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Merge remote-tracking branch 'upstream/develop' into oo_filters

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Sterling Harper 2016-08-01 10:56:24 -05:00
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13
docs/source/pythonapi/examples/mgxs-part-i.ipynb
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@ -383,6 +383,9 @@
"* `ScatterMatrixXS`\n",
"* `NuScatterMatrixXS`\n",
"* `Chi`\n",
"* `ChiPrompt`\n",
"* `InverseVelocity`\n",
"* `PromptNuFissionXS`\n",
"\n",
"These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group total, absorption and scattering cross sections with our 2-group structure."
]
@ -1164,21 +1167,21 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 2",
"language": "python",
"name": "python3"
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,

15
docs/source/pythonapi/examples/mgxs-part-iii.ipynb
View file

@ -549,6 +549,9 @@
"* `ScatterMatrixXS` (`\"scatter matrix\"`)\n",
"* `NuScatterMatrixXS` (`\"nu-scatter matrix\"`)\n",
"* `Chi` (`\"chi\"`)\n",
"* `ChiPrompt` (`\"chi prompt\"`)\n",
"* `InverseVelocity` (`\"inverse-velocity\"`)\n",
"* `PromptNuFissionXS` (`\"prompt-nu-fission\"`)\n",
"\n",
"In this case, let's create the multi-group cross sections needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we will define `\"transport\"`, `\"nu-fission\"`, `'\"fission\"`, `\"nu-scatter matrix\"` and `\"chi\"` cross sections for our `Library`.\n",
"\n",
@ -571,7 +574,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports `\"material,\"` `\"cell,\"` and `\"universe\"` domain types. We will use a `\"cell\"` domain type here to compute cross sections in each of the cells in the fuel assembly geometry.\n",
"Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports `\"material\"`, `\"cell\"`, `\"universe\"`, and `\"mesh\"` domain types. We will use a `\"cell\"` domain type here to compute cross sections in each of the cells in the fuel assembly geometry.\n",
"\n",
"**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell or universe) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property. In our case, we wish to compute multi-group cross sections in each and every cell since they will be needed in our downstream OpenMOC calculation on the identical combinatorial geometry mesh."
]
@ -1596,21 +1599,21 @@
],
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"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 2",
"language": "python",
"name": "python3"
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,

14
docs/source/pythonapi/examples/mgxs-part-iv.ipynb
View file

@ -519,9 +519,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports \"material,\" \"cell,\" and \"universe\" domain types. In this simple example, we wish to compute multi-group cross sections only for each material andtherefore will use a \"material\" domain type.\n",
"Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports \"material\" \"cell\", \"universe\", and \"mesh\" domain types. In this simple example, we wish to compute multi-group cross sections only for each material and therefore will use a \"material\" domain type.\n",
"\n",
"**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell or universe) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property."
"**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell, universe, or mesh) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property."
]
},
{
@ -1437,21 +1437,21 @@
],
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"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 2",
"language": "python",
"name": "python3"
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
"pygments_lexer": "ipython2",
"version": "2.7.11"
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"nbformat": 4,

506
docs/source/pythonapi/examples/nuclear-data.ipynb
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File diff suppressed because one or more lines are too long

3
docs/source/pythonapi/index.rst
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@ -269,13 +269,16 @@ Multi-group Cross Sections
openmc.mgxs.AbsorptionXS
openmc.mgxs.CaptureXS
openmc.mgxs.Chi
openmc.mgxs.ChiPrompt
openmc.mgxs.FissionXS
openmc.mgxs.InverseVelocity
openmc.mgxs.KappaFissionXS
openmc.mgxs.MultiplicityMatrixXS
openmc.mgxs.NuFissionXS
openmc.mgxs.NuFissionMatrixXS
openmc.mgxs.NuScatterXS
openmc.mgxs.NuScatterMatrixXS
openmc.mgxs.PromptNuFissionXS
openmc.mgxs.ScatterXS
openmc.mgxs.ScatterMatrixXS
openmc.mgxs.TotalXS

4
docs/source/usersguide/input.rst
View file

@ -1764,6 +1764,10 @@ The ``<tally>`` element accepts the following sub-elements:
| |fission. This score type is not used in the |
| |multi-group :ref:`energy_mode`. |
+----------------------+---------------------------------------------------+
|prompt-nu-fission |Total production of prompt neutrons due to |
| |fission. This score type is not used in the |
| |multi-group :ref:`energy_mode`. |
+----------------------+---------------------------------------------------+
|nu-fission |Total production of neutrons due to fission. |
+----------------------+---------------------------------------------------+
|nu-scatter, |These scores are similar in functionality to their |

2
openmc/__init__.py
View file

@ -9,8 +9,8 @@ from openmc.plots import *
from openmc.settings import *
from openmc.surface import *
from openmc.universe import *
from openmc.mgxs_library import *
from openmc.mesh import *
from openmc.mgxs_library import *
from openmc.filter import *
from openmc.trigger import *
from openmc.tallies import *

2
openmc/checkvalue.py
View file

@ -212,7 +212,7 @@ def check_less_than(name, value, maximum, equality=False):
raise ValueError(msg)
def check_greater_than(name, value, minimum, equality=False):
"""Ensure that an object's value is less than a given value.
"""Ensure that an object's value is greater than a given value.
Parameters
----------

