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Merge pull request #1909 from mkreher13/dif-coef-fix
Removing Legendre filter in diffusion coefficient results
This commit is contained in:
Adam Nelson
GitHub
commit
e61db57485
7 changed files with 76 additions and 204 deletions
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@ -3127,6 +3127,32 @@ class DiffusionCoefficient(TransportXS):
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@property
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def rxn_rate_tally(self):
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if self._rxn_rate_tally is None:
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# Switch EnergyoutFilter to EnergyFilter.
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p1_tally = self.tallies['scatter-1']
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old_filt = p1_tally.filters[-2]
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new_filt = openmc.EnergyFilter(old_filt.values)
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p1_tally.filters[-2] = new_filt
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# Slice Legendre expansion filter and change name of score
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p1_tally = p1_tally.get_slice(filters=[openmc.LegendreFilter],
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filter_bins=[('P1',)],
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squeeze=True)
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p1_tally._scores = ['scatter-1']
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transport = self.tallies['total'] - p1_tally
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self._rxn_rate_tally = transport**(-1) / 3.0
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self._rxn_rate_tally.sparse = self.sparse
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return self._rxn_rate_tally
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@property
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def xs_tally(self):
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if self._xs_tally is None:
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if self.tallies is None:
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msg = 'Unable to get xs_tally since tallies have ' \
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'not been loaded from a statepoint'
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raise ValueError(msg)
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# Switch EnergyoutFilter to EnergyFilter
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p1_tally = self.tallies['scatter-1']
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old_filt = p1_tally.filters[-2]
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@ -3143,136 +3169,16 @@ class DiffusionCoefficient(TransportXS):
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total_xs = self.tallies['total'] / self.tallies['flux (tracklength)']
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# Compute transport correction term
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trans_corr = self.tallies['scatter-1'] / self.tallies['flux (analog)']
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trans_corr = p1_tally / self.tallies['flux (analog)']
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# Compute the diffusion coefficient
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transport = total_xs - trans_corr
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dif_coef = transport**(-1) / 3.0
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self._rxn_rate_tally = dif_coef * self.tallies['flux (tracklength)']
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self._rxn_rate_tally.sparse = self.sparse
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return self._rxn_rate_tally
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@property
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def xs_tally(self):
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if self._xs_tally is None:
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if self.tallies is None:
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msg = 'Unable to get xs_tally since tallies have ' \
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'not been loaded from a statepoint'
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raise ValueError(msg)
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self._xs_tally = self.rxn_rate_tally / self.tallies['flux (tracklength)']
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diff_coef = transport**(-1) / 3.0
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self._xs_tally = diff_coef
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self._compute_xs()
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return self._xs_tally
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def get_condensed_xs(self, coarse_groups):
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"""Construct an energy-condensed version of this cross section.
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Parameters
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----------
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coarse_groups : openmc.mgxs.EnergyGroups
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The coarse energy group structure of interest
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Returns
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-------
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MGXS
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A new MGXS condensed to the group structure of interest
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"""
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cv.check_type('coarse_groups', coarse_groups, EnergyGroups)
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cv.check_less_than('coarse groups', coarse_groups.num_groups,
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self.num_groups, equality=True)
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cv.check_value('upper coarse energy', coarse_groups.group_edges[-1],
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[self.energy_groups.group_edges[-1]])
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cv.check_value('lower coarse energy', coarse_groups.group_edges[0],
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[self.energy_groups.group_edges[0]])
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# Clone this MGXS to initialize the condensed version
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condensed_xs = copy.deepcopy(self)
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if self._rxn_rate_tally is None:
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p1_tally = self.tallies['scatter-1']
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old_filt = p1_tally.filters[-2]
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new_filt = openmc.EnergyFilter(old_filt.values)
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p1_tally.filters[-2] = new_filt
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# Slice Legendre expansion filter and change name of score
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p1_tally = p1_tally.get_slice(filters=[openmc.LegendreFilter],
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filter_bins=[('P1',)],
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squeeze=True)
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p1_tally._scores = ['scatter-1']
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total = self.tallies['total'] / self.tallies['flux (tracklength)']
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trans_corr = p1_tally / self.tallies['flux (analog)']
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transport = (total - trans_corr)
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dif_coef = transport**(-1) / 3.0
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dif_coef *= self.tallies['flux (tracklength)']
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else:
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dif_coef = self.rxn_rate_tally
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flux_tally = condensed_xs.tallies['flux (tracklength)']
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condensed_xs._tallies = OrderedDict()
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condensed_xs._tallies[self._rxn_type] = dif_coef
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condensed_xs._tallies['flux (tracklength)'] = flux_tally
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condensed_xs._rxn_rate_tally = dif_coef
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condensed_xs._xs_tally = None
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condensed_xs._sparse = False
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condensed_xs._energy_groups = coarse_groups
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# Build energy indices to sum across
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energy_indices = []
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for group in range(coarse_groups.num_groups, 0, -1):
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low, high = coarse_groups.get_group_bounds(group)
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low_index = np.where(self.energy_groups.group_edges == low)[0][0]
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energy_indices.append(low_index)
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fine_edges = self.energy_groups.group_edges
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# Condense each of the tallies to the coarse group structure
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for tally in condensed_xs.tallies.values():
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# Make condensed tally derived and null out sum, sum_sq
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tally._derived = True
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tally._sum = None
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tally._sum_sq = None
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# Get tally data arrays reshaped with one dimension per filter
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mean = tally.get_reshaped_data(value='mean')
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std_dev = tally.get_reshaped_data(value='std_dev')
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# Sum across all applicable fine energy group filters
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for i, tally_filter in enumerate(tally.filters):
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if not isinstance(tally_filter,
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(openmc.EnergyFilter,
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openmc.EnergyoutFilter)):
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continue
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elif len(tally_filter.bins) != len(fine_edges):
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continue
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elif not np.allclose(tally_filter.bins, fine_edges):
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continue
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else:
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tally_filter.bins = coarse_groups.group_edges
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mean = np.add.reduceat(mean, energy_indices, axis=i)
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std_dev = np.add.reduceat(std_dev**2, energy_indices,
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axis=i)
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std_dev = np.sqrt(std_dev)
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# Reshape condensed data arrays with one dimension for all filters
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mean = np.reshape(mean, tally.shape)
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std_dev = np.reshape(std_dev, tally.shape)
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# Override tally's data with the new condensed data
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tally._mean = mean
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tally._std_dev = std_dev
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# Compute the energy condensed multi-group cross section
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condensed_xs.sparse = self.sparse
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return condensed_xs
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class AbsorptionXS(MGXS):
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r"""An absorption multi-group cross section.
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@ -214,24 +214,16 @@
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28 2 2 y-min out 1 total 4.548 0.156691
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mesh 1 group in nuclide mean std. dev.
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x y z
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1 1 1 1 0 total 1.039567 0.052248
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0 1 1 1 1 total 0.289572 0.043864
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5 1 2 1 0 total 1.079961 0.085600
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4 1 2 1 1 total 0.304543 0.038994
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3 2 1 1 0 total 1.088126 0.082813
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2 2 1 1 1 total 0.304326 0.051269
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7 2 2 1 0 total 1.037036 0.121171
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6 2 2 1 1 total 0.308008 0.027855
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0 1 1 1 1 total 0.757948 0.035459
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2 1 2 1 1 total 0.779112 0.047034
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1 2 1 1 1 total 0.787475 0.050368
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3 2 2 1 1 total 0.769656 0.058065
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mesh 1 group in nuclide mean std. dev.
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x y z
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1 1 1 1 0 total 1.039567 0.052248
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0 1 1 1 1 total 0.289572 0.043864
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5 1 2 1 0 total 1.079555 0.085584
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4 1 2 1 1 total 0.304543 0.038994
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3 2 1 1 0 total 1.088126 0.082813
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2 2 1 1 1 total 0.304326 0.051269
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7 2 2 1 0 total 1.037036 0.121171
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6 2 2 1 1 total 0.308008 0.027855
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0 1 1 1 1 total 0.757948 0.035459
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2 1 2 1 1 total 0.778934 0.047027
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1 2 1 1 1 total 0.787475 0.050368
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3 2 2 1 1 total 0.769656 0.058065
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mesh 1 delayedgroup group in nuclide mean std. dev.
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x y z
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0 1 1 1 1 1 total 0.000006 3.699363e-07
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@ -54,12 +54,10 @@
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0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...),) 1 total 0.088451 0.003512
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sum(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, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...),) 1 1 total 0.082789 0.005683
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sum(distribcell) group in legendre nuclide mean std. dev.
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0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...),) 1 P0 total 5.212993 1.398847
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1 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...),) 1 P1 total 0.806252 0.027980
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sum(distribcell) group in legendre nuclide mean std. dev.
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0 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...),) 1 P0 total 5.226298 1.407011
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1 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...),) 1 P1 total 0.806564 0.028011
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sum(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, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...),) 1 total 0.806252 0.022131
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sum(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, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...),) 1 total 0.806564 0.022166
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sum(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, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...),) 1 1 total 0.000020 8.047454e-07
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1 ((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...),) 2 1 total 0.000108 4.184372e-06
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@ -144,15 +144,11 @@ domain=1 type=current
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[2.08326667e-02 3.48425028e-02 0.00000000e+00 0.00000000e+00
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7.95361553e-02 6.66783323e-02 0.00000000e+00 0.00000000e+00]]]
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domain=1 type=diffusion-coefficient
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[[2.89572488e-01 6.04647594e+01]
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[1.03956656e+00 1.39871892e+01]]
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[[4.38638506e-02 2.26850956e+03]
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[5.22481383e-02 1.22780818e+01]]
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[1.03956656e+00 2.89572488e-01]
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[4.66257756e-02 3.47571359e-02]
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domain=1 type=nu-diffusion-coefficient
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[[2.89572488e-01 6.04647594e+01]
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[1.03956656e+00 1.39871892e+01]]
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[[4.38638506e-02 2.26850956e+03]
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[5.22481383e-02 1.22780818e+01]]
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[1.03956656e+00 2.89572488e-01]
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[4.66257756e-02 3.47571359e-02]
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domain=1 type=delayed-nu-fission
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[[1.37840363e-06 3.03296462e-05]
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[8.45663047e-06 1.56552364e-04]
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@ -212,26 +212,18 @@
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30 2 2 y-max out 1 total 0.0244 0.024400
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29 2 2 y-min in 1 total 0.2326 0.042782
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28 2 2 y-min out 1 total 0.1778 0.009484
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mesh 1 group in legendre nuclide mean std. dev.
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x y z
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0 1 1 1 1 P0 total 26.352505 16.110503
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1 1 1 1 1 P1 total 4.600555 0.424935
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4 1 2 1 1 P0 total 32.890094 15.569252
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5 1 2 1 1 P1 total 4.572518 0.218792
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2 2 1 1 1 P0 total 27.128188 17.388447
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3 2 1 1 1 P1 total 4.493168 0.383445
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6 2 2 1 1 P0 total 21.584163 5.964683
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7 2 2 1 1 P1 total 4.489908 0.236773
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mesh 1 group in legendre nuclide mean std. dev.
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x y z
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0 1 1 1 1 P0 total 26.352505 16.110503
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1 1 1 1 1 P1 total 4.600555 0.424935
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4 1 2 1 1 P0 total 32.996545 15.690979
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5 1 2 1 1 P1 total 4.571696 0.218502
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2 2 1 1 1 P0 total 27.434506 17.764465
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3 2 1 1 1 P1 total 4.496020 0.383903
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6 2 2 1 1 P0 total 21.761315 6.095339
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7 2 2 1 1 P1 total 4.492825 0.237270
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mesh 1 group in nuclide mean std. dev.
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x y z
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0 1 1 1 1 total 4.600555 0.368212
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2 1 2 1 1 total 4.572518 0.204129
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1 2 1 1 1 total 4.493168 0.329560
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3 2 2 1 1 total 4.489908 0.228574
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mesh 1 group in nuclide mean std. dev.
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x y z
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0 1 1 1 1 total 4.600555 0.368212
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2 1 2 1 1 total 4.571696 0.203824
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1 2 1 1 1 total 4.496020 0.330019
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3 2 2 1 1 total 4.492825 0.229077
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mesh 1 delayedgroup group in nuclide mean std. dev.
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x y z
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0 1 1 1 1 1 total 0.000007 4.371033e-07
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@ -151,17 +151,13 @@ prompt-nu-fission matrix
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1 1 2 1 total 0.495450 0.012592
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0 1 2 2 total 0.000000 0.000000
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diffusion-coefficient
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material group in legendre nuclide mean std. dev.
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||||
2 1 1 P0 total 13.267434 9.377780
|
||||
3 1 1 P1 total 0.881035 0.050823
|
||||
0 1 2 P0 total 1.242844 0.316720
|
||||
1 1 2 P1 total 0.519910 0.064395
|
||||
material group in nuclide mean std. dev.
|
||||
1 1 1 total 0.881035 0.038500
|
||||
0 1 2 total 0.519910 0.049301
|
||||
nu-diffusion-coefficient
|
||||
material group in legendre nuclide mean std. dev.
|
||||
2 1 1 P0 total 13.360612 9.496523
|
||||
3 1 1 P1 total 0.880816 0.050843
|
||||
0 1 2 P0 total 1.242844 0.316720
|
||||
1 1 2 P1 total 0.519910 0.064395
|
||||
material group in nuclide mean std. dev.
|
||||
1 1 1 total 0.880816 0.038534
|
||||
0 1 2 total 0.519910 0.049301
|
||||
(n,elastic)
|
||||
material group in nuclide mean std. dev.
|
||||
1 1 1 total 0.361427 0.011879
|
||||
|
|
@ -457,17 +453,13 @@ prompt-nu-fission matrix
|
|||
1 2 2 1 total 0.0 0.0
|
||||
0 2 2 2 total 0.0 0.0
|
||||
diffusion-coefficient
|
||||
material group in legendre nuclide mean std. dev.
|
||||
2 2 1 P0 total 133.637358 904.804108
|
||||
3 2 1 P1 total 1.210594 0.082668
|
||||
0 2 2 P0 total 34.754997 241.483530
|
||||
1 2 2 P1 total 1.110491 0.131041
|
||||
material group in nuclide mean std. dev.
|
||||
1 2 1 total 1.210594 0.070439
|
||||
0 2 2 total 1.110491 0.099580
|
||||
nu-diffusion-coefficient
|
||||
material group in legendre nuclide mean std. dev.
|
||||
2 2 1 P0 total 133.637358 904.804108
|
||||
3 2 1 P1 total 1.210594 0.082668
|
||||
0 2 2 P0 total 34.754997 241.483530
|
||||
1 2 2 P1 total 1.110491 0.131041
|
||||
material group in nuclide mean std. dev.
|
||||
1 2 1 total 1.210594 0.070439
|
||||
0 2 2 total 1.110491 0.099580
|
||||
(n,elastic)
|
||||
material group in nuclide mean std. dev.
|
||||
1 2 1 total 0.301494 0.009403
|
||||
|
|
@ -763,17 +755,13 @@ prompt-nu-fission matrix
|
|||
1 3 2 1 total 0.0 0.0
|
||||
0 3 2 2 total 0.0 0.0
|
||||
diffusion-coefficient
|
||||
material group in legendre nuclide mean std. dev.
|
||||
2 3 1 P0 total 11.244053 8.794903
|
||||
3 3 1 P1 total 1.141761 0.081143
|
||||
0 3 2 P0 total -2.888333 3.736106
|
||||
1 3 2 P1 total 0.227648 0.021613
|
||||
material group in nuclide mean std. dev.
|
||||
1 3 1 total 1.141761 0.074641
|
||||
0 3 2 total 0.227648 0.018035
|
||||
nu-diffusion-coefficient
|
||||
material group in legendre nuclide mean std. dev.
|
||||
2 3 1 P0 total 11.244053 8.794903
|
||||
3 3 1 P1 total 1.141761 0.081143
|
||||
0 3 2 P0 total -2.888333 3.736106
|
||||
1 3 2 P1 total 0.227648 0.021613
|
||||
material group in nuclide mean std. dev.
|
||||
1 3 1 total 1.141761 0.074641
|
||||
0 3 2 total 0.227648 0.018035
|
||||
(n,elastic)
|
||||
material group in nuclide mean std. dev.
|
||||
1 3 1 total 0.683777 0.015496
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
bf460584607a2a7b2f3fca008762839f5b3a5bbc85721a990eb568df5d0417c4f4eca3e0c2c12380c0761e15faeccc662af1876171ff8de0102ba86c81b4bd04
|
||||
b8706c9586aeeb5ee558829c28772f7f6e18a0d3fe4e30a2059b6e74564d1ebbbd0ab5473965c4e6156dff8b8178ce76c74d8db1d63f032c3d4da5f7eb9eae2c
|
||||
Loading…
Add table
Add a link
Reference in a new issue