Planar coil optimization for the Eos stellarator using sparse regression

Abstract A challenge in the design of stellarators for confining plasma at conditions relevant to fusion energy generation is designing a feasible set of magnetic field coils which can create the necessary confining field. One active direction of investigation involves the creation of a set of simplified planar coils to approximate the desired magnetic field, split between large plasma encircling coils which generate the majority of the field and small shaping coils near the plasma surface which correct remaining errors in the field geometry. The problem of optimizing currents in these coils is inherently ill-posed due to the Biot–Savart Law. In this work, the problem of optimizing the current distribution of the shaping coils is posed as a sparse regression to minimize coil count while targeting <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>B</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:mo fence="false" stretchy="false">⟨</mml:mo> <mml:mrow> <mml:mi mathvariant="bold">B</mml:mi> </mml:mrow> <mml:mo>⋅</mml:mo> <mml:mrow> <mml:mi mathvariant="bold">n</mml:mi> </mml:mrow> <mml:mo fence="false" stretchy="false">⟩</mml:mo> </mml:mrow> </mml:math> , a metric of confinement quality, as a least-squares objective. The goal is to improve manufacturability and conductor efficiency by using fewer coils at higher currents. Using an ensemble of sparse optimization algorithms such as LASSO, relax-and-split, and heuristic methods, Pareto fronts between coil sparsity and B n properties are identified. Differences between the optimization algorithms are evaluated, demonstrating up to 20% reductions in mean B n at the same sparsity compared to a previously used heuristic method. The perturbation sensitivity of the sparse solutions to manufacturing misalignment is also evaluated. The results from the study demonstrate the potential and diversity of sparse optimization strategies in stellarator coil design problems.

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Publication Details

Journal
Plasma Physics and Controlled Fusion
Published
2025-02-13
DOI
https://doi.org/10.1088/1361-6587/adb5b7
Citations
1
Primary Topic
Astronomical Observations and Instrumentation
Type
article
Field-Weighted Citation Impact
1.33
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article

Planar coil optimization for the Eos stellarator using sparse regression

Ryan Wu, Charles Swanson, Thomas Krüger
1 citations
Plasma Physics and Controlled Fusion
Astronomical Observations and Instrumentation
1.33
article

Planar coil optimization for the Eos stellarator using sparse regression

Ryan Wu, Charles Swanson, Thomas Krüger
article en
1 citations

Abstract

Abstract A challenge in the design of stellarators for confining plasma at conditions relevant to fusion energy generation is designing a feasible set of magnetic field coils which can create the necessary confining field. One active direction of investigation involves the creation of a set of simplified planar coils to approximate the desired magnetic field, split between large plasma encircling coils which generate the majority of the field and small shaping coils near the plasma surface which correct remaining errors in the field geometry. The problem of optimizing currents in these coils is inherently ill-posed due to the Biot–Savart Law. In this work, the problem of optimizing the current distribution of the shaping coils is posed as a sparse regression to minimize coil count while targeting <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>B</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>=</mml:mo> <mml:mo fence="false" stretchy="false">⟨</mml:mo> <mml:mrow> <mml:mi mathvariant="bold">B</mml:mi> </mml:mrow> <mml:mo>⋅</mml:mo> <mml:mrow> <mml:mi mathvariant="bold">n</mml:mi> </mml:mrow> <mml:mo fence="false" stretchy="false">⟩</mml:mo> </mml:mrow> </mml:math> , a metric of confinement quality, as a least-squares objective. The goal is to improve manufacturability and conductor efficiency by using fewer coils at higher currents. Using an ensemble of sparse optimization algorithms such as LASSO, relax-and-split, and heuristic methods, Pareto fronts between coil sparsity and B n properties are identified. Differences between the optimization algorithms are evaluated, demonstrating up to 20% reductions in mean B n at the same sparsity compared to a previously used heuristic method. The perturbation sensitivity of the sparse solutions to manufacturing misalignment is also evaluated. The results from the study demonstrate the potential and diversity of sparse optimization strategies in stellarator coil design problems.

Plasma Physics and Controlled FusionVol. 67(3)
United States Department of Energy (US)
Openalex Percentile: Top 27%
Astronomical Observations and Instrumentation
1.33
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