FW CADIS Weight Windows#
Creating and utilizing a weight window to accelerate deep shielding simulations
This example simulates a shield room / bunker with corridor entrance and a neutron source in the center of the room. This example implements a FW-CADIS of weight window generation.
In this tutorial we shall focus on generating a weight window to accelerate the simulation of particles through a shield.
Weight Windows are found using the FW-CADIS method and used to accelerate the simulation.
The variance reduction method used for this simulation is well documented in the OpenMC documentation https://docs.openmc.org/en/stable/usersguide/variance_reduction.html
First we import openmc and other packages needed for the example and configure the nuclear data path
import time # used to time the simulation
import numpy as np
import copy
from matplotlib import pyplot as plt
from matplotlib.colors import LogNorm # used for plotting log scale graphs
import openmc
from pathlib import Path
# Setting the cross section path to the correct location in the docker image.
# If you are running this outside the docker image you will have to change this path to your local cross section path.
openmc.config['cross_sections'] = Path.home() / 'nuclear_data' / 'cross_sections.xml'
We create a couple of materials for the simulation
mat_air = openmc.Material(name="air")
mat_air.add_element("N", 0.784431)
mat_air.add_element("O", 0.210748)
mat_air.add_element("Ar", 0.0046)
mat_air.set_density("g/cc", 0.001205)
mat_concrete = openmc.Material(name='concrete')
mat_concrete.add_element("H",0.168759)
mat_concrete.add_element("C",0.001416)
mat_concrete.add_element("O",0.562524)
mat_concrete.add_element("Na",0.011838)
mat_concrete.add_element("Mg",0.0014)
mat_concrete.add_element("Al",0.021354)
mat_concrete.add_element("Si",0.204115)
mat_concrete.add_element("K",0.005656)
mat_concrete.add_element("Ca",0.018674)
mat_concrete.add_element("Fe",0.00426)
mat_concrete.set_density("g/cm3", 2.3)
materials_continuous_xs = openmc.Materials([mat_air, mat_concrete])
Now we define and plot the geometry. This geometry is defined by parameters for every width and height. The parameters input into the geometry in a stacked manner so they can easily be adjusted to change the geometry without creating overlapping cells.
width_a = 100
width_b = 100
width_c = 500
width_d = 100
width_e = 100
width_f = 100
width_g = 100
depth_a = 100
depth_b = 100
depth_c = 700
depth_d = 600
depth_e = 100
depth_f = 100
height_j = 100
height_k = 500
height_l = 100
xplane_0 = openmc.XPlane(x0=0, boundary_type="vacuum")
xplane_1 = openmc.XPlane(x0=xplane_0.x0 + width_a)
xplane_2 = openmc.XPlane(x0=xplane_1.x0 + width_b)
xplane_3 = openmc.XPlane(x0=xplane_2.x0 + width_c)
xplane_4 = openmc.XPlane(x0=xplane_3.x0 + width_d)
xplane_5 = openmc.XPlane(x0=xplane_4.x0 + width_e)
xplane_6 = openmc.XPlane(x0=xplane_5.x0 + width_f)
xplane_7 = openmc.XPlane(x0=xplane_6.x0 + width_g, boundary_type="vacuum")
yplane_0 = openmc.YPlane(y0=0, boundary_type="vacuum")
yplane_1 = openmc.YPlane(y0=yplane_0.y0 + depth_a)
yplane_2 = openmc.YPlane(y0=yplane_1.y0 + depth_b)
yplane_3 = openmc.YPlane(y0=yplane_2.y0 + depth_c)
yplane_4 = openmc.YPlane(y0=yplane_3.y0 + depth_d)
yplane_5 = openmc.YPlane(y0=yplane_4.y0 + depth_e)
yplane_6 = openmc.YPlane(y0=yplane_5.y0 + depth_f, boundary_type="vacuum")
zplane_1 = openmc.ZPlane(z0=0, boundary_type="vacuum")
zplane_2 = openmc.ZPlane(z0=zplane_1.z0 + height_j)
zplane_3 = openmc.ZPlane(z0=zplane_2.z0 + height_k)
zplane_4 = openmc.ZPlane(z0=zplane_3.z0 + height_l, boundary_type="vacuum")
outside_left_region = +xplane_0 & -xplane_1 & +yplane_1 & -yplane_5 & +zplane_1 & -zplane_4
wall_left_region = +xplane_1 & -xplane_2 & +yplane_2 & -yplane_4 & +zplane_2 & -zplane_3
wall_right_region = +xplane_5 & -xplane_6 & +yplane_2 & -yplane_5 & +zplane_2 & -zplane_3
wall_top_region = +xplane_1 & -xplane_4 & +yplane_4 & -yplane_5 & +zplane_2 & -zplane_3
outside_top_region = +xplane_0 & -xplane_7 & +yplane_5 & -yplane_6 & +zplane_1 & -zplane_4
wall_bottom_region = +xplane_1 & -xplane_6 & +yplane_1 & -yplane_2 & +zplane_2 & -zplane_3
outside_bottom_region = +xplane_0 & -xplane_7 & +yplane_0 & -yplane_1 & +zplane_1 & -zplane_4
wall_middle_region = +xplane_3 & -xplane_4 & +yplane_3 & -yplane_4 & +zplane_2 & -zplane_3
outside_right_region = +xplane_6 & -xplane_7 & +yplane_1 & -yplane_5 & +zplane_1 & -zplane_4
room_region = +xplane_2 & -xplane_3 & +yplane_2 & -yplane_4 & +zplane_2 & -zplane_3
gap_region = +xplane_3 & -xplane_4 & +yplane_2 & -yplane_3 & +zplane_2 & -zplane_3
corridor_region = +xplane_4 & -xplane_5 & +yplane_2 & -yplane_5 & +zplane_2 & -zplane_3
roof_region = +xplane_1 & -xplane_6 & +yplane_1 & -yplane_5 & +zplane_1 & -zplane_2
floor_region = +xplane_1 & -xplane_6 & +yplane_1 & -yplane_5 & +zplane_3 & -zplane_4
outside_left_cell = openmc.Cell(region=outside_left_region, fill=mat_air)
outside_right_cell = openmc.Cell(region=outside_right_region, fill=mat_air)
outside_top_cell = openmc.Cell(region=outside_top_region, fill=mat_air)
outside_bottom_cell = openmc.Cell(region=outside_bottom_region, fill=mat_air)
wall_left_cell = openmc.Cell(region=wall_left_region, fill=mat_concrete)
wall_right_cell = openmc.Cell(region=wall_right_region, fill=mat_concrete)
wall_top_cell = openmc.Cell(region=wall_top_region, fill=mat_concrete)
wall_bottom_cell = openmc.Cell(region=wall_bottom_region, fill=mat_concrete)
wall_middle_cell = openmc.Cell(region=wall_middle_region, fill=mat_concrete)
room_cell = openmc.Cell(region=room_region, fill=mat_air)
gap_cell = openmc.Cell(region=gap_region, fill=mat_air)
corridor_cell = openmc.Cell(region=corridor_region, fill=mat_air)
roof_cell = openmc.Cell(region=roof_region, fill=mat_concrete)
floor_cell = openmc.Cell(region=floor_region, fill=mat_concrete)
geometry = openmc.Geometry(
[
outside_bottom_cell,
outside_top_cell,
outside_left_cell,
outside_right_cell,
wall_left_cell,
wall_right_cell,
wall_top_cell,
wall_bottom_cell,
wall_middle_cell,
room_cell,
gap_cell,
corridor_cell,
roof_cell,
floor_cell,
]
)
Now we plot the geometry and color by materials.
plot = geometry.plot(basis='xy', color_by='material')
plot.figure.savefig('geometry_top_down_view.png', bbox_inches="tight")
Next we create a point source, this also uses the same geometry parameters to place in the center of the room regardless of the values of the parameters.
