Bubble Characteristics Required for the Complete Removal of Alumina Inclusions from Steel Melts
Bibliographic record
Abstract
Gas bubbling can be an effective means to float out alumina inclusions from liquid steel in a ladle. However, large spherical cap bubbles are formed when using porous plugs, as the liquid steel is nonwetting to the porous refractory. These bubbles rise rapidly through the liquid steel, forming a fast‐moving bubble plume, restricting contact times. Sized microbubbles, by contrast, have now been generated in liquid metals by shearing methods, involving linear crossflows to an entering flow of gas, or alternatively by rotational shearing. Combined with these convective shearing forces, local kinetic energy of turbulence can also play an important part in determining final microbubble size distributions. As microbubbles have much smaller rise velocities and present a far greater inclusion capture surface area than those of a single large bubble of the same gross volume, this will allow us to remove sub‐50 μm inclusions from liquid steel. It is expected that this goal will require a redesign of current ladle shrouds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".