From e131e079483ee76535edd8a3c546d7f036de1139 Mon Sep 17 00:00:00 2001 From: Miriam Rathbun Date: Tue, 16 Nov 2021 13:58:40 -0700 Subject: [PATCH] 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. --- openmc/mgxs/mgxs.py | 152 ++++-------------- .../mgxs_library_condense/results_true.dat | 24 +-- .../mgxs_library_distribcell/results_true.dat | 10 +- .../mgxs_library_hdf5/results_true.dat | 12 +- .../mgxs_library_mesh/results_true.dat | 32 ++-- .../mgxs_library_no_nuclides/results_true.dat | 48 +++--- .../mgxs_library_nuclides/results_true.dat | 2 +- 7 files changed, 76 insertions(+), 204 deletions(-) diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 89b223a35..ff739fe85 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -3127,6 +3127,32 @@ class DiffusionCoefficient(TransportXS): @property def rxn_rate_tally(self): if self._rxn_rate_tally is None: + # Switch EnergyoutFilter to EnergyFilter. + p1_tally = self.tallies['scatter-1'] + old_filt = p1_tally.filters[-2] + new_filt = openmc.EnergyFilter(old_filt.values) + p1_tally.filters[-2] = new_filt + + # Slice Legendre expansion filter and change name of score + p1_tally = p1_tally.get_slice(filters=[openmc.LegendreFilter], + filter_bins=[('P1',)], + squeeze=True) + p1_tally._scores = ['scatter-1'] + + transport = self.tallies['total'] - p1_tally + self._rxn_rate_tally = transport**(-1) / 3.0 + self._rxn_rate_tally.sparse = self.sparse + + return self._rxn_rate_tally + + @property + def xs_tally(self): + if self._xs_tally is None: + if self.tallies is None: + msg = 'Unable to get xs_tally since tallies have ' \ + 'not been loaded from a statepoint' + raise ValueError(msg) + # Switch EnergyoutFilter to EnergyFilter p1_tally = self.tallies['scatter-1'] old_filt = p1_tally.filters[-2] @@ -3143,136 +3169,16 @@ class DiffusionCoefficient(TransportXS): total_xs = self.tallies['total'] / self.tallies['flux (tracklength)'] # Compute transport correction term - trans_corr = self.tallies['scatter-1'] / self.tallies['flux (analog)'] + trans_corr = p1_tally / self.tallies['flux (analog)'] # Compute the diffusion coefficient transport = total_xs - trans_corr - dif_coef = transport**(-1) / 3.0 - - self._rxn_rate_tally = dif_coef * self.tallies['flux (tracklength)'] - self._rxn_rate_tally.sparse = self.sparse - - return self._rxn_rate_tally - - @property - def xs_tally(self): - if self._xs_tally is None: - if self.tallies is None: - msg = 'Unable to get xs_tally since tallies have ' \ - 'not been loaded from a statepoint' - raise ValueError(msg) - - self._xs_tally = self.rxn_rate_tally / self.tallies['flux (tracklength)'] + diff_coef = transport**(-1) / 3.0 + self._xs_tally = diff_coef self._compute_xs() return self._xs_tally - def get_condensed_xs(self, coarse_groups): - """Construct an energy-condensed version of this cross section. - Parameters - ---------- - coarse_groups : openmc.mgxs.EnergyGroups - The coarse energy group structure of interest - - Returns - ------- - MGXS - A new MGXS condensed to the group structure of interest - """ - - cv.check_type('coarse_groups', coarse_groups, EnergyGroups) - cv.check_less_than('coarse groups', coarse_groups.num_groups, - self.num_groups, equality=True) - cv.check_value('upper coarse energy', coarse_groups.group_edges[-1], - [self.energy_groups.group_edges[-1]]) - cv.check_value('lower coarse energy', coarse_groups.group_edges[0], - [self.energy_groups.group_edges[0]]) - - # Clone this MGXS to initialize the condensed version - condensed_xs = copy.deepcopy(self) - - if self._rxn_rate_tally is None: - - p1_tally = self.tallies['scatter-1'] - old_filt = p1_tally.filters[-2] - new_filt = openmc.EnergyFilter(old_filt.values) - p1_tally.filters[-2] = new_filt - - # Slice Legendre expansion filter and change name of score - p1_tally = p1_tally.get_slice(filters=[openmc.LegendreFilter], - filter_bins=[('P1',)], - squeeze=True) - p1_tally._scores = ['scatter-1'] - - total = self.tallies['total'] / self.tallies['flux (tracklength)'] - trans_corr = p1_tally / self.tallies['flux (analog)'] - transport = (total - trans_corr) - dif_coef = transport**(-1) / 3.0 - dif_coef *= self.tallies['flux (tracklength)'] - - else: - dif_coef = self.rxn_rate_tally - - flux_tally = condensed_xs.tallies['flux (tracklength)'] - condensed_xs._tallies = OrderedDict() - condensed_xs._tallies[self._rxn_type] = dif_coef - condensed_xs._tallies['flux (tracklength)'] = flux_tally - condensed_xs._rxn_rate_tally = dif_coef - condensed_xs._xs_tally = None - condensed_xs._sparse = False - condensed_xs._energy_groups = coarse_groups - - # Build energy indices to sum across - energy_indices = [] - for group in range(coarse_groups.num_groups, 0, -1): - low, high = coarse_groups.get_group_bounds(group) - low_index = np.where(self.energy_groups.group_edges == low)[0][0] - energy_indices.append(low_index) - - fine_edges = self.energy_groups.group_edges - - # Condense each of the tallies to the coarse group structure - for tally in condensed_xs.tallies.values(): - - # Make condensed tally derived and null out sum, sum_sq - tally._derived = True - tally._sum = None - tally._sum_sq = None - - # Get tally data arrays reshaped with one dimension per filter - mean = tally.get_reshaped_data(value='mean') - std_dev = tally.get_reshaped_data(value='std_dev') - - # Sum across all applicable fine energy group filters - for i, tally_filter in enumerate(tally.filters): - if not isinstance(tally_filter, - (openmc.EnergyFilter, - openmc.EnergyoutFilter)): - continue - elif len(tally_filter.bins) != len(fine_edges): - continue - elif not np.allclose(tally_filter.bins, fine_edges): - continue - else: - tally_filter.bins = coarse_groups.group_edges - mean = np.add.reduceat(mean, energy_indices, axis=i) - std_dev = np.add.reduceat(std_dev**2, energy_indices, - axis=i) - std_dev = np.sqrt(std_dev) - - # Reshape condensed data arrays with one dimension for all filters - mean = np.reshape(mean, tally.shape) - std_dev = np.reshape(std_dev, tally.shape) - - # Override tally's data with the new condensed data - tally._mean = mean - tally._std_dev = std_dev - - # Compute the energy condensed multi-group cross section - condensed_xs.sparse = self.sparse - return condensed_xs - - class AbsorptionXS(MGXS): r"""An absorption multi-group cross section. diff --git a/tests/regression_tests/mgxs_library_condense/results_true.dat b/tests/regression_tests/mgxs_library_condense/results_true.dat index 6ba7c0755..349d9debe 100644 --- a/tests/regression_tests/mgxs_library_condense/results_true.dat +++ b/tests/regression_tests/mgxs_library_condense/results_true.dat @@ -214,24 +214,16 @@ 28 2 2 y-min out 1 total 4.548 0.156691 mesh 1 group in nuclide mean std. dev. x y z -1 1 1 1 0 total 1.039567 0.052248 -0 1 1 1 1 total 0.289572 0.043864 -5 1 2 1 0 total 1.079961 0.085600 -4 1 2 1 1 total 0.304543 0.038994 -3 2 1 1 0 total 1.088126 0.082813 -2 2 1 1 1 total 0.304326 0.051269 -7 2 2 1 0 total 1.037036 0.121171 -6 2 2 1 1 total 0.308008 0.027855 +0 1 1 1 1 total 0.757948 0.035459 +2 1 2 1 1 total 0.779112 0.047034 +1 2 1 1 1 total 0.787475 0.050368 +3 2 2 1 1 total 0.769656 0.058065 mesh 1 group in nuclide mean std. dev. x y z -1 1 1 1 0 total 1.039567 0.052248 -0 1 1 1 1 total 0.289572 0.043864 -5 1 2 1 0 total 1.079555 0.085584 -4 1 2 1 1 total 0.304543 0.038994 -3 2 1 1 0 total 1.088126 0.082813 -2 2 1 1 1 total 0.304326 0.051269 -7 2 2 1 0 total 1.037036 0.121171 -6 2 2 1 1 total 0.308008 0.027855 +0 1 1 1 1 total 0.757948 0.035459 +2 1 2 1 1 total 0.778934 0.047027 +1 2 1 1 1 total 0.787475 0.050368 +3 2 2 1 1 total 0.769656 0.058065 mesh 1 delayedgroup group in nuclide mean std. dev. x y z 0 1 1 1 1 1 total 0.000006 3.699363e-07 diff --git a/tests/regression_tests/mgxs_library_distribcell/results_true.dat b/tests/regression_tests/mgxs_library_distribcell/results_true.dat index d72632245..9fff8479d 100644 --- a/tests/regression_tests/mgxs_library_distribcell/results_true.dat +++ b/tests/regression_tests/mgxs_library_distribcell/results_true.dat @@ -54,12 +54,10 @@ 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 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, 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 - sum(distribcell) group in legendre nuclide mean std. dev. -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 -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 - sum(distribcell) group in legendre nuclide mean std. dev. -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 -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 + sum(distribcell) group in nuclide mean std. dev. +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 + sum(distribcell) group in nuclide mean std. dev. +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 sum(distribcell) delayedgroup group in nuclide mean std. dev. 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 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 diff --git a/tests/regression_tests/mgxs_library_hdf5/results_true.dat b/tests/regression_tests/mgxs_library_hdf5/results_true.dat index bcea46925..f49ae97ab 100644 --- a/tests/regression_tests/mgxs_library_hdf5/results_true.dat +++ b/tests/regression_tests/mgxs_library_hdf5/results_true.dat @@ -144,15 +144,11 @@ domain=1 type=current [2.08326667e-02 3.48425028e-02 0.00000000e+00 0.00000000e+00 7.95361553e-02 6.66783323e-02 0.00000000e+00 0.00000000e+00]]] domain=1 type=diffusion-coefficient -[[2.89572488e-01 6.04647594e+01] - [1.03956656e+00 1.39871892e+01]] -[[4.38638506e-02 2.26850956e+03] - [5.22481383e-02 1.22780818e+01]] +[1.03956656e+00 2.89572488e-01] +[4.66257756e-02 3.47571359e-02] domain=1 type=nu-diffusion-coefficient -[[2.89572488e-01 6.04647594e+01] - [1.03956656e+00 1.39871892e+01]] -[[4.38638506e-02 2.26850956e+03] - [5.22481383e-02 1.22780818e+01]] +[1.03956656e+00 2.89572488e-01] +[4.66257756e-02 3.47571359e-02] domain=1 type=delayed-nu-fission [[1.37840363e-06 3.03296462e-05] [8.45663047e-06 1.56552364e-04] diff --git a/tests/regression_tests/mgxs_library_mesh/results_true.dat b/tests/regression_tests/mgxs_library_mesh/results_true.dat index a56e8b0e9..0acc2f407 100644 --- a/tests/regression_tests/mgxs_library_mesh/results_true.dat +++ b/tests/regression_tests/mgxs_library_mesh/results_true.dat @@ -212,26 +212,18 @@ 30 2 2 y-max out 1 total 0.0244 0.024400 29 2 2 y-min in 1 total 0.2326 0.042782 28 2 2 y-min out 1 total 0.1778 0.009484 - mesh 1 group in legendre nuclide mean std. dev. - x y z -0 1 1 1 1 P0 total 26.352505 16.110503 -1 1 1 1 1 P1 total 4.600555 0.424935 -4 1 2 1 1 P0 total 32.890094 15.569252 -5 1 2 1 1 P1 total 4.572518 0.218792 -2 2 1 1 1 P0 total 27.128188 17.388447 -3 2 1 1 1 P1 total 4.493168 0.383445 -6 2 2 1 1 P0 total 21.584163 5.964683 -7 2 2 1 1 P1 total 4.489908 0.236773 - mesh 1 group in legendre nuclide mean std. dev. - x y z -0 1 1 1 1 P0 total 26.352505 16.110503 -1 1 1 1 1 P1 total 4.600555 0.424935 -4 1 2 1 1 P0 total 32.996545 15.690979 -5 1 2 1 1 P1 total 4.571696 0.218502 -2 2 1 1 1 P0 total 27.434506 17.764465 -3 2 1 1 1 P1 total 4.496020 0.383903 -6 2 2 1 1 P0 total 21.761315 6.095339 -7 2 2 1 1 P1 total 4.492825 0.237270 + mesh 1 group in nuclide mean std. dev. + x y z +0 1 1 1 1 total 4.600555 0.368212 +2 1 2 1 1 total 4.572518 0.204129 +1 2 1 1 1 total 4.493168 0.329560 +3 2 2 1 1 total 4.489908 0.228574 + mesh 1 group in nuclide mean std. dev. + x y z +0 1 1 1 1 total 4.600555 0.368212 +2 1 2 1 1 total 4.571696 0.203824 +1 2 1 1 1 total 4.496020 0.330019 +3 2 2 1 1 total 4.492825 0.229077 mesh 1 delayedgroup group in nuclide mean std. dev. x y z 0 1 1 1 1 1 total 0.000007 4.371033e-07 diff --git a/tests/regression_tests/mgxs_library_no_nuclides/results_true.dat b/tests/regression_tests/mgxs_library_no_nuclides/results_true.dat index f2f66b0d0..5afe3b24c 100644 --- a/tests/regression_tests/mgxs_library_no_nuclides/results_true.dat +++ b/tests/regression_tests/mgxs_library_no_nuclides/results_true.dat @@ -151,17 +151,13 @@ prompt-nu-fission matrix 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 diff --git a/tests/regression_tests/mgxs_library_nuclides/results_true.dat b/tests/regression_tests/mgxs_library_nuclides/results_true.dat index ddc7fa46e..fc005b133 100644 --- a/tests/regression_tests/mgxs_library_nuclides/results_true.dat +++ b/tests/regression_tests/mgxs_library_nuclides/results_true.dat @@ -1 +1 @@ -bf460584607a2a7b2f3fca008762839f5b3a5bbc85721a990eb568df5d0417c4f4eca3e0c2c12380c0761e15faeccc662af1876171ff8de0102ba86c81b4bd04 \ No newline at end of file +b8706c9586aeeb5ee558829c28772f7f6e18a0d3fe4e30a2059b6e74564d1ebbbd0ab5473965c4e6156dff8b8178ce76c74d8db1d63f032c3d4da5f7eb9eae2c \ No newline at end of file