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Fixed MGXS.get_subdomain_avg_xs(...) to use track density-weighted averaging
This commit is contained in:
Will Boyd
parent
b94d1bf661
commit
0faa406cea
3 changed files with 151 additions and 118 deletions
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@ -192,7 +192,9 @@ class MGXS(object):
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clone._rxn_rate_tally = copy.deepcopy(self._rxn_rate_tally, memo)
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clone._xs_tally = copy.deepcopy(self._xs_tally, memo)
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clone._sparse = self.sparse
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clone._loaded_sp = self._loaded_sp
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clone._derived = self.derived
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clone._hdf5_key = self._hdf5_key
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clone._tallies = OrderedDict()
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for tally_type, tally in self.tallies.items():
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@ -325,7 +327,7 @@ class MGXS(object):
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@property
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def num_subdomains(self):
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if self.domain_type.startswith('avg('):
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if self.domain_type.startswith('sum('):
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domain_type = self.domain_type[4:-1]
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else:
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domain_type = self.domain_type
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@ -789,16 +791,22 @@ class MGXS(object):
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if not isinstance(subdomains, string_types):
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cv.check_iterable_type('subdomains', subdomains, Integral,
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max_depth=3)
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filters.append(_DOMAIN_TO_FILTER[self.domain_type])
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subdomain_bins = []
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for subdomain in subdomains:
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filters.append(_DOMAIN_TO_FILTER[self.domain_type])
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filter_bins.append((subdomain,))
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subdomain_bins.append(subdomain)
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filter_bins.append(tuple(subdomain_bins))
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# Construct list of energy group bounds tuples for all requested groups
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if not isinstance(groups, string_types):
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cv.check_iterable_type('groups', groups, Integral)
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filters.append(openmc.EnergyFilter)
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energy_bins = []
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for group in groups:
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filters.append(openmc.EnergyFilter)
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filter_bins.append((self.energy_groups.get_group_bounds(group),))
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energy_bins.append(
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(self.energy_groups.get_group_bounds(group),))
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filter_bins.append(tuple(energy_bins))
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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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@ -958,30 +966,27 @@ class MGXS(object):
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# Construct a collection of the subdomain filter bins to average across
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if not isinstance(subdomains, string_types):
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cv.check_iterable_type('subdomains', subdomains, Integral)
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subdomains = [(subdomain,) for subdomain in subdomains]
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subdomains = [tuple(subdomains)]
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elif self.domain_type == 'distribcell':
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subdomains = np.arange(self.num_subdomains)
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subdomains = [i for i in range(self.num_subdomains)]
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subdomains = [tuple(subdomains)]
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else:
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subdomains = None
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# Clone this MGXS to initialize the subdomain-averaged version
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avg_xs = copy.deepcopy(self)
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avg_xs._rxn_rate_tally = None
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avg_xs._xs_tally = None
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if self.derived:
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avg_xs._rxn_rate_tally = avg_xs.rxn_rate_tally.average(
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filter_type=_DOMAIN_TO_FILTER[self.domain_type],
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filter_bins=subdomains)
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else:
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avg_xs._rxn_rate_tally = None
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avg_xs._xs_tally = None
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# Average each of the tallies across subdomains
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for tally_type, tally in avg_xs.tallies.items():
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filt_type = _DOMAIN_TO_FILTER[self.domain_type]
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tally_avg = tally.summation(filter_type=filt_type,
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filter_bins=subdomains)
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avg_xs.tallies[tally_type] = tally_avg
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# Average each of the tallies across subdomains
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for tally_type, tally in avg_xs.tallies.items():
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filt_type = _DOMAIN_TO_FILTER[self.domain_type]
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tally_avg = tally.average(filter_type=filt_type,
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filter_bins=subdomains)
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avg_xs.tallies[tally_type] = tally_avg
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avg_xs._domain_type = 'avg({0})'.format(self.domain_type)
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avg_xs._domain_type = 'sum({0})'.format(self.domain_type)
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avg_xs.sparse = self.sparse
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return avg_xs
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@ -1304,8 +1309,8 @@ class MGXS(object):
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cv.check_iterable_type('subdomains', subdomains, Integral)
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elif self.domain_type == 'distribcell':
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subdomains = np.arange(self.num_subdomains, dtype=np.int)
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elif self.domain_type == 'avg(distribcell)':
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domain_filter = self.xs_tally.find_filter('avg(distribcell)')
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elif self.domain_type == 'sum(distribcell)':
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domain_filter = self.xs_tally.find_filter('sum(distribcell)')
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subdomains = domain_filter.bins
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elif self.domain_type == 'mesh':
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xyz = [range(1, x+1) for x in self.domain.dimension]
