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Random Ray AutoMagic Setup (#3351)
Co-authored-by: Paul Romano <paul.k.romano@gmail.com>
This commit is contained in:
John Tramm
GitHub
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26 changed files with 1239 additions and 88 deletions
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@ -11,6 +11,76 @@ active batches <usersguide_batches>`. However, there are a couple of settings
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that are unique to the random ray solver and a few areas that the random ray
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run strategy differs, both of which will be described in this section.
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.. _quick_start:
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-----------
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Quick Start
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-----------
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While this page contains a comprehensive guide to the random ray solver and
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its various parameters, the process of converting an existing continuous energy
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Monte Carlo model to a random ray model can be largely automated via convenience
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functions in OpenMC's Python interface::
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# Define continuous energy model as normal
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model = openmc.Model()
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...
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# Convert model to multigroup (will auto-generate MGXS library if needed)
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model.convert_to_multigroup()
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# Convert model to random ray and initialize random ray parameters
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# to reasonable defaults based on the specifics of the geometry
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model.convert_to_random_ray()
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# (Optional) Overlay source region decomposition mesh to improve fidelity of the
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# random ray solver. Adjust 'n' for fidelity vs runtime.
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n = 100
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mesh = openmc.RegularMesh()
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mesh.dimension = (n, n, n)
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mesh.lower_left = model.geometry.bounding_box.lower_left
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mesh.upper_right = model.geometry.bounding_box.upper_right
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model.settings.random_ray['source_region_meshes'] = [(mesh, [model.geometry.root_universe])]
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# (Optional) Improve fidelity of the random ray solver by enabling linear sources
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model.settings.random_ray['source_shape'] = 'linear'
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# (Optional) Increase the number of rays/batch, to reduce uncertainty
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model.settings.particles = 500
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The above strategy first converts the continuous energy model to a multigroup
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one using the :meth:`openmc.Model.convert_to_multigroup` method. By default,
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this will internally run a coarsely converged continuous energy Monte Carlo
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simulation to produce an estimated multigroup macroscopic cross section set for
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each material specified in the model, and store this data into a multigroup
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cross section library file (``mgxs.h5``) that can be used by the random ray
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solver.
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The :meth:`openmc.Model.convert_to_random_ray` method enables random ray mode
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and performs an analysis of the model geometry to determine reasonable values
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for all required parameters. If default behavior is not satisfactory, the user
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can manually adjust the settings in the :attr:`~openmc.Settings.random_ray`
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dictionary in the :class:`openmc.Settings` as described in the sections below.
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Finally a few optional steps are shown. The first (recommended) step overlays a
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mesh over the geometry to create smaller source regions so that source
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resolution improves and the random ray solver becomes more accurate. Varying the
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mesh resolution can be used to trade off between accuracy and runtime.
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High-fidelity fission reactor simulation may require source region sizes below 1
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cm, while larger fixed source problems with some tolerance for error may be able
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to use source regions of 10 or 100 cm.
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We also enable linear sources, which can improve the accuracy of the random ray
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solver and/or allow for a much coarser mesh resolution to be overlaid. Finally,
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the number of rays per batch is adjusted. The goal here is to ensure that the
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source region miss rate is below 1%, which is reported by OpenMC at the end of
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the simulation (or before via a warning if it is very high).
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.. warning::
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If using a mesh filter for tallying or weight window generation, ensure that
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the same mesh is used for source region decomposition via
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``model.settings.random_ray['source_region_meshes']``.
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------------------------
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Enabling Random Ray Mode
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------------------------
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@ -557,29 +627,118 @@ variety of problem types (or through a multidimensional parameter sweep of
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design variables) with only modest errors and at greatly reduced cost as
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compared to using only continuous energy Monte Carlo.
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~~~~~~~~~~~~
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The Easy Way
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~~~~~~~~~~~~
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The easiest way to generate a multigroup cross section library is to use the
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:meth:`openmc.Model.convert_to_multigroup` method. This method will
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automatically output a multigroup cross section library file (``mgxs.h5``) from
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a continuous energy Monte Carlo model and alter the material definitions in the
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model to use these multigroup cross sections. An example is given below::
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# Assume we already have a working continuous energy model
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model.convert_to_multigroup(
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method="material_wise",
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groups="CASMO-2",
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nparticles=2000,
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overwrite_mgxs_library=False,
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mgxs_path="mgxs.h5",
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correction=None
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)
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The most important parameter to set is the ``method`` parameter, which can be
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either "stochastic_slab", "material_wise", or "infinite_medium". An overview
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of these methods is given below:
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.. list-table:: Comparison of Automatic MGXS Generation Methods
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:header-rows: 1
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:widths: 10 30 30 30
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* - Method
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- Description
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- Pros
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- Cons
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* - ``material_wise`` (default)
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- * Higher Fidelity
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* Runs a CE simulation with the original geometry and source, tallying
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cross sections with a material filter.
