mirror of
https://github.com/openmc-dev/openmc.git
synced 2026-07-27 21:55:41 -04:00
working on the writing of the MGXS lib
This commit is contained in:
Adam Nelson
parent
8e10d9e00f
commit
f126d4387a
4 changed files with 55 additions and 39 deletions
|
|
@ -868,15 +868,13 @@ class RealFilter(Filter):
|
|||
return np.allclose(self.bins, other.bins)
|
||||
|
||||
def get_bin_index(self, filter_bin):
|
||||
# Use lower energy bound to find index for RealFilters
|
||||
deltas = np.abs(self.bins - filter_bin[1]) / filter_bin[1]
|
||||
min_delta = np.min(deltas)
|
||||
if min_delta < 1E-3:
|
||||
return deltas.argmin() - 1
|
||||
else:
|
||||
i = np.where(self.bins == filter_bin[1])[0]
|
||||
if len(i) == 0:
|
||||
msg = 'Unable to get the bin index for Filter since "{0}" ' \
|
||||
'is not one of the bins'.format(filter_bin)
|
||||
raise ValueError(msg)
|
||||
else:
|
||||
return i[0] - 1
|
||||
|
||||
def get_bin(self, bin_index):
|
||||
cv.check_type('bin_index', bin_index, Integral)
|
||||
|
|
@ -907,6 +905,17 @@ class EnergyFilter(RealFilter):
|
|||
|
||||
"""
|
||||
|
||||
def get_bin_index(self, filter_bin):
|
||||
# Use lower energy bound to find index for RealFilters
|
||||
deltas = np.abs(self.bins - filter_bin[1]) / filter_bin[1]
|
||||
min_delta = np.min(deltas)
|
||||
if min_delta < 1E-3:
|
||||
return deltas.argmin() - 1
|
||||
else:
|
||||
msg = 'Unable to get the bin index for Filter since "{0}" ' \
|
||||
'is not one of the bins'.format(filter_bin)
|
||||
raise ValueError(msg)
|
||||
|
||||
def check_bins(self, bins):
|
||||
for edge in bins:
|
||||
if not isinstance(edge, Real):
|
||||
|
|
|
|||
|
|
@ -875,13 +875,13 @@ class Library(object):
|
|||
return pickle.load(open(full_filename, 'rb'))
|
||||
|
||||
def get_xsdata(self, domain, xsdata_name, nuclide='total', xs_type='macro',
|
||||
order=None, subdomain=None):
|
||||
subdomain=None):
|
||||
"""Generates an openmc.XSdata object describing a multi-group cross section
|
||||
dataset for writing to an openmc.MGXSLibrary object.
|
||||
|
||||
Note that this method does not build an XSdata
|
||||
object with nested temperature tables. The temperature of each
|
||||
XSdata object will be left at the default value of 300K.
|
||||
XSdata object will be left at the default value of 294K.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -896,10 +896,6 @@ class Library(object):
|
|||
Provide the macro or micro cross section in units of cm^-1 or
|
||||
barns. Defaults to 'macro'. If the Library object is not tallied by
|
||||
nuclide this will be set to 'macro' regardless.
|
||||
order : int
|
||||
Scattering order for this data entry. Default is None,
|
||||
which will set the XSdata object to use the order of the
|
||||
Library.
|
||||
subdomain : iterable of int
|
||||
This parameter is not used unless using a mesh domain. In that
|
||||
case, the subdomain is an [i,j,k] index (1-based indexing) of the
|
||||
|
|
@ -929,10 +925,6 @@ class Library(object):
|
|||
cv.check_type('xsdata_name', xsdata_name, basestring)
|
||||
cv.check_type('nuclide', nuclide, basestring)
|
||||
cv.check_value('xs_type', xs_type, ['macro', 'micro'])
|
||||
cv.check_type('order', order, (type(None), Integral))
|
||||
if order is not None:
|
||||
cv.check_greater_than('order', order, 0, equality=True)
|
||||
cv.check_less_than('order', order, 10, equality=True)
|
||||
if subdomain is not None:
|
||||
cv.check_iterable_type('subdomain', subdomain, Integral,
|
||||
max_depth=3)
|
||||
|
|
@ -953,14 +945,6 @@ class Library(object):
|
|||
name += '_' + nuclide
|
||||
xsdata = openmc.XSdata(name, self.energy_groups)
|
||||
|
||||
if order is None:
|
||||
# Set the order to the Library's order (the default behavior)
|
||||
xsdata.order = self.legendre_order
|
||||
else:
|
||||
# Set the order of the xsdata object to the minimum of
|
||||
# the provided order or the Library's order.
|
||||
xsdata.order = min(order, self.legendre_order)
|
||||
|
||||
# Right now only isotropic weighting is supported
|
||||
self.representation = 'isotropic'
|
||||
|
||||
|
|
@ -1033,6 +1017,7 @@ class Library(object):
|
|||
using_multiplicity = False
|
||||
|
||||
if using_multiplicity:
|
||||
# import pdb; pdb.set_trace()
|
||||
nuscatt_mgxs = self.get_mgxs(domain, 'nu-scatter matrix')
|
||||
xsdata.set_scatter_matrix_mgxs(nuscatt_mgxs, xs_type=xs_type,
|
||||
nuclide=[nuclide],
|
||||
|
|
@ -1049,10 +1034,17 @@ class Library(object):
