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Exposed tabular_legendre to the openmc.Library interface to the MGXS Data library. Also ever-so slightly sped up the tabular scattering runtime

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This commit is contained in:
Adam Nelson 2016-06-05 15:09:32 -04:00
parent 908e5680a9
commit f747bb4fbd
3 changed files with 75 additions and 16 deletions
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68
openmc/mgxs/library.py
View file

@ -753,7 +753,8 @@ class Library(object):
return pickle.load(open(full_filename, 'rb'))
def get_xsdata(self, domain, xsdata_name, nuclide='total', xs_type='macro',
xs_id='1m', order=None):
xs_id='1m', order=None, tabular_legendre=None,
tabular_points=33):
"""Generates an openmc.XSdata object describing a multi-group cross section
data set for eventual combination in to an openmc.MGXSLibrary object
(i.e., the library).
@ -773,9 +774,23 @@ class Library(object):
nuclide this will be set to 'macro' regardless.
xs_ids : str
Cross section set identifier. Defaults to '1m'.
order : Scattering order for this data entry. Default is None,
order : int
Scattering order for this data entry. Default is None,
which will set the XSdata object to use the order of the
Library.
tabular_legendre : {None, bool}
Flag to denote whether or not the Legendre expansion of the
scattering angular distribution is to be converted to a tabular
representation by OpenMC. A value of `True` means that it is to be
converted while a value of 'False' means that it will not be.
Defaults to `None` which leaves the default behavior of OpenMC in
place (the distribution is not converted to a tabular
representation).
tabular_points : {int}
This parameter is not used unless the `tabular_legendre` is set to
`True`. In this case, this parameter sets the number of
equally-spaced points in the domain of [-1,1] to be used in
building the tabular distribution. Default is `33`.
Returns
-------
@ -804,6 +819,10 @@ class Library(object):
if order is not None:
cv.check_greater_than('order', order, 0, equality=True)
cv.check_less_than('order', order, 10, equality=True)
cv.check_type('tabular_legendre', tabular_legendre,
(type(None), bool))
if tabular_points is not None:
cv.check_greater_than('tabular_points', tabular_points, 1)
# Make sure statepoint has been loaded
if self._sp_filename is None:
@ -830,6 +849,11 @@ class Library(object):
# the provided order or the Library's order.
xsdata.order = min(order, self.legendre_order)
# Set the tabular_legendre option if needed
if tabular_legendre is not None:
xsdata.tabular_legendre = {'enable': tabular_legendre,
'num_points': tabular_points}
if nuclide is not 'total':
xsdata.zaid = self._nuclides[nuclide][0]
xsdata.awr = self._nuclides[nuclide][1]
@ -906,7 +930,8 @@ class Library(object):
return xsdata
def create_mg_library(self, xs_type='macro', xsdata_names=None,
xs_ids=None):
xs_ids=None, tabular_legendre=None,
tabular_points=33):
"""Creates an openmc.MGXSLibrary object to contain the MGXS data for the
Multi-Group mode of OpenMC.
@ -923,6 +948,19 @@ class Library(object):
Cross section set identifier (i.e., '71c') for all
data sets (if only str) or for each individual one
(if iterable of str). Defaults to '1m'.
tabular_legendre : {None, bool}
Flag to denote whether or not the Legendre expansion of the
scattering angular distribution is to be converted to a tabular
representation by OpenMC. A value of `True` means that it is to be
converted while a value of 'False' means that it will not be.
Defaults to `None` which leaves the default behavior of OpenMC in
place (the distribution is not converted to a tabular
representation).
tabular_points : {int}
This parameter is not used unless the `tabular_legendre` is set to
`True`. In this case, this parameter sets the number of
equally-spaced points in the domain of [-1,1] to be used in
building the tabular distribution. Default is `33`.
Returns
-------
@ -983,13 +1021,16 @@ class Library(object):
xsdata_name += '_' + nuclide
xsdata = self.get_xsdata(domain, xsdata_name, nuclide=nuclide,
xs_type=xs_type, xs_id=xs_ids[i])
xs_type=xs_type, xs_id=xs_ids[i],
tabular_legendre=tabular_legendre,
tabular_points=tabular_points)
mgxs_file.add_xsdata(xsdata)
return mgxs_file
def create_mg_mode(self, xsdata_names=None, xs_ids=None):
def create_mg_mode(self, xsdata_names=None, xs_ids=None,
tabular_legendre=None, tabular_points=33):
"""Creates an openmc.MGXSLibrary object to contain the MGXS data for the
Multi-Group mode of OpenMC as well as the associated openmc.Materials
and openmc.Geometry objects. The created Geometry is the same as that
@ -1007,6 +1048,19 @@ class Library(object):
Cross section set identifier (i.e., '71c') for all
data sets (if only str) or for each individual one
(if iterable of str). Defaults to '1m'.
tabular_legendre : {None, bool}
Flag to denote whether or not the Legendre expansion of the
scattering angular distribution is to be converted to a tabular
representation by OpenMC. A value of `True` means that it is to be
converted while a value of 'False' means that it will not be.
Defaults to `None` which leaves the default behavior of OpenMC in
place (the distribution is not converted to a tabular
representation).
tabular_points : {int}
This parameter is not used unless the `tabular_legendre` is set to
`True`. In this case, this parameter sets the number of
equally-spaced points in the domain of [-1,1] to be used in
building the tabular distribution. Default is `33`.
Returns
-------
@ -1071,7 +1125,9 @@ class Library(object):
# Create XSdata and Macroscopic for this domain
xsdata = self.get_xsdata(domain, xsdata_name, nuclide='total',
xs_type=xs_type, xs_id=xs_ids[i])
xs_type=xs_type, xs_id=xs_ids[i],
tabular_legendre=tabular_legendre,
tabular_points=tabular_points)
mgxs_file.add_xsdata(xsdata)
macroscopic = openmc.Macroscopic(name=xsdata_name, xs=xs_ids[i])

2
openmc/mgxs_library.py
View file

@ -445,7 +445,7 @@ class XSdata(object):
raise ValueError(msg)
if 'num_points' in tabular_legendre:
num_points = tabular_legendre['num_points']
check_value('num_points', num_points, Integral)
check_type('num_points', num_points, Integral)
check_greater_than('num_points', num_points, 0)
else:
if not enable:

21
src/scattdata_header.F90
View file

@ -329,7 +329,7 @@ contains
real(8), intent(in) :: coeffs(:, :, :) ! Coefficients to use
integer :: imu, gin, gout, groups, order
real(8) :: norm
real(8) :: norm, m, mu0, mu1, p0
real(8), allocatable :: energy(:, :)
real(8), allocatable :: matrix(:, :, :)
@ -410,13 +410,17 @@ contains
this % fmu(gin) % data(:, gout) / norm
end if
! Now create CDF from fmu with trapezoidal rule
! Now create CDF from fmu with the analytical integral
this % dist(gin) % data(1, gout) = ZERO
do imu = 2, order
this % dist(gin) % data(imu, gout) = &
this % dist(gin) % data(imu - 1, gout) + &
HALF * this % dmu * (this % fmu(gin) % data(imu - 1, gout) + &
this % fmu(gin) % data(imu, gout))
p0 = this % fmu(gin) % data(imu - 1, gout)
mu0 = this % mu(imu - 1)
mu1 = this % mu(imu)
m = (this % fmu(gin) % data(imu, gout) - p0) / (mu1 - mu0)
this % dist(gin) % data(imu, gout) = HALF * m * mu1 * mu1 + &
(p0 - m * mu0) * mu1 + &
(HALF * m * mu0 * mu0 - p0 * mu0)
end do
! Ensure we normalize to 1 still
norm = this % dist(gin) % data(order, gout)
@ -628,11 +632,10 @@ contains
p1 = this % fmu(gin) % data(k + 1, gout)
mu1 = this % mu(k + 1)
frac = (p1 - p0) / (mu1 - mu0)
if (frac == ZERO) then
if (p0 == p1) then
mu = mu0 + (xi - c_k) / p0
else
frac = (p1 - p0) / (mu1 - mu0)
mu = mu0 + &
(sqrt(max(ZERO, p0 * p0 + TWO * frac * (xi - c_k))) - p0) / frac
end if