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Record W4406913224 · doi:10.1088/1741-4326/ada56c

Stellarator fusion systems enabled by arrays of planar coils

2025· article· en· W4406913224 on OpenAlexaff
D. Gates, Mohammed Aslam Shaikh, B. Berzin, P. J. Bonofiglo, A. Côté, Daniel Dudt, E. Flom, D. Fort, A.D. Koen, Thomas Krüger, S. T. A. Kumar, Marie Martin, Angelica Ottaviano, Samuel Pasmann, Paul Romano, Charles Swanson, L. Tang, E. Winkler, Ryan Wu

Bibliographic record

VenueNuclear Fusion · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsHyperion Technologies (Canada)
Fundersnot available
KeywordsStellaratorPlanarFusionNuclear engineeringMaterials scienceMagnetic confinement fusionPhysicsPlasmaNuclear magnetic resonanceNuclear physicsComputer scienceTokamakEngineeringPhilosophy

Abstract

fetched live from OpenAlex

Abstract We present an overview of a novel electromagnetic coil configuration for stellarators and its application to two near-term fusion systems. The novel coil configuration is the planar coil stellarator, able to implement precisely-quasisymmetric 3D magnetic fields using a set of planar, plasma-encircling coils and a set of planar, field-shaping coils situated on a surface surrounding the plasma. This configuration combines the stellarator’s advantages of steady-state operation, stability, low recirculating power fraction, and a mature physics basis, with the benefits of using simpler, planar coils which allow for a maintenance scheme leveraging large ports, and the ability to control magnets individually. The initial near-term use case considered is a steady state deuterium–deuterium stellarator neutron source, called Eos. The second near-term use case considered is a deuterium–tritium stellarator fusion pilot plant, called Helios, that would be approximately twice the linear dimension of the Eos design.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.231
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2025
Admission routes1
Has abstractyes

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