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Fixed per @paulromano comments

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This commit is contained in:
Adam Nelson 2016-06-08 19:38:44 -04:00
parent 826d5a43d8
commit dffb9858e8
10 changed files with 771 additions and 795 deletions
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172
docs/source/pythonapi/examples/mgxs-part-iv.ipynb
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File diff suppressed because one or more lines are too long

4
docs/source/usersguide/mgxs_library.rst
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@ -171,7 +171,9 @@ attributes/sub-elements required to describe the meta-data:
provided via the ``scatt_type`` element above, is represented and thus used
during the scattering process. Specifically, the options are to either
convert the Legendre expansion to a tabular representation or leave it as
a set of Legendre coefficients. Converting to a tabular representation will cost memory but can allow for a decrease in runtime compared to leaving as a set of Legendre coefficients. This element has the following
a set of Legendre coefficients. Converting to a tabular representation
will cost memory but can allow for a decrease in runtime compared to
leaving as a set of Legendre coefficients. This element has the following
attributes/sub-elements:
:enable:

42
openmc/mgxs/library.py
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@ -778,18 +778,18 @@ class Library(object):
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}
tabular_legendre : None or 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.
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 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`.
tabular_points : int
This parameter is not used unless the ``tabular_legendre``
parameter 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
-------
@ -947,18 +947,18 @@ 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}
tabular_legendre : None or 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.
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 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`.
tabular_points : int
This parameter is not used unless the ``tabular_legendre``
parameter 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
-------
@ -1046,18 +1046,18 @@ 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}
tabular_legendre : None or 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.
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 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`.
tabular_points : int
This parameter is not used unless the ``tabular_legendre``
parameter 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
-------

14
src/mgxs_header.F90
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@ -277,7 +277,7 @@ module mgxs_header
real(8), allocatable :: scatt_coeffs(:, :, :)
real(8), allocatable :: input_scatt(:, :, :)
real(8), allocatable :: temp_scatt(:, :, :)
real(8) :: dmu, mu, norm, p0, m, mu0
real(8) :: dmu, mu, norm
integer :: order, order_dim, gin, gout, l, arr_len
integer :: legendre_mu_points, imu
@ -498,18 +498,6 @@ module mgxs_header
(scatt_coeffs(imu - 1, gout, gin) + &
scatt_coeffs(imu, gout, gin))
end if
! Now create CDF from fmu with the analytical integral of a
! piecewise linear function
if (imu > 1) then
p0 = scatt_coeffs(imu - 1, gout, gin)
mu0 = mu - dmu
m = (scatt_coeffs(imu, gout, gin) - &
scatt_coeffs(imu - 1, gout, gin)) / dmu
norm = norm + &
HALF * m * mu * mu + &
(p0 - m * mu0) * mu + &
(HALF * m * mu0 * mu0 - p0 * mu0)
end if
end do
! Now that we have the integral, lets ensure that the distribution
! is normalized such that it preserves the original scattering xs

20
src/scattdata_header.F90
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@ -398,32 +398,18 @@ contains
end do
! Re-normalize fmu for numerical integration issues and in case
! the negative fix-up introduced un-normalized data
! the negative fix-up introduced un-normalized data while
! accruing the CDF
norm = ZERO
do imu = 2, order
norm = norm + HALF * this % dmu * &
(this % fmu(gin) % data(imu - 1, gout) + &
this % fmu(gin) % data(imu, gout))
this % dist(gin) % data(imu, gout) = norm
end do
if (norm > ZERO) then
this % fmu(gin) % data(:, gout) = &
this % fmu(gin) % data(:, gout) / norm
end if
! Now create CDF from fmu with the analytical integral
this % dist(gin) % data(1, gout) = ZERO
do imu = 2, order
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)
if (norm > ZERO) then
this % dist(gin) % data(:, gout) = &
this % dist(gin) % data(:, gout) / norm
end if

2
tests/test_mg_basic/results_true.dat
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@ -1,2 +1,2 @@
k-combined:
1.003952E+00 4.160185E-02
1.047136E+00 2.765964E-02

2
tests/test_mg_max_order/results_true.dat
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@ -1,2 +1,2 @@
k-combined:
1.026398E+00 7.824554E-02
1.102093E+00 4.962190E-02

2
tests/test_mg_nuclide/results_true.dat
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@ -1,2 +1,2 @@
k-combined:
1.003952E+00 4.160185E-02
1.047136E+00 2.765964E-02

1306
tests/test_mg_tallies/results_true.dat
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2
tests/test_mgxs_library_ce_to_mg/results_true.dat
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@ -1,2 +1,2 @@
k-combined:
1.151644E+00 3.044607E-02
1.140804E+00 2.937150E-02