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Merge pull request #27 from fusion-energy/develop
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Updating the paramak version used to 0.6.7
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@shimwell
shimwell authored Feb 15, 2022
2 parents 1012c90 + 1cde4cc commit 84939c6
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55 changes: 16 additions & 39 deletions Dockerfile
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Expand Up @@ -28,7 +28,6 @@
# docker build -t fusion-neutronics-workflow:embree-avx --build-arg compile_cores=7 --build-arg build_double_down=ON --build-arg include_avx=false .


# TODO save build time by basing this on FROM ghcr.io/fusion-energy/paramak:latest
# This can't be done currently as the base images uses conda installs for moab / dagmc which don't compile with OpenMC
FROM ghcr.io/openmc-data-storage/miniconda3_4.9.2_endfb-7.1_nndc_tendl_2019:latest

Expand Down Expand Up @@ -64,9 +63,13 @@ RUN pip install cython
RUN conda install -c anaconda numpy==1.21.2

# Installing CadQuery
RUN conda install -c conda-forge -c cadquery cadquery=2.1 && \
RUN conda install -c conda-forge -c cadquery cadquery=master && \
pip install jupyter-cadquery==2.1.0

# Installing Gmsh
RUN conda install -c conda-forge gmsh && \
conda install -c conda-forge python-gmsh

# Install neutronics dependencies from Debian package manager
RUN apt-get install -y \
wget \
Expand All @@ -89,20 +92,6 @@ RUN apt-get --yes install libeigen3-dev && \
apt-get --yes install libtbb-dev && \
apt-get --yes install libglfw3-dev

# install Cubit dependencies
RUN apt-get install -y libx11-6
RUN apt-get install -y libxt6
RUN apt-get install -y libgl1
RUN apt-get install -y libglu1-mesa
RUN apt-get install -y libgl1-mesa-glx
RUN apt-get install -y libxcb-icccm4
RUN apt-get install -y libxcb-image0
RUN apt-get install -y libxcb-keysyms1
RUN apt-get install -y libxcb-render-util0
RUN apt-get install -y libxkbcommon-x11-0
RUN apt-get install -y libxcb-randr0
RUN apt-get install -y libxcb-xinerama0


# Clone and install Embree
# embree from conda is not supported yet
Expand Down Expand Up @@ -196,10 +185,8 @@ RUN mkdir DAGMC && \
# Clone and install OpenMC with DAGMC
# TODO clone a specific release when the next release containing (PR 1825) is avaialble.
RUN cd /opt && \
git clone --single-branch --branch develop https://github.com/openmc-dev/openmc.git && \
git clone --single-branch --branch v0.13.0 --depth 1 https://github.com/openmc-dev/openmc.git && \
cd openmc && \
# this commit is from this PR https://github.com/openmc-dev/openmc/pull/1900
git checkout 0157dc219ff8dca814859b3140c6cef1e78cdee1 && \
cd /opt/openmc && \
mkdir build && \
cd build && \
Expand All @@ -212,33 +199,23 @@ RUN cd /opt && \
cd .. && \
pip install -e .[test]

# Download Cubit
RUN wget -O coreform-cubit-2021.5.deb https://f002.backblazeb2.com/file/cubit-downloads/Coreform-Cubit/Releases/Linux/Coreform-Cubit-2021.5%2B15962_5043ef39-Lin64.deb

# Install cubit
RUN dpkg -i coreform-cubit-2021.5.deb

# installs svalinn plugin for cubit
RUN wget https://github.com/svalinn/Cubit-plugin/releases/download/0.2.1/svalinn-plugin_debian-10.10_cubit_2021.5.tgz
RUN tar -xzvf svalinn-plugin_debian-10.10_cubit_2021.5.tgz -C /opt/Coreform-Cubit-2021.5

# writes a non commercial license file
RUN mkdir -p /root/.config/Coreform/licenses
RUN printf 'Fri May 28 2021' >> /root/.config/Coreform/licenses/cubit-learn.lic

# needed to prevent hdf5 conflict between MOAB and Cubit
ENV HDF5_DISABLE_VERSION_CHECK=1

# helps to identify Cubit related errrors
ENV CUBIT_VERBOSE=5


COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt

# installs python packages and nuclear data
# solves binary incompatabilitiy error
RUN pip install numpy --upgrade


# installs python packages and nuclear data (quick as it does not overwrite existing h5 files)
RUN openmc_data_downloader -d nuclear_data -e all -i H3 -l ENDFB-7.1-NNDC TENDL-2019 -p neutron photon --no-overwrite

# solves OSError: libXft.so.2: cannot open shared object file: No such file or directory
RUN apt-get install libxft2
#libxft2:i386 lib64ncurses5
#RUN apt install libxext6
#RUN apt install libxext6:i386

# setting enviromental varibles
ENV OPENMC_CROSS_SECTIONS=/nuclear_data/cross_sections.xml
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41 changes: 22 additions & 19 deletions README.md
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Expand Up @@ -2,12 +2,12 @@

[![CI with install](https://github.com/fusion-energy/fusion_neutronics_workflow/actions/workflows/ci_with_install.yml/badge.svg)](https://github.com/fusion-energy/fusion_neutronics_workflow/actions/workflows/ci_with_install.yml)

<!-- [![docker-publish-release](https://github.com/fusion-energy/fusion_neutronics_workflow/actions/workflows/docker_publish.yml/badge.svg)](https://github.com/fusion-energy/fusion_neutronics_workflow/actions/workflows/docker_publish.yml) -->
[![docker-publish-release](https://github.com/fusion-energy/fusion_neutronics_workflow/actions/workflows/docker_publish.yml/badge.svg)](https://github.com/fusion-energy/fusion_neutronics_workflow/actions/workflows/docker_publish.yml)


# Fusion Neutronics Workflow

![fusion neutronics workflow](https://user-images.githubusercontent.com/8583900/145485618-d4733a82-4f8a-4f8d-a669-7419f076a567.png)
![fusion neutronics workflow](https://user-images.githubusercontent.com/8583900/150701623-29fbf50b-0203-4818-b318-2f3a996e1db7.png)

Diagram showing the connectivity of software packages that make up the Fusion
Neutronics Workflow. Software that the user interacts with directly are shown
Expand All @@ -27,7 +27,8 @@ that is:
- Automated - the entire workflow can be run automatically. Care has been taken
to code out all manual processes so that the neutronics analysis can be
performed in an API manner. As a result it is therefore possible to drive
the workflow with machine learning, optimization or parameter space sampling methods.
the workflow with machine learning, optimization or parameter space
sampling methods.
- Scalable - by using efficient interfaces (Embree, Double Down) and
accelerated geometry (DAGMC) together with scalable Monte Carlo transport
(OpenMC) the resulting workflow scales well with computational power.
Expand Down Expand Up @@ -101,36 +102,43 @@ Links to the packages that are utilized by the fusion-neutronics-workflow
needed for OpenMC neutronics simulations.

* [openmc-plasma-source](https://github.com/fusion-energy/openmc-plasma-source/)
Creates a plasma source as an openmc.source object from input parameters that describe the plasma

* [spectrum_plotter](https://github.com/fusion-energy/spectrum_plotter)
A Python package for creating publication quality plots for neutron / photon / particle spectrum

* [openmc_mesh_tally_to_vtk](https://github.com/fusion-energy/openmc_mesh_tally_to_vtk)
A Python package for converting OpenMC mesh tallies to VTK files and optionally converting the units

* [regular_mesh_plotter](https://github.com/fusion-energy/regular_mesh_plotter)
A Python package for plotting regular mesh tally results from neutronics simulations.

* [dagmc_geometry_slice_plotter](https://github.com/fusion-energy/dagmc_geometry_slice_plotter)
* [dagmc_geometry_slice_plotter](https://github.com/fusion-energy/dagmc_geometry_slice_plotter) A minimal Python package that produces slice plots through h5m DAGMC geometry files

* [dagmc_bounding_box](https://github.com/fusion-energy/dagmc_bounding_box)
Finds the bounding box and related properties of a DAGMC geometry

* [remove_dagmc_tags](https://github.com/svalinn/remove_dagmc_tags)
* [remove_dagmc_tags](https://github.com/svalinn/remove_dagmc_tags) A python package and command line tool for removing DAGMC tags such as graveyard and vacuum

* [dagmc_h5m_file_inspector](https://github.com/fusion-energy/dagmc_h5m_file_inspector)
Extracts information from DAGMC h5m files including volumes number, material tags

* [brep_to_h5m](https://github.com/fusion-energy/brep_to_h5m) Converts Brep CAD geometry files to h5m geometry files compatible with DAGMC simulations

* [brep_part_finder](https://github.com/fusion-energy/brep_part_finder) A Python package to identify the part ID number in Brep format CAD files

* Open source projects that are utilized and contributed to

* [OpenMC](https://github.com/openmc-dev/openmc) The OpenMC project, a
Monte Carlo particle transport code based on modern methods.

* [DAGMC](https://github.com/svalinn/DAGMC) Direct Accelerated Geometry
Monte Carlo Toolkit
Monte Carlo Toolkit

* [Svalinn Cubit Plugin](https://github.com/svalinn/Cubit-plugin/) a plugin
and command extensions for Cubit that allows h5m files to be exported.
* [SphinxCadQuery](https://github.com/CadQuery/sphinxcadquery) An extension to visualize CadQuery 3D files in your Sphinx documentation

* [SphinxCadQuery](https://github.com/CadQuery/sphinxcadquery)


* OpenMC source projects utilized in the software stack
* Open source projects utilized in the software stack

* [MOAB and pymoab](https://github.com/svalinn/Cubit-plugin/) the
Mesh-Oriented datABase MOAB is a component for representing and evaluating
Expand All @@ -145,12 +153,7 @@ Links to the packages that are utilized by the fusion-neutronics-workflow
* [Embree](https://github.com/embree/embree) high-performance ray tracing
kernels

* [Gmsh](https://gitlab.onelab.info/gmsh/gmsh) A three-dimensional finite
element mesh generator with built-in pre- and post-processing facilities

* Scipy, Numpy, Plotly, Pint and other standard scientific Python packages


* Commercial codes (with non commercial license available)

* [Coreform Cubit](https://github.com/svalinn/Cubit-plugin/) - advanced
meshing for challenging simulations. Supports imprinting and merging of
surfaces which speed up the neutronics transport time required for
simulations through faceted geometry.
19 changes: 6 additions & 13 deletions example_01_single_volume_cell_tally/1_create_cad_and_convert_to_dagmc.py
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Expand Up @@ -2,23 +2,16 @@
# A surrounding volume called a graveyard is needed for neutronics simulations

import paramak
from stl_to_h5m import stl_to_h5m

import os

my_shape = paramak.ExtrudeStraightShape(
name='my_material',
points=[(400, 100), (400, 200), (600, 200), (600, 100)],
distance=180,
)

my_shape.export_stl("steel.stl")

# This script converts the CAD stp files generated into h5m files that can be
# used in DAGMC enabled codes. One of the key aspects of this is the assignment
# of materials to the volumes present in the CAD files.
my_shape.export_dagmc_h5m('dagmc.h5m')

stl_to_h5m(
files_with_tags=[
("steel.stl", "mat1"),
],
h5m_filename="dagmc.h5m",
)
# this converts the neutronics geometry h5m file into a vtk file for
# visualisation in Paraview or Visit
os.system('mbconvert dagmc.h5m dagmc.vtk')
10 changes: 5 additions & 5 deletions example_01_single_volume_cell_tally/2_run_neutronics_simulation.py
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Expand Up @@ -24,20 +24,20 @@
# sets the material to use
materials = odw.Materials(
h5m_filename='dagmc.h5m',
correspondence_dict={"mat1": "eurofer"}
correspondence_dict={"mat_my_material": "eurofer"}
)

# creates a cell tally for neutron flux
tally1 = odw.CellTally(
tally_type="neutron_flux",
target="mat1",
target="mat_my_material",
materials=materials
)

# creates a cell tally for neutron spectra
tally2 = odw.CellTally(
tally_type="neutron_spectra",
target="mat1",
target="mat_my_material",
materials=materials
)

Expand All @@ -63,7 +63,7 @@
statepoint = openmc.StatePoint(filepath=statepoint_file)

# gets the first tally using its name
my_tally_1 = statepoint.get_tally(name="mat1_neutron_flux")
my_tally_1 = statepoint.get_tally(name="mat_my_material_neutron_flux")

# gets number of neutron for a 1.3 mega joule shot
source_strength = otuc.find_source_strength(fusion_energy_per_second_or_per_pulse=1.3e6)
Expand All @@ -78,7 +78,7 @@
print(f"flux per second = {result}", end="\n\n")

# gets the second tally using its name
my_tally_2 = statepoint.get_tally(name="mat1_neutron_spectra")
my_tally_2 = statepoint.get_tally(name="mat_my_material_neutron_spectra")

# returns the tally converted to MeV, scaled and normalisation for source strength
result = otuc.process_spectra_tally(
Expand Down
54 changes: 8 additions & 46 deletions example_02_multi_volume_cell_tally/1_create_cad_and_convert_to_dagmc.py
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Expand Up @@ -3,6 +3,7 @@
# A surrounding volume called a graveyard is needed for neutronics simulations

import paramak
import os


my_reactor = paramak.BallReactor(
Expand All @@ -20,52 +21,13 @@
elongation=2.75,
triangularity=0.5,
number_of_tf_coils=16,
rotation_angle=360
rotation_angle=180
)

# exports the reactor shapes to stp files. The list of stp filenames provided
# is made from the shape names with ".stp" added to each one using python
# list comprehension.
# exports the reactor shapes as a DAGMC h5m file which can be used as
# neutronics geometry by OpenMC
my_reactor.export_dagmc_h5m('dagmc.h5m')


# exports the reactor shapes as separate stp files using default stp filenames
stp_filenames = my_reactor.export_stp()


# This script converts the CAD stp files generated into h5m files that can be
# used in DAGMC enabled codes. h5m files created in this way are imprinted,
# merged, faceted and ready for use in OpenMC.

# One of the key aspects of this is the assignment of materials to the volumes present in the CAD files.
# The stp files along with their material tags are based on the names of the
# shapes in the reactor using dictionary comprehension.


# method uses cubit to create dagmc h5m file.

# files_with_tags = [{'cad_filename':stp_filename, 'material_tag':name} for name, stp_filename in zip(my_reactor.name, stp_filenames)]
# produces a list of dictionaries of with cad_filename and material_tag as the keys.
# the values are the stp filename and the name of the component
# [{'cad_filename': 'plasma.stp', 'material_tag': 'plasma'}, {'cad_filename': 'inboard_tf_coils.stp', 'material_tag': 'inboard_tf_coils'}, {'cad_filename': 'center_column_shield.stp', 'material_tag': 'center_column_shield'}, {'cad_filename': 'firstwall.stp', 'material_tag': 'firstwall'}, {'cad_filename': 'blanket.stp', 'material_tag': 'blanket'}, {'cad_filename': 'blanket_rear_wall.stp', 'material_tag': 'blanket_rear_wall'}, {'cad_filename': 'divertor.stp', 'material_tag': 'divertor'}]

# from cad_to_h5m import cad_to_h5m
# cad_to_h5m(
# files_with_tags=files_with_tags,
# h5m_filename='dagmc.h5m',
# cubit_path='/opt/Coreform-Cubit-2021.5/bin/'
# )


# in case you don't have Cubit with the Svalin plugin installed you could use
# stl_to_h5m. This doesn't imprint and merge the geometry so there could be
# overlaps and tunneling particles.
from stl_to_h5m import stl_to_h5m

# exports the reactor shapes as separate stl files using default stl filenames
stl_filenames = my_reactor.export_stl()
files_with_tags = [(stl_filename, name) for name, stl_filename in zip(my_reactor.name, stl_filenames)]

stl_to_h5m(
files_with_tags=files_with_tags,
h5m_filename='dagmc_not_imprinted_and_merged.h5m',
)
# this converts the neutronics geometry h5m file into a vtk file for
# visualisation in Paraview or Visit
os.system('mbconvert dagmc.h5m dagmc.vtk')
37 changes: 21 additions & 16 deletions example_02_multi_volume_cell_tally/2_run_neutronics_simulation.py
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Expand Up @@ -7,25 +7,28 @@
import openmc_plasma_source as ops
import openmc_tally_unit_converter as otuc

dagmc_filename = "dagmc_not_imprinted_and_merged.h5m"
# geometry = odw.Geometry(h5m_filename="dagmc.h5m")
geometry = odw.Geometry(h5m_filename=dagmc_filename)
# makes an OpenMC geometry object from the dagmc file
geometry = odw.Geometry(
h5m_filename="dagmc.h5m",
reflective_angles=(0, 180)
)


# this links the material tags in the dagmc h5m file with materials.
# these materials are input as strings so they will be looked up in the
# neutronics material maker package
material_tag_to_material_dict = {
"plasma": "DT_plasma",
"inboard_tf_coils": "copper",
"center_column_shield": "tungsten",
"firstwall": "tungsten",
"blanket": "Li4SiO4",
"blanket_rear_wall": "eurofer",
"divertor": "tungsten",
}

materials = odw.Materials(
h5m_filename=dagmc_filename, correspondence_dict=material_tag_to_material_dict
h5m_filename=geometry.h5m_filename,
correspondence_dict={
"mat_plasma": "DT_plasma",
"mat_inboard_tf_coils": "copper",
"mat_center_column_shield": "tungsten",
"mat_firstwall": "tungsten",
"mat_blanket": "Li4SiO4",
"mat_blanket_rear_wall": "eurofer",
"mat_divertor_upper": "tungsten",
"mat_divertor_lower": "tungsten",
}
)

tally1 = odw.CellTally(tally_type="TBR", target="blanket", materials=materials)
Expand All @@ -39,7 +42,7 @@
tallies = openmc.Tallies([tally1, tally2])

settings = odw.FusionSettings()
settings.batches = 1
settings.batches = 2
settings.particles = 100
# assigns a ring source of DT energy neutrons to the source using the
# openmc_plasma_source package
Expand All @@ -64,7 +67,9 @@

# returns the tally with normalisation for source strength
result = otuc.process_dose_tally(
source_strength=1.3e6, tally=my_tally_2, required_units="Sv / second",
source_strength=1.3e6,
tally=my_tally_2,
required_units="Sv / second",
volume= 100 # in the future this volume of the component will be found automatically
)
print(f"effective dose = {result}", end="\n\n")
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