kevinm387 / neutronics-workshop

A workshop covering a range of fusion relevant analysis and simulations with OpenMC, DAGMC, Paramak and other open source fusion neutronics tools

Fusion Neutronics workshop

A selection of resources for learning fusion neutronics simulations with a particular focus on OpenMC with DAGMC and the Paramak

There are a few slides that introduce the workshop, cover some theory and show the expected outputs of each task.

The use of OpenMC, DAGMC and Paramak for neutronics analysis requires several software packages and nuclear data. These have all been installed inside a Docker container. The video below gives a brief explainer of what to expect in the workshop and some motivation for learning neutronics.

The neutronics workshop was created by and is maintained by Jonathan Shimwell. The repository has benefited greatly from contributions from John Billingsley and others

The resource has proven most useful as it is one of the few open source and accessible fusion neutronics training resources.

The repository has benefited greatly from user feedback. Please feel free to raise Github issues or reach out in the discussions section if you spot anything that needs fixing or think of an improvement. Contributions are also very welcome as pull requests to the develop branch.

Run in the cloud (low CPU count)

One advantage of the containerization of the entire software stack is that it can be deployed in the cloud and launched in your browser on demand.

To demonstrate this I've deployed the workshop on a minimal cloud computer which can be spun up by anyone going to the following URL. This is mainly intended for demonstration purposes and the CPU count is low to keep my costs down. For the the best performance there are other options for deployment which take more effort than clicking a link but provide more computing power. :point_right: Try the workshop in your browser

Run in the cloud (higher CPU count)

The repository is also ready for deployment on Github Codespaces which allows users to launch the containerized environment on more powerful cloud computers without installing anything locally.

• To get started sign up to the free 👉 codespaces Beta.

• Then follow 👉 this link to config a compute instance 👉

  

• VS Code will then launch in the browser, once loaded you must select the conda python interpreter to enable the correct Python environment.

Local Installation

There are video tutorials for this section which accompany the step by step instructions below.

• Ubuntu installation video 👉

  

• Windows installation video 👉

  

• Mac installation video 👉

  

1. Install Docker CE for Ubuntu, Mac OS, or Windows, including the part where you enable docker use as a non-root user.

2. Pull the docker image from the store by typing the following command in a terminal window, or Windows users might prefer PowerShell.

   docker pull ghcr.io/fusion-energy/neutronics-workshop

   Expand - Having permission denied errors?

   
        If you are running the command from Linux or Ubuntu terminal and getting permission denied messages back.
        Try running the same command with with elevated user permissions by adding sudo at the front.
        sudo docker pull ghcr.io/fusion-energy/neutronics-workshop
        Then enter your password when prompted.
        

3. Now that you have the docker image you can enable graphics linking between your os and docker, and then run the docker container by typing the following commands in a terminal window.

   docker run -p 8888:8888 ghcr.io/fusion-energy/neutronics-workshop

   Expand - Having permission denied errors?

   
        If you are running the command from Linux or Ubuntu terminal and getting permission denied messages back.
        Try running the same command with with elevated user permissions by adding sudo at the front.
        sudo docker run -p 8888:8888 ghcr.io/fusion-energy/neutronics-workshop
        Then enter your password when prompted.
        

4. A URL should be displayed in the terminal and can now be opened in the internet browser of your choice. Select and open the URL at the end of the terminal print out (highlighted below)

5. Some tasks require the use of Paraview to view the 3D meshes produced. Parview can be download from here.

   Expand - Ubuntu terminal commands for Paraview install

   
        sudo apt update && sudo apt-get install paraview
        

6. Some tasks require the use of CAD software to view the 3D geometry produced. FreeCAD is one option for this and can be downloaded here.

   Expand - Ubuntu terminal commands for FreeCAD install

   
            sudo apt update && sudo apt-get install freecad
            

Workshop tasks

The task videos are all available on a Gather Town space which is great for working through the workshop with colleagues.

Tasks	Keywords	Video(s)
Task 1 - Cross sections	Nuclear data, cross-sections, MT numbers, Doppler	link1 link2 link3 link4
Task 2 - Materials	Materials, Neutronics Material Maker, Mixed materials	link
Task 3 - CSG geometry	CSG geometry, Geometry visualisation	link
Task 4 - Sources	Neutron point sources, Gamma sources, Plasma sources, Neutron track visualisation	link
Task 5 - TBR	Tritium Breeding Ratio, Cell tallies, Simulations	link
Task 6 - DPA	Displacements Per Atom, Cell tallies, Simulations, Volume calculations	link
Task 7 - Neutron and photon spectra	Neutron Spectra, Photon Spectra, Cell tallies, Energy group structures, Flux, Current	link
Task 8 - Mesh tallies	Mesh tallies, Structured meshes	link
Task 9 - Dose	Dose, Cell tallies, Dose coefficients
Task 10 - Making CAD geometry	Parametric CAD geometry, Paramak, Geometry visualisation	link
Task 11 - CAD Cell tallies	CAD-based neutronics, Cell tallies, DAGMC, Heating
Task 12 - CAD Mesh tallies	CAD-based neutronics, Mesh tallies, Paramak, DAGMC, Fast flux
Task 13 - Variance_reduction	Variance reduction, weight windows
Task 14 - Activation transmutation depletion	Isotope build up and tally variation as a function of time
Task 15 - Techniques for sampling parameter space	Sampling, Interpolation, Multi-dimensional parameter studies
Task 16 - Parameter study optimisation	Data science machine learning approaches