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Record W2988531367 · doi:10.1088/1741-4326/ab945c

Application of high frequency biasing and its effect in STOR-M tokamak

2020· article· en· W2988531367 on OpenAlexaff

Bibliographic record

VenueNuclear Fusion · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBiasingTokamakVoltageAmplifierRADIUSPlasmaPower (physics)Electric field

Abstract

fetched live from OpenAlex

A pulsed oscillating power amplifier has been developed to apply high frequency biasing voltage to an electrode at the edge of the STOR-M tokamak plasma. The power amplifier can deliver a peak-to-peak oscillating voltage ±60 V and current 30 A within the frequency range 1 kHz–50 kHz. The electrode is located in the equatorial plane at radius ρ = 0.88. The frequency of the applied voltage has been varied between discharges. It is observed that the plasma density and soft x-ray intensity from the plasma core region usually increase at lower frequency regime 1 kHz–5 kHz as well as relatively higher frequency regime 20 kHz–25 kHz but seldom increase in between them. Increment of τ \! E has been observed 40% and 20% for the frequency regimes of 1 kHz–5 kHz and 20 kHz–25 kHz, respectively, and τ \! p increment is 25% for both frequency regimes. Transport simulation has been carried out using the ASTRA simulation code for STOR-M tokamak parameters to understand the physical process behind experimental observations at the higher frequency branch. The model is based on geodesic acoustic mode(GAM) excitement at resonance frequency associated with Ware-pinch due to the oscillating electric field produced by biasing voltage, which can suppress anomalous transport. Simulation results reproduce the experimental trends quite well in terms of the density, particle confinement, as well as energy confinement time evolution. All the results indicate that high frequency biasing is capable of improving confinement efficiently.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.988

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.010
GPT teacher head0.238
Teacher spread0.229 · 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 designObservational
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

Citations7
Published2020
Admission routes1
Has abstractyes

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