Modelling the effect of ferrite on an inductively coupled plasma torch: II. Finite ferrite permeability
Bibliographic record
Abstract
A model for an inductively coupled plasma with ferrite around the coil is developed. Vector representation is used for modelling and calculation of the electromagnetic fields. The permeability of ferrite is taken into account (not assumed to be infinite as in a previous work by the authors) but is assumed not to change with magnetic fields. The power dissipation in ferrite is neglected (no imaginary part for permeability). Under these assumptions, the role of ferrite can be completely replaced by the magnetization current density on the surface of ferrite. Through the magnetic field boundary condition on the surface of ferrite, the magnetization current density is derived and added to vector potential equations as a source term. The commercial Computational Fluid Dynamics (CFD) software FLUENT is used to solve the governing equations of plasma flow with vector potential equation and the flow, temperature, and electromagnetic fields. The effect of different shapes of ferrites around the coil was simulated. The results are compared with the results from the same model without ferrite and a previously proposed model with infinite permeability of ferrite. The computation results show that the effect of ferrite is not only to shield the electromagnetic radiation but it also decreases the coil current necessary to sustain the same power plasma torch. Accordingly, it increases the power coupling efficiency as well.
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) | 0.001 | 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 | 0.001 | 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".