From f139ce8dc12ae036e73ddf46c9ed5ba1a563be1c Mon Sep 17 00:00:00 2001 From: Sterling Harper Date: Fri, 24 Feb 2017 15:01:21 -0500 Subject: [PATCH 01/12] Fix printing bug for tallies with AggregateNuclide --- openmc/tallies.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/openmc/tallies.py b/openmc/tallies.py index 20e03129b..9eef5f374 100644 --- a/openmc/tallies.py +++ b/openmc/tallies.py @@ -195,7 +195,7 @@ class Tally(object): if isinstance(nuclide, openmc.Nuclide): string += nuclide.name + ' ' else: - string += nuclide + ' ' + string += str(nuclide) + ' ' string += '\n' From fc3df18eab72265016f733aedb257c2ae8e7bba5 Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sat, 25 Feb 2017 08:17:10 -0500 Subject: [PATCH 02/12] Added an openmc.run mode (summary, which just shows the timing and summary results; fixed the generation of cells from a mesh (incorrect ordering of universes in the lattice), xs_shapes for delayed groups had wrong ordering (fixed) in openmc.MGXSLibrary and openmc.Plotter, fixed error in multi-group plots which only plotted one group not all, fixed issue with setting max order to 0 for Legendre scattering (not legendre converted to tabular w/in OpenMC, the default), removed superfluous xs assignments in MG mode which only slowed things down --- openmc/executor.py | 26 ++++++++++++++++++++------ openmc/mesh.py | 35 +++++++++++++---------------------- openmc/mgxs/library.py | 1 + openmc/mgxs_library.py | 4 ++-- openmc/plotter.py | 24 ++++++++++++------------ src/mgxs_header.F90 | 8 +------- src/tally.F90 | 2 +- src/tracking.F90 | 2 -- 8 files changed, 50 insertions(+), 52 deletions(-) diff --git a/openmc/executor.py b/openmc/executor.py index 3b9b8abd8..efc28a0b9 100644 --- a/openmc/executor.py +++ b/openmc/executor.py @@ -5,21 +5,33 @@ import sys from six import string_types +summary_indicator = "TIMING STATISTICS" + def _run(command, output, cwd): # Launch a subprocess p = subprocess.Popen(command, shell=True, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True) + storage_flag = False + # Capture and re-print OpenMC output in real-time while True: # If OpenMC is finished, break loop line = p.stdout.readline() - if not line and p.poll() != None: + if not line and p.poll() is not None: break - # If user requested output, print to screen - if output: + if output == 'full': + # If user requested output, print to screen + print(line, end='') + elif output == 'summary' and summary_indicator in line: + # If they requested a summary, look for the start of the summary + storage_flag = True + + if storage_flag: + # If a summary is requested, and we have reached the summary, + # then print it print(line, end='') # Return the returncode (integer, zero if no problems encountered) @@ -44,7 +56,7 @@ def plot_geometry(output=True, openmc_exec='openmc', cwd='.'): def run(particles=None, threads=None, geometry_debug=False, - restart_file=None, tracks=False, output=True, cwd='.', + restart_file=None, tracks=False, output="full", cwd='.', openmc_exec='openmc', mpi_args=None): """Run an OpenMC simulation. @@ -63,8 +75,10 @@ def run(particles=None, threads=None, geometry_debug=False, Path to restart file to use tracks : bool, optional Write tracks for all particles. Defaults to False. - output : bool, optional - Capture OpenMC output from standard out. Defaults to True. + output : {"full", "summary", "none", False}, optional + Degree of OpenMC output captured from standard out. "full" prints all + output; "summary" prints only the results summary, and "none" or False + does not show the output. Defaults to "full". cwd : str, optional Path to working directory to run in. Defaults to the current working directory. diff --git a/openmc/mesh.py b/openmc/mesh.py index 24c39b530..4d9726292 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -189,21 +189,21 @@ class Mesh(EqualityMixin): def cell_generator(self): """Generator function to traverse through every [i,j,k] index - of the mesh. + of the mesh in the same order that would be done by the Fortran For example the following code: .. code-block:: python for mesh_index in mymesh.cell_generator(): - print mesh_index + print(mesh_index) will produce the following output for a 3-D 2x2x2 mesh in mymesh:: [1, 1, 1] + [2, 1, 1] [1, 1, 2] [1, 2, 1] - [1, 2, 2] ... @@ -213,13 +213,13 @@ class Mesh(EqualityMixin): for x in range(self.dimension[0]): yield [x + 1, 1, 1] elif len(self.dimension) == 2: - for x in range(self.dimension[0]): - for y in range(self.dimension[1]): + for y in range(self.dimension[1]): + for x in range(self.dimension[0]): yield [x + 1, y + 1, 1] else: - for x in range(self.dimension[0]): + for z in range(self.dimension[2]): for y in range(self.dimension[1]): - for z in range(self.dimension[2]): + for x in range(self.dimension[0]): yield [x + 1, y + 1, z + 1] def to_xml_element(self): @@ -321,25 +321,17 @@ class Mesh(EqualityMixin): # Build the universes which will be used for each of the [i,j,k] # locations within the mesh. - # We will also have to build cells to assign to these universes - universes = np.ndarray(self.dimension[::-1], dtype=np.object) + # We will concurrently build cells to assign to these universes cells = [] + universes = [] for [i, j, k] in self.cell_generator(): - if len(self.dimension) == 1: - universes[i - 1] = openmc.Universe() - cells.append(openmc.Cell()) - universes[i - 1].add_cells([cells[-1]]) - elif len(self.dimension) == 2: - universes[j - 1, i - 1] = openmc.Universe() - cells.append(openmc.Cell()) - universes[j - 1, i - 1].add_cells([cells[-1]]) - else: - universes[k - 1, j - 1, i - 1] = openmc.Universe() - cells.append(openmc.Cell()) - universes[k - 1, j - 1, i - 1].add_cells([cells[-1]]) + cells.append(openmc.Cell()) + universes.append(openmc.Universe()) + universes[-1].add_cell(cells[-1]) lattice = openmc.RectLattice() lattice.lower_left = self.lower_left + lattice.universes = np.reshape(universes, self.dimension) if self.width is not None: lattice.pitch = self.width @@ -359,7 +351,6 @@ class Mesh(EqualityMixin): dz = ((self.upper_right[2] - self.lower_left[2]) / self.dimension[2]) lattice.pitch = [dx, dy, dz] - lattice.universes = universes # Fill Cell with the Lattice root_cell.fill = lattice diff --git a/openmc/mgxs/library.py b/openmc/mgxs/library.py index 13fbe5e87..627fb2508 100644 --- a/openmc/mgxs/library.py +++ b/openmc/mgxs/library.py @@ -1317,6 +1317,7 @@ class Library(object): root_cell, cells = \ self.domains[0].build_cells(bc) root.add_cell(root_cell) + geometry = openmc.Geometry() geometry.root_universe = root materials = openmc.Materials() diff --git a/openmc/mgxs_library.py b/openmc/mgxs_library.py index af6dc8f98..db69055a7 100644 --- a/openmc/mgxs_library.py +++ b/openmc/mgxs_library.py @@ -351,8 +351,8 @@ class XSdata(object): self._xs_shapes["[DG]"] = (self.num_delayed_groups,) self._xs_shapes["[DG][G]"] = (self.num_delayed_groups, self.energy_groups.num_groups) - self._xs_shapes["[DG'][G']"] = (self.num_delayed_groups, - self.energy_groups.num_groups) + self._xs_shapes["[DG][G']"] = (self.num_delayed_groups, + self.energy_groups.num_groups) self._xs_shapes["[DG][G][G']"] = (self.num_delayed_groups, self.energy_groups.num_groups, self.energy_groups.num_groups) diff --git a/openmc/plotter.py b/openmc/plotter.py index 83f5722cc..80a2077ac 100644 --- a/openmc/plotter.py +++ b/openmc/plotter.py @@ -676,7 +676,7 @@ def calculate_mgxs(this, types, orders=None, temperature=294., for line in range(len(types)): for g in range(library.energy_groups.num_groups): - data[g * 2: g * 2 + 2] = mgxs[line, g] + data[line, g * 2: g * 2 + 2] = mgxs[line, g] return energy_grid[::-1], data @@ -775,28 +775,28 @@ def _calculate_mgxs_nuc_macro(this, types, library, orders=None, data[i, :] = temp_data[orders[i]] else: data[i, :] = np.sum(temp_data[:]) - elif shape in (xsdata.xs_shapes["[G'][DG]"], - xsdata.xs_shapes["[G][DG]"]): + elif shape in (xsdata.xs_shapes["[DG][G']"], + xsdata.xs_shapes["[DG][G]"]): # Then we have an array vs groups with values for each # delayed group. The user-provided value of orders tells us # which delayed group we want. If none are provided, then # we sum all the delayed groups together. if orders[i]: - if orders[i] < len(shape[1]): - data[i, :] = temp_data[:, orders[i]] + if orders[i] < len(shape[0]): + data[i, :] = temp_data[orders[i], :] else: - data[i, :] = np.sum(temp_data[:, :], axis=1) - elif shape == xsdata.xs_shapes["[G][G'][DG]"]: + data[i, :] = np.sum(temp_data[:, :], axis=0) + elif shape == xsdata.xs_shapes["[DG][G][G']"]: # Then we have a delayed group matrix. We will first # remove the outgoing group dependency - temp_data = np.sum(temp_data, axis=1) + temp_data = np.sum(temp_data, axis=-1) # And then proceed in exactly the same manner as the - # "[G'][DG]" of "[G][DG]" shapes in the previous block. + # "[DG][G']" or "[DG][G]" shapes in the previous block. if orders[i]: - if orders[i] < len(shape[1]): - data[i, :] = temp_data[:, orders[i]] + if orders[i] < len(shape[0]): + data[i, :] = temp_data[orders[i], :] else: - data[i, :] = np.sum(temp_data[:, :], axis=1) + data[i, :] = np.sum(temp_data[:, :], axis=0) elif shape == xsdata.xs_shapes["[G][G'][Order]"]: # This is a scattering matrix with angular data # First remove the outgoing group dependence diff --git a/src/mgxs_header.F90 b/src/mgxs_header.F90 index 04443e6d5..faf0fbaff 100644 --- a/src/mgxs_header.F90 +++ b/src/mgxs_header.F90 @@ -2340,7 +2340,7 @@ module mgxs_header ! Now need to compare this material maximum scattering order with ! the problem wide max scatt order and use whichever is lower - order = min(mat_max_order, max_order) + order = min(mat_max_order, max_order + 1) ! Ok, got our order, store the dimensionality order_dim = order @@ -3467,9 +3467,7 @@ module mgxs_header type(MaterialMacroXS), intent(inout) :: xs ! Resultant Mgxs Data xs % total = this % xs(this % index_temp) % total(gin) - xs % elastic = this % xs(this % index_temp) % scatter % scattxs(gin) xs % absorption = this % xs(this % index_temp) % absorption(gin) - xs % fission = this % xs(this % index_temp) % fission(gin) xs % nu_fission = & this % xs(this % index_temp) % prompt_nu_fission(gin) + & sum(this % xs(this % index_temp) % delayed_nu_fission(:, gin)) @@ -3487,12 +3485,8 @@ module mgxs_header call find_angle(this % polar, this % azimuthal, uvw, iazi, ipol) xs % total = this % xs(this % index_temp) % & total(gin, iazi, ipol) - xs % elastic = this % xs(this % index_temp) % & - scatter(iazi, ipol) % obj % scattxs(gin) xs % absorption = this % xs(this % index_temp) % & absorption(gin, iazi, ipol) - xs % fission = this % xs(this % index_temp) % & - fission(gin, iazi, ipol) xs % nu_fission = this % xs(this % index_temp) % & prompt_nu_fission(gin, iazi, ipol) + & sum(this % xs(this % index_temp) % & diff --git a/src/tally.F90 b/src/tally.F90 index df1b5d2d6..650e91b66 100644 --- a/src/tally.F90 +++ b/src/tally.F90 @@ -2027,7 +2027,7 @@ contains end do SCORE_LOOP - nullify(matxs,nucxs) + nullify(matxs, nucxs) end subroutine score_general_mg !=============================================================================== diff --git a/src/tracking.F90 b/src/tracking.F90 index 1b4f6b99b..a673b6751 100644 --- a/src/tracking.F90 +++ b/src/tracking.F90 @@ -105,9 +105,7 @@ contains p % coord(p % n_coord) % uvw, material_xs) else material_xs % total = ZERO - material_xs % elastic = ZERO material_xs % absorption = ZERO - material_xs % fission = ZERO material_xs % nu_fission = ZERO end if end if From 6aa561c291e4c4e615a9d55f3298dec7666152ef Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sat, 25 Feb 2017 08:52:16 -0500 Subject: [PATCH 03/12] Updated mgxs-iv notebook --- .../pythonapi/examples/mgxs-part-iv.ipynb | 351 ++++-------------- 1 file changed, 62 insertions(+), 289 deletions(-) diff --git a/docs/source/pythonapi/examples/mgxs-part-iv.ipynb b/docs/source/pythonapi/examples/mgxs-part-iv.ipynb index dfab3b2be..f0fce27c2 100644 --- a/docs/source/pythonapi/examples/mgxs-part-iv.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-iv.ipynb @@ -338,7 +338,7 @@ "outputs": [], "source": [ "# OpenMC simulation parameters\n", - "batches = 50\n", + "batches = 500\n", "inactive = 10\n", "particles = 5000\n", "\n", @@ -425,7 +425,7 @@ "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVDA5OjM5OjEwLTA2OjAwOkcAiwAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVDA4OjQ2OjA0LTA1OjAwRiB1hwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] @@ -571,7 +571,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/mgxs/library.py:412: RuntimeWarning: The P0 correction will be ignored since the scattering order 0 is greater than zero\n", + "/home/nelsonag/git/openmc/openmc/mgxs/library.py:412: RuntimeWarning: The P0 correction will be ignored since the scattering order 0 is greater than zero\n", " warn(msg, RuntimeWarning)\n" ] } @@ -684,7 +684,7 @@ "collapsed": true }, "source": [ - "Time to run the calculation and get our results!" + "Time to run the calculation and get our results! This time we will suppress the OpenMC output except for the summary information." ] }, { @@ -698,144 +698,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | 7ca46e809ce01fe7857ae36072822a1718f01aaf\n", - " Date/Time | 2017-02-23 09:39:10\n", - " OpenMP Threads | 4\n", - "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", - " Reading settings XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", - " Reading cross sections XML file...\n", - " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", - " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", - " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/romano/openmc/scripts/nndc_hdf5/B10.h5\n", - " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", - " Reading tallies XML file...\n", - " Building neighboring cells lists for each surface...\n", - " Initializing source particles...\n", - "\n", - " ===========================================================================\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", - "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.05169 \n", - " 2/1 1.02178 \n", - " 3/1 1.02778 \n", - " 4/1 1.01814 \n", - " 5/1 1.02993 \n", - " 6/1 1.05836 \n", - " 7/1 1.00011 \n", - " 8/1 1.04961 \n", - " 9/1 1.00908 \n", - " 10/1 1.00134 \n", - " 11/1 1.04275 \n", - " 12/1 1.04456 1.04366 +/- 0.00090\n", - " 13/1 1.00699 1.03143 +/- 0.01224\n", - " 14/1 1.05509 1.03735 +/- 0.01048\n", - " 15/1 1.01645 1.03317 +/- 0.00913\n", - " 16/1 1.02813 1.03233 +/- 0.00750\n", - " 17/1 1.03686 1.03298 +/- 0.00637\n", - " 18/1 1.04389 1.03434 +/- 0.00569\n", - " 19/1 1.01404 1.03208 +/- 0.00550\n", - " 20/1 1.01838 1.03071 +/- 0.00511\n", - " 21/1 1.02745 1.03042 +/- 0.00463\n", - " 22/1 1.03013 1.03039 +/- 0.00422\n", - " 23/1 1.02808 1.03022 +/- 0.00389\n", - " 24/1 1.04615 1.03135 +/- 0.00378\n", - " 25/1 1.03395 1.03153 +/- 0.00352\n", - " 26/1 1.05256 1.03284 +/- 0.00355\n", - " 27/1 1.03201 1.03279 +/- 0.00333\n", - " 28/1 1.00136 1.03105 +/- 0.00359\n", - " 29/1 1.01409 1.03015 +/- 0.00351\n", - " 30/1 1.01159 1.02923 +/- 0.00346\n", - " 31/1 1.02533 1.02904 +/- 0.00330\n", - " 32/1 1.04169 1.02962 +/- 0.00320\n", - " 33/1 1.02904 1.02959 +/- 0.00305\n", - " 34/1 1.04991 1.03044 +/- 0.00304\n", - " 35/1 1.03223 1.03051 +/- 0.00292\n", - " 36/1 1.02835 1.03043 +/- 0.00281\n", - " 37/1 1.02690 1.03029 +/- 0.00270\n", - " 38/1 1.02993 1.03028 +/- 0.00261\n", - " 39/1 1.03601 1.03048 +/- 0.00252\n", - " 40/1 1.04026 1.03081 +/- 0.00246\n", - " 41/1 1.00972 1.03013 +/- 0.00247\n", - " 42/1 1.04266 1.03052 +/- 0.00243\n", - " 43/1 1.01825 1.03015 +/- 0.00238\n", - " 44/1 1.05496 1.03087 +/- 0.00242\n", - " 45/1 1.01621 1.03046 +/- 0.00239\n", - " 46/1 1.04001 1.03072 +/- 0.00234\n", - " 47/1 1.04887 1.03121 +/- 0.00233\n", - " 48/1 1.02937 1.03116 +/- 0.00226\n", - " 49/1 1.01104 1.03065 +/- 0.00226\n", - " 50/1 1.03586 1.03078 +/- 0.00221\n", - " Creating state point statepoint.50.h5...\n", - "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.2488E-01 seconds\n", - " Reading cross sections = 3.1421E-01 seconds\n", - " Total time in simulation = 1.7708E+01 seconds\n", - " Time in transport only = 1.7283E+01 seconds\n", - " Time in inactive batches = 1.7937E+00 seconds\n", - " Time in active batches = 1.5915E+01 seconds\n", - " Time synchronizing fission bank = 5.8977E-03 seconds\n", - " Sampling source sites = 3.9998E-03 seconds\n", - " SEND/RECV source sites = 1.8525E-03 seconds\n", - " Time accumulating tallies = 1.4703E-04 seconds\n", - " Total time for finalization = 4.6920E-06 seconds\n", - " Total time elapsed = 1.8150E+01 seconds\n", - " Calculation Rate (inactive) = 27874.7 neutrons/second\n", - " Calculation Rate (active) = 12567.0 neutrons/second\n", + " Total time for initialization = 3.2303E-01 seconds\n", + " Reading cross sections = 2.4414E-01 seconds\n", + " Total time in simulation = 1.4351E+02 seconds\n", + " Time in transport only = 1.4018E+02 seconds\n", + " Time in inactive batches = 7.9702E-01 seconds\n", + " Time in active batches = 1.4272E+02 seconds\n", + " Time synchronizing fission bank = 8.1742E-02 seconds\n", + " Sampling source sites = 5.6844E-02 seconds\n", + " SEND/RECV source sites = 2.4302E-02 seconds\n", + " Time accumulating tallies = 1.9605E-03 seconds\n", + " Total time for finalization = 6.3080E-06 seconds\n", + " Total time elapsed = 1.4385E+02 seconds\n", + " Calculation Rate (inactive) = 62733.9 neutrons/second\n", + " Calculation Rate (active) = 17166.9 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02955 +/- 0.00250\n", - " k-effective (Track-length) = 1.03078 +/- 0.00221\n", - " k-effective (Absorption) = 1.02896 +/- 0.00244\n", - " Combined k-effective = 1.03019 +/- 0.00179\n", + " k-effective (Collision) = 1.02519 +/- 0.00068\n", + " k-effective (Track-length) = 1.02548 +/- 0.00075\n", + " k-effective (Absorption) = 1.02621 +/- 0.00064\n", + " Combined k-effective = 1.02580 +/- 0.00053\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -852,8 +737,8 @@ } ], "source": [ - "# Run OpenMC\n", - "openmc.run()" + "# Run OpenMC, showing only the summary information at the end.\n", + "openmc.run(output='summary')" ] }, { @@ -971,11 +856,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" ] } @@ -1084,9 +969,9 @@ "outputs": [ { "data": { - "image/png": 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VAYB+7bdLe967+iAO7t+Bv70zh4uemcb6bVZ8T5TgD/fGNI6HekXkNo7A64JG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\nOBdbj8MYY5otL72qpro/bgMuiG04TWO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bYjVOqg4A9OuAPdrz7rWHcGj/Dvz17Xlc8swMNm636nuiBH+4NyY5HuoVkXMc\ngdcFjRz3fNWGzVqRWp0w/jt9eaKL0Ox4CRwZqrou6PlGj68zMdSuIIfHLxjBbWMHMWXRBo67dzJf\nLN6Q6GI1S1VBX/1DfdCH5R4aKtgEAkeVx5xJqBrHC183jw/UG1+dnegiNDteAsD7IvKBiFwoIhfi\nLBv7bmyLZbwQES48qA9vXHUQhXlZnPfE19z21lyb6yrOgj/b/TRVBWoMoQJHRoQaRyh1BxEuXred\nW163D1QTG16S4zcAjwJDgH2Bx1T1xlgXzHg3qGsr3r7mEC48sDfjv1zGCfdP5rsUa25IZbVqHI1I\njldFyHV4znHU2WcLhJlYihg4RCRTRD5S1ddU9TpV/a2qvh6vwhnvWuRkcttJe/P8r37GrvIqTn/4\nS/794UJf34BN4wR/uPvqjhuhqSqQcA+1rybHUSs5Xjt0+Jml1xi/IgYOVa3CmW4kvbPNaeSgvh14\n/zeHcvLQrtz38SJOfegLFq61aRliKXiyvSo/NY5I+yIElVCr/VkXWhNPXnIcpcBsd/W/+wKPWBfM\nNF7rFtn8+6yhPPKL4awqKeWE+ybz7w8XUlZpuY9YaGxyPFI36kBX3UjnC95TNzme6kvJ+rW1NPS0\nJCY2vASOd3C6304CZgY9TJIbs09nPvztoZwwuAv3fbyI4++dzPRlmxJdrLQTXOPwN44j/L6INY6a\npiqtt625OuG+yYkuQrOSFW6HiBQBRar6TJ3t+wC2UESKaN8yl3vOGcYpw7rxh9fncOYjU/n5z3py\n03EDaZVnU45FQ2OT45EaqwLxotJrd9xmVsOoa8WmXYkuQrMSqcZxP84a43V1A+6NTXFMrIwe0JEP\nrzuUXx3chxenLeeof33Oe7NX26jzKAgOFo1Jjofc5waVSIEoUlOVJcdNLEUKHINV9fO6G1X1A5yu\nuXEhIgUi8oyIPC4i58XruukoPyeLP544iDeuOogOLXO58vlvOP/JaSyy5HmTBDdPlfnoBuslOR6q\nW23duoVqdNccN6YhkQJHpHaMJrVxiMhTIrJORObU2T5GRBaIyGIRucndfBrwiqpeCpzUlOsax5Du\nbXjr6oO4/aS9+b64hDH3Tub2iXPDrntgIgtOYPsZfBmxxuHu3FUR4XwRxnE096YrE1uRAsciETm+\n7kYROQ5Y0sTrjgfG1DlvJvAgcBwwCDhXRAYB3YEV7mHWLShKsjIz+OWBvfn0d6M5e/8ejP9yGYff\n9RkTpi3GZmJLAAAgAElEQVS3pWp9Cq5x7AyxDGo4kZqTAv8FkQLH7qVn/Z3bmKaKFDh+C9wjIuNF\n5Br38QxOfuPaplxUVScBdbv3jAQWq+oSVS0HXsRZdbAYJ3hELK+IXCYiM0Rkxvr165tSvGalfctc\n/nbqYCZefTB7FhVw82uzOeG+yXwyf63lPzwKzkNEq8YR6I4b8nx1B/vZf1NSeXHackbc8WGt3nbp\nJuwHsaouBAYDnwO93cfnwBB3X7R1Y3fNApyA0Q14DThdRB4GJkYo72OqOkJVRxQVhcrpm0j26daa\nly4fxf3nDmNXRRUXj5/BWY9OZYZ1321QoOdTQU4mO/0EDg/7vl5a//ef6cYNmxQgOf35zbls2F7O\nmjRerTBsd1wAVS0Dno5TWUI1yqqq7gAu8nQCkbHA2L59+0a1YM2FiDB2366M2acz/52+gns/XsQZ\nj0zlqL068rtjBzCwc6tEFzEpVbg1jsK8bF+BI9I30ki1vexM5/teuTugU8TW0EgmGRlAFZTsrKBr\nmxaJLk5MJNP06MVAj6Dn3YFVfk7QXNbjiLXszAx+cUAvPr9hNDccO4Cvl25izD2Tufy5GcwuTs9F\nspoikBNq1SIrcjI7zOugfu+p4LhRN4jkZDl/toGApRo50Jj4Cvy/pvNo9mQKHNOBfiLSR0RygHOA\ntxJcpmYtPyeLqw7vy+TfH861R/Zj6o8bGfvAFC54ahrTQjShNFeB5HirvGx2lleycXsZN7w8q8F8\nR/DAwbpJ9eDVAet28a2pcVhbVVKqCRxp3EvRy9KxBSKSEfQ8Q0Tym3JREZkATAUGiEixiFyiqpXA\n1cAHwA/AS6o61+d503rp2ERpk5/Db4/uzxc3HcHvxwxg7sotnPXoVM56ZCofzF3T7HthBb75t2qR\nTWlFNf/6cCEvzyzm1W+KAeeb56XPzmDdttpt3r9/5fuan+s2cQVXIOoGoGw3yWFTpyenwJ/D1lLv\nPexSjZcax8dAcKDIBz5qykVV9VxV7aKq2araXVWfdLe/q6r9VXVPVf2/RpzXmqpiqDAvm1+P7suU\nG4/gTycOonjzTi5/biaj7/qUJyYvSeuqeSSB2kL7ghxg9wd6pVsjeGVGMR/OW8tDn/7Y4DkCgmNx\n3eavnKxMoHY34LotVc09mCeDZl3jAPJUdXvgiftzk2ocsWI1jvhokZPJJQf3YdLvD+eh8/ajc6s8\n7njnB0b97WNufXNOsxuJHqgRdCjMBXY3LUWatqpuTqJ+Un33/m11vrkGahyBwLGyZBfzVm+tdUxp\nhdVGEqE0KMin8xcpL4Fjh4jsF3giIsOBpJxRzGoc8ZWVmcHxg7vw8hUHMvHqgzl27868MG05R989\nidMf/pKXZqzwNSAuVe1wP/QDNY4y98Mj0nrhdWsIdYNDcIXh2Hsm1dqXU9Oravf5H5tUe0yunyS9\niZ6SnbuDxdZd6fvej9gd1/Ub4GURCfRw6gKcHbsiRcGGRfD0CYkuRbMyGPg3cGefatZvL2PdhjJK\n36pi7kShfcscOrTMpTAvK75TYQw+A0Z46sndJLvc4NixVR6wu/YQaEn6eulGoHYto+5ysec98TXL\nxu1+z6oq7Qpy2LSjvN71sgI5jghVmnRuJklmm3fu/v9K5yl8GgwcqjpdRAYCA3DGWsxX1fT9jZgm\nyc7MoGvrFnRpnce20krWbStjw/Zy1m0rIyczg/Ytc2hfkEtBbmZsg8ia2c6/cQgcO8qryM4Uurd1\n+uwHkuCBnlEfzF3rPt/9muoGus9+uiD87AeBXlWvf1sc9ph/frCg4YKbqNu8Izhw1A/6fs7R1q3B\nJqNI63EcoaqfiMhpdXb1ExFU9bUYl823WgMAL3on0cVp1gRo5T52lFXy0Q9rmThrFZ8vXE/FeqVP\nhwKO2bsTR+3ViWE92pCVGeWe4XGsce4qr6JFdibd3cFea7a4gaNOgjp4/igfs68D8NWSjTzz5TLe\nm7OGQ/p1ACLnMZZv2unvAiYqNrtNVUWFuWzY3rjAMeyvHwLUqoEmm0g1jsOAT4CxIfYpzlQgSUVV\nJwITR4wYcWmiy2J2K8jN4uSh3Th5aDdKdpbz3pw1vP39Kp6cvJRHP19Cm/xsRvcv4pB+RYzs047u\nbVuk1DThm3eW07bAaY7Lycyo6YY5q3gLt721u0d5QzUOVQ173+c89lXNz1N/3BilkptoC9Q2B3Yu\nZNnGHRGPveuDBRRv3sk95wyLR9GiKmzgUNVb3X9jX9c3zUab/BzOHdmTc0f2ZGtpBZMXbuDj+Wv5\nbMF63vjOSaN1apXL8F5t6VvUkj2KWtK2IIfsDGHjDqfJa8vOcjq2ymPMPp3p0DI3wXcEG7aX0b4g\nh4wMYa8uhcxyR9d/9EPthTIj5TgA3vxuFacM61Zr2+MXjODSZ2fU2paZIb7WNm8unpi8hG5tWtCt\nbQu6tWlBu4KcuH8BWbu1jOxMoV/HQmb+tDnisQ98uhiAf581lIyM1PmiBB5yHCLSHrgVOBinpjEF\n+IuqJt3XHpurKrW0ysvmhCFdOGFIF6qrlYXrtjF96SamL9vMrOIS3p+zJuK63H99ex7n/awXV47e\nk6LCxAWQjdvL6dHO6aG+X6+2NYGjruBYEdyM1b9TSxau3c6idU435uB28qMHdap3HhsxHtod7/xQ\n63mL7MyaIBL4t2e7fHq3L6BXh/yYLJ28essuOrXKo6gwl53lVewsryQ/J/LH7MYd5Ql9/zaGl15V\nLwKTgNPd5+cB/wWOilWhGsuaqlJXRoYwsHMrBnZuxfmjegNQVlnFik072bKrkvLKatq3zKGoZS6t\nWmTz4/rtPDZpCc9MXcYL037i5yN7cdLQruzbvXWD3zLXbSvlp4072b93O19lDPTRz8vOZOHabVz0\n9HRe//WBLN+0kwP2aA/AYf2LePqLZSFfH9w8FRwQx50+hNMe+pJXZ67kN0f1r2njDrjpuIGMe29+\nzXObliq07/58NMWbd7GyZBcr6/w7e+WWej3U2hfk0Ku9E0h6dyigf6dCBnVpRY92jW8qXbphB306\nFNC+pZPY3rCtnJ7t63/MBtc+V2/ZxWvfFPP39+az8I7jPF/r4x/WMrh7azoW5jWqrE3hJXC0U9W/\nBj2/Q0ROiVWBjAnIzcqkb8fCkPv6dyrkrjP35arD+3Lfx4t4ZuoynvpiKW3ysxnSvQ1/3byD3KxM\nfliwjg4FueTnZqKqzFm5ldsmzqVkZwX3nD20XtPQrvIqSnaV06V1/VlNT3pgChkivP+bQ3lqylJW\nluzi8clL2FleRf9OTjkP61/ERQf1Dhk8qhW27KzgwHEfc+cZu1dfHtajDQBrtpbS7w/v1XvdFYft\nycI123jt25U129rkZ9caM2CcZtA2+Tns0y30OK6d5ZWs2LSLpRt28NPGHSzbuINlG3by1ZKNtX63\nLXOz2KuLE0T27dGGYT3b0rt9foPBpKpa+XHdds4c0YOebg106cYd9Gxff7x08HQkq0pKedQdhxOq\n+3VdZZVVDL39Q3ZVVLFHUQGfXD+6wddEmzQ0q6aI3AXMAF5yN50B7B3IgSSjESNG6IwZMxo+0KSN\nkp3lfPzDOqYv28R3K0q4ffP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0rQxKyD06l9G3eyX9e3SiX/dOjOjXjeF9urY6bkEKlmh+4v8O464XP+CuFz/k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\nNBYYO2LEiFyH4pxrRXuXNDw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LgfPzF1LmJJ0InDhs2LBCh+KcS6K9SxqennIvZVWVpApgJzPbA9gd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\ntPx/RfHQqmw1MBwAAAAASUVORK5CYII=\n", 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Ti4uLkx2KiKS4Q/t14p6LRvLMNWMYPqAztzw/nxNvm87T76+iqS/Ujbnj1UUc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\n", 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RXDz5Tb73+DzKq2o4++DBqQ5LRKRFzV05XEjTo+C2umG8GcuBQVHrA4GV8RzAzMaZ2aTN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\n1e7uZnYawZXGvWZ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1143,7 +1028,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "At this point, the problem is set up and we can run the multi-group calculation." + "At this point, the problem is set up and we can run the multi-group calculation, again with only summary information being displayed." ] }, { @@ -1158,141 +1043,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - " %%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%%%%%%%%%\n", - " ################## %%%%%%%%%%%%%%%%%%%%%%%\n", - " ################### %%%%%%%%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%%%%%%\n", - " ##################### %%%%%%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%%\n", - " ####################### %%%%%%%%%%%%%%%%%\n", - " ###################### %%%%%%%%%%%%%%%%%\n", - " #################### %%%%%%%%%%%%%%%%%\n", - " ################# %%%%%%%%%%%%%%%%%\n", - " ############### %%%%%%%%%%%%%%%%\n", - " ############ %%%%%%%%%%%%%%%\n", - " ######## %%%%%%%%%%%%%%\n", - " %%%%%%%%%%%\n", - "\n", - " | The OpenMC Monte Carlo Code\n", - " Copyright | 2011-2017 Massachusetts Institute of Technology\n", - " License | http://openmc.readthedocs.io/en/latest/license.html\n", - " Version | 0.8.0\n", - " Git SHA1 | 7ca46e809ce01fe7857ae36072822a1718f01aaf\n", - " Date/Time | 2017-02-23 09:39:37\n", - " OpenMP Threads | 4\n", - "\n", - " ===========================================================================\n", - " ========================> INITIALIZATION <=========================\n", - " ===========================================================================\n", - "\n", - " Reading settings XML file...\n", - " Reading geometry XML file...\n", - " Reading materials XML file...\n", - " Reading cross sections HDF5 file...\n", - " Reading tallies XML file...\n", - " Loading Cross Section Data...\n", - " Loading fuel Data...\n", - " Loading zircaloy Data...\n", - " Loading water Data...\n", - " Building neighboring cells lists for each surface...\n", - " Initializing source particles...\n", - "\n", - " ===========================================================================\n", - " ====================> K EIGENVALUE SIMULATION <====================\n", - " ===========================================================================\n", - "\n", - " Bat./Gen. k Average k \n", - " ========= ======== ==================== \n", - " 1/1 1.00368 \n", - " 2/1 0.99772 \n", - " 3/1 1.04317 \n", - " 4/1 1.02487 \n", - " 5/1 1.01766 \n", - " 6/1 1.03964 \n", - " 7/1 1.02340 \n", - " 8/1 1.02634 \n", - " 9/1 1.03388 \n", - " 10/1 1.02952 \n", - " 11/1 1.01872 \n", - " 12/1 1.02315 1.02093 +/- 0.00222\n", - " 13/1 1.02926 1.02371 +/- 0.00306\n", - " 14/1 1.01288 1.02100 +/- 0.00346\n", - " 15/1 1.04072 1.02494 +/- 0.00477\n", - " 16/1 1.01872 1.02391 +/- 0.00403\n", - " 17/1 1.00295 1.02091 +/- 0.00454\n", - " 18/1 1.02724 1.02170 +/- 0.00401\n", - " 19/1 1.01843 1.02134 +/- 0.00355\n", - " 20/1 1.02340 1.02155 +/- 0.00318\n", - " 21/1 1.03938 1.02317 +/- 0.00331\n", - " 22/1 1.00397 1.02157 +/- 0.00342\n", - " 23/1 1.03516 1.02261 +/- 0.00331\n", - " 24/1 1.03595 1.02357 +/- 0.00321\n", - " 25/1 1.03557 1.02437 +/- 0.00309\n", - " 26/1 1.03415 1.02498 +/- 0.00296\n", - " 27/1 1.02624 1.02505 +/- 0.00278\n", - " 28/1 1.02469 1.02503 +/- 0.00262\n", - " 29/1 1.00769 1.02412 +/- 0.00264\n", - " 30/1 1.02584 1.02420 +/- 0.00251\n", - " 31/1 0.99119 1.02263 +/- 0.00286\n", - " 32/1 1.00246 1.02172 +/- 0.00287\n", - " 33/1 1.00718 1.02108 +/- 0.00282\n", - " 34/1 1.01662 1.02090 +/- 0.00270\n", - " 35/1 1.01834 1.02080 +/- 0.00260\n", - " 36/1 1.04397 1.02169 +/- 0.00265\n", - " 37/1 1.01419 1.02141 +/- 0.00256\n", - " 38/1 1.02268 1.02145 +/- 0.00247\n", - " 39/1 1.04256 1.02218 +/- 0.00249\n", - " 40/1 1.03746 1.02269 +/- 0.00246\n", - " 41/1 1.02199 1.02267 +/- 0.00238\n", - " 42/1 1.03030 1.02291 +/- 0.00232\n", - " 43/1 1.00489 1.02236 +/- 0.00231\n", - " 44/1 1.03025 1.02259 +/- 0.00225\n", - " 45/1 1.05954 1.02365 +/- 0.00243\n", - " 46/1 1.03444 1.02395 +/- 0.00238\n", - " 47/1 1.00532 1.02345 +/- 0.00237\n", - " 48/1 1.03364 1.02371 +/- 0.00232\n", - " 49/1 1.02660 1.02379 +/- 0.00226\n", - " 50/1 1.04569 1.02434 +/- 0.00227\n", - " Creating state point statepoint.50.h5...\n", - "\n", - " ===========================================================================\n", - " ======================> SIMULATION FINISHED <======================\n", - " ===========================================================================\n", - "\n", - "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 2.6345E-02 seconds\n", - " Reading cross sections = 3.9616E-03 seconds\n", - " Total time in simulation = 1.6005E+01 seconds\n", - " Time in transport only = 1.5841E+01 seconds\n", - " Time in inactive batches = 1.3158E+00 seconds\n", - " Time in active batches = 1.4689E+01 seconds\n", - " Time synchronizing fission bank = 6.9577E-03 seconds\n", - " Sampling source sites = 4.4024E-03 seconds\n", - " SEND/RECV source sites = 2.4544E-03 seconds\n", - " Time accumulating tallies = 1.4749E-04 seconds\n", - " Total time for finalization = 3.7060E-06 seconds\n", - " Total time elapsed = 1.6053E+01 seconds\n", - " Calculation Rate (inactive) = 37998.4 neutrons/second\n", - " Calculation Rate (active) = 13615.9 neutrons/second\n", + " Total time for initialization = 4.3662E-02 seconds\n", + " Reading cross sections = 5.8801E-03 seconds\n", + " Total time in simulation = 1.7518E+02 seconds\n", + " Time in transport only = 1.7154E+02 seconds\n", + " Time in inactive batches = 8.0088E-01 seconds\n", + " Time in active batches = 1.7438E+02 seconds\n", + " Time synchronizing fission bank = 9.8651E-02 seconds\n", + " Sampling source sites = 6.7951E-02 seconds\n", + " SEND/RECV source sites = 2.9902E-02 seconds\n", + " Time accumulating tallies = 2.2328E-03 seconds\n", + " Total time for finalization = 5.9420E-06 seconds\n", + " Total time elapsed = 1.7525E+02 seconds\n", + " Calculation Rate (inactive) = 62431.1 neutrons/second\n", + " Calculation Rate (active) = 14049.6 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.02412 +/- 0.00199\n", - " k-effective (Track-length) = 1.02434 +/- 0.00227\n", - " k-effective (Absorption) = 1.02703 +/- 0.00143\n", - " Combined k-effective = 1.02632 +/- 0.00142\n", + " k-effective (Collision) = 1.02429 +/- 0.00068\n", + " k-effective (Track-length) = 1.02408 +/- 0.00075\n", + " k-effective (Absorption) = 1.02514 +/- 0.00050\n", + " Combined k-effective = 1.02488 +/- 0.00045\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -1309,8 +1082,8 @@ } ], "source": [ - "# Run the Multi-Group OpenMC Simulation\n", - "openmc.run()" + "# Run OpenMC, showing only the summary information at the end.\n", + "openmc.run(output='summary')" ] }, { @@ -1373,9 +1146,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Continuous-Energy keff = 1.030194\n", - "Multi-Group keff = 1.026316\n", - "bias [pcm]: 387.9\n" + "Continuous-Energy keff = 1.025799\n", + "Multi-Group keff = 1.024883\n", + "bias [pcm]: 91.6\n" ] } ], @@ -1472,7 +1245,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 41, @@ -1481,9 +1254,9 @@ }, { "data": { - "image/png": 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bhpre1wXZ0GZmbqBX7NuFfk3Pvtz56qiyFZ4+4ILQqHfoGWqYGB/v3fl+qPln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\nQmCC\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1520,7 +1293,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1534,7 +1307,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.0" } }, "nbformat": 4, From bd9b32b804b60def8639f667bcdb35896a4a5882 Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sat, 25 Feb 2017 14:36:09 -0500 Subject: [PATCH 04/12] Removed Nu* MGXS classes. --- .../pythonapi/examples/mgxs-part-i.ipynb | 54 +- .../pythonapi/examples/mgxs-part-ii.ipynb | 84 +-- .../pythonapi/examples/mgxs-part-iii.ipynb | 90 ++- openmc/mgxs/mgxs.py | 666 +++--------------- 4 files changed, 224 insertions(+), 670 deletions(-) diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb index d076823be..2b3d5a77c 100644 --- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -369,21 +369,19 @@ "\n", "* `TotalXS`\n", "* `TransportXS`\n", - "* `NuTransportXS`\n", "* `AbsorptionXS`\n", "* `CaptureXS`\n", "* `FissionXS`\n", - "* `NuFissionXS`\n", "* `KappaFissionXS`\n", "* `ScatterXS`\n", - "* `NuScatterXS`\n", "* `ScatterMatrixXS`\n", - "* `NuScatterMatrixXS`\n", "* `Chi`\n", "* `ChiPrompt`\n", "* `InverseVelocity`\n", "* `PromptNuFissionXS`\n", "\n", + "Of course, we know that the transport (`TransportXS`), fission (`FissionXS`), scattering (`ScatterXS`), and scattering-matrix (`ScatterMatrixXS`) cross sections can potentially incorporate neutron multiplication ($\\nu$). For these types, the multpiplication can be accomodated by setting the `nu` parameter to `True` as shown below.\n", + "\n", "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group total, absorption and scattering cross sections with our 2-group structure." ] }, @@ -398,7 +396,11 @@ "# Instantiate a few different sections\n", "total = mgxs.TotalXS(domain=cell, groups=groups)\n", "absorption = mgxs.AbsorptionXS(domain=cell, groups=groups)\n", - "scattering = mgxs.ScatterXS(domain=cell, groups=groups)" + "scattering = mgxs.ScatterXS(domain=cell, groups=groups)\n", + "\n", + "# Note that if we wanted to incorporate neutron multiplication in the\n", + "# scattering cross section we would write the previous line as:\n", + "# scattering = mgxs.ScatterXS(domain=cell, groups=groups, nu=True)" ] }, { @@ -520,8 +522,8 @@ " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.8.0\n", - " Git SHA1 | 54b65c8bda6af5788bd762b8cf9855d1a8008238\n", - " Date/Time | 2017-02-12 13:36:24\n", + " Git SHA1 | 60a1f157dae88b62e1865a5fe3efd7ef0773a068\n", + " Date/Time | 2017-02-25 14:26:54\n", " OpenMP Threads | 8\n", "\n", " ===========================================================================\n", @@ -592,7 +594,7 @@ " 42/1 1.13779 1.16177 +/- 0.00531\n", " 43/1 1.15066 1.16143 +/- 0.00516\n", " 44/1 1.12174 1.16026 +/- 0.00514\n", - " 45/1 1.17479 1.16068 +/- 0.00501\n", + " 45/1 1.17478 1.16068 +/- 0.00501\n", " 46/1 1.14146 1.16014 +/- 0.00489\n", " 47/1 1.20464 1.16135 +/- 0.00491\n", " 48/1 1.15119 1.16108 +/- 0.00479\n", @@ -607,20 +609,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.1327E-01 seconds\n", - " Reading cross sections = 3.2638E-01 seconds\n", - " Total time in simulation = 2.2324E+00 seconds\n", - " Time in transport only = 2.1226E+00 seconds\n", - " Time in inactive batches = 3.0650E-01 seconds\n", - " Time in active batches = 1.9259E+00 seconds\n", - " Time synchronizing fission bank = 2.7640E-03 seconds\n", - " Sampling source sites = 2.0198E-03 seconds\n", - " SEND/RECV source sites = 7.0929E-04 seconds\n", - " Time accumulating tallies = 4.5355E-05 seconds\n", - " Total time for finalization = 4.1885E-04 seconds\n", - " Total time elapsed = 2.6534E+00 seconds\n", - " Calculation Rate (inactive) = 81567.1 neutrons/second\n", - " Calculation Rate (active) = 51923.2 neutrons/second\n", + " Total time for initialization = 3.5070E-01 seconds\n", + " Reading cross sections = 2.4151E-01 seconds\n", + " Total time in simulation = 2.3276E+00 seconds\n", + " Time in transport only = 2.2350E+00 seconds\n", + " Time in inactive batches = 2.5677E-01 seconds\n", + " Time in active batches = 2.0708E+00 seconds\n", + " Time synchronizing fission bank = 2.7683E-03 seconds\n", + " Sampling source sites = 2.0233E-03 seconds\n", + " SEND/RECV source sites = 7.1007E-04 seconds\n", + " Time accumulating tallies = 5.0753E-05 seconds\n", + " Total time for finalization = 3.8695E-04 seconds\n", + " Total time elapsed = 2.6857E+00 seconds\n", + " Calculation Rate (inactive) = 97364.6 neutrons/second\n", + " Calculation Rate (active) = 48290.8 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -901,7 +903,7 @@ " 6.250000e-01\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", - " -2.664535e-15\n", + " -5.551115e-15\n", " 0.011292\n", " \n", " \n", @@ -911,7 +913,7 @@ " 2.000000e+07\n", " total\n", " (((total / flux) - (absorption / flux)) - (sca...\n", - " -3.330669e-16\n", + " -1.110223e-16\n", " 0.002570\n", " \n", " \n", @@ -924,8 +926,8 @@ "1 1 6.25e-01 2.00e+07 total \n", "\n", " score mean std. dev. \n", - "0 (((total / flux) - (absorption / flux)) - (sca... -2.66e-15 1.13e-02 \n", - "1 (((total / flux) - (absorption / flux)) - (sca... -3.33e-16 2.57e-03 " + "0 (((total / flux) - (absorption / flux)) - (sca... -5.55e-15 1.13e-02 \n", + "1 (((total / flux) - (absorption / flux)) - (sca... -1.11e-16 2.57e-03 " ] }, "execution_count": 22, diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb index 359fd4eaa..a0e47fa12 100644 --- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb @@ -34,7 +34,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/miniconda3/envs/python3/lib/python3.5/site-packages/matplotlib/__init__.py:1401: UserWarning: This call to matplotlib.use() has no effect\n", + "/usr/lib/python3.6/site-packages/matplotlib/__init__.py:1401: UserWarning: This call to matplotlib.use() has no effect\n", "because the backend has already been chosen;\n", "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", @@ -342,8 +342,8 @@ " xs_library[cell.id] = {}\n", " xs_library[cell.id]['transport'] = mgxs.TransportXS(groups=fine_groups)\n", " xs_library[cell.id]['fission'] = mgxs.FissionXS(groups=fine_groups)\n", - " xs_library[cell.id]['nu-fission'] = mgxs.NuFissionXS(groups=fine_groups)\n", - " xs_library[cell.id]['nu-scatter'] = mgxs.NuScatterMatrixXS(groups=fine_groups)\n", + " xs_library[cell.id]['nu-fission'] = mgxs.FissionXS(groups=fine_groups, nu=True)\n", + " xs_library[cell.id]['nu-scatter'] = mgxs.ScatterMatrixXS(groups=fine_groups, nu=True)\n", " xs_library[cell.id]['chi'] = mgxs.Chi(groups=fine_groups)" ] }, @@ -454,9 +454,9 @@ " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.8.0\n", - " Git SHA1 | dfc6f1d9bf1b31fc94dad1cb30bd672b25f47e04\n", - " Date/Time | 2017-02-21 15:07:51\n", - " OpenMP Threads | 4\n", + " Git SHA1 | 60a1f157dae88b62e1865a5fe3efd7ef0773a068\n", + " Date/Time | 2017-02-25 14:28:21\n", + " OpenMP Threads | 8\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -466,11 +466,11 @@ " Reading geometry XML file...\n", " Reading materials XML file...\n", " Reading cross sections XML file...\n", - " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", - " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", - " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", + " Reading U235 from /opt/xsdata/nndc/U235.h5\n", + " Reading U238 from /opt/xsdata/nndc/U238.h5\n", + " Reading O16 from /opt/xsdata/nndc/O16.h5\n", + " Reading H1 from /opt/xsdata/nndc/H1.h5\n", + " Reading Zr90 from /opt/xsdata/nndc/Zr90.h5\n", " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", @@ -532,7 +532,7 @@ " 48/1 1.22204 1.22140 +/- 0.00239\n", " 49/1 1.22077 1.22139 +/- 0.00232\n", " 50/1 1.23166 1.22164 +/- 0.00228\n", - " Triggers unsatisfied, max unc./thresh. is 1.17623 for flux in tally 10057\n", + " Triggers unsatisfied, max unc./thresh. is 1.17623 for flux in tally 10050\n", " The estimated number of batches is 66\n", " Creating state point statepoint.050.h5...\n", " 51/1 1.20071 1.22113 +/- 0.00228\n", @@ -551,7 +551,7 @@ " 64/1 1.23955 1.22122 +/- 0.00200\n", " 65/1 1.21143 1.22104 +/- 0.00197\n", " 66/1 1.21791 1.22099 +/- 0.00194\n", - " Triggers unsatisfied, max unc./thresh. is 1.13207 for flux in tally 10057\n", + " Triggers unsatisfied, max unc./thresh. is 1.13207 for flux in tally 10050\n", " The estimated number of batches is 82\n", " 67/1 1.24897 1.22148 +/- 0.00196\n", " 68/1 1.22221 1.22149 +/- 0.00193\n", @@ -579,20 +579,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 3.6241E-01 seconds\n", - " Reading cross sections = 2.5754E-01 seconds\n", - " Total time in simulation = 9.0901E+01 seconds\n", - " Time in transport only = 9.0045E+01 seconds\n", - " Time in inactive batches = 2.9786E+00 seconds\n", - " Time in active batches = 8.7923E+01 seconds\n", - " Time synchronizing fission bank = 2.7221E-02 seconds\n", - " Sampling source sites = 1.6637E-02 seconds\n", - " SEND/RECV source sites = 1.0474E-02 seconds\n", - " Time accumulating tallies = 9.2191E-04 seconds\n", - " Total time for finalization = 1.2513E-02 seconds\n", - " Total time elapsed = 9.1307E+01 seconds\n", - " Calculation Rate (inactive) = 33572.9 neutrons/second\n", - " Calculation Rate (active) = 4549.45 neutrons/second\n", + " Total time for initialization = 3.2089E-01 seconds\n", + " Reading cross sections = 2.2960E-01 seconds\n", + " Total time in simulation = 3.1332E+01 seconds\n", + " Time in transport only = 3.1007E+01 seconds\n", + " Time in inactive batches = 1.6660E+00 seconds\n", + " Time in active batches = 2.9666E+01 seconds\n", + " Time synchronizing fission bank = 1.7043E-02 seconds\n", + " Sampling source sites = 1.1813E-02 seconds\n", + " SEND/RECV source sites = 5.1688E-03 seconds\n", + " Time accumulating tallies = 5.0509E-04 seconds\n", + " Total time for finalization = 1.6880E-02 seconds\n", + " Total time elapsed = 3.1699E+01 seconds\n", + " Calculation Rate (inactive) = 60024.2 neutrons/second\n", + " Calculation Rate (active) = 13483.6 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -733,7 +733,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] } @@ -803,7 +803,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] }, @@ -1151,11 +1151,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" ] } @@ -1510,11 +1510,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" ] } @@ -1990,7 +1990,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] }, @@ -2006,9 +2006,9 @@ }, { "data": { - "image/png": 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qukFE6gO/AQ8HOc4kCJfLxbUDO5TbVlRcEt+rnnmqhtxP/v0DzEJrD7CSgIlF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\nxhiT4P4fD4z+wrudGZ0AAAAASUVORK5CYII=\n", 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vsAd7sLd+ywmYRBQuENwJTANaALep6mYRaYwzjPT9dZE4k9hcLhfXHtqlwrqS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\nqrrV27HsLRG5KfZJM6bmerd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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2123,7 +2123,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -2137,7 +2137,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb index 69297d884..a41fb1374 100644 --- a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb @@ -32,7 +32,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/miniconda3/envs/python3/lib/python3.5/site-packages/matplotlib/__init__.py:1401: UserWarning: This call to matplotlib.use() has no effect\n", + "/usr/lib/python3.6/site-packages/matplotlib/__init__.py:1401: UserWarning: This call to matplotlib.use() has no effect\n", "because the backend has already been chosen;\n", "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n", "or matplotlib.backends is imported for the first time.\n", @@ -458,7 +458,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "" ] @@ -537,17 +537,13 @@ "Now, we must specify to the `Library` which types of cross sections to compute. In particular, the following are the multi-group cross section `MGXS` subclasses that are mapped to string codes accepted by the `Library` class:\n", "\n", "* `TotalXS` (`\"total\"`)\n", - "* `TransportXS` (`\"transport\"`)\n", - "* `NuTransportXS` (`\"nu-transport\"`)\n", + "* `TransportXS` (`\"transport\"` or `\"nu-transport` with `nu` set to `True`)\n", "* `AbsorptionXS` (`\"absorption\"`)\n", "* `CaptureXS` (`\"capture\"`)\n", - "* `FissionXS` (`\"fission\"`)\n", - "* `NuFissionXS` (`\"nu-fission\"`)\n", + "* `FissionXS` (`\"fission\"` or `\"nu-fission\"` with `nu` set to `True`)\n", "* `KappaFissionXS` (`\"kappa-fission\"`)\n", - "* `ScatterXS` (`\"scatter\"`)\n", - "* `NuScatterXS` (`\"nu-scatter\"`)\n", - "* `ScatterMatrixXS` (`\"scatter matrix\"`)\n", - "* `NuScatterMatrixXS` (`\"nu-scatter matrix\"`)\n", + "* `ScatterXS` (`\"scatter\"` or `\"nu-scatter\"` with `nu` set to `True`)\n", + "* `ScatterMatrixXS` (`\"scatter matrix\"` or `\"nu-scatter matrix\"` with `nu` set to `True`)\n", "* `Chi` (`\"chi\"`)\n", "* `ChiPrompt` (`\"chi prompt\"`)\n", "* `InverseVelocity` (`\"inverse-velocity\"`)\n", @@ -743,9 +739,9 @@ " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.8.0\n", - " Git SHA1 | 7ca46e809ce01fe7857ae36072822a1718f01aaf\n", - " Date/Time | 2017-02-23 09:36:51\n", - " OpenMP Threads | 4\n", + " Git SHA1 | 60a1f157dae88b62e1865a5fe3efd7ef0773a068\n", + " Date/Time | 2017-02-25 14:32:59\n", + " OpenMP Threads | 8\n", "\n", " ===========================================================================\n", " ========================> INITIALIZATION <=========================\n", @@ -755,12 +751,12 @@ " Reading geometry XML file...\n", " Reading materials XML file...\n", " Reading cross sections XML file...\n", - " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n", - " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n", - " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n", - " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n", - " Reading B10 from /home/romano/openmc/scripts/nndc_hdf5/B10.h5\n", - " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n", + " Reading U235 from /opt/xsdata/nndc/U235.h5\n", + " Reading U238 from /opt/xsdata/nndc/U238.h5\n", + " Reading O16 from /opt/xsdata/nndc/O16.h5\n", + " Reading H1 from /opt/xsdata/nndc/H1.h5\n", + " Reading B10 from /opt/xsdata/nndc/B10.h5\n", + " Reading Zr90 from /opt/xsdata/nndc/Zr90.h5\n", " Maximum neutron transport energy: 2.00000E+07 eV for U235\n", " Reading tallies XML file...\n", " Building neighboring cells lists for each surface...\n", @@ -831,20 +827,20 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.2238E-01 seconds\n", - " Reading cross sections = 3.1986E-01 seconds\n", - " Total time in simulation = 1.3628E+01 seconds\n", - " Time in transport only = 1.3342E+01 seconds\n", - " Time in inactive batches = 1.1012E+00 seconds\n", - " Time in active batches = 1.2527E+01 seconds\n", - " Time synchronizing fission bank = 3.7750E-03 seconds\n", - " Sampling source sites = 2.4760E-03 seconds\n", - " SEND/RECV source sites = 1.2253E-03 seconds\n", - " Time accumulating tallies = 6.5502E-04 seconds\n", - " Total time for finalization = 9.4890E-06 seconds\n", - " Total time elapsed = 1.4059E+01 seconds\n", - " Calculation Rate (inactive) = 22702.0 neutrons/second\n", - " Calculation Rate (active) = 7982.70 neutrons/second\n", + " Total time for initialization = 3.4863E-01 seconds\n", + " Reading cross sections = 2.6337E-01 seconds\n", + " Total time in simulation = 6.2906E+00 seconds\n", + " Time in transport only = 6.0984E+00 seconds\n", + " Time in inactive batches = 5.0785E-01 seconds\n", + " Time in active batches = 5.7827E+00 seconds\n", + " Time synchronizing fission bank = 2.6573E-03 seconds\n", + " Sampling source sites = 1.9038E-03 seconds\n", + " SEND/RECV source sites = 7.1726E-04 seconds\n", + " Time accumulating tallies = 2.9242E-04 seconds\n", + " Total time for finalization = 7.4980E-06 seconds\n", + " Total time elapsed = 6.6484E+00 seconds\n", + " Calculation Rate (inactive) = 49227.5 neutrons/second\n", + " Calculation Rate (active) = 17292.9 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", @@ -971,7 +967,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] }, @@ -1108,7 +1104,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1506: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1506: RuntimeWarning: invalid value encountered in true_divide\n", " data = self.std_dev[indices] / self.mean[indices]\n" ] } @@ -1135,11 +1131,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" ] } @@ -1213,7 +1209,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n" ] }, @@ -1326,11 +1322,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n", " self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n", " other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n", - "/home/romano/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", + "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n", " new_tally._mean = data['self']['mean'] / data['other']['mean']\n" ] } @@ -1627,7 +1623,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 43, @@ -1636,9 +1632,9 @@ }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1665,7 +1661,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1679,7 +1675,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 33bdeb83a..284be6ca3 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -149,6 +149,8 @@ class MGXS(object): Name of the multi-group cross section rxn_type : str Reaction type (e.g., 'total', 'nu-fission', etc.) + nu : bool + If True, the cross section data will include neutron multiplication by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -230,6 +232,7 @@ class MGXS(object): self._derived = False self._hdf5_key = None self._valid_estimators = ESTIMATOR_TYPES + self._nu = False self.name = name self.by_nuclide = by_nuclide @@ -408,6 +411,10 @@ class MGXS(object): def rxn_type(self): return self._rxn_type + @property + def nu(self): + return self._nu + @property def by_nuclide(self): return self._by_nuclide @@ -573,6 +580,11 @@ class MGXS(object): cv.check_type('name', name, string_types) self._name = name + @nu.setter + def nu(self, nu): + cv.check_type('nu', nu, bool) + self._nu = nu + @by_nuclide.setter def by_nuclide(self, by_nuclide): cv.check_type('by_nuclide', by_nuclide, bool) @@ -704,7 +716,7 @@ class MGXS(object): elif mgxs_type == 'transport': mgxs = TransportXS(domain, domain_type, energy_groups) elif mgxs_type == 'nu-transport': - mgxs = NuTransportXS(domain, domain_type, energy_groups) + mgxs = TransportXS(domain, domain_type, energy_groups, nu=True) elif mgxs_type == 'absorption': mgxs = AbsorptionXS(domain, domain_type, energy_groups) elif mgxs_type == 'capture': @@ -712,17 +724,17 @@ class MGXS(object): elif mgxs_type == 'fission': mgxs = FissionXS(domain, domain_type, energy_groups) elif mgxs_type == 'nu-fission': - mgxs = NuFissionXS(domain, domain_type, energy_groups) + mgxs = FissionXS(domain, domain_type, energy_groups, nu=True) elif mgxs_type == 'kappa-fission': mgxs = KappaFissionXS(domain, domain_type, energy_groups) elif mgxs_type == 'scatter': mgxs = ScatterXS(domain, domain_type, energy_groups) elif mgxs_type == 'nu-scatter': - mgxs = NuScatterXS(domain, domain_type, energy_groups) + mgxs = ScatterXS(domain, domain_type, energy_groups, nu=True) elif mgxs_type == 'scatter matrix': mgxs = ScatterMatrixXS(domain, domain_type, energy_groups) elif mgxs_type == 'nu-scatter matrix': - mgxs = NuScatterMatrixXS(domain, domain_type, energy_groups) + mgxs = ScatterMatrixXS(domain, domain_type, energy_groups, nu=True) elif mgxs_type == 'multiplicity matrix': mgxs = MultiplicityMatrixXS(domain, domain_type, energy_groups) elif mgxs_type == 'nu-fission matrix': @@ -2001,6 +2013,8 @@ class MatrixMGXS(MGXS): Name of the multi-group cross section rxn_type : str Reaction type (e.g., 'total', 'nu-fission', etc.) + nu : bool + If True, the cross section data will include neutron multiplication by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -2555,9 +2569,8 @@ class TotalXS(MGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(TotalXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) @@ -2607,6 +2620,9 @@ class TransportXS(MGXS): The domain type for spatial homogenization groups : openmc.mgxs.EnergyGroups The energy group structure for energy condensation + nu : bool + If True, the cross section data will include neutron multiplication; + defaults to True. by_nuclide : bool If true, computes cross sections for each nuclide in domain name : str, optional @@ -2625,6 +2641,8 @@ class TransportXS(MGXS): Name of the multi-group cross section rxn_type : str Reaction type (e.g., 'total', 'nu-fission', etc.) + nu : bool + If True, the cross section data will include neutron multiplication by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -2685,19 +2703,32 @@ class TransportXS(MGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, nu=False, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(TransportXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) - self._rxn_type = 'transport' + if not nu: + self._rxn_type = 'transport' + else: + self._rxn_type = 'nu-transport' self._estimator = 'analog' self._valid_estimators = ['analog'] + self.nu = nu @property def scores(self): - return ['flux', 'total', 'scatter-1'] + if not self.nu: + return ['flux', 'total', 'scatter-1'] + else: + return ['flux', 'total', 'nu-scatter-1'] + + @property + def tally_keys(self): + if not self.nu: + return super(TransportXS, self).tally_keys + else: + return ['flux', 'total', 'scatter-1'] @property def filters(self): @@ -2724,131 +2755,6 @@ class TransportXS(MGXS): return self._rxn_rate_tally -class NuTransportXS(TransportXS): - r"""A transport-corrected total multi-group cross section which - accounts for neutron multiplicity in scattering reactions. - - This class can be used for both OpenMC input generation and tally data - post-processing to compute spatially-homogenized and energy-integrated - multi-group cross sections for multi-group neutronics calculations. At a - minimum, one needs to set the :attr:`NuTransportXS.energy_groups` and - :attr:`NuTransportXS.domain` properties. Tallies for the flux and - appropriate reaction rates over the specified domain are generated - automatically via the :attr:`NuTransportXS.tallies` property, which can then - be appended to a :class:`openmc.Tallies` instance. - - For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the - necessary data to compute multi-group cross sections from a - :class:`openmc.StatePoint` instance. The derived multi-group cross section - can then be obtained from the :attr:`NuTransportXS.xs_tally` property. - - The calculation of the transport-corrected cross section is the same as that - for :class:`TransportXS` except that the scattering multiplicity is - accounted for. - - Parameters - ---------- - domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh - The domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - The domain type for spatial homogenization - groups : openmc.mgxs.EnergyGroups - The energy group structure for energy condensation - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - name : str, optional - Name of the multi-group cross section. Used as a label to identify - tallies in OpenMC 'tallies.xml' file. - num_polar : Integral, optional - Number of equi-width polar angle bins for angle discretization; - defaults to one bin - num_azimuthal : Integral, optional - Number of equi-width azimuthal angle bins for angle discretization; - defaults to one bin - - Attributes - ---------- - name : str, optional - Name of the multi-group cross section - rxn_type : str - Reaction type (e.g., 'total', 'nu-fission', etc.) - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - domain : Material or Cell or Universe or Mesh - Domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - Domain type for spatial homogenization - energy_groups : openmc.mgxs.EnergyGroups - Energy group structure for energy condensation - num_polar : Integral - Number of equi-width polar angle bins for angle discretization - num_azimuthal : Integral - Number of equi-width azimuthal angle bins for angle discretization - tally_trigger : openmc.Trigger - An (optional) tally precision trigger given to each tally used to - compute the cross section - scores : list of str - The scores in each tally used to compute the multi-group cross section - filters : list of openmc.Filter - The filters in each tally used to compute the multi-group cross section - tally_keys : list of str - The keys into the tallies dictionary for each tally used to compute - the multi-group cross section - estimator : 'analog' - The tally estimator used to compute the multi-group cross section - tallies : collections.OrderedDict - OpenMC tallies needed to compute the multi-group cross section. The keys - are strings listed in the :attr:`NuTransportXS.tally_keys` property and - values are instances of :class:`openmc.Tally`. - rxn_rate_tally : openmc.Tally - Derived tally for the reaction rate tally used in the numerator to - compute the multi-group cross section. This attribute is None - unless the multi-group cross section has been computed. - xs_tally : openmc.Tally - Derived tally for the multi-group cross section. This attribute - is None unless the multi-group cross section has been computed. - num_subdomains : int - The number of subdomains is unity for 'material', 'cell' and 'universe' - domain types. This is equal to the number of cell instances - for 'distribcell' domain types (it is equal to unity prior to loading - tally data from a statepoint file). - num_nuclides : int - The number of nuclides for which the multi-group cross section is - being tracked. This is unity if the by_nuclide attribute is False. - nuclides : Iterable of str or 'sum' - The optional user-specified nuclides for which to compute cross - sections (e.g., 'U238', 'O16'). If by_nuclide is True but nuclides - are not specified by the user, all nuclides in the spatial domain - are included. This attribute is 'sum' if by_nuclide is false. - sparse : bool - Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format - for compressed data storage - loaded_sp : bool - Whether or not a statepoint file has been loaded with tally data - derived : bool - Whether or not the MGXS is merged from one or more other MGXS - hdf5_key : str - The key used to index multi-group cross sections in an HDF5 data store - - """ - - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): - super(NuTransportXS, self).__init__(domain, domain_type, - groups, by_nuclide, name, - num_polar, num_azimuthal) - self._rxn_type = 'nu-transport' - - @property - def scores(self): - return ['flux', 'total', 'nu-scatter-1'] - - @property - def tally_keys(self): - return ['flux', 'total', 'scatter-1'] - - class AbsorptionXS(MGXS): r"""An absorption multi-group cross section. @@ -2966,9 +2872,8 @@ class AbsorptionXS(MGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(AbsorptionXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) @@ -3094,9 +2999,8 @@ class CaptureXS(MGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(CaptureXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) @@ -3150,6 +3054,9 @@ class FissionXS(MGXS): The domain type for spatial homogenization groups : openmc.mgxs.EnergyGroups The energy group structure for energy condensation + nu : bool + If True, the cross section data will include neutron multiplication; + defaults to False by_nuclide : bool If true, computes cross sections for each nuclide in domain name : str, optional @@ -3168,6 +3075,8 @@ class FissionXS(MGXS): Name of the multi-group cross section rxn_type : str Reaction type (e.g., 'total', 'nu-fission', etc.) + nu : bool + If True, the cross section data will include neutron multiplication by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -3228,136 +3137,15 @@ class FissionXS(MGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, nu=False, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(FissionXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) - self._rxn_type = 'fission' - - -class NuFissionXS(MGXS): - r"""A fission neutron production multi-group cross section. - - This class can be used for both OpenMC input generation and tally data - post-processing to compute spatially-homogenized and energy-integrated - multi-group fission neutron production cross sections for multi-group - neutronics calculations. At a minimum, one needs to set the - :attr:`NuFissionXS.energy_groups` and :attr:`NuFissionXS.domain` - properties. Tallies for the flux and appropriate reaction rates over the - specified domain are generated automatically via the - :attr:`NuFissionXS.tallies` property, which can then be appended to a - :class:`openmc.Tallies` instance. - - For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the - necessary data to compute multi-group cross sections from a - :class:`openmc.StatePoint` instance. The derived multi-group cross section - can then be obtained from the :attr:`NuFissionXS.xs_tally` property. - - For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the - fission neutron production cross section is calculated as: - - .. math:: - - \frac{\int_{r \in V} dr \int_{4\pi} d\Omega \int_{E_g}^{E_{g-1}} dE \; - \nu\sigma_f (r, E) \psi (r, E, \Omega)}{\int_{r \in V} dr \int_{4\pi} - d\Omega \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega)}. - - - Parameters - ---------- - domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh - The domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - The domain type for spatial homogenization - groups : openmc.mgxs.EnergyGroups - The energy group structure for energy condensation - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - name : str, optional - Name of the multi-group cross section. Used as a label to identify - tallies in OpenMC 'tallies.xml' file. - num_polar : Integral, optional - Number of equi-width polar angle bins for angle discretization; - defaults to one bin - num_azimuthal : Integral, optional - Number of equi-width azimuthal angle bins for angle discretization; - defaults to one bin - - Attributes - ---------- - name : str, optional - Name of the multi-group cross section - rxn_type : str - Reaction type (e.g., 'total', 'nu-fission', etc.) - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - domain : Material or Cell or Universe or Mesh - Domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - Domain type for spatial homogenization - energy_groups : openmc.mgxs.EnergyGroups - Energy group structure for energy condensation - num_polar : Integral - Number of equi-width polar angle bins for angle discretization - num_azimuthal : Integral - Number of equi-width azimuthal angle bins for angle discretization - tally_trigger : openmc.Trigger - An (optional) tally precision trigger given to each tally used to - compute the cross section - scores : list of str - The scores in each tally used to compute the multi-group cross section - filters : list of openmc.Filter - The filters in each tally used to compute the multi-group cross section - tally_keys : list of str - The keys into the tallies dictionary for each tally used to compute - the multi-group cross section - estimator : {'tracklength', 'collision', 'analog'} - The tally estimator used to compute the multi-group cross section - tallies : collections.OrderedDict - OpenMC tallies needed to compute the multi-group cross section. The keys - are strings listed in the :attr:`NuFissionXS.tally_keys` property and - values are instances of :class:`openmc.Tally`. - rxn_rate_tally : openmc.Tally - Derived tally for the reaction rate tally used in the numerator to - compute the multi-group cross section. This attribute is None - unless the multi-group cross section has been computed. - xs_tally : openmc.Tally - Derived tally for the multi-group cross section. This attribute - is None unless the multi-group cross section has been computed. - num_subdomains : int - The number of subdomains is unity for 'material', 'cell' and 'universe' - domain types. This is equal to the number of cell instances - for 'distribcell' domain types (it is equal to unity prior to loading - tally data from a statepoint file). - num_nuclides : int - The number of nuclides for which the multi-group cross section is - being tracked. This is unity if the by_nuclide attribute is False. - nuclides : Iterable of str or 'sum' - The optional user-specified nuclides for which to compute cross - sections (e.g., 'U238', 'O16'). If by_nuclide is True but nuclides - are not specified by the user, all nuclides in the spatial domain - are included. This attribute is 'sum' if by_nuclide is false. - sparse : bool - Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format - for compressed data storage - loaded_sp : bool - Whether or not a statepoint file has been loaded with tally data - derived : bool - Whether or not the MGXS is merged from one or more other MGXS - hdf5_key : str - The key used to index multi-group cross sections in an HDF5 data store - - """ - - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): - super(NuFissionXS, self).__init__(domain, domain_type, - groups, by_nuclide, name, num_polar, - num_azimuthal) - self._rxn_type = 'nu-fission' + if not nu: + self._rxn_type = 'fission' + else: + self._rxn_type = 'nu-fission' class KappaFissionXS(MGXS): @@ -3479,9 +3267,8 @@ class KappaFissionXS(MGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(KappaFissionXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) @@ -3526,6 +3313,9 @@ class ScatterXS(MGXS): The domain type for spatial homogenization groups : openmc.mgxs.EnergyGroups The energy group structure for energy condensation + nu : bool + If True, the cross section data will include neutron multiplication; + defaults to False by_nuclide : bool If true, computes cross sections for each nuclide in domain name : str, optional @@ -3544,6 +3334,8 @@ class ScatterXS(MGXS): Name of the multi-group cross section rxn_type : str Reaction type (e.g., 'total', 'nu-fission', etc.) + nu : bool + If True, the cross section data will include neutron multiplication by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -3604,142 +3396,21 @@ class ScatterXS(MGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, nu=False, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(ScatterXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) - self._rxn_type = 'scatter' + if not nu: + self._rxn_type = 'scatter' + else: + self._rxn_type = 'nu-scatter' + # Only analog estimators are valid so change from the defaults + # to reflect this + self._estimator = 'analog' + self._valid_estimators = ['analog'] - -class NuScatterXS(MGXS): - r"""A scattering neutron production multi-group cross section. - - The neutron production from scattering is defined as the average number of - neutrons produced from all neutron-producing reactions except for fission. - - This class can be used for both OpenMC input generation and tally data - post-processing to compute spatially-homogenized and energy-integrated - multi-group cross sections for multi-group neutronics calculations. At a - minimum, one needs to set the :attr:`NuScatterXS.energy_groups` and - :attr:`NuScatterXS.domain` properties. Tallies for the flux and appropriate - reaction rates over the specified domain are generated automatically via the - :attr:`NuScatterXS.tallies` property, which can then be appended to a - :class:`openmc.Tallies` instance. - - For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the - necessary data to compute multi-group cross sections from a - :class:`openmc.StatePoint` instance. The derived multi-group cross section - can then be obtained from the :attr:`NuScatterXS.xs_tally` property. - - For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the - scattering neutron production cross section is calculated as: - - .. math:: - - \frac{\int_{r \in V} dr \int_{4\pi} d\Omega \int_{E_g}^{E_{g-1}} dE \; - \sum_i \upsilon_i \sigma_i (r, E) \psi (r, E, \Omega)}{\int_{r \in V} dr - \int_{4\pi} d\Omega \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega)}. - - where :math:`\upsilon_i` is the multiplicity of the :math:`i`-th scattering - reaction. - - Parameters - ---------- - domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh - The domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - The domain type for spatial homogenization - groups : openmc.mgxs.EnergyGroups - The energy group structure for energy condensation - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - name : str, optional - Name of the multi-group cross section. Used as a label to identify - tallies in OpenMC 'tallies.xml' file. - num_polar : Integral, optional - Number of equi-width polar angle bins for angle discretization; - defaults to one bin - num_azimuthal : Integral, optional - Number of equi-width azimuthal angle bins for angle discretization; - defaults to one bin - - Attributes - ---------- - name : str, optional - Name of the multi-group cross section - rxn_type : str - Reaction type (e.g., 'total', 'nu-fission', etc.) - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - domain : Material or Cell or Universe or Mesh - Domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - Domain type for spatial homogenization - energy_groups : openmc.mgxs.EnergyGroups - Energy group structure for energy condensation - num_polar : Integral - Number of equi-width polar angle bins for angle discretization - num_azimuthal : Integral - Number of equi-width azimuthal angle bins for angle discretization - tally_trigger : openmc.Trigger - An (optional) tally precision trigger given to each tally used to - compute the cross section - scores : list of str - The scores in each tally used to compute the multi-group cross section - filters : list of openmc.Filter - The filters in each tally used to compute the multi-group cross section - tally_keys : list of str - The keys into the tallies dictionary for each tally used to compute - the multi-group cross section - estimator : 'analog' - The tally estimator used to compute the multi-group cross section - tallies : collections.OrderedDict - OpenMC tallies needed to compute the multi-group cross section. The keys - are strings listed in the :attr:`NuScatterXS.tally_keys` property and - values are instances of :class:`openmc.Tally`. - rxn_rate_tally : openmc.Tally - Derived tally for the reaction rate tally used in the numerator to - compute the multi-group cross section. This attribute is None - unless the multi-group cross section has been computed. - xs_tally : openmc.Tally - Derived tally for the multi-group cross section. This attribute - is None unless the multi-group cross section has been computed. - num_subdomains : int - The number of subdomains is unity for 'material', 'cell' and 'universe' - domain types. This is equal to the number of cell instances - for 'distribcell' domain types (it is equal to unity prior to loading - tally data from a statepoint file). - num_nuclides : int - The number of nuclides for which the multi-group cross section is - being tracked. This is unity if the by_nuclide attribute is False. - nuclides : Iterable of str or 'sum' - The optional user-specified nuclides for which to compute cross - sections (e.g., 'U238', 'O16'). If by_nuclide is True but nuclides - are not specified by the user, all nuclides in the spatial domain - are included. This attribute is 'sum' if by_nuclide is false. - sparse : bool - Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format - for compressed data storage - loaded_sp : bool - Whether or not a statepoint file has been loaded with tally data - derived : bool - Whether or not the MGXS is merged from one or more other MGXS - hdf5_key : str - The key used to index multi-group cross sections in an HDF5 data store - - """ - - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): - super(NuScatterXS, self).__init__(domain, domain_type, - groups, by_nuclide, name, num_polar, - num_azimuthal) - self._rxn_type = 'nu-scatter' - self._estimator = 'analog' - self._valid_estimators = ['analog'] + self.nu = nu class ScatterMatrixXS(MatrixMGXS): @@ -3793,6 +3464,9 @@ class ScatterMatrixXS(MatrixMGXS): The domain type for spatial homogenization groups : openmc.mgxs.EnergyGroups The energy group structure for energy condensation + nu : bool + If True, the cross section data will include neutron multiplication; + defaults to False by_nuclide : bool If true, computes cross sections for each nuclide in domain name : str, optional @@ -3824,6 +3498,8 @@ class ScatterMatrixXS(MatrixMGXS): Name of the multi-group cross section rxn_type : str Reaction type (e.g., 'total', 'nu-fission', etc.) + nu : bool + If True, the cross section data will include neutron multiplication by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -3884,20 +3560,24 @@ class ScatterMatrixXS(MatrixMGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, nu=False, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(ScatterMatrixXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_azimuthal) - self._rxn_type = 'scatter' + if not nu: + self._rxn_type = 'scatter' + self._hdf5_key = 'scatter matrix' + else: + self._rxn_type = 'nu-scatter' + self._hdf5_key = 'nu-scatter matrix' self._correction = 'P0' self._scatter_format = 'legendre' self._legendre_order = 0 self._histogram_bins = 16 - self._hdf5_key = 'scatter matrix' self._estimator = 'analog' self._valid_estimators = ['analog'] + self.nu = nu def __deepcopy__(self, memo): clone = super(ScatterMatrixXS, self).__deepcopy__(memo) @@ -4601,128 +4281,6 @@ class ScatterMatrixXS(MatrixMGXS): print(string) -class NuScatterMatrixXS(ScatterMatrixXS): - """A scattering production matrix multi-group cross section for one or - more Legendre moments. - - This class can be used for both OpenMC input generation and tally data - post-processing to compute spatially-homogenized and energy-integrated - multi-group cross sections for multi-group neutronics calculations. At a - minimum, one needs to set the :attr:`NuScatterMatrixXS.energy_groups` and - :attr:`NuScatterMatrixXS.domain` properties. Tallies for the flux and - appropriate reaction rates over the specified domain are generated - automatically via the :attr:`NuScatterMatrixXS.tallies` property, which can - then be appended to a :class:`openmc.Tallies` instance. - - For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the - necessary data to compute multi-group cross sections from a - :class:`openmc.StatePoint` instance. The derived multi-group cross section - can then be obtained from the :attr:`NuScatterMatrixXS.xs_tally` property. - - The calculation of the scattering-production matrix is the same as that for - :class:`ScatterMatrixXS` except that the scattering multiplicity is - accounted for. - - Parameters - ---------- - domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh - The domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - The domain type for spatial homogenization - groups : openmc.mgxs.EnergyGroups - The energy group structure for energy condensation - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - name : str, optional - Name of the multi-group cross section. Used as a label to identify - tallies in OpenMC 'tallies.xml' file. - num_polar : Integral, optional - Number of equi-width polar angle bins for angle discretization; - defaults to one bin - num_azimuthal : Integral, optional - Number of equi-width azimuthal angle bins for angle discretization; - defaults to one bin - - Attributes - ---------- - correction : 'P0' or None - Apply the P0 correction to scattering matrices if set to 'P0' - legendre_order : int - The highest legendre moment in the scattering matrix (default is 0) - name : str, optional - Name of the multi-group cross section - rxn_type : str - Reaction type (e.g., 'total', 'nu-fission', etc.) - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - domain : Material or Cell or Universe or Mesh - Domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - Domain type for spatial homogenization - energy_groups : openmc.mgxs.EnergyGroups - Energy group structure for energy condensation - num_polar : Integral - Number of equi-width polar angle bins for angle discretization - num_azimuthal : Integral - Number of equi-width azimuthal angle bins for angle discretization - tally_trigger : openmc.Trigger - An (optional) tally precision trigger given to each tally used to - compute the cross section - scores : list of str - The scores in each tally used to compute the multi-group cross section - filters : list of openmc.Filter - The filters in each tally used to compute the multi-group cross section - tally_keys : list of str - The keys into the tallies dictionary for each tally used to compute - the multi-group cross section - estimator : 'analog' - The tally estimator used to compute the multi-group cross section - tallies : collections.OrderedDict - OpenMC tallies needed to compute the multi-group cross section. The keys - are strings listed in the :attr:`NuScatterMatrixXS.tally_keys` property - and values are instances of :class:`openmc.Tally`. - rxn_rate_tally : openmc.Tally - Derived tally for the reaction rate tally used in the numerator to - compute the multi-group cross section. This attribute is None - unless the multi-group cross section has been computed. - xs_tally : openmc.Tally - Derived tally for the multi-group cross section. This attribute - is None unless the multi-group cross section has been computed. - num_subdomains : int - The number of subdomains is unity for 'material', 'cell' and 'universe' - domain types. This is equal to the number of cell instances - for 'distribcell' domain types (it is equal to unity prior to loading - tally data from a statepoint file). - num_nuclides : int - The number of nuclides for which the multi-group cross section is - being tracked. This is unity if the by_nuclide attribute is False. - nuclides : Iterable of str or 'sum' - The optional user-specified nuclides for which to compute cross - sections (e.g., 'U238', 'O16'). If by_nuclide is True but nuclides - are not specified by the user, all nuclides in the spatial domain - are included. This attribute is 'sum' if by_nuclide is false. - sparse : bool - Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format - for compressed data storage - loaded_sp : bool - Whether or not a statepoint file has been loaded with tally data - derived : bool - Whether or not the MGXS is merged from one or more other MGXS - hdf5_key : str - The key used to index multi-group cross sections in an HDF5 data store - - """ - - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): - super(NuScatterMatrixXS, self).__init__(domain, domain_type, - groups, by_nuclide, name, - num_polar, num_azimuthal) - self._rxn_type = 'nu-scatter' - self._hdf5_key = 'nu-scatter matrix' - - class MultiplicityMatrixXS(MatrixMGXS): r"""The scattering multiplicity matrix. @@ -4847,15 +4405,15 @@ class MultiplicityMatrixXS(MatrixMGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(MultiplicityMatrixXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) self._rxn_type = 'multiplicity matrix' self._estimator = 'analog' self._valid_estimators = ['analog'] + self.nu = True @property def scores(self): @@ -5009,9 +4567,8 @@ class NuFissionMatrixXS(MatrixMGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(NuFissionMatrixXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) @@ -5019,6 +4576,7 @@ class NuFissionMatrixXS(MatrixMGXS): self._hdf5_key = 'nu-fission matrix' self._estimator = 'analog' self._valid_estimators = ['analog'] + self.nu = True class Chi(MGXS): @@ -5138,14 +4696,14 @@ class Chi(MGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(Chi, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) self._rxn_type = 'chi' self._estimator = 'analog' self._valid_estimators = ['analog'] + self.nu = True @property def _dont_squeeze(self): @@ -5691,9 +5249,8 @@ class ChiPrompt(Chi): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(ChiPrompt, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) @@ -5820,9 +5377,8 @@ class InverseVelocity(MGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(InverseVelocity, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) @@ -5850,8 +5406,8 @@ class InverseVelocity(MGXS): if xs_type == 'macro': return 'second/cm' else: - raise ValueError('Unable to return the units of InverseVelocity for' - ' xs_type other than "macro"') + raise ValueError('Unable to return the units of InverseVelocity' + ' for xs_type other than "macro"') class PromptNuFissionXS(MGXS): @@ -5966,13 +5522,13 @@ class PromptNuFissionXS(MGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(PromptNuFissionXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) self._rxn_type = 'prompt-nu-fission' + self.nu = True class PromptNuFissionMatrixXS(MatrixMGXS): @@ -6092,9 +5648,8 @@ class PromptNuFissionMatrixXS(MatrixMGXS): """ - def __init__(self, domain=None, domain_type=None, - groups=None, by_nuclide=False, name='', num_polar=1, - num_azimuthal=1): + def __init__(self, domain=None, domain_type=None, groups=None, + by_nuclide=False, name='', num_polar=1, num_azimuthal=1): super(PromptNuFissionMatrixXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) @@ -6102,3 +5657,4 @@ class PromptNuFissionMatrixXS(MatrixMGXS): self._hdf5_key = 'prompt-nu-fission matrix' self._estimator = 'analog' self._valid_estimators = ['analog'] + self.nu = True From c40a1ca2fd3ed4d9fbc5b23563516a436bd90c7a Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sat, 25 Feb 2017 14:54:03 -0500 Subject: [PATCH 05/12] whoops, missed some old Nu* references --- openmc/mgxs_library.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/openmc/mgxs_library.py b/openmc/mgxs_library.py index af6dc8f98..d1a46df09 100644 --- a/openmc/mgxs_library.py +++ b/openmc/mgxs_library.py @@ -1074,12 +1074,12 @@ class XSdata(object): def set_nu_fission_mgxs(self, nu_fission, temperature=294., nuclide='total', xs_type='macro', subdomain=None): - """This method allows for an openmc.mgxs.NuFissionXS + """This method allows for an openmc.mgxs.FissionXS to be used to set the nu-fission cross section for this XSdata object. Parameters ---------- - nu_fission: openmc.mgxs.NuFissionXS + nu_fission: openmc.mgxs.FissionXS MGXS Object containing the nu-fission cross section for the domain of interest. temperature : float @@ -1102,8 +1102,9 @@ class XSdata(object): """ - check_type('nu_fission', nu_fission, (openmc.mgxs.NuFissionXS, + check_type('nu_fission', nu_fission, (openmc.mgxs.FissionXS, openmc.mgxs.NuFissionMatrixXS)) + check_value('nu', nu_fission.nu, [True]) check_value('energy_groups', nu_fission.energy_groups, [self.energy_groups]) check_value('domain_type', nu_fission.domain_type, @@ -1598,8 +1599,9 @@ class XSdata(object): """ - check_type('nuscatter', nuscatter, (openmc.mgxs.NuScatterMatrixXS, + check_type('nuscatter', nuscatter, (openmc.mgxs.ScatterMatrixXS, openmc.mgxs.MultiplicityMatrixXS)) + check_value('nu', nuscatter.nu, [True]) check_value('energy_groups', nuscatter.energy_groups, [self.energy_groups]) check_value('domain_type', nuscatter.domain_type, From 5e313cf5f1d601074ad95c17ae589bf564972adb Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sun, 26 Feb 2017 05:33:32 -0500 Subject: [PATCH 06/12] Revisions per @wbinventors comments --- docs/source/pythonapi/examples/mgxs-part-i.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb index 2b3d5a77c..17d64ee2d 100644 --- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb @@ -380,7 +380,9 @@ "* `InverseVelocity`\n", "* `PromptNuFissionXS`\n", "\n", - "Of course, we know that the transport (`TransportXS`), fission (`FissionXS`), scattering (`ScatterXS`), and scattering-matrix (`ScatterMatrixXS`) cross sections can potentially incorporate neutron multiplication ($\\nu$). For these types, the multpiplication can be accomodated by setting the `nu` parameter to `True` as shown below.\n", + "Of course, we are aware that the fission cross section (`FissionXS`) can sometimes be paired with the fission neutron multiplication to become $\\nu\\sigma_f$. This can be accomodated in to the `FissionXS` class by setting the `nu` parameter to `True` as shown below.\n", + "\n", + "Additionally, scattering reactions (like (n,2n)) can also be defined to take in to account the neutron multiplication to become $\\nu\\sigma_s$. This can be accomodated in the the transport (`TransportXS`), scattering (`ScatterXS`), and scattering-matrix (`ScatterMatrixXS`) cross sections types by setting the `nu` parameter to `True` as shown below.\n", "\n", "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group total, absorption and scattering cross sections with our 2-group structure." ] From 14f6185744399413eaef85503b4ae5fc256262c6 Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sun, 26 Feb 2017 05:39:20 -0500 Subject: [PATCH 07/12] resolved @wbinventor comments on the mesh and executor --- openmc/executor.py | 6 +++--- openmc/mesh.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/openmc/executor.py b/openmc/executor.py index efc28a0b9..6f7e22311 100644 --- a/openmc/executor.py +++ b/openmc/executor.py @@ -5,7 +5,7 @@ import sys from six import string_types -summary_indicator = "TIMING STATISTICS" +_summary_indicator = "TIMING STATISTICS" def _run(command, output, cwd): @@ -25,7 +25,7 @@ def _run(command, output, cwd): if output == 'full': # If user requested output, print to screen print(line, end='') - elif output == 'summary' and summary_indicator in line: + elif output == 'summary' and _summary_indicator in line: # If they requested a summary, look for the start of the summary storage_flag = True @@ -56,7 +56,7 @@ def plot_geometry(output=True, openmc_exec='openmc', cwd='.'): def run(particles=None, threads=None, geometry_debug=False, - restart_file=None, tracks=False, output="full", cwd='.', + restart_file=None, tracks=False, output='full', cwd='.', openmc_exec='openmc', mpi_args=None): """Run an OpenMC simulation. diff --git a/openmc/mesh.py b/openmc/mesh.py index 4d9726292..6e3f2a266 100644 --- a/openmc/mesh.py +++ b/openmc/mesh.py @@ -188,8 +188,8 @@ class Mesh(EqualityMixin): return mesh def cell_generator(self): - """Generator function to traverse through every [i,j,k] index - of the mesh in the same order that would be done by the Fortran + """Generator function to traverse through every [i,j,k] index of the + mesh For example the following code: @@ -202,8 +202,8 @@ class Mesh(EqualityMixin): [1, 1, 1] [2, 1, 1] - [1, 1, 2] [1, 2, 1] + [2, 2, 1] ... From 413e04c5cf36d1d424e0d4fb4912a6ae18ab0054 Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sun, 26 Feb 2017 06:19:40 -0500 Subject: [PATCH 08/12] Added a bit more documentation for nu, merged the Prompt* MGXS types with their total counterparts using the same method used for nu, and udpated mdgxs-part-i. --- .../pythonapi/examples/mdgxs-part-i.ipynb | 227 ++++----- openmc/mgxs/mgxs.py | 481 ++++-------------- 2 files changed, 201 insertions(+), 507 deletions(-) diff --git a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb index f7582fcbe..05366758a 100644 --- a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb @@ -365,20 +365,15 @@ "\n", "* `TotalXS`\n", "* `TransportXS`\n", - "* `NuTransportXS`\n", "* `AbsorptionXS`\n", "* `CaptureXS`\n", "* `FissionXS`\n", - "* `NuFissionXS`\n", + "* `NuFissionMatrixXS`\n", "* `KappaFissionXS`\n", "* `ScatterXS`\n", - "* `NuScatterXS`\n", "* `ScatterMatrixXS`\n", - "* `NuScatterMatrixXS`\n", "* `Chi`\n", - "* `ChiPrompt`\n", "* `InverseVelocity`\n", - "* `PromptNuFissionXS`\n", "\n", "A separate abstract `MDGXS` class is used for cross-sections and parameters that involve delayed neutrons. The subclasses of `MDGXS` include:\n", "\n", @@ -387,7 +382,11 @@ "* `Beta`\n", "* `DecayRate`\n", "\n", - "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group chi-prompt, chi-delayed, and prompt-nu-fission cross sections with our 100-energy-group structure and multi-group delayed-nu-fission and beta cross sections with our 100-energy-group and 6-delayed-group structures. " + "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. \n", + "\n", + "In this case, let's create the multi-group chi-prompt, chi-delayed, and prompt-nu-fission cross sections with our 100-energy-group structure and multi-group delayed-nu-fission and beta cross sections with our 100-energy-group and 6-delayed-group structures. \n", + "\n", + "The prompt chi and nu-fission data can actually be gathered using the `Chi` and `FissionXS` classes, respectively, but passing in a value of `True` for the optional `prompt` parameter upon initialization." ] }, { @@ -399,8 +398,8 @@ "outputs": [], "source": [ "# Instantiate a few different sections\n", - "chi_prompt = mgxs.ChiPrompt(domain=cell, groups=energy_groups, by_nuclide=True)\n", - "prompt_nu_fission = mgxs.PromptNuFissionXS(domain=cell, groups=energy_groups, by_nuclide=True)\n", + "chi_prompt = mgxs.Chi(domain=cell, groups=energy_groups, by_nuclide=True, prompt=True)\n", + "prompt_nu_fission = mgxs.FissionXS(domain=cell, groups=energy_groups, by_nuclide=True, nu=True, prompt=True)\n", "chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n", "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n", "beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n", @@ -542,8 +541,8 @@ " Copyright | 2011-2017 Massachusetts Institute of Technology\n", " License | http://openmc.readthedocs.io/en/latest/license.html\n", " Version | 0.8.0\n", - " Git SHA1 | 6e3f6bf8b11cb3f6f171f1c351ddcaf85dd515ec\n", - " Date/Time | 2017-02-11 14:15:38\n", + " Git SHA1 | 5e313cf5f1d601074ad95c17ae589bf564972adb\n", + " Date/Time | 2017-02-26 06:05:10\n", " OpenMP Threads | 8\n", "\n", " ===========================================================================\n", @@ -617,10 +616,10 @@ " 44/1 1.24424 1.23133 +/- 0.00389\n", " 45/1 1.24767 1.23179 +/- 0.00381\n", " 46/1 1.22998 1.23174 +/- 0.00370\n", - " 47/1 1.26352 1.23260 +/- 0.00370\n", - " 48/1 1.23155 1.23257 +/- 0.00360\n", - " 49/1 1.22059 1.23227 +/- 0.00352\n", - " 50/1 1.24724 1.23264 +/- 0.00345\n", + " 47/1 1.26195 1.23256 +/- 0.00369\n", + " 48/1 1.23146 1.23253 +/- 0.00359\n", + " 49/1 1.22059 1.23222 +/- 0.00351\n", + " 50/1 1.24724 1.23260 +/- 0.00345\n", " Creating state point statepoint.50.h5...\n", "\n", " ===========================================================================\n", @@ -630,27 +629,27 @@ "\n", " =======================> TIMING STATISTICS <=======================\n", "\n", - " Total time for initialization = 4.1132E-01 seconds\n", - " Reading cross sections = 3.2075E-01 seconds\n", - " Total time in simulation = 1.3772E+01 seconds\n", - " Time in transport only = 1.2971E+01 seconds\n", - " Time in inactive batches = 6.3146E-01 seconds\n", - " Time in active batches = 1.3140E+01 seconds\n", - " Time synchronizing fission bank = 5.4819E-03 seconds\n", - " Sampling source sites = 3.8838E-03 seconds\n", - " SEND/RECV source sites = 1.5557E-03 seconds\n", - " Time accumulating tallies = 5.3270E-04 seconds\n", - " Total time for finalization = 4.9668E-02 seconds\n", - " Total time elapsed = 1.4246E+01 seconds\n", - " Calculation Rate (inactive) = 79181.7 neutrons/second\n", - " Calculation Rate (active) = 15220.4 neutrons/second\n", + " Total time for initialization = 3.8846E-01 seconds\n", + " Reading cross sections = 3.0221E-01 seconds\n", + " Total time in simulation = 1.2666E+01 seconds\n", + " Time in transport only = 1.2196E+01 seconds\n", + " Time in inactive batches = 5.1652E-01 seconds\n", + " Time in active batches = 1.2150E+01 seconds\n", + " Time synchronizing fission bank = 5.1914E-03 seconds\n", + " Sampling source sites = 3.6297E-03 seconds\n", + " SEND/RECV source sites = 1.5222E-03 seconds\n", + " Time accumulating tallies = 5.2027E-04 seconds\n", + " Total time for finalization = 4.8293E-02 seconds\n", + " Total time elapsed = 1.3117E+01 seconds\n", + " Calculation Rate (inactive) = 96801.2 neutrons/second\n", + " Calculation Rate (active) = 16461.5 neutrons/second\n", "\n", " ============================> RESULTS <============================\n", "\n", - " k-effective (Collision) = 1.23260 +/- 0.00309\n", - " k-effective (Track-length) = 1.23264 +/- 0.00345\n", + " k-effective (Collision) = 1.23256 +/- 0.00308\n", + " k-effective (Track-length) = 1.23260 +/- 0.00345\n", " k-effective (Absorption) = 1.23111 +/- 0.00186\n", - " Combined k-effective = 1.23135 +/- 0.00185\n", + " Combined k-effective = 1.23135 +/- 0.00184\n", " Leakage Fraction = 0.00000 +/- 0.00000\n", "\n" ] @@ -759,17 +758,17 @@ { "data": { "text/plain": [ - "array([[[ 5.14239169e-06, 1.16429778e-06]],\n", + "array([[[ 5.14223507e-06, 1.16426087e-06]],\n", "\n", - " [[ 2.65434434e-05, 7.58244504e-06]],\n", + " [[ 2.65426350e-05, 7.58220468e-06]],\n", "\n", - " [[ 2.53406770e-05, 5.73814391e-06]],\n", + " [[ 2.53399053e-05, 5.73796202e-06]],\n", "\n", - " [[ 5.68158884e-05, 1.04761254e-05]],\n", + " [[ 5.68141581e-05, 1.04757933e-05]],\n", "\n", - " [[ 2.32937121e-05, 5.45676114e-06]],\n", + " [[ 2.32930026e-05, 5.45658817e-06]],\n", "\n", - " [[ 9.75765501e-06, 1.65156185e-06]]])" + " [[ 9.75735783e-06, 1.65150949e-06]]])" ] }, "execution_count": 18, @@ -819,8 +818,8 @@ " 1\n", " 1\n", " U235\n", - " 9.533842e-11\n", - " 4.789050e-11\n", + " 9.534320e-11\n", + " 4.789291e-11\n", " \n", " \n", " 199\n", @@ -828,8 +827,8 @@ " 1\n", " 1\n", " Pu239\n", - " 1.606499e-11\n", - " 8.071081e-12\n", + " 1.606580e-11\n", + " 8.071486e-12\n", " \n", " \n", " 398\n", @@ -837,8 +836,8 @@ " 2\n", " 1\n", " U235\n", - " 1.224131e-09\n", - " 6.149449e-10\n", + " 1.224152e-09\n", + " 6.149552e-10\n", " \n", " \n", " 399\n", @@ -846,8 +845,8 @@ " 2\n", " 1\n", " Pu239\n", - " 2.602518e-10\n", - " 1.307590e-10\n", + " 2.602562e-10\n", + " 1.307612e-10\n", " \n", " \n", " 598\n", @@ -855,8 +854,8 @@ " 3\n", " 1\n", " U235\n", - " 9.033000e-10\n", - " 4.537601e-10\n", + " 9.032969e-10\n", + " 4.537585e-10\n", " \n", " \n", " 599\n", @@ -864,8 +863,8 @@ " 3\n", " 1\n", " Pu239\n", - " 1.522295e-10\n", - " 7.648264e-11\n", + " 1.522290e-10\n", + " 7.648238e-11\n", " \n", " \n", " 798\n", @@ -873,8 +872,8 @@ " 4\n", " 1\n", " U235\n", - " 1.749138e-09\n", - " 8.786432e-10\n", + " 1.749268e-09\n", + " 8.787082e-10\n", " \n", " \n", " 799\n", @@ -882,8 +881,8 @@ " 4\n", " 1\n", " Pu239\n", - " 2.400317e-10\n", - " 1.205943e-10\n", + " 2.400495e-10\n", + " 1.206032e-10\n", " \n", " \n", " 998\n", @@ -909,14 +908,14 @@ ], "text/plain": [ " cell delayedgroup group in nuclide mean std. dev.\n", - "198 1 1 1 U235 9.533842e-11 4.789050e-11\n", - "199 1 1 1 Pu239 1.606499e-11 8.071081e-12\n", - "398 1 2 1 U235 1.224131e-09 6.149449e-10\n", - "399 1 2 1 Pu239 2.602518e-10 1.307590e-10\n", - "598 1 3 1 U235 9.033000e-10 4.537601e-10\n", - "599 1 3 1 Pu239 1.522295e-10 7.648264e-11\n", - "798 1 4 1 U235 1.749138e-09 8.786432e-10\n", - "799 1 4 1 Pu239 2.400317e-10 1.205943e-10\n", + "198 1 1 1 U235 9.534320e-11 4.789291e-11\n", + "199 1 1 1 Pu239 1.606580e-11 8.071486e-12\n", + "398 1 2 1 U235 1.224152e-09 6.149552e-10\n", + "399 1 2 1 Pu239 2.602562e-10 1.307612e-10\n", + "598 1 3 1 U235 9.032969e-10 4.537585e-10\n", + "599 1 3 1 Pu239 1.522290e-10 7.648238e-11\n", + "798 1 4 1 U235 1.749268e-09 8.787082e-10\n", + "799 1 4 1 Pu239 2.400495e-10 1.206032e-10\n", "998 1 5 1 U235 2.724017e-10 1.368376e-10\n", "999 1 5 1 Pu239 4.749191e-11 2.386080e-11" ] @@ -998,7 +997,7 @@ " 1\n", " U235\n", " 0.120780\n", - " 0.000551\n", + " 0.000549\n", " \n", " \n", " 5\n", @@ -1007,7 +1006,7 @@ " 1\n", " Pu239\n", " 0.113370\n", - " 0.000452\n", + " 0.000451\n", " \n", " \n", " 6\n", @@ -1016,7 +1015,7 @@ " 1\n", " U235\n", " 0.302780\n", - " 0.001381\n", + " 0.001378\n", " \n", " \n", " 7\n", @@ -1025,7 +1024,7 @@ " 1\n", " Pu239\n", " 0.292500\n", - " 0.001166\n", + " 0.001163\n", " \n", " \n", " 8\n", @@ -1034,7 +1033,7 @@ " 1\n", " U235\n", " 0.849490\n", - " 0.003875\n", + " 0.003865\n", " \n", " \n", " 9\n", @@ -1043,7 +1042,7 @@ " 1\n", " Pu239\n", " 0.857490\n", - " 0.003419\n", + " 0.003411\n", " \n", " \n", " 10\n", @@ -1052,7 +1051,7 @@ " 1\n", " U235\n", " 2.853000\n", - " 0.013013\n", + " 0.012980\n", " \n", " \n", " 11\n", @@ -1061,7 +1060,7 @@ " 1\n", " Pu239\n", " 2.729700\n", - " 0.010884\n", + " 0.010858\n", " \n", " \n", "\n", @@ -1073,14 +1072,14 @@ "1 1 1 1 Pu239 0.013271 0.000053\n", "2 1 2 1 U235 0.032739 0.000149\n", "3 1 2 1 Pu239 0.030881 0.000123\n", - "4 1 3 1 U235 0.120780 0.000551\n", - "5 1 3 1 Pu239 0.113370 0.000452\n", - "6 1 4 1 U235 0.302780 0.001381\n", - "7 1 4 1 Pu239 0.292500 0.001166\n", - "8 1 5 1 U235 0.849490 0.003875\n", - "9 1 5 1 Pu239 0.857490 0.003419\n", - "10 1 6 1 U235 2.853000 0.013013\n", - "11 1 6 1 Pu239 2.729700 0.010884" + "4 1 3 1 U235 0.120780 0.000549\n", + "5 1 3 1 Pu239 0.113370 0.000451\n", + "6 1 4 1 U235 0.302780 0.001378\n", + "7 1 4 1 Pu239 0.292500 0.001163\n", + "8 1 5 1 U235 0.849490 0.003865\n", + "9 1 5 1 Pu239 0.857490 0.003411\n", + "10 1 6 1 U235 2.853000 0.012980\n", + "11 1 6 1 Pu239 2.729700 0.010858" ] }, "execution_count": 20, @@ -1169,7 +1168,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -1180,7 +1179,7 @@ "data": { "image/png": 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trXiWGahyBYsB/AvArgObHrJDe3EqNB+AipNeuJY8TvBBMFZCSg8o94AyTwjIA7MrFPhB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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1251,8 +1250,8 @@ " 1\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 8.779406e-08\n", - " 4.667924e-10\n", + " 8.779139e-08\n", + " 4.658590e-10\n", " \n", " \n", " 1\n", @@ -1260,8 +1259,8 @@ " 1\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 7.150041e-09\n", - " 3.565013e-11\n", + " 7.149814e-09\n", + " 3.559010e-11\n", " \n", " \n", " 2\n", @@ -1269,8 +1268,8 @@ " 2\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 9.528171e-07\n", - " 5.066035e-09\n", + " 9.527880e-07\n", + " 5.055905e-09\n", " \n", " \n", " 3\n", @@ -1278,8 +1277,8 @@ " 2\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.303200e-07\n", - " 6.497762e-10\n", + " 1.303159e-07\n", + " 6.486820e-10\n", " \n", " \n", " 4\n", @@ -1287,8 +1286,8 @@ " 3\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.353975e-07\n", - " 1.251585e-09\n", + " 2.353903e-07\n", + " 1.249083e-09\n", " \n", " \n", " 5\n", @@ -1296,8 +1295,8 @@ " 3\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.032960e-08\n", - " 1.013634e-10\n", + " 2.032895e-08\n", + " 1.011928e-10\n", " \n", " \n", " 6\n", @@ -1305,8 +1304,8 @@ " 4\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 4.720335e-07\n", - " 2.509756e-09\n", + " 4.720191e-07\n", + " 2.504737e-09\n", " \n", " \n", " 7\n", @@ -1314,8 +1313,8 @@ " 4\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.626392e-08\n", - " 1.309520e-10\n", + " 2.626309e-08\n", + " 1.307315e-10\n", " \n", " \n", " 8\n", @@ -1323,8 +1322,8 @@ " 5\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.828001e-08\n", - " 1.503620e-10\n", + " 2.827915e-08\n", + " 1.500614e-10\n", " \n", " \n", " 9\n", @@ -1332,8 +1331,8 @@ " 5\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 2.430664e-09\n", - " 1.211930e-11\n", + " 2.430587e-09\n", + " 1.209889e-11\n", " \n", " \n", " 10\n", @@ -1341,8 +1340,8 @@ " 6\n", " U235\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 1.477575e-09\n", - " 7.856122e-12\n", + " 1.477530e-09\n", + " 7.840413e-12\n", " \n", " \n", " 11\n", @@ -1350,8 +1349,8 @@ " 6\n", " Pu239\n", " (((delayed-nu-fission / nu-fission) * (delayed...\n", - " 6.994534e-11\n", - " 3.487477e-13\n", + " 6.994312e-11\n", + " 3.481605e-13\n", " \n", " \n", "\n", @@ -1373,18 +1372,18 @@ "11 1 6 Pu239 \n", "\n", " score mean std. dev. \n", - "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08 4.67e-10 \n", - "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.57e-11 \n", - "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07 5.07e-09 \n", - "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 6.50e-10 \n", + "0 (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08 4.66e-10 \n", + "1 (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09 3.56e-11 \n", + "2 (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07 5.06e-09 \n", + "3 (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07 6.49e-10 \n", "4 (((delayed-nu-fission / nu-fission) * (delayed... 2.35e-07 1.25e-09 \n", "5 (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08 1.01e-10 \n", - "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 2.51e-09 \n", + "6 (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07 2.50e-09 \n", "7 (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08 1.31e-10 \n", "8 (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08 1.50e-10 \n", "9 (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09 1.21e-11 \n", - "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.86e-12 \n", - "11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.49e-13 " + "10 (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09 7.84e-12 \n", + "11 (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11 3.48e-13 " ] }, "execution_count": 24, @@ -1438,7 +1437,7 @@ "data": { "image/png": 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OMwwjp+hRI4vuZsiQIWzZsiXq3ObNm9ljjz3YsGFDxDB9++23A+4iwtNOO41zzjmHb37z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\nLMiGYDlO3H4DbgZ+LSLHAE9lQ7AcJlGfAZwP3NO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q3njjDfr06RNZUBeE9oQtv/766ykuLua1117j/vvvjyiX9mBhy41086v9lka2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\nhqrqf1S1vXaKo4B1qvqu195iXGO5X1m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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 284be6ca3..3ff17b35f 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -151,6 +151,8 @@ class MGXS(object): Reaction type (e.g., 'total', 'nu-fission', etc.) nu : bool If True, the cross section data will include neutron multiplication + prompt : bool + If true, computes cross sections which only includes prompt neutrons by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -233,6 +235,7 @@ class MGXS(object): self._hdf5_key = None self._valid_estimators = ESTIMATOR_TYPES self._nu = False + self._prompt = False self.name = name self.by_nuclide = by_nuclide @@ -415,6 +418,10 @@ class MGXS(object): def nu(self): return self._nu + @property + def prompt(self): + return self._prompt + @property def by_nuclide(self): return self._by_nuclide @@ -585,6 +592,11 @@ class MGXS(object): cv.check_type('nu', nu, bool) self._nu = nu + @prompt.setter + def prompt(self, prompt): + cv.check_type('prompt', prompt, bool) + self._prompt = prompt + @by_nuclide.setter def by_nuclide(self, by_nuclide): cv.check_type('by_nuclide', by_nuclide, bool) @@ -742,13 +754,14 @@ class MGXS(object): elif mgxs_type == 'chi': mgxs = Chi(domain, domain_type, energy_groups) elif mgxs_type == 'chi-prompt': - mgxs = ChiPrompt(domain, domain_type, energy_groups) + mgxs = Chi(domain, domain_type, energy_groups, prompt=True) elif mgxs_type == 'inverse-velocity': mgxs = InverseVelocity(domain, domain_type, energy_groups) elif mgxs_type == 'prompt-nu-fission': - mgxs = PromptNuFissionXS(domain, domain_type, energy_groups) + mgxs = FissionXS(domain, domain_type, energy_groups, prompt=True) elif mgxs_type == 'prompt-nu-fission matrix': - mgxs = PromptNuFissionMatrixXS(domain, domain_type, energy_groups) + mgxs = NuFissionMatrixXS(domain, domain_type, energy_groups, + prompt=True) mgxs.by_nuclide = by_nuclide mgxs.name = name @@ -2612,6 +2625,9 @@ class TransportXS(MGXS): \sigma_{tr} &= \frac{\langle \sigma_t \phi \rangle - \langle \sigma_{s1} \phi \rangle}{\langle \phi \rangle} + To incorporate the effect of scattering multiplication in the above + relation, the `nu` parameter can be set to `True`. + Parameters ---------- domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh @@ -3046,6 +3062,16 @@ class FissionXS(MGXS): \sigma_f (r, E) \psi (r, E, \Omega)}{\int_{r \in V} dr \int_{4\pi} d\Omega \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega)}. + To incorporate the effect of neutron multiplication in the above + relation, the `nu` parameter can be set to `True`. + + This class can also be used to gather a prompt-nu-fission cross section + (which only includes the contributions from prompt neutrons). This is + accomplished by setting the :attr:`FissionXS.prompt` attribute to `True`. + Since the prompt-nu-fission cross section requires neutron multiplication, + the `nu` parameter will automatically be set to `True` if `prompt` is also + `True`. + Parameters ---------- domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh @@ -3057,6 +3083,10 @@ class FissionXS(MGXS): nu : bool If True, the cross section data will include neutron multiplication; defaults to False + prompt : bool + If true, computes cross sections which only includes prompt neutrons; + defaults to False which includes prompt and delayed in total. Setting + this to True will also set nu to True by_nuclide : bool If true, computes cross sections for each nuclide in domain name : str, optional @@ -3077,6 +3107,8 @@ class FissionXS(MGXS): Reaction type (e.g., 'total', 'nu-fission', etc.) nu : bool If True, the cross section data will include neutron multiplication + prompt : bool + If true, computes cross sections which only includes prompt neutrons by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -3138,15 +3170,20 @@ class FissionXS(MGXS): """ def __init__(self, domain=None, domain_type=None, groups=None, nu=False, - by_nuclide=False, name='', num_polar=1, num_azimuthal=1): + prompt=False, by_nuclide=False, name='', num_polar=1, + num_azimuthal=1): super(FissionXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) - if not nu: - self._rxn_type = 'fission' + if not prompt: + if not nu: + self._rxn_type = 'fission' + else: + self._rxn_type = 'nu-fission' + self.nu = nu else: - self._rxn_type = 'nu-fission' - + self._rxn_type = 'prompt-nu-fission' + self.nu = True class KappaFissionXS(MGXS): r"""A recoverable fission energy production rate multi-group cross section. @@ -3305,6 +3342,9 @@ class ScatterXS(MGXS): \Omega) \right ]}{\int_{r \in V} dr \int_{4\pi} d\Omega \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega)}. + To incorporate the effect of scattering multiplication from (n,xn) + reactions in the above relation, the `nu` parameter can be set to `True`. + Parameters ---------- domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh @@ -3455,6 +3495,8 @@ class ScatterMatrixXS(MatrixMGXS): \phi \rangle - \delta_{gg'} \sum_{g''} \langle \sigma_{s,1,g''\rightarrow g} \phi \rangle}{\langle \phi \rangle} + To incorporate the effect of neutron multiplication from (n,xn) reactions + in the above relation, the `nu` parameter can be set to `True`. Parameters ---------- @@ -4481,6 +4523,11 @@ class NuFissionMatrixXS(MatrixMGXS): \nu\sigma_{f,g'\rightarrow g} &= \frac{\langle \nu\sigma_{f,g'\rightarrow g} \phi \rangle}{\langle \phi \rangle} + This class can also be used to gather a prompt-nu-fission cross section + (which only includes the contributions from prompt neutrons). This is + accomplished by setting the :attr:`NuFissionMatrixXS.prompt` attribute to + `True`. + Parameters ---------- domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh @@ -4489,6 +4536,9 @@ class NuFissionMatrixXS(MatrixMGXS): The domain type for spatial homogenization groups : openmc.mgxs.EnergyGroups The energy group structure for energy condensation + prompt : bool + If true, computes cross sections which only includes prompt neutrons; + defaults to False which includes prompt and delayed in total by_nuclide : bool If true, computes cross sections for each nuclide in domain name : str, optional @@ -4507,6 +4557,8 @@ class NuFissionMatrixXS(MatrixMGXS): Name of the multi-group cross section rxn_type : str Reaction type (e.g., 'total', 'nu-fission', etc.) + prompt : bool + If true, computes cross sections which only includes prompt neutrons by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -4568,12 +4620,17 @@ class NuFissionMatrixXS(MatrixMGXS): """ def __init__(self, domain=None, domain_type=None, groups=None, - by_nuclide=False, name='', num_polar=1, num_azimuthal=1): + prompt=False, by_nuclide=False, name='', num_polar=1, + num_azimuthal=1): super(NuFissionMatrixXS, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) - self._rxn_type = 'nu-fission' - self._hdf5_key = 'nu-fission matrix' + if not prompt: + self._rxn_type = 'nu-fission' + self._hdf5_key = 'nu-fission matrix' + else: + self._rxn_type = 'prompt-nu-fission' + self._hdf5_key = 'prompt-nu-fission matrix' self._estimator = 'analog' self._valid_estimators = ['analog'] self.nu = True @@ -4610,6 +4667,10 @@ class Chi(MGXS): \chi_g &= \frac{\langle \nu\sigma_{f,g' \rightarrow g} \phi \rangle} {\langle \nu\sigma_f \phi \rangle} + This class can also be used to gather a prompt-chi (which only includes the + outgoing energy spectrum of prompt neutrons). This is accomplished by + setting the :attr:`Chi.prompt` attribute to `True`. + Parameters ---------- domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh @@ -4618,6 +4679,9 @@ class Chi(MGXS): The domain type for spatial homogenization groups : openmc.mgxs.EnergyGroups The energy group structure for energy condensation + prompt : bool + If true, computes cross sections which only includes prompt neutrons; + defaults to False which includes prompt and delayed in total by_nuclide : bool If true, computes cross sections for each nuclide in domain name : str, optional @@ -4636,6 +4700,8 @@ class Chi(MGXS): Name of the multi-group cross section rxn_type : str Reaction type (e.g., 'total', 'nu-fission', etc.) + prompt : bool + If true, computes cross sections which only includes prompt neutrons by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -4697,13 +4763,18 @@ class Chi(MGXS): """ def __init__(self, domain=None, domain_type=None, groups=None, - by_nuclide=False, name='', num_polar=1, num_azimuthal=1): + prompt=False, by_nuclide=False, name='', num_polar=1, + num_azimuthal=1): super(Chi, self).__init__(domain, domain_type, groups, by_nuclide, name, num_polar, num_azimuthal) - self._rxn_type = 'chi' + if not prompt: + self._rxn_type = 'chi' + else: + self._rxn_type = 'chi-prompt' self._estimator = 'analog' self._valid_estimators = ['analog'] self.nu = True + self.prompt = prompt @property def _dont_squeeze(self): @@ -4717,7 +4788,10 @@ class Chi(MGXS): @property def scores(self): - return ['nu-fission', 'nu-fission'] + if not self.prompt: + return ['nu-fission', 'nu-fission'] + else: + return ['prompt-nu-fission', 'prompt-nu-fission'] @property def filters(self): @@ -5132,135 +5206,6 @@ class Chi(MGXS): return '%' -class ChiPrompt(Chi): - r"""The prompt fission spectrum. - - This class can be used for both OpenMC input generation and tally data - post-processing to compute spatially-homogenized and energy-integrated - multi-group cross sections for multi-group neutronics calculations. At a - minimum, one needs to set the :attr:`ChiPrompt.energy_groups` and - :attr:`ChiPrompt.domain` properties. Tallies for the flux and appropriate - reaction rates over the specified domain are generated automatically via the - :attr:`ChiPrompt.tallies` property, which can then be appended to a - :class:`openmc.Tallies` instance. - - For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the - necessary data to compute multi-group cross sections from a - :class:`openmc.StatePoint` instance. The derived multi-group cross section - can then be obtained from the :attr:`ChiPrompt.xs_tally` property. - - For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the - fission spectrum is calculated as: - - .. math:: - - \langle \nu^p \sigma_{f,g' \rightarrow g} \phi \rangle &= \int_{r \in V} - dr \int_{4\pi} d\Omega' \int_0^\infty dE' \int_{E_g}^{E_{g-1}} dE \; - \chi(E)^p \nu^p \sigma_f (r, E') \psi(r, E', \Omega')\\ - \langle \nu^p \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi} - d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu^p \sigma_f (r, - E') \psi(r, E', \Omega') \\ - \chi_g^p &= \frac{\langle \nu^p \sigma_{f,g' \rightarrow g} \phi \rangle} - {\langle \nu^p \sigma_f \phi \rangle} - - Parameters - ---------- - domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh - The domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - The domain type for spatial homogenization - groups : openmc.mgxs.EnergyGroups - The energy group structure for energy condensation - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - name : str, optional - Name of the multi-group cross section. Used as a label to identify - tallies in OpenMC 'tallies.xml' file. - num_polar : Integral, optional - Number of equi-width polar angle bins for angle discretization; - defaults to one bin - num_azimuthal : Integral, optional - Number of equi-width azimuthal angle bins for angle discretization; - defaults to one bin - - Attributes - ---------- - name : str, optional - Name of the multi-group cross section - rxn_type : str - Reaction type (e.g., 'total', 'nu-fission', etc.) - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - domain : Material or Cell or Universe or Mesh - Domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - Domain type for spatial homogenization - energy_groups : openmc.mgxs.EnergyGroups - Energy group structure for energy condensation - num_polar : Integral - Number of equi-width polar angle bins for angle discretization - num_azimuthal : Integral - Number of equi-width azimuthal angle bins for angle discretization - tally_trigger : openmc.Trigger - An (optional) tally precision trigger given to each tally used to - compute the cross section - scores : list of str - The scores in each tally used to compute the multi-group cross section - filters : list of openmc.Filter - The filters in each tally used to compute the multi-group cross section - tally_keys : list of str - The keys into the tallies dictionary for each tally used to compute - the multi-group cross section - estimator : 'analog' - The tally estimator used to compute the multi-group cross section - tallies : collections.OrderedDict - OpenMC tallies needed to compute the multi-group cross section. The keys - are strings listed in the :attr:`ChiPrompt.tally_keys` property and - values are instances of :class:`openmc.Tally`. - rxn_rate_tally : openmc.Tally - Derived tally for the reaction rate tally used in the numerator to - compute the multi-group cross section. This attribute is None - unless the multi-group cross section has been computed. - xs_tally : openmc.Tally - Derived tally for the multi-group cross section. This attribute - is None unless the multi-group cross section has been computed. - num_subdomains : int - The number of subdomains is unity for 'material', 'cell' and 'universe' - domain types. This is equal to the number of cell instances - for 'distribcell' domain types (it is equal to unity prior to loading - tally data from a statepoint file). - num_nuclides : int - The number of nuclides for which the multi-group cross section is - being tracked. This is unity if the by_nuclide attribute is False. - nuclides : Iterable of str or 'sum' - The optional user-specified nuclides for which to compute cross - sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides - are not specified by the user, all nuclides in the spatial domain - are included. This attribute is 'sum' if by_nuclide is false. - sparse : bool - Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format - for compressed data storage - loaded_sp : bool - Whether or not a statepoint file has been loaded with tally data - derived : bool - Whether or not the MGXS is merged from one or more other MGXS - hdf5_key : str - The key used to index multi-group cross sections in an HDF5 data store - - """ - - def __init__(self, domain=None, domain_type=None, groups=None, - by_nuclide=False, name='', num_polar=1, num_azimuthal=1): - super(ChiPrompt, self).__init__(domain, domain_type, groups, - by_nuclide, name, num_polar, - num_azimuthal) - self._rxn_type = 'chi-prompt' - - @property - def scores(self): - return ['prompt-nu-fission', 'prompt-nu-fission'] - - class InverseVelocity(MGXS): r"""An inverse velocity multi-group cross section. @@ -5408,253 +5353,3 @@ class InverseVelocity(MGXS): else: raise ValueError('Unable to return the units of InverseVelocity' ' for xs_type other than "macro"') - - -class PromptNuFissionXS(MGXS): - r"""A prompt fission neutron production multi-group cross section. - - This class can be used for both OpenMC input generation and tally data - post-processing to compute spatially-homogenized and energy-integrated - multi-group cross sections for multi-group neutronics calculations. At a - minimum, one needs to set the :attr:`PromptNuFissionXS.energy_groups` and - :attr:`PromptNuFissionXS.domain` properties. Tallies for the flux and - appropriate reaction rates over the specified domain are generated - automatically via the :attr:`PromptNuFissionXS.tallies` property, which can - then be appended to a :class:`openmc.Tallies` instance. - - For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the - necessary data to compute multi-group cross sections from a - :class:`openmc.StatePoint` instance. The derived multi-group cross section - can then be obtained from the :attr:`PromptNuFissionXS.xs_tally` property. - - For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the - fission spectrum is calculated as: - - .. math:: - - \frac{\int_{r \in V} dr \int_{4\pi} d\Omega \int_{E_g}^{E_{g-1}} dE \; - \nu\sigma_f^p (r, E) \psi (r, E, \Omega)}{\int_{r \in V} dr \int_{4\pi} - d\Omega \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega)}. - - Parameters - ---------- - domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh - The domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - The domain type for spatial homogenization - groups : openmc.mgxs.EnergyGroups - The energy group structure for energy condensation - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - name : str, optional - Name of the multi-group cross section. Used as a label to identify - tallies in OpenMC 'tallies.xml' file. - num_polar : Integral, optional - Number of equi-width polar angle bins for angle discretization; - defaults to one bin - num_azimuthal : Integral, optional - Number of equi-width azimuthal angle bins for angle discretization; - defaults to one bin - - Attributes - ---------- - name : str, optional - Name of the multi-group cross section - rxn_type : str - Reaction type (e.g., 'total', 'nu-fission', etc.) - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - domain : Material or Cell or Universe or Mesh - Domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - Domain type for spatial homogenization - energy_groups : openmc.mgxs.EnergyGroups - Energy group structure for energy condensation - num_polar : Integral - Number of equi-width polar angle bins for angle discretization - num_azimuthal : Integral - Number of equi-width azimuthal angle bins for angle discretization - tally_trigger : openmc.Trigger - An (optional) tally precision trigger given to each tally used to - compute the cross section - scores : list of str - The scores in each tally used to compute the multi-group cross section - filters : list of openmc.Filter - The filters in each tally used to compute the multi-group cross section - tally_keys : list of str - The keys into the tallies dictionary for each tally used to compute - the multi-group cross section - estimator : {'tracklength', 'collision', 'analog'} - The tally estimator used to compute the multi-group cross section - tallies : collections.OrderedDict - OpenMC tallies needed to compute the multi-group cross section. The keys - are strings listed in the :attr:`PromptNuFissionXS.tally_keys` property - and values are instances of :class:`openmc.Tally`. - rxn_rate_tally : openmc.Tally - Derived tally for the reaction rate tally used in the numerator to - compute the multi-group cross section. This attribute is None - unless the multi-group cross section has been computed. - xs_tally : openmc.Tally - Derived tally for the multi-group cross section. This attribute - is None unless the multi-group cross section has been computed. - num_subdomains : int - The number of subdomains is unity for 'material', 'cell' and 'universe' - domain types. This is equal to the number of cell instances - for 'distribcell' domain types (it is equal to unity prior to loading - tally data from a statepoint file). - num_nuclides : int - The number of nuclides for which the multi-group cross section is - being tracked. This is unity if the by_nuclide attribute is False. - nuclides : Iterable of str or 'sum' - The optional user-specified nuclides for which to compute cross - sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides - are not specified by the user, all nuclides in the spatial domain - are included. This attribute is 'sum' if by_nuclide is false. - sparse : bool - Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format - for compressed data storage - loaded_sp : bool - Whether or not a statepoint file has been loaded with tally data - derived : bool - Whether or not the MGXS is merged from one or more other MGXS - hdf5_key : str - The key used to index multi-group cross sections in an HDF5 data store - - """ - - def __init__(self, domain=None, domain_type=None, groups=None, - by_nuclide=False, name='', num_polar=1, num_azimuthal=1): - super(PromptNuFissionXS, self).__init__(domain, domain_type, groups, - by_nuclide, name, num_polar, - num_azimuthal) - self._rxn_type = 'prompt-nu-fission' - self.nu = True - - -class PromptNuFissionMatrixXS(MatrixMGXS): - r"""A prompt fission neutron production matrix multi-group cross section. - - This class can be used for both OpenMC input generation and tally data - post-processing to compute spatially-homogenized and energy-integrated - multi-group cross sections for multi-group neutronics calculations. At a - minimum, one needs to set the :attr:`PromptNuFissionMatrixXS.energy_groups` - and :attr:`PromptNuFissionMatrixXS.domain` properties. Tallies for the flux - and appropriate reaction rates over the specified domain are generated - automatically via the :attr:`PromptNuFissionMatrixXS.tallies` property, - which can then be appended to a :class:`openmc.Tallies` instance. - - For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the - necessary data to compute multi-group cross sections from a - :class:`openmc.StatePoint` instance. The derived multi-group cross section - can then be obtained from the :attr:`PromptNuFissionMatrixXS.xs_tally` - property. - - For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the - fission spectrum is calculated as: - - .. math:: - - \langle \nu\sigma_{f,g'\rightarrow g} \phi \rangle &= \int_{r \in V} dr - \int_{4\pi} d\Omega' \int_{E_{g'}}^{E_{g'-1}} dE' \int_{E_g}^{E_{g-1}} dE - \; \chi(E) \nu\sigma_f^p (r, E') \psi(r, E', \Omega')\\ - \langle \phi \rangle &= \int_{r \in V} dr \int_{4\pi} d\Omega - \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega) \\ - \nu\sigma_{f,g'\rightarrow g} &= \frac{\langle \nu\sigma_{f,g'\rightarrow - g}^p \phi \rangle}{\langle \phi \rangle} - - Parameters - ---------- - domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh - The domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - The domain type for spatial homogenization - groups : openmc.mgxs.EnergyGroups - The energy group structure for energy condensation - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - name : str, optional - Name of the multi-group cross section. Used as a label to identify - tallies in OpenMC 'tallies.xml' file. - num_polar : Integral, optional - Number of equi-width polar angle bins for angle discretization; - defaults to one bin - num_azimuthal : Integral, optional - Number of equi-width azimuthal angle bins for angle discretization; - defaults to one bin - - Attributes - ---------- - name : str, optional - Name of the multi-group cross section - rxn_type : str - Reaction type (e.g., 'total', 'nu-fission', etc.) - by_nuclide : bool - If true, computes cross sections for each nuclide in domain - domain : Material or Cell or Universe or Mesh - Domain for spatial homogenization - domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'} - Domain type for spatial homogenization - energy_groups : openmc.mgxs.EnergyGroups - Energy group structure for energy condensation - num_polar : Integral - Number of equi-width polar angle bins for angle discretization - num_azimuthal : Integral - Number of equi-width azimuthal angle bins for angle discretization - tally_trigger : openmc.Trigger - An (optional) tally precision trigger given to each tally used to - compute the cross section - scores : list of str - The scores in each tally used to compute the multi-group cross section - filters : list of openmc.Filter - The filters in each tally used to compute the multi-group cross section - tally_keys : list of str - The keys into the tallies dictionary for each tally used to compute - the multi-group cross section - estimator : 'analog' - The tally estimator used to compute the multi-group cross section - tallies : collections.OrderedDict - OpenMC tallies needed to compute the multi-group cross section. The keys - are strings listed in the :attr:`PromptNuFissionXS.tally_keys` property - and values are instances of :class:`openmc.Tally`. - rxn_rate_tally : openmc.Tally - Derived tally for the reaction rate tally used in the numerator to - compute the multi-group cross section. This attribute is None - unless the multi-group cross section has been computed. - xs_tally : openmc.Tally - Derived tally for the multi-group cross section. This attribute - is None unless the multi-group cross section has been computed. - num_subdomains : int - The number of subdomains is unity for 'material', 'cell' and 'universe' - domain types. This is equal to the number of cell instances - for 'distribcell' domain types (it is equal to unity prior to loading - tally data from a statepoint file). - num_nuclides : int - The number of nuclides for which the multi-group cross section is - being tracked. This is unity if the by_nuclide attribute is False. - nuclides : Iterable of str or 'sum' - The optional user-specified nuclides for which to compute cross - sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides - are not specified by the user, all nuclides in the spatial domain - are included. This attribute is 'sum' if by_nuclide is false. - sparse : bool - Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format - for compressed data storage - loaded_sp : bool - Whether or not a statepoint file has been loaded with tally data - derived : bool - Whether or not the MGXS is merged from one or more other MGXS - hdf5_key : str - The key used to index multi-group cross sections in an HDF5 data store - - """ - - def __init__(self, domain=None, domain_type=None, groups=None, - by_nuclide=False, name='', num_polar=1, num_azimuthal=1): - super(PromptNuFissionMatrixXS, self).__init__(domain, domain_type, - groups, by_nuclide, name, - num_polar, num_azimuthal) - self._rxn_type = 'prompt-nu-fission' - self._hdf5_key = 'prompt-nu-fission matrix' - self._estimator = 'analog' - self._valid_estimators = ['analog'] - self.nu = True From 58517587aa536a82c851cdcaa01f80991106f2b5 Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sun, 26 Feb 2017 06:26:24 -0500 Subject: [PATCH 09/12] I again forgot about updating the mgxs_library module for the removal of the prompt types --- openmc/mgxs_library.py | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/openmc/mgxs_library.py b/openmc/mgxs_library.py index d1a46df09..79630ca16 100644 --- a/openmc/mgxs_library.py +++ b/openmc/mgxs_library.py @@ -1125,13 +1125,13 @@ class XSdata(object): subdomain=None): """Sets the prompt-nu-fission cross section. - This method allows for an openmc.mgxs.PromptNuFissionXS or - openmc.mgxs.PromptNuFissionMatrixXS to be used to set the - prompt-nu-fission cross section for this XSdata object. + This method allows for an openmc.mgxs.FissionXS or + openmc.mgxs.NuFissionMatrixXS to be used to set the prompt-nu-fission + cross section for this XSdata object. Parameters ---------- - prompt_nu_fission: openmc.mgxs.PromptNuFissionXS or openmc.mgxs.PromptNuFissionMatrixXS + prompt_nu_fission: openmc.mgxs.FissionXS or openmc.mgxs.NuFissionMatrixXS MGXS Object containing the prompt-nu-fission cross section for the domain of interest. temperature : float @@ -1155,8 +1155,8 @@ class XSdata(object): """ check_type('prompt_nu_fission', prompt_nu_fission, - (openmc.mgxs.PromptNuFissionXS, - openmc.mgxs.PromptNuFissionMatrixXS)) + (openmc.mgxs.FissionXS, openmc.mgxs.NuFissionMatrixXS)) + check_value('prompt', prompt_nu_fission.prompt, [True]) check_value('energy_groups', prompt_nu_fission.energy_groups, [self.energy_groups]) check_value('domain_type', prompt_nu_fission.domain_type, @@ -1308,12 +1308,12 @@ class XSdata(object): def set_chi_prompt_mgxs(self, chi_prompt, temperature=294., nuclide='total', xs_type='macro', subdomain=None): - """This method allows for an openmc.mgxs.ChiPrompt - to be used to set chi-prompt for this XSdata object. + """This method allows for an openmc.mgxs.Chi to be used to set + chi-prompt for this XSdata object. Parameters ---------- - chi_prompt: openmc.mgxs.ChiPrompt + chi_prompt: openmc.mgxs.Chi MGXS Object containing chi-prompt for the domain of interest. temperature : float Temperature (in units of Kelvin) of the provided dataset. Defaults @@ -1335,7 +1335,8 @@ class XSdata(object): """ - check_type('chi_prompt', chi_prompt, openmc.mgxs.ChiPrompt) + check_type('chi_prompt', chi_prompt, openmc.mgxs.Chi) + check_value('prompt', chi_prompt.prompt, [True]) check_value('energy_groups', chi_prompt.energy_groups, [self.energy_groups]) check_value('domain_type', chi_prompt.domain_type, From 6ce91c2b96bd9ce58d96319b2c6fd8d00b7f95c2 Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sun, 26 Feb 2017 14:51:19 -0500 Subject: [PATCH 10/12] moved nu and prompt attributes to only the MGXS children who need them and added deepcopy capabilities for the nu/prompt data. --- .../pythonapi/examples/mdgxs-part-i.ipynb | 2 +- openmc/mgxs/mgxs.py | 113 +++++++++++++----- 2 files changed, 84 insertions(+), 31 deletions(-) diff --git a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb index 05366758a..6d84ca3c2 100644 --- a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb +++ b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb @@ -386,7 +386,7 @@ "\n", "In this case, let's create the multi-group chi-prompt, chi-delayed, and prompt-nu-fission cross sections with our 100-energy-group structure and multi-group delayed-nu-fission and beta cross sections with our 100-energy-group and 6-delayed-group structures. \n", "\n", - "The prompt chi and nu-fission data can actually be gathered using the `Chi` and `FissionXS` classes, respectively, but passing in a value of `True` for the optional `prompt` parameter upon initialization." + "The prompt chi and nu-fission data can actually be gathered using the `Chi` and `FissionXS` classes, respectively, by passing in a value of `True` for the optional `prompt` parameter upon initialization." ] }, { diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 3ff17b35f..92c449f70 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -149,10 +149,6 @@ class MGXS(object): Name of the multi-group cross section rxn_type : str Reaction type (e.g., 'total', 'nu-fission', etc.) - nu : bool - If True, the cross section data will include neutron multiplication - prompt : bool - If true, computes cross sections which only includes prompt neutrons by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -234,8 +230,6 @@ class MGXS(object): self._derived = False self._hdf5_key = None self._valid_estimators = ESTIMATOR_TYPES - self._nu = False - self._prompt = False self.name = name self.by_nuclide = by_nuclide @@ -414,14 +408,6 @@ class MGXS(object): def rxn_type(self): return self._rxn_type - @property - def nu(self): - return self._nu - - @property - def prompt(self): - return self._prompt - @property def by_nuclide(self): return self._by_nuclide @@ -587,16 +573,6 @@ class MGXS(object): cv.check_type('name', name, string_types) self._name = name - @nu.setter - def nu(self, nu): - cv.check_type('nu', nu, bool) - self._nu = nu - - @prompt.setter - def prompt(self, prompt): - cv.check_type('prompt', prompt, bool) - self._prompt = prompt - @by_nuclide.setter def by_nuclide(self, by_nuclide): cv.check_type('by_nuclide', by_nuclide, bool) @@ -2026,8 +2002,6 @@ class MatrixMGXS(MGXS): Name of the multi-group cross section rxn_type : str Reaction type (e.g., 'total', 'nu-fission', etc.) - nu : bool - If True, the cross section data will include neutron multiplication by_nuclide : bool If true, computes cross sections for each nuclide in domain domain : Material or Cell or Universe or Mesh @@ -2732,6 +2706,11 @@ class TransportXS(MGXS): self._valid_estimators = ['analog'] self.nu = nu + def __deepcopy__(self, memo): + clone = super(TransportXS, self).__deepcopy__(memo) + clone._nu = self.nu + return clone + @property def scores(self): if not self.nu: @@ -2770,6 +2749,15 @@ class TransportXS(MGXS): return self._rxn_rate_tally + @property + def nu(self): + return self._nu + + @nu.setter + def nu(self, nu): + cv.check_type('nu', nu, bool) + self._nu = nu + class AbsorptionXS(MGXS): r"""An absorption multi-group cross section. @@ -3184,6 +3172,32 @@ class FissionXS(MGXS): else: self._rxn_type = 'prompt-nu-fission' self.nu = True + self.prompt = prompt + + def __deepcopy__(self, memo): + clone = super(FissionXS, self).__deepcopy__(memo) + clone._nu = self.nu + clone._prompt = self.prompt + return clone + + @property + def nu(self): + return self._nu + + @property + def prompt(self): + return self._prompt + + @nu.setter + def nu(self, nu): + cv.check_type('nu', nu, bool) + self._nu = nu + + @prompt.setter + def prompt(self, prompt): + cv.check_type('prompt', prompt, bool) + self._prompt = prompt + class KappaFissionXS(MGXS): r"""A recoverable fission energy production rate multi-group cross section. @@ -3449,9 +3463,22 @@ class ScatterXS(MGXS): # to reflect this self._estimator = 'analog' self._valid_estimators = ['analog'] - self.nu = nu + def __deepcopy__(self, memo): + clone = super(ScatterXS, self).__deepcopy__(memo) + clone._nu = self.nu + return clone + + @property + def nu(self): + return self._nu + + @nu.setter + def nu(self, nu): + cv.check_type('nu', nu, bool) + self._nu = nu + class ScatterMatrixXS(MatrixMGXS): r"""A scattering matrix multi-group cross section with the cosine of the @@ -3627,6 +3654,7 @@ class ScatterMatrixXS(MatrixMGXS): clone._scatter_format = self.scatter_format clone._legendre_order = self.legendre_order clone._histogram_bins = self.histogram_bins + clone._nu = self.nu return clone @property @@ -3645,6 +3673,10 @@ class ScatterMatrixXS(MatrixMGXS): else: return (1, 2) + @property + def nu(self): + return self._nu + @property def correction(self): return self._correction @@ -3721,6 +3753,11 @@ class ScatterMatrixXS(MatrixMGXS): return self._rxn_rate_tally + @nu.setter + def nu(self, nu): + cv.check_type('nu', nu, bool) + self._nu = nu + @correction.setter def correction(self, correction): cv.check_value('correction', correction, ('P0', None)) @@ -4455,7 +4492,6 @@ class MultiplicityMatrixXS(MatrixMGXS): self._rxn_type = 'multiplicity matrix' self._estimator = 'analog' self._valid_estimators = ['analog'] - self.nu = True @property def scores(self): @@ -4633,7 +4669,11 @@ class NuFissionMatrixXS(MatrixMGXS): self._hdf5_key = 'prompt-nu-fission matrix' self._estimator = 'analog' self._valid_estimators = ['analog'] - self.nu = True + + def __deepcopy__(self, memo): + clone = super(NuFissionMatrixXS, self).__deepcopy__(memo) + clone._prompt = self.prompt + return clone class Chi(MGXS): @@ -4773,9 +4813,17 @@ class Chi(MGXS): self._rxn_type = 'chi-prompt' self._estimator = 'analog' self._valid_estimators = ['analog'] - self.nu = True self.prompt = prompt + def __deepcopy__(self, memo): + clone = super(Chi, self).__deepcopy__(memo) + clone._prompt = self.prompt + return clone + + @property + def prompt(self): + return self._prompt + @property def _dont_squeeze(self): """Create a tuple of axes which should not be removed during the get_xs @@ -4832,6 +4880,11 @@ class Chi(MGXS): return self._xs_tally + @prompt.setter + def prompt(self, prompt): + cv.check_type('prompt', prompt, bool) + self._prompt = prompt + def get_homogenized_mgxs(self, other_mgxs): """Construct a homogenized mgxs with other MGXS objects. From f1c17ad150ac4ca524995fe9e41e0c0451e7e9e2 Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sun, 26 Feb 2017 15:00:57 -0500 Subject: [PATCH 11/12] missing setting of prompt in NuFissionMatrixXS --- openmc/mgxs/mgxs.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py index 92c449f70..10e10738e 100644 --- a/openmc/mgxs/mgxs.py +++ b/openmc/mgxs/mgxs.py @@ -4669,6 +4669,16 @@ class NuFissionMatrixXS(MatrixMGXS): self._hdf5_key = 'prompt-nu-fission matrix' self._estimator = 'analog' self._valid_estimators = ['analog'] + self.prompt = prompt + + @property + def prompt(self): + return self._prompt + + @prompt.setter + def prompt(self, prompt): + cv.check_type('prompt', prompt, bool) + self._prompt = prompt def __deepcopy__(self, memo): clone = super(NuFissionMatrixXS, self).__deepcopy__(memo) From c82cf1bea4f8f2751bede004b97c1d9acd5ef065 Mon Sep 17 00:00:00 2001 From: Adam Nelson Date: Sun, 26 Feb 2017 15:15:02 -0500 Subject: [PATCH 12/12] I should quit while im ahead. this is three times in a row i missed this file --- openmc/mgxs_library.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/openmc/mgxs_library.py b/openmc/mgxs_library.py index 79630ca16..1803562b7 100644 --- a/openmc/mgxs_library.py +++ b/openmc/mgxs_library.py @@ -1104,7 +1104,8 @@ class XSdata(object): check_type('nu_fission', nu_fission, (openmc.mgxs.FissionXS, openmc.mgxs.NuFissionMatrixXS)) - check_value('nu', nu_fission.nu, [True]) + if isinstance(nu_fission, openmc.mgxs.FissionXS): + check_value('nu', nu_fission.nu, [True]) check_value('energy_groups', nu_fission.energy_groups, [self.energy_groups]) check_value('domain_type', nu_fission.domain_type, @@ -1602,7 +1603,6 @@ class XSdata(object): check_type('nuscatter', nuscatter, (openmc.mgxs.ScatterMatrixXS, openmc.mgxs.MultiplicityMatrixXS)) - check_value('nu', nuscatter.nu, [True]) check_value('energy_groups', nuscatter.energy_groups, [self.energy_groups]) check_value('domain_type', nuscatter.domain_type,