Dust acoustic shock waves in arbitrarily charged dusty plasma with low and high temperature non-thermal ions
Bibliographic record
Abstract
Using the well-known reductive perturbation technique, the three-dimensional (3D) Burgers equation and modified 3D Burgers equation have been derived for a plasma system comprising of non-thermal ions, Maxwellian electrons, and negatively charged fluctuating dust particles. The salient features of nonlinear propagation of shock waves in such plasmas have been investigated in detail. The different temperature non-thermal ions and Maxwellian electrons are found to play an important role in the shock waves solution. The analytical solution of the 3D Burgers equation and modified 3D Burgers equation ratifying the propagation of dust acoustic shock waves are derived using the well-known tanh method. On increasing the population of non-thermal ions, an enhancement in the amplitude of shock waves is seen for negatively charged dust particles. A striking dependence of amplitude and width of shock waves on the ratio of ion temperatures and densities are also reported. Finally we introduced a new stretching coordinate and perturbation for the nth-order nonlinear 3D Burgers equation and its solution by the use of the tanh method. We found that, due to higher nonlinearity, the amplitude of shock waves decreases while width remains constant for all plasma parameters considered in the present investigation. The features accounted here could be relevant in the case of different space and astrophysical plasmas and laboratory dusty plasma for negatively charged dust fluctuation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".