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Forgot universe_id=0 for root in example nb (fixed), resolved comments from @paulromano, and made sure the example problem worked still (it did, but I simplified it a bit since data doesnt need to be numpy arrays anymore.
This commit is contained in:
Adam Nelson
parent
4bec584ddb
commit
071e8d3e30
6 changed files with 172 additions and 133 deletions
File diff suppressed because one or more lines are too long
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@ -22,9 +22,9 @@ materials.
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.. _XML: http://www.w3.org/XML/
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------------------------------------------------
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--------------------------------------
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MGXS Library Specification -- mgxs.xml
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------------------------------------------------
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--------------------------------------
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The multi-group library meta-data is contained within the groups_,
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group_structure_, and inverse_velocities_ elements.
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@ -33,7 +33,7 @@ The actual multi-group data itself is contained within the xsdata_ element.
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.. _groups:
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``<groups>`` Element
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----------------------------------
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--------------------
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The ``<groups>`` element has no attributes and simply provides the number of
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energy groups contained within the library.
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@ -1,4 +1,3 @@
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import numpy as np
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import openmc
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import openmc.mgxs
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@ -12,7 +11,7 @@ inactive = 10
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particles = 1000
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###############################################################################
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# Exporting to OpenMC mg_cross_sections.xml file
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# Exporting to OpenMC mgxs.xml file
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###############################################################################
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# Instantiate the energy group data
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@ -22,45 +21,43 @@ groups = openmc.mgxs.EnergyGroups(group_edges=[1E-11, 0.0635E-6, 10.0E-6,
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# Instantiate the 7-group (C5G7) cross section data
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uo2_xsdata = openmc.XSdata('UO2.300K', groups)
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uo2_xsdata.order = 0
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uo2_xsdata.total = np.array([0.1779492, 0.3298048, 0.4803882, 0.5543674,
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0.3118013, 0.3951678, 0.5644058])
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uo2_xsdata.absorption = np.array([8.0248E-03, 3.7174E-03, 2.6769E-02, 9.6236E-02,
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3.0020E-02, 1.1126E-01, 2.8278E-01])
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scatter = [[[0.1275370, 0.0423780, 0.0000094, 0.0000000, 0.0000000, 0.0000000, 0.0000000],
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[0.0000000, 0.3244560, 0.0016314, 0.0000000, 0.0000000, 0.0000000, 0.0000000],
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[0.0000000, 0.0000000, 0.4509400, 0.0026792, 0.0000000, 0.0000000, 0.0000000],
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[0.0000000, 0.0000000, 0.0000000, 0.4525650, 0.0055664, 0.0000000, 0.0000000],
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[0.0000000, 0.0000000, 0.0000000, 0.0001253, 0.2714010, 0.0102550, 0.0000000],
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[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0012968, 0.2658020, 0.0168090],
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[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0085458, 0.2730800]]]
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uo2_xsdata.scatter = np.array(scatter[:][:])
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uo2_xsdata.fission = np.array([7.21206E-03, 8.19301E-04, 6.45320E-03,
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1.85648E-02, 1.78084E-02, 8.30348E-02,
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2.16004E-01])
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uo2_xsdata.nu_fission = np.array([2.005998E-02, 2.027303E-03, 1.570599E-02,
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4.518301E-02, 4.334208E-02, 2.020901E-01,
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5.257105E-01])
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uo2_xsdata.chi = np.array([5.8791E-01, 4.1176E-01, 3.3906E-04, 1.1761E-07,
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0.0000E+00, 0.0000E+00, 0.0000E+00])
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uo2_xsdata.total = [0.1779492, 0.3298048, 0.4803882, 0.5543674,
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0.3118013, 0.3951678, 0.5644058]
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uo2_xsdata.absorption = [8.0248E-03, 3.7174E-03, 2.6769E-02, 9.6236E-02,
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3.0020E-02, 1.1126E-01, 2.8278E-01]
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uo2_xsdata.scatter = [[[0.1275370, 0.0423780, 0.0000094, 0.0000000, 0.0000000, 0.0000000, 0.0000000],
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[0.0000000, 0.3244560, 0.0016314, 0.0000000, 0.0000000, 0.0000000, 0.0000000],
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[0.0000000, 0.0000000, 0.4509400, 0.0026792, 0.0000000, 0.0000000, 0.0000000],
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[0.0000000, 0.0000000, 0.0000000, 0.4525650, 0.0055664, 0.0000000, 0.0000000],
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[0.0000000, 0.0000000, 0.0000000, 0.0001253, 0.2714010, 0.0102550, 0.0000000],
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[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0012968, 0.2658020, 0.0168090],
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[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0085458, 0.2730800]]]
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uo2_xsdata.fission = [7.21206E-03, 8.19301E-04, 6.45320E-03,
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1.85648E-02, 1.78084E-02, 8.30348E-02,
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2.16004E-01]
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uo2_xsdata.nu_fission = [2.005998E-02, 2.027303E-03, 1.570599E-02,
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4.518301E-02, 4.334208E-02, 2.020901E-01,
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5.257105E-01]
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uo2_xsdata.chi = [5.8791E-01, 4.1176E-01, 3.3906E-04, 1.1761E-07,
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0.0000E+00, 0.0000E+00, 0.0000E+00]
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h2o_xsdata = openmc.XSdata('LWTR.300K', groups)
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h2o_xsdata.order = 0
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h2o_xsdata.total = np.array([0.15920605, 0.412969593, 0.59030986, 0.58435,
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0.718, 1.2544497, 2.650379])
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h2o_xsdata.absorption = np.array([6.0105E-04, 1.5793E-05, 3.3716E-04,
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1.9406E-03, 5.7416E-03, 1.5001E-02,
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3.7239E-02])
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scatter = [[[0.0444777, 0.1134000, 0.0007235, 0.0000037, 0.0000001, 0.0000000, 0.0000000],
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[0.0000000, 0.2823340, 0.1299400, 0.0006234, 0.0000480, 0.0000074, 0.0000010],
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[0.0000000, 0.0000000, 0.3452560, 0.2245700, 0.0169990, 0.0026443, 0.0005034],
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[0.0000000, 0.0000000, 0.0000000, 0.0910284, 0.4155100, 0.0637320, 0.0121390],
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[0.0000000, 0.0000000, 0.0000000, 0.0000714, 0.1391380, 0.5118200, 0.0612290],
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[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0022157, 0.6999130, 0.5373200],
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[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.1324400, 2.4807000]]]
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h2o_xsdata.scatter = np.array(scatter)
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h2o_xsdata.total = [0.15920605, 0.412969593, 0.59030986, 0.58435,
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0.718, 1.2544497, 2.650379]
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h2o_xsdata.absorption = [6.0105E-04, 1.5793E-05, 3.3716E-04,
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1.9406E-03, 5.7416E-03, 1.5001E-02,
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3.7239E-02]
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h2o_xsdata.scatter = [[[0.0444777, 0.1134000, 0.0007235, 0.0000037, 0.0000001, 0.0000000, 0.0000000],
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[0.0000000, 0.2823340, 0.1299400, 0.0006234, 0.0000480, 0.0000074, 0.0000010],
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[0.0000000, 0.0000000, 0.3452560, 0.2245700, 0.0169990, 0.0026443, 0.0005034],
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[0.0000000, 0.0000000, 0.0000000, 0.0910284, 0.4155100, 0.0637320, 0.0121390],
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[0.0000000, 0.0000000, 0.0000000, 0.0000714, 0.1391380, 0.5118200, 0.0612290],
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[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0022157, 0.6999130, 0.5373200],
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[0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.0000000, 0.1324400, 2.4807000]]]
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mg_cross_sections_file = openmc.MGXSLibrary(groups)
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mg_cross_sections_file.add_xsdatas([uo2_xsdata,h2o_xsdata])
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mg_cross_sections_file.add_xsdatas([uo2_xsdata, h2o_xsdata])
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mg_cross_sections_file.export_to_xml()
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@ -134,7 +131,7 @@ geometry.export_to_xml()
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# Instantiate a Settings object, set all runtime parameters, and export to XML
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settings_file = openmc.Settings()
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settings_file.energy_mode = "multi-group"
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settings_file.cross_sections = "./mg_cross_sections.xml"
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settings_file.cross_sections = "./mgxs.xml"
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settings_file.batches = batches
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settings_file.inactive = inactive
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settings_file.particles = particles
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@ -247,8 +247,7 @@ class Library(object):
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@domain_type.setter
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def domain_type(self, domain_type):
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cv.check_value('domain type', domain_type,
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tuple(openmc.mgxs.DOMAIN_TYPES))
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cv.check_value('domain type', domain_type, openmc.mgxs.DOMAIN_TYPES)
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self._domain_type = domain_type
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@domains.setter
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@ -722,8 +721,8 @@ class Library(object):
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# Load and return pickled Library object
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return pickle.load(open(full_filename, 'rb'))
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def get_xsdata(self, domain, domain_name, nuclide='total', xs_type='macro',
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xs_id='1m', order=-1):
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def get_xsdata(self, domain, xsdata_name, nuclide='total', xs_type='macro',
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xs_id='1m', order=None):
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"""Generates an openmc.XSdata object describing a multi-group cross section
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data set for eventual combination in to an openmc.MGXSLibrary object
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(i.e., the library).
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@ -732,7 +731,7 @@ class Library(object):
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----------
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domain : openmc.Material or openmc.Cell or openmc.Universe
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The domain for spatial homogenization
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domain_name : str
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xsdata_name : str
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Name to apply to the "xsdata" entry produced by this method
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nuclide : str
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A nuclide name string (e.g., 'U-235'). Defaults to 'total' to
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@ -743,7 +742,7 @@ class Library(object):
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nuclide this will be set to 'macro' regardless.
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xs_ids : str
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Cross section set identifier. Defaults to '1m'.
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order : Scattering order for this dataset entry. Default is -1,
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order : Scattering order for this dataset entry. Default is None,
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which will force the XSdata object to use whatever the maximum
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order available.
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@ -766,11 +765,11 @@ class Library(object):
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cv.check_type('domain', domain, (openmc.Material, openmc.Cell,
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openmc.Cell))
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cv.check_type('domain_name', domain_name, basestring)
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cv.check_type('xsdata_name', xsdata_name, basestring)
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cv.check_type('nuclide', nuclide, basestring)
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cv.check_value('xs_type', xs_type, ['macro', 'micro'])
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cv.check_type('xs_id', xs_id, basestring)
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cv.check_type('order', order, Integral)
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cv.check_type('order', order, (type(None), Integral))
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cv.check_greater_than('order', order, -1, equality=True)
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# Make sure statepoint has been loaded
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@ -784,12 +783,18 @@ class Library(object):
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xs_type = 'macro'
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# Build & add metadata to XSdata object
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name = domain_name
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name = xsdata_name
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if nuclide is not 'total':
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name += '_' + nuclide
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name += '.' + xs_id
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xsdata = openmc.XSdata(name, self.energy_groups)
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xsdata.order = order
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if order is 0:
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xsdata.order = order
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else:
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msg = 'Generating anisotropic scattering from openmc.Library' \
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'objects has not yet been implemented.'
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raise NotImplementedError(msg)
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if nuclide is not 'total':
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xsdata.zaid = self._nuclides[nuclide][0]
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xsdata.awr = self._nuclides[nuclide][1]
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@ -852,7 +857,7 @@ class Library(object):
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return xsdata
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def create_mg_library(self, xs_type='macro', domain_names=None,
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def create_mg_library(self, xs_type='macro', xsdata_names=None,
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xs_ids=None):
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"""Creates an openmc.MGXSLibrary object to contain the MGXS data for the
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Multi-Group mode of OpenMC.
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@ -863,7 +868,7 @@ class Library(object):
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Provide the macro or micro cross section in units of cm^-1 or
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barns. Defaults to 'macro'. If the Library object is not tallied by
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nuclide this will be set to 'macro' regardless.
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domain_names : Iterable of str
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xsdata_names : Iterable of str
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List of names to apply to the "xsdata" entries in the
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resultant mgxs data file. Defaults to 'set1', 'set2', ...
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xs_ids : str or Iterable of str
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@ -894,8 +899,8 @@ class Library(object):
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self.check_library_for_openmc_mgxs()
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cv.check_value('xs_type', xs_type, ['macro', 'micro'])
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if domain_names is not None:
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cv.check_iterable_type('domain_names', domain_names, basestring)
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if xsdata_names is not None:
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cv.check_iterable_type('xsdata_names', xsdata_names, basestring)
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if xs_ids is not None:
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if isinstance(xs_ids, basestring):
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# If we only have a string lets convert it now to a list
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@ -927,14 +932,14 @@ class Library(object):
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nuclides = ['total']
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for nuclide in nuclides:
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# Build & add metadata to XSdata object
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if domain_names is None:
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name = 'set' + str(i + 1)
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if xsdata_names is None:
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xsdata_name = 'set' + str(i + 1)
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else:
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name = domain_names[i]
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xsdata_name = xsdata_names[i]
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if nuclide is not 'total':
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name += '_' + nuclide
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xsdata_name += '_' + nuclide
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xsdata = self.get_xsdata(domain, name, nuclide=nuclide,
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xsdata = self.get_xsdata(domain, xsdata_name, nuclide=nuclide,
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xs_type=xs_type, xs_id=xs_ids[i],
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order=order)
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@ -952,14 +957,17 @@ class Library(object):
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The rules to check include:
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- Either total or transport should be present.
