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Record W2232408099 · doi:10.1063/1.4936478

Development of long pulse RF heating and current drive for H-mode scenarios with metallic walls in WEST

2015· article· en· W2232408099 on OpenAlexaboutno aff

Bibliographic record

VenueAIP conference proceedings · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsTokamakDivertorNuclear engineeringTore SupraMaterials scienceCyclotronPlasmaDielectric heatingHeat fluxRF power amplifierRadio frequencyPulse (music)Current (fluid)TungstenEnvironmental sciencePhysicsOptoelectronicsNuclear physicsDetectorElectrical engineeringOpticsMechanicsHeat transferEngineeringAmplifier

Abstract

fetched live from OpenAlex

The longstanding expertise of the Tore Supra team in long pulse heating and current drive with radiofrequency (RF) systems will now be exploited in the WEST device (tungsten-W Environment in Steady-state Tokamak) [1]. WEST will allow an integrated long pulse tokamak programme for testing W-divertor components at ITER-relevant heat flux (10-20 MW/m2), while treating crucial aspects for ITER-operation, such as avoidance of W-accumulation in long discharges, monitoring and control of heat fluxes on the metallic plasma facing components (PFCs) and coupling of RF waves in H-mode plasmas. Scenario modelling using the METIS-code shows that ITER-relevant heat fluxes are compatible with the sustainment of long pulse H-mode discharges, at high power (up to 15 MW / 30 s at IP = 0.8 MA) or high fluence (up to 10 MW / 1000 s at IP = 0.6 MA) [2], all based on RF heating and current drive using Ion Cyclotron Resonance Heating (ICRH) and Lower Hybrid Current Drive (LHCD). This paper gives a description of the ICRH and LHCD systems in WEST, together with the modelling of the power deposition of the RF waves in the WEST-scenarios.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.936

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)0.0010.000

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 head0.042
GPT teacher head0.303
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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2015
Admission routes1
Has abstractyes

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