9
openmc/data/data.py
View file

@ -122,10 +122,11 @@ ATOMIC_SYMBOL = {1: 'H', 2: 'He', 3: 'Li', 4: 'Be', 5: 'B', 6: 'C', 7: 'N',
114: 'Fl', 116: 'Lv'}
ATOMIC_NUMBER = {value: key for key, value in ATOMIC_SYMBOL.items()}
REACTION_NAME = {1: '(n,total)', 2: '(n,elastic)', 4: '(n,level)', 5: '(n,misc)', 11: '(n,2nd)',
16: '(n,2n)', 17: '(n,3n)', 18: '(n,fission)', 19: '(n,f)',
20: '(n,nf)', 21: '(n,2nf)', 22: '(n,na)', 23: '(n,n3a)',
24: '(n,2na)', 25: '(n,3na)', 28: '(n,np)', 29: '(n,n2a)',
REACTION_NAME = {1: '(n,total)', 2: '(n,elastic)', 4: '(n,level)',
5: '(n,misc)', 11: '(n,2nd)', 16: '(n,2n)', 17: '(n,3n)',
18: '(n,fission)', 19: '(n,f)', 20: '(n,nf)', 21: '(n,2nf)',
22: '(n,na)', 23: '(n,n3a)', 24: '(n,2na)', 25: '(n,3na)',
27: '(n,absorption)', 28: '(n,np)', 29: '(n,n2a)',
30: '(n,2n2a)', 32: '(n,nd)', 33: '(n,nt)', 34: '(n,nHe-3)',
35: '(n,nd2a)', 36: '(n,nt2a)', 37: '(n,4n)', 38: '(n,3nf)',
41: '(n,2np)', 42: '(n,3np)', 44: '(n,n2p)', 45: '(n,npa)',

48
openmc/data/function.py
View file

@ -80,50 +80,46 @@ class Tabulated1D(object):
# Get indices for interpolation
idx = np.searchsorted(self.x, x, side='right') - 1
# Find lowest valid index
i_low = np.searchsorted(idx, 0)
# Loop over interpolation regions
for k in range(len(self.breakpoints)):
# Determine which x values are within this interpolation range
i_high = np.searchsorted(idx, self.breakpoints[k] - 1)
# Get indices for the begining and ending of this region
i_begin = self.breakpoints[k-1] - 1 if k > 0 else 0
i_end = self.breakpoints[k] - 1
# Get x values and bounding (x,y) pairs
xk = x[i_low:i_high]
xi = self.x[idx[i_low:i_high]]
xi1 = self.x[idx[i_low:i_high] + 1]
yi = self.y[idx[i_low:i_high]]
yi1 = self.y[idx[i_low:i_high] + 1]
# Figure out which idx values lie within this region
contained = (idx >= i_begin) & (idx < i_end)
xk = x[contained] # x values in this region
xi = self.x[idx[contained]] # low edge of corresponding bins
xi1 = self.x[idx[contained] + 1] # high edge of corresponding bins
yi = self.y[idx[contained]]
yi1 = self.y[idx[contained] + 1]
if self.interpolation[k] == 1:
# Histogram
y[i_low:i_high] = yi
y[contined] = yi
elif self.interpolation[k] == 2:
# Linear-linear
y[i_low:i_high] = yi + (xk - xi)/(xi1 - xi)*(yi1 - yi)
y[contained] = yi + (xk - xi)/(xi1 - xi)*(yi1 - yi)
elif self.interpolation[k] == 3:
# Linear-log
y[i_low:i_high] = yi + np.log(xk/xi)/np.log(xi1/xi)*(yi1 - yi)
y[contained] = yi + np.log(xk/xi)/np.log(xi1/xi)*(yi1 - yi)
elif self.interpolation[k] == 4:
# Log-linear
y[i_low:i_high] = yi*np.exp((xk - xi)/(xi1 - xi)*np.log(yi1/yi))
y[contained] = yi*np.exp((xk - xi)/(xi1 - xi)*np.log(yi1/yi))
elif self.interpolation[k] == 5:
# Log-log
y[i_low:i_high] = yi*np.exp(np.log(xk/xi)/np.log(xi1/xi)*np.log(yi1/yi))
y[contained] = (yi*np.exp(np.log(xk/xi)/np.log(xi1/xi)
*np.log(yi1/yi)))
i_low = i_high
# In some cases, the first/last point of x may be less than the first
# value of self.x due only to precision, so we check if they're close
# and set them equal if so. Otherwise, the interpolated value might be
# out of range (and thus zero)
if np.isclose(x[0], self.x[0], 1e-8):
y[0] = self.y[0]
if np.isclose(x[-1], self.x[-1], 1e-8):
y[-1] = self.y[-1]
# In some cases, x values might be outside the tabulated region due only
# to precision, so we check if they're close and set them equal if so.
y[np.isclose(x, self.x[0], atol=1e-14)] = self.y[0]
y[np.isclose(x, self.x[-1], atol=1e-14)] = self.y[-1]
return y if iterable else y[0]

11
openmc/data/neutron.py
View file

@ -326,6 +326,17 @@ class IncidentNeutron(object):
tgroup = group['total_nu']
rx.derived_products.append(Product.from_hdf5(tgroup))
# Build summed reactions. Start from the highest MT number because high
# MTs never depend on lower MTs.
for mt_sum in sorted(SUM_RULES, reverse=True):
if mt_sum not in data:
xs_components = [data[mt].xs for mt in SUM_RULES[mt_sum]
if mt in data]
if len(xs_components) > 0:
rxn = Reaction(mt_sum)
rxn.xs = Sum(xs_components)
data.summed_reactions[mt_sum] = rxn
# Read unresolved resonance probability tables
if 'urr' in group:
urr_group = group['urr']

2
openmc/data/reaction.py
View file

@ -400,7 +400,7 @@ class Reaction(object):
# Read cross section
if 'xs' in group:
xs = group['xs'].value
rx.xs = Tabulated1D(energy, xs)
rx.xs = Tabulated1D(energy[rx.threshold_idx:], xs)
# Determine number of products
n_product = 0

2
openmc/filter.py
View file

@ -568,7 +568,7 @@ class Filter(object):
# Initialize dictionary to build Pandas Multi-index column
filter_dict = {}
# Append Mesh ID as outermost index of mult-index
# Append Mesh ID as outermost index of multi-index
mesh_key = 'mesh {0}'.format(self.mesh.id)
# Find mesh dimensions - use 3D indices for simplicity

