Modeling the Impact of Varying Levels of Inclusion Adhesion on Deposition in a Pilot‐Scale Nozzle
Bibliographic record
Abstract
The deposition and accumulation of inclusions is the dominant mechanism in nozzle clogging of the submerged entry nozzle. Previous modeling attempts of inclusion deposition have assumed that any contact between the inclusion and nozzle wall results in adhesion. Herein, an Eulerian–Lagrangian simulation with a stochastic adhesion model is used to study the effects of different inclusion‐wall sticking probability ( S wall ) on inclusion deposition. The results indicate that inclusion deposition is affected by both melt height and S wall . Lower melt heights result in increased deposition deeper into the nozzle and greater maximum particle area number density. The effect of S wall on the global deposition ratio can be divided into two regimes. When S wall increases from 0–0.05, there is a rapid rise in the global deposition ratio. When S wall > 0.05, the global deposition ratio increases only modestly with S wall . Changes to S wall also affect the location of deposition. When S wall decreases, the high and mid cases show greater relative deposition in the cone and taper sections of the nozzle, while the low melt height case shows greater relative deposition in the straight section of 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 | 0.000 |
| 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) | 0.000 | 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".