a denser and denser plot)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f90b1409-5644-495d-8e31-ccb4234478ed",
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_kwargs = {\n",
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" 'color_by' : 'material',\n",
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" 'n_samples': 500,\n",
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" 'plane_tolerance': height/2, # any of the poinst over this point\n",
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" 'source_kwargs': {'marker': '.'},\n",
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" 'colors': {inner: 'magenta', outer: 'lightgreen'}\n",
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"}\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eae79157-5b03-405f-b6b9-daf52dbd3587",
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"metadata": {},
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"source": [
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"The source shows up as a single point, as expected. Also note that when we don't specify anything for the angle distribution, it defaults to isotropic and the energy distribution defaults to a Watt fission spectrum.\n",
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"\n",
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"Let's change the source to a box source covering the inner cylinder."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "d993442e-f61a-472a-b2ad-efb5e42d566e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "09d067a7-cc64-415f-8787-ca35a96e7cea",
|
|
"metadata": {},
|
|
"source": [
|
|
"The `openmc.stats.Box` will uniformly sample within a paralleliped - the same source distribution could also have been achieved by independently setting the sampling distributions for the x, y, and z directions with the `openmc.stats.CartesianIndependent` class."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "602553ea-bce3-4cb4-b4a5-b6419c8c1444",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"bbox = inner.bounding_box"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "54f1e987-c95a-468e-a1a1-d55dfd3848ac",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"space = openmc.stats.Box(lower_left=bbox.lower_left , upper_right=bbox.upper_right)\n",
|
|
"model.settings.source = openmc.IndependentSource(space=space)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "654aacd0-8b84-4873-b042-be607310d023",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5623b756-5634-41fe-a705-bac96a38c99b",
|
|
"metadata": {},
|
|
"source": [
|
|
"If we want the points to appear only in the cylinder, we can use the `openmc.stats.CylindricalIndependent` class."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "d4e5174d-893d-422c-8646-b9d11ae2fc76",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rs = openmc.stats.Uniform(0,10)\n",
|
|
"phis = openmc.stats.Uniform(0, 2*math.pi)\n",
|
|
"zs = openmc.stats.delta_function(0)\n",
|
|
"\n",
|
|
"space = openmc.stats.CylindricalIndependent(r=rs , phi=phis , z=zs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "3063e942-b682-4aed-99ea-a2d9ffc37710",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.settings.source = openmc.IndependentSource(space=space)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "0aaa283b-8815-4fac-bd81-5395bb601c48",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "959775ad-02db-40d2-ae2e-ec6869b647c1",
|
|
"metadata": {},
|
|
"source": [
|
|
"Notice that the points are not uniformly distributed over the cylinder (_volumetrically_), preferrentially clustering at smaller radii. To make it uniform, the radial distribution needs to be power-law distributed (which has a PDF of $cx^ndx$). Here, we are essentially weighting by `r`, because the cylindrical differential volume element is $rdrd\\theta dz$."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "dd10af9a-2902-404a-b629-792ea9d4d500",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"rs = openmc.stats.PowerLaw(0,10, 1)\n",
|
|
"phis = openmc.stats.Uniform(0, 2*math.pi)\n",
|
|
"zs = openmc.stats.delta_function(0)\n",
|
|
"\n",
|
|
"space = openmc.stats.CylindricalIndependent(r=rs , phi=phis , z=zs)\n",
|
|
"model.settings.source = openmc.IndependentSource(space=space)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "6cd0c68a-9b5c-4f43-81f9-3b8d3649709b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dfd8da37-acee-432c-9d0b-175dafda3c8f",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Constraints\n",
|
|
"\n",
|
|
