Slag entraining vortexing funnel formation during ladle teeming: similarity criteria and scale-up relationships
Bibliographic record
Abstract
There are several motivations for minimising slag entrainment during the teeming of steelmaking ladles. Cleaner steel, improved yield, and higher productivity are all at stake. As one of several identifiable contributors to slag entrainment, vortexing has received considerable attention in the past decade and a half. What is commonly referred to as 'vortexing' in fact comprises two distinct phenomena, namely, vortexing funnels and non-vortexing funnels, each controlled by entirely different sets of variables. Dimensionless correlations describing the two phenomena were determined, and validated, using separate sets of dimensional analyses and appropriately designed scale model experiments. The importance of these findings to the teeming of steel is discussed. Performance results of a patented 'vortex buster' device, developed on the basis of the understanding gained from these studies, and validated in water models as well as in a 12 ton tundish, are also presented.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".