16
openmc/mgxs/library.py
View file

@ -54,9 +54,9 @@ class Library(object):
If true, computes cross sections for each nuclide in each domain
mgxs_types : Iterable of str
The types of cross sections in the library (e.g., ['total', 'scatter'])
domain_type : {'material', 'cell', 'distribcell', 'universe'}
domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'}
Domain type for spatial homogenization
domains : Iterable of openmc.Material, openmc.Cell or openmc.Universe
domains : Iterable of openmc.Material, openmc.Cell, openmc.Universe or openmc.Mesh
The spatial domain(s) for which MGXS in the Library are computed
correction : {'P0', None}
Apply the P0 correction to scattering matrices if set to 'P0'
@ -183,6 +183,8 @@ class Library(object):
return self.openmc_geometry.get_all_material_cells()
elif self.domain_type == 'universe':
return self.openmc_geometry.get_all_universes()
elif self.domain_type == 'mesh':
raise ValueError('Unable to get domains for Mesh domain type')
else:
raise ValueError('Unable to get domains without a domain type')
else:
@ -273,6 +275,12 @@ class Library(object):
elif self.domain_type == 'universe':
cv.check_iterable_type('domain', domains, openmc.Universe)
all_domains = self.openmc_geometry.get_all_universes()
elif self.domain_type == 'mesh':
cv.check_iterable_type('domain', domains, openmc.Mesh)
# The mesh and geometry are independent, so set all_domains
# to the input domains
all_domains = domains
else:
raise ValueError('Unable to set domains with domain '
'type "{}"'.format(self.domain_type))
@ -452,7 +460,7 @@ class Library(object):
----------
domain : Material or Cell or Universe or Integral
The material, cell, or universe object of interest (or its ID)
mgxs_type : {'total', 'transport', 'nu-transport', 'absorption', 'capture', 'fission', 'nu-fission', 'kappa-fission', 'scatter', 'nu-scatter', 'scatter matrix', 'nu-scatter matrix', 'multiplicity matrix', 'nu-fission matrix', chi'}
mgxs_type : {'total', 'transport', 'nu-transport', 'absorption', 'capture', 'fission', 'nu-fission', 'kappa-fission', 'scatter', 'nu-scatter', 'scatter matrix', 'nu-scatter matrix', 'multiplicity matrix', 'nu-fission matrix', chi', 'chi-prompt', 'inverse-velocity', 'prompt-nu-fission'}
The type of multi-group cross section object to return
Returns
@ -474,6 +482,8 @@ class Library(object):
cv.check_type('domain', domain, (openmc.Cell, Integral))
elif self.domain_type == 'universe':
cv.check_type('domain', domain, (openmc.Universe, Integral))
elif self.domain_type == 'mesh':
cv.check_type('domain', domain, (openmc.Mesh, Integral))
# Check that requested domain is included in library
if isinstance(domain, Integral):

762
openmc/mgxs/mgxs.py
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File diff suppressed because it is too large Load diff

2
openmc/tallies.py
View file

@ -1293,7 +1293,7 @@ class Tally(object):
# Create list of 2- or 3-tuples tuples for mesh cell bins
if self_filter.type == 'mesh':
dimension = self_filter.mesh.dimension
xyz = map(lambda x: np.arange(1, x+1), dimension)
xyz = [range(1, x+1) for x in dimension]
bins = list(itertools.product(*xyz))
# Create list of 2-tuples for energy boundary bins

5
src/constants.F90
View file

@ -289,7 +289,7 @@ module constants
EVENT_ABSORB = 2
! Tally score type
integer, parameter :: N_SCORE_TYPES = 20
integer, parameter :: N_SCORE_TYPES = 21
integer, parameter :: &
SCORE_FLUX = -1, & ! flux
SCORE_TOTAL = -2, & ! total reaction rate
@ -310,7 +310,8 @@ module constants
SCORE_NU_SCATTER_YN = -17, & ! angular flux-weighted nu-scattering moment (0:N)
SCORE_EVENTS = -18, & ! number of events
SCORE_DELAYED_NU_FISSION = -19, & ! delayed neutron production rate
SCORE_INVERSE_VELOCITY = -20 ! flux-weighted inverse velocity
SCORE_PROMPT_NU_FISSION = -20, & ! prompt neutron production rate
SCORE_INVERSE_VELOCITY = -21 ! flux-weighted inverse velocity
! Maximum scattering order supported
integer, parameter :: MAX_ANG_ORDER = 10

2
src/endf.F90
View file

@ -42,6 +42,8 @@ contains
string = "nu-fission"
case (SCORE_DELAYED_NU_FISSION)
string = "delayed-nu-fission"
case (SCORE_PROMPT_NU_FISSION)
string = "prompt-nu-fission"
case (SCORE_KAPPA_FISSION)
string = "kappa-fission"
case (SCORE_CURRENT)

6
src/input_xml.F90
View file

@ -3635,6 +3635,12 @@ contains
! Set tally estimator to analog
t % estimator = ESTIMATOR_ANALOG
end if
case ('prompt-nu-fission')
t % score_bins(j) = SCORE_PROMPT_NU_FISSION
if (t % find_filter(FILTER_ENERGYOUT) > 0) then
! Set tally estimator to analog
t % estimator = ESTIMATOR_ANALOG
end if
! Disallow for MG mode since data not present
if (.not. run_CE) then

1
src/output.F90
View file

@ -774,6 +774,7 @@ contains
score_names(abs(SCORE_NU_SCATTER_PN)) = "Scattering Prod. Rate Moment"
score_names(abs(SCORE_NU_SCATTER_YN)) = "Scattering Prod. Rate Moment"
score_names(abs(SCORE_DELAYED_NU_FISSION)) = "Delayed-Nu-Fission Rate"
score_names(abs(SCORE_PROMPT_NU_FISSION)) = "Prompt-Nu-Fission Rate"
score_names(abs(SCORE_INVERSE_VELOCITY)) = "Flux-Weighted Inverse Velocity"
! Create filename for tally output

258
src/tally.F90
View file

@ -400,7 +400,7 @@ contains
! neutrons were emitted with different energies, multiple
! outgoing energy bins may have been scored to. The following
! logic treats this special case and results to multiple bins
call score_fission_eout_ce(p, t, score_index)
call score_fission_eout_ce(p, t, score_index, score_bin)
cycle SCORE_LOOP
end if
end if
@ -434,6 +434,67 @@ contains
end if
case (SCORE_PROMPT_NU_FISSION)
if (t % estimator == ESTIMATOR_ANALOG) then
if (survival_biasing .or. p % fission) then
if (t % find_filter(FILTER_ENERGYOUT) > 0) then
! Normally, we only need to make contributions to one scoring
! bin. However, in the case of fission, since multiple fission
! neutrons were emitted with different energies, multiple
! outgoing energy bins may have been scored to. The following
! logic treats this special case and results to multiple bins
call score_fission_eout_ce(p, t, score_index, score_bin)
cycle SCORE_LOOP
end if
end if
if (survival_biasing) then
! No fission events occur if survival biasing is on -- need to
! calculate fraction of absorptions that would have resulted in
! prompt-nu-fission
if (micro_xs(p % event_nuclide) % absorption > ZERO) then
score = p % absorb_wgt * micro_xs(p % event_nuclide) % fission &
* nuclides(p % event_nuclide) % nu(E, EMISSION_PROMPT) &
/ micro_xs(p % event_nuclide) % absorption
else
score = ZERO
end if
else
! Skip any non-fission events
if (.not. p % fission) cycle SCORE_LOOP
! If there is no outgoing energy filter, than we only need to
! score to one bin. For the score to be 'analog', we need to
! score the number of particles that were banked in the fission
! bank as prompt neutrons. Since this was weighted by 1/keff, we
! multiply by keff to get the proper score.
score = keff * p % wgt_bank * (ONE - sum(p % n_delayed_bank) &
/ real(p % n_bank, 8))
end if
else
if (i_nuclide > 0) then
score = micro_xs(i_nuclide) % fission * nuclides(i_nuclide) % &
nu(E, EMISSION_PROMPT) * atom_density * flux
else
score = ZERO
! Loop over all nuclides in the current material
do l = 1, materials(p % material) % n_nuclides
! Get atom density
atom_density_ = materials(p % material) % atom_density(l)
! Get index in nuclides array
i_nuc = materials(p % material) % nuclide(l)
! Accumulate the contribution from each nuclide
score = score + micro_xs(i_nuc) % fission * nuclides(i_nuc) % &
nu(E, EMISSION_PROMPT) * atom_density_ * flux
end do
end if
end if
case (SCORE_DELAYED_NU_FISSION)
! make sure the correct energy is used
@ -454,7 +515,7 @@ contains
! neutrons were emitted with different energies, multiple
! outgoing energy bins may have been scored to. The following
! logic treats this special case and results to multiple bins
call score_fission_delayed_eout(p, t, score_index)
call score_fission_eout_ce(p, t, score_index, score_bin)
cycle SCORE_LOOP
end if
end if
@ -1607,12 +1668,18 @@ contains
! neutrons produced with different energies.
!===============================================================================
subroutine score_fission_eout_ce(p, t, i_score)
type(Particle), intent(in) :: p
subroutine score_fission_eout_ce(p, t, i_score, score_bin)
type(Particle), intent(in) :: p
type(TallyObject), intent(inout) :: t
integer, intent(in) :: i_score ! index for score
integer, intent(in) :: i_score ! index for score
integer, intent(in) :: score_bin
integer :: i ! index of outgoing energy filter
integer :: j ! index of delayedgroup filter
integer :: d ! delayed group
integer :: g ! another delayed group
integer :: d_bin ! delayed group bin index
integer :: n ! number of energies on filter
integer :: k ! loop index for bank sites
integer :: bin_energyout ! original outgoing energy bin
@ -1625,12 +1692,12 @@ contains
i = t % find_filter(FILTER_ENERGYOUT)
bin_energyout = matching_bins(i)
! Declare the filter type
select type(filt => t % filters(i) % obj)
! declare the energyout filter type
select type(eo_filt => t % filters(i) % obj)
type is (EnergyoutFilter)
! Get number of energies on filter
n = size(filt % bins)
n = size(eo_filt % bins)
! Since the creation of fission sites is weighted such that it is
! expected to create n_particles sites, we need to multiply the
@ -1639,6 +1706,10 @@ contains
! loop over number of particles banked
do k = 1, p % n_bank
! get the delayed group
g = fission_bank(n_bank - p % n_bank + k) % delayed_group
! determine score based on bank site weight and keff
score = keff * fission_bank(n_bank - p % n_bank + k) % wgt
@ -1646,19 +1717,66 @@ contains
E_out = fission_bank(n_bank - p % n_bank + k) % E
! check if outgoing energy is within specified range on filter
if (E_out < filt % bins(1) .or. E_out > filt % bins(n)) cycle
if (E_out < eo_filt % bins(1) .or. E_out > eo_filt % bins(n)) cycle
! change outgoing energy bin
matching_bins(i) = binary_search(filt % bins, n, E_out)
Matching_bins(i) = binary_search(eo_filt % bins, n, E_out)
! determine scoring index and weight for this filter combination
i_filter = sum((matching_bins(1:size(t%filters)) - 1) * t % stride) + 1
filter_weight = product(filter_weights(:size(t % filters)))
! Case for tallying prompt neutrons
if (score_bin == SCORE_NU_FISSION .or. &
(score_bin == SCORE_PROMPT_NU_FISSION .and. g == 0)) then
! Add score to tally
! determine scoring index and weight for this filter combination
i_filter = sum((matching_bins(1:size(t%filters)) - 1) * t % stride) &
+ 1
filter_weight = product(filter_weights(:size(t % filters)))
! Add score to tally
!$omp atomic
t % results(i_score, i_filter) % value = &
t % results(i_score, i_filter) % value + score * filter_weight
t % results(i_score, i_filter) % value = &
t % results(i_score, i_filter) % value + score * filter_weight
! Case for tallying delayed emissions
else if (score_bin == SCORE_DELAYED_NU_FISSION .and. g /= 0) then
! Get the index of delayed group filter
j = t % find_filter(FILTER_DELAYEDGROUP)
! if the delayed group filter is present, tally to corresponding
! delayed group bin if it exists
if (j > 0) then
! declare the delayed group filter type
select type(dg_filt => t % filters(j) % obj)
type is (DelayedGroupFilter)
! loop over delayed group bins until the corresponding bin is
! found
do d_bin = 1, dg_filt % n_bins
d = dg_filt % groups(d_bin)
! check whether the delayed group of the particle is equal to
! the delayed group of this bin
if (d == g) then
call score_fission_delayed_dg(t, d_bin, score, i_score)
end if
end do
end select
! if the delayed group filter is not present, add score to tally
else
! determine scoring index and weight for this filter combination
i_filter = sum((matching_bins(1:size(t%filters)) - 1) * t % stride)&
+ 1
filter_weight = product(filter_weights(:size(t % filters)))
! Add score to tally
!$omp atomic
t % results(i_score, i_filter) % value = &
t % results(i_score, i_filter) % value + score * filter_weight
end if
end if
end do
end select
@ -1751,114 +1869,6 @@ contains
end subroutine score_fission_eout_mg
!===============================================================================
! SCORE_FISSION_DELAYED_EOUT handles a special case where we need to store
! delayed neutron production rate with an outgoing energy filter (think of a
! fission matrix). In this case, we may need to score to multiple bins if there
! were multiple neutrons produced with different energies.
!===============================================================================
subroutine score_fission_delayed_eout(p, t, i_score)
type(Particle), intent(in) :: p
type(TallyObject), intent(inout) :: t
integer, intent(in) :: i_score ! index for score
integer :: i ! index of outgoing energy filter
integer :: j ! index of delayedgroup filter
integer :: d ! delayed group
integer :: g ! another delayed group
integer :: d_bin ! delayed group bin index
integer :: n ! number of energies on filter
integer :: k ! loop index for bank sites
integer :: bin_energyout ! original outgoing energy bin
integer :: i_filter ! index for matching filter bin combination
real(8) :: filter_weight ! combined weight of all filters
real(8) :: score ! actual score
real(8) :: E_out ! energy of fission bank site
! Save original outgoing energy bin
i = t % find_filter(FILTER_ENERGYOUT)
bin_energyout = matching_bins(i)
! Get the index of delayed group filter
j = t % find_filter(FILTER_DELAYEDGROUP)
! Declare the energyout filter type
select type(eo_filt => t % filters(i) % obj)
type is (EnergyoutFilter)
! Get number of energies on filter
n = size(eo_filt % bins)
! Since the creation of fission sites is weighted such that it is
! expected to create n_particles sites, we need to multiply the
! score by keff to get the true delayed-nu-fission rate.
! loop over number of particles banked
do k = 1, p % n_bank
! get the delayed group
g = fission_bank(n_bank - p % n_bank + k) % delayed_group
! check if the particle was born delayed
if (g /= 0) then
! determine score based on bank site weight and keff
score = keff * fission_bank(n_bank - p % n_bank + k) % wgt
! determine outgoing energy from fission bank
E_out = fission_bank(n_bank - p % n_bank + k) % E
! check if outgoing energy is within specified range on filter
if (E_out < eo_filt % bins(1) .or. E_out > eo_filt % bins(n)) cycle
! change outgoing energy bin
matching_bins(i) = binary_search(eo_filt % bins, n, E_out)
! if the delayed group filter is present, tally to corresponding
! delayed group bin if it exists
if (j > 0) then
! Declare the delayed group filter type
select type(dg_filt => t % filters(j) % obj)
type is (DelayedGroupFilter)
! loop over delayed group bins until the corresponding bin is
! found
do d_bin = 1, dg_filt % n_bins
d = dg_filt % groups(d_bin)
! check whether the delayed group of the particle is equal to
! the delayed group of this bin
if (d == g) then
call score_fission_delayed_dg(t, d_bin, score, i_score)
end if
end do
end select
! if the delayed group filter is not present, add score to tally
else
! determine scoring index and weight for this filter combination
i_filter = sum((matching_bins(1:size(t%filters)) - 1) * t % stride)&
+ 1
filter_weight = product(filter_weights(:size(t % filters)))
! Add score to tally
!$omp atomic
t % results(i_score, i_filter) % value = &
t % results(i_score, i_filter) % value + score * filter_weight
end if
end if
end do
end select
! reset outgoing energy bin
matching_bins(i) = bin_energyout
end subroutine score_fission_delayed_eout
!===============================================================================
! SCORE_FISSION_DELAYED_DG helper function used to increment the tally when a
! delayed group filter is present.

