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Record W2809080815 · doi:10.1063/1.5024237

Nonlinear structures of lower-hybrid waves driven by the ion beam

2018· article· en· W2809080815 on OpenAlexafffund
Oleksandr Koshkarov, A. I. Smolyakov, Alexander Kapulkin, Yevgeny Raitses, Igor Kaganovich

Bibliographic record

VenuePhysics of Plasmas · 2018
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsPhysicsInstabilityGyroradiusNonlinear systemMechanicsPlasmaKink instabilityTwo-stream instabilityClassical mechanicsBeam (structure)WavenumberLinear stabilityEigenvalues and eigenvectorsQuantum mechanicsPlasma instabilityOptics

Abstract

fetched live from OpenAlex

The lower-hybrid waves can be driven unstable by the transverse ion beam in a partially magnetized plasma of a finite length. This instability mechanism, which relies on the presence of fixed potential boundary conditions, is of particular relevance to axially propagating modes in a Hall effect thruster. The linear and nonlinear regimes of this instability are studied here with numerical simulations. In the linear regime, our results agree with analytical and numerical eigenvalue analysis conducted by Kapulkin and Behar [IEEE Trans. Plasma Sci. 43, 64 (2015)]. It is shown that in nonlinear regimes, the mode saturation results in coherent nonlinear structures. For the aperiodic instability [with Re(ω)=0—odd Pierce zones], the unstable eigen-function saturates into new stationary nonlinear equilibrium. In the case of oscillatory instability [Re(ω)≠0—even Pierce zones], the instability results in the nonlinear oscillating standing wave. It is also shown that finite Larmor radius effects stabilize instability for parameters corresponding to a large number of Pierce zones, and therefore, only few first zones remain relevant.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.283

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 head0.215
Teacher spread0.207 · 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 designBench or experimental
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

Citations12
Published2018
Admission routes2
Has abstractyes

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