# location of the point source
source_x = width_a + width_b + width_c * 0.5
source_y = depth_a + depth_b + depth_c * 0.75
source_z = height_j + height_k * 0.5
space = openmc.stats.Point((source_x, source_y, source_z))
# all (100%) of source particles are 2.5MeV energy
source = openmc.IndependentSource(
space=space,
angle=openmc.stats.Isotropic(),
energy=openmc.stats.Discrete([2.5e6], [1.0]),
particle="neutron"
)
Make the settings for our continuous energy MC solver
# Create settings
settings = openmc.Settings()
settings.run_mode = "fixed source"
settings.source = source
settings.particles = 80
settings.batches = 100
# Normally in fixed source problems we don't use inactivie batches.
# However when using Random Ray we do need to use inactive batches
# More info here https://docs.openmc.org/en/stable/usersguide/random_ray.html#batches
settings.inactive = 50
ce_model = openmc.Model(geometry, materials_continuous_xs, settings)
At this point, we have a valid continuous energy Monte Carlo model!
Convert to Multigroup and Random Ray#
We begin by making a clone of our original continuous energy deck, and then convert it to multigroup.
This step will automatically compute material-wise multigroup cross sections for us by running a continous energy OpenMC simulation internally.
rr_model = copy.deepcopy(ce_model)
rr_model.convert_to_multigroup(
# I tend to use "stochastic_slab" method here.
# Using the "material_wise" method is more accurate but slower
# In problems where one needs weight windows to solve we don't really want
# the calculation of weight windows to be slow.
# In extreme cases the "material_wise" method could require its own weight windows to solve.
# The "stochastic_slab" method is much faster and works well for most problems.
# more details here https://docs.openmc.org/en/latest/usersguide/random_ray.html#the-easy-way
method="stochastic_slab",
overwrite_mgxs_library=True, # overrights the
nparticles=2000 # this is the default but can be adjusted upward to improve the fidelity of the generated cross section library
)
/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=40.
warn(msg, IDWarning)
/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=2.
warn(msg, IDWarning)
/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=11.
warn(msg, IDWarning)
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| The OpenMC Monte Carlo Code
Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors
License | https://docs.openmc.org/en/latest/license.html
Version | 0.15.3-dev233
Commit Hash | 6cf0eac20578463a6d5aea3be55774488015211e
Date/Time | 2025-08-02 09:27:02
OpenMP Threads | 4
Reading model XML file 'model.xml' ...
Reading cross sections XML file...
Reading N14 from /home/runner/nuclear_data/neutron/N14.h5
Reading N15 from /home/runner/nuclear_data/neutron/N15.h5
Reading O16 from /home/runner/nuclear_data/neutron/O16.h5
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Reading Ar36 from /home/runner/nuclear_data/neutron/Ar36.h5
WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K
Reading Ar38 from /home/runner/nuclear_data/neutron/Ar38.h5
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Minimum neutron data temperature: 294.0 K
Maximum neutron data temperature: 294.0 K
Preparing distributed cell instances...
Writing summary.h5 file...
Maximum neutron transport energy: 20000000.0 eV for N15
===============> FIXED SOURCE TRANSPORT SIMULATION <===============
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=======================> TIMING STATISTICS <=======================
Total time for initialization = 3.6144e+00 seconds
Reading cross sections = 3.6019e+00 seconds
Total time in simulation = 3.7371e+01 seconds
Time in transport only = 3.7311e+01 seconds
Time in active batches = 3.7371e+01 seconds
Time accumulating tallies = 1.7862e-02 seconds
Time writing statepoints = 9.1510e-03 seconds
Total time for finalization = 1.8100e-07 seconds
Total time elapsed = 4.0990e+01 seconds
Calculation Rate (active) = 10703.6 particles/second
============================> RESULTS <============================
Leakage Fraction = 0.00000 +/- 0.00000
We now have a valid multigroup Monte Carlo input deck, complete with a “mgxs.h5” multigroup cross section library file. Next, we convert the model to use random ray instead of multigroup monte carlo.
Random ray is needed for use with the FW-CADIS algorithm (which requires global adjoint flux information that the random ray solver generates).
The below function will analyze the geometry and initialize the random ray solver with reasonable parameters.
Users are free to tweak these parameters to improve the performance of the random ray solver, but the defaults are likely sufficient for weight window generation purposes for most cases.
rr_model.convert_to_random_ray()
Create a Mesh for: Tallies / Weight Windows / Random Ray Source Region Subdivision#
Now we setup a mesh that will be used in three ways:
For a mesh flux tally for viewing results
For subdividing the random ray source regions into smaller cells
For computing weight window parameters on
mesh = openmc.RegularMesh().from_domain(geometry)
mesh.dimension = (100, 100, 1)
mesh.id = 1
# 1. Make a flux tally for viewing the results of the simulation
mesh_filter = openmc.MeshFilter(mesh)
flux_tally = openmc.Tally(name="flux tally")
flux_tally.filters = [mesh_filter]
flux_tally.scores = ["flux"]
flux_tally.id = 42 # we set the ID because we need to access this later
tallies = openmc.Tallies([flux_tally])
# 2. Subdivide random ray source regions
rr_model.settings.random_ray['source_region_meshes'] = [(mesh, [rr_model.geometry.root_universe])]
/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another MeshBase instance already exists with id=1.
warn(msg, IDWarning)
/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another Tally instance already exists with id=42.
warn(msg, IDWarning)
Not required for WW generation, but one can run the regular (forward flux) random ray solver to make sure things are working before we attempt to generate weight windows with the command random_ray_wwg_statepoint = rr_model.run()
Use FW-CADIS to Generate Weight Windows#
Now add the weight window generator
rr_model.settings.weight_window_generators = openmc.WeightWindowGenerator(
method='fw_cadis',
mesh=mesh,
max_realizations=settings.batches
)
Then run the random ray model. This will automatically run a normal forward flux solve followed by a subsequent adjoint solve that is used to compute the weight windows. Our outputted flux tally data will be in terms of adjoint fluxes.
random_ray_wwg_statepoint = rr_model.run()
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| The OpenMC Monte Carlo Code
Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors
License | https://docs.openmc.org/en/latest/license.html
Version | 0.15.3-dev233
Commit Hash | 6cf0eac20578463a6d5aea3be55774488015211e
Date/Time | 2025-08-02 09:27:43
OpenMP Threads | 4
Reading model XML file 'model.xml' ...
WARNING: The maximum number of specified tally realizations (100) is greater
than the number of active batches (50).
WARNING: Other XML file input(s) are present. These files may be ignored in
favor of the model.xml file.
Reading cross sections HDF5 file...
Loading cross section data...
Loading air data...
Loading concrete data...
Preparing distributed cell instances...
Writing summary.h5 file...
======================> FORWARD FLUX SOLVE <=======================
=====> FIXED SOURCE TRANSPORT SIMULATION (RANDOM RAY SOLVER) <=====
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Creating state point statepoint.100.h5...
Exporting weight windows to weight_windows.h5...
