Modeling Slag Behavior When Using Micro‐Bubble Swarms for the Deep Cleaning of Liquid Steel in Tundishes
Bibliographic record
Abstract
Gas bubbling through a ladle shroud can be an effective approach for the removal of inclusions. However, an exposed eye of steel often appears in the slag cover surrounding the ladle shroud, owing to re‐surfacing bubbles leading to the formation of an exposed eye of steel, termed a “slag eye,” resulting in heat losses and re‐oxidation of the liquid steel. In order to control the formation of such a “slag eye” during gas injection into the shroud, a novel ladle shroud is employed to produce micro‐bubbles, as small as 0.54 mm in diameter. The water model results revealed that reducing the sizes of bubbles that are created in the ladle shroud could effectively limit the formation of a “slag eye,” provided the bubbles are sufficiently small and dispersed. This allows them to pass through the top layer of slag without breaking it up. For a given inflow velocity and shroud configuration, the critical condition for forming a “slag eye” depends on a balance between the gas flow rate and the individual sizes of bubbles penetrating the upper slag layer. Slag layer behavior is also simulated numerically, using a three‐dimensional CFD model. Numerical predictions are in good agreement with corresponding experimental results.
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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 | 0.000 |
| 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 | 0.001 | 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".