Spectroscopic validation of the supersonic plasma jet model
Bibliographic record
Abstract
Optical emission spectroscopy is applied to validate numerical simulations of supersonic plasma flow generated by induction torch with a convergent-divergent nozzle. The plasmas exhausting from the discharge tube with the pressure 0.4-1.4 atm. through two nozzle configurations (the outlet Mach number equals 1.5 and 3) into low-pressure (1.8 kPa) chamber are compared. Both modelling and experiments show that the effect of the nozzle geometry on physical properties of plasma jet is significant. The profiles of electron number density obtained from modeling and spectroscopy agree well and show the deviations from local thermodynamic equilibrium. Analysis of intercoupling between different sorts of nonequilibrium processes is performed. The results reveal that the ion recombination is more essential in the nozzle with the higher outlet number than in the nozzle with the lower outlet number. It is demonstrated that in the jets the axial electron temperature is quite low (3000-8000 K). For spectroscopic data interpretation we propose a method based on the definition of two excitation temperatures. We suppose that in mildly under expanded argon jets with frozen ion recombination the electron temperature can be defined by the electronic transitions from level 5p (the energy E = 14.5 eV) to level 4p ( E = 13.116 eV). The obtained results are useful for the optimization of plasma reactors for plasma chemistry and plasma processing applications.
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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 | 0.001 | 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 | 0.000 |
| 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".