Numerical Simulation of Melting Kinetics of Metal Particles during Tapping with Argon-Bottom Stirring
Bibliographic record
Abstract
Molten steel is alloyed during tapping from the melting furnace to the argon-bottom stirred ladle. The metallic additions thrown to the ladle during the ladle filling time are at room temperature. The melting rates or kinetics of sinking-metals, like nickel, are simulated through a multiphase Euler–Lagrangian mathematical model during this operation. The melting rate of a metallic particle depends on its trajectory within regions of the melt with high or low turbulence levels, delaying or speeding up their melting process. At low steel levels in the ladle, the melting rates are higher on the opposite side of the plume zone induced by the bottom gas stirring. This effect is due to its deviation after the impact of the impinging jet on the ladle bottom. The higher melting kinetics are located on both sides at high steel levels due to the more extensive recirculation flows formed in taller baths. Making the additions above the eye of the argon plume spout increases the melting rate of nickel particles. The increase of the superheat makes the heat flux more significant from the melt to the particle, increasing its melting rate. At higher superheats, the melting kinetics become less dependent on the fluid dynamics of the melt.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".