Multi-dimensional transient process for a pulse ablating capillary discharge: modeling and experiment
Bibliographic record
Abstract
In recent years, ablative plasma generated by capillary discharge attracted considerable attention because of its possible applications in electrothermal launchers, laser-driven particle accelerators, thin-film deposition and soft X-ray lasers, etc. An electrical discharge through a capillary insulator heats the capillary plasma that provides further evaporation of the capillary wall and electrode. The created plasma is confined by the capillary wall, electrode material and flow in a specified chamber through a hollow electrode. The mass flux leaving the capillary in axial motion is replenished by a radial inward flow of matter. Thus the radial component of the mass flux plays a principal role in the mass and energy balance. In this paper, we present a theoretical model for a time-dependent magneto-hydrodynamical simulation to calculate the dynamic evolution of plasma flow and transportation in two-dimensional configurations combined with turbulent effect. The thermodynamic and transport properties are characterized by a model that describes the plasma composition, equation of state, internal energy, viscosity and thermal and electrical conductivity for a partially ionized multi-component plasma in the weakly non-ideal region, similar to that which exists in the ablation-controlled arcs. Our model results show that some of the well-known experimental features of this kind of discharge are confirmed, particularly the radial mass and energy transportation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".