Non-metallic inclusion (NMI) deposition in a slide-gate submerged entry nozzle (SEN)
Bibliographic record
Abstract
Abstract Clogging of the submerged entry nozzle (SEN) is a common and persistent issue in the continuous casting process for steel alloys. The dominant clogging mechanism has been attributed to the deposition and accumulation of non-metallic inclusions (NMI) in the steel melt. Prior studies of NMI deposition assume that every collision between an inclusion and the nozzle wall results in adhesion, which is unrealistic. In this study, a macroscopic transport model for fluid and NMI motion is combined with a microscale model for NMI adhesion and applied to a slide-gate nozzle. Eight NMI sticking probabilities ( S ) and three slide-gate linear opening positions are explored. Simulation results indicated that the more closed the slide-gate, the greater the total deposition of NMI. When the slide-gate was partially open, the particle area number density was highest above, within and just below the slide-gate. But when the slide-gate was fully opened the deposition was concentrated in the upper tundish nozzle. Deposition behaviour fell into two regimes based on S . When S ≤ 0.05, the particle deposition was low and increased rapidly with sticking probability. When S ≥ 0.05, the particle deposition was high but changed very little with sticking probability. Changes to sticking probability did not significantly affect the deposition locations or relative distribution of particles within the nozzle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".