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Local adjoint source for Random Ray (#3717)
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Jack Fletcher
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77 changed files with 2663 additions and 462 deletions
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@ -944,6 +944,8 @@ as::
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which will greatly improve the quality of the linear source term in 2D
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simulations.
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.. _usersguide_random_ray_run_modes:
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---------------------------------
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Fixed Source and Eigenvalue Modes
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---------------------------------
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@ -1073,22 +1075,47 @@ The adjoint flux random ray solver mode can be enabled as::
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settings.random_ray['adjoint'] = True
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When enabled, OpenMC will first run a forward transport simulation followed by
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an adjoint transport simulation. The purpose of the forward solve is to compute
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the adjoint external source when an external source is present in the
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simulation. Simulation settings (e.g., number of rays, batches, etc.) will be
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identical for both simulations. At the conclusion of the run, all results (e.g.,
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tallies, plots, etc.) will be derived from the adjoint flux rather than the
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forward flux but are not labeled any differently. The initial forward flux
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solution will not be stored or available in the final statepoint file. Those
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wishing to do analysis requiring both the forward and adjoint solutions will
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need to run two separate simulations and load both statepoint files.
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When enabled, OpenMC will first run a forward transport simulation if there are
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no user-specified adjoint sources present, followed by an adjoint transport
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simulation. Fixed adjoint sources can be specified on the
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:attr:`openmc.Settings.random_ray` dictionary as follows::
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# Geometry definition
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...
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detector_cell = openmc.Cell(fill=detector_mat, name='cell where detector will be')
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...
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# Define fixed adjoint neutron source
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strengths = [1.0]
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midpoints = [1.0e-4]
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energy_distribution = openmc.stats.Discrete(x=midpoints, p=strengths)
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adj_source = openmc.IndependentSource(
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energy=energy_distribution,
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constraints={'domains': [detector_cell]}
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)
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# Add to random_ray dict
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settings.random_ray['adjoint_source'] = adj_source
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The same constraints apply to the user-defined adjoint source as to the forward
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source, described in the :ref:`Fixed Source and Eigenvalue section
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<usersguide_random_ray_run_modes>`. If this source is not provided, a forward
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solve must take place to compute the adjoint external source when a forward
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external source is present in the problem. Simulation settings (e.g., number of
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rays, batches, etc.) will be identical for both calculations. At the
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conclusion of the run, all results (e.g., tallies, plots, etc.) will be
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derived from the adjoint flux rather than the forward flux but are not labeled
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any differently. The initial forward flux solution will not be stored or
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available in the final statepoint file. Those wishing to do analysis requiring
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both the forward and adjoint solutions will need to run two separate
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simulations and load both statepoint files.
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.. note::
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When adjoint mode is selected, OpenMC will always perform a full forward
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solve and then run a full adjoint solve immediately afterwards. Statepoint
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and tally results will be derived from the adjoint flux, but will not be
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labeled any differently.
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Use of the automated
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:ref:`FW-CADIS weight window generator<usersguide_fw_cadis>` is not
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currently compatible with user-defined adjoint sources. Instead, the
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initial forward calculation is used to assign "forward-weighted" adjoint
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sources to the tally regions of interest.
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---------------------------------------
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Putting it All Together: Example Inputs
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@ -4,26 +4,27 @@
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Variance Reduction
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==================
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Global variance reduction in OpenMC is accomplished by weight windowing
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or source biasing techniques, the latter of which additionally provides a
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local variance reduction capability. OpenMC is capable of generating weight
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windows using either the MAGIC or FW-CADIS methods. Both techniques will
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produce a ``weight_windows.h5`` file that can be loaded and used later on. In
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Global and local variance reduction are possible in OpenMC through both weight
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windowing and source biasing techniques. OpenMC is capable of generating weight
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windows using either the MAGIC or FW-CADIS methods, the latter with an optional
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capability for local variance reduction. Both techniques will produce a
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``weight_windows.h5`` file that can be loaded and used later on. In
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this section, we first break down the steps required to generate and apply
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weight windows, then describe how source biasing may be applied.
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.. _ww_generator:
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------------------------------------
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Generating Weight Windows with MAGIC
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------------------------------------
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-------------------------------------------
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Generating Global Weight Windows with MAGIC
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-------------------------------------------
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As discussed in the :ref:`methods section <methods_variance_reduction>`, MAGIC
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is an iterative method that uses flux tally information from a Monte Carlo
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simulation to produce weight windows for a user-defined mesh. While generating
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the weight windows, OpenMC is capable of applying the weight windows generated
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from a previous batch while processing the next batch, allowing for progressive
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improvement in the weight window quality across iterations.
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simulation to produce weight windows for a user-defined mesh with the objective
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of global variance reduction. While generating the weight windows, OpenMC is
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capable of applying the weight windows generated from a previous batch while
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processing the next batch, allowing for progressive improvement in the weight
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window quality across iterations.
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The typical way of generating weight windows is to define a mesh and then add an
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:class:`openmc.WeightWindowGenerator` object to an :attr:`openmc.Settings`
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@ -71,15 +72,20 @@ At the end of the simulation, a ``weight_windows.h5`` file will be saved to disk
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for later use. Loading it in another subsequent simulation will be discussed in
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the "Using Weight Windows" section below.
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------------------------------------------------------
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Generating Weight Windows with FW-CADIS and Random Ray
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------------------------------------------------------
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.. _usersguide_fw_cadis:
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----------------------------------------------------------------------
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Generating Global or Local Weight Windows with FW-CADIS and Random Ray
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----------------------------------------------------------------------
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Weight window generation with FW-CADIS and random ray in OpenMC uses the same
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exact strategy as with MAGIC. An :class:`openmc.WeightWindowGenerator` object is
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added to the :attr:`openmc.Settings` object, and a ``weight_windows.h5`` will be
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generated at the end of the simulation. The only difference is that the code
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must be run in random ray mode. A full description of how to enable and setup
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exact strategy as with MAGIC. Using FW-CADIS, however, also enables
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local variance reduction in fixed source problems through the :attr:`targets`
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attribute, which is described later in this section. To enable FW-CADIS, an
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:class:`openmc.WeightWindowGenerator` object is added to the
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:attr:`openmc.Settings` object, and a ``weight_windows.h5`` will be generated
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at the end of the simulation. The only procedural difference is that the code
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must be run in random ray mode. A full description of how to enable and setup
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random ray mode can be found in the :ref:`Random Ray User Guide <random_ray>`.
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.. note::
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@ -90,7 +96,7 @@ 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. Begin by making a deepy copy of your continuous energy Python model and then
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1. Begin by making a deep 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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@ -148,7 +154,53 @@ random ray mode can be found in the :ref:`Random Ray User Guide <random_ray>`.
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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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3. When running your multigroup random ray input deck, OpenMC will automatically
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3. (Optional) If local variance reduction is desired in a fixed-source problem,
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populate the :attr:`targets` attribute with an :class:`openmc.Tallies`
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instance or an iterable of tally IDs indicating the tallies of interest for
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variance reduction::
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# Build a new example and WWG for local variance reduction
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from openmc.examples import random_ray_three_region_cube_with_detectors
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new_model = random_ray_three_region_cube_with_detectors()
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ww_mesh = openmc.RegularMesh()
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n = 7
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width = 35.0
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ww_mesh.dimension = (n, n, n)
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ww_mesh.lower_left = (0.0, 0.0, 0.0)
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ww_mesh.upper_right = (width, width, width)
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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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max_realizations=new_model.settings.batches
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)
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new_model.settings.weight_window_generators = wwg
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new_model.settings.random_ray['volume_estimator'] = 'naive'
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# Get the tallies of interest
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target_tallies = openmc.Tallies()
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for tally in list(new_model.tallies):
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if tally.name in {"Detector 1 Tally", "Detector 2 Tally"}:
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target_tallies.append(tally)
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# Add to WeightWindowGenerator
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wwg.targets = target_tallies
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.. warning::
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The tallies designated as FW-CADIS targets to the
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:class:`~openmc.WeightWindowGenerator` must be present under the
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:class:`~openmc.model.Model.tallies` attribute of the
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:class:`~openmc.model.Model` as well in order to be recognized as valid
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local variance reduction targets. This check is performed when the
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:func:`openmc.model.Model.export_to_model_xml` or
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:func:`openmc.model.Model.export_to_xml` functions are called, meaning
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that the standalone :func:`openmc.Settings.export_to_xml` and
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:func:`openmc.Tallies.export_to_xml` methods should not be used with
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FW-CADIS local variance reduction.
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4. 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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