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Record W2079932584 · doi:10.1088/0029-5515/45/8/011

Modelling of tungsten migration during limiter ramp-down in the ASDEX Upgrade divertor tokamak

2005· article· en· W2079932584 on OpenAlexaff
A. Geier, K. Krieger, R. Neu, D. Coster, J.D. Elder, R. Pugno, V. Rohde, the ASDEX Upgrade Team

Bibliographic record

VenueNuclear Fusion · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsASDEX UpgradeDivertorLimiterTokamakNuclear engineeringTungstenMaterials scienceUpgradePlasmaNuclear physicsPhysicsComputer science

Abstract

fetched live from OpenAlex

The suitability of tungsten as a plasma facing material in magnetic fusion devices is currently under investigation at the ASDEX Upgrade divertor tokamak. Recently, the complete central column, which is also used as a ramp-up and ramp-down limiter was covered with W coated carbon tiles. Experimentally, the transient limiter phases were identified to dominate the gross influx of eroded tungsten and thus they could have an effect on the post-campaign surface analysis measurements. Therefore, the migration of W during limiter contact was assessed using the DIVIMP impurity transport code. The results indicate that most of the tungsten eroded during the limiter phases is redeposited locally on the central column and that only an average of less than 10% of the eroded W is deposited somewhere else on the wall. Accordingly, campaign integrated surface analysis measurements of tungsten deposition in the divertor are not influenced by tungsten deposited during ramp-up or ramp-down. Moreover, the comparison of tungsten migration during the limiter phases and the much longer flat-top divertor phases is not contradictory to the experimental result that only about 10% of the tungsten, eroded at the central column, is found in the divertor by post-campaign tile analysis.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.974

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)0.0270.000

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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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 designSimulation or modeling
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

Citations12
Published2005
Admission routes1
Has abstractyes

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