Turbulence Modeling of Inductively Coupled Plasma Flows
Bibliographic record
Abstract
Abstract Various turbulent models (Spalart-Allmaras, Standard k-ε, RNG k-ε, Realizable k-ε and Reynolds Stress Models) along with Standard, and two-zonal wall functions are used to simulate inductively coupled plasma flows. The computational results can be classified into two categories: All the turbulent models that include low Reynolds number effects, such as, Low Reynolds number k-ε model, Spalart-Allmaras one-equation model, Standard k-ε model with two-zonal wall function, RNG model with turbulent viscosity determined by a differential equation, RSM etc., give similar modelling results. These models predict almost the same temperature contours which are similar to the one predicted by laminar model. The viscosity ratios in plasma region predicted by these models are very close to zero except for in the wall-neighbouring cells, which means the plasma flow is almost laminar. The other category contains those models that do not include the low Reynolds number effects, such as Standard, RNG and so-called Realizable k-ε models with standard wall function. They predict the plasma flow to be turbulence-dominated. In comparison with the results of experimentally measured heat fluxes to a substrate, the heat fluxes predicted by these models that include low Reynolds number effect are very close to experimental measurements while these models that do not include low Reynolds number effects deviate greatly from experimental measurements. It is found that the Reynolds stress model(RSM) appears to be the best predictive model.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".