mirror of
https://github.com/openmc-dev/openmc.git
synced 2026-07-28 22:26:08 -04:00
Corrected issue in certain Mgxs classes which had the number density divided out for microscopic xs that should not be. Specifically, this was in MultiplicityMatrixXS, and ScatterProbabilityMatrix.
This commit is contained in:
Adam Nelson
parent
7661c77e32
commit
9abaf3fb2e
1 changed files with 34 additions and 65 deletions
|
|
@ -222,6 +222,10 @@ class MGXS:
|
|||
|
||||
"""
|
||||
|
||||
# Store whether or not the number density should be removed for microscopic
|
||||
# values of this data
|
||||
_divide_by_density = True
|
||||
|
||||
def __init__(self, domain=None, domain_type=None,
|
||||
energy_groups=None, by_nuclide=False, name='', num_polar=1,
|
||||
num_azimuthal=1):
|
||||
|
|
@ -1081,7 +1085,7 @@ class MGXS:
|
|||
nuclides=query_nuclides, value=value)
|
||||
|
||||
# Divide by atom number densities for microscopic cross sections
|
||||
if xs_type == 'micro':
|
||||
if xs_type == 'micro' and self._divide_by_density:
|
||||
if self.by_nuclide:
|
||||
densities = self.get_nuclide_densities(nuclides)
|
||||
else:
|
||||
|
|
@ -1938,7 +1942,7 @@ class MGXS:
|
|||
df = df[df['group out'].isin(groups)]
|
||||
|
||||
# If user requested micro cross sections, divide out the atom densities
|
||||
if xs_type == 'micro':
|
||||
if xs_type == 'micro' and self._divide_by_density:
|
||||
if self.by_nuclide:
|
||||
densities = self.get_nuclide_densities(nuclides)
|
||||
else:
|
||||
|
|
@ -2101,10 +2105,9 @@ class MatrixMGXS(MGXS):
|
|||
|
||||
return self._add_angle_filters(filters)
|
||||
|
||||
def get_xs(self, in_groups='all', out_groups='all',
|
||||
subdomains='all', nuclides='all',
|
||||
xs_type='macro', order_groups='increasing',
|
||||
row_column='inout', value='mean', squeeze=True, **kwargs):
|
||||
def get_xs(self, in_groups='all', out_groups='all', subdomains='all',
|
||||
nuclides='all', xs_type='macro', order_groups='increasing',
|
||||
row_column='inout', value='mean', squeeze=True, **kwargs):
|
||||
"""Returns an array of multi-group cross sections.
|
||||
|
||||
This method constructs a 4D NumPy array for the requested
|
||||
|
|
@ -2217,7 +2220,7 @@ class MatrixMGXS(MGXS):
|
|||
nuclides=query_nuclides, value=value)
|
||||
|
||||
# Divide by atom number densities for microscopic cross sections
|
||||
if xs_type == 'micro':
|
||||
if xs_type == 'micro' and self._divide_by_density:
|
||||
if self.by_nuclide:
|
||||
densities = self.get_nuclide_densities(nuclides)
|
||||
else:
|
||||
|
|
@ -4413,7 +4416,7 @@ class ScatterMatrixXS(MatrixMGXS):
|
|||
nuclides=query_nuclides, value=value)
|
||||
|
||||
# Divide by atom number densities for microscopic cross sections
|
||||
if xs_type == 'micro':
|
||||
if xs_type == 'micro' and self._divide_by_density:
|
||||
if self.by_nuclide:
|
||||
densities = self.get_nuclide_densities(nuclides)
|
||||
else:
|
||||
|
|
@ -4820,6 +4823,12 @@ class MultiplicityMatrixXS(MatrixMGXS):
|
|||
|
||||
"""
|
||||
|
||||
# Store whether or not the number density should be removed for microscopic
|
||||
# values of this data; since a multiplicity matrix should reflect the
|
||||
# multiplication relative to 1, this class will not divide by density
|
||||
# for microscopic data
|
||||
_divide_by_density = False
|
||||
|
||||
def __init__(self, domain=None, domain_type=None, groups=None,
|
||||
by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
|
||||
super().__init__(domain, domain_type, groups, by_nuclide, name,
|
||||
|
|
@ -4987,6 +4996,11 @@ class ScatterProbabilityMatrix(MatrixMGXS):
|
|||
|
||||
"""
|
||||
|
||||
# Store whether or not the number density should be removed for microscopic
|
||||
# values of this data; since this probability matrix is always normalized
|
||||
# to 1.0, this density division is not necessary
|
||||
_divide_by_density = False
|
||||
|
||||
def __init__(self, domain=None, domain_type=None, groups=None,
|
||||
by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
|
||||
super().__init__(domain, domain_type, groups, by_nuclide,
