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pshriwise/angular_filters_after.ipynb

Last active August 10, 2023 20:36
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SurfaceFilter and MuFitler combination
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angular_filters_after.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import openmc\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# openmc_data = \"/home/zoeprieto/Doctorado/openmc_ncrystal_conda/endfb-viii.0-hdf5/cross_sections.xml\"\n",
"openmc_data = '/Users/pshriwise/data/xs/openmc/nndc_hdf5/cross_sections.xml'\n",
"os.environ[\"OPENMC_CROSS_SECTIONS\"] = openmc_data\n",
"openmc.config['cross_sections'] = openmc_data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"mat=openmc.Material(material_id=1) \n",
"mat.add_nuclide(\"H1\",2)\n",
"mat.add_nuclide(\"O16\",1)\n",
"mat.add_s_alpha_beta('c_H_in_H2O')\n",
"mat.set_density('g/cm3', 1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x13bd955d0>"
]
},
"execution_count": 4,
"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": [
"Lx,Ly,Lz=10.0,10.0,10.0\n",
"\n",
"surf01=openmc.XPlane(boundary_type = \"vacuum\", surface_id = 1)\n",
"surf02=openmc.YPlane(boundary_type = \"vacuum\", surface_id = 2)\n",
"surf03=openmc.ZPlane(boundary_type = \"vacuum\", surface_id = 3) \n",
"surf04=openmc.XPlane(Lx,boundary_type = \"vacuum\", surface_id = 4)\n",
"surf05=openmc.YPlane(Ly,boundary_type = \"vacuum\", surface_id = 5)\n",
"surf06=openmc.ZPlane(Lz,boundary_type = \"vacuum\", surface_id = 6)\n",
"\n",
"cell=openmc.Cell(region = +surf01 & -surf04 & +surf02 & -surf05 & +surf03 & -surf06, fill=mat , cell_id = 1 )\n",
"\n",
"univ=openmc.Universe(cells = [cell], universe_id = 1)\n",
"\n",
"univ.plot(origin=(Lx/2, Ly/2, Lz/2), width=(Lx*1.5, Lz*1.5), pixels=(200, 200), basis='xy', color_by='cell')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"sets = openmc.Settings()\n",
"sets.run_mode = \"fixed source\"\n",
"sets.particles = 10000\n",
"sets.batches = 110\n",
"sets.inactive = 10\n",
"sets.photon_transport = True\n",
"\n",
"sets.surf_source_write = {\n",
" 'surface_ids': [6],\n",
" 'max_particles': 10000\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/pshriwise/soft/openmc/openmc/source.py:388: FutureWarning: This class is deprecated in favor of 'IndependentSource'\n",
" warnings.warn(\"This class is deprecated in favor of 'IndependentSource'\", FutureWarning)\n"
]
}
],
"source": [
"E_f=1.0e6\n",
"\n",
"X=np.linspace(0,Lx,50)\n",
"Y=np.linspace(0,Ly,50)\n",
"PX=np.sin(np.pi*X/(Lx))\n",
"PY=np.sin(np.pi*Y/Lx)\n",
"Z=[1e-6,]\n",
"PZ=[1.0,]\n",
"\n",
"source=openmc.Source()\n",
"source.space = openmc.stats.CartesianIndependent(openmc.stats.Tabular(X,PX),openmc.stats.Tabular(Y,PY),openmc.stats.Discrete(Z,PZ))\n",
"\n",
"mu=openmc.stats.Uniform(1.0,1.0)\n",
"phi=openmc.stats.Uniform(-np.pi,+np.pi)\n",
"source.angle= openmc.stats.PolarAzimuthal(mu, phi, reference_uvw=(0.0, 0.0, 1.0))\n",
"\n",
"source.energy = openmc.stats.Discrete([E_f], [1.0]) \n",
