mirror of
https://github.com/openmc-dev/openmc.git
synced 2026-07-29 06:35:48 -04:00
Local adjoint source for Random Ray (#3717)
This commit is contained in:
Jack Fletcher
GitHub
parent
1116c4bdc0
commit
368ea069ca
77 changed files with 2663 additions and 462 deletions
|
|
@ -597,7 +597,7 @@ found in the :ref:`random ray user guide <random_ray>`.
|
|||
|
||||
*Default*: None
|
||||
|
||||
:source:
|
||||
:ray_source:
|
||||
Specifies the starting ray distribution, and follows the format for
|
||||
:ref:`source_element`. It must be uniform in space and angle and cover the
|
||||
full domain. It does not represent a physical neutron or photon source -- it
|
||||
|
|
@ -605,6 +605,35 @@ found in the :ref:`random ray user guide <random_ray>`.
|
|||
|
||||
*Default*: None
|
||||
|
||||
:adjoint_source:
|
||||
Specifies an adjoint fixed source for adjoint transport simulations, and
|
||||
follows the format for :ref:`source_element`. The distributions which make
|
||||
up the adjoint source are subject to the same restrictions as forward
|
||||
fixed sources in Random Ray mode.
|
||||
|
||||
*Default*: None
|
||||
|
||||
:adjoint:
|
||||
Specifies whether to perform adjoint transport. The default is 'False',
|
||||
corresponding to forward transport.
|
||||
|
||||
*Default*: None
|
||||
|
||||
:volume_estimator:
|
||||
Specifies choice of volume estimator for the random ray solver. Options
|
||||
are 'naive', 'simulation_averaged', or 'hybrid'. The default is 'hybrid'.
|
||||
|
||||
*Default*: None
|
||||
|
||||
:volume_normalized_flux_tallies:
|
||||
Specifies whether to normalize flux tallies by volume (bool). The
|
||||
default is 'False'. When enabled, flux tallies will be reported in units
|
||||
of cm/cm^3. When disabled, flux tallies will be reported in units of cm
|
||||
(i.e., total distance traveled by neutrons in the spatial tally
|
||||
region).
|
||||
|
||||
*Default*: None
|
||||
|
||||
:sample_method:
|
||||
Specifies the method for sampling the starting ray distribution. This
|
||||
element can be set to "prng" or "halton".
|
||||
|
|
@ -1696,6 +1725,14 @@ mesh-based weight windows.
|
|||
The ratio of the lower to upper weight window bounds.
|
||||
|
||||
*Default*: 5.0
|
||||
|
||||
For FW-CADIS:
|
||||
|
||||
:targets:
|
||||
A sequence of IDs corresponding to the tallies which cover phase
|
||||
space regions of interest for local variance reduction.
|
||||
|
||||
*Default*: None
|
||||
|
||||
---------------------------------------
|
||||
``<weight_window_checkpoints>`` Element
|
||||
|
|
|
|||
|
|
@ -1081,28 +1081,32 @@ lifetimes.
|
|||
|
||||
In OpenMC, the random ray adjoint solver is implemented simply by transposing
|
||||
the scattering matrix, swapping :math:`\nu\Sigma_f` and :math:`\chi`, and then
|
||||
running a normal transport solve. When no external fixed source is present, no
|
||||
additional changes are needed in the transport process. However, if an external
|
||||
fixed forward source is present in the simulation problem, then an additional
|
||||
step is taken to compute the accompanying fixed adjoint source. In OpenMC, the
|
||||
adjoint flux does *not* represent a response function for a particular detector
|
||||
region. Rather, the adjoint flux is the global response, making it appropriate
|
||||
for use with weight window generation schemes for global variance reduction.