=====================> SIMULATION STATISTICS <=====================
Total Iterations = 100
Number of Rays per Iteration = 4284
Inactive Distance = 2142.428528562855 cm
Active Distance = 10712.142642814273 cm
Source Regions (SRs) = 25620
SRs Containing External Sources = 1
Total Geometric Intersections = 4.3763e+08
Avg per Iteration = 4.3763e+06
Avg per Iteration per SR = 170.82
Avg SR Miss Rate per Iteration = 0.0000%
Energy Groups = 2
Total Integrations = 8.7527e+08
Avg per Iteration = 8.7527e+06
Volume Estimator Type = Hybrid
Adjoint Flux Mode = OFF
Source Shape = Flat
Sample Method = PRNG
Transport XS Stabilization Used = NO
=======================> TIMING STATISTICS <=======================
Total time for initialization = 2.1590e-02 seconds
Reading cross sections = 3.2467e-03 seconds
Total simulation time = 2.2190e+01 seconds
Transport sweep only = 2.2050e+01 seconds
Source update only = 1.9264e-02 seconds
Tally conversion only = 3.5889e-02 seconds
MPI source reductions only = 0.0000e+00 seconds
Other iteration routines = 8.4924e-02 seconds
Time in active batches = 1.1015e+01 seconds
Time writing statepoints = 5.6800e-03 seconds
Total time for finalization = 1.5087e-02 seconds
Time per integration = 2.5193e-08 seconds
======================> ADJOINT FLUX SOLVE <=======================
=====> FIXED SOURCE TRANSPORT SIMULATION (RANDOM RAY SOLVER) <=====
Simulating batch 1 (inactive)
Simulating batch 2 (inactive)
Simulating batch 3 (inactive)
Simulating batch 4 (inactive)
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Simulating batch 100
Creating state point statepoint.100.h5...
Exporting weight windows to weight_windows.h5...
=====================> SIMULATION STATISTICS <=====================
Total Iterations = 100
Number of Rays per Iteration = 4284
Inactive Distance = 2142.428528562855 cm
Active Distance = 10712.142642814273 cm
Source Regions (SRs) = 25620
SRs Containing External Sources = 25620
Total Geometric Intersections = 4.3763e+08
Avg per Iteration = 4.3763e+06
Avg per Iteration per SR = 170.82
Avg SR Miss Rate per Iteration = 0.0000%
Energy Groups = 2
Total Integrations = 8.7527e+08
Avg per Iteration = 8.7527e+06
Volume Estimator Type = Hybrid
Adjoint Flux Mode = ON
Source Shape = Flat
Sample Method = PRNG
Transport XS Stabilization Used = NO
=======================> TIMING STATISTICS <=======================
Total time for initialization = 0.0000e+00 seconds
Reading cross sections = 0.0000e+00 seconds
Total simulation time = 2.1064e+01 seconds
Transport sweep only = 2.1003e+01 seconds
Source update only = 1.8838e-02 seconds
Tally conversion only = 9.4080e-03 seconds
MPI source reductions only = 0.0000e+00 seconds
Other iteration routines = 3.2710e-02 seconds
Time in active batches = 1.0554e+01 seconds
Time writing statepoints = 5.8934e-03 seconds
Total time for finalization = 1.7762e-02 seconds
Time per integration = 2.3996e-08 seconds
Plot the ADJOINT flux. Not necessary, but interesting to see how they compare to the forward flux solve.
Now we should see a weight_windows.h5 file has been created
!ls -lh weight_windows.h5
-rw-r--r-- 1 runner docker 169K Aug 2 09:28 weight_windows.h5
Plot the resulting weight windows with the geometry overlay
weight_windows = openmc.hdf5_to_wws('weight_windows.h5')
ax1 = plt.subplot()
im = ax1.imshow(
weight_windows[0].lower_ww_bounds.squeeze().T,
origin='lower',
extent=mesh.bounding_box.extent['xy'],
norm=LogNorm()
)
plt.colorbar(im, ax=ax1)
ax1 = rr_model.plot(
outline='only',
extent=rr_model.bounding_box.extent['xz'],
axes=ax1, # Use the same axis as ax1\n",
pixels=10_000_000, #avoids rounded corners on outline
color_by='material',
)
ax1.set_title("lower_ww_bounds")
/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/openmc/weight_windows.py:772: FutureWarning: This function is deprecated in favor of 'WeightWindowsList.from_hdf5'
warnings.warn(
/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/openmc/mixin.py:70: IDWarning: Another MeshBase instance already exists with id=1.
warn(msg, IDWarning)
Text(0.5, 1.0, 'lower_ww_bounds')
Use Weight Windows to Accelerate Original Continuous Energy Monte Carlo Solve#
Now, to utilize the weight windows, we will add the weight windows into our settings object and configure things for continuous energy MC.