2
tests/test_mgxs_library_condense/inputs_true.dat
View file

@ -1 +1 @@
855919f7a333acff6423527b82656d6a472ea8416002fb475c2d21c676e2f75f5c040d209a3e9a3a6118cf944f2c1bfb8cf2318273b890861d790b1927791a35
e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e

18
tests/test_mgxs_library_condense/results_true.dat
View file

@ -34,6 +34,12 @@
0 10000 1 1 total 0.085835 0.005592
material group out nuclide mean std. dev.
0 10000 1 total 1.0 0.046071
material group out nuclide mean std. dev.
0 10000 1 total 1.0 0.051471
material group in nuclide mean std. dev.
0 10000 1 total 4.996730e-07 3.650635e-08
material group in nuclide mean std. dev.
0 10000 1 total 0.090004 0.006367
material group in nuclide mean std. dev.
0 10001 1 total 0.311594 0.013793
material group in nuclide mean std. dev.
@ -70,6 +76,12 @@
0 10001 1 1 total 0.0 0.0
material group out nuclide mean std. dev.
0 10001 1 total 0.0 0.0
material group out nuclide mean std. dev.
0 10001 1 total 0.0 0.0
material group in nuclide mean std. dev.
0 10001 1 total 5.454760e-07 4.949800e-08
material group in nuclide mean std. dev.
0 10001 1 total 0.0 0.0
material group in nuclide mean std. dev.
0 10002 1 total 0.904999 0.043964
material group in nuclide mean std. dev.
@ -106,3 +118,9 @@
0 10002 1 1 total 0.0 0.0
material group out nuclide mean std. dev.
0 10002 1 total 0.0 0.0
material group out nuclide mean std. dev.
0 10002 1 total 0.0 0.0
material group in nuclide mean std. dev.
0 10002 1 total 5.773006e-07 5.322132e-08
material group in nuclide mean std. dev.
0 10002 1 total 0.0 0.0

