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Record W6922085911 · doi:10.1063/5.0228749

Analytical estimates of the vertical displacement growth rate in tokamaks with a resistive wall

2025· article· en· W6922085911 on OpenAlexaboutno aff

Bibliographic record

VenuePhysics of Plasmas · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsResistive touchscreenTokamakPlasmaDisplacement (psychology)DiffusionVertical displacementGrowth rateCross section (physics)

Abstract

fetched live from OpenAlex

Analytical evaluation of the vertical displacement event (VDE) growth rate is the main subject here. The attention is attracted to the fact that the model with the plasma and wall boundaries treated as confocal ellipses, introduced by Laval et al. [Phys. Fluids 17, 835 (1974)] and used up until now in analytics, not only restricts the applicability range, but also strongly affects the outcome. In particular, improper utilization of the aforementioned model (i.e., by neglecting the assumption of the wall's confocality) leads to the prediction that the resistive wall with circular cross section cannot slow down the VDE—a conclusion beyond the applicability of the model. Here, we propose a new model and a new approach assuming rigid plasma motion, where the plasma is separated from the wall, and the main element in the task is the plasma–wall electromagnetic interaction. First, we propose a VDE growth time evaluation based on integral equation for the poloidal flux diffusion through the thin resistive shell, which is applicable for the walls of arbitrary shape. Then, the task is simplified by using the large-aspect-ratio expansion of the Green's function. As a result, a slow wall-stabilized motion is predicted in wide ranges of the plasma elongations and the width of the plasma–wall gap. The differences between the existing results and our new predictions are explained.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.263
Teacher spread0.254 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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