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Record W2774081181 · doi:10.1139/cjp-2017-0589

Generation of dispersive shock waves in nonextensive plasmas

2017· article· en· W2774081181 on OpenAlexvenueno aff
Bahareh Azarvand-Hassanfard, A. Esfandyari-Kalejahi, M. Akbari-Moghanjoughi

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsShock waveKorteweg–de Vries equationDissipative systemDispersion (optics)PlasmaShock (circulatory)ElectronQuantum electrodynamicsViscosityDispersion relationClassical mechanicsQuantum mechanicsMechanicsNonlinear system

Abstract

fetched live from OpenAlex

In this research we use a generalized hydrodynamic model to numerically investigate the quasi-neutral expansion and compression of an electron–ion plasma with nonextensive electron velocity distribution into or from vacuum. We study the effects of kinematic viscosity and electron–ion collisions on the expansion profile and compare our results to the numerical solutions of the standard Korteweg – de Vries (KdV) equation. It is found that the quasi-neutrality assumption in the hydrodynamic approach in the absence of viscosity and collisions, which leads to elimination of Poisson’s equation, sets the weak dispersion limit and becomes equivalent to the standard weakly dispersive KdV model. In the weak dispersion limit our model, as well as the KdV with small dispersion effect, predicts that a pulse-like initial profile evolves into solitary wave train. We further show that in a plasma expansion different shock profiles, such as purely dispersive, diffusive–dispersive, and dissipative ones, with significantly different characters may form. Finally, the effect of electron nonextensivity on oscillatory shock waves shows that the expansion profile is affected by changes of q-parameter. Our numerical solution is in qualitative agreement with some distinguished experiments showing the possibility of dispersive shock wave formation in rarefied plasma during an expansion into vacuum.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.636

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 head0.038
GPT teacher head0.244
Teacher spread0.206 · 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 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

Citations2
Published2017
Admission routes1
Has abstractyes

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