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@ -1784,17 +1789,19 @@ class MatrixMGXS(MGXS):
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if not isinstance(subdomains, string_types):
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cv.check_iterable_type('subdomains', subdomains, Integral,
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max_depth=3)
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filters.append(_DOMAIN_TO_FILTER[self.domain_type])
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subdomain_bins = []
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for subdomain in subdomains:
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filters.append(_DOMAIN_TO_FILTER[self.domain_type])
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filter_bins.append((subdomain,))
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subdomain_bins.append(subdomain)
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filter_bins.append(tuple(subdomain_bins))
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# Construct list of energy group bounds tuples for all requested groups
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if not isinstance(in_groups, string_types):
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cv.check_iterable_type('groups', in_groups, Integral)
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filters.append(openmc.EnergyFilter)
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for group in in_groups:
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filters.append(openmc.EnergyFilter)
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filter_bins.append((
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self.energy_groups.get_group_bounds(group),))
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energy_bins.append((self.energy_groups.get_group_bounds(group),))
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filter_bins.append(tuple(energy_bins))
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# Construct list of energy group bounds tuples for all requested groups
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if not isinstance(out_groups, string_types):
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@ -3618,16 +3625,21 @@ class ScatterMatrixXS(MatrixMGXS):
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# Construct a collection of the domain filter bins
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if not isinstance(subdomains, string_types):
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cv.check_iterable_type('subdomains', subdomains, Integral, max_depth=3)
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filters.append(_DOMAIN_TO_FILTER[self.domain_type])
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subdomain_bins = []
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for subdomain in subdomains:
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filters.append(_DOMAIN_TO_FILTER[self.domain_type])
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filter_bins.append((subdomain,))
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subdomain_bins.append(subdomain)
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filter_bins.append(tuple(subdomain_bins))
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# Construct list of energy group bounds tuples for all requested groups
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if not isinstance(in_groups, string_types):
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cv.check_iterable_type('groups', in_groups, Integral)
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filters.append(openmc.EnergyFilter)
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energy_bins = []
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for group in in_groups:
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filters.append(openmc.EnergyFilter)
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filter_bins.append((self.energy_groups.get_group_bounds(group),))
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energy_bins.append(
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(self.energy_groups.get_group_bounds(group),))
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filter_bins.append(tuple(energy_bins))
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# Construct list of energy group bounds tuples for all requested groups
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if not isinstance(out_groups, string_types):
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@ -4606,16 +4618,21 @@ class Chi(MGXS):
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# Construct a collection of the domain filter bins
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if not isinstance(subdomains, string_types):
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cv.check_iterable_type('subdomains', subdomains, Integral, max_depth=3)
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filters.append(_DOMAIN_TO_FILTER[self.domain_type])
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subdomain_bins = []
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for subdomain in subdomains:
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filters.append(_DOMAIN_TO_FILTER[self.domain_type])
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filter_bins.append((subdomain,))
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subdomain_bins.append(subdomain)
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filter_bins.append(tuple(subdomain_bins))
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# Construct list of energy group bounds tuples for all requested groups
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if not isinstance(groups, string_types):
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cv.check_iterable_type('groups', groups, Integral)
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filters.append(openmc.EnergyoutFilter)
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energy_bins = []
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for group in groups:
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filters.append(openmc.EnergyoutFilter)
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filter_bins.append((self.energy_groups.get_group_bounds(group),))
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energy_bins.append(
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(self.energy_groups.get_group_bounds(group),))
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filter_bins.append(tuple(energy_bins))
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# If chi was computed for each nuclide in the domain
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if self.by_nuclide:
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@ -1327,7 +1327,7 @@ class Tally(object):
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# Create list of cell instance IDs for distribcell Filters
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elif isinstance(self_filter, openmc.DistribcellFilter):
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bins = [i for i in range(self_filter.num_bins)]
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bins = [b for b in range(self_filter.num_bins)]
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# Create list of IDs for bins for all other filter types
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else:
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@ -2258,12 +2258,12 @@ class Tally(object):
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# Construct lists of tuples for the bins in each of the two filters
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filters = [type(filter1), type(filter2)]
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if isinstance(filter1, openmc.DistribcellFilter):
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filter1_bins = [i for i in range(filter1.num_bins)]
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filter1_bins = [b for b in range(filter1.num_bins)]
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else:
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filter1_bins = [filter1.get_bin(i) for i in range(filter1.num_bins)]
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if isinstance(filter2, openmc.DistribcellFilter):
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filter2_bins = [i for i in range(filter2.num_bins)]
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filter2_bins = [b for b in range(filter2.num_bins)]
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else:
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filter2_bins = [filter2.get_bin(i) for i in range(filter2.num_bins)]
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@ -3108,8 +3108,16 @@ class Tally(object):
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# Sum across the bins in the user-specified filter
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for i, self_filter in enumerate(self.filters):
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if isinstance(self_filter, filter_type):
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shape = mean.shape
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mean = np.take(mean, indices=bin_indices, axis=i)
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std_dev = np.take(std_dev, indices=bin_indices, axis=i)
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# NumPy take introduces a new dimension in output array
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# for some special cases that must be removed
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if len(mean.shape) > len(shape):
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mean = np.squeeze(mean, axis=i)
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std_dev = np.squeeze(std_dev, axis=i)
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mean = np.sum(mean, axis=i, keepdims=True)
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std_dev = np.sum(std_dev**2, axis=i, keepdims=True)
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std_dev = np.sqrt(std_dev)
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@ -3255,8 +3263,16 @@ class Tally(object):
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# Average across the bins in the user-specified filter
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for i, self_filter in enumerate(self.filters):
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if isinstance(self_filter, filter_type):
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shape = mean.shape
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mean = np.take(mean, indices=bin_indices, axis=i)
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std_dev = np.take(std_dev, indices=bin_indices, axis=i)
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# NumPy take introduces a new dimension in output array
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# for some special cases that must be removed
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if len(mean.shape) > len(shape):
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mean = np.squeeze(mean, axis=i)
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std_dev = np.squeeze(std_dev, axis=i)
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mean = np.nanmean(mean, axis=i, keepdims=True)
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std_dev = np.nanmean(std_dev**2, axis=i, keepdims=True)
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std_dev /= len(bin_indices)
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@ -1,79 +1,79 @@
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.457353 0.010474
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.405649 0.015784
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.405641 0.015787
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.066556 0.00251
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.028979 0.002712
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.037577 0.001487
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.092377 0.003628
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 7.276707e+06 287579.26286
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.390797 0.008717
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.387332 0.014241
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avg(distribcell) group in group out nuclide moment mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P0 0.387009 0.014230
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1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P1 0.047179 0.004923
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2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P2 0.015713 0.003654
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3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P3 0.005378 0.003137
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avg(distribcell) group in group out nuclide moment mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P0 0.387332 0.014241
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1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P1 0.047187 0.004933
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2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P2 0.015727 0.003654
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3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total P3 0.005387 0.003141
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avg(distribcell) group in group out nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 1.000834 0.037242
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avg(distribcell) group in group out nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.094516 0.0059
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avg(distribcell) group out nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 1.0 0.080455
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avg(distribcell) group out nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 1.0 0.080541
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 5.139437e-07 2.133314e-08
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avg(distribcell) group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 total 0.091725 0.003604
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avg(distribcell) group in group out nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.093985 0.005872
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avg(distribcell) delayedgroup group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.000021 8.253907e-07
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1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 2 1 total 0.000112 4.284000e-06
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2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 3 1 total 0.000109 4.105197e-06
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3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4 1 total 0.000252 9.271420e-06
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4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 5 1 total 0.000112 3.888625e-06
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5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 6 1 total 0.000047 1.625563e-06
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avg(distribcell) delayedgroup group out nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.0 0.000000
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1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 2 1 total 1.0 1.414214
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2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 3 1 total 1.0 1.414214
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3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4 1 total 0.0 0.000000
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4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 5 1 total 0.0 0.000000
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5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 6 1 total 1.0 1.414214
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avg(distribcell) delayedgroup group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.000227 0.000012
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1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 2 1 total 0.001209 0.000061
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2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 3 1 total 0.001177 0.000059
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3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4 1 total 0.002727 0.000135
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4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 5 1 total 0.001210 0.000058
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5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 6 1 total 0.000504 0.000024
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avg(distribcell) delayedgroup group in nuclide mean std. dev.
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0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 total 0.000000 0.000000
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1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 2 1 total 0.032739 0.046300
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2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 3 1 total 0.120780 0.170809
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3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4 1 total 0.000000 0.000000
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4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 5 1 total 0.000000 0.000000
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5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 6 1 total 2.853000 4.034751
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avg(distribcell) delayedgroup group in group out nuclide mean std. dev.
|
||||
0 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 1 1 1 total 0.000000 0.000000
|
||||
1 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 2 1 1 total 0.000175 0.000175
|
||||
2 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 3 1 1 total 0.000178 0.000178
|
||||
3 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 4 1 1 total 0.000000 0.000000
|
||||
4 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 5 1 1 total 0.000000 0.000000
|
||||
5 (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... 6 1 1 total 0.000178 0.000178
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 0.457353 0.010474
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 0.405649 0.015784
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 0.405641 0.015787
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 0.066556 0.00251
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 0.028979 0.002712
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 0.037577 0.001487
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 0.092377 0.003628
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 7.276707e+06 287579.26286
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 0.390797 0.008717
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 0.387332 0.014241
|
||||
sum(distribcell) group in group out nuclide moment mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total P0 0.387009 0.014230
|
||||
1 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total P1 0.047179 0.004923
|
||||
2 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total P2 0.015713 0.003654
|
||||
3 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total P3 0.005378 0.003137
|
||||
sum(distribcell) group in group out nuclide moment mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total P0 0.387332 0.014241
|
||||
1 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total P1 0.047187 0.004933
|
||||
2 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total P2 0.015727 0.003654
|
||||
3 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total P3 0.005387 0.003141
|
||||
sum(distribcell) group in group out nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total 1.000834 0.037242
|
||||
sum(distribcell) group in group out nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total 0.094516 0.0059
|
||||
sum(distribcell) group out nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 1.0 0.080455
|
||||
sum(distribcell) group out nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 1.0 0.080541
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 5.139437e-07 2.133314e-08
|
||||
sum(distribcell) group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 total 0.091725 0.003604
|
||||
sum(distribcell) group in group out nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total 0.093985 0.005872
|
||||
sum(distribcell) delayedgroup group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total 0.000021 8.253907e-07
|
||||
1 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 2 1 total 0.000112 4.284000e-06
|
||||
2 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 3 1 total 0.000109 4.105197e-06
|
||||
3 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 4 1 total 0.000252 9.271420e-06
|
||||
4 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 5 1 total 0.000112 3.888625e-06
|
||||
5 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 6 1 total 0.000047 1.625563e-06
|
||||
sum(distribcell) delayedgroup group out nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total 0.0 0.000000
|
||||
1 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 2 1 total 1.0 1.414214
|
||||
2 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 3 1 total 1.0 1.414214
|
||||
3 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 4 1 total 0.0 0.000000
|
||||
4 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 5 1 total 0.0 0.000000
|
||||
5 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 6 1 total 1.0 1.414214
|
||||
sum(distribcell) delayedgroup group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total 0.000227 0.000012
|
||||
1 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 2 1 total 0.001209 0.000061
|
||||
2 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 3 1 total 0.001177 0.000059
|
||||
3 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 4 1 total 0.002727 0.000135
|
||||
4 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 5 1 total 0.001210 0.000058
|
||||
5 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 6 1 total 0.000504 0.000024
|
||||
sum(distribcell) delayedgroup group in nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 total 0.000000 0.000000
|
||||
1 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 2 1 total 0.032739 0.046300
|
||||
2 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 3 1 total 0.120780 0.170809
|
||||
3 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 4 1 total 0.000000 0.000000
|
||||
4 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 5 1 total 0.000000 0.000000
|
||||
5 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 6 1 total 2.853000 4.034751
|
||||
sum(distribcell) delayedgroup group in group out nuclide mean std. dev.
|
||||
0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 1 1 1 total 0.000000 0.000000
|
||||
1 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 2 1 1 total 0.000175 0.000175
|
||||
2 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 3 1 1 total 0.000178 0.000178
|
||||
3 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 4 1 1 total 0.000000 0.000000
|
||||
4 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 5 1 1 total 0.000000 0.000000
|
||||
5 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13... 6 1 1 total 0.000178 0.000178
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue