Removal of Inclusions Using Micro-bubble Swarms in a Four-strand, Full-scale, Water Model Tundish
Bibliographic record
Abstract
Water model experiments were performed in a full-scale, delta-shaped water model tundish, in order to study the removal of inclusions by micro-bubbles. Micro-bubbles were generated using a specially designed ladle shroud with twelve laser-drilled orifices. Gas flow rates, injection positions and multi-port injection were all taken into consideration to create different bubble conditions. Bubbles were recorded using a high speed camera and post-processed with commercial software, Image J. Hollow glass borosilicate microspheres, smaller than 100 µm, were used to simulate inclusions, and detected, in-situ, using a new generation of the Aqueous Particle Sensor, APS III. The results revealed that the effect of micro-bubbles on inclusion removal depends greatly on the gas injection protocols used. The optimum gas flow rate was an intermediate value, which indicates a minimum particle number density, np, of about 7.85/ml. This results from the counter-balancing effects of bubble sizes against the total number of bubbles. The highest inclusion removal rate was 80%, when gas was injected through the four ports located closest to the slide gate, at a gas flow rate of 0.2 L/min.
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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 | 0.000 | 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".