MétaCan
Menu
Back to cohort
Record W2988470692 · doi:10.1063/1.5109947

Gyro-kinetic theory and global simulations of the collisionless tearing instability: The impact of trapped particles through the magnetic field curvature

2019· article· en· W2988470692 on OpenAlexaff
D. Zarzoso, S. Nasr, X. Garbet, A. I. Smolyakov, S. Benkadda

Bibliographic record

VenuePhysics of Plasmas · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Saskatchewan
FundersAgence Nationale de la RechercheEUROfusion
KeywordsPhysicsTearingInstabilityCurvatureMagnetic fieldGyrokineticsWeibel instabilityPlasmaClassical mechanicsMechanicsCondensed matter physicsTokamakQuantum mechanics

Abstract

fetched live from OpenAlex

The linear instability of the tearing mode is analyzed using a gyrokinetic approach within a Hamiltonian formalism, where the interaction between particles and the tearing mode through the wave-particle resonance is retained. On the one hand, the curvature of the magnetic field is shown to play no role in the linear instability when only passing particles are present in the plasma. On the other hand, the presence of trapped particles leads to an overall increase in the growth rate. Gyrokinetic simulations using the state-of-the-art Gkw code confirm these findings and are further used to investigate the impact of the magnetic field curvature and the temperature gradient on tearing modes including the effect of trapped particles. Without the temperature gradient, wave-particle resonance with the trapped electrons tends to stabilize the tearing mode, while with the finite temperature gradient, the magnetic curvature tends to destabilize the tearing mode, suggesting an interchange mechanism. The balance of these two stabilizing/destabilizing effects leads to a threshold in the temperature gradient beyond which the magnetic curvature plays a destabilizing role. This opens the way for a deeper understanding and control of the tearing instability in fusion plasmas.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.999

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)0.0020.000

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 head0.013
GPT teacher head0.290
Teacher spread0.277 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePhysics of PlasmasSame topicMagnetic confinement fusion researchFrench-language works237,207