Modeling of Non-Equilibrium Supersonic Plasma Jets at Low Pressure
Bibliographic record
Abstract
Abstract Low Pressure Plasma Spraying (LPPS) is a spraying technique developed in the 1970s that uses a converging-diverging nozzle (i.e. de Laval nozzle) inside a controlled atmosphere chamber. With such a set-up, it is possible to obtain a plasma jet in the supersonic flow regime, which may encounter several non-equilibrium phenomena. First, the operating pressures (i.e. chamber and torch exit) determine the aerodynamic state of the supersonic plasma jet. Operating at the nozzle design pressure insures aerodynamic equilibrium of the plasma jet while other chamber pressure brings a state of “aerodynamic non-equilibrium” for which expansion, compression and shock waves appear. Second, a low chamber pressure corresponds to a decrease in the number of collisions between electrons and heavy species (i.e. atoms and ions). This rarefaction of the plasma causes a state of “thermal non-equilibrium” for which there exist two different temperature distributions, Te and Th, respectively representing the electrons and the heavy species. Third, the high velocity of the plasma jet can bring a state of “chemical non-equilibrium” as the hydrodynamic time scale of the flow might become smaller than its chemical time scale. Hence it is a challenging task to model a phenomenon as complex as the low pressure supersonic plasma jet. The model developed in this work uses a commercial code in which recent advances in the field of plasma transport properties are implemented. It considers chemical non-equilibrium through the solution of the electrons conservation equation while the two temperature distributions each require the solution of an energy balance. The validation of the model is possible from recent measurements with a new method enhancing the capabilities of the enthalpy probe technique to supersonic jets in aerodynamic non-equilibrium. Abstract only; no full-text paper available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".