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Record W2738335794 · doi:10.1063/1.4994547

Numerical study of the synergy effects of electron cyclotron wave and two lower-hybrid waves in the current drive process

2017· article· en· W2738335794 on OpenAlexaboutno aff
Y. Yang, Nong Xiang, Yemin Hu

Bibliographic record

VenuePhysics of Plasmas · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsPhysicsElectronLower hybrid oscillationThermonuclear fusionComputational physicsCyclotronTokamakWave propagationAtomic physicsPlasmaNuclear physicsOptics

Abstract

fetched live from OpenAlex

In recent experiments on the experimental advanced superconducting tokamak, the electron cyclotron wave and the two lower-hybrid waves at different frequencies, i.e., 4.6 GHz and 2.45 GHz, are applied simultaneously to sustain and control the plasma current. To investigate the synergy effects of the three waves, the Fokker-Planck equation with the quasi-linear diffusions induced by the three waves is solved numerically with the CQL3D code [R. W. Harvey and M. G. McCoy, in Proceedings of IAEA Technical Committee Meeting on Advances in Simulation and Modeling of Thermonuclear Plasmas, Montreal, Canada (1992)]. It is found that there might be strong synergy effects between the three waves. The electrons in the low velocity region in the velocity space can be accelerated perpendicularly by the electron cyclotron wave, and their parallel velocities can be increased due to scattering and fall into the resonance regions of the lower-hybrid waves. Therefore, such processes may bring more electrons to resonate with the lower-hybrid waves and enhance the current drive of the lower-hybrid waves. The synergy effects strongly depend on the distance between the resonance regions in the velocity space of the three waves.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.322

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)0.0000.000

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 head0.011
GPT teacher head0.294
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations8
Published2017
Admission routes1
Has abstractyes

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