2
tests/test_mgxs_library_distribcell/inputs_true.dat
View file

@ -1 +1 @@
3a3b7f75b326c94a8e5c7efe3046b2fdb887e9f75ecf6eb27587f9450c77cf8fd6acc4198c15bffb4e7ceead6d7b4327c19536bbf9cc35dfaae3f4ce4c26cc1a
2d948f3b12293294eaeca231a3df9d51195379e8bb38dd3e68d3bc512a7d08ed52a1109054ca381684ec127268710f6d6e9210ac8154c9b379608e996627624a

6
tests/test_mgxs_library_distribcell/results_true.dat
View file

@ -34,3 +34,9 @@
0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.0 0.0
avg(distribcell) group out nuclide mean std. dev.
0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.0 0.0
avg(distribcell) group out nuclide mean std. dev.
0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.0 0.0
avg(distribcell) group in nuclide mean std. dev.
0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.000001 6.946255e-07
avg(distribcell) group in nuclide mean std. dev.
0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.0 0.0

2
tests/test_mgxs_library_hdf5/inputs_true.dat
View file

@ -1 +1 @@
855919f7a333acff6423527b82656d6a472ea8416002fb475c2d21c676e2f75f5c040d209a3e9a3a6118cf944f2c1bfb8cf2318273b890861d790b1927791a35
e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e

27
tests/test_mgxs_library_hdf5/results_true.dat
View file

@ -63,6 +63,15 @@ domain=10000 type=nu-fission matrix
domain=10000 type=chi
[ 1. 0.]
[ 0.04607052 0. ]
domain=10000 type=chi-prompt
[ 1. 0.]
[ 0.05147146 0. ]
domain=10000 type=inverse-velocity
[ 5.70932329e-08 2.85573950e-06]
[ 4.68792969e-09 2.44216503e-07]
domain=10000 type=prompt-nu-fission
[ 0.01923922 0.46671903]
[ 0.00130951 0.04141087]
domain=10001 type=total
[ 0.31373767 0.3008214 ]
[ 0.0155819 0.02805245]
@ -128,6 +137,15 @@ domain=10001 type=nu-fission matrix
domain=10001 type=chi
[ 0. 0.]
[ 0. 0.]
domain=10001 type=chi-prompt
[ 0. 0.]
[ 0. 0.]
domain=10001 type=inverse-velocity
[ 5.99598048e-08 2.98549021e-06]
[ 4.55309296e-09 3.41701554e-07]
domain=10001 type=prompt-nu-fission
[ 0. 0.]
[ 0. 0.]
domain=10002 type=total
[ 0.66457226 2.05238401]
[ 0.03121475 0.22434291]
@ -193,3 +211,12 @@ domain=10002 type=nu-fission matrix
domain=10002 type=chi
[ 0. 0.]
[ 0. 0.]
domain=10002 type=chi-prompt
[ 0. 0.]
[ 0. 0.]
domain=10002 type=inverse-velocity
[ 6.02207831e-08 3.04495537e-06]
[ 3.78043696e-09 3.60007673e-07]
domain=10002 type=prompt-nu-fission
[ 0. 0.]
[ 0. 0.]

1
tests/test_mgxs_library_mesh/inputs_true.dat Normal file
View file

@ -0,0 +1 @@
a4cd030bea212e45fdb159e75a7fb3d1947e9bf3d0384ac5d37a72298d67dcfdd1b9eb5c6af8ac6e5983bd5b47de9c17a2ea472b467b7222a4909ee070bf1ca3

