Generation of dispersive shock waves in nonextensive plasmas
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".