Plasma torch and its associated MHD fields using the FLUENT code
Bibliographic record
Abstract
Summary form only given. Reported studies dealing with the mathematical modeling of the r.f. inductively coupled plasma provide information about 2D flow, temperature and species concentration as well as electromagnetic fields in the discharge. The majority of the proposed models require, however, a lengthy iterative process to converge, where sometimes several thousands of iterations can be needed. In order to speed up the calculation and increase the flexibility of the model, the CFD code FLUENT is used. The later is based on the solution of the Navier-Stokes equations using the control volume method of Patankar. User defined subroutines are added to the code to implement the vector potential equation and the source terms, as Lorentz force, radiation and joule heating acting on the plasma. Subroutines are also implemented to allow thermodynamics and transport properties to be function of temperature and pressure. The torch is modeled by a fully axisymmetric configuration. A laminar flow field is assumed, for an optically thin plasma under local thermodynamic equilibrium. The inductor is represented by a series of parallel current ring. In the present study, emphasis is placed on the formulation of the MHD induction equation where the electromagnetic fields are not limited to the torch itself, but extend well beyond the torch.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.097 | 0.018 |
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".