MétaCan
Menu
Back to cohort
Record W4408871699 · doi:10.1063/5.0227040

Drift-kinetic PIC simulations of plasma flow and energy transport in the magnetic mirror configuration

2025· article· en· W4408871699 on OpenAlexafffund
M. Tyushev, A. I. Smolyakov, ALEX J. SABO, R. E. Groenewald, A. Nečas, P. N. Yushmanov

Bibliographic record

VenuePhysics of Plasmas · 2025
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsKinetic energyPlasmaMagnetic mirrorEnergy transportFlow (mathematics)Atomic physicsPlasma instabilityEnergy flowMagnetic fieldEnergy (signal processing)Computational physicsMechanicsNuclear physicsClassical mechanics

Abstract

fetched live from OpenAlex

Plasma flow and acceleration in a magnetic mirror configuration are studied using a drift-kinetic particles-in-cell model in the paraxial approximation, with an emphasis on finite temperature effects and energy transport. Energy conversion between electrons and ions, overall energy balance, and axial energy losses are investigated. The simulations of plasma flow, acceleration, and energy transport in the magnetic mirror are extended into the high-density regimes with implicit particle-in-cell simulations. It is shown that profiles of the anisotropic ion temperatures and heat fluxes obtained with the full drift-kinetic model compare favorably with the results of a fluid model, which includes collisionless ion heat fluxes beyond the two-pressure adiabatic equations. The effects of collisions on trapped electrons and the resulting impacts on electron temperature and electric field profiles are investigated using a model collision operator.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.323

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)0.0000.000

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 head0.208
Teacher spread0.201 · 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 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

Citations6
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuePhysics of PlasmasSame topicPlasma Diagnostics and ApplicationsFrench-language works237,207