A CFD and Experimental Investigation of Slag Eye in Gas Stirred Ladle
Bibliographic record
Abstract
In ladle metallurgy, gas stirring and behavior of the slag layer are very important for the quality of the steel.When gas is injected through a nozzle located at the bottom of the ladle into the metal bath, the gas jet exiting the nozzle breaks up into gas bubbles.The rising bubbles break the slag layer and create a slag eye.In this paper, the behavior of the slag eye area for different gas flow rates is been investigated through experimental measurements and CFD simulations.A 1/5-scale water model of 150 ton-ladle was deployed for the experimental measurements and for studying the effect of gas flow rate on the slag eye diameter.The physical modelling results show that the slag eye area changes from 20 to 182 cm 2 when the gas flow rate increases from 1.5 to 15 NL/min.The dimensionless area of the open eye was found to be in agreement with earlier studies.The simulations were carried out in the commercial CFD code ANSYS Fluent with mesh generation in ANSYS Workbench.The numerical model developed is based on the Eulerian Multiphase Volume of Fluid (VOF) approach and employs standard 𝑘 -𝜀 turbulence model for solving the turbulent liquid flow induced by bubble-liquid interaction.The simulation results of slag eye area showed a good agreement when compared to experimental results measured.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".