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- Both can be available if one wants, but we should
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use whatever corresponds to Library.correction (if P0: transport)
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- Absorption and total (or transport) are required.
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- A nu-fission cross section and chi values are not required as a
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fixed source problem could be the target.
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- Fission and kappa-fission are not required as they are only
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needed to support tallies the user may wish to request.
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- A nu-scatter matrix is required.
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- Having both nu-scatter (of any order) and scatter
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(at least isotropic) matrices is preferred
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- If only nu-scatter, need total (not transport), to
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@ -283,7 +283,7 @@ class MGXS(object):
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@domain_type.setter
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def domain_type(self, domain_type):
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cv.check_value('domain type', domain_type, tuple(DOMAIN_TYPES))
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cv.check_value('domain type', domain_type, DOMAIN_TYPES)
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self._domain_type = domain_type
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@energy_groups.setter
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@ -346,6 +346,16 @@ class XSdata(object):
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self.energy_groups.num_groups,
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self.energy_groups.num_groups)
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@property
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def pn_matrix_shape(self):
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if self.representation is 'isotropic':
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return (self.num_orders, self.energy_groups.num_groups,
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self.energy_groups.num_groups)
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elif self.representation is 'angle':
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return (self.num_polar, self.num_azimuthal, self.num_orders,
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self.energy_groups.num_groups,
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self.energy_groups.num_groups)
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@name.setter
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def name(self, name):
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check_type('name for XSdata', name, basestring)
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@ -449,38 +459,49 @@ class XSdata(object):
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@total.setter
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def total(self, total):
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check_type('total', total, np.ndarray, expected_iter_type=Real)
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check_value('total shape', total.shape, self.vector_shape)
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check_type('total', total, Iterable, expected_iter_type=Real)
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# Convert to a numpy array so we can easily get the shape for
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# checking
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nptotal = np.array(total)
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check_value('total shape', nptotal.shape, [self.vector_shape])
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self._total = total
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self._total = nptotal
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@absorption.setter
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def absorption(self, absorption):
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check_type('absorption', absorption, np.ndarray,
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expected_iter_type=Real)
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check_value('absorption shape', absorption.shape, self.vector_shape)
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check_type('absorption', absorption, Iterable, expected_iter_type=Real)
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# Convert to a numpy array so we can easily get the shape for
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# checking
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npabsorption = np.array(absorption)
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check_value('absorption shape', npabsorption.shape,
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[self.vector_shape])
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self._absorption = absorption
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self._absorption = npabsorption
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@fission.setter
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def fission(self, fission):
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check_type('fission', fission, np.ndarray,
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expected_iter_type=Real)
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check_value('fission shape', fission.shape, self.vector_shape)
