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Record W4388821709 · doi:10.1063/5.0171845

Plasma flow and instabilities in the magnetic mirror with ion recycling and neutral back-flow

2023· article· en· W4388821709 on OpenAlexafffund
Oleksandr Chapurin, Marilyn Jimenez, A. I. Smolyakov, P. N. Yushmanov, Sean Dettrick

Bibliographic record

VenuePhysics of Plasmas · 2023
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Air ForceAir Force Office of Scientific ResearchAlliance de recherche numérique du Canada
KeywordsPhysicsPlasmaMagnetic mirrorIonAtomic physicsElectronIonizationKinetic energyMagnetic confinement fusionComputational physicsEnergetic neutral atomMechanicsNuclear physicsClassical mechanics

Abstract

fetched live from OpenAlex

Magnetic mirror configurations are observed in natural settings and have various applications in laboratory plasmas, such as a magnetic expander of the open mirror fusion devices. The axial plasma flow in open mirror systems is significantly influenced by atomic processes involving neutrals, such as ionization and charge-exchange collisions. A quasi-two-dimensional computational model was developed to study these effects on accelerated plasma flow in magnetic mirror configurations. This model includes an emitting wall, a quasineutral flow/acceleration region with a magnetic expander, and a recycling/absorbing wall. Implemented in a hybrid quasineutral code, the model incorporates drift-kinetic ions, fluid electrons, and fully kinetic neutral atoms with collision processes simulated using the direct simulation Monte Carlo approach. Ion recycling on the wall is accounted for using empirical methods. The model demonstrates that slow atoms with short mean free paths create a dense plasma layer near the wall, modifying the plasma potential which can lead to large-scale perturbations due to ion–ion streaming instabilities.

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 score0.126
Threshold uncertainty score0.333

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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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