import regular files from docker_msre repo
This commit is contained in:
Erik B Knudsen
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ac9365798d
commit
2dd71c135c
17 changed files with 783 additions and 0 deletions
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msre_docker/.gitattributes
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msre_docker/.gitattributes
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msre_simple.h5m filter=lfs diff=lfs merge=lfs -text
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dagmc_controlrods.h5m filter=lfs diff=lfs merge=lfs -text
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msre_control_in.h5m filter=lfs diff=lfs merge=lfs -text
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msre_control_out.h5m filter=lfs diff=lfs merge=lfs -text
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msre_docker/.gitignore
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msre_docker/.gitignore
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notebooks/
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msre_docker/Dockerfile
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msre_docker/Dockerfile
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FROM debian:11
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ARG compile_cores=8
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#update system
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RUN apt-get --allow-releaseinfo-change update
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RUN DEBIAN_FRONTEND=noninteractive && apt-get --yes update && apt-get --yes upgrade
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RUN DEBIAN_FRONTEND=noninteractive && apt-get --yes install git sudo bash wget apt-utils xz-utils python3 python3-pip
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#RUN git clone git@github.com:openmsr/openmc_install_scripts
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RUN groupadd -g 1000 usr
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RUN useradd -rm -u 1000 usr -G sudo -g usr -s /bin/bash \
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&& sed -i '/%sudo.*ALL/a %sudo ALL=(ALL) NOPASSWD: ALL' /etc/sudoers
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USER usr
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WORKDIR /home/usr
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#clone the install scripts and run them
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#RUN git clone git@github.com:openmsr/openmc_install_scripts.git
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#this is done step by step to avoid invalidating the docker cache.
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COPY openmc_install_scripts/Debian11/nuclear_data-install.sh .
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RUN ./nuclear_data-install.sh
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COPY openmc_install_scripts/Debian11/embree-install.sh .
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RUN ./embree-install.sh "$compile_cores"
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COPY openmc_install_scripts/Debian11/moab-install.sh .
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RUN ./moab-install.sh "$compile_cores"
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COPY openmc_install_scripts/Debian11/double_down-install.sh .
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RUN ./double_down-install.sh "$compile_cores"
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COPY openmc_install_scripts/Debian11/dagmc-install.sh .
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RUN DEBIAN_FRONTEND=noninteractive && ./dagmc-install.sh "$compile_cores"
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COPY openmc_install_scripts/Debian11/openmc-install.sh .
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RUN DEBIAN_FRONTEND=noninteractive && ./openmc-install.sh "$compile_cores"
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#clean up a bit
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RUN rm *-install.sh
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RUN rm *-install.sh.done
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RUN rm $HOME/openmc/nuclear_data/*.xz
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RUN rm $HOME/openmc/nuclear_data/*-install.sh.done
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RUN sudo pip install --no-cache-dir requests jupyterlab
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#Here should be added COPYING in MSRE-data directories - probably needs meshes and h5m-files as well
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#include neither cubit nor onshape can be distributed like this.
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RUN mkdir msre
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COPY msre_simple.h5m msre/
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COPY msre_control*.h5m msre/
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#COPY msre_*.py msre/
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RUN mkdir example_notebooks
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COPY MSRE.ipynb example_notebooks/
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ENV OPENMC_CROSS_SECTIONS=/home/usr/openmc/nuclear_data/mcnp_endfb71/cross_sections.xml
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#we are now ready to run the msre
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EXPOSE 8888
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ENTRYPOINT ["jupyter","lab","--ip=0.0.0.0","--allow-root"]
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632
msre_docker/MSRE.ipynb
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msre_docker/MSRE.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a28f447d-a699-4495-bf71-bace1a4c0833",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Notebook for running MSRE calculations directly from a CAD drawing\n",
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"This notebook show an example of running computations of a model of the Moten Salt Reactor Experiment of MSRE in short.\n",
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"The model itself has been generated from tehe original drawings from Oak Ridge National lab \n",
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"\n",
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"The CAD-model is available on github at https://github.com/openmsr/msre, which in turn is generated from a long list of documents which have been compiled at https://github.com/openmsr/msr-archive\n",
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"\n",
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"The simulation backend is run using the Open Source Monte Carlo particle transport code OpenMC (https://openmc.org), through its' python interface.\n",
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"\n",
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"**Important: If you want your work to be available after you shutdown the docker, you must copy your notebooks to a location mounted on your local machine.**\n",
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"\n",
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"If you started the docker using the supplied ```run_docker.sh```-script, the ```notebooks```-directory has been mounted like this."
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]
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},
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{
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"cell_type": "markdown",
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"id": "fb624881-ed0a-4962-b528-fb1ee5141f35",
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"metadata": {},
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"source": [
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"## The (obvious) 1st step is to import the OpenMC python interface"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "85306562-ad94-4072-9cd4-1734fc7b0ab0",
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"metadata": {},
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"outputs": [],
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"source": [
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"import openmc"
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]
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},
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{
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"cell_type": "markdown",
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"id": "51730852-f7a5-4491-8b1d-de7609dce96f",
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"metadata": {},
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"source": [
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"Next we define a set of materials objects that form the core of the MSRE, graphite, hastelloy N / inor-8, inconel, the fuel salt, and helium. Lastly these are exported to am OpenMC-xml control file."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6df036db-7b00-4a73-a4e0-dd9a407d7395",
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"metadata": {},
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"outputs": [],
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"source": [
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"graphite=openmc.Material(name='graphite')\n",
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"graphite.add_element('C',1.0,'ao')\n",
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"graphite.set_density('g/cc',2.26)\n",
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"\n",
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"#Hastelloy N / INOR-8 nominal material composition from\n",
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"#ORNL-TM-4189\n",
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"inor=openmc.Material(name='inor')\n",
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"inor.add_element('Ni',0.72)\n",
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"inor.add_element('Mo',0.16)\n",
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"inor.add_element('Cr',0.07)\n",
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"inor.add_element('Fe',0.05)\n",
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"inor.set_density('g/cc',9)\n",
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"\n",
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"# LiF,BeF2,UF4,ZrF4 [0.67,0.23,0.05,0.0079] mol % @ 33% enrichment \n",
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"molar_comp={'LiF':0.67,'BeF2':0.23, 'ZrF4':0.05, 'UF4':0.0079}\n",
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"enrichment=0.3333\n",
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"salt=openmc.Material(name='salt')\n",
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"salt.add_element('F',molar_comp['LiF']*1/2+molar_comp['BeF2']*2/3+molar_comp['ZrF4']*4/5+molar_comp['UF4']*4/5,'ao')\n",
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"salt.add_nuclide('Li7',molar_comp['LiF']*1/2,'ao')\n",
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"salt.add_element('Be',molar_comp['BeF2']*1/3,'ao')\n",
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"salt.add_element('Zr',molar_comp['ZrF4']*1/5,'ao')\n",
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"salt.add_nuclide('U235',enrichment*molar_comp['UF4']*1/5,'ao')\n",
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"salt.add_nuclide('U238',(1-enrichment)*molar_comp['UF4']*1/5,'ao')\n",
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"salt.set_density('g/cc',2.2)\n",
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"\n",
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"# The natural isotopes have been used for this alloy\n",
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"# The density is set to that of Ni.\n",
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"inconel=openmc.Material(name='inconel')\n",
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"inconel.add_element('Ni',0.72,'ao')\n",
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"inconel.add_element('Cr',0.20,'ao')\n",
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"inconel.add_element('Fe',0.08,'ao')\n",
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"inconel.set_density('g/cc',8.9)\n",
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"\n",
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"helium=openmc.Material(name='helium')\n",
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"helium.add_nuclide('He4',1.0,'ao')\n",
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"helium.set_density('g/cc',1.0e-4)\n",
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"\n",
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"materials=openmc.Materials([helium,salt,graphite,inconel,inor])\n",
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"materials.export_to_xml()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3e21759c-4d3f-44f4-8b61-eb1a519d7d68",
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"metadata": {},
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"source": [
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"As a control we can inspect the materials object. Notice how OpenMC has in the revant cases expanded our material definition to consist the naturally occurring isotope concentrations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "82b7ea1e-28d6-450d-9bad-e5f5262c5272",
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"metadata": {},
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"outputs": [],
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"source": [
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"materials"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1a5f8f08-ec15-4ab4-b091-c3e0b2147490",
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"metadata": {},
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"outputs": [],
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"source": [
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"#geometry\n",
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"h5m_filepath=\"../msre/msre_simple.h5m\"\n",
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"dag_univ = openmc.DAGMCUniverse(h5m_filepath)\n",
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"geom = openmc.Geometry(root=dag_univ)\n",
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"geom.export_to_xml()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "be73fe16-ea81-4efa-939f-3934bc5b77bc",
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"metadata": {},
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"source": [
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"We can now plot our geometry to verify that this is in fact the geometry we want. We plot two slices (xz and xy) through the centre of the MSRE core, and color the geometry by constituent material."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dde16460-008e-4e6f-ac81-32e82dd066d4",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"xwidth = 350\n",
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"yheight = 350\n",
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"material_colors={salt:'red', inor:'lightblue', inconel:'blue',helium:'white',graphite:'gray'}\n",
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"#xz plot\n",
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"p1 = openmc.Plot()\n",
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"p1.background='white'\n",
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"p1.basis = 'xz'\n",
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"p1.width = (xwidth,yheight)\n",
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"p1.origin=(0,0,125)\n",
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"p1.pixels = (800, 800)\n",
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"p1.color_by = 'material'\n",
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"p1.colors=material_colors\n",
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"#xy plot\n",
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"p2 = openmc.Plot()\n",
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"p2.background='white'\n",
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"p2.basis='xy'\n",
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"p2.width=(xwidth, yheight)\n",
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"p2.pixels = (800,800)\n",
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"p2.origin=(0,0,100)\n",
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"p2.color_by='material'\n",
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"p2.colors=material_colors\n",
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"\n",
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"plots=openmc.Plots([p1,p2])\n",
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"openmc.plot_inline(plots)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8d02c4c6-2f47-44ac-989c-1ecaf9441450",
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"metadata": {},
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"source": [
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"Now we need to define som settings for our calculations.\n",
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"\n",
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"First of all - we need to some neutrons to kick-start or chain reaction. In OpenMC this is doen by defining a source region. Here this is simply defined as being a region that encloses the MSRE core.\n",
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"\n",
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"Next we define some settings for the Monte Carlo-computation, such as how many particles we would initially run with. \n",
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"\n",
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"After the members of the settings python object have been filled to our desires, we export this to a settings xml-file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6a17a765-10dd-4666-9f66-2d7f7931bb3e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a neutron source for kick-starting\n",
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"source_volume=openmc.stats.Box([-125,-125,0],[125,125,500], only_fissionable=True)\n",
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"source = openmc.Source(space=source_volume)\n",
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"source.angle=openmc.stats.Isotropic()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ee524ba8-d65f-48ab-8024-e05f3409a6ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Finally we build a settings object for OpenMC where we define parameters for the run.\n",
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"settings = openmc.Settings()\n",
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"settings.source = source\n",
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"settings.batches = 20\n",
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"settings.inactive = 5\n",
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"settings.particles = 20000\n",
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"settings.export_to_xml()\n",
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"\n",
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"openmc.run()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dfadbeb7-077b-42c6-8714-c92829536998",
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"metadata": {},
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"source": [
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"Now that we have a running model let's try to do some more useful work with and extract some data from the model. To do this we need to specify what information we want to extract before \n",
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"starting simulations. In many Monte Carlo particle transport codes, we add objects known as tallies to our models. In this respect OpenMC is no different.\n",
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"\n",
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"We will add tallies to monitor the neutron flux, and the fission sites in volumes along the geomtrical slices through our reactor that we plotted earlier.\n",
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"\n",
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"A tally needs to know what to measure and where to measure that. In OpenMC the \"where\" is known as a filter and the \"what\" is known as a score.\n",
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"In or case we'd like to spatially resolve the flux so we first generate mesh object as filters and then add that to tally objects. In addtion we assign a list of scores to the score-member of the tallies. Lastly (as always) we export this to an xml-file which will be read by OpenMC."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3383b0ba-4cec-4733-a271-a032079d0b00",
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"metadata": {},
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"outputs": [],
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"source": [
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"mesh1=openmc.RegularMesh()\n",
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"mesh1.dimension = [400,400,1]\n",
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"mesh1.lower_left = [-125, -125, 90]\n",
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"mesh1.upper_right = [125, 125, 110]\n",
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"mesh1_filter = openmc.MeshFilter(mesh1)\n",
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"\n",
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"mesh2=openmc.RegularMesh()\n",
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"mesh2.dimension = [400,1,400]\n",
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"mesh2.lower_left = [-175, -10, -50]\n",
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"mesh2.upper_right = [175, 10, 400]\n",
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"mesh2_filter = openmc.MeshFilter(mesh2)\n",
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"\n",
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"t1 = openmc.Tally(name='flux1')\n",
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"t1.filters =[mesh1_filter]\n",
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"t1.scores = ['flux','fission']\n",
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"\n",
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"t2 = openmc.Tally(name='flux2')\n",
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"t2.filters =[mesh2_filter]\n",
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"t2.scores = ['flux','fission']\n",
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"\n",
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"tallies=openmc.Tallies([t1,t2])\n",
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"tallies.export_to_xml()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e62c1ebc-75bf-44eb-8dde-90fef06e0030",
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"metadata": {},
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"source": [
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"We have to re-run our simulation after generating the ```tallies.xml``` file.\n",
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"\n",
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"Note that we forcibly remove the _old_ datafiles first."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4e624e13-f734-4e58-9307-f3b4d56151a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.system(\"rm -f summary.h5 statepoint.20.h5\")\n",
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"openmc.run()"
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]
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},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "430aff88-5238-4a68-8d7a-2d0de99eb77d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"After the run has finished the data we are after resides in the \"statepoint\" file that OpenMC saves.\n",
|
||||
"In the below code, we will open that and extract the mean values for neutron flux."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d7bebead-b04a-4a38-8802-0d01239e2c57",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sp=openmc.StatePoint('statepoint.20.h5')\n",
|
||||
"\n",
|
||||
"tl1=sp.get_tally(name='flux1')\n",
|
||||
"tl2=sp.get_tally(name='flux2')\n",
|
||||
"\n",
|
||||
"flux1=tl1.get_slice(scores=['flux'])\n",
|
||||
"flux2=tl2.get_slice(scores=['flux'])\n",
|
||||
"\n",
|
||||
"flux1.mean.shape=(400,400)\n",
|
||||
"flux2.mean.shape=(400,400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "8205bf93-0351-458e-9d44-f8b43b0509ac",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The last 2 lines are necessary to reshape the flux maps into a 400x400 grid.\n",
|
||||
"\n",
|
||||
"In the end we plot the maps using matplotlib"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "84b1be81-188c-467a-9da5-74e7054792d4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"fig,(ax1,ax2)=plt.subplots(ncols=2,figsize=(20,16), constrained_layout=True)\n",
|
||||
"\n",
|
||||
"ax1.set_xticks(np.arange(0,401,399/4))\n",
|
||||
"ax1.set_xticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax2.set_xticks(np.arange(0,400,399/4))\n",
|
||||
"ax2.set_xticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax1.set_yticks(np.arange(0,400,399/4))\n",
|
||||
"ax1.set_yticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax2.set_yticks(np.arange(0,400,399/4))\n",
|
||||
"ax2.set_yticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax1.set_xlabel('X / cm')\n",
|
||||
"ax1.set_ylabel('Y / cm')\n",
|
||||
"\n",
|
||||
"ax2.set_xlabel('X / cm')\n",
|
||||
"ax2.set_ylabel('Z / cm')\n",
|
||||
"im1=ax1.imshow(flux1.mean)\n",
|
||||
"fig.colorbar(im1,ax=ax1,shrink=0.4)\n",
|
||||
"im2=ax2.imshow(flux2.mean)\n",
|
||||
"fig.colorbar(im2,ax=ax2,shrink=0.4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1099e99a-e4a8-46c8-b3e4-00259de64c82",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Similarly we can plot fission reactions:\n",
|
||||
"\n",
|
||||
"fission1=tl1.get_slice(scores=['fission'])\n",
|
||||
"fission2=tl2.get_slice(scores=['fission'])\n",
|
||||
"\n",
|
||||
"fission1.mean.shape=(400,400)\n",
|
||||
"fission2.mean.shape=(400,400)\n",
|
||||
"\n",
|
||||
"fig,(ax1,ax2)=plt.subplots(ncols=2,figsize=(20,16), constrained_layout=True)\n",
|
||||
"\n",
|
||||
"ax1.set_xticks(np.arange(0,401,399/4))\n",
|
||||
"ax1.set_xticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax2.set_xticks(np.arange(0,400,399/4))\n",
|
||||
"ax2.set_xticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax1.set_yticks(np.arange(0,400,399/4))\n",
|
||||
"ax1.set_yticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax2.set_yticks(np.arange(0,400,399/4))\n",
|
||||
"ax2.set_yticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax1.set_xlabel('X / cm')\n",
|
||||
"ax1.set_ylabel('Y / cm')\n",
|
||||
"\n",
|
||||
"ax2.set_xlabel('X / cm')\n",
|
||||
"ax2.set_ylabel('Z / cm')\n",
|
||||
"im1=ax1.imshow(fission1.mean)\n",
|
||||
"fig.colorbar(im1,ax=ax1,shrink=0.4)\n",
|
||||
"im2=ax2.imshow(fission2.mean)\n",
|
||||
"fig.colorbar(im2,ax=ax2,shrink=0.4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cd6f56f4-6ade-4e4f-b751-e67df40b3d8c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Suppose we now would like to see what the energy spectrum of the neutrons generated in our reactor is. To explore this we will add another talliy to our simulation. This time however, instead of a spatial regular mesh, the tally will have an energy filter. Furthermore, we restrict the tally to neutron flux and fission events within the fuel salt, by means of a material filter. Unfortunately to fill the new tally we have to re-run the simulation."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e440a7a0-3f4d-4067-ba47-09245d82400e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#define a lograrithmic binning from 1keV to 10 MeV\n",
|
||||
"ef=energyrange=openmc.EnergyFilter(np.logspace(3,7,200))\n",
|
||||
"te = openmc.Tally(name='energy')\n",
|
||||
"\n",
|
||||
"sf = openmc.MaterialFilter(salt)\n",
|
||||
"te.filters =[ef,sf]\n",
|
||||
"te.scores = ['flux','fission']\n",
|
||||
"\n",
|
||||
"tallies.append(te)\n",
|
||||
"tallies.export_to_xml()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "aa856b84-144c-49fc-8184-1c35eedc0449",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"os.system(\"rm -f summary.h5 statepoint.20.h5\")\n",
|
||||
"openmc.run()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7f431317-75ca-4960-ba3a-fae1c13e643b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sp=openmc.StatePoint('statepoint.20.h5')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4920fa40-9468-4fdc-a7e6-4cfe1a88612d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tl=sp.get_tally(name='energy')\n",
|
||||
"\n",
|
||||
"e1=tl.get_slice(scores=['flux'])\n",
|
||||
"e2=tl.get_slice(scores=['fission'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "22a6b009-f2d9-441b-ac98-4cb71c6c99a7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig,ax=plt.subplots(figsize=(7,3.5),squeeze=True)\n",
|
||||
"ax.plot(ef.values[:199],e1.mean[:,0,0])\n",
|
||||
"ax.plot(ef.values[:199],e2.mean[:,0,0])\n",
|
||||
"ax.set_xscale('log')\n",
|
||||
"ax.set_yscale('log')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a9aefede-8fdf-4494-b0c0-d19e41fb6c4f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"A reactor with a k_{eff} significantly higher than 1 is likely not what you want. Therefore we need to modify our initial model to also include a set of control rods. In the MSRE these rods consisted of three sets of cylindrical elements made from a Al_2O_3/Gd_2O_3-mixture. These absorb neutrons to \"dampen\" the nuclear process - something also known as poisoning.\n",
|
||||
"The cylindrical elements were stacked to form the control rods (~=80''), which can be inserted into the reactor core in 3 of the 4 voids visible in the centre of the XZ-geometry of the reactor.\n",
|
||||
"\n",
|
||||
"To run our reactor model with control-rods we will now simply point openmc at a different geometry-file, and append the missing materials to the materials list. To visualize we also add tallies to track the absorption."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "57d11f47-2c4a-4549-8944-ef1aacf81c7e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#geometry\n",
|
||||
"os.system('rm -f materials.xml geometry.xml tallies.xml')\n",
|
||||
"h5m_filepath=\"../msre/msre_control_in.h5m\"\n",
|
||||
"dag_univ = openmc.DAGMCUniverse(h5m_filepath)\n",
|
||||
"geom = openmc.Geometry(root=dag_univ)\n",
|
||||
"geom.export_to_xml()\n",
|
||||
"\n",
|
||||
"b_w_conc={'Al2O3':0.3,'Gd2O3':0.7} # Wt. (D. Shen et.al. Nucl. Sc. & Eng., v. 195, pp. 825, 2021)\n",
|
||||
"A_w={'Al':26.9815385,'Gd':157.25, 'O':15.99} #g/mol, (Webelements.com)\n",
|
||||
"rho={'Al2O3':3.987,'Gd2O3':7.07} # g /cc, (Wikipedia: Aluminium_oxide & Gadolinium(III)_oxide)\n",
|
||||
"b_mol_w={'Al2O3':2*A_w['Al']+ 3*A_w['O'],'Gd2O3':2*A_w['Gd']+3*A_w['O']}\n",
|
||||
"bush_mol_comp={'Al2O3':(b_w_conc['Al2O3']/b_mol_w['Al2O3'])/( b_w_conc['Al2O3']/b_mol_w['Al2O3'] + b_w_conc['Gd2O3']/b_mol_w['Gd2O3'] ),\n",
|
||||
" 'Gd2O3':(b_w_conc['Gd2O3']/b_mol_w['Gd2O3'])/( b_w_conc['Al2O3']/b_mol_w['Al2O3'] + b_w_conc['Gd2O3']/b_mol_w['Gd2O3'] )}\n",
|
||||
"\n",
|
||||
"bush=openmc.Material(name='bush')\n",
|
||||
"bush.add_element('Al',2/5*bush_mol_comp['Al2O3'],'ao')\n",
|
||||
"bush.add_element('Gd',2/5*bush_mol_comp['Gd2O3'],'ao')\n",
|
||||
"bush.add_element('O',3/5*bush_mol_comp['Al2O3']+3/5*bush_mol_comp['Gd2O3'],'ao')\n",
|
||||
"bush.set_density('g/cc',b_w_conc['Al2O3']*rho['Al2O3'] +b_w_conc['Gd2O3']*rho['Gd2O3'] )\n",
|
||||
"\n",
|
||||
"t1 = openmc.Tally(name='flux1')\n",
|
||||
"t1.filters =[mesh1_filter]\n",
|
||||
"t1.scores = ['flux','fission','absorption']\n",
|
||||
"\n",
|
||||
"t2 = openmc.Tally(name='flux2')\n",
|
||||
"t2.filters =[mesh2_filter]\n",
|
||||
"t2.scores = ['flux','fission','absorption']\n",
|
||||
"\n",
|
||||
"tallies=openmc.Tallies([t1,t2])\n",
|
||||
"tallies.export_to_xml()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"materials=openmc.Materials([helium,salt,graphite,inconel,inor,bush])\n",
|
||||
"materials.export_to_xml()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ea8d4900-f24e-4f0e-b5af-39ad3342bebc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xwidth = 350\n",
|
||||
"yheight = 350\n",
|
||||
"material_colors={salt:'red', inor:'lightblue', inconel:'blue',helium:'white',graphite:'gray',bush:'purple'}\n",
|
||||
"#xz plot\n",
|
||||
"p1 = openmc.Plot()\n",
|
||||
"p1.background='white'\n",
|
||||
"p1.basis = 'xz'\n",
|
||||
"p1.width = (xwidth,yheight)\n",
|
||||
"p1.origin=(0,0,125)\n",
|
||||
"p1.pixels = (800, 800)\n",
|
||||
"p1.color_by = 'material'\n",
|
||||
"p1.colors=material_colors\n",
|
||||
"#xy plot\n",
|
||||
"p2 = openmc.Plot()\n",
|
||||
"p2.background='white'\n",
|
||||
"p2.basis='xy'\n",
|
||||
"p2.width=(xwidth, yheight)\n",
|
||||
"p2.pixels = (800,800)\n",
|
||||
"p2.origin=(0,0,100)\n",
|
||||
"p2.color_by='material'\n",
|
||||
"p2.colors=material_colors\n",
|
||||
"\n",
|
||||
"p3 = openmc.Plot()\n",
|
||||
"p3.background='white'\n",
|
||||
"p3.basis='xy'\n",
|
||||
"p3.width=(xwidth/10, yheight/10)\n",
|
||||
"p3.pixels = (800,800)\n",
|
||||
"p3.origin=(0,0,100)\n",
|
||||
"p3.color_by='material'\n",
|
||||
"p3.colors=material_colors\n",
|
||||
"\n",
|
||||
"plots=openmc.Plots([p1,p2,p3])\n",
|
||||
"openmc.plot_inline(plots)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c024f9cd-3ca8-4f8d-a5d0-60eca95cac3c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"os.system(\"rm -f summary.h5 statepoint.20.h5\")\n",
|
||||
"openmc.run()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "046eb354-e11f-4dfa-90e8-59768cc4c063",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Plot maps of the absorption\n",
|
||||
"sp=openmc.StatePoint('statepoint.20.h5')\n",
|
||||
"\n",
|
||||
"tl1=sp.get_tally(name='flux1')\n",
|
||||
"tl2=sp.get_tally(name='flux2')\n",
|
||||
"\n",
|
||||
"abs1=tl1.get_slice(scores=['absorption'])\n",
|
||||
"abs2=tl2.get_slice(scores=['absorption'])\n",
|
||||
"\n",
|
||||
"abs1.mean.shape=(400,400)\n",
|
||||
"abs2.mean.shape=(400,400)\n",
|
||||
"\n",
|
||||
"fig,(ax1,ax2)=plt.subplots(ncols=2,figsize=(20,16), constrained_layout=True)\n",
|
||||
"\n",
|
||||
"ax1.set_xticks(np.arange(0,401,399/4))\n",
|
||||
"ax1.set_xticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax2.set_xticks(np.arange(0,400,399/4))\n",
|
||||
"ax2.set_xticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax1.set_yticks(np.arange(0,400,399/4))\n",
|
||||
"ax1.set_yticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax2.set_yticks(np.arange(0,400,399/4))\n",
|
||||
"ax2.set_yticklabels(np.arange(-175,176,350./4))\n",
|
||||
"ax1.set_xlabel('X / cm')\n",
|
||||
"ax1.set_ylabel('Y / cm')\n",
|
||||
"\n",
|
||||
"ax2.set_xlabel('X / cm')\n",
|
||||
"ax2.set_ylabel('Z / cm')\n",
|
||||
"im1=ax1.imshow(abs1.mean)\n",
|
||||
"fig.colorbar(im1,ax=ax1,shrink=0.4)\n",
|
||||
"im2=ax2.imshow(abs2.mean)\n",
|
||||
"fig.colorbar(im2,ax=ax2,shrink=0.4)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
61
msre_docker/README.md
Normal file
61
msre_docker/README.md
Normal file
|
|
@ -0,0 +1,61 @@
|
|||
# docker-msre
|
||||
This repository is created for the purpose of creating a docker conatiner which includes not only support for OpenMC with DAGMC/MOAB, embree, and double_down libraries, but also a Jupyter notebook server. The "raison d'etre" for this is to run a virtual version of The Molten Salt Reactor Experiment (MSRE). The experiment itself was performed physically in the '60s at Oak Ridge National Lab (ORNL), Tennessee, US.
|
||||
|
||||
# Getting started:
|
||||
1. Install the docker engine on your system (for windows and MacOS: docker-desktop, for Linux: docker server)
|
||||
Instructions for how to do this may be found on the Docker website at: [Docker installation](https://docs.docker.com/engine/install/)
|
||||
2. Run the msre docker container.
|
||||
- Linux:
|
||||
1. Open a terminal, navigate to a directory where you want to run.
|
||||
2. Download the msre-docker runscript, and run it:
|
||||
```{bash tidy=false}
|
||||
wget https://raw.githubusercontent.com/openmsr/msre_docker/main/run_docker.sh
|
||||
chmod u+x run_docker.sh
|
||||
./run_docker.sh
|
||||
```
|
||||
The script contains a call to ```docker run``` with some options preset. Among other things it sets up a subdirectory called notebooks which is shared between the conatiner and the host so that you can keep data between runs.
|
||||
Feel free to inspect the runscript if you prefer to run your docker manually. If you'd prefer for instance to run commands in the docker from a shell
|
||||
you may do so by adding the option ```--entrypoint /bin/bash``` to the docker run command.
|
||||
3. If you used the run-script "as is" you should now be able to open a browser and point it to "https://127.0.0.1:8888" and be treated with a login page to Jupyter.
|
||||
Please enter "docker" in the password box, which should provide you with the base Jupter Lab screen.
|
||||
4. In the example_notebooks folder open the MSRE.ipynb file. You are now ready to run!
|
||||
|
||||
## Troubleshooting:
|
||||
- If the wget command fails with a message complaining about certificates you may bypass this by adding ```--no-check-certificate``` to the command.
|
||||
- If you get an error message saying: "docker: Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?." that likely means that you need to start the docker engine. This can be done using the command:
|
||||
```sudo dockerd```
|
||||
- If you get an error similar to: "Got permission denied while trying to connect to the Docker daemon socket...", it is likely that your user needs to be added to the "docker" group. To check which groups your user belongs to you may use the groups command. To add your user to the docker group you may use:
|
||||
```sudo usermod -aG docker <username>```
|
||||
where \<username\> is your unix username.
|
||||
|
||||
- Windows:
|
||||
There are two options for running the docker on a windows system:
|
||||
1. Install the docker-desktop application.
|
||||
Once you have downloaded and installed the application in the normal manner from docker.com, you may proceed by:
|
||||
1. Open the command prompt and issue: docker pull copenhagenatomics/msre:0.1.0. This will look like this 
|
||||
3. Open the docker-desktop application. The msre docker image should now be visible on the "images"-tab. 
|
||||
4. Click on the run-button and then on options. Here we need to set a few parameters to mimic the run_docker.sh script. You should now have a dialog: 
|
||||
4. Once the docker has been downloaded and started, you should see among many other messages something about a Jupyter Server at "http://127.0.0.1:8888/lab?token=...".  Please copy this entire URL and paste it into the adress field of your browser. You should now see the Jupyter lab interface and may proceed.
|
||||
|
||||
2. Install Windows Subsystem for Linux (WSL) and run the docker from the command-line there.
|
||||
N.b. In fact the docker-desktop application also relies on WSL.
|
||||
WSL is a native windows system that enables users to run anyh and all Linux applications natively on Windows 10 and 11, including parallelization and even access to acceleration through GPUs.
|
||||
Please note that the process may require updates to Windows to be installed and also rebooting your system. Furthermore, it may also require you to edit settings in the BIOS to enable virtualization on your system.
|
||||
First you must install a Linux kernel in WSL. Open a command prompt and run the command
|
||||
```wsl --install --distribution debian``` (or shorter ```wsl --install -d debian```)
|
||||
|
||||
|
||||
Once this is done wsl will start and you can proceed proceed to get the run-script using wget:
|
||||
```wget --no-check-certificate https://raw.githubusercontent.com/openmsr/msre_docker/main/run_docker.sh```
|
||||
Set the executable bit on this script and run it to start the docker as if on Linux:
|
||||
```chmod u+x run_docker.sh```
|
||||
At this point you should be able to run the script ```./run_docker.sh``` and point your browser to "127.0.0.1:8888" or "localhost:8888" to get into Jupyter notebook.
|
||||
|
||||
## Troubleshooting:
|
||||
Should you run into problems, please ensure that the following options have been set.
|
||||
First, we need to tell docker to use the debian kernel in WSL. This is done in the setting pane under resources 
|
||||
Second, a number of windows features need to be turned on:
|
||||
|
||||
Should you still have problems - please contact us (for instance by opening an issue in this repo) and we'll help you out.
|
||||
3. Jupiter notebook
|
||||
The default password for the jupyter notebook is 'docker'.
|
||||
2
msre_docker/build_docker.sh
Executable file
2
msre_docker/build_docker.sh
Executable file
|
|
@ -0,0 +1,2 @@
|
|||
#!/usr/bin/env bash
|
||||
docker build -t copenhagenatomics/msre:0.1.1 .
|
||||
12
msre_docker/run_docker.sh
Executable file
12
msre_docker/run_docker.sh
Executable file
|
|
@ -0,0 +1,12 @@
|
|||
#!/usr/bin/env bash
|
||||
PWD=`pwd`
|
||||
|
||||
mountdir=$1
|
||||
if [ "a$1" == "a" ]; then
|
||||
mountdir=notebooks
|
||||
fi
|
||||
if [ ! -d $mountdir ]; then
|
||||
mkdir $mountdir
|
||||
fi
|
||||
|
||||
docker run -it -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -e JUPYTER_TOKEN=docker -v ${PWD}/${mountdir}:/home/usr/notebooks copenhagenatomics/msre:0.1.1
|
||||
12
msre_docker/run_docker_daemon.sh
Executable file
12
msre_docker/run_docker_daemon.sh
Executable file
|
|
@ -0,0 +1,12 @@
|
|||
#!/usr/bin/env bash
|
||||
PWD=`pwd`
|
||||
|
||||
mountdir=$1
|
||||
if [ "a$1" == "a" ]; then
|
||||
mountdir=notebooks
|
||||
fi
|
||||
if [ ! -d $mountdir ]; then
|
||||
mkdir $mountdir
|
||||
fi
|
||||
|
||||
docker run -d --restart unless-stopped -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -e JUPYTER_TOKEN=docker -v ${PWD}/${mountdir}:/home/usr/notebooks copenhagenatomics/msre:0.1.1
|
||||
Loading…
Add table
Add a link
Reference in a new issue