"OpenMC allows for source distributions to be further constrained, by setting the `constraints` parameter to a dictionary. For instance, this could be used to further reject source sites if they are not within a particular domain (cell, material, universe)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "d08df809-05db-4b57-8cad-e5422eac1e60",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"space = openmc.stats.Box(lower_left= bbox.lower_left, upper_right = bbox.upper_right)\n",
|
|
"model.settings.source= openmc.IndependentSource (space = space, constraints={'domains': [outer]})\n",
|
|
"model.settings.source= openmc.IndependentSource (space = space, constraints={'fissionable': True})\n",
|
|
"\n",
|
|
"# rejection is 5% \n",
|
|
"# TRISO thing ...\n",
|
|
"\n",
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3743c88e-df6a-4586-9c92-965f66a5e478",
|
|
"metadata": {},
|
|
"source": [
|
|
"Or, we could have restricted the source to only the fissionable regions."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "09002ecf-449f-4a49-b39a-e71c62e24888",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "36090e1c-6385-4ba5-9c6a-2e78b395b24c",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Angle and Energy Distributions\n",
|
|
"\n",
|
|
"To change the angle or energy distribution, we use the `angle=` and `energy=` arguments when creating an instance of `IndependentSource`. For the angle distribution, our options are:\n",
|
|
"\n",
|
|
"- `openmc.stats.Isotropic`\n",
|
|
"- `openmc.stats.Monodirectional`\n",
|
|
"- `openmc.stats.PolarAzimuthal`\n",
|
|
"\n",
|
|
"For the energy distribution, any univariate distribution is accepted, which includes:\n",
|
|
"\n",
|
|
"- `openmc.stats.Discrete`\n",
|
|
"- `openmc.stats.ContinuousTabular`\n",
|
|
"- `openmc.stats.Uniform`\n",
|
|
"- `openmc.stats.Mixture`\n",
|
|
"- `openmc.stats.Normal`\n",
|
|
"- ...\n",
|
|
"\n",
|
|
"See [here](https://docs.openmc.org/en/latest/pythonapi/stats.html) for the full list of univariate distributions. Also note that for the common case where you want the source at a single energy, you can use the `delta_function` function (or a `Discrete` distribution with a single energy value).\n",
|
|
"\n",
|
|
"For example, to adjust our neutron source to be a combination of neutrons at 10 MeV (40% of the neutrons) and 14.1 MeV (60% of the neutrons), traveling in the $+\\hat{y}$ direction, we would use the following."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "8a2b35ae-f999-4b99-8068-2d3545145808",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# things like gamma emmsions and stuff\n",
|
|
"energy = openmc.stats.Discrete([10e6, 14.1e6],[0.4, 0.6])\n",
|
|
"angle = openmc.stats.Monodirectional([0,1,0])\n",
|
|
"\n",
|
|
"model.settings.source = openmc.IndependentSource(energy=energy, angle=angle)\n",
|
|
"# there is particle tracking feature."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bf738a01-0021-4e0e-9bbe-fcf8f38aaed7",
|
|
"metadata": {},
|
|
"source": [
|
|
"## `FileSource`\n",
|
|
"\n",
|
|
"The `FileSource` class allows you to specify a source from an external HDF5 file that contains a finite number of discrete source sites. Source files can be created in a number of manners:\n",
|
|
"\n",
|
|
"- $k$-eigenvalue calculations will store the initial fission source in the statepoint file (or write any batch's source to a separate source file).\n",
|
|
"- A surface source can be collected during a simulation using the `Settings.surf_source_write` setting.\n",
|
|
"- Discrete source sites can be created in the Python API and written to file using the `openmc.ParticleList` class.\n",
|
|
"- An [MCPL](https://mctools.github.io/mcpl/) (Monte Carlo Particle Lists) file written from another code can be used."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1661e02f-1d50-4a08-aa32-517b0a2663f1",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's demonstrate how we can create a source file using the `ParticleList` class. This class takes a list of `SourceParticle` objects that we create individually. Let's start by creating monoenergetic points along a ring."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "0ffb8432-2d00-431b-8441-9b8d180a93ce",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"radius = 10\n",
|
|
"\n",
|
|
"particles = openmc.ParticleList()\n",
|
|
"for i in range(1000):\n",
|
|
" phi = random.uniform(0., 2.*math.pi)\n",
|
|
" x = math.cos(phi) * radius\n",
|
|
" y = math.sin(phi) * radius\n",
|
|
" p = openmc.SourceParticle(r=(x, y, 0.0))\n",
|
|
" particles.append(p)\n",
|
|
"\n",
|
|
"particles.export_to_hdf5('source.h5')\n",