settings = openmc.Settings()
settings.weight_window_checkpoints = {'collision': True, 'surface': True}
settings.survival_biasing = False
settings.weight_windows = weight_windows
settings.particles = 40000
settings.batches = 12
settings.source = source
settings.run_mode = "fixed source"
tallies = openmc.Tallies([flux_tally])
First, to demonstrate the impacts of the weight windows, let’s run OpenMC without using them and plot the relative error in the fluxes. Given that the shield is optically thick, many regions will receive no tallies and/or have very high errors.
settings.weight_windows_on = False # Turn off weight windows for now
simulation_using_ww_off = openmc.Model(geometry, materials_continuous_xs, settings, tallies)
Creating a plotting function that we will reuse to plot results from the different simulations
def run_and_plot(model: openmc.Model, image_filename: str) -> openmc.StatePoint:
!rm s*.h5 || true
sp_filename = model.run()
with openmc.StatePoint(sp_filename) as sp:
flux_tally = sp.get_tally(name="flux tally")
mesh_extent = mesh.bounding_box.extent['xy']
# create a plot of the mean flux values
flux_mean = flux_tally.get_reshaped_data(value='mean', expand_dims=True).squeeze()
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 5))
ax1.imshow(
flux_mean.T,
origin="lower",
extent=mesh_extent,
norm=LogNorm(),
)
ax1 = model.plot(
outline='only',
extent=model.bounding_box.extent['xz'],
axes=ax1, # Use the same axis as ax1\n",
pixels=10_000_000, #avoids rounded corners on outline
color_by='material',
)
ax1.set_title("Flux Mean")
# create a plot of the flux relative error
flux_std_dev = flux_tally.get_reshaped_data(value='std_dev', expand_dims=True).squeeze()
ax2.imshow(
flux_std_dev.T,
origin="lower",
extent=mesh_extent,
norm=LogNorm(),
)
ax2 = model.plot(
outline='only',
extent=model.bounding_box.extent['xz'],
axes=ax2, # Use the same axis as ax2\n",
pixels=10_000_000, #avoids rounded corners on outline
color_by='material',
)
ax2.set_title("Flux Std. Dev.")
plt.savefig(image_filename)
return sp
run_and_plot(simulation_using_ww_off, "no_fw_cadis.png")
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| The OpenMC Monte Carlo Code
Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors
License | https://docs.openmc.org/en/latest/license.html
Version | 0.15.3-dev233
Commit Hash | 6cf0eac20578463a6d5aea3be55774488015211e
Date/Time | 2025-08-02 09:28:29
OpenMP Threads | 4
Reading model XML file 'model.xml' ...
WARNING: Other XML file input(s) are present. These files may be ignored in
favor of the model.xml file.
Reading cross sections XML file...
Reading N14 from /home/runner/nuclear_data/neutron/N14.h5
Reading N15 from /home/runner/nuclear_data/neutron/N15.h5
Reading O16 from /home/runner/nuclear_data/neutron/O16.h5
Reading O17 from /home/runner/nuclear_data/neutron/O17.h5
Reading O18 from /home/runner/nuclear_data/neutron/O18.h5
Reading Ar36 from /home/runner/nuclear_data/neutron/Ar36.h5
WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K
Reading Ar38 from /home/runner/nuclear_data/neutron/Ar38.h5
Reading Ar40 from /home/runner/nuclear_data/neutron/Ar40.h5
Reading H1 from /home/runner/nuclear_data/neutron/H1.h5
Reading H2 from /home/runner/nuclear_data/neutron/H2.h5
Reading C12 from /home/runner/nuclear_data/neutron/C12.h5
Reading C13 from /home/runner/nuclear_data/neutron/C13.h5
Reading Na23 from /home/runner/nuclear_data/neutron/Na23.h5
Reading Mg24 from /home/runner/nuclear_data/neutron/Mg24.h5
Reading Mg25 from /home/runner/nuclear_data/neutron/Mg25.h5
Reading Mg26 from /home/runner/nuclear_data/neutron/Mg26.h5
Reading Al27 from /home/runner/nuclear_data/neutron/Al27.h5
Reading Si28 from /home/runner/nuclear_data/neutron/Si28.h5
Reading Si29 from /home/runner/nuclear_data/neutron/Si29.h5
Reading Si30 from /home/runner/nuclear_data/neutron/Si30.h5
Reading K39 from /home/runner/nuclear_data/neutron/K39.h5
Reading K40 from /home/runner/nuclear_data/neutron/K40.h5
Reading K41 from /home/runner/nuclear_data/neutron/K41.h5
Reading Ca40 from /home/runner/nuclear_data/neutron/Ca40.h5
Reading Ca42 from /home/runner/nuclear_data/neutron/Ca42.h5
Reading Ca43 from /home/runner/nuclear_data/neutron/Ca43.h5
Reading Ca44 from /home/runner/nuclear_data/neutron/Ca44.h5
Reading Ca46 from /home/runner/nuclear_data/neutron/Ca46.h5
Reading Ca48 from /home/runner/nuclear_data/neutron/Ca48.h5
Reading Fe54 from /home/runner/nuclear_data/neutron/Fe54.h5
Reading Fe56 from /home/runner/nuclear_data/neutron/Fe56.h5
Reading Fe57 from /home/runner/nuclear_data/neutron/Fe57.h5
Reading Fe58 from /home/runner/nuclear_data/neutron/Fe58.h5
Minimum neutron data temperature: 294.0 K
Maximum neutron data temperature: 294.0 K
Preparing distributed cell instances...
Writing summary.h5 file...
Maximum neutron transport energy: 20000000.0 eV for N15
===============> FIXED SOURCE TRANSPORT SIMULATION <===============
Simulating batch 1
Simulating batch 2
Simulating batch 3
Simulating batch 4
Simulating batch 5
Simulating batch 6
Simulating batch 7
Simulating batch 8
Simulating batch 9
Simulating batch 10
Simulating batch 11
Simulating batch 12
Creating state point statepoint.12.h5...
=======================> TIMING STATISTICS <=======================
Total time for initialization = 3.6187e+00 seconds
Reading cross sections = 3.5911e+00 seconds
Total time in simulation = 4.2784e+01 seconds
Time in transport only = 4.2775e+01 seconds
Time in active batches = 4.2784e+01 seconds
Time accumulating tallies = 1.7299e-04 seconds
Time writing statepoints = 5.7006e-03 seconds
Total time for finalization = 8.8105e-03 seconds
Total time elapsed = 4.6416e+01 seconds
Calculation Rate (active) = 11219.2 particles/second
============================> RESULTS <============================
Leakage Fraction = 0.00193 +/- 0.00006
<openmc.statepoint.StatePoint at 0x7f4fcd4f0fb0>
As expected, the error spikes quickly once entering the shield, with most areas deep into the shield receiving no tallies at all.
On this computer the simulation speed was 32553 particles per second for the simulation without weight windows and 6379 particles per second when weight windows were turned on. The particle splitting means individual particles take longer to simulate as the continue to be transported more.
So the simulation with weight window took longer to run and consumed more compute, to make this a fair comparison we should chage the particles per batch or batches so that the simulations both have the same compute.
Next, let’s run OpenMC with weight windows enabled, and see the impact.
settings.weight_windows_on = True # Now, turn on the FW-CADIS generated weight windows
settings.batches = 2
simulation_using_ww_on = openmc.Model(geometry, materials_continuous_xs, settings, tallies)
run_and_plot(simulation_using_ww_on, "fw_cadis.png")
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| The OpenMC Monte Carlo Code
Copyright | 2011-2025 MIT, UChicago Argonne LLC, and contributors
License | https://docs.openmc.org/en/latest/license.html
Version | 0.15.3-dev233
Commit Hash | 6cf0eac20578463a6d5aea3be55774488015211e
Date/Time | 2025-08-02 09:29:20
OpenMP Threads | 4
Reading model XML file 'model.xml' ...
WARNING: Other XML file input(s) are present. These files may be ignored in
favor of the model.xml file.
Reading cross sections XML file...
Reading N14 from /home/runner/nuclear_data/neutron/N14.h5
Reading N15 from /home/runner/nuclear_data/neutron/N15.h5
Reading O16 from /home/runner/nuclear_data/neutron/O16.h5
Reading O17 from /home/runner/nuclear_data/neutron/O17.h5
Reading O18 from /home/runner/nuclear_data/neutron/O18.h5
Reading Ar36 from /home/runner/nuclear_data/neutron/Ar36.h5
WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K
Reading Ar38 from /home/runner/nuclear_data/neutron/Ar38.h5
Reading Ar40 from /home/runner/nuclear_data/neutron/Ar40.h5
Reading H1 from /home/runner/nuclear_data/neutron/H1.h5
Reading H2 from /home/runner/nuclear_data/neutron/H2.h5
Reading C12 from /home/runner/nuclear_data/neutron/C12.h5
Reading C13 from /home/runner/nuclear_data/neutron/C13.h5
Reading Na23 from /home/runner/nuclear_data/neutron/Na23.h5
Reading Mg24 from /home/runner/nuclear_data/neutron/Mg24.h5
Reading Mg25 from /home/runner/nuclear_data/neutron/Mg25.h5
Reading Mg26 from /home/runner/nuclear_data/neutron/Mg26.h5
Reading Al27 from /home/runner/nuclear_data/neutron/Al27.h5
Reading Si28 from /home/runner/nuclear_data/neutron/Si28.h5
Reading Si29 from /home/runner/nuclear_data/neutron/Si29.h5
Reading Si30 from /home/runner/nuclear_data/neutron/Si30.h5
Reading K39 from /home/runner/nuclear_data/neutron/K39.h5
Reading K40 from /home/runner/nuclear_data/neutron/K40.h5
Reading K41 from /home/runner/nuclear_data/neutron/K41.h5
Reading Ca40 from /home/runner/nuclear_data/neutron/Ca40.h5
Reading Ca42 from /home/runner/nuclear_data/neutron/Ca42.h5
Reading Ca43 from /home/runner/nuclear_data/neutron/Ca43.h5
Reading Ca44 from /home/runner/nuclear_data/neutron/Ca44.h5
Reading Ca46 from /home/runner/nuclear_data/neutron/Ca46.h5
Reading Ca48 from /home/runner/nuclear_data/neutron/Ca48.h5
Reading Fe54 from /home/runner/nuclear_data/neutron/Fe54.h5
Reading Fe56 from /home/runner/nuclear_data/neutron/Fe56.h5
Reading Fe57 from /home/runner/nuclear_data/neutron/Fe57.h5
Reading Fe58 from /home/runner/nuclear_data/neutron/Fe58.h5
Minimum neutron data temperature: 294.0 K
Maximum neutron data temperature: 294.0 K
Preparing distributed cell instances...
Writing summary.h5 file...
Maximum neutron transport energy: 20000000.0 eV for N15
===============> FIXED SOURCE TRANSPORT SIMULATION <===============
Simulating batch 1
Simulating batch 2
Creating state point statepoint.2.h5...
=======================> TIMING STATISTICS <=======================
Total time for initialization = 3.6674e+00 seconds
Reading cross sections = 3.6394e+00 seconds
Total time in simulation = 2.4729e+01 seconds
Time in transport only = 2.4724e+01 seconds
Time in active batches = 2.4729e+01 seconds
Time accumulating tallies = 3.7961e-05 seconds
Time writing statepoints = 5.5021e-03 seconds
Total time for finalization = 9.1208e-03 seconds
Total time elapsed = 2.8411e+01 seconds
Calculation Rate (active) = 3235.05 particles/second
============================> RESULTS <============================
Leakage Fraction = 0.00201 +/- 0.00012
<openmc.statepoint.StatePoint at 0x7f4ff9886de0>