132
tests/test_mgxs_library_mesh/results_true.dat Normal file
View file

@ -0,0 +1,132 @@
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.640786 0.044177
1 1 2 1 1 total 0.660597 0.128423
2 2 1 1 1 total 0.615276 0.104046
3 2 2 1 1 total 0.646999 0.186709
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.36665 0.048814
1 1 2 1 1 total 0.40784 0.096486
2 2 1 1 1 total 0.36356 0.074111
3 2 2 1 1 total 0.41456 0.160443
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.366650 0.048814
1 1 2 1 1 total 0.407840 0.096486
2 2 1 1 1 total 0.363560 0.074111
3 2 2 1 1 total 0.414593 0.160436
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.025749 0.002863
1 1 2 1 1 total 0.028400 0.005275
2 2 1 1 1 total 0.022988 0.004099
3 2 2 1 1 total 0.027589 0.010350
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.015861 0.002876
1 1 2 1 1 total 0.017280 0.004371
2 2 1 1 1 total 0.014403 0.003542
3 2 2 1 1 total 0.018061 0.010110
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.009888 0.001077
1 1 2 1 1 total 0.011121 0.002456
2 2 1 1 1 total 0.008585 0.001552
3 2 2 1 1 total 0.009527 0.003659
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.026065 0.002907
1 1 2 1 1 total 0.029084 0.006430
2 2 1 1 1 total 0.022596 0.004062
3 2 2 1 1 total 0.025066 0.009687
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 1.938476 0.211550
1 1 2 1 1 total 2.177360 0.480780
2 2 1 1 1 total 1.682799 0.303764
3 2 2 1 1 total 1.864890 0.715661
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.615037 0.041754
1 1 2 1 1 total 0.632196 0.123878
2 2 1 1 1 total 0.592288 0.100439
3 2 2 1 1 total 0.619410 0.177190
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.584014 0.054315
1 1 2 1 1 total 0.622514 0.111323
2 2 1 1 1 total 0.587256 0.084833
3 2 2 1 1 total 0.613792 0.168612
mesh 1 group in group out nuclide moment mean std. dev.
x y z
0 1 1 1 1 1 total P0 0.584014 0.054315
1 1 1 1 1 1 total P1 0.243427 0.025488
2 1 1 1 1 1 total P2 0.089236 0.007357
3 1 1 1 1 1 total P3 0.008994 0.005768
4 1 2 1 1 1 total P0 0.622514 0.111323
5 1 2 1 1 1 total P1 0.239376 0.042594
6 1 2 1 1 1 total P2 0.088386 0.017200
7 1 2 1 1 1 total P3 -0.001243 0.005639
8 2 1 1 1 1 total P0 0.587256 0.084833
9 2 1 1 1 1 total P1 0.245120 0.041033
10 2 1 1 1 1 total P2 0.086784 0.016255
11 2 1 1 1 1 total P3 0.008660 0.004755
12 2 2 1 1 1 total P0 0.612950 0.167940
13 2 2 1 1 1 total P1 0.226176 0.061882
14 2 2 1 1 1 total P2 0.086593 0.026126
15 2 2 1 1 1 total P3 0.009672 0.011995
mesh 1 group in group out nuclide moment mean std. dev.
x y z
0 1 1 1 1 1 total P0 0.584014 0.054315
1 1 1 1 1 1 total P1 0.243427 0.025488
2 1 1 1 1 1 total P2 0.089236 0.007357
3 1 1 1 1 1 total P3 0.008994 0.005768
4 1 2 1 1 1 total P0 0.622514 0.111323
5 1 2 1 1 1 total P1 0.239376 0.042594
6 1 2 1 1 1 total P2 0.088386 0.017200
7 1 2 1 1 1 total P3 -0.001243 0.005639
8 2 1 1 1 1 total P0 0.587256 0.084833
9 2 1 1 1 1 total P1 0.245120 0.041033
10 2 1 1 1 1 total P2 0.086784 0.016255
11 2 1 1 1 1 total P3 0.008660 0.004755
12 2 2 1 1 1 total P0 0.613792 0.168612
13 2 2 1 1 1 total P1 0.226142 0.061856
14 2 2 1 1 1 total P2 0.086174 0.025979
15 2 2 1 1 1 total P3 0.009721 0.012027
mesh 1 group in group out nuclide mean std. dev.
x y z
0 1 1 1 1 1 total 1.000000 0.088094
1 1 2 1 1 1 total 1.000000 0.160891
2 2 1 1 1 1 total 1.000000 0.126864
3 2 2 1 1 1 total 1.001374 0.305883
mesh 1 group in group out nuclide mean std. dev.
x y z
0 1 1 1 1 1 total 0.027395 0.004680
1 1 2 1 1 1 total 0.022914 0.006025
2 2 1 1 1 1 total 0.019384 0.002846
3 2 2 1 1 1 total 0.029629 0.006292
mesh 1 group out nuclide mean std. dev.
x y z
0 1 1 1 1 total 1.0 0.220956
1 1 2 1 1 total 1.0 0.316565
2 2 1 1 1 total 1.0 0.132140
3 2 2 1 1 total 1.0 0.181577
mesh 1 group out nuclide mean std. dev.
x y z
0 1 1 1 1 total 1.0 0.222246
1 1 2 1 1 total 1.0 0.316565
2 2 1 1 1 total 1.0 0.132140
3 2 2 1 1 total 1.0 0.181577
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 3.610522e-07 3.169931e-08
1 1 2 1 1 total 3.942353e-07 8.459167e-08
2 2 1 1 1 total 3.097784e-07 5.252025e-08
3 2 2 1 1 total 3.799163e-07 1.806470e-07
mesh 1 group in nuclide mean std. dev.
x y z
0 1 1 1 1 total 0.025735 0.002840
1 1 2 1 1 total 0.028773 0.006349
2 2 1 1 1 total 0.022306 0.004010
3 2 2 1 1 total 0.024549 0.009379

81
tests/test_mgxs_library_mesh/test_mgxs_library_mesh.py Normal file
View file

@ -0,0 +1,81 @@
#!/usr/bin/env python
import os
import sys
import glob
import hashlib
sys.path.insert(0, os.pardir)
from testing_harness import PyAPITestHarness
import openmc
import openmc.mgxs
class MGXSTestHarness(PyAPITestHarness):
def _build_inputs(self):
# Generate inputs using parent class routine
super(MGXSTestHarness, self)._build_inputs()
# Initialize a one-group structure
energy_groups = openmc.mgxs.EnergyGroups(group_edges=[0, 20.])
# Initialize MGXS Library for a few cross section types
# for one material-filled cell in the geometry
self.mgxs_lib = openmc.mgxs.Library(self._input_set.geometry)
self.mgxs_lib.by_nuclide = False
# Test all MGXS types
self.mgxs_lib.mgxs_types = openmc.mgxs.MGXS_TYPES
self.mgxs_lib.energy_groups = energy_groups
self.mgxs_lib.legendre_order = 3
self.mgxs_lib.domain_type = 'mesh'
# Instantiate a tally mesh
mesh = openmc.Mesh(mesh_id=1)
mesh.type = 'regular'
mesh.dimension = [2, 2]
mesh.lower_left = [-100., -100.]
mesh.width = [100., 100.]
self.mgxs_lib.domains = [mesh]
self.mgxs_lib.build_library()
# Initialize a tallies file
self._input_set.tallies = openmc.Tallies()
self.mgxs_lib.add_to_tallies_file(self._input_set.tallies, merge=False)
self._input_set.tallies.export_to_xml()
def _get_results(self, hash_output=False):
"""Digest info in the statepoint and return as a string."""
# Read the statepoint file.
statepoint = glob.glob(os.path.join(os.getcwd(), self._sp_name))[0]
sp = openmc.StatePoint(statepoint)
# Load the MGXS library from the statepoint
self.mgxs_lib.load_from_statepoint(sp)
# Build a string from Pandas Dataframe for each 1-group MGXS
outstr = ''
for domain in self.mgxs_lib.domains:
for mgxs_type in self.mgxs_lib.mgxs_types:
mgxs = self.mgxs_lib.get_mgxs(domain, mgxs_type)
df = mgxs.get_pandas_dataframe()
outstr += df.to_string() + '\n'
# Hash the results if necessary
if hash_output:
sha512 = hashlib.sha512()
sha512.update(outstr.encode('utf-8'))
outstr = sha512.hexdigest()
return outstr
def _cleanup(self):
super(MGXSTestHarness, self)._cleanup()
f = os.path.join(os.getcwd(), 'tallies.xml')
if os.path.exists(f): os.remove(f)
if __name__ == '__main__':
harness = MGXSTestHarness('statepoint.10.*', True)
harness.main()