|
|||
# accounted for approximately by using an adjusted
|
||||
# absorption cross section.
|
||||
if 'total' in self.mgxs_types or 'transport' in self.mgxs_types:
|
||||
for i in range(len(xsdata.temperatures)):
|
||||
xsdata._absorption[i] = \
|
||||
np.subtract(xsdata._total[i], np.sum(
|
||||
xsdata._scatter_matrix[i][0, :, :], axis=1))
|
||||
if xsdata.scatter_format == 'legendre':
|
||||
for i in range(len(xsdata.temperatures)):
|
||||
xsdata._absorption[i] = \
|
||||
np.subtract(xsdata._total[i], np.sum(
|
||||
xsdata._scatter_matrix[i][0, :, :], axis=1))
|
||||
elif xsdata.scatter_format == 'histogram':
|
||||
for i in range(len(xsdata.temperatures)):
|
||||
xsdata._absorption[i] = \
|
||||
np.subtract(xsdata._total[i], np.sum(np.sum(
|
||||
xsdata._scatter_matrix[i][:, :, :], axis=0),
|
||||
axis=1))
|
||||
|
||||
return xsdata
|
||||
|
||||
|
|
|
|||
|
|
@ -3410,7 +3410,8 @@ class ScatterMatrixXS(MatrixMGXS):
|
|||
else:
|
||||
filters = [[energy], [energy, energyout]]
|
||||
elif self.scatter_format == 'histogram':
|
||||
bins = np.linspace(-1., 1., num=self.histogram_bins, endpoint=True)
|
||||
bins = np.linspace(-1., 1., num=self.histogram_bins + 1,
|
||||
endpoint=True)
|
||||
filters = [[energy], [energy, energyout, openmc.MuFilter(bins)]]
|
||||
|
||||
return filters
|
||||
|
|
@ -3758,9 +3759,19 @@ class ScatterMatrixXS(MatrixMGXS):
|
|||
else:
|
||||
num_out_groups = len(out_groups)
|
||||
|
||||
if self.scatter_format == 'histogram':
|
||||
num_mu_bins = self.histogram_bins
|
||||
else:
|
||||
num_mu_bins = 1
|
||||
|
||||
# Reshape tally data array with separate axes for domain and energy
|
||||
num_subdomains = int(xs.shape[0] / (num_in_groups * num_out_groups))
|
||||
new_shape = (num_subdomains, num_in_groups, num_out_groups)
|
||||
num_subdomains = int(xs.shape[0] /
|
||||
(num_mu_bins * num_in_groups * num_out_groups))
|
||||
if self.scatter_format == 'histogram':
|
||||
new_shape = (num_subdomains, num_in_groups, num_out_groups,
|
||||
num_mu_bins)
|
||||
else:
|
||||
new_shape = (num_subdomains, num_in_groups, num_out_groups)
|
||||
new_shape += xs.shape[1:]
|
||||
xs = np.reshape(xs, new_shape)
|
||||
|
||||
|
|
@ -3771,7 +3782,7 @@ class ScatterMatrixXS(MatrixMGXS):
|
|||
# Reverse data if user requested increasing energy groups since
|
||||
# tally data is stored in order of increasing energies
|
||||
if order_groups == 'increasing':
|
||||
xs = xs[:, ::-1, ::-1, :]
|
||||
xs = xs[:, ::-1, ::-1, ...]
|
||||
|
||||
if squeeze:
|
||||
xs = np.squeeze(xs)
|
||||
|
|
|
|||
|
|
@ -1031,21 +1031,21 @@ class XSdata(object):
|
|||
|
||||
i = np.where(self.temperatures == temperature)[0][0]
|
||||
if self.representation == 'isotropic':
|
||||
# Get the scattering orders in the outermost dimension
|
||||
self._scatter_matrix[i] = np.zeros((self.num_orders,
|
||||
self.energy_groups.num_groups,
|
||||
self.energy_groups.num_groups))
|
||||
if self.scatter_format == 'legendre':
|
||||
# Get the scattering orders in the outermost dimension
|
||||
self._scatter_matrix[i] = np.zeros((self.num_orders,
|
||||
self.energy_groups.num_groups,
|
||||
self.energy_groups.num_groups))
|
||||
for moment in range(self.num_orders):
|
||||
self._scatter_matrix[i][moment, :, :] = \
|
||||
scatter.get_xs(nuclides=nuclide, xs_type=xs_type,
|
||||
moment=moment, subdomains=subdomain)
|
||||
else:
|
||||
self._scatter_matrix[i][:, :, :] = \
|
||||
self._scatter_matrix[i] = \
|
||||
scatter.get_xs(nuclides=nuclide, xs_type=xs_type,
|
||||
subdomains=subdomain)
|
||||
import pdb; pdb.set_trace()
|
||||
|
||||
# self._scatter_matrix[i] = \
|
||||
# np.swapaxes(self._scatter_matrix[i], 0, 2)
|
||||
elif self.representation == 'angle':
|
||||
msg = 'Angular-Dependent MGXS have not yet been implemented'
|
||||
raise ValueError(msg)
|
||||
|
|
@ -1124,6 +1124,10 @@ class XSdata(object):
|
|||
scatt = scatter.get_xs(nuclides=nuclide,
|
||||
xs_type=xs_type, moment=0,
|
||||
subdomains=subdomain)
|
||||
if scatter.scatter_format == 'histogram':
|
||||
scatt = np.sum(scatt, axis=4)
|
||||
if nuscatter.scatter_format == 'histogram':
|
||||
nuscatt = np.sum(nuscatt, axis=4)
|
||||
self._multiplicity_matrix[i] = np.divide(nuscatt, scatt)
|
||||
elif self.representation == 'angle':
|
||||
msg = 'Angular-Dependent MGXS have not yet been implemented'
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue