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Initial implementation of scattering moments for ScatterMatrixXS
This commit is contained in:
Will Boyd
parent
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commit
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4 changed files with 115 additions and 59 deletions
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@ -2,6 +2,7 @@ from __future__ import division
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from collections import Iterable, OrderedDict
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from numbers import Integral
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import warnings
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import os
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import sys
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import copy
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@ -1412,12 +1413,6 @@ class MGXS(object):
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else:
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df = self.xs_tally.get_pandas_dataframe(summary=summary)
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# Remove the score column since it is homogeneous and redundant
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if summary and 'distribcell' in self.domain_type:
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df = df.drop('score', level=0, axis=1)
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else:
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df = df.drop('score', axis=1)
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# Override energy groups bounds with indices
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all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int)
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all_groups = np.repeat(all_groups, self.num_nuclides)
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@ -1431,7 +1426,8 @@ class MGXS(object):
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df.rename(columns={'energyout low [MeV]': 'group out'},
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inplace=True)
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out_groups = np.tile(all_groups, df.shape[0] / all_groups.size)
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out_groups = np.repeat(all_groups, self.xs_tally.num_scores)
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out_groups = np.tile(out_groups, df.shape[0] / out_groups.size)
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df['group out'] = out_groups
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del df['energyout high [MeV]']
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columns = ['group in', 'group out']
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@ -1843,6 +1839,8 @@ class ScatterMatrixXS(MGXS):
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----------
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correction : 'P0' or None
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Apply the P0 correction to scattering matrices if set to 'P0'
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order : int
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The highest order in the scattering matrix (default is 0)
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"""
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@ -1852,6 +1850,7 @@ class ScatterMatrixXS(MGXS):
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groups, by_nuclide, name)
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self._rxn_type = 'scatter matrix'
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self._correction = 'P0'
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self._order = 0
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def __deepcopy__(self, memo):
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clone = super(ScatterMatrixXS, self).__deepcopy__(memo)
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@ -1862,6 +1861,10 @@ class ScatterMatrixXS(MGXS):
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def correction(self):
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return self._correction
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@property
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def order(self):
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return self._order
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@property
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def tallies(self):
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"""Construct the OpenMC tallies needed to compute this cross section.
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@ -1879,13 +1882,19 @@ class ScatterMatrixXS(MGXS):
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energy = openmc.Filter('energy', group_edges)
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energyout = openmc.Filter('energyout', group_edges)
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# Create a list of scores for each Tally to be created
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if self.correction == 'P0':
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scores = ['flux', 'scatter', 'scatter-P1']
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filters = [[energy], [energy, energyout], [energyout]]
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else:
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scores = ['flux', 'scatter']
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filters = [[energy], [energy, energyout]]
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# Create lists of scores, filters for each Tally to be created
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scores = ['flux']
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filters = [[energy]]
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# Create separate tallies for each moment
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for moment in range(self.order+1):
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scores.append('scatter-{}'.format(moment))
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filters.append([energy, energyout])
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# Append to the lists for the P0 approximation if needed
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if self.correction == 'P0' and self.order == 0:
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scores.append('scatter-1')
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filters.append([energyout])
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estimator = 'analog'
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keys = scores
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@ -1899,16 +1908,25 @@ class ScatterMatrixXS(MGXS):
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def rxn_rate_tally(self):
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if self._rxn_rate_tally is None:
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# If using P0 correction subtract scatter-P1 from the diagonal
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if self.correction == 'P0':
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scatter_p1 = self.tallies['scatter-P1']
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scatter_p1 = scatter_p1.get_slice(scores=['scatter-P1'])
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energy_filter = self.tallies['scatter'].find_filter('energy')
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if self.correction == 'P0' and self.order == 0:
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scatter_p1 = self.tallies['scatter-1']
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scatter_p1 = scatter_p1.get_slice(scores=['scatter-1'])
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energy_filter = self.tallies['scatter-0'].find_filter('energy')
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energy_filter = copy.deepcopy(energy_filter)
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scatter_p1 = scatter_p1.diagonalize_filter(energy_filter)
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self._rxn_rate_tally = self.tallies['scatter'] - scatter_p1
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self._rxn_rate_tally = self.tallies['scatter-0'] - scatter_p1
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# Merge all scattering moments into a single reaction rate Tally
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else:
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self._rxn_rate_tally = self.tallies['scatter']
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rxn_rate_tally = self.tallies['scatter-0']
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for moment in range(1, self.order+1):
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scatter_key = 'scatter-{}'.format(moment)
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scatter_pn = self.tallies[scatter_key]
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rxn_rate_tally = rxn_rate_tally.merge(scatter_pn)
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self._rxn_rate_tally = rxn_rate_tally
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self._rxn_rate_tally.sparse = self.sparse
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@ -1917,8 +1935,27 @@ class ScatterMatrixXS(MGXS):
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@correction.setter
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def correction(self, correction):
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cv.check_value('correction', correction, ('P0', None))
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if correction == 'P0' and self.order > 0:
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msg = 'The P0 correction will be ignored since the scattering ' \
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'order {} is greater than zero'.format(self.order)
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warnings.warn(msg)
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self._correction = correction
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@order.setter
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def order(self, order):
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cv.check_type('order', order, Integral)
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cv.check_greater_than('order', order, 0, equality=True)
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if self.correction == 'P0' and self.order > 0:
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msg = 'The P0 correction will be ignored since the scattering ' \
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'order {} is greater than zero'.format(self.order)
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warnings.warn(msg, RuntimeWarning)
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self._order = order
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# FIXME: Add order param
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def get_slice(self, nuclides=[], in_groups=[], out_groups=[]):
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"""Build a sliced ScatterMatrix for the specified nuclides and
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energy groups.
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@ -1971,8 +2008,8 @@ class ScatterMatrixXS(MGXS):
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slice_xs.sparse = self.sparse
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return slice_xs
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def get_xs(self, in_groups='all', out_groups='all',
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subdomains='all', nuclides='all', xs_type='macro',
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def get_xs(self, in_groups='all', out_groups='all', subdomains='all',
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nuclides='all', moment='all', xs_type='macro',
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order_groups='increasing', value='mean'):
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"""Returns an array of multi-group cross sections.
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@ -1992,6 +2029,10 @@ class ScatterMatrixXS(MGXS):
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special string 'all' will return the cross sections for all nuclides
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in the spatial domain. The special string 'sum' will return the
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cross section summed over all nuclides. Defaults to 'all'.
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moment : int or 'all'
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The scattering matrix moment to return. All moments will be
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returned if the moment is 'all' (default); otherwise, a specific
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moment will be returned.
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xs_type: {'macro', 'micro'}
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Return the macro or micro cross section in units of cm^-1 or barns.
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Defaults to 'macro'.
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@ -2044,6 +2085,15 @@ class ScatterMatrixXS(MGXS):
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filters.append('energyout')
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filter_bins.append((self.energy_groups.get_group_bounds(group),))
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# Construct CrossScore for requested scattering moment
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if moment != 'all':
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cv.check_type('moment', moment, Integral)
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cv.check_greater_than('moment', moment, 0, equality=True)
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cv.check_less_than('moment', moment, self.order, equality=True)
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scores = [self.xs_tally.scores[moment]]
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else:
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scores = []
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# Construct a collection of the nuclides to retrieve from the xs tally
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if self.by_nuclide:
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if nuclides == 'all' or nuclides == 'sum' or nuclides == ['sum']:
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@ -2056,10 +2106,10 @@ class ScatterMatrixXS(MGXS):
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# Use tally summation if user requested the sum for all nuclides
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if nuclides == 'sum' or nuclides == ['sum']:
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xs_tally = self.xs_tally.summation(nuclides=query_nuclides)
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xs = xs_tally.get_values(filters=filters,
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xs = xs_tally.get_values(scores=scores, filters=filters,
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filter_bins=filter_bins, value=value)
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else:
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xs = self.xs_tally.get_values(filters=filters,
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xs = self.xs_tally.get_values(scores=scores, filters=filters,
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filter_bins=filter_bins,
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nuclides=query_nuclides, value=value)
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@ -2101,7 +2151,8 @@ class ScatterMatrixXS(MGXS):
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return xs
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def print_xs(self, subdomains='all', nuclides='all', xs_type='macro'):
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def print_xs(self, subdomains='all', nuclides='all',
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xs_type='macro', moment=0):
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"""Prints a string representation for the multi-group cross section.
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Parameters
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@ -2118,6 +2169,8 @@ class ScatterMatrixXS(MGXS):
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xs_type: {'macro', 'micro'}
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Return the macro or micro cross section in units of cm^-1 or barns.
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Defaults to 'macro'.
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moment : int
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The scattering moment to print (default is 0)
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"""
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@ -2142,9 +2195,14 @@ class ScatterMatrixXS(MGXS):
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cv.check_value('xs_type', xs_type, ['macro', 'micro'])
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if self.correction != 'P0':
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rxn_type= '{0} (moment {1})'.format(self.rxn_type, moment)
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else:
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rxn_type = self.rxn_type
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# Build header for string with type and domain info
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string = 'Multi-Group XS\n'
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string += '{0: <16}=\t{1}\n'.format('\tReaction Type', self.rxn_type)
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string += '{0: <16}=\t{1}\n'.format('\tReaction Type', rxn_type)
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string += '{0: <16}=\t{1}\n'.format('\tDomain Type', self.domain_type)
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string += '{0: <16}=\t{1}\n'.format('\tDomain ID', self.domain.id)
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@ -2189,11 +2247,11 @@ class ScatterMatrixXS(MGXS):
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string += template.format('', in_group, out_group)
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average = \
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self.get_xs([in_group], [out_group],
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[subdomain], [nuclide],
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[subdomain], [nuclide], moment=moment,
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xs_type=xs_type, value='mean')
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rel_err = \
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self.get_xs([in_group], [out_group],
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[subdomain], [nuclide],
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[subdomain], [nuclide], moment=moment,
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xs_type=xs_type, value='rel_err')
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average = average.flatten()[0]
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rel_err = rel_err.flatten()[0] * 100.
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@ -2206,6 +2264,8 @@ class ScatterMatrixXS(MGXS):
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print(string)
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# FIXME: Add order property to Library
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class NuScatterMatrixXS(ScatterMatrixXS):
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"""A scattering production matrix multi-group cross section."""
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@ -2228,28 +2288,35 @@ class NuScatterMatrixXS(ScatterMatrixXS):
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# Instantiate tallies if they do not exist
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if self._tallies is None:
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# Create the non-domain specific Filters for the Tallies
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group_edges = self.energy_groups.group_edges
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energy = openmc.Filter('energy', group_edges)
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energyout = openmc.Filter('energyout', group_edges)
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# Create a list of scores for each Tally to be created
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if self.correction == 'P0':
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scores = ['flux', 'nu-scatter', 'scatter-P1']
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estimator = 'analog'
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keys = ['flux', 'scatter', 'scatter-P1']
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filters = [[energy], [energy, energyout], [energyout]]
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else:
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scores = ['flux', 'nu-scatter']
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estimator = 'analog'
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keys = ['flux', 'scatter']
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filters = [[energy], [energy, energyout]]
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# Create lists of scores, filters for each Tally to be created
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scores = ['flux']
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filters = [[energy]]
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keys = ['flux']
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# Create separate tallies for each moment
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for moment in range(self.order+1):
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scores.append('nu-scatter-{}'.format(moment))
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filters.append([energy, energyout])
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keys.append('scatter-{}'.format(moment))
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# Append to the lists for the P0 approximation if needed
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if self.correction == 'P0' and self.order == 0:
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scores.append('scatter-1')
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filters.append([energyout])
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keys.append('scatter-1')
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estimator = 'analog'
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# Intialize the Tallies
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self._create_tallies(scores, filters, keys, estimator)
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return self._tallies
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class Chi(MGXS):
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"""The fission spectrum."""
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@ -1637,7 +1637,7 @@ class Tally(object):
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for score in self.scores:
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if isinstance(score, (basestring, CrossScore)):
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scores.append(score)
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scores.append(str(score))
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elif isinstance(score, AggregateScore):
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scores.append(score.name)
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column_name = '{0}(score)'.format(score.aggregate_op)
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@ -3338,28 +3338,17 @@ contains
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case ('nu-scatter')
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t % score_bins(j) = SCORE_NU_SCATTER
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! Set tally estimator to analog
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t % estimator = ESTIMATOR_ANALOG
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case ('scatter-n')
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if (n_order == 0) then
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t % score_bins(j) = SCORE_SCATTER
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else
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t % score_bins(j) = SCORE_SCATTER_N
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! Set tally estimator to analog
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t % estimator = ESTIMATOR_ANALOG
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end if
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t % score_bins(j) = SCORE_SCATTER_N
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t % moment_order(j) = n_order
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t % estimator = ESTIMATOR_ANALOG
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case ('nu-scatter-n')
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! Set tally estimator to analog
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t % estimator = ESTIMATOR_ANALOG
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if (n_order == 0) then
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t % score_bins(j) = SCORE_NU_SCATTER
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else
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t % score_bins(j) = SCORE_NU_SCATTER_N
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end if
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t % score_bins(j) = SCORE_NU_SCATTER_N
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t % moment_order(j) = n_order
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t % estimator = ESTIMATOR_ANALOG
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case ('scatter-pn')
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t % estimator = ESTIMATOR_ANALOG
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@ -330,11 +330,11 @@ contains
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MOMENT_LOOP: do j = 1, tally % n_user_score_bins
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select case(tally % score_bins(k))
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case (SCORE_SCATTER_N, SCORE_NU_SCATTER_N)
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str_array(k) = 'P' // trim(to_str(tally % moment_order(k)))
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str_array(k) = trim(to_str(tally % moment_order(k)))
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k = k + 1
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case (SCORE_SCATTER_PN, SCORE_NU_SCATTER_PN)
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do n_order = 0, tally % moment_order(k)
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str_array(k) = 'P' // trim(to_str(n_order))
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str_array(k) = trim(to_str(n_order))
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k = k + 1
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end do
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case (SCORE_SCATTER_YN, SCORE_NU_SCATTER_YN, SCORE_FLUX_YN, &
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