Bibliographic record
Abstract
Supersonic plasma jets find applications in plasma chemistry and plasma processing, metallurgy, experimental physics, and space technology. Usually the plasma in these jets deviates from chemical and thermal equilibrium. To optimize the industrial process detailed study of nonequilibrium effects in supersonic flow is required. In the article we apply numerical simulation to study the supersonically accelerated argon plasma flow downstream of the induction plasma torch. We compare the jets exhausting from two different convergent-divergent nozzles by means of a two-temperature model. The results show that the axial electron number density is rather convective flux controlled than recombination-ionization reaction controlled in both cases. However, the recombination resulting in electron gas heating is more essential in the jet flowing from the nozzle with a higher outlet Mach number. The composition of the jet exhausting from the nozzle with a lower outlet Mach number remains almost unchanged (“frozen”) until the end of the first expansion zone. These results confirm that the chamber pressure and the nozzle design changing leads to the induction plasma jets with different chemical conditions. For low-pressure supersonic plasma, these conditions vary from frozen to recombining. The conclusion is that depending on the industrial process, one can choose the proper torch nozzle geometry to have nonequilibrium plasma with the required properties.
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 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".