|
||||
Thus, if using a fixed source, the external source for the adjoint mode is
|
||||
simply computed as being :math:`1 / \phi`, where :math:`\phi` is the forward
|
||||
scalar flux that results from a normal forward solve (which OpenMC will run
|
||||
first automatically when in adjoint mode). The adjoint external source will be
|
||||
computed for each source region in the simulation mesh, independent of any
|
||||
tallies. The adjoint external source is always flat, even when a linear
|
||||
scattering and fission source shape is used. When in adjoint mode, all reported
|
||||
results (e.g., tallies, eigenvalues, etc.) are derived from the adjoint flux,
|
||||
even when the physical meaning is not necessarily obvious. These values are
|
||||
still reported, though we emphasize that the primary use case for adjoint mode
|
||||
is for producing adjoint flux tallies to support subsequent perturbation studies
|
||||
and weight window generation.
|
||||
running a normal transport solve. When no external fixed forward source is
|
||||
present, or if an adjoint fixed source is specifically provided, no additional
|
||||
changes are needed in the transport process. This adjoint source can
|
||||
correspond, for example, to a detector response function in a particular
|
||||
region. However, if an external fixed forward source is present in the
|
||||
simulation problem without an adjoint fixed source, an additional step is taken
|
||||
to compute the accompanying forward-weighted adjoint source. In this case, the
|
||||
adjoint flux does *not* represent the importance of locations in phase space to
|
||||
detector response; rather, the "response" in question is a uniform distribution
|
||||
of Monte Carlo particle density, making the importance provided by the adjoint
|
||||
flux appropriate for use with weight window generation schemes for global
|
||||
variance reduction. Thus, if using a fixed source, the forward-weighted
|
||||
external source for adjoint mode is simply computed as being :math:`1 / \phi`,
|
||||
where :math:`\phi` is the forward scalar flux that results from a normal
|
||||
forward solve (which OpenMC will run first automatically when in adjoint mode).
|
||||
The adjoint external source will be computed for each source region in the
|
||||
simulation mesh, independent of any tallies. The adjoint external source is
|
||||
always flat, even when a linear scattering and fission source shape is used.
|
||||
|
||||
Note that the adjoint :math:`k_{eff}` is statistically the same as the forward
|
||||
:math:`k_{eff}`, despite the flux distributions taking different shapes.
|
||||
When in adjoint mode, all reported results (e.g., tallies, eigenvalues, etc.)
|
||||
are derived from the adjoint flux, even when the physical meaning is not
|
||||
necessarily obvious. These values are still reported, though we emphasize that
|
||||
the primary use case for adjoint mode is for producing adjoint flux tallies to
|
||||
support subsequent perturbation studies and weight window generation. Note
|
||||
however that the adjoint :math:`k_{eff}` is statistically the same as the
|
||||
forward :math:`k_{eff}`, despite the flux distributions taking different shapes.
|
||||
|
||||
---------------------------
|
||||
Fundamental Sources of Bias
|
||||
|
|
|
|||
|
|
@ -82,8 +82,8 @@ where it was born from.
|
|||
|
||||
The Forward-Weighted Consistent Adjoint Driven Importance Sampling method, or
|
||||
`FW-CADIS method <https://doi.org/10.13182/NSE12-33>`_, produces weight windows
|
||||
for global variance reduction given adjoint flux information throughout the
|
||||
entire domain. The weight window lower bound is defined in Equation
|
||||
for global or local variance reduction given adjoint flux information throughout
|
||||
the entire domain. The weight window lower bound is defined in Equation
|
||||
:eq:`fw_cadis`, and also involves a normalization step not shown here.
|
||||
|
||||
.. math::
|
||||
|
|
@ -135,6 +135,18 @@ aware of this.
|
|||
|
||||
\text{FOM} = \frac{1}{\text{Time} \times \sigma^2}
|
||||
|
||||
Finally, one unique capability of the FW-CADIS weight window generator is to
|
||||
produce weight windows for local variance reduction, given a list of the
|
||||
responses of interest. This is controlled by optionally specifying target
|
||||
tallies from the :class:`openmc.model.Model` to the
|
||||
:class:`openmc.WeightWindowGenerator`, as illustrated in the
|
||||
:ref:`user guide<variance_reduction>`. If target tallies for local variance
|
||||
reduction are supplied, then the adjoint sources are only populated after the
|
||||
initial forward simulation in the source regions associated with those tallies.
|
||||
In other regions, the adjoint source term is instead set to zero. The Random
|
||||
Ray solver then determines the adjoint flux map used to generate FW-CADIS
|
||||
weight windows following the usual technique.
|
||||
|
||||
.. _methods_source_biasing:
|
||||
|
||||
--------------
|
||||
|
|
|
|||
|
|
@ -944,6 +944,8 @@ as::
|
|||
which will greatly improve the quality of the linear source term in 2D
|
||||
simulations.
|
||||
|
||||
.. _usersguide_random_ray_run_modes:
|
||||
|
||||
---------------------------------
|
||||
Fixed Source and Eigenvalue Modes
|
||||
---------------------------------
|
||||
|
|
@ -1073,22 +1075,47 @@ The adjoint flux random ray solver mode can be enabled as::
|
|||
|
||||
settings.random_ray['adjoint'] = True
|
||||
|
||||
When enabled, OpenMC will first run a forward transport simulation followed by
|
||||
an adjoint transport simulation. The purpose of the forward solve is to compute
|
||||
the adjoint external source when an external source is present in the
|
||||
simulation. Simulation settings (e.g., number of rays, batches, etc.) will be
|
||||
identical for both simulations. At the conclusion of the run, all results (e.g.,
|
||||
tallies, plots, etc.) will be derived from the adjoint flux rather than the
|
||||
forward flux but are not labeled any differently. The initial forward flux
|
||||
solution will not be stored or available in the final statepoint file. Those
|
||||
wishing to do analysis requiring both the forward and adjoint solutions will
|
||||
need to run two separate simulations and load both statepoint files.
|
||||
When enabled, OpenMC will first run a forward transport simulation if there are
|
||||
no user-specified adjoint sources present, followed by an adjoint transport
|
||||
simulation. Fixed adjoint sources can be specified on the
|
||||
:attr:`openmc.Settings.random_ray` dictionary as follows::
|
||||
|
||||
# Geometry definition
|
||||
...
|
||||
detector_cell = openmc.Cell(fill=detector_mat, name='cell where detector will be')
|
||||
...
|
||||
# Define fixed adjoint neutron source
|
||||
strengths = [1.0]
|
||||
midpoints = [1.0e-4]
|
||||
energy_distribution = openmc.stats.Discrete(x=midpoints, p=strengths)
|
||||
|
||||
adj_source = openmc.IndependentSource(
|
||||
energy=energy_distribution,
|
||||
constraints={'domains': [detector_cell]}
|
||||
)
|
||||
|
||||
# Add to random_ray dict
|
||||
settings.random_ray['adjoint_source'] = adj_source
|
||||
|
||||
The same constraints apply to the user-defined adjoint source as to the forward
|
||||
source, described in the :ref:`Fixed Source and Eigenvalue section
|
||||
<usersguide_random_ray_run_modes>`. If this source is not provided, a forward
|
||||
solve must take place to compute the adjoint external source when a forward
|
||||
external source is present in the problem. Simulation settings (e.g., number of
|
||||
rays, batches, etc.) will be identical for both calculations. At the
|
||||
conclusion of the run, all results (e.g., tallies, plots, etc.) will be
|
||||
derived from the adjoint flux rather than the forward flux but are not labeled
|
||||
any differently. The initial forward flux solution will not be stored or
|
||||
available in the final statepoint file. Those wishing to do analysis requiring
|
||||
both the forward and adjoint solutions will need to run two separate
|
||||
simulations and load both statepoint files.
|
||||
|
||||
.. note::
|
||||
When adjoint mode is selected, OpenMC will always perform a full forward
|
||||
solve and then run a full adjoint solve immediately afterwards. Statepoint
|
||||
and tally results will be derived from the adjoint flux, but will not be
|
||||
labeled any differently.
|
||||
Use of the automated
|
||||
:ref:`FW-CADIS weight window generator<usersguide_fw_cadis>` is not
|
||||
currently compatible with user-defined adjoint sources. Instead, the
|
||||
initial forward calculation is used to assign "forward-weighted" adjoint
|
||||
sources to the tally regions of interest.
|
||||
|
||||
---------------------------------------
|
||||
Putting it All Together: Example Inputs
|
||||
|
|
|
|||
|
|
@ -4,26 +4,27 @@
|
|||
Variance Reduction
|
||||
==================
|
||||
|
||||
Global variance reduction in OpenMC is accomplished by weight windowing
|
||||
or source biasing techniques, the latter of which additionally provides a
|
||||
local variance reduction capability. OpenMC is capable of generating weight
|
||||
windows using either the MAGIC or FW-CADIS methods. Both techniques will
|
||||
produce a ``weight_windows.h5`` file that can be loaded and used later on. In
|
||||
Global and local variance reduction are possible in OpenMC through both weight
|
||||
windowing and source biasing techniques. OpenMC is capable of generating weight
|
||||
windows using either the MAGIC or FW-CADIS methods, the latter with an optional
|
||||
capability for local variance reduction. Both techniques will produce a
|
||||
``weight_windows.h5`` file that can be loaded and used later on. In
|
||||
this section, we first break down the steps required to generate and apply
|
||||
weight windows, then describe how source biasing may be applied.
|
||||
|
||||
.. _ww_generator:
|
||||
|
||||
------------------------------------
|
||||
Generating Weight Windows with MAGIC
|
||||
------------------------------------
|
||||
-------------------------------------------
|
||||
Generating Global Weight Windows with MAGIC
|
||||
-------------------------------------------
|
||||
|
||||
As discussed in the :ref:`methods section <methods_variance_reduction>`, MAGIC
|
||||
is an iterative method that uses flux tally information from a Monte Carlo
|
||||
simulation to produce weight windows for a user-defined mesh. While generating
|
||||
the weight windows, OpenMC is capable of applying the weight windows generated
|
||||
from a previous batch while processing the next batch, allowing for progressive
|
||||
improvement in the weight window quality across iterations.
|
||||
simulation to produce weight windows for a user-defined mesh with the objective
|
||||
of global variance reduction. While generating the weight windows, OpenMC is
|
||||
capable of applying the weight windows generated from a previous batch while
|
||||
processing the next batch, allowing for progressive improvement in the weight
|
||||
window quality across iterations.
|
||||
|
||||
The typical way of generating weight windows is to define a mesh and then add an
|
||||
:class:`openmc.WeightWindowGenerator` object to an :attr:`openmc.Settings`
|
||||
|
|
@ -71,15 +72,20 @@ At the end of the simulation, a ``weight_windows.h5`` file will be saved to disk
|
|||
for later use. Loading it in another subsequent simulation will be discussed in
|
||||
the "Using Weight Windows" section below.
|
||||
|
||||
------------------------------------------------------
|
||||
Generating Weight Windows with FW-CADIS and Random Ray
|
||||
------------------------------------------------------
|
||||
.. _usersguide_fw_cadis:
|
||||
|
||||
----------------------------------------------------------------------
|
||||
Generating Global or Local Weight Windows with FW-CADIS and Random Ray
|
||||
----------------------------------------------------------------------
|
||||
|
||||
Weight window generation with FW-CADIS and random ray in OpenMC uses the same
|
||||
exact strategy as with MAGIC. An :class:`openmc.WeightWindowGenerator` object is
|
||||
added to the :attr:`openmc.Settings` object, and a ``weight_windows.h5`` will be
|
||||
generated at the end of the simulation. The only difference is that the code
|
||||
must be run in random ray mode. A full description of how to enable and setup
|
||||
exact strategy as with MAGIC. Using FW-CADIS, however, also enables
|
||||
local variance reduction in fixed source problems through the :attr:`targets`
|
||||
attribute, which is described later in this section. To enable FW-CADIS, an
|
||||
:class:`openmc.WeightWindowGenerator` object is added to the
|
||||
:attr:`openmc.Settings` object, and a ``weight_windows.h5`` will be generated
|
||||
at the end of the simulation. The only procedural difference is that the code
|
||||
must be run in random ray mode. A full description of how to enable and setup
|
||||
random ray mode can be found in the :ref:`Random Ray User Guide <random_ray>`.
|
||||
|
||||
.. note::
|
||||
|
|
@ -90,7 +96,7 @@ random ray mode can be found in the :ref:`Random Ray User Guide <random_ray>`.
|
|||
ray solver. A high level overview of the current workflow for generation of
|
||||
weight windows with FW-CADIS using random ray is given below.
|
||||
|
||||
1. Begin by making a deepy copy of your continuous energy Python model and then
|
||||
1. Begin by making a deep copy of your continuous energy Python model and then
|
||||
convert the copy to be multigroup and use the random ray transport solver.
|
||||
The conversion process can largely be automated as described in more detail
|
||||
in the :ref:`random ray quick start guide <quick_start>`, summarized below::
|
||||
|
|
@ -148,7 +154,53 @@ random ray mode can be found in the :ref:`Random Ray User Guide <random_ray>`.
|
|||
assigning to ``model.settings.random_ray['source_region_meshes']``) and for
|
||||
weight window generation.
|
||||
|
||||
3. When running your multigroup random ray input deck, OpenMC will automatically
|
||||
3. (Optional) If local variance reduction is desired in a fixed-source problem,
|
||||
populate the :attr:`targets` attribute with an :class:`openmc.Tallies`
|
||||
instance or an iterable of tally IDs indicating the tallies of interest for
|
||||
variance reduction::
|
||||
|
||||
# Build a new example and WWG for local variance reduction
|
||||
from openmc.examples import random_ray_three_region_cube_with_detectors
|
||||
new_model = random_ray_three_region_cube_with_detectors()
|
||||
|
||||
ww_mesh = openmc.RegularMesh()
|
||||
n = 7
|
||||
width = 35.0
|
||||
ww_mesh.dimension = (n, n, n)
|
||||
ww_mesh.lower_left = (0.0, 0.0, 0.0)
|
||||
ww_mesh.upper_right = (width, width, width)
|
||||
|
||||
wwg = openmc.WeightWindowGenerator(
|
||||
method="fw_cadis",
|
||||
mesh=ww_mesh,
|
||||
max_realizations=new_model.settings.batches
|
||||
)
|
||||
new_model.settings.weight_window_generators = wwg
|
||||
new_model.settings.random_ray['volume_estimator'] = 'naive'
|
||||
|
||||
# Get the tallies of interest
|
||||
target_tallies = openmc.Tallies()
|
||||
|
||||
for tally in list(new_model.tallies):
|
||||
if tally.name in {"Detector 1 Tally", "Detector 2 Tally"}:
|
||||
target_tallies.append(tally)
|
||||
|
||||
# Add to WeightWindowGenerator
|
||||
wwg.targets = target_tallies
|
||||
|
||||
.. warning::
|
||||
The tallies designated as FW-CADIS targets to the
|
||||
:class:`~openmc.WeightWindowGenerator` must be present under the
|
||||
:class:`~openmc.model.Model.tallies` attribute of the
|
||||
:class:`~openmc.model.Model` as well in order to be recognized as valid
|
||||
local variance reduction targets. This check is performed when the
|
||||
:func:`openmc.model.Model.export_to_model_xml` or
|
||||
:func:`openmc.model.Model.export_to_xml` functions are called, meaning
|
||||
that the standalone :func:`openmc.Settings.export_to_xml` and
|
||||
:func:`openmc.Tallies.export_to_xml` methods should not be used with
|
||||
FW-CADIS local variance reduction.
|
||||
|
||||
4. When running your multigroup random ray input deck, OpenMC will automatically
|
||||
run a forward solve followed by an adjoint solve, with a
|
||||
``weight_windows.h5`` file generated at the end. The ``weight_windows.h5``
|
||||
file will contain FW-CADIS generated weight windows. This file can be used in
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue