How we are helping to model fusion energy devices
In fusion energy research, confining hot plasma is one of the key challenges in achieving a stable fusion device, also called a tokamak. A tokamak uses magnetic fields to levitate fuel plasma in a doughnut-shaped chamber. The leading approach to the confinement of plasma requires precise control of the magnetic fields. FreeGSNKE (pronounced “free-gee-snake”) is a newly developed open-source Python-based simulation code designed to help researchers study and optimise plasma shaping and control.
Developed through the Fusion Computing Lab collaboration with the UK Atomic Energy Authority, FreeGSNKE is a publicly available, validated code for modelling two-dimensional, free-boundary plasma equilibria. It builds upon the well-established FreeGS code, and uses FreeGS4E, a fork of FreeGS, to solve different types of free-boundary problems. These powerful solvers include a static solver to determine plasma shape and a dynamic solver to model how the plasma evolves over time. Additionally, these tools have been tested and validated using real-world data from the MAST-U tokamak, where they assist in modelling strongly shaped plasmas.
One of FreeGSNKE’s biggest advantages is its compatibility with machine learning. Unlike many other simulation tools, it integrates seamlessly with machine learning libraries, making it easier to develop new plasma control techniques. This opens exciting possibilities for reinforcement learning, where AI can be trained to make real-time control decisions and support classical control algorithms used in traditional plasma shaping studies.
By making FreeGSNKE open-source, we aim to encourage collaboration and innovation in plasma research. This tool is available to the fusion community, helping researchers worldwide explore new ideas in plasma control and equilibrium modelling. We believe that by sharing high-quality research openly, we can accelerate progress towards commercial fusion energy.
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Research Publications
2024

FreeGSNKE: A Python-based dynamic free-boundary toroidal plasma equilibrium solver
N. C. Amorisco et al, “FreeGSNKE: A Python-based dynamic free-boundary toroidal plasma equilibrium solver”, Physics of Plasmas, 31, 042517 (2024). DOI: 10.1063/5.0188467.
Keywords: Non linear dynamics, Eddy current, Machine learning, Programming languages, Control theory, Newton Raphson method, Magnetohydrodynamics, Plasma confinement, Tokamaks

Emulation Techniques for Scenario and Classical Control Design of Tokamak Plasmas
A. Agnello et al, “Emulation techniques for scenario and classical control design of tokamak plasmas”, Physics of Plasmas, 31, 043091 (2024). DOI: 10.1063/5.0187822.
Keywords: Machine learning, Programming languages, Control theory, Plasma confinement, Tokamaks

K. Pentland et al, “Validation of the static forward Grad-Shafranov equilibrium solvers in FreeGSNKE and Fiesta using EFIT++ reconstructions from MAST-U”, Physica Scripta (2024). DOI: 10.1088/1402-4896/ada192.
Keywords: Machine learning, Programming languages, Control theory, Plasma confinement, Tokamaks