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check_type('fission', fission, Iterable, expected_iter_type=Real)
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# Convert to a numpy array so we can easily get the shape for
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# checking
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npfission = np.array(fission)
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check_value('fission shape', npfission.shape, [self.vector_shape])
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self._fission = fission
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self._fission = npfission
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if np.sum(self._fission) > 0.0:
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self._fissionable = True
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@kappa_fission.setter
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def kappa_fission(self, kappa_fission):
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check_type('kappa_fission', kappa_fission, np.ndarray,
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check_type('kappa_fission', kappa_fission, Iterable,
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expected_iter_type=Real)
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check_value('kappa fission shape', kappa_fission.shape,
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self.vector_shape)
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# Convert to a numpy array so we can easily get the shape for
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# checking
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npkappa_fission = np.array(kappa_fission)
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check_value('kappa fission shape', npkappa_fission.shape,
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[self.vector_shape])
|
||||
|
||||
self._kappa_fission = kappa_fission
|
||||
self._kappa_fission = npkappa_fission
|
||||
|
||||
if np.sum(self._kappa_fission) > 0.0:
|
||||
self._fissionable = True
|
||||
|
|
@ -493,30 +514,39 @@ class XSdata(object):
|
|||
'matrix'
|
||||
raise ValueError(msg)
|
||||
|
||||
check_type('chi', chi, np.ndarray, expected_iter_type=Real)
|
||||
check_value('chi shape', chi.shape, self.vector_shape)
|
||||
check_type('chi', chi, Iterable, expected_iter_type=Real)
|
||||
# Convert to a numpy array so we can easily get the shape for
|
||||
# checking
|
||||
npchi = np.array(chi)
|
||||
check_value('chi shape', npchi.shape, [self.vector_shape])
|
||||
|
||||
self._chi = chi
|
||||
self._chi = npchi
|
||||
|
||||
if self._use_chi is not None:
|
||||
self._use_chi = True
|
||||
|
||||
@scatter.setter
|
||||
def scatter(self, scatter):
|
||||
check_type('scatter', scatter, np.ndarray, expected_iter_type=Real,
|
||||
max_depth=len(scatter.shape))
|
||||
check_value('scatter shape', scatter.shape, self.pn_matrix_shape)
|
||||
# Convert to a numpy array so we can easily get the shape for
|
||||
# checking
|
||||
npscatter = np.array(scatter)
|
||||
check_iterable_type('scatter', npscatter, Real,
|
||||
max_depth=len(npscatter.shape))
|
||||
check_value('scatter shape', npscatter.shape, [self.pn_matrix_shape])
|
||||
|
||||
self._scatter = scatter
|
||||
self._scatter = npscatter
|
||||
|
||||
@multiplicity.setter
|
||||
def multiplicity(self, multiplicity):
|
||||
check_type('multiplicity', multiplicity, np.ndarray,
|
||||
expected_iter_type=Real, max_depth=len(multiplicity.shape))
|
||||
check_value('multiplicity shape', multiplicity.shape,
|
||||
self.matrix_shape)
|
||||
# Convert to a numpy array so we can easily get the shape for
|
||||
# checking
|
||||
npmultiplicity = np.array(multiplicity)
|
||||
check_iterable_type('multiplicity', npmultiplicity, Real,
|
||||
max_depth=len(npmultiplicity.shape))
|
||||
check_value('multiplicity shape', npmultiplicity.shape,
|
||||
[self.matrix_shape])
|
||||
|
||||
self._multiplicity = multiplicity
|
||||
self._multiplicity = npmultiplicity
|
||||
|
||||
@nu_fission.setter
|
||||
def nu_fission(self, nu_fission):
|
||||
|
|
@ -530,27 +560,31 @@ class XSdata(object):
|
|||
# chi already has been set. If not, we just check that this is OK
|
||||
# and set the use_chi flag accordingly
|
||||
|
||||
check_type('nu_fission', nu_fission, np.ndarray,
|
||||
expected_iter_type=Real, max_depth=len(nu_fission.shape))
|
||||
# Convert to a numpy array so we can easily get the shape for
|
||||
# checking
|
||||
npnu_fission = np.array(nu_fission)
|
||||
|
||||
check_iterable_type('nu_fission', npnu_fission, Real,
|
||||
max_depth=len(npnu_fission.shape))
|
||||
|
||||
if self._use_chi is not None:
|
||||
if self._use_chi:
|
||||
check_value('nu_fission shape', nu_fission.shape,
|
||||
self.vector_shape)
|
||||
check_value('nu_fission shape', npnu_fission.shape,
|
||||
[self.vector_shape])
|
||||
else:
|
||||
check_value('nu_fission shape', nu_fission.shape,
|
||||
self.matrix_shape)
|
||||
check_value('nu_fission shape', npnu_fission.shape,
|
||||
[self.matrix_shape])
|
||||
else:
|
||||
check_value('nu_fission shape', nu_fission.shape,
|
||||
(self.vector_shape, self.matrix_shape))
|
||||
check_value('nu_fission shape', npnu_fission.shape,
|
||||
[self.vector_shape, self.matrix_shape])
|
||||
# Find out if we have a nu-fission matrix or vector
|
||||
# and set a flag to allow other methods to check this later.
|
||||
if nu_fission.shape == self.vector_shape:
|
||||
if npnu_fission.shape == self.vector_shape:
|
||||
self._use_chi = True
|
||||
else:
|
||||
self._use_chi = False
|
||||
|
||||
self._nu_fission = nu_fission
|
||||
self._nu_fission = npnu_fission
|
||||
if np.sum(self._nu_fission) > 0.0:
|
||||
self._fissionable = True
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue