Near-axis quasi-isodynamic database

In this work we investigate the landscape of quasi-isodynamic (QI) stellarators using the near-axis expansion of the magnetic field. Building on recent theoretical developments, we construct a database of more than 800 000 stable, approximately QI vacuum magnetic configurations. These configurations span a range of field period numbers and other geometric control parameters, including the magnetic axis shape and plasma elongation. To evaluate each configuration, we use a broad set of measures, including effective ripple, sensitivity of the Shafranov shift to changes in plasma $\beta$ , the prevalence of maximum- $\mathcal{J}$ trapped particles and the Rosenbluth–Hinton residual, among others. This enables an exhaustive, thorough and quantitative characterisation of the database. Statistical analysis and modern machine learning techniques are then employed to find correlations and identify key descriptors and heuristics to help understand tendencies that govern the behaviour of numerical optimisation. The database provides baseline configurations for further studies and to serve as tailored initial conditions for optimisation. With this work we initiate a long-term program to complete a systematic exploration of QI stellarator design space.

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Publication Details

Journal
Journal of Plasma Physics
Published
2026-05-21
DOI
https://doi.org/10.1017/s0022377826101688
Primary Topic
Magnetic confinement fusion research
Type
article
Field-Weighted Citation Impact
0.00

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article

Near-axis quasi-isodynamic database

Eduardo Rodríguez, Gabriel Plunk
Journal of Plasma Physics
Magnetic confinement fusion research
article

Near-axis quasi-isodynamic database

Eduardo Rodríguez, Gabriel Plunk
article en

Abstract

In this work we investigate the landscape of quasi-isodynamic (QI) stellarators using the near-axis expansion of the magnetic field. Building on recent theoretical developments, we construct a database of more than 800 000 stable, approximately QI vacuum magnetic configurations. These configurations span a range of field period numbers and other geometric control parameters, including the magnetic axis shape and plasma elongation. To evaluate each configuration, we use a broad set of measures, including effective ripple, sensitivity of the Shafranov shift to changes in plasma $\beta$ , the prevalence of maximum- $\mathcal{J}$ trapped particles and the Rosenbluth–Hinton residual, among others. This enables an exhaustive, thorough and quantitative characterisation of the database. Statistical analysis and modern machine learning techniques are then employed to find correlations and identify key descriptors and heuristics to help understand tendencies that govern the behaviour of numerical optimisation. The database provides baseline configurations for further studies and to serve as tailored initial conditions for optimisation. With this work we initiate a long-term program to complete a systematic exploration of QI stellarator design space.

Journal of Plasma PhysicsVol. 92(3)
Max Planck Institute for Plasma Physics (DE)
EUROfusion, European Commission, Max-Planck-Gesellschaft, Euratom Research and Training Programme
Sustainable cities and communities
Openalex Percentile: Top 6%
Magnetic confinement fusion research
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