MétaCan
Menu
Back to cohort
Record W3094258575 · doi:10.1088/1741-4326/abc355

Effects of a shallow SAS divertor on detachment in KSTAR

2020· article· en· W3094258575 on OpenAlexaff
Ookjoo Ra, Kyu Been Kwon, L. Casali, Houyang Guo, P.C. Stangeby, Min Sup Hur

Bibliographic record

VenueNuclear Fusion · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Toronto
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsDivertorKSTARTokamakPlasmaDIII-DMaterials scienceDeuteriumNuclear engineeringAtomic physicsPhysicsNuclear physics

Abstract

fetched live from OpenAlex

Abstract For long pulse operation of fusion reactors, it is important to reduce sputter-erosion and power loading of the divertor target by means of plasma detachment. It has been reported that the small-angle-slot (SAS) divertor employed by the DIII-D tokamak can initiate detachment at a relatively low upstream plasma density as it can effectively dissipate heat by concentrating neutrals near the target. Motivated by these findings in DIII-D, we investigated the effects of a SAS-like divertor in KSTAR using SOLPS-ITER simulations without drifts. One remarkable feature revealed by our simulation study is that even a very shallow SAS can lead to a considerably lower heat load on the divertor target than the original flat, open divertor of KSTAR. Deuterium neutrals are concentrated along the divertor separatrix line in the shallow SAS, while deuterium density in the open divertor peaks in the far-scrape-off layer. Furthermore it was found that the neutral density and temperature-drop induced by SAS are both fairly incentive to the depth of the slot. The highest heat dissipation was obtained for a SAS depth of 10.3 cm.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.940

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)0.0610.000

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueNuclear FusionSame topicMagnetic confinement fusion researchFrench-language works237,207