2
tests/test_mgxs_library_no_nuclides/inputs_true.dat
View file

@ -1 +1 @@
855919f7a333acff6423527b82656d6a472ea8416002fb475c2d21c676e2f75f5c040d209a3e9a3a6118cf944f2c1bfb8cf2318273b890861d790b1927791a35
e2cdca7ea5b3532050af5b12fac26d7ef212d2696bb1b73cdd00929b2243c40d100ad02438c7b090555b49815d0de6c48cf1b4ebf437a48bc80c2d2b4bad292e

27
tests/test_mgxs_library_no_nuclides/results_true.dat
View file

@ -75,6 +75,15 @@
material group out nuclide mean std. dev.
1 10000 1 total 1.0 0.046071
0 10000 2 total 0.0 0.000000
material group out nuclide mean std. dev.
1 10000 1 total 1.0 0.051471
0 10000 2 total 0.0 0.000000
material group in nuclide mean std. dev.
1 10000 1 total 5.709323e-08 4.687930e-09
0 10000 2 total 2.855740e-06 2.442165e-07
material group in nuclide mean std. dev.
1 10000 1 total 0.019239 0.001310
0 10000 2 total 0.466719 0.041411
material group in nuclide mean std. dev.
1 10001 1 total 0.313738 0.015582
0 10001 2 total 0.300821 0.028052
@ -152,6 +161,15 @@
material group out nuclide mean std. dev.
1 10001 1 total 0.0 0.0
0 10001 2 total 0.0 0.0
material group out nuclide mean std. dev.
1 10001 1 total 0.0 0.0
0 10001 2 total 0.0 0.0
material group in nuclide mean std. dev.
1 10001 1 total 5.995980e-08 4.553093e-09
0 10001 2 total 2.985490e-06 3.417016e-07
material group in nuclide mean std. dev.
1 10001 1 total 0.0 0.0
0 10001 2 total 0.0 0.0
material group in nuclide mean std. dev.
1 10002 1 total 0.664572 0.031215
0 10002 2 total 2.052384 0.224343
@ -229,3 +247,12 @@
material group out nuclide mean std. dev.
1 10002 1 total 0.0 0.0
0 10002 2 total 0.0 0.0
material group out nuclide mean std. dev.
1 10002 1 total 0.0 0.0
0 10002 2 total 0.0 0.0
material group in nuclide mean std. dev.
1 10002 1 total 6.022078e-08 3.780437e-09
0 10002 2 total 3.044955e-06 3.600077e-07
material group in nuclide mean std. dev.
1 10002 1 total 0.0 0.0
0 10002 2 total 0.0 0.0

2
tests/test_mgxs_library_nuclides/inputs_true.dat
View file

@ -1 +1 @@
739796983940a1bad601998cf9ea2f90453a994477c7f675c2fd404d1864fe04a7fbfb5a15c5fe7cf9bad016b78432ba0910baba6f9cc026143761e9f526b62a
e4a5f03ab6167e96462c4ef537533fe33b98d7878ae00824c5619356bda8d548b3c71af01ba8c88d5a9b46dd1471d331e6f678a164af922200f2ee3642be6340

2
tests/test_mgxs_library_nuclides/results_true.dat
View file

@ -1 +1 @@
d56c6bae6bf3cd8950d3f50f089458c1c6c807be780fc97570532c6af6eb7e3057968d3345bd3c363f01315271129ef7b2ca028b05767353e601dc37b035f8a7
e494320a213b5704a2ac915a2ba504857be91961ceb6735b6ad05d81eb31c44c9584d5bd9d40baececf1dcb5b030e6ecec63cfbd20639baf69bcb596c5c46591

2
tests/test_tallies/inputs_true.dat
View file

@ -1 +1 @@
2f6728effcafd8cdba60604f5c695a1fbaa591c0ee6bd907d7a942ee8df05386502aeae4027a4bf455ac1d072211c77010fe624f4a86ac538a53fc7159695eaa
930af242a043f2676a000dbc5a2db6b148edcb31ed8c87dbaa35a8efb37a3be8cff30cdf4dc03f9c5c7eb4021f7e4c3327e64681cdd8fd8722c95c69db850227

2
tests/test_tallies/results_true.dat
View file

@ -1 +1 @@
d890ab7b790d6672ce94ab4144c5f6b1a7ffae63cfe3bff5cdcf75a1458e3819650b547de9c137a223bc2725c7d7670b06636377f55b69078274a89ebc7c5851
a51db2a4efc681805f85968e04411dc33beee0532c202f5179b9a82880ab60a75e53fa9141c81045ea1d2842372f2d8da900326f09382ea61dd80a3c9b43bba1

3
tests/test_tallies/test_tallies.py
View file

@ -123,7 +123,8 @@ class TalliesTestHarness(PyAPITestHarness):
t.filters = [cell_filter]
t.scores = ['absorption', 'delayed-nu-fission', 'events', 'fission',
'inverse-velocity', 'kappa-fission', '(n,2n)', '(n,n1)',
'(n,gamma)', 'nu-fission', 'scatter', 'elastic', 'total']
'(n,gamma)', 'nu-fission', 'scatter', 'elastic', 'total',
'prompt-nu-fission']
score_tallies[0].estimator = 'tracklength'
score_tallies[1].estimator = 'analog'
score_tallies[2].estimator = 'collision'