"sets.source=source"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"tallies = openmc.Tallies()\n",
"\n",
"# Grilla\n",
"mesh_xy = openmc.RegularMesh(mesh_id = 1)\n",
"mesh_xy.lower_left = [0, 0, 0]\n",
"mesh_xy.upper_right = [Lx, Ly, Lz]\n",
"mesh_xy.dimension = [100, 100, 1]\n",
"\n",
"mesh_xz = openmc.RegularMesh(mesh_id = 2)\n",
"mesh_xz.lower_left = [0, 0, 0]\n",
"mesh_xz.upper_right = [Lx, Ly, Lz]\n",
"mesh_xz.dimension = [100, 1, 100]\n",
"\n",
"\n",
"# Filtro de grilla\n",
"filter_mesh_xy = openmc.filter.MeshFilter(mesh_xy, filter_id = 1)\n",
"filter_mesh_xz = openmc.filter.MeshFilter(mesh_xz, filter_id = 2)\n",
"\n",
"# Filtro de particulas\n",
"filter_neutron = openmc.ParticleFilter(['neutron'], filter_id = 6)\n",
"filter_photon = openmc.ParticleFilter(['photon'], filter_id = 7)\n",
"\n",
"#Filtro de energías\n",
"filter_energy_neutron = openmc.filter.EnergyFilter(np.logspace(np.log10(1e-5), np.log10(2e7), 201),filter_id = 8)\n",
"\n",
"#Filtro de ángulos\n",
"filter_mu = openmc.MuFilter(np.linspace(-1.0,1.0,50),filter_id=10)\n",
"filter_theta = openmc.PolarFilter(np.linspace(0,np.pi,50),filter_id=11)\n",
"filter_phi= openmc.AzimuthalFilter(np.linspace(-np.pi,+np.pi,50),filter_id=12)\n",
"\n",
"# Tallies\n",
"flux_neutron_xy = openmc.Tally(name = 'flux_neutron_xy', tally_id = 1)\n",
"flux_neutron_xy.scores = [\"flux\"]\n",
"flux_neutron_xy.filters = [filter_mesh_xy, filter_neutron]\n",
"tallies.append(flux_neutron_xy)\n",
"\n",
"\n",
"flux_neutron_xz = openmc.Tally(name = 'flux_neutron_xz', tally_id = 3)\n",
"flux_neutron_xz.scores = [\"flux\"]\n",
"flux_neutron_xz.filters = [filter_mesh_xz, filter_neutron]\n",
"tallies.append(flux_neutron_xz)\n",
"\n",
"spectra_neutron = openmc.Tally(name = 'spectra_neutron', tally_id = 9)\n",
"spectra_neutron.scores = ['flux']\n",
"spectra_neutron.filters = [filter_energy_neutron, filter_neutron]\n",
"tallies.append(spectra_neutron)\n",
"\n",
"leak_salida_mu = openmc.Tally(name='leakage_salida_mu')\n",
"leak_salida_mu.filters = [openmc.SurfaceFilter(surf06), filter_mu]\n",
"leak_salida_mu.scores = ['current']\n",
"tallies.append(leak_salida_mu)\n",
"\n",
"leak_salida_phi = openmc.Tally(name='leakage_salida_phi')\n",
"leak_salida_phi.filters = [openmc.SurfaceFilter(surf06), filter_phi]\n",
"leak_salida_phi.scores = ['current']\n",
"tallies.append(leak_salida_phi)\n",
"\n",
"leak_salida_theta = openmc.Tally(name='leakage_salida_theta')\n",
"leak_salida_theta.filters = [openmc.SurfaceFilter(surf06), filter_theta]\n",
"leak_salida_theta.scores = ['current']\n",
"tallies.append(leak_salida_theta)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/pshriwise/soft/openmc/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=3.\n",
" warn(msg, IDWarning)\n"
]
}
],
"source": [
"geom = openmc.Geometry(univ)\n",
"mats = openmc.Materials(geom.get_all_materials().values()) \n",
"mats.cross_sections = openmc_data\n",
"\n",
"\n",
"geom.export_to_xml()\n",
"mats.export_to_xml()\n",
"sets.export_to_xml()\n",
"tallies.export_to_xml()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"rm: statepoint.110.h5: No such file or directory\n",
"rm: summary.h5: No such file or directory\n",
" %%%%%%%%%%%%%%%\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 | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
" License | https://docs.openmc.org/en/latest/license.html\n",
" Version | 0.13.4-dev\n",
" Git SHA1 | 922b3f9de6ccf3eb1e61171c3b48d51b62875884\n",
" Date/Time | 2023-08-10 15:33:03\n",
"\n",
" Reading settings XML file...\n",
" Reading cross sections XML file...\n",
" Reading materials XML file...\n",
" Reading geometry XML file...\n",
" Reading H1 from /Users/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n",
" Reading H from /Users/pshriwise/data/xs/openmc/nndc_hdf5/photon/H.h5 \n",
" Reading O16 from /Users/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n",
" Reading O from /Users/pshriwise/data/xs/openmc/nndc_hdf5/photon/O.h5 \n",
" Reading c_H_in_H2O from /Users/pshriwise/data/xs/openmc/nndc_hdf5/c_H_in_H2O.h5\n",
" Minimum neutron data temperature: 294 K\n",
" Maximum neutron data temperature: 294 K\n",
" Reading tallies XML file...\n",
" WARNING: You are tallying the 'current' score and haven't used a particle\n",
" filter. This score will include contributions from all particles.\n",
" WARNING: You are tallying the 'current' score and haven't used a particle\n",
" filter. This score will include contributions from all particles.\n",
" WARNING: You are tallying the 'current' score and haven't used a particle\n",
" filter. This score will include contributions from all particles.\n",
" Preparing distributed cell instances...\n",
" Reading plot XML file...\n",
" Writing summary.h5 file...\n",
" Maximum neutron transport energy: 20000000 eV for H1\n",
"\n",
" ===============> FIXED SOURCE TRANSPORT SIMULATION <===============\n",
"\n",
" Simulating batch 1\n",
" Simulating batch 2\n",
" Simulating batch 3\n",
" Simulating batch 4\n",
" Simulating batch 5\n",
" Simulating batch 6\n",
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" Simulating batch 71\n"
]
}
],
"source": [
"!rm statepoint.110.h5\n",
"!rm summary.h5\n",
"openmc.run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sp=openmc.StatePoint('statepoint.110.h5')\n",
"S0=1.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tally=sp.get_tally(name='flux_neutron_xy') \n",
"mesh_filter=tally.find_filter(openmc.filter.MeshFilter)\n",
"data=tally.get_slice(scores=['flux'])\n",
"\n",
"Nx=mesh_filter.mesh.dimension[0]\n",
"xmin=mesh_filter.mesh.lower_left[0]\n",
"xmax=mesh_filter.mesh.upper_right[0]\n",
"dx=xmax-xmin\n",
"\n",
"Ny=mesh_filter.mesh.dimension[1]\n",
"ymin=mesh_filter.mesh.lower_left[1]\n",
"ymax=mesh_filter.mesh.upper_right[1]\n",
"dy=ymax-ymin\n",
"\n",
"Nz=mesh_filter.mesh.dimension[2]\n",
"zmin=mesh_filter.mesh.lower_left[2]\n",
"zmax=mesh_filter.mesh.upper_right[2]\n",
"dz=zmax-zmin\n",
"\n",
"data.mean.shape=(Nx,Ny)\n",
"data.std_dev.shape=(Nx,Ny)\n",
"\n",
"data_mean = data.mean*S0/(dx/Nx*dy/Ny*dz/Nz)\n",
"data_stdv = data.std_dev*S0/(dx/Nx*dy/Ny*dz/Nz)\n",
"\n",
"plt.imshow(data_mean, origin='lower', interpolation='none', extent=(xmin,xmax,ymin,ymax), cmap='viridis')\n",
"plt.colorbar()\n",
"plt.xlabel('x [cm]')\n",
"plt.ylabel('y [cm]')\n",
"plt.title('flux XY')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tally=sp.get_tally(name='flux_neutron_xz') \n",
"mesh_filter=tally.find_filter(openmc.filter.MeshFilter)\n",
"data=tally.get_slice(scores=['flux'])\n",
"\n",
"Nx=mesh_filter.mesh.dimension[0]\n",
"xmin=mesh_filter.mesh.lower_left[0]\n",
"xmax=mesh_filter.mesh.upper_right[0]\n",
"dx=xmax-xmin\n",
"\n",
"Ny=mesh_filter.mesh.dimension[1]\n",
"ymin=mesh_filter.mesh.lower_left[1]\n",
"ymax=mesh_filter.mesh.upper_right[1]\n",
"dy=ymax-ymin\n",
"\n",
"Nz=mesh_filter.mesh.dimension[2]\n",
"zmin=mesh_filter.mesh.lower_left[2]\n",
"zmax=mesh_filter.mesh.upper_right[2]\n",
"dz=zmax-zmin\n",
"\n",
"data.mean.shape=(Nx,Nz)\n",
"data.std_dev.shape=(Nx,Nz)\n",
"\n",
"data_mean = data.mean*S0/(dx/Nx*dy/Ny*dz/Nz)\n",
"data_stdv = data.std_dev*S0/(dx/Nx*dy/Ny*dz/Nz)\n",
"\n",
"plt.imshow(data_mean, origin='lower', interpolation='none', extent=(xmin,xmax,zmin,zmax), cmap='viridis')\n",
"plt.colorbar()\n",
"plt.xlabel('x [cm]')\n",
"plt.ylabel('z [cm]')\n",
"plt.title('flux XZ')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dV=Lx*Ly*Lz\n",
"data=sp.get_tally(name='spectra_neutron').get_pandas_dataframe(nuclides=False)\n",
"data.columns=['Emin','Emax','particle','score','mean','stdv']\n",
"Emin=data['Emin'].values\n",
"Emax=data['Emax'].values\n",
"E=(Emin+Emax)/2.0\n",
"dE=Emax-Emin\n",
"\n",
"data_mean=data['mean']*S0/(dE*dV)\n",
"data_stdv=data['stdv']*S0/(dE*dV)\n",
"\n",
"plt.loglog(E,data_mean)\n",
"plt.xlabel('E [eV]')\n",
"plt.ylabel('$\\phi(E)$ [n/(cm² s eV)]')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dS=Lx*Ly\n",
"data=sp.get_tally(name='leakage_salida_mu').get_pandas_dataframe(nuclides=False)\n",
"data.columns=['surface','mu_min','mu_max','score','mean','stdv']\n",
"mu_min=data['mu_min'].values\n",
"mu_max=data['mu_max'].values\n",
"mu=(mu_min+mu_max)/2.0\n",
"dmu=mu_max-mu_min\n",
"data_mean_mu=data['mean']*S0/(dmu*dS)\n",
"data_stdv_mu=data['stdv']*S0/(dmu*dS)\n",
"\n",
"plt.plot(mu,data_mean_mu)\n",
"plt.xlabel('$\\mu$')\n",
"plt.ylabel('$J(\\mu)$')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dS=Lx*Ly\n",
"data=sp.get_tally(name='leakage_salida_theta').get_pandas_dataframe(nuclides=False)\n",
"\n",
"data.columns=['surface','theta_min','theta_max','score','mean','stdv']\n",
"theta_min=data['theta_min'].values\n",
"theta_max=data['theta_max'].values\n",
"cos_theta=(np.cos(theta_min)+np.cos(theta_max))/2.0\n",
"dcos_theta=np.cos(theta_min)-np.cos(theta_max)\n",
"data_mean_theta=data['mean']*S0/(dcos_theta*dS)\n",
"data_stdv_theta=data['stdv']*S0/(dcos_theta*dS)\n",
"\n",
"plt.plot(cos_theta,data_mean_theta)\n",
"plt.xlabel('$\\mu$')\n",
"plt.ylabel('$J(\\mu)$')\n",
"plt.grid()\n",
"plt.show()"
]
}
],
"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": "3.10.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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