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Record W2064161180 · doi:10.1063/1.1835344

Particle-in-cell simulations of heat flux driven ion acoustic instability

2004· article· en· W2064161180 on OpenAlexafffund
F. Detering, W. Rozmus, A. V. Brantov, V. Yu. Bychenkov, C. E. Capjack, R. D. Sydora

Bibliographic record

VenuePhysics of Plasmas · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsCollisionalityInstabilityAtomic physicsElectronWeibel instabilityPlasmaIonHeat fluxTwo-stream instabilityElectron temperatureAcoustic waveIon acoustic waveParticle-in-cellBremsstrahlungHeat transferMechanicsOpticsNuclear physicsTokamak

Abstract

fetched live from OpenAlex

The return current instability of ion acoustic waves in a laser heated plasma is studied by means of a collisional particle-in-cell code and theoretical analysis in the regime of nonlocal heat transport. The physical scenario of localized, inverse Bremsstrahlung heating in a single laser hot spot, electron thermal transport, return current of cold electrons, instability of ion acoustic waves, and resulting ion acoustic turbulence are examined in a self-consistent kinetic collisional particle simulation. The observed growth of the return current instability is in excellent agreement with predictions of a linear, nonlocal theory. Ion acoustic fluctuations contribute to the inhibition of thermal transport, which leads to the enhancement of the electron temperature in the center of a hot spot. Increased electron collisionality and hot ion tail production are the dominant saturation mechanisms of the return current instability in a one-dimensional geometry. The effects of the ion acoustic turbulence on other interaction processes are also discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.497

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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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

Citations10
Published2004
Admission routes2
Has abstractyes

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