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- * Typically the most accurate of the three methods
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* Accurately captures (averaged over the full problem domain)
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both spatial and resonance self shielding effects
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- * Potentially slower as the full geometry must be run
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* If a material is only present far from the source and doesn't get tallied
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to in the CE simulation, the MGXS will be zero for that material.
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* - ``stochastic_slab``
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- * Medium Fidelity
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* Runs a CE simulation with a greatly simplified geometry, where materials
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are randomly assigned to layers in a 1D "stochastic slab sandwich" geometry
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- * Still captures resonant self shielding and resonance effects between materials
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* Fast due to the simplified geometry
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* Able to produce cross section data for all materials, regardless of how
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far they are from the source in the original geometry
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- * Does not capture most spatial self shielding effects, e.g., no lattice physics.
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* - ``infinite_medium``
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- * Lower Fidelity
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* Runs one CE simulation per material independently. Each simulation is just
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an infinite medium slowing down problem, with an assumed external source term.
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- * Simple
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- * Poor accuracy (no spatial information, no lattice physics, no resonance effects
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between materials)
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* May hang if a material has a k-infinity greater than 1.0
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When selecting a non-default energy group structure, you can manually define
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group boundaries or specify the name of a known group structure (a list of which
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can be found at :data:`openmc.mgxs.GROUP_STRUCTURES`). The ``nparticles``
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parameter can be adjusted upward to improve the fidelity of the generated cross
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section library. The ``correction`` parameter can be set to ``"P0"`` to enable
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P0 transport correction. The ``overwrite_mgxs_library`` parameter can be set to
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``True`` to overwrite an existing MGXS library file, or ``False`` to skip
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generation and use an existing library file.
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.. note::
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MGXS transport correction (via setting the ``correction`` parameter in the
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:meth:`openmc.Model.convert_to_multigroup` method to ``"P0"``) may
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result in negative in-group scattering cross sections, which can cause
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numerical instability. To mitigate this, during a random ray solve OpenMC
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will automatically apply
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`diagonal stabilization <https://doi.org/10.1016/j.anucene.2018.10.036>`_
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with a :math:`\rho` default value of 1.0, which can be adjusted with the
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``settings.random_ray['diagonal_stabilization_rho']`` parameter.
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Ultimately, the methods described above are all just approximations.
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Approximations in the generated MGXS data will fundamentally limit the potential
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accuracy of the random ray solver. However, the methods described above are all
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useful in that they can provide a good starting point for a random ray
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simulation, and if more fidelity is needed the user may wish to follow the
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instructions below or experiment with transport correction techniques to improve
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the fidelity of the generated MGXS data.
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~~~~~~~~~~~~
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The Hard Way
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~~~~~~~~~~~~
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We give here a quick summary of how to produce a multigroup cross section data
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file (``mgxs.h5``) from a starting point of a typical continuous energy Monte
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Carlo input file. Notably, continuous energy input files define materials as a
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mixture of nuclides with different densities, whereas multigroup materials are
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simply defined by which name they correspond to in a ``mgxs.h5`` library file.
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Carlo model. Notably, continuous energy models define materials as a mixture of
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nuclides with different densities, whereas multigroup materials are simply
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defined by which name they correspond to in a ``mgxs.h5`` library file.
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To generate the cross section data, we begin with a continuous energy Monte
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Carlo input deck and add in the required tallies that will be needed to generate
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our library. In this example, we will specify material-wise cross sections and a
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two group energy decomposition::
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Carlo model and add in the tallies that are needed to generate our library. In
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this example, we will specify material-wise cross sections and a two-group
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energy decomposition::
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# Define geometry
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...
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...
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geometry = openmc.Geometry()
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...
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...
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# Initialize MGXS library with a finished OpenMC geometry object
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mgxs_lib = openmc.mgxs.Library(geometry)
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# Pick energy group structure
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groups = openmc.mgxs.EnergyGroups(openmc.mgxs.GROUP_STRUCTURES['CASMO-2'])
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groups = openmc.mgxs.EnergyGroups('CASMO-2')
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mgxs_lib.energy_groups = groups
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# Disable transport correction
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@ -587,7 +746,7 @@ two group energy decomposition::
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# Specify needed cross sections for random ray
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mgxs_lib.mgxs_types = ['total', 'absorption', 'nu-fission', 'fission',
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'nu-scatter matrix', 'multiplicity matrix', 'chi']
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'nu-scatter matrix', 'multiplicity matrix', 'chi']
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# Specify a "cell" domain type for the cross section tally filters
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mgxs_lib.domain_type = "material"
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@ -614,13 +773,13 @@ two group energy decomposition::
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...
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When selecting an energy decomposition, you can manually define group boundaries
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or pick out a group structure already known to OpenMC (a list of which can be
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found at :class:`openmc.mgxs.GROUP_STRUCTURES`). Once the above input deck has
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been run, the resulting statepoint file will contain the needed flux and
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reaction rate tally data so that a MGXS library file can be generated. Below is
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the postprocessing script needed to generate the ``mgxs.h5`` library file given
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a statepoint file (e.g., ``statepoint.100.h5``) file and summary file (e.g.,
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``summary.h5``) that resulted from running our previous example::
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or specify the name of known group structure (a list of which can be found at
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:data:`openmc.mgxs.GROUP_STRUCTURES`). Once the above model has been run, the
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resulting statepoint file will contain the needed flux and reaction rate tally
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data so that a MGXS library file can be generated. Below is the postprocessing
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script needed to generate the ``mgxs.h5`` library file given a statepoint file
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(e.g., ``statepoint.100.h5``) file and summary file (e.g., ``summary.h5``) that
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resulted from running our previous example::
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import openmc
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@ -628,10 +787,7 @@ a statepoint file (e.g., ``statepoint.100.h5``) file and summary file (e.g.,
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geom = summary.geometry
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mats = summary.materials
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statepoint_filename = 'statepoint.100.h5'
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sp = openmc.StatePoint(statepoint_filename)
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groups = openmc.mgxs.EnergyGroups(openmc.mgxs.GROUP_STRUCTURES['CASMO-2'])
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groups = openmc.mgxs.EnergyGroups('CASMO-2')
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mgxs_lib = openmc.mgxs.Library(geom)
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mgxs_lib.energy_groups = groups
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mgxs_lib.correction = None
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@ -653,10 +809,10 @@ a statepoint file (e.g., ``statepoint.100.h5``) file and summary file (e.g.,
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# Construct all tallies needed for the multi-group cross section library
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mgxs_lib.build_library()
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mgxs_lib.load_from_statepoint(sp)
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with openmc.StatePoint('statepoint.100.h5') as sp:
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mgxs_lib.load_from_statepoint(sp)
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names = []
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for mat in mgxs_lib.domains: names.append(mat.name)
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names = [mat.name for mat in mgxs_lib.domains]
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# Create a MGXS File which can then be written to disk
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mgxs_file = mgxs_lib.create_mg_library(xs_type='macro', xsdata_names=names)
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@ -665,8 +821,8 @@ a statepoint file (e.g., ``statepoint.100.h5``) file and summary file (e.g.,
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mgxs_file.export_to_hdf5("mgxs.h5")
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Notably, the postprocessing script needs to match the same
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:class:`openmc.mgxs.Library` settings that were used to generate the tallies,
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but otherwise is able to discern the rest of the simulation details from the
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:class:`openmc.mgxs.Library` settings that were used to generate the tallies but
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is otherwise able to discern the rest of the simulation details from the
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statepoint and summary files. Once the postprocessing script is successfully
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run, the ``mgxs.h5`` file can be loaded by subsequent runs of OpenMC.
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@ -701,11 +857,11 @@ multigroup library instead of defining their isotopic contents, as::
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water_data = openmc.Macroscopic('Hot borated water')
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# Instantiate some Materials and register the appropriate Macroscopic objects
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fuel= openmc.Material(name='UO2 (2.4%)')
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fuel = openmc.Material(name='UO2 (2.4%)')
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fuel.set_density('macro', 1.0)
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fuel.add_macroscopic(fuel_data)
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water= openmc.Material(name='Hot borated water')
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water = openmc.Material(name='Hot borated water')
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water.set_density('macro', 1.0)
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water.add_macroscopic(water_data)
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@ -88,59 +88,66 @@ random ray mode can be found in the :ref:`Random Ray User Guide <random_ray>`.
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ray solver. A high level overview of the current workflow for generation of
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weight windows with FW-CADIS using random ray is given below.
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1. Produce approximate multigroup cross section data (stored in a ``mgxs.h5``
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library). There is more information on generating multigroup cross sections
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via OpenMC in the :ref:`multigroup materials <create_mgxs>` user guide, and a
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specific example of generating cross section data for use with random ray in
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the :ref:`random ray MGXS guide <mgxs_gen>`.
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1. Begin by making a deepy copy of your continuous energy Python model and then
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convert the copy to be multigroup and use the random ray transport solver.
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The conversion process can largely be automated as described in more detail
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in the :ref:`random ray quick start guide <quick_start>`, summarized below::
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2. Make a copy of your continuous energy Python input file. You'll edit the new
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file to work in multigroup mode with random ray for producing weight windows.
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# Define continuous energy model
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ce_model = openmc.pwr_pin_cell() # example, replace with your model
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3. Adjust the material definitions in your new multigroup Python file to utilize
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the multigroup cross sections instead of nuclide-wise continuous energy data.
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There is a specific example of making this conversion in the :ref:`random ray
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MGXS guide <mgxs_gen>`.
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# Make a copy to convert to multigroup and random ray
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model = copy.deepcopy(ce_model)
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4. Configure OpenMC to run in random ray mode (by adding several standard random
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ray input flags and settings to the :attr:`openmc.Settings.random_ray`
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dictionary). More information can be found in the :ref:`Random Ray User
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Guide <random_ray>`.
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# Convert model to multigroup (will auto-generate MGXS library if needed)
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model.convert_to_multigroup()
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5. Add in a :class:`~openmc.WeightWindowGenerator` in a similar manner as for
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# Convert model to random ray and initialize random ray parameters
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# to reasonable defaults based on the specifics of the geometry
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model.convert_to_random_ray()
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# (Optional) Overlay source region decomposition mesh to improve fidelity of the
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# random ray solver. Adjust 'n' for fidelity vs runtime.
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n = 10
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mesh = openmc.RegularMesh()
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mesh.dimension = (n, n, n)
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mesh.lower_left = model.geometry.bounding_box.lower_left
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mesh.upper_right = model.geometry.bounding_box.upper_right
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model.settings.random_ray['source_region_meshes'] = [(mesh, [model.geometry.root_universe])]
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# (Optional) Improve fidelity of the random ray solver by enabling linear sources
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model.settings.random_ray['source_shape'] = 'linear'
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# (Optional) Increase the number of rays/batch, to reduce uncertainty
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model.settings.particles = 500
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If you need to improve the fidelity of the MGXS library, there is more
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information on generating multigroup cross sections via OpenMC in the
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:ref:`random ray MGXS guide <mgxs_gen>`.
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2. Add in a :class:`~openmc.WeightWindowGenerator` in a similar manner as for
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MAGIC generation with Monte Carlo and set the :attr:`method` attribute set to
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``"fw_cadis"``::
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# Define weight window spatial mesh
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ww_mesh = openmc.RegularMesh()
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ww_mesh.dimension = (10, 10, 10)
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ww_mesh.lower_left = (0.0, 0.0, 0.0)
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ww_mesh.upper_right = (100.0, 100.0, 100.0)
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# Create weight window object and adjust parameters
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# Create weight window object and adjust parameters, using the same mesh
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# we used for source region decomposition
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wwg = openmc.WeightWindowGenerator(
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method='fw_cadis',
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mesh=ww_mesh,
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mesh=mesh,
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max_realizations=settings.batches
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)
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# Add generator to openmc.settings object
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settings.weight_window_generators = wwg
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.. warning::
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If using FW-CADIS weight window generation, ensure that the selected weight
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window mesh does not subdivide any source regions in the problem. This can
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be ensured by assigning the weight window tally mesh to the root universe so
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as to create source region boundaries that conform to the mesh, as in the
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example below.
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be ensured by using the same mesh for both source region subdivision (i.e.,
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assigning to ``model.settings.random_ray['source_region_meshes']``) and for
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weight window generation.
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::
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root = model.geometry.root_universe
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settings.random_ray['source_region_meshes'] = [(ww_mesh, [root])]
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6. When running your multigroup random ray input deck, OpenMC will automatically
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3. When running your multigroup random ray input deck, OpenMC will automatically
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run a forward solve followed by an adjoint solve, with a
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``weight_windows.h5`` file generated at the end. The ``weight_windows.h5``
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file will contain FW-CADIS generated weight windows. This file can be used in
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