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Record W3020857127 · doi:10.1063/5.0002548

Error field feedback control system in the Keda Torus eXperiment and open loop control experiment

2020· article· en· W3020857127 on OpenAlexaff
Yanqi Wu, Hong Li, Adil Yolbarsop, Yuan Zhang, Wentan Yan, Zheng Chen, Xianhao Rao, Kezhu Song, Jinlin Xie, Tao Lan, Adi Liu, Wenzhe Mao, Chu Zhou, Zixi Liu, C. Xiao, Weixing Ding, G. Zhuang, Wandong Liu

Bibliographic record

VenuePhysics of Plasmas · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Saskatchewan
FundersNational Magnetic Confinement Fusion Program of ChinaNational Natural Science Foundation of China
KeywordsPhysicsElectromagnetic coilControl systemPlasmaTorusReversed field pinchMagnetic fieldAmplifierControl theory (sociology)Electrical engineeringToroidControl (management)OptoelectronicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

A feedback control system has been designed for the Keda Torus eXperiment device to achieve the following goals: (a) suppression of the error field at the poloidal gap, (b) three-dimensional plasma stability control, and (c) improvement of plasma discharge quality. The system consists of a boundary electromagnetic probe array, a field-programmable gate array, a linear power amplifier, and an active control coil array. The system adopts a compound control method that includes both active and passive control methods. To control the plasma, an active control coil array is used to generate a localized radial magnetic field as needed at two poloidal gaps of the device. In the open-loop control experiment, the active control coils near the poloidal gaps reduced the original error field and thus increased the plasma discharge current amplitude by 50% and effectively extended the plasma discharge duration.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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 designBench or experimental
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

Citations5
Published2020
Admission routes1
Has abstractyes

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