Numerical model for alumina deoxidation inclusion size distributions
Bibliographic record
Abstract
A numerical model was developed to predict alumina deoxidation inclusion size distributions during steelmaking. The model was based on the Kampmann-Wagner numerical (KWN) model. This is the first time the KWN model has been used to describe precipitation of two solutes from a liquid phase. Besides inclusion nucleation and diffusion growth, the KWN model was extended to include reoxidation, collision growth and inclusion removal. Collision growth included three different types of collisions: Brownian collisions, Stokes collisions and turbulent collisions. The modified KWN model results were compared to three previously published numerical models using the same initial conditions and parameters. The slopes of the size distributions were similar in all cases. However, the magnitudes of the size distributions varied significantly. This was explained by the different mechanisms and assumptions used by each model. The model results from this work were also compared to experimental size distributions by simulating an entire ladle treatment from tapping at the converter to the end of the ladle treatment, including ladle additions and the starting and stopping of stirring. The slopes of the size distributions predicted by this model matched those in all the experimental size distributions. The modified KWN model predicted the correct size distribution magnitude at the start of the ladle treatment. However, the magnitude at the end of the ladle treatment was poorly predicted. This was explained by the loss of alumina inclusions due to their gradual modification by calcium. The slope of the size distributions was shown to be controlled by turbulent collision growth. However, this slope could not be modified by varying the turbulent energy dissipation rate.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".