|
||||
|
|
@ -5257,7 +5271,7 @@ class Chi(MGXS):
|
|||
by_nuclide : bool
|
||||
If true, computes cross sections for each nuclide in domain
|
||||
domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.RegularMesh
|
||||
Domain for spatial homogenization
|
||||
Domain for spatial homogenization`
|
||||
domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'}
|
||||
Domain type for spatial homogenization
|
||||
energy_groups : openmc.mgxs.EnergyGroups
|
||||
|
|
@ -5314,6 +5328,11 @@ class Chi(MGXS):
|
|||
|
||||
"""
|
||||
|
||||
# Store whether or not the number density should be removed for microscopic
|
||||
# values of this data; since this chi data is normalized to 1.0, the
|
||||
# data should not be divided by the number density
|
||||
_divide_by_density = False
|
||||
|
||||
def __init__(self, domain=None, domain_type=None, groups=None,
|
||||
prompt=False, by_nuclide=False, name='', num_polar=1,
|
||||
num_azimuthal=1):
|
||||
|
|
@ -5695,62 +5714,6 @@ class Chi(MGXS):
|
|||
|
||||
return xs
|
||||
|
||||
def get_pandas_dataframe(self, groups='all', nuclides='all',
|
||||
xs_type='macro', paths=False):
|
||||
"""Build a Pandas DataFrame for the MGXS data.
|
||||
|
||||
This method leverages :meth:`openmc.Tally.get_pandas_dataframe`, but
|
||||
renames the columns with terminology appropriate for cross section data.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
groups : Iterable of Integral or 'all'
|
||||
Energy groups of interest. Defaults to 'all'.
|
||||
nuclides : Iterable of str or 'all' or 'sum'
|
||||
The nuclides of the cross-sections to include in the dataframe. This
|
||||
may be a list of nuclide name strings (e.g., ['U235', 'U238']).
|
||||
The special string 'all' will include the cross sections for all
|
||||
nuclides in the spatial domain. The special string 'sum' will
|
||||
include the cross sections summed over all nuclides. Defaults to
|
||||
'all'.
|
||||
xs_type: {'macro', 'micro'}
|
||||
Return macro or micro cross section in units of cm^-1 or barns.
|
||||
Defaults to 'macro'.
|
||||
paths : bool, optional
|
||||
Construct columns for distribcell tally filters (default is True).
|
||||
The geometric information in the Summary object is embedded into
|
||||
a Multi-index column with a geometric "path" to each distribcell
|
||||
instance.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pandas.DataFrame
|
||||
A Pandas DataFrame for the cross section data.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
When this method is called before the multi-group cross section is
|
||||
computed from tally data.
|
||||
|
||||
"""
|
||||
|
||||
# Build the dataframe using the parent class method
|
||||
df = super().get_pandas_dataframe(groups, nuclides, xs_type, paths=paths)
|
||||
|
||||
# If user requested micro cross sections, multiply by the atom
|
||||
# densities to cancel out division made by the parent class method
|
||||
if xs_type == 'micro':
|
||||
if self.by_nuclide:
|
||||
densities = self.get_nuclide_densities(nuclides)
|
||||
else:
|
||||
densities = self.get_nuclide_densities('sum')
|
||||
tile_factor = int(df.shape[0] / len(densities))
|
||||
df['mean'] *= np.tile(densities, tile_factor)
|
||||
df['std. dev.'] *= np.tile(densities, tile_factor)
|
||||
|
||||
return df
|
||||
|
||||
def get_units(self, xs_type='macro'):
|
||||
"""Returns the units of Chi.
|
||||
|
||||
|
|
@ -5892,6 +5855,12 @@ class InverseVelocity(MGXS):
|
|||
|
||||
"""
|
||||
|
||||
# Store whether or not the number density should be removed for microscopic
|
||||
# values of this data; since the inverse velocity does not contain number
|
||||
# density scaling, we should not remove the number density from microscopic
|
||||
# values
|
||||
_divide_by_density = False
|
||||
|
||||
def __init__(self, domain=None, domain_type=None, groups=None,
|
||||
by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
|
||||
super().__init__(domain, domain_type, groups, by_nuclide, name,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue