CFD Predictions for Mixing Times in an Elliptical Ladle Using Single- and Dual-Plug Configurations
Bibliographic record
Abstract
Argon bottom stirring is commonly practiced in secondary steelmaking processes due to its positive effects on achieving uniform temperatures and chemical compositions throughout a steel melt. It can also be used to facilitate slag metal refining reactions. The inter-mixing phenomena associated with argon gas injection through porous plugs set in the bottom and its stirring efficiency can be summarized by evaluations of 95% mixing times. This study focuses on investigating the impact of different plug positions and ratios of argon flow rates from two plugs on mixing behavior within a 110-tonne, elliptical-shaped industrial ladle. A quasi-single-phase modeling technique was employed for this purpose. The CFD findings revealed that the optimal position of the second plug is to be placed diametrically opposite the existing one at an equal mid-radius distance (R/2). An equal distribution of argon flow rates yielded the best results in terms of refractory erosion. A comparative study was conducted between single- and dual-plug-configured ladles based on flow behavior and wall shear stresses using this method. Furthermore, a transient multiphase model was developed to examine the formation of slag open eyes (SOE) for both single- and dual-plug configurations using a volume of fluid (VOF) model. The results indicated that the dual-plug configuration outperformed the current single-plug configuration.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".