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Fixed issue where both P0 and P1 moments were displayed in the diffusion coefficient results. With this fix, the Legendre filter is successfully sliced. Also, there was previously concern that get_condensed_xs needed to be specially catered toward DiffusionCoefficient. However, since the MGXS get_condensed_xs method condenses the tallies before calculating the cross section, that should work great for DiffusionCoefficient as well. Tests updated.
This commit is contained in:
Miriam Rathbun
parent
c8003e0243
commit
e131e07948
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
|
||||
0 1 2 2 total 0.000000 0.000000
|
||||
diffusion-coefficient
|
||||
material group in legendre nuclide mean std. dev.
|
||||
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