|
|
"model.settings.source = openmc.FileSource('source.h5')\n",
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d40753e2-6101-47e9-88dc-70a615e5689e",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's add a little \"wobble\" to the sampled source sites by making the radius a sine function."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "3dfb0d50-5a3a-42d5-9f27-ac626c708440",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"particles = openmc.ParticleList()\n",
|
|
"for i in range(1000):\n",
|
|
" phi = random.uniform(0., 2.*math.pi)\n",
|
|
" x = math.cos(phi) * (radius + 0.8*math.sin(10*phi))\n",
|
|
" y = math.sin(phi) * (radius + 0.2*math.sin(10*phi))\n",
|
|
" p = openmc.SourceParticle(r=(x, y, 0.0))\n",
|
|
" particles.append(p)\n",
|
|
"\n",
|
|
"particles.export_to_hdf5('source.h5')\n",
|
|
"model.settings.source = openmc.FileSource('source.h5')\n",
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8d6306c0-ed6a-473e-a99c-fc6ffc94b917",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's also show how to write the fission source from a k-eigenvalue calculation. Since the source changes with each batch, we need to explicitly indicate at which batch we want to write the source. We write the source to a separate file, named `source.<batch>.h5`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "e0c45804-1aae-4d5f-a9e6-f745c111c655",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" %%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%\n",
|
|
" ################# %%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%\n",
|
|
" ############ %%%%%%%%%%%%%%%\n",
|
|
" ######## %%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%\n",
|
|
"\n",
|
|
" | The OpenMC Monte Carlo Code\n",
|
|
" Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n",
|
|
" License | https://docs.openmc.org/en/latest/license.html\n",
|
|
" Version | 0.15.3\n",
|
|
" Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n",
|
|
" Date/Time | 2025-11-25 10:54:15\n",
|
|
" OpenMP Threads | 2\n",
|
|
"\n",
|
|
" Reading model XML file 'model.xml' ...\n",
|
|
" Reading source file from\n",
|
|
" /home/ubuntu/openmc-nea-course/notebooks/sources/source.h5...\n",
|
|
" Reading chain file: /home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml...\n",
|
|
" Reading cross sections XML file...\n",
|
|
" Reading U235 from /home/ubuntu/data/endfb71_hdf5/U235.h5\n",
|
|
" Reading H1 from /home/ubuntu/data/endfb71_hdf5/H1.h5\n",
|
|
" Reading H2 from /home/ubuntu/data/endfb71_hdf5/H2.h5\n",
|
|
" Reading O16 from /home/ubuntu/data/endfb71_hdf5/O16.h5\n",
|
|
" Reading O17 from /home/ubuntu/data/endfb71_hdf5/O17.h5\n",
|
|
" Minimum neutron data temperature: 294 K\n",
|
|
" Maximum neutron data temperature: 294 K\n",
|
|
" Preparing distributed cell instances...\n",
|
|
" Writing summary.h5 file...\n",
|
|
" Maximum neutron transport energy: 20000000 eV for U235\n",
|
|
" Initializing source particles...\n",
|
|
"\n",
|
|
" ====================> K EIGENVALUE SIMULATION <====================\n",
|
|
"\n",
|
|
" Bat./Gen. k Average k\n",
|
|
" ========= ======== ====================\n",
|
|
" 1/1 1.09681\n",
|
|
" Creating source file source.01.h5 with 1000 particles ...\n",
|
|
" 2/1 0.95879\n",
|
|
" 3/1 0.95441\n",
|
|
" 4/1 1.01266\n",
|
|
" 5/1 1.00746\n",
|
|
" 6/1 0.91291\n",
|
|
" 7/1 0.98159\n",
|
|
" 8/1 0.94322\n",
|
|
" 9/1 1.08337\n",
|
|
" 10/1 0.93785\n",
|
|
" 11/1 1.05086\n",
|
|
" 12/1 1.05058\n",
|
|
" 13/1 0.98731\n",
|
|
" 14/1 0.96838\n",
|
|
" 15/1 1.00996\n",
|
|
" 16/1 0.95018\n",
|
|
" 17/1 1.02050\n",
|
|
" 18/1 1.01451\n",
|
|
" 19/1 0.91769\n",
|
|
" 20/1 1.05611\n",
|
|
" 21/1 1.01592\n",
|
|
" 22/1 0.97807\n",
|
|
" 23/1 0.98630\n",
|
|
" 24/1 0.86717\n",
|
|
" 25/1 0.90069\n",
|
|
" 26/1 0.96758\n",
|
|
" 27/1 1.02729\n",
|
|
" 28/1 1.06527\n",
|
|
" 29/1 0.94838\n",
|
|
" 30/1 0.99318\n",
|
|
" 31/1 0.96592\n",
|
|
" 32/1 0.98120\n",
|
|
" 33/1 1.02657\n",
|
|
" 34/1 0.97603\n",
|
|
" 35/1 0.96644\n",
|
|
" 36/1 1.02594\n",
|
|
" 37/1 1.11307\n",
|
|
" 38/1 0.97701\n",
|
|
" 39/1 0.94736\n",
|
|
" 40/1 1.05564\n",
|
|
" 41/1 0.97125\n",
|
|
" 42/1 0.94140\n",
|
|
" 43/1 0.94205\n",
|
|
" 44/1 0.94725\n",
|
|
" 45/1 0.94376\n",
|
|
" 46/1 1.02569\n",
|
|
" 47/1 0.90881\n",
|
|
" 48/1 1.01074\n",
|
|
" 49/1 0.91866\n",
|
|
" 50/1 0.95732\n",
|
|
" 51/1 0.93950\n",
|
|
" 52/1 0.98715 0.96332 +/- 0.02382\n",
|
|
" 53/1 0.99274 0.97313 +/- 0.01689\n",
|
|
" 54/1 0.99106 0.97761 +/- 0.01276\n",
|
|
" 55/1 0.93591 0.96927 +/- 0.01293\n",
|
|
" Creating state point statepoint.55.h5...\n",
|
|
" Creating source file source.55.h5 with 1000 particles ...\n",
|
|
"\n",
|
|
" =======================> TIMING STATISTICS <=======================\n",
|
|
"\n",
|
|
" Total time for initialization = 2.2741e-01 seconds\n",
|
|
" Reading cross sections = 6.9474e-02 seconds\n",
|
|
" Total time in simulation = 6.5182e-01 seconds\n",
|
|
" Time in transport only = 6.4499e-01 seconds\n",
|
|
" Time in inactive batches = 5.9363e-01 seconds\n",
|
|
" Time in active batches = 5.8184e-02 seconds\n",
|
|
" Time synchronizing fission bank = 2.1057e-03 seconds\n",
|
|
" Sampling source sites = 1.8940e-03 seconds\n",
|
|
" SEND/RECV source sites = 1.9240e-04 seconds\n",
|
|
" Time accumulating tallies = 1.2494e-05 seconds\n",
|
|
" Time writing statepoints = 1.0689e-03 seconds\n",
|
|
" Total time for finalization = 1.7980e-06 seconds\n",
|
|
" Total time elapsed = 8.8042e-01 seconds\n",
|
|
" Calculation Rate (inactive) = 84227 particles/second\n",
|
|
" Calculation Rate (active) = 85934.4 particles/second\n",
|
|
"\n",
|
|
" ============================> RESULTS <============================\n",
|
|
"\n",
|
|
" k-effective (Collision) = 0.95808 +/- 0.00564\n",
|
|
" k-effective (Track-length) = 0.96927 +/- 0.01293\n",
|
|
" k-effective (Absorption) = 0.98779 +/- 0.00787\n",
|
|
" Combined k-effective = 0.98136 +/- 0.01977\n",
|
|
" Leakage Fraction = 0.31660 +/- 0.00739\n",
|
|
"\n"
|
|
]
|
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},
|
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{
|
|
"data": {
|
|
"text/plain": [
|
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"PosixPath('/home/ubuntu/openmc-nea-course/notebooks/sources/statepoint.55.h5')"
|
|
]
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.settings.particles = 1000\n",
|
|
"model.settings.inactive = 50\n",
|
|
"model.settings.batches = 55\n",
|
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"\n",
|
|
"model.settings.sourcepoint = {'batches': [1,55], 'separate' : True, 'write': True}\n",
|
|
"model.run()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "aa7d5252-f9b8-46ef-8073-f846c5b4ef71",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[0m\u001b[01;34mSolution\u001b[0m/ model.xml source.55.h5 statepoint.55.h5\n",
|
|
"Sources.ipynb source.01.h5 source.h5 summary.h5\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"ls"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "44a90f5c-3fa6-4900-a1fc-8b65971efebb",
|
|
"metadata": {},
|
|
"source": [
|
|
"We could then load this source into a new simulation. By inspecting the two sources, we can clearly see how after just one batch, the remnants of our sine wave source are still sort of visible (not easy for our neutrons to reach far into the fuel to cause fission, as many fission in the outer layer of the fuel)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"id": "8e6707ae-55d8-4d06-a90d-81e203fef326",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
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"text/plain": [
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"<Figure size 258.065x259.74 with 1 Axes>"
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]
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},
|
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"metadata": {},
|
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"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.settings.source = openmc.FileSource('source.01.h5')\n",
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "248383bf-75d9-4207-bfb4-b15987efb4f1",
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.settings.source = openmc.FileSource('source.55.h5')\n",
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "5aadf523-354a-42ca-91e1-67f34e58132e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9bb04822-33b8-4abe-9b93-ceb5531dc041",
|
|
"metadata": {},
|
|
"source": [
|
|
"## `MeshSource` and `MeshSpatial`\n",
|
|
"\n",
|
|
"Finally, there are two classes that allow us to define a source distributed on a mesh:\n",
|
|
"\n",
|
|
"- `MeshSpatial`: for this class, we can specify source intensities on each mesh element and then use it within `IndependentSource`\n",
|
|
"- `MeshSource`: this class behaves like `MeshSpatial` except it allows unique energy and angle distributions for each mesh element.\n",
|
|
"\n",
|
|
"We'll demonstrate the simpler `MeshSpatial` class."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"id": "a3b1b5d7-2f61-43ce-9022-2777960d1c93",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"mesh = openmc.RegularMesh()\n",
|
|
"mesh.lower_left = (-10, -10, -height/2)\n",
|
|
"mesh.upper_right = bbox.upper_right\n",
|
|
"mesh.dimension = (3,3,1)\n",
|
|
"\n",
|
|
"source = openmc.stats.MeshSpatial(mesh, strengths=[9,9,1,3,5,6,7,4,3])\n",
|
|
"model.settings.source = openmc.IndependentSource(space = source)\n",
|
|
"\n",
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b9a3691a-1c8f-4543-9c76-1756843d8513",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Combining Multiple Sources\n",
|
|
"\n",
|
|
"You can also superimpose multiple sources - this is done by passing a list to `model.settings.source`. The default source strength in OpenMC is unity, so we can control the relative amounts of each source using the `strength` parameter. If the sources have very different strengths, you may want to set `uniform_source_sampling` to `True` so that they are equally sampled (with weights that now reflect the relative source strengths)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"id": "dd10762c-970c-4030-9237-7268c34992e0",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 41,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.settings.source = [openmc.IndependentSource(space=space, strength=0.7), openmc.FileSource(path='source.h5', strength=0.3)]\n",
|
|
"model.settings.uniform_source_sampling = True\n",
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1c135d44-87c4-49a8-bdd2-7622e0ae7fcf",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Surface Sources\n",
|
|
"\n",
|
|
"Let's now demonstrate how to write a surface source with OpenMC, which you may use to then run a second OpenMC calculation starting from those source particles. For example, this could be used to find the surface source on the outside of a reactor (with vacuum outside) using a k-eigenvalue calculation, to then load this source into a second fixed source calculation only considering the shielding outside the reactor. Of course, this example is an approximation to the integrated problem when both domains are present - but this notion of splitting a domain into separate runs is often used for experiment analysis.\n",
|
|
"\n",
|
|
"Let's record all the particles which cross from the aluminum into the fuel. To do so, we'll set a dictionary named `surf_source_write` in the settings. The particles added to the surface source will simply be added starting from the active batches, until reaching the desired number per rank."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "3a9a269a-e31b-4067-be95-4303934eec11",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# example would be to make the source for a irradiation verification"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"id": "ee3c00b9-228c-4606-9155-20d45ab80591",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.settings.surf_source_write = {'surface_ids' : [cyl1.id], 'max_particles' : 1000, 'cellfrom' : outer.id}\n",
|
|
"model.settings.particles = 4000"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"id": "97ccc3d1-8c40-4722-b685-7dad27d91128",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" %%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%%\n",
|
|
" ####################### %%%%%%%%%%%%%%%%%\n",
|
|
" ###################### %%%%%%%%%%%%%%%%%\n",
|
|
" #################### %%%%%%%%%%%%%%%%%\n",
|
|
" ################# %%%%%%%%%%%%%%%%%\n",
|
|
" ############### %%%%%%%%%%%%%%%%\n",
|
|
" ############ %%%%%%%%%%%%%%%\n",
|
|
" ######## %%%%%%%%%%%%%%\n",
|
|
" %%%%%%%%%%%\n",
|
|
"\n",
|
|
" | The OpenMC Monte Carlo Code\n",
|
|
" Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors\n",
|
|
" License | https://docs.openmc.org/en/latest/license.html\n",
|
|
" Version | 0.15.3\n",
|
|
" Commit Hash | 27e38e894697bb32a1dac7848d2618818b6b8daf\n",
|
|
" Date/Time | 2025-11-25 11:09:53\n",
|
|
" OpenMP Threads | 2\n",
|
|
"\n",
|
|
" Reading model XML file 'model.xml' ...\n",
|
|
" Reading source file from\n",
|
|
" /home/ubuntu/openmc-nea-course/notebooks/sources/source.h5...\n",
|
|
" Reading chain file: /home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml...\n",
|
|
" Reading cross sections XML file...\n",
|
|
" Reading U235 from /home/ubuntu/data/endfb71_hdf5/U235.h5\n",
|
|
" Reading H1 from /home/ubuntu/data/endfb71_hdf5/H1.h5\n",
|
|
" Reading H2 from /home/ubuntu/data/endfb71_hdf5/H2.h5\n",
|
|
" Reading O16 from /home/ubuntu/data/endfb71_hdf5/O16.h5\n",
|
|
" Reading O17 from /home/ubuntu/data/endfb71_hdf5/O17.h5\n",
|
|
" Minimum neutron data temperature: 294 K\n",
|
|
" Maximum neutron data temperature: 294 K\n",
|
|
" Preparing distributed cell instances...\n",
|
|
" Writing summary.h5 file...\n",
|
|
" Maximum neutron transport energy: 20000000 eV for U235\n",
|
|
" Initializing source particles...\n",
|
|
"\n",
|
|
" ====================> K EIGENVALUE SIMULATION <====================\n",
|
|
"\n",
|
|
" Bat./Gen. k Average k\n",
|
|
" ========= ======== ====================\n",
|
|
" 1/1 0.94226\n",
|
|
" Creating source file source.01.h5 with 4000 particles ...\n",
|
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" 2/1 0.94921\n",
|
|
" 3/1 1.00186\n",
|
|
" 4/1 0.94355\n",
|
|
" 5/1 0.95609\n",
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" 6/1 0.97632\n",
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" 7/1 1.00746\n",
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" 8/1 0.98547\n",
|
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" 9/1 0.97645\n",
|
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" 10/1 0.98297\n",
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" 11/1 0.93932\n",
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" 12/1 0.95849\n",
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" 13/1 0.94799\n",
|
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" 14/1 0.94845\n",
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" 15/1 0.94103\n",
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" 16/1 0.99539\n",
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" 17/1 0.94643\n",
|
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" 18/1 0.97490\n",
|
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" 19/1 0.92903\n",
|
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" 20/1 0.95781\n",
|
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" 21/1 0.98086\n",
|
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" 22/1 0.99211\n",
|
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" 23/1 0.97522\n",
|
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" 24/1 0.97925\n",
|
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" 25/1 0.98336\n",
|
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" 26/1 0.96271\n",
|
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" 27/1 0.97072\n",
|
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" 28/1 0.97655\n",
|
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" 29/1 0.94341\n",
|
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" 30/1 0.99869\n",
|
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" 31/1 0.99588\n",
|
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" 32/1 0.95113\n",
|
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" 33/1 0.95397\n",
|
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" 34/1 0.95596\n",
|
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" 35/1 0.95003\n",
|
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" 36/1 0.94741\n",
|
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" 37/1 0.93601\n",
|
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" 38/1 0.95350\n",
|
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" 39/1 0.95983\n",
|
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" 40/1 0.93609\n",
|
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" 41/1 0.96682\n",
|
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" 42/1 0.97665\n",
|
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" 43/1 0.96092\n",
|
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" 44/1 0.95950\n",
|
|
" 45/1 0.99311\n",
|
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" 46/1 0.94287\n",
|
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" 47/1 0.97707\n",
|
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" 48/1 0.95967\n",
|
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" 49/1 0.95609\n",
|
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" 50/1 1.01842\n",
|
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" 51/1 1.00296\n",
|
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" Creating source file surface_source.h5 with 1000 particles ...\n",
|
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" 52/1 0.95019 0.97658 +/- 0.02638\n",
|
|
" 53/1 0.96560 0.97292 +/- 0.01566\n",
|
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" 54/1 0.96246 0.97030 +/- 0.01138\n",
|
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" 55/1 0.95428 0.96710 +/- 0.00938\n",
|
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" Creating state point statepoint.55.h5...\n",
|
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" Creating source file source.55.h5 with 4000 particles ...\n",
|
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"\n",
|
|
" =======================> TIMING STATISTICS <=======================\n",
|
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"\n",
|
|
" Total time for initialization = 2.2475e-01 seconds\n",
|
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" Reading cross sections = 6.7036e-02 seconds\n",
|
|
" Total time in simulation = 2.4711e+00 seconds\n",
|
|
" Time in transport only = 2.4522e+00 seconds\n",
|
|
" Time in inactive batches = 2.2450e+00 seconds\n",
|
|
" Time in active batches = 2.2612e-01 seconds\n",
|
|
" Time synchronizing fission bank = 8.9547e-03 seconds\n",
|
|
" Sampling source sites = 7.9264e-03 seconds\n",
|
|
" SEND/RECV source sites = 9.9997e-04 seconds\n",
|
|
" Time accumulating tallies = 1.8762e-05 seconds\n",
|
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" Time writing statepoints = 1.1340e-03 seconds\n",
|
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" Total time for finalization = 1.7550e-06 seconds\n",
|
|
" Total time elapsed = 2.6981e+00 seconds\n",
|
|
" Calculation Rate (inactive) = 89088.4 particles/second\n",
|
|
" Calculation Rate (active) = 88447.4 particles/second\n",
|
|
"\n",
|
|
" ============================> RESULTS <============================\n",
|
|
"\n",
|
|
" k-effective (Collision) = 0.97051 +/- 0.00545\n",
|
|
" k-effective (Track-length) = 0.96710 +/- 0.00938\n",
|
|
" k-effective (Absorption) = 0.96137 +/- 0.00894\n",
|
|
" Combined k-effective = 0.98447 +/- 0.01030\n",
|
|
" Leakage Fraction = 0.32515 +/- 0.00229\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"PosixPath('/home/ubuntu/openmc-nea-course/notebooks/sources/statepoint.55.h5')"
|
|
]
|
|
},
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"model.run()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7e03474a-4347-4aa3-85ae-1c449e6c873e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"!ls"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9ebcaf58-4be3-4e13-82b4-485b993957cb",
|
|
"metadata": {},
|
|
"source": [
|
|
"To read in this surface source, we can load it with the `surf_source_read` parameter, which should be passed the path to the surface source file. Let's now modify our geometry to replace the fuel with some other goemetry, as if we were using a surface source for a typical experiment design procedure. If we want to run with only that surface source, we need to remove the wobbly ring source and our mesh source from earlier."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"id": "e3440386-6f6a-4720-9091-fe15796b6150",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.settings.source = []\n",
|
|
"model.settings.surf_source_write = {}\n",
|
|
"model.settings.surf_source_read = {'path': \"surface_source.h5\"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"id": "c2f8f686-9559-4d6f-865e-499e1d08f7cf",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
|
|
]
|
|
},
|
|
"execution_count": 48,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
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",
|
|
"text/plain": [
|
|
"<Figure size 258.065x259.74 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"model.plot(**plot_kwargs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "07f9786f-b7c4-445d-99ef-176781cb6e3b",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Fixed Source Calculations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "eeecbabe-9dc0-4492-b031-7505453c6959",
|
|
"metadata": {},
|
|
"source": [
|
|
"To run a fixed source calculation, all we need to do is modify the `run_mode` (which defaults to `eigenvalue`). With a fixed source calculation, there is no notion of inactive batches, so we will run all of our batches as active."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 50,
|
|
"id": "f52e0fc5-b5f2-4067-b1e3-56cb8466df26",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.settings.run_mode = 'fixed source'\n",
|
|
"model.settings.photon_transport = True"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "98dd12c7-ea43-4cec-9192-3b3a6031c750",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can also customize the particle type for each source, with the `particle` parameter. Make sure that photon transport is enabled if you have a photon source! (or if you want to track the photons created from neutron-induced reactions)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "217f5f2e-2935-4438-a32d-cd21e5a73404",
|
|
"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": "3.12.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|