Experimental measurements of bubble size distributions in a water model and its influence on the aluminum kinetics degassing
Bibliographic record
Abstract
ABSTRACT An experimental study of the gas‐liquid dynamics in a water model of an aluminum ladle was conducted. The rotor degassing performance was evaluated for two commercial rotor‐injector devices compared to a new rotor design. In this work, the influence of the turbulent properties of the flow fields on the bubble size distribution is analyzed for a better understanding of its impact on the degassing efficiency in aluminum refinement operations. The degassing process was analyzed by two different methods: (a) a high‐speed camera was used to obtain the bubble size distribution into the container; and (b) the particle image velocimetry technique (PIV) was employed to obtain the liquid flow properties. It was found that the rotor geometry plays an important role on the average size and distribution of the bubbles. The energy dissipation rate contours show significant differences for the distinct rotors tested. These hydrodynamic states and bubbles distribution dominate the kinetic and efficiency of the degassing processes. It was shown that the new rotor configuration enhances the degassing kinetics, due to a more suitable bubble dispersion compared to the commercial rotors tested.
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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 | 0.001 |
| 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) | 0.001 | 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".