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Record W2599077429 · doi:10.1063/1.4977524

Synergy effects during current drive by two lower-hybrid waves

2017· article· en· W2599077429 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
KeywordsPhysicsTokamakElectronThermonuclear fusionLower hybrid oscillationCurrent (fluid)PlasmaResonance (particle physics)Computational physicsQuantum electrodynamicsAtomic physicsNuclear physics

Abstract

fetched live from OpenAlex

In recent lower-hybrid current drive experiments on the experimental advanced superconducting tokamak, two lower-hybrid waves are launched simultaneously from different locations with different phase velocities to drive the plasma current. To understand the synergy effects of the two LH waves, the analytical expression for the electron velocity distribution is obtained based on Fuchs' model [Fuchs et al., Phys. Fluids 28(12), 3619–3628 (1985)], which is in good agreement with that obtained by solving the quasi-linear equation numerically via 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)]. The synergy factor is also obtained analytically. It is found that the existence of two resonant regions may bring more resonant electrons interacting with each wave and the perpendicular dynamics can further enhance the synergy effect by increasing the effective electron temperature, which in turn increases the number of electrons in the resonance with each wave.

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 score0.778
Threshold uncertainty score0.997

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)0.0030.000

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 head0.009
GPT teacher head0.274
Teacher spread0.266 · 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
Published2017
Admission routes1
Has abstractyes

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