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Linear Source Random Ray (#3072)

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Co-authored-by: John Tramm <john.tramm@gmail.com>
Co-authored-by: Paul Romano <paul.k.romano@gmail.com>
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docs/source/usersguide/random_ray.rst
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@ -447,6 +447,34 @@ in the `OpenMC Jupyter notebook collection
separate materials can be defined each with a separate multigroup dataset
corresponding to a given temperature.
--------------
Linear Sources
--------------
Linear Sources (LS), are supported with the eigenvalue and fixed source random
ray solvers. General 3D LS can be toggled by setting the ``source_shape`` field
in the :attr:`openmc.Settings.random_ray` dictionary to ``'linear'`` as::
settings.random_ray['source_shape'] = 'linear'
LS enables the use of coarser mesh discretizations and lower ray populations,
offsetting the increased computation per ray.
While OpenMC has no specific mode for 2D simulations, such simulations can be
performed implicitly by leaving one of the dimensions of the geometry unbounded
or by imposing reflective boundary conditions with no variation in between them
in that dimension. When 3D linear sources are used in a 2D random ray
simulation, the extremely long (or potentially infinite) spatial dimension along
one of the axes can cause the linear source to become noisy, leading to
potentially large increases in variance. To mitigate this, the user can force
the z-terms of the linear source to zero by setting the ``source_shape`` field
as::
settings.random_ray['source_shape'] = 'linear_xy'
which will greatly improve the quality of the linear source term in 2D
simulations.
---------------------------------
Fixed Source and Eigenvalue Modes
---------------------------------