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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 teacher head, not a consensus.

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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