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 (Swall) on inclusion deposition. The results indicate that inclusion deposition is affected by both melt height and Swall. Lower melt heights result in increased deposition deeper into the nozzle and greater maximum particle area number density. The effect of Swall on the global deposition ratio can be divided into two regimes. When Swall increases from 0–0.05, there is a rapid rise in the global deposition ratio. When Swall > 0.05, the global deposition ratio increases only modestly with Swall. Changes to Swall also affect the location of deposition. When Swall 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".