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Improved automatic MGXS generation for random ray (#3658)

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Co-authored-by: Paul Romano <paul.k.romano@gmail.com>
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John Tramm 2025-12-03 01:48:38 -06:00 • committed by GitHub
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docs/source/usersguide/random_ray.rst
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@ -644,7 +644,8 @@ model to use these multigroup cross sections. An example is given below::
nparticles=2000,
overwrite_mgxs_library=False,
mgxs_path="mgxs.h5",
correction=None
correction=None,
source_energy=None
)
The most important parameter to set is the ``method`` parameter, which can be
@ -706,6 +707,31 @@ generation and use an existing library file.
with a :math:`\rho` default value of 1.0, which can be adjusted with the
``settings.random_ray['diagonal_stabilization_rho']`` parameter.
When generating MGXS data with either the ``stochastic_slab`` or
``infinite_medium`` methods, by default the simulation will use a uniform source
distribution spread evenly over all energy groups. This ensures that all energy
groups receive tallies and therefore produce non-zero total multigroup cross
sections. Additionally, the function will convert any sources in the model into
simplified spatial sources that retain the original energy distributions. If
sources are present, they will be used 99% of the time to sample source energies
during MGXS generation. The other 1% of the time, energies will be sampled
uniformly over all energy groups to ensure that all groups receive some tallies.
However, the user may wish to specify a different source energy spectrum (for
instance, if they are using a FileSource, such that the energy distribution
cannot be extracted from the python source object). This can be done by
providing a :class:`openmc.stats.Univariate` distribution as the
``source_energy`` parameter of the :meth:`openmc.Model.convert_to_multigroup`
method. If provided, it will override any sources present in the model and will
be used 99% of the time to sample source energies during MGXS generation. The
other 1% of the time, energies will be sampled uniformly over all energy groups
to ensure that all groups receive some tallies.
For instance, a D-D fusion simulation may involve a complex file source. In this
case, the user may wish to provide a discrete 2.45 MeV energy source
distribution for MGXS generation as::
source_energy = openmc.stats.delta_function(2.45e6)
Ultimately, the methods described above are all just approximations.
Approximations in the generated MGXS data will fundamentally limit the potential
accuracy of the random